diff --git a/.travis.yml b/.travis.yml index 72f98ac97c..984b5dbae8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,6 +16,8 @@ matrix: env: DISTRIB="conda" PYTHON_VERSION="3.4" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" - os: linux env: DISTRIB="conda" PYTHON_VERSION="3.5" COVERAGE="true" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" + - os: linux + env: DISTRIB="conda" PYTHON_VERSION="3.6" MINICONDA_URL="https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh" # Temporarily disabling OSX builds because thy take too long # Set language to generic to not break travis-ci @@ -56,7 +58,7 @@ before_install: - conda update --yes conda - conda create -n testenv --yes python=$PYTHON_VERSION pip wheel nose - source activate testenv - - conda install --yes gcc + - conda install --yes gcc swig - echo "Using GCC at "`which gcc` - export CC=`which gcc` # Fixes version `GLIBCXX_3.4.21' not found (on Ubuntu 16.04) @@ -64,12 +66,12 @@ before_install: install: # Install general requirements the way setup.py suggests - - pip install coverage pep8 python-coveralls + - pip install pep8 codecov - cat requirements.txt | xargs -n 1 -L 1 pip install # Install openml dependency for metadata generation unittest - pip install xmltodict requests - pip install git+https://github.com/renatopp/liac-arff - - pip install git+https://github.com/openml/openml-python@master --no-deps + - pip install git+https://github.com/openml/openml-python@0b9009b0436fda77d9f7c701bd116aff4158d5e1 --no-deps - mkdir ~/.openml - echo "apikey = 610344db6388d9ba34f6db45a3cf71de" > ~/.openml/config # Debug output to know all exact package versions! diff --git a/README.md b/README.md index 43ffff17e9..d0de400572 100644 --- a/README.md +++ b/README.md @@ -8,10 +8,10 @@ Status for master branch: [![Build Status](https://travis-ci.org/automl/auto-sklearn.svg?branch=master)](https://travis-ci.org/automl/auto-sklearn) [![Code Health](https://landscape.io/github/automl/auto-sklearn/master/landscape.png)](https://landscape.io/github/automl/auto-sklearn/master) -[![Coverage Status](https://coveralls.io/repos/automl/auto-sklearn/badge.svg?branch=master&service=github)](https://coveralls.io/github/automl/auto-sklearn?branch=master) +[![codecov](https://codecov.io/gh/automl/auto-sklearn/branch/master/graph/badge.svg)](https://codecov.io/gh/automl/auto-sklearn) Status for development branch [![Build Status](https://travis-ci.org/automl/auto-sklearn.svg?branch=development)](https://travis-ci.org/automl/auto-sklearn) [![Code Health](https://landscape.io/github/automl/auto-sklearn/development/landscape.png)](https://landscape.io/github/automl/auto-sklearn/development) -[![Coverage Status](https://coveralls.io/repos/automl/auto-sklearn/badge.svg?branch=development&service=github)](https://coveralls.io/github/automl/auto-sklearn?branch=development) +[![codecov](https://codecov.io/gh/automl/auto-sklearn/branch/development/graph/badge.svg)](https://codecov.io/gh/automl/auto-sklearn) diff --git a/autosklearn/__init__.py b/autosklearn/__init__.py index 2a7ccf2719..caf743ae27 100644 --- a/autosklearn/__init__.py +++ b/autosklearn/__init__.py @@ -4,13 +4,12 @@ __MANDATORY_PACKAGES__ = ''' -numpy>=1.9,<1.12 -scikit-learn==0.17.1 -smac==0.3.0 +numpy>=1.9 +scikit-learn==0.18.1 +smac==0.5.0 lockfile>=0.10 -ConfigSpace>=0.3.1,<0.4 -pyrfr==0.2.0 -xgboost==0.4a30 +ConfigSpace>=0.3.3,<0.4 +pyrfr>=0.4.0,<0.5 ''' dependencies.verify_packages(__MANDATORY_PACKAGES__) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index dd8e2bbd73..539982fe5c 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.1.3" \ No newline at end of file +__version__ = "0.2.0" \ No newline at end of file diff --git a/autosklearn/automl.py b/autosklearn/automl.py index bf3ddb1f40..ab517a388e 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -1,6 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function - from collections import defaultdict import io import json @@ -16,18 +14,42 @@ from sklearn.base import BaseEstimator from smac.tae.execute_ta_run import StatusType from smac.stats.stats import Stats -from sklearn.grid_search import _CVScoreTuple +from sklearn.externals import joblib from autosklearn.constants import * +from autosklearn.metrics import Scorer from autosklearn.data.competition_data_manager import CompetitionDataManager from autosklearn.data.xy_data_manager import XYDataManager from autosklearn.evaluation import ExecuteTaFuncWithQueue -from autosklearn.evaluation import calculate_score +from autosklearn.metrics import calculate_score from autosklearn.util import StopWatch, get_logger, setup_logger, \ pipeline from autosklearn.ensemble_builder import EnsembleBuilder from autosklearn.smbo import AutoMLSMBO -from autosklearn.util.hash import hash_numpy_array +from autosklearn.util.hash import hash_array_or_matrix + + +def _model_predict(self, X, batch_size, identifier): + def send_warnings_to_log( + message, category, filename, lineno, file=None, line=None): + self._logger.debug('%s:%s: %s:%s' % + (filename, lineno, category.__name__, message)) + return + model = self.models_[identifier] + X_ = X.copy() + with warnings.catch_warnings(): + warnings.showwarning = send_warnings_to_log + if self._task in REGRESSION_TASKS: + prediction = model.predict(X_, batch_size=batch_size) + else: + prediction = model.predict_proba(X_, batch_size=batch_size) + if len(prediction.shape) < 1 or len(X_.shape) < 1 or \ + X_.shape[0] < 1 or prediction.shape[0] != X_.shape[0]: + self._logger.warning("Prediction shape for model %s is %s " + "while X_.shape is %s" % + (model, str(prediction.shape), + str(X_.shape))) + return prediction class AutoML(BaseEstimator): @@ -119,7 +141,7 @@ def __init__(self, def fit(self, X, y, task=MULTICLASS_CLASSIFICATION, - metric='acc_metric', + metric=None, feat_type=None, dataset_name=None): if not self._shared_mode: @@ -136,7 +158,7 @@ def fit(self, X, y, self._backend.context.create_directories() if dataset_name is None: - dataset_name = hash_numpy_array(X) + dataset_name = hash_array_or_matrix(X) self._backend.save_start_time(self._seed) self._stopwatch = StopWatch() @@ -145,8 +167,11 @@ def fit(self, X, y, self._logger = self._get_logger(dataset_name) - if isinstance(metric, str): - metric = STRING_TO_METRIC[metric] + if metric is None: + raise ValueError('No metric given.') + if not isinstance(metric, Scorer): + raise ValueError('Metric must be instance of ' + 'autosklearn.metric.Scorer.') if feat_type is not None and len(feat_type) != X.shape[1]: raise ValueError('Array feat_type does not have same number of ' @@ -164,14 +189,12 @@ def fit(self, X, y, self._data_memory_limit = None loaded_data_manager = XYDataManager(X, y, task=task, - metric=metric, feat_type=feat_type, - dataset_name=dataset_name, - encode_labels=False) + dataset_name=dataset_name) - return self._fit(loaded_data_manager) + return self._fit(loaded_data_manager, metric) - def fit_automl_dataset(self, dataset): + def fit_automl_dataset(self, dataset, metric): self._stopwatch = StopWatch() self._backend.save_start_time(self._seed) @@ -185,15 +208,14 @@ def fit_automl_dataset(self, dataset): # Encoding the labels will be done after the metafeature calculation! self._data_memory_limit = float(self._ml_memory_limit) / 3 loaded_data_manager = CompetitionDataManager( - dataset, encode_labels=False, - max_memory_in_mb=self._data_memory_limit) + dataset, max_memory_in_mb=self._data_memory_limit) loaded_data_manager_str = str(loaded_data_manager).split('\n') for part in loaded_data_manager_str: self._logger.debug(part) - return self._fit(loaded_data_manager) + return self._fit(loaded_data_manager, metric) - def fit_on_datamanager(self, datamanager): + def fit_on_datamanager(self, datamanager, metric): self._stopwatch = StopWatch() self._backend.save_start_time(self._seed) @@ -203,7 +225,7 @@ def fit_on_datamanager(self, datamanager): self._dataset_name = name self._logger = self._get_logger(name) - self._fit(datamanager) + self._fit(datamanager, metric) def _get_logger(self, name): logger_name = 'AutoML(%d):%s' % (self._seed, name) @@ -249,6 +271,7 @@ def _do_dummy_prediction(self, datamanager, num_run): initial_num_run=num_run, logger=self._logger, stats=stats, + metric=self._metric, memory_limit=memory_limit, disable_file_output=self._disable_evaluator_output, **self._resampling_strategy_arguments) @@ -258,17 +281,27 @@ def _do_dummy_prediction(self, datamanager, num_run): if status == StatusType.SUCCESS: self._logger.info("Finished creating dummy predictions.") else: - self._logger.error('Error creating dummy predictions:%s ', - additional_info) + self._logger.error('Error creating dummy predictions: %s ', + str(additional_info)) return ta.num_run - def _fit(self, datamanager): + def _fit(self, datamanager, metric): # Reset learnt stuff self.models_ = None self.ensemble_ = None # Check arguments prior to doing anything! + if not isinstance(self._disable_evaluator_output, (bool, list)): + raise ValueError('disable_evaluator_output must be of type bool ' + 'or list.') + if isinstance(self._disable_evaluator_output, list): + allowed_elements = ['model', 'y_optimization'] + for element in self._disable_evaluator_output: + if element not in allowed_elements: + raise ValueError("List member '%s' for argument " + "'disable_evaluator_output' must be one " + "of " + str(allowed_elements)) if self._resampling_strategy not in ['holdout', 'holdout-iterative-fit', 'cv', 'partial-cv', 'partial-cv-iterative-fit']: @@ -296,7 +329,7 @@ def _fit(self, datamanager): if not self._shared_mode: raise - self._metric = datamanager.info['metric'] + self._metric = metric self._task = datamanager.info['task'] self._label_num = datamanager.info['label_num'] @@ -401,6 +434,7 @@ def _fit(self, datamanager): smac_iters=self._max_iter_smac, seed=self._seed, metadata_directory=self._metadata_directory, + metric=self._metric, resampling_strategy=self._resampling_strategy, resampling_strategy_args=self._resampling_strategy_arguments, acquisition_function=self.acquisition_function, @@ -411,14 +445,14 @@ def _fit(self, datamanager): exclude_preprocessors=self._exclude_preprocessors, disable_file_output=self._disable_evaluator_output, configuration_mode=self._configuration_mode) - self.runhistory_, self.trajectory_ = _proc_smac.run_smbo() - runhistory_filename = os.path.join(self._backend.temporary_directory, - 'runhistory.json',) - self.runhistory_.save_json(runhistory_filename) + self.runhistory_, self.trajectory_, self.fANOVA_input_ = \ + _proc_smac.run_smbo() trajectory_filename = os.path.join( - self._backend.temporary_directory, 'trajectory.json') - saveable_trajectory = [entry[:2] + [entry[2].get_dictionary()] + entry[3:] - for entry in self.trajectory_] + self._backend.get_smac_output_directory(self._seed) + '_run1', + 'trajectory.json') + saveable_trajectory = \ + [list(entry[:2]) + [entry[2].get_dictionary()] + list(entry[3:]) + for entry in self.trajectory_] with open(trajectory_filename, 'w') as fh: json.dump(saveable_trajectory, fh) @@ -434,7 +468,7 @@ def _fit(self, datamanager): def refit(self, X, y): def send_warnings_to_log(message, category, filename, lineno, - file=None): + file=None, line=None): self._logger.debug('%s:%s: %s:%s' % (filename, lineno, category.__name__, message)) return @@ -462,16 +496,34 @@ def send_warnings_to_log(message, category, filename, lineno, warnings.showwarning = send_warnings_to_log model.fit(X.copy(), y.copy()) break - except ValueError: + except ValueError as e: indices = list(range(X.shape[0])) random_state.shuffle(indices) X = X[indices] y = y[indices] + if i == 9: + raise e + self._can_predict = True return self - def predict(self, X): + def predict(self, X, batch_size=None, n_jobs=1): + """predict. + + Parameters + ---------- + X: array-like, shape = (n_samples, n_features) + + batch_size: int or None, defaults to None + batch_size controls whether the pipelines will be + called on small chunks of the data. Useful when calling the + predict method on the whole array X results in a MemoryError. + + n_jobs: int, defaults to 1 + Parallelize the predictions across the models with n_jobs + processes. + """ if self._keep_models is not True: raise ValueError( "Predict can only be called if 'keep_models==True'") @@ -486,31 +538,11 @@ def predict(self, X): self.ensemble_ is None: self._load_models() - def send_warnings_to_log(message, category, filename, lineno, - file=None): - self._logger.debug('%s:%s: %s:%s' % - (filename, lineno, category.__name__, message)) - return - - all_predictions = [] - for identifier in self.ensemble_.get_model_identifiers(): - model = self.models_[identifier] - - X_ = X.copy() - with warnings.catch_warnings(): - warnings.showwarning = send_warnings_to_log - if self._task in REGRESSION_TASKS: - prediction = model.predict(X_) - else: - prediction = model.predict_proba(X_) - - if len(prediction.shape) < 1 or len(X_.shape) < 1 or \ - X_.shape[0] < 1 or prediction.shape[0] != X_.shape[0]: - self._logger.warning("Prediction shape for model %s is %s " - "while X_.shape is %s" % - (model, str(prediction.shape), - str(X_.shape))) - all_predictions.append(prediction) + # Parallelize predictions across models with n_jobs processes. + # Each process computes predictions in chunks of batch_size rows. + all_predictions = joblib.Parallel(n_jobs=n_jobs)( + joblib.delayed(_model_predict)(self, X, batch_size, identifier) + for identifier in self.ensemble_.get_model_identifiers()) if len(all_predictions) == 0: raise ValueError('Something went wrong generating the predictions. ' @@ -596,49 +628,33 @@ def _load_models(self): if self.ensemble_: identifiers = self.ensemble_.identifiers_ self.models_ = self._backend.load_models_by_identifiers(identifiers) - else: - self.models_ = self._backend.load_all_models(seed) + if len(self.models_) == 0 and self._resampling_strategy not in \ + ['partial-cv', 'partial-cv-iterative-fit']: + raise ValueError('No models fitted!') + + elif self._disable_evaluator_output is False or \ + (isinstance(self._disable_evaluator_output, list) and + 'model' not in self._disable_evaluator_output): + model_names = self._backend.list_all_models(seed) + + if len(model_names) == 0 and self._resampling_strategy not in \ + ['partial-cv', 'partial-cv-iterative-fit']: + raise ValueError('No models fitted!') - if len(self.models_) == 0 and self._resampling_strategy not in \ - ['partial-cv', 'partial-cv-iterative-fit']: - raise ValueError('No models fitted!') + self.models = [] + + else: + self.models = [] def score(self, X, y): # fix: Consider only index 1 of second dimension # Don't know if the reshaping should be done there or in calculate_score prediction = self.predict(X) - return calculate_score(y, prediction, self._task, - self._metric, self._label_num, - logger=self._logger) - - @property - def grid_scores_(self): - grid_scores = list() - - scores_per_config = defaultdict(list) - config_list = list() - - for run_key in self.runhistory_.data: - run_value = self.runhistory_.data[run_key] - - config_id = run_key.config_id - cost = run_value.cost - - if config_id not in config_list: - config_list.append(config_id) - - scores_per_config[config_id].append(cost) - - for config_id in config_list: - scores = [1 - score for score in scores_per_config[config_id]] - mean_score = np.mean(scores) - config = self.runhistory_.ids_config[config_id] - - grid_score = _CVScoreTuple(config.get_dictionary(), mean_score, - scores) - grid_scores.append(grid_score) - - return grid_scores + return calculate_score(solution=y, + prediction=prediction, + task_type=self._task, + metric=self._metric, + all_scoring_functions=False) @property def cv_results_(self): @@ -730,7 +746,7 @@ def sprint_statistics(self): sio = io.StringIO() sio.write('auto-sklearn results:\n') sio.write(' Dataset name: %s\n' % self._dataset_name) - sio.write(' Metric: %s\n' % METRIC_TO_STRING[self._metric]) + sio.write(' Metric: %s\n' % self._metric) idx_best_run = np.argmax(cv_results['mean_test_score']) best_score = cv_results['mean_test_score'][idx_best_run] sio.write(' Best validation score: %f\n' % best_score) @@ -748,7 +764,6 @@ def sprint_statistics(self): 'limit: %d\n' % num_memout) return sio.getvalue() - def show_models(self): if self.models_ is None or len(self.models_) == 0 or \ self.ensemble_ is None: diff --git a/autosklearn/constants.py b/autosklearn/constants.py index e234bb2a04..85427f031f 100644 --- a/autosklearn/constants.py +++ b/autosklearn/constants.py @@ -22,50 +22,3 @@ 'multiclass.classification': MULTICLASS_CLASSIFICATION, 'multilabel.classification': MULTILABEL_CLASSIFICATION, 'regression': REGRESSION} - - -ACC_METRIC = 5 -AUC_METRIC = 6 -BAC_METRIC = 7 -F1_METRIC = 8 -PAC_METRIC = 9 -CLASSIFICATION_METRICS = [ACC_METRIC, AUC_METRIC, BAC_METRIC, - F1_METRIC, PAC_METRIC] - -R2_METRIC = 10 -A_METRIC = 11 -REGRESSION_METRICS = [R2_METRIC, A_METRIC] -METRIC = CLASSIFICATION_METRICS + REGRESSION_METRICS -STRING_TO_METRIC = { - 'acc': ACC_METRIC, - 'acc_metric': ACC_METRIC, - 'auc': AUC_METRIC, - 'auc_metric': AUC_METRIC, - 'bac': BAC_METRIC, - 'bac_metric': BAC_METRIC, - 'f1': F1_METRIC, - 'f1_metric': F1_METRIC, - 'pac': PAC_METRIC, - 'pac_metric': PAC_METRIC, - 'r2': R2_METRIC, - 'r2_metric': R2_METRIC, - 'a': A_METRIC, - 'a_metric': A_METRIC} - -METRIC_TO_STRING = { - ACC_METRIC: 'acc_metric', - AUC_METRIC: 'auc_metric', - BAC_METRIC: 'bac_metric', - F1_METRIC: 'f1_metric', - PAC_METRIC: 'pac_metric', - R2_METRIC: 'r2_metric', - A_METRIC: 'a_metric'} - -METRICS_SHORT_TO_LONG_FORM = { - 'acc': 'acc_metric', - 'auc': 'auc_metric', - 'bac': 'bac_metric', - 'f1': 'f1_metric', - 'pac': 'pac_metric', - 'r2': 'r2_metric', - 'a': 'a_metric'} \ No newline at end of file diff --git a/autosklearn/data/abstract_data_manager.py b/autosklearn/data/abstract_data_manager.py index 4c9cff0564..f7140efb0d 100644 --- a/autosklearn/data/abstract_data_manager.py +++ b/autosklearn/data/abstract_data_manager.py @@ -1,9 +1,35 @@ # -*- encoding: utf-8 -*- import abc - +import numpy as np import scipy.sparse -from autosklearn.data.generic_one_hot_encoding import perform_one_hot_encoding +from autosklearn.pipeline.implementations.OneHotEncoder import OneHotEncoder +from autosklearn.util import predict_RAM_usage + + +def perform_one_hot_encoding(sparse, categorical, data): + predicted_RAM_usage = float( + predict_RAM_usage(data[0], categorical)) / 1024 / 1024 + + if predicted_RAM_usage > 1000: + sparse = True + + rvals = [] + if any(categorical): + encoder = OneHotEncoder(categorical_features=categorical, + dtype=np.float32, + sparse=sparse) + rvals.append(encoder.fit_transform(data[0])) + for d in data[1:]: + rvals.append(encoder.transform(d)) + + if not sparse and scipy.sparse.issparse(rvals[0]): + for i in range(len(rvals)): + rvals[i] = rvals[i].todense() + else: + rvals = data + + return rvals, sparse class AbstractDataManager(): diff --git a/autosklearn/data/competition_data_manager.py b/autosklearn/data/competition_data_manager.py index 147982b293..2307d25898 100644 --- a/autosklearn/data/competition_data_manager.py +++ b/autosklearn/data/competition_data_manager.py @@ -3,16 +3,15 @@ # Functions performing various input/output operations for the ChaLearn # AutoML challenge -from __future__ import print_function import os import re -import time +import warnings import numpy as np import scipy.sparse from autosklearn.constants import MULTILABEL_CLASSIFICATION, \ - STRING_TO_TASK_TYPES, MULTICLASS_CLASSIFICATION, STRING_TO_METRIC + STRING_TO_TASK_TYPES, MULTICLASS_CLASSIFICATION from autosklearn.data.abstract_data_manager import AbstractDataManager from autosklearn.util import convert_to_num try: @@ -156,7 +155,7 @@ def load_labels(filename): class CompetitionDataManager(AbstractDataManager): - def __init__(self, name, encode_labels=True, max_memory_in_mb=1048576): + def __init__(self, name, max_memory_in_mb=1048576): """ max_memory_size in Mb """ if name.endswith("/"): name = name[:-1] @@ -214,9 +213,6 @@ def __init__(self, name, encode_labels=True, max_memory_in_mb=1048576): except (IOError, OSError): pass - if encode_labels: - self.perform1HotEncoding() - def load_data(self, filename, num_points, max_memory_in_mb): """Get the data from a text file in one of 3 formats: matrix, sparse, binary_sparse""" @@ -331,7 +327,9 @@ def get_info(self, filename): 'file.') self.info['task'] = STRING_TO_TASK_TYPES[self.info['task']] - self.info['metric'] = STRING_TO_METRIC[self.info['metric']] + warnings.warn('auto-sklearn will no longer take the metric given by ' + 'the data manager into account. Please specify the ' + 'metric when calling fit().') return self.info diff --git a/autosklearn/data/generic_one_hot_encoding.py b/autosklearn/data/generic_one_hot_encoding.py deleted file mode 100644 index 21714fbec2..0000000000 --- a/autosklearn/data/generic_one_hot_encoding.py +++ /dev/null @@ -1,31 +0,0 @@ -import numpy as np -import scipy.sparse - -from autosklearn.pipeline.implementations.OneHotEncoder import OneHotEncoder - -from autosklearn.util import predict_RAM_usage - - -def perform_one_hot_encoding(sparse, categorical, data): - - predicted_RAM_usage = float(predict_RAM_usage(data[0], categorical)) / 1024 / 1024 - - if predicted_RAM_usage > 1000: - sparse = True - - rvals = [] - if any(categorical): - encoder = OneHotEncoder(categorical_features=categorical, - dtype=np.float32, - sparse=sparse) - rvals.append(encoder.fit_transform(data[0])) - for d in data[1:]: - rvals.append(encoder.transform(d)) - - if not sparse and scipy.sparse.issparse(rvals[0]): - for i in range(len(rvals)): - rvals[i] = rvals[i].todense() - else: - rvals = data - - return rvals, sparse \ No newline at end of file diff --git a/autosklearn/data/xy_data_manager.py b/autosklearn/data/xy_data_manager.py index 15c4fdd714..e117c70e3f 100644 --- a/autosklearn/data/xy_data_manager.py +++ b/autosklearn/data/xy_data_manager.py @@ -10,18 +10,13 @@ class XYDataManager(AbstractDataManager): - def __init__(self, data_x, y, task, metric, feat_type, dataset_name, - encode_labels): + def __init__(self, data_x, y, task, feat_type, dataset_name): super(XYDataManager, self).__init__(dataset_name) if isinstance(task, six.string_types): task = STRING_TO_TASK_TYPES[task] - if isinstance(metric, six.string_types): - metric = STRING_TO_METRIC[metric] - self.info['task'] = task - self.info['metric'] = metric if sparse.issparse(data_x): self.info['is_sparse'] = 1 self.info['has_missing'] = np.all(np.isfinite(data_x.data)) @@ -40,6 +35,14 @@ def __init__(self, data_x, y, task, metric, feat_type, dataset_name, self.data['X_train'] = data_x self.data['Y_train'] = y + + if feat_type is not None: + for feat in feat_type: + allowed_types = ['numerical', 'categorical'] + if feat.lower() not in allowed_types: + raise ValueError("Entry '%s' in feat_type not in %s" % + (feat.lower(), str(allowed_types))) + self.feat_type = feat_type # TODO: try to guess task type! @@ -58,6 +61,3 @@ def __init__(self, data_x, y, task, metric, feat_type, dataset_name, raise ValueError('X and feat type must have the same dimensions, ' 'but are %d and %d.' % (data_x.shape[1], len(self.feat_type))) - - if encode_labels: - self.perform1HotEncoding() diff --git a/autosklearn/ensemble_builder.py b/autosklearn/ensemble_builder.py index 3651bc07c7..682e52a205 100644 --- a/autosklearn/ensemble_builder.py +++ b/autosklearn/ensemble_builder.py @@ -11,9 +11,9 @@ import numpy as np import pynisher -from autosklearn.constants import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, \ - MULTILABEL_CLASSIFICATION, CLASSIFICATION_TASKS, BAC_METRIC, F1_METRIC -from autosklearn.evaluation.util import calculate_score +from autosklearn.constants import BINARY_CLASSIFICATION, \ + MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION, CLASSIFICATION_TASKS +from autosklearn.metrics import calculate_score from autosklearn.util import StopWatch from autosklearn.ensembles.ensemble_selection import EnsembleSelection from autosklearn.util.logging_ import get_logger @@ -22,8 +22,7 @@ class EnsembleBuilder(multiprocessing.Process): def __init__(self, backend, dataset_name, task_type, metric, limit, ensemble_size=None, ensemble_nbest=None, - seed=1, shared_mode=False, max_iterations=-1, precision="32", - low_precision=True): + seed=1, shared_mode=False, max_iterations=-1, precision="32"): super(EnsembleBuilder, self).__init__() self.backend = backend @@ -37,7 +36,6 @@ def __init__(self, backend, dataset_name, task_type, metric, self.shared_mode = shared_mode self.max_iterations = max_iterations self.precision = precision - self.low_precision = low_precision logger_name = 'EnsembleBuilder(%d):%s' % (self.seed, self.dataset_name) self.logger = get_logger(logger_name) @@ -188,9 +186,11 @@ def main(self): else: predictions = np.load(fh) - score = calculate_score(targets_ensemble, predictions, - self.task_type, self.metric, - predictions.shape[1]) + score = calculate_score(solution=targets_ensemble, + prediction=predictions, + task_type=self.task_type, + metric=self.metric, + all_scoring_functions=False) except Exception as e: self.logger.warning('Error loading %s: %s - %s', @@ -309,11 +309,11 @@ def main(self): used_time = watch.wall_elapsed('ensemble_builder') time.sleep(2) continue - except Exception as e: - self.logger.error('Caught error! %s', str(e)) - used_time = watch.wall_elapsed('ensemble_builder') - time.sleep(2) - continue + #except Exception as e: + # self.logger.error('Caught error! %s', str(e)) + # used_time = watch.wall_elapsed('ensemble_builder') + # time.sleep(2) + # continue # Output the score self.logger.info('Training performance: %f' % ensemble.train_score_) @@ -356,35 +356,9 @@ def main(self): ensemble_predictions_valid = ensemble.predict(all_predictions_valid) if self.task_type == BINARY_CLASSIFICATION: ensemble_predictions_valid = ensemble_predictions_valid[:, 1] - if self.low_precision: - if self.task_type in [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION]: - ensemble_predictions_valid[ensemble_predictions_valid < 1e-4] = 0. - if self.metric in [BAC_METRIC, F1_METRIC]: - bin_array = np.zeros(ensemble_predictions_valid.shape, dtype=np.int32) - if (self.task_type != MULTICLASS_CLASSIFICATION) or ( - ensemble_predictions_valid.shape[1] == 1): - bin_array[ensemble_predictions_valid >= 0.5] = 1 - else: - sample_num = ensemble_predictions_valid.shape[0] - for i in range(sample_num): - j = np.argmax(ensemble_predictions_valid[i, :]) - bin_array[i, j] = 1 - ensemble_predictions_valid = bin_array - if self.task_type in CLASSIFICATION_TASKS: - if ensemble_predictions_valid.size < (20000 * 20): - precision = 3 - else: - precision = 2 - else: - if ensemble_predictions_valid.size > 1000000: - precision = 4 - else: - # File size maximally 2.1MB - precision = 6 self.backend.save_predictions_as_txt(ensemble_predictions_valid, - 'valid', index_run, prefix=self.dataset_name, - precision=precision) + 'valid', index_run, prefix=self.dataset_name) else: self.logger.info('Could not find as many validation set predictions (%d)' 'as ensemble predictions (%d)!.', @@ -397,35 +371,9 @@ def main(self): ensemble_predictions_test = ensemble.predict(all_predictions_test) if self.task_type == BINARY_CLASSIFICATION: ensemble_predictions_test = ensemble_predictions_test[:, 1] - if self.low_precision: - if self.task_type in [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, MULTILABEL_CLASSIFICATION]: - ensemble_predictions_test[ensemble_predictions_test < 1e-4] = 0. - if self.metric in [BAC_METRIC, F1_METRIC]: - bin_array = np.zeros(ensemble_predictions_test.shape, - dtype=np.int32) - if (self.task_type != MULTICLASS_CLASSIFICATION) or ( - ensemble_predictions_test.shape[1] == 1): - bin_array[ensemble_predictions_test >= 0.5] = 1 - else: - sample_num = ensemble_predictions_test.shape[0] - for i in range(sample_num): - j = np.argmax(ensemble_predictions_test[i, :]) - bin_array[i, j] = 1 - ensemble_predictions_test = bin_array - if self.task_type in CLASSIFICATION_TASKS: - if ensemble_predictions_test.size < (20000 * 20): - precision = 3 - else: - precision = 2 - else: - if ensemble_predictions_test.size > 1000000: - precision = 4 - else: - precision = 6 self.backend.save_predictions_as_txt(ensemble_predictions_test, - 'test', index_run, prefix=self.dataset_name, - precision=precision) + 'test', index_run, prefix=self.dataset_name) else: self.logger.info('Could not find as many test set predictions (%d) as ' 'ensemble predictions (%d)!', diff --git a/autosklearn/ensembles/ensemble_selection.py b/autosklearn/ensembles/ensemble_selection.py index 1b76e0fd35..59794c683b 100644 --- a/autosklearn/ensembles/ensemble_selection.py +++ b/autosklearn/ensembles/ensemble_selection.py @@ -6,7 +6,8 @@ from autosklearn.constants import * from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble -from autosklearn.evaluation.util import calculate_score +from autosklearn.metrics import calculate_score +from autosklearn.metrics import Scorer class EnsembleSelection(AbstractEnsemble): @@ -25,8 +26,8 @@ def fit(self, predictions, labels, identifiers): raise ValueError('Ensemble size cannot be less than one!') if not self.task_type in TASK_TYPES: raise ValueError('Unknown task type %s.' % self.task_type) - if not self.metric in METRIC: - raise ValueError('Unknown metric %s.' % self.metric) + if not isinstance(self.metric, Scorer): + raise ValueError('Metric must be of type scorer') if self.mode not in ('fast', 'slow'): raise ValueError('Unknown mode %s' % self.mode) @@ -81,8 +82,11 @@ def _fast(self, predictions, labels): fant_ensemble_prediction = weighted_ensemble_prediction + \ (1. / float(s + 1)) * pred scores[j] = calculate_score( - labels, fant_ensemble_prediction, self.task_type, - self.metric, fant_ensemble_prediction.shape[1]) + solution=labels, + prediction=fant_ensemble_prediction, + task_type=self.task_type, + metric=self.metric, + all_scoring_functions=False) best = np.nanargmax(scores) ensemble.append(predictions[best]) trajectory.append(scores[best]) @@ -114,8 +118,11 @@ def _slow(self, predictions, labels): order.append(idx) ensemble_ = np.array(ensemble).mean(axis=0) ensemble_performance = calculate_score( - labels, ensemble_, self.task_type, self.metric, - ensemble_.shape[1]) + solution=labels, + prediction=ensemble_, + task_type=self.task_type, + metric=self.metric, + all_scoring_functions=False) trajectory.append(ensemble_performance) ensemble_size -= n_best @@ -124,9 +131,12 @@ def _slow(self, predictions, labels): for j, pred in enumerate(predictions): ensemble.append(pred) ensemble_prediction = np.mean(np.array(ensemble), axis=0) - scores[j] = calculate_score(labels, ensemble_prediction, - self.task_type, self.metric, - ensemble_prediction.shape[1]) + scores[j] = calculate_score( + solution=labels, + prediction=ensemble_prediction, + task_type=self.task_type, + metric=self.metric, + all_scoring_functions=False) ensemble.pop() best = np.nanargmax(scores) ensemble.append(predictions[best]) diff --git a/autosklearn/estimators.py b/autosklearn/estimators.py index 1066431bc4..e53942f893 100644 --- a/autosklearn/estimators.py +++ b/autosklearn/estimators.py @@ -1,16 +1,18 @@ # -*- encoding: utf-8 -*- import numpy as np -import six import warnings -import autosklearn.automl -from autosklearn.constants import * -from autosklearn.util.backend import create +from sklearn.metrics.classification import type_of_target from sklearn.base import BaseEstimator import sklearn.utils import scipy.sparse +import autosklearn.automl +from autosklearn.metrics import f1_macro, accuracy, r2 +from autosklearn.constants import * +from autosklearn.util.backend import create + class AutoMLDecorator(object): @@ -50,37 +52,27 @@ def refit(self, X, y): def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, ensemble_size=None): - """Build the ensemble. - This method only needs to be called in the parallel mode. - - Returns - ------- - self - """ return self._automl.fit_ensemble(y, task, metric, precision, dataset_name, ensemble_nbest, ensemble_size) - def predict(self, X): - return self._automl.predict(X) + def predict(self, X, batch_size=None, n_jobs=1): + return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) def score(self, X, y): return self._automl.score(X, y) def show_models(self): - """Return a representation of the final ensemble found by auto-sklearn + """Return a representation of the final ensemble found by auto-sklearn. Returns ------- str + """ return self._automl.show_models() - @property - def grid_scores_(self): - return self._automl.grid_scores_ - @property def cv_results_(self): return self._automl.cv_results_ @@ -89,6 +81,10 @@ def cv_results_(self): def trajectory_(self): return self._automl.trajectory_ + @property + def fANOVA_input_(self): + return self._automl.fANOVA_input_ + def sprint_statistics(self): return self._automl.sprint_statistics() @@ -124,6 +120,7 @@ def __init__(self, models. By increasing this value, *auto-sklearn* has a higher chance of finding better models. + per_run_time_limit : int, optional (default=360) Time limit for a single call to the machine learning model. Model fitting will be terminated if the machine learning @@ -138,8 +135,8 @@ def __init__(self, should start from scratch. ensemble_size : int, optional (default=50) - Number of models added to the ensemble built by `Ensemble - selection from libraries of models. Models are drawn with + Number of models added to the ensemble built by *Ensemble + selection from libraries of models*. Models are drawn with replacement. ensemble_nbest : int, optional (default=50) @@ -207,30 +204,36 @@ def __init__(self, ``delete_tmp_folder_after_terminate`` and ``delete_output_folder_after_terminate`` are set to False. - disable_evaluator_output: bool, optional (False) - Disable model and prediction output. Cannot be used together with - ensemble building. predict() cannot be used when setting this - flag to True. + disable_evaluator_output: bool or list, optional (False) + If True, disable model and prediction output. Cannot be used + together with ensemble building. ``predict()`` cannot be used when + setting this True. Can also be used as a list to pass more + fine-grained information on what to save. Allowed elements in the + list are: + + * ``'y_optimization'`` : do not save the predictions for the + optimization/validation set, which would later on be used to build + an ensemble. + * ``'model'`` : do not save any model files + + configuration_mode : ``SMAC`` or ``ROAR`` + Defines the configuration mode as described in the paper + `Sequential Model-Based Optimization for General Algorithm + Configuration `_: + + * ``SMAC`` (default): Sequential Model-based Algorithm + Configuration, which is a Bayesian optimization algorithm + * ``ROAR``: Random Online Aggressive Racing, which is basically + random search Attributes ---------- - grid_scores\_ : list of named tuples - Contains scores for all parameter combinations in param_grid. - Each entry corresponds to one parameter setting. - Each named tuple has the attributes: - - * ``parameters``, a dict of parameter settings - * ``mean_validation_score``, the mean score over the - cross-validation folds - * ``cv_validation_scores``, the list of scores for each fold cv_results\_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. - This attribute is a backward port to already support the advanced - output of scikit-learn 0.18. Not all keys returned by scikit-learn - are supported yet. + Not all keys returned by scikit-learn are supported yet. """ self.time_left_for_this_task = time_left_for_this_task @@ -303,6 +306,46 @@ def fit(self, *args, **kwargs): def fit_ensemble(self, y, task=None, metric=None, precision='32', dataset_name=None, ensemble_nbest=None, ensemble_size=None): + """Fit an ensemble to models trained during an optimization process. + + All parameters are ``None`` by default. If no other value is given, + the default values which were set in a call to ``fit()`` are used. + + Parameters + ---------- + y : array-like + Target values. + + task : int + A constant from the module ``autosklearn.constants``. Determines + the task type (binary classification, multiclass classification, + multilabel classification or regression). + + metric : callable, optional + An instance of :class:`autosklearn.metrics.Scorer` as created by + :meth:`autosklearn.metrics.make_scorer`. These are the `Built-in + Metrics`_. + + precision : str + Numeric precision used when loading ensemble data. Can be either + ``'16'``, ``'32'`` or ``'64'``. + + dataset_name : str + Name of the current data set. + + ensemble_nbest : int + Determines how many models should be considered from the ensemble + building. This is inspired by a concept called library pruning + introduced in `Getting Most out of Ensemble Selection`. + + ensemble_size : int + Size of the ensemble built by `Ensomble Selection`. + + Returns + ------- + self + + """ if self._automl is None: self._automl = self.build_automl() return self._automl.fit_ensemble(y, task, metric, precision, @@ -313,6 +356,7 @@ def fit_ensemble(self, y, task=None, metric=None, precision='32', class AutoSklearnClassifier(AutoSklearnEstimator): """ This class implements the classification task. + """ def build_automl(self): @@ -320,7 +364,7 @@ def build_automl(self): return AutoMLClassifier(automl) def fit(self, X, y, - metric='acc_metric', + metric=None, feat_type=None, dataset_name=None): """Fit *auto-sklearn* to given training set (X, y). @@ -334,15 +378,18 @@ def fit(self, X, y, y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target classes. - metric : str, optional (default='acc_metric') - The metric to optimize for. Can be one of: ['acc_metric', - 'auc_metric', 'bac_metric', 'f1_metric', 'pac_metric']. A - description of the metrics can be found in `the paper describing - the AutoML Challenge. + metric : callable, optional (default='autosklearn.metrics.accuracy') + An instance of :class:`autosklearn.metrics.Scorer` as created by + :meth:`autosklearn.metrics.make_scorer`. These are the `Built-in + Metrics`_. + feat_type : list, optional (default=None) List of str of `len(X.shape[1])` describing the attribute type. Possible types are `Categorical` and `Numerical`. `Categorical` - attributes will be automatically One-Hot encoded. + attributes will be automatically One-Hot encoded. The values + used for a categorical attribute must be integers, obtained for + example by `sklearn.preprocessing.LabelEncoder + `_. dataset_name : str, optional (default=None) Create nicer output. If None, a string will be determined by the @@ -357,7 +404,7 @@ def fit(self, X, y, feat_type=feat_type, dataset_name=dataset_name) - def predict(self, X): + def predict(self, X, batch_size=None, n_jobs=1): """Predict classes for X. Parameters @@ -370,9 +417,10 @@ def predict(self, X): The predicted classes. """ - return super(AutoSklearnClassifier, self).predict(X) + return super(AutoSklearnClassifier, self).predict( + X, batch_size=batch_size, n_jobs=n_jobs) - def predict_proba(self, X): + def predict_proba(self, X, batch_size=None, n_jobs=1): """Predict probabilities of classes for all samples X. @@ -384,13 +432,16 @@ def predict_proba(self, X): ------- y : array of shape = [n_samples, n_classes] or [n_samples, n_labels] The predicted class probabilities. + """ - return self._automl.predict_proba(X) + return self._automl.predict_proba( + X, batch_size=batch_size, n_jobs=n_jobs) class AutoSklearnRegressor(AutoSklearnEstimator): """ This class implements the regression task. + """ def build_automl(self): @@ -398,7 +449,7 @@ def build_automl(self): return AutoMLRegressor(automl) def fit(self, X, y, - metric='r2_metric', + metric=None, feat_type=None, dataset_name=None): """Fit *autosklearn* to given training set (X, y). @@ -412,11 +463,10 @@ def fit(self, X, y, y : array-like, shape = [n_samples] or [n_samples, n_outputs] The regression target. - metric : str, optional (default='r2_metric') - The metric to optimize for. Can be one of: ['r2_metric', - 'a_metric']. A description of the metrics can be found in - `the paper describing the AutoML Challenge - `_. + metric : callable, optional (default='autosklearn.metrics.r2') + An instance of :class:`autosklearn.metrics.Scorer` as created by + :meth:`autosklearn.metrics.make_scorer`. These are the `Built-in + Metrics`_. feat_type : list, optional (default=None) List of str of `len(X.shape[1])` describing the attribute type. @@ -438,7 +488,7 @@ def fit(self, X, y, feat_type=feat_type, dataset_name=dataset_name) - def predict(self, X): + def predict(self, X, batch_size=None, n_jobs=1): """Predict regression target for X. Parameters @@ -451,7 +501,8 @@ def predict(self, X): The predicted values. """ - return super(AutoSklearnRegressor, self).predict(X) + return super(AutoSklearnRegressor, self).predict( + X, batch_size=batch_size, n_jobs=n_jobs) class AutoMLClassifier(AutoMLDecorator): @@ -464,26 +515,33 @@ def __init__(self, automl): super(AutoMLClassifier, self).__init__(automl) def fit(self, X, y, - metric='acc_metric', + metric=None, loss=None, feat_type=None, - dataset_name=None, - ): - # From sklearn.tree.DecisionTreeClassifier + dataset_name=None): X = sklearn.utils.check_array(X, accept_sparse="csr", force_all_finite=False) + y = sklearn.utils.check_array(y, ensure_2d=False) + if scipy.sparse.issparse(X): X.sort_indices() - y = self._process_target_classes(y) + y_task = type_of_target(y) + task_mapping = {'multilabel-indicator': MULTILABEL_CLASSIFICATION, + 'multiclass': MULTICLASS_CLASSIFICATION, + 'binary': BINARY_CLASSIFICATION} - if self._n_outputs > 1: - task = MULTILABEL_CLASSIFICATION - else: - if len(self._classes[0]) == 2: - task = BINARY_CLASSIFICATION + task = task_mapping.get(y_task) + if task is None: + raise ValueError('Cannot work on data of type %s' % y_task) + + if metric is None: + if task == MULTILABEL_CLASSIFICATION: + metric = f1_macro else: - task = MULTICLASS_CLASSIFICATION + metric = accuracy + + y = self._process_target_classes(y) return self._automl.fit(X, y, task, metric, feat_type, dataset_name) @@ -514,51 +572,50 @@ def _process_target_classes(self, y): self._classes = [] self._n_classes = [] - for k in six.moves.range(self._n_outputs): + for k in range(self._n_outputs): classes_k, y[:, k] = np.unique(y[:, k], return_inverse=True) self._classes.append(classes_k) self._n_classes.append(classes_k.shape[0]) self._n_classes = np.array(self._n_classes, dtype=np.int) - # TODO: fix metafeatures calculation to allow this! if y.shape[1] == 1: y = y.flatten() return y + def predict(self, X, batch_size=None, n_jobs=1): + predicted_probabilities = self._automl.predict( + X, batch_size=batch_size, n_jobs=n_jobs) - def predict(self, X): - predicted_probabilities = self._automl.predict(X) if self._n_outputs == 1: predicted_indexes = np.argmax(predicted_probabilities, axis=1) predicted_classes = self._classes[0].take(predicted_indexes) return predicted_classes else: - argmax_v = np.vectorize(np.argmax, otypes=[int]) - predicted_indexes = argmax_v(predicted_probabilities) - #predicted_indexes = np.argmax(predicted_probabilities, axis=1) + predicted_indices = (predicted_probabilities > 0.5).astype(int) n_samples = predicted_probabilities.shape[0] - predicted_classes = np.zeros((n_samples, self._n_outputs), dtype=object) + predicted_classes = np.zeros((n_samples, self._n_outputs)) - for k in six.moves.range(self._n_outputs): - output_predicted_indexes = predicted_indexes[:, k].reshape(-1) + for k in range(self._n_outputs): + output_predicted_indexes = predicted_indices[:, k].reshape(-1) predicted_classes[:, k] = self._classes[k].take(output_predicted_indexes) return predicted_classes - def predict_proba(self, X): - return self._automl.predict(X) + def predict_proba(self, X, batch_size=None, n_jobs=1): + return self._automl.predict(X, batch_size=batch_size, n_jobs=n_jobs) class AutoMLRegressor(AutoMLDecorator): def fit(self, X, y, - metric='r2_metric', - loss=None, + metric=None, feat_type=None, dataset_name=None, ): + if metric is None: + metric = r2 return self._automl.fit(X=X, y=y, task=REGRESSION, metric=metric, feat_type=feat_type, dataset_name=dataset_name) diff --git a/autosklearn/evaluation/__init__.py b/autosklearn/evaluation/__init__.py index 25a4076a1d..07a99192fa 100644 --- a/autosklearn/evaluation/__init__.py +++ b/autosklearn/evaluation/__init__.py @@ -1,54 +1,77 @@ # -*- encoding: utf-8 -*- +import copy +import functools import logging import math import multiprocessing +from queue import Empty +import traceback -import numpy as np import pynisher -from sklearn.cross_validation import ShuffleSplit, StratifiedShuffleSplit, KFold, \ +from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit, KFold, \ StratifiedKFold -from smac.tae.execute_ta_run import StatusType +from smac.tae.execute_ta_run import StatusType, BudgetExhaustedException from smac.tae.execute_func import AbstractTAFunc +from ConfigSpace import Configuration -from .abstract_evaluator import * -from .train_evaluator import * -from .test_evaluator import * -from .util import * -from autosklearn.constants import REGRESSION_TASKS, CLASSIFICATION_TASKS, \ - MULTILABEL_CLASSIFICATION +import autosklearn.evaluation.train_evaluator +import autosklearn.evaluation.test_evaluator +import autosklearn.evaluation.util +from autosklearn.constants import CLASSIFICATION_TASKS, MULTILABEL_CLASSIFICATION WORST_POSSIBLE_RESULT = 1.0 +def fit_predict_try_except_decorator(ta, queue, **kwargs): + + try: + return ta(queue=queue, **kwargs) + except Exception as e: + if isinstance(e, MemoryError): + # Re-raise the memory error to let the pynisher handle that + # correctly + raise e + + exception_traceback = traceback.format_exc() + error_message = repr(e) + + queue.put({'loss': WORST_POSSIBLE_RESULT, + 'additional_run_info': {'traceback': exception_traceback, + 'error': error_message}, + 'status': StatusType.CRASHED, + 'final_queue_element': True}) + + # TODO potentially log all inputs to this class to pickle them in order to do # easier debugging of potential crashes class ExecuteTaFuncWithQueue(AbstractTAFunc): - def __init__(self, backend, autosklearn_seed, resampling_strategy, + def __init__(self, backend, autosklearn_seed, resampling_strategy, metric, logger, initial_num_run=1, stats=None, runhistory=None, - run_obj='quality', par_factor=1, with_predictions=True, - all_scoring_functions=False, output_y_test=True, - include=None, exclude=None, memory_limit=None, - disable_file_output=False, **resampling_strategy_args): + run_obj='quality', par_factor=1, all_scoring_functions=False, + output_y_hat_optimization=True, include=None, exclude=None, + memory_limit=None, disable_file_output=False, + **resampling_strategy_args): if resampling_strategy == 'holdout': - eval_function = eval_holdout + eval_function = autosklearn.evaluation.train_evaluator.eval_holdout elif resampling_strategy == 'holdout-iterative-fit': - eval_function = eval_iterative_holdout + eval_function = autosklearn.evaluation.train_evaluator.eval_iterative_holdout elif resampling_strategy == 'cv': - eval_function = eval_cv + eval_function = autosklearn.evaluation.train_evaluator.eval_cv elif resampling_strategy == 'partial-cv': - eval_function = eval_partial_cv + eval_function = autosklearn.evaluation.train_evaluator.eval_partial_cv elif resampling_strategy == 'partial-cv-iterative-fit': - eval_function = eval_partial_cv_iterative + eval_function = autosklearn.evaluation.train_evaluator.eval_partial_cv_iterative elif resampling_strategy == 'test': - eval_function = eval_t - output_y_test = False - with_predictions = False + eval_function = autosklearn.evaluation.test_evaluator.eval_t + output_y_hat_optimization = False else: raise ValueError('Unknown resampling strategy %s' % resampling_strategy) + eval_function = functools.partial(fit_predict_try_except_decorator, + ta=eval_function) super().__init__(ta=eval_function, stats=stats, runhistory=runhistory, run_obj=run_obj, par_factor=par_factor) @@ -56,12 +79,12 @@ def __init__(self, backend, autosklearn_seed, resampling_strategy, self.autosklearn_seed = autosklearn_seed self.resampling_strategy = resampling_strategy self.num_run = initial_num_run + self.metric = metric self.resampling_strategy = resampling_strategy self.resampling_strategy_args = resampling_strategy_args - self.with_predictions = with_predictions self.all_scoring_functions = all_scoring_functions - # TODO deactivate output_y_test and let the respective evaluator decide - self.output_y_test = output_y_test + # TODO deactivate output_y_hat_optimization and let the respective evaluator decide + self.output_y_hat_optimization = output_y_hat_optimization self.include = include self.exclude = exclude self.disable_file_output = disable_file_output @@ -71,27 +94,54 @@ def __init__(self, backend, autosklearn_seed, resampling_strategy, memory_limit = int(math.ceil(memory_limit)) self.memory_limit = memory_limit - def start(self, config, instance, - cutoff=None, - seed=12345, - instance_specific=None): - # Overwrite the start function here. This allows us to abort target - # algorithm runs if the time us over without having the start method - # of the parent class adding the run to the runhistory + def start(self, config: Configuration, + instance: str, + cutoff: float = None, + seed: int = 12345, + instance_specific: str = "0", + capped: bool = False): + """ + wrapper function for ExecuteTARun.start() to cap the target algorithm + runtime if it would run over the total allowed runtime. - # Restrict the cutoff to not go over the final time limit, but stop ten - # seconds earlier + Parameters + ---------- + config : Configuration + mainly a dictionary param -> value + instance : string + problem instance + cutoff : float + runtime cutoff + seed : int + random seed + instance_specific: str + instance specific information (e.g., domain file or solution) + capped: bool + if true and status is StatusType.TIMEOUT, + uses StatusType.CAPPED + Returns + ------- + status: enum of StatusType (int) + {SUCCESS, TIMEOUT, CRASHED, ABORT} + cost: float + cost/regret/quality (float) (None, if not returned by TA) + runtime: float + runtime (None if not returned by TA) + additional_info: dict + all further additional run information + """ remaining_time = self.stats.get_remaing_time_budget() + if remaining_time - 5 < cutoff: cutoff = int(remaining_time - 5) - if cutoff <= 0: - self.logger.debug( - "Skip target algorithm run due to exhausted configuration budget") - return StatusType.ABORT, np.nan, 0, {"misc": "exhausted bugdet -- ABORT"} + if cutoff <= 1.0: + raise BudgetExhaustedException() + cutoff = int(cutoff) return super().start(config=config, instance=instance, cutoff=cutoff, - seed=seed, instance_specific=instance_specific) + seed=seed, instance_specific=instance_specific, + capped=capped) def run(self, config, instance=None, cutoff=None, @@ -101,37 +151,31 @@ def run(self, config, instance=None, D = self.backend.load_datamanager() queue = multiprocessing.Queue() - if instance_specific is None or instance_specific == '0': - instance_specific = {} - else: - instance_specific = [specific.split('=') for specific in instance_specific.split(',')] - instance_specific = {specific[0]: specific[1] for specific in instance_specific} - subsample = instance_specific.get('subsample') - subsample = int(subsample) if subsample is not None else None + if not (instance_specific is None or instance_specific == '0'): + raise ValueError(instance_specific) arguments = dict(logger=logging.getLogger("pynisher"), wall_time_in_s=cutoff, - mem_in_mb=self.memory_limit, - grace_period_in_s=15) + mem_in_mb=self.memory_limit) obj_kwargs = dict(queue=queue, config=config, - data=D, + datamanager=D, backend=self.backend, + metric=self.metric, seed=self.autosklearn_seed, num_run=self.num_run, - with_predictions=self.with_predictions, all_scoring_functions=self.all_scoring_functions, - output_y_test=self.output_y_test, - subsample=subsample, + output_y_hat_optimization=self.output_y_hat_optimization, include=self.include, exclude=self.exclude, - disable_file_output=self.disable_file_output) + disable_file_output=self.disable_file_output, + instance=instance) if self.resampling_strategy != 'test': cv = self.get_splitter(D) obj_kwargs['cv'] = cv - if instance is not None: - obj_kwargs['instance'] = instance + #if instance is not None: + # obj_kwargs['instance'] = instance obj = pynisher.enforce_limits(**arguments)(self.ta) obj(**obj_kwargs) @@ -141,39 +185,46 @@ def run(self, config, instance=None, # the target algorithm wrote something into the queue - then we # treat it as a succesful run try: - info = get_last_result(queue) - result = info[1] - additional_run_info = info[3] + info = autosklearn.evaluation.util.get_last_result(queue) + result = info['loss'] + status = info['status'] + additional_run_info = info['additional_run_info'] + additional_run_info['info'] = 'Run stopped because of timeout.' - if obj.exit_status == pynisher.TimeoutException and result is not None: - status = StatusType.SUCCESS + if status == StatusType.SUCCESS: cost = result else: - status = StatusType.CRASHED cost = WORST_POSSIBLE_RESULT - except Exception: + + except Empty: status = StatusType.TIMEOUT cost = WORST_POSSIBLE_RESULT - additional_run_info = 'Timeout' + additional_run_info = {'error': 'Timeout'} elif obj.exit_status is pynisher.MemorylimitException: status = StatusType.MEMOUT cost = WORST_POSSIBLE_RESULT - additional_run_info = 'Memout' + additional_run_info = {'error': 'Memout (used more than %d MB).' % + self.memory_limit} + else: try: - info = get_last_result(queue) - result = info[1] - additional_run_info = info[3] + info = autosklearn.evaluation.util.get_last_result(queue) + result = info['loss'] + status = info['status'] + additional_run_info = info['additional_run_info'] - if obj.exit_status == 0 and result is not None: - status = StatusType.SUCCESS + if obj.exit_status == 0: cost = result else: status = StatusType.CRASHED cost = WORST_POSSIBLE_RESULT - except Exception as e0: - additional_run_info = 'Unknown error (%s) %s' % (type(e0), e0) + additional_run_info['info'] = 'Run treated as crashed ' \ + 'because the pynisher exit ' \ + 'status %s is unknown.' % \ + str(obj.exit_status) + except Empty: + additional_run_info = {'error': 'Result queue is empty'} status = StatusType.CRASHED cost = WORST_POSSIBLE_RESULT @@ -183,39 +234,38 @@ def run(self, config, instance=None, def get_splitter(self, D): y = D.data['Y_train'].ravel() - n = D.data['Y_train'].shape[0] + if D.info['task'] in CLASSIFICATION_TASKS and \ D.info['task'] != MULTILABEL_CLASSIFICATION: if self.resampling_strategy in ['holdout', 'holdout-iterative-fit']: try: - cv = StratifiedShuffleSplit(y=y, n_iter=1, train_size=0.67, + cv = StratifiedShuffleSplit(n_splits=1, train_size=0.67, test_size=0.33, random_state=1) + test_cv = copy.deepcopy(cv) + next(test_cv.split(y, y)) except ValueError as e: if 'The least populated class in y has only' in e.args[0]: - cv = ShuffleSplit(n=n, n_iter=1, train_size=0.67, + cv = ShuffleSplit(n_splits=1, train_size=0.67, test_size=0.33, random_state=1) else: raise elif self.resampling_strategy in ['cv', 'partial-cv', 'partial-cv-iterative-fit']: - cv = StratifiedKFold(y=y, - n_folds=self.resampling_strategy_args[ - 'folds'], + cv = StratifiedKFold(n_splits=self.resampling_strategy_args['folds'], shuffle=True, random_state=1) else: raise ValueError(self.resampling_strategy) else: if self.resampling_strategy in ['holdout', 'holdout-iterative-fit']: - cv = ShuffleSplit(n=n, n_iter=1, train_size=0.67, + cv = ShuffleSplit(n_splits=1, train_size=0.67, test_size=0.33, random_state=1) elif self.resampling_strategy in ['cv', 'partial-cv', 'partial-cv-iterative-fit']: - cv = KFold(n=n, - n_folds=self.resampling_strategy_args['folds'], + cv = KFold(n_splits=self.resampling_strategy_args['folds'], shuffle=True, random_state=1) else: raise ValueError(self.resampling_strategy) diff --git a/autosklearn/evaluation/abstract_evaluator.py b/autosklearn/evaluation/abstract_evaluator.py index b47c6e4c93..943190b7a6 100644 --- a/autosklearn/evaluation/abstract_evaluator.py +++ b/autosklearn/evaluation/abstract_evaluator.py @@ -1,5 +1,4 @@ import os -import queue import time import warnings @@ -11,7 +10,7 @@ import autosklearn.pipeline.regression from autosklearn.constants import * from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel -from autosklearn.evaluation.util import calculate_score +from autosklearn.metrics import calculate_score from autosklearn.util.logging_ import get_logger from ConfigSpace import Configuration @@ -82,11 +81,11 @@ def estimator_supports_iterative_fit(self): class AbstractEvaluator(object): - def __init__(self, Datamanager, backend, queue, configuration=None, - with_predictions=False, + def __init__(self, datamanager, backend, queue, metric, + configuration=None, all_scoring_functions=False, seed=1, - output_y_test=False, + output_y_hat_optimization=True, num_run=None, subsample=None, include=None, @@ -99,19 +98,18 @@ def __init__(self, Datamanager, backend, queue, configuration=None, self.backend = backend self.queue = queue - self.D = Datamanager + self.datamanager = datamanager self.include = include self.exclude = exclude - self.X_valid = Datamanager.data.get('X_valid') - self.X_test = Datamanager.data.get('X_test') + self.X_valid = datamanager.data.get('X_valid') + self.X_test = datamanager.data.get('X_test') - self.metric = Datamanager.info['metric'] - self.task_type = Datamanager.info['task'] + self.metric = metric + self.task_type = datamanager.info['task'] self.seed = seed - self.output_y_test = output_y_test - self.with_predictions = with_predictions + self.output_y_hat_optimization = output_y_hat_optimization self.all_scoring_functions = all_scoring_functions self.disable_file_output = disable_file_output @@ -131,7 +129,7 @@ def __init__(self, Datamanager, backend, queue, configuration=None, self.predict_function = self._predict_proba categorical_mask = [] - for feat in Datamanager.feat_type: + for feat in datamanager.feat_type: if feat.lower() == 'numerical': categorical_mask.append(False) elif feat.lower() == 'categorical': @@ -151,7 +149,7 @@ def __init__(self, Datamanager, backend, queue, configuration=None, self.subsample = subsample logger_name = '%s(%d):%s' % (self.__class__.__name__.split('.')[-1], - self.seed, self.D.name) + self.seed, self.datamanager.name) self.logger = get_logger(logger_name) def _get_model(self): @@ -161,7 +159,7 @@ def _get_model(self): init_params=self._init_params) else: dataset_properties = {'task': self.task_type, - 'sparse': self.D.info['is_sparse'] == 1, + 'sparse': self.datamanager.info['is_sparse'] == 1, 'multilabel': self.task_type == MULTILABEL_CLASSIFICATION, 'multiclass': self.task_type == @@ -182,8 +180,7 @@ def _loss(self, y_true, y_hat): return 1.0 score = calculate_score( - y_true, y_hat, self.task_type, - self.metric, self.D.info['label_num'], + y_true, y_hat, self.task_type, self.metric, all_scoring_functions=self.all_scoring_functions) if hasattr(score, '__len__'): @@ -193,7 +190,8 @@ def _loss(self, y_true, y_hat): return err - def finish_up(self, loss, opt_pred, valid_pred, test_pred, file_output=True): + def finish_up(self, loss, opt_pred, valid_pred, test_pred, + file_output=True, final_call=True): """This function does everything necessary after the fitting is done: * predicting @@ -208,7 +206,7 @@ def finish_up(self, loss, opt_pred, valid_pred, test_pred, file_output=True): loss_, additional_run_info_ = self.file_output( opt_pred, valid_pred, test_pred) else: - loss_, additional_run_info_ = None, None + loss_, additional_run_info_ = None, {} if loss_ is not None: return self.duration, loss_, self.seed, additional_run_info_ @@ -216,21 +214,26 @@ def finish_up(self, loss, opt_pred, valid_pred, test_pred, file_output=True): num_run = str(self.num_run).zfill(5) if isinstance(loss, dict): loss_ = loss - loss = loss_[self.D.info['metric']] + loss = loss_[self.metric.name] else: loss_ = {} - additional_run_info = ';'.join(['%s: %s' % - (METRIC_TO_STRING[metric] if metric in METRIC_TO_STRING else metric, value) - for metric, value in loss_.items()]) - additional_run_info += ';' + 'duration: ' + str(self.duration) - additional_run_info += ';' + 'num_run:' + num_run - self.queue.put((self.duration, loss, self.seed, additional_run_info, - StatusType.SUCCESS)) + additional_run_info = {metric_name: value for metric_name, value in + loss_.items()} + additional_run_info['duration'] = self.duration + additional_run_info['num_run'] = self.num_run + + rval_dict = {'loss': loss, + 'additional_run_info': additional_run_info, + 'status': StatusType.SUCCESS} + if final_call: + rval_dict['final_queue_element'] = True + + self.queue.put(rval_dict) def file_output(self, Y_optimization_pred, Y_valid_pred, Y_test_pred): - if self.disable_file_output: - return None, None + if self.disable_file_output is True: + return None, {} seed = self.seed @@ -238,30 +241,37 @@ def file_output(self, Y_optimization_pred, Y_valid_pred, Y_test_pred): # obviously no output should be saved. if self.Y_optimization is not None and \ self.Y_optimization.shape[0] != Y_optimization_pred.shape[0]: - return 1.0, "Targets %s and prediction %s don't have the same " \ - "length. Probably training didn't finish" % ( - self.Y_optimization.shape, Y_optimization_pred.shape) + return 1.0, {'error': "Targets %s and prediction %s don't have " + "the same length. Probably training didn't " + "finish" % (self.Y_optimization.shape, + Y_optimization_pred.shape)} if not np.all(np.isfinite(Y_optimization_pred)): - return 1.0, 'Model predictions for optimization set contains NaNs.' - for y, s in [[Y_valid_pred, 'validation'], - [Y_test_pred, 'test']]: + return 1.0, {'error': 'Model predictions for optimization set ' \ + 'contains NaNs.'} + for y, s in [[Y_valid_pred, 'validation'], [Y_test_pred, 'test']]: if y is not None and not np.all(np.isfinite(y)): - return 1.0, 'Model predictions for %s set contains NaNs.' % s + return 1.0, {'error': 'Model predictions for %s set contains ' + 'NaNs.' % s} num_run = str(self.num_run).zfill(5) - if os.path.exists(self.backend.get_model_dir()): - self.backend.save_model(self.model, self.num_run, seed) - if self.output_y_test: - try: - os.makedirs(self.backend.output_directory) - except OSError: - pass - self.backend.save_targets_ensemble(self.Y_optimization) - - self.backend.save_predictions_as_npy(Y_optimization_pred, 'ensemble', - seed, num_run) + if not isinstance(self.disable_file_output, list) or \ + 'model' not in self.disable_file_output: + if os.path.exists(self.backend.get_model_dir()): + self.backend.save_model(self.model, self.num_run, seed) + + if not isinstance(self.disable_file_output, list) or \ + 'y_optimization' not in self.disable_file_output: + if self.output_y_hat_optimization: + try: + os.makedirs(self.backend.output_directory) + except OSError: + pass + self.backend.save_targets_ensemble(self.Y_optimization) + + self.backend.save_predictions_as_npy(Y_optimization_pred, 'ensemble', + seed, num_run) if Y_valid_pred is not None: self.backend.save_predictions_as_npy(Y_valid_pred, 'valid', @@ -275,7 +285,7 @@ def file_output(self, Y_optimization_pred, Y_valid_pred, Y_test_pred): def _predict_proba(self, X, model, task_type, Y_train): def send_warnings_to_log(message, category, filename, lineno, - file=None): + file=None, line=None): self.logger.debug('%s:%s: %s:%s' % (filename, lineno, category.__name__, message)) return @@ -289,7 +299,7 @@ def send_warnings_to_log(message, category, filename, lineno, def _predict_regression(self, X, model, task_type, Y_train=None): def send_warnings_to_log(message, category, filename, lineno, - file=None): + file=None, line=None): self.logger.debug('%s:%s: %s:%s' % (filename, lineno, category.__name__, message)) return @@ -304,7 +314,7 @@ def send_warnings_to_log(message, category, filename, lineno, return Y_pred def _ensure_prediction_array_sizes(self, prediction, Y_train): - num_classes = self.D.info['label_num'] + num_classes = self.datamanager.info['label_num'] if self.task_type == MULTICLASS_CLASSIFICATION and \ prediction.shape[1] < num_classes: @@ -330,7 +340,7 @@ def _ensure_prediction_array_sizes(self, prediction, Y_train): def _fit_and_suppress_warnings(self, model, X, y): def send_warnings_to_log(message, category, filename, lineno, - file=None): + file=None, line=None): self.logger.debug('%s:%s: %s:%s' % (filename, lineno, category.__name__, message)) return diff --git a/autosklearn/evaluation/test_evaluator.py b/autosklearn/evaluation/test_evaluator.py index ba777ad834..3fdccd638d 100644 --- a/autosklearn/evaluation/test_evaluator.py +++ b/autosklearn/evaluation/test_evaluator.py @@ -2,7 +2,7 @@ from smac.tae.execute_ta_run import StatusType from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator -from autosklearn.evaluation.util import calculate_score +from autosklearn.metrics import calculate_score __all__ = [ @@ -13,33 +13,34 @@ class TestEvaluator(AbstractEvaluator): - def __init__(self, Datamanager, backend, queue, + def __init__(self, datamanager, backend, queue, metric, configuration=None, - with_predictions=False, all_scoring_functions=False, seed=1, include=None, exclude=None, disable_file_output=False): super(TestEvaluator, self).__init__( - Datamanager, backend, queue=queue, + datamanager=datamanager, + backend=backend, + queue=queue, configuration=configuration, - with_predictions=with_predictions, + metric=metric, all_scoring_functions=all_scoring_functions, seed=seed, - output_y_test=False, - num_run='-1', + output_y_hat_optimization=False, + num_run=-1, subsample=None, include=include, exclude=exclude, disable_file_output= disable_file_output) self.configuration = configuration - self.X_train = Datamanager.data['X_train'] - self.Y_train = Datamanager.data['Y_train'] + self.X_train = datamanager.data['X_train'] + self.Y_train = datamanager.data['Y_train'] - self.X_test = Datamanager.data.get('X_test') - self.Y_test = Datamanager.data.get('Y_test') + self.X_test = datamanager.data.get('X_test') + self.Y_test = datamanager.data.get('Y_test') self.model = self._get_model() @@ -58,7 +59,6 @@ def predict_and_loss(self, train=False): prediction=Y_pred, task_type=self.task_type, metric=self.metric, - num_classes=self.D.info['label_num'], all_scoring_functions=self.all_scoring_functions) else: Y_pred = self.predict_function(self.X_test, self.model, @@ -68,7 +68,6 @@ def predict_and_loss(self, train=False): prediction=Y_pred, task_type=self.task_type, metric=self.metric, - num_classes=self.D.info['label_num'], all_scoring_functions=self.all_scoring_functions) if hasattr(score, '__len__'): @@ -81,12 +80,12 @@ def predict_and_loss(self, train=False): # create closure for evaluating an algorithm # Has a stupid name so nosetests doesn't regard it as a test -def eval_t(queue, config, data, backend, seed, num_run, subsample, - with_predictions, all_scoring_functions, - output_y_test, include, exclude, disable_file_output): - evaluator = TestEvaluator(Datamanager=data, configuration=config, - backend=backend, seed=seed, queue=queue, - with_predictions=with_predictions, +def eval_t(queue, config, datamanager, backend, metric, seed, num_run, instance, + all_scoring_functions, output_y_hat_optimization, include, + exclude, disable_file_output): + evaluator = TestEvaluator(datamanager=datamanager, configuration=config, + backend=backend, metric=metric, seed=seed, + queue=queue, all_scoring_functions=all_scoring_functions, include=include, exclude=exclude, disable_file_output=disable_file_output) diff --git a/autosklearn/evaluation/train_evaluator.py b/autosklearn/evaluation/train_evaluator.py index d6ded0f6c3..ce20369523 100644 --- a/autosklearn/evaluation/train_evaluator.py +++ b/autosklearn/evaluation/train_evaluator.py @@ -1,5 +1,7 @@ +import json + import numpy as np -import sklearn.cross_validation +import sklearn.model_selection from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator from autosklearn.constants import * @@ -9,13 +11,21 @@ 'eval_cv', 'eval_partial_cv', 'eval_partial_cv_iterative'] +def _get_y_array(y, task_type): + if task_type in CLASSIFICATION_TASKS and task_type != \ + MULTILABEL_CLASSIFICATION: + return y.ravel() + else: + return y + + + class TrainEvaluator(AbstractEvaluator): - def __init__(self, Datamanager, backend, queue, + def __init__(self, datamanager, backend, queue, metric, configuration=None, - with_predictions=False, all_scoring_functions=False, seed=1, - output_y_test=False, + output_y_hat_optimization=True, cv=None, num_run=None, subsample=None, @@ -24,12 +34,14 @@ def __init__(self, Datamanager, backend, queue, exclude=None, disable_file_output=False): super().__init__( - Datamanager, backend, queue, + datamanager=datamanager, + backend=backend, + queue=queue, configuration=configuration, - with_predictions=with_predictions, + metric=metric, all_scoring_functions=all_scoring_functions, seed=seed, - output_y_test=output_y_test, + output_y_hat_optimization=output_y_hat_optimization, num_run=num_run, subsample=subsample, include=include, @@ -37,9 +49,9 @@ def __init__(self, Datamanager, backend, queue, disable_file_output=disable_file_output) self.cv = cv - self.cv_folds = cv.n_folds if hasattr(cv, 'n_folds') else cv.n_iter - self.X_train = self.D.data['X_train'] - self.Y_train = self.D.data['Y_train'] + self.cv_folds = cv.n_splits + self.X_train = self.datamanager.data['X_train'] + self.Y_train = self.datamanager.data['Y_train'] self.Y_optimization = None self.Y_targets = [None] * self.cv_folds self.models = [None] * self.cv_folds @@ -58,7 +70,7 @@ def fit_predict_and_loss(self, iterative=False): raise ValueError('Cannot use partial fitting together with full' 'cross-validation!') - for train_split, test_split in self.cv: + for train_split, test_split in self.cv.split(self.X_train, self.Y_train): self.Y_optimization = self.Y_train[test_split] self._partial_fit_and_predict(0, train_indices=train_split, test_indices=test_split, @@ -72,7 +84,11 @@ def fit_predict_and_loss(self, iterative=False): Y_valid_pred = [None] * self.cv_folds Y_test_pred = [None] * self.cv_folds - for i, (train_split, test_split) in enumerate(self.cv): + + y = _get_y_array(self.Y_train, self.task_type) + for i, (train_split, test_split) in enumerate(self.cv.split( + self.X_train, y)): + opt_pred, valid_pred, test_pred = self._partial_fit_and_predict( i, train_indices=train_split, test_indices=test_split) @@ -118,13 +134,16 @@ def fit_predict_and_loss(self, iterative=False): self._added_empty_model = True self.finish_up(loss, Y_optimization_pred, Y_valid_pred, Y_test_pred, - file_output=True) + file_output=True, final_call=True) def partial_fit_predict_and_loss(self, fold, iterative=False): if fold > self.cv_folds: raise ValueError('Cannot evaluate a fold %d which is higher than ' 'the number of folds %d.' % (fold, self.cv_folds)) - for i, (train_split, test_split) in enumerate(self.cv): + + y = _get_y_array(self.Y_train, self.task_type) + for i, (train_split, test_split) in enumerate(self.cv.split( + self.X_train, y)): if i != fold: continue else: @@ -150,7 +169,7 @@ def partial_fit_predict_and_loss(self, fold, iterative=False): self._added_empty_model = True self.finish_up(loss, opt_pred, valid_pred, test_pred, - file_output=False) + file_output=False, final_call=True) def _partial_fit_and_predict(self, fold, train_indices, test_indices, iterative=False): @@ -168,8 +187,8 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, file_output = True if self.cv_folds == 1 else False if model.estimator_supports_iterative_fit(): - Xt, fit_params = model.pre_transform(self.X_train[train_indices], - self.Y_train[train_indices]) + Xt, fit_params = model.fit_transformer(self.X_train[train_indices], + self.Y_train[train_indices]) n_iter = 2 while not model.configuration_fully_fitted(): @@ -183,8 +202,13 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) + if model.configuration_fully_fitted(): + final_call = True + else: + final_call = False self.finish_up(loss, Y_optimization_pred, Y_valid_pred, - Y_test_pred, file_output=file_output) + Y_test_pred, file_output=file_output, + final_call=final_call) n_iter *= 2 return @@ -202,7 +226,8 @@ def _partial_fit_and_predict(self, fold, train_indices, test_indices, model=model, train_indices=train_indices, test_indices=test_indices) loss = self._loss(self.Y_train[test_indices], Y_optimization_pred) self.finish_up(loss, Y_optimization_pred, Y_valid_pred, - Y_test_pred, file_output=file_output) + Y_test_pred, file_output=file_output, + final_call=True) return else: @@ -231,7 +256,7 @@ def subsample_indices(self, train_indices): if len(train_indices) > self.subsample: indices = np.arange(len(train_indices)) - cv_indices_train, _ = sklearn.cross_validation.train_test_split( + cv_indices_train, _ = sklearn.model_selection.train_test_split( indices, stratify=stratify, train_size=self.subsample, random_state=1) train_indices = train_indices[cv_indices_train] @@ -265,84 +290,113 @@ def _predict(self, model, test_indices, train_indices): # create closure for evaluating an algorithm -def eval_holdout(queue, config, data, backend, cv, seed, num_run, - subsample, with_predictions, all_scoring_functions, - output_y_test, include, exclude, disable_file_output, - iterative=False): - evaluator = TrainEvaluator(data, backend, queue, +def eval_holdout(queue, config, datamanager, backend, cv, metric, seed, num_run, + instance, all_scoring_functions, output_y_hat_optimization, + include, exclude, disable_file_output, iterative=False): + instance = json.loads(instance) if instance is not None else {} + subsample = instance.get('subsample') + evaluator = TrainEvaluator(datamanager=datamanager, + backend=backend, + queue=queue, cv=cv, + metric=metric, configuration=config, seed=seed, num_run=num_run, subsample=subsample, - with_predictions=with_predictions, all_scoring_functions=all_scoring_functions, - output_y_test=output_y_test, + output_y_hat_optimization=output_y_hat_optimization, include=include, exclude=exclude, disable_file_output=disable_file_output) evaluator.fit_predict_and_loss(iterative=iterative) -def eval_iterative_holdout(queue, config, data, backend, cv, seed, - num_run, subsample, with_predictions, - all_scoring_functions, output_y_test, - include, exclude, disable_file_output): - return eval_holdout(queue=queue, config=config, data=data, backend=backend, - cv=cv, seed=seed, num_run=num_run, subsample=subsample, - with_predictions=with_predictions, +def eval_iterative_holdout(queue, config, datamanager, backend, cv, metric, + seed, num_run, instance, all_scoring_functions, + output_y_hat_optimization, include, exclude, + disable_file_output): + return eval_holdout(queue=queue, + config=config, + datamanager=datamanager, + backend=backend, + metric=metric, + cv=cv, + seed=seed, + num_run=num_run, all_scoring_functions=all_scoring_functions, - output_y_test=output_y_test, - include=include, exclude=exclude, - disable_file_output=disable_file_output, iterative=True) - - -def eval_partial_cv(queue, config, data, backend, cv, seed, num_run, instance, - subsample, with_predictions, all_scoring_functions, - output_y_test, include, exclude, disable_file_output, - iterative=False): - evaluator = TrainEvaluator(data, backend, queue, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + instance=instance, + disable_file_output=disable_file_output, + iterative=True) + + +def eval_partial_cv(queue, config, datamanager, backend, cv, metric, seed, + num_run, instance, all_scoring_functions, + output_y_hat_optimization, include, exclude, + disable_file_output, iterative=False): + instance = json.loads(instance) if instance is not None else {} + subsample = instance.get('subsample') + fold = instance['fold'] + + evaluator = TrainEvaluator(datamanager=datamanager, + backend=backend, + queue=queue, + metric=metric, configuration=config, cv=cv, seed=seed, num_run=num_run, subsample=subsample, - with_predictions=with_predictions, all_scoring_functions=all_scoring_functions, - output_y_test=False, + output_y_hat_optimization=False, include=include, exclude=exclude, disable_file_output=disable_file_output) - evaluator.partial_fit_predict_and_loss(fold=instance, iterative=iterative) - - -def eval_partial_cv_iterative(queue, config, data, backend, cv, seed, num_run, - instance, subsample, with_predictions, - all_scoring_functions, output_y_test, - include, exclude, disable_file_output): - return eval_partial_cv(queue=queue, config=config, data=data, backend=backend, - cv=cv, seed=seed, num_run=num_run, instance=instance, - subsample=subsample, with_predictions=with_predictions, + evaluator.partial_fit_predict_and_loss(fold=fold, iterative=iterative) + + +def eval_partial_cv_iterative(queue, config, datamanager, backend, cv, metric, + seed, num_run, instance, all_scoring_functions, + output_y_hat_optimization, include, exclude, + disable_file_output): + return eval_partial_cv(queue=queue, + config=config, + datamanager=datamanager, + backend=backend, + metric=metric, + cv=cv, + seed=seed, + num_run=num_run, + instance=instance, all_scoring_functions=all_scoring_functions, - output_y_test=output_y_test, include=include, - exclude=exclude, disable_file_output=disable_file_output, + output_y_hat_optimization=output_y_hat_optimization, + include=include, + exclude=exclude, + disable_file_output=disable_file_output, iterative=True) # create closure for evaluating an algorithm -def eval_cv(queue, config, data, backend, cv, seed, num_run, - subsample, with_predictions, all_scoring_functions, - output_y_test, include, exclude, disable_file_output): - evaluator = TrainEvaluator(data, backend, queue, +def eval_cv(queue, config, datamanager, backend, cv, metric, seed, num_run, + instance, all_scoring_functions, output_y_hat_optimization, + include, exclude, disable_file_output): + instance = json.loads(instance) if instance is not None else {} + subsample = instance.get('subsample') + evaluator = TrainEvaluator(datamanager=datamanager, + backend=backend, + queue=queue, + metric=metric, configuration=config, seed=seed, num_run=num_run, cv=cv, subsample=subsample, - with_predictions=with_predictions, all_scoring_functions=all_scoring_functions, - output_y_test=output_y_test, + output_y_hat_optimization=output_y_hat_optimization, include=include, exclude=exclude, disable_file_output=disable_file_output) diff --git a/autosklearn/evaluation/util.py b/autosklearn/evaluation/util.py index 7486300d01..eb897a175a 100644 --- a/autosklearn/evaluation/util.py +++ b/autosklearn/evaluation/util.py @@ -1,49 +1,11 @@ import queue -from autosklearn.constants import * -from autosklearn.metrics import sanitize_array, \ - regression_metrics, classification_metrics - __all__ = [ - 'calculate_score', 'get_last_result' ] -def calculate_score(solution, prediction, task_type, metric, num_classes, - all_scoring_functions=False, logger=None): - - if task_type not in TASK_TYPES: - raise NotImplementedError(task_type) - - if all_scoring_functions: - score = dict() - if task_type in REGRESSION_TASKS: - # TODO put this into the regression metric itself - cprediction = sanitize_array(prediction) - for metric_ in REGRESSION_METRICS: - score[metric_] = regression_metrics.calculate_score( - metric_, solution, cprediction) - - else: - for metric_ in CLASSIFICATION_METRICS: - score[metric_] = classification_metrics.calculate_score( - metric_, solution, prediction, task_type) - - else: - if task_type in REGRESSION_TASKS: - # TODO put this into the regression metric itself - cprediction = sanitize_array(prediction) - score = regression_metrics.calculate_score( - metric, solution, cprediction) - else: - score = classification_metrics.calculate_score( - metric, solution, prediction, task=task_type) - - return score - - def get_last_result(queue_): stack = [] while True: @@ -51,5 +13,20 @@ def get_last_result(queue_): rval = queue_.get(timeout=1) except queue.Empty: break + + # Check if there is a special placeholder value which tells us that + # we don't have to wait until the queue times out in order to + # retrieve the final value! + if 'final_queue_element' in rval: + del rval['final_queue_element'] + do_break = True + else: + do_break = False stack.append(rval) - return stack.pop() + if do_break: + break + + if len(stack) == 0: + raise queue.Empty + else: + return stack.pop() diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/algorithm_runs.arff b/autosklearn/metalearning/files/a_metric_regression_dense/algorithm_runs.arff deleted file mode 100644 index 31025d5675..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_dense/algorithm_runs.arff +++ /dev/null @@ -1,120 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_ALGORITHM_RUNS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE algorithm STRING -@ATTRIBUTE a_metric NUMERIC -@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} - -@DATA -344_r2_metric,1.0,47,0.00513410568237,ok -344_r2_metric,1.0,48,0.00649279356003,ok -422_r2_metric,1.0,18,0.9564473629,ok 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-45,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,187,median,,,,,,,,,,,,,,,,,,,,,,,,,,adaboost,0.40436963527687636,exponential,10,338,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -46,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,adaboost,0.03881375740244802,linear,8,423,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -47,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.964568605961244,None,6,3,0.0,100,,,,,,,,,,,,,,,standardize -48,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.638463312412601,None,5,4,0.0,100,,,,,,,,,,,,,,,none -49,mean,0.06837813706602064,True,feature_agglomeration,,,,,,,,,,,,,manhattan,average,31,median,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.816610219718326,None,3,12,0.0,100,,,,,,,,,,,,,,,standardize -50,mean,0.06588990666418214,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.64312817587696,None,4,16,0.0,100,,,,,,,,,,,,,,,none -51,median,0.035413129022298255,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6934727071735427,False,0.001393180598569528,True,1,squared_epsilon_insensitive,1.1129869858587773e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -52,mean,0.05169879074187555,True,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69.5278515347773,f_regression,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.286891705724335,None,7,11,0.0,100,,,,,,,,,,,,,,,minmax -53,mean,0.013307227780517586,True,select_percentile_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,77.39722264573916,f_regression,decision_tree,,,,,,,,,,,,,mse,1.1806384544117086,1.0,None,9,20,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -54,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,decision_tree,,,,,,,,,,,,,mse,0.5,1.0,None,1,2,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -55,mean,0.0023994786419236558,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,decision_tree,,,,,,,,,,,,,mse,1.926017202951233,1.0,None,1,2,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -56,mean,0.00012980286034531858,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.004609606728225581,True,2.5557817408084165e-05,0.026579153138680305,True,,invscaling,epsilon_insensitive,969,l2,0.0929887923927151,standardize -57,most_frequent,0.001559668909077697,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.082881730018572,False,0.06166886997671436,True,1,squared_epsilon_insensitive,3.292634310699719e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -58,most_frequent,0.14346177037935146,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.002871580718687084,True,0.0004685256778738382,,,,,,,,,,,,minmax -59,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.6863994469812906,5776,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.522914874445429e-05,False,,0.030016048237273864,True,5.623348010918567e-09,constant,squared_loss,306,elasticnet,,minmax -60,mean,0.0008795607847802394,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,adaboost,0.09926696771782668,linear,3,111,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -61,most_frequent,0.0007532614729206898,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,ard_regression,,,,,0.00042906735025235033,1.3695555361436012e-10,True,0.00016402959364960796,1.2762015128704412e-10,300,93670.42273046337,0.0008077649594743829,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,mean,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.620187571887624,3028,,,,,,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0021734462607454676,True,2.7960144386696057e-05,,,,,,,,,,,,standardize -63,most_frequent,0.0001625707154796164,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.967466412432852,1594,,,,,,,,,,,,,,,,,,,ard_regression,,,,,0.00036257796572477033,1.4604413147509483e-08,True,0.0001843468315027027,1.3570161502719352e-08,300,2869.361951102668,0.006034061980184114,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -64,most_frequent,0.21338776746252336,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,213,mean,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.852162823305485,None,12,15,0.0,100,,,,,,,,,,,,,,,none -65,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,1.1462353774599199,rbf,56,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.743394673354396e-06,True,0.00847857431871685,0.09971388475732029,True,0.00033284954626829543,optimal,huber,46,elasticnet,,normalize -66,median,0.0968556998482573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,8,4,1.0,30,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0641375748610752,,,0.23204376605619673,,linear,-1,True,0.008566344762428551,,,,,,,,,,,,,,,,,,,,,,,,standardize -67,most_frequent,0.005607066924937062,True,extra_trees_preproc_for_regression,True,mse,None,2.6112604563622552,12,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.045789159051221,,5,0.004784503543114743,4.627298382526854,rbf,-1,False,2.8494792916708405e-05,,,,,,,,,,,,,,,,,,,,,,,,minmax -68,median,,False,extra_trees_preproc_for_regression,True,mse,None,0.993528657198836,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1933.5929428405466,,2,0.015939473931353206,0.7858707127149234,rbf,-1,False,0.0037322094857651553,,,,,,,,,,,,,,,,,,,,,,,,minmax -69,most_frequent,0.0014457709291250823,True,kernel_pca,,,,,,,,,,,,,,,,,-0.01416546049641254,3,0.23060510905667822,poly,1413,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0017837392449809443,True,0.00345075877020118,0.09774090746458727,True,,optimal,epsilon_insensitive,195,l2,,none -70,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.683440436880492,,4,0.008766678436592141,0.10578131451502429,rbf,-1,False,0.08678739387617436,,,,,,,,,,,,,,,,,,,,,,,,none -71,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.5828695478528918,,1.2846036338050844,sigmoid,355,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.793413587557181e-06,False,2.7540645871255865e-05,0.006462379282943462,True,,optimal,huber,325,l1,,minmax -72,mean,0.08562251742396482,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.5905557599097153,rbf,74,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.052990481379682e-06,False,2.2842654098672913e-05,0.09620557628291702,True,0.012660036055398494,optimal,huber,209,elasticnet,,none -73,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.010148480388225148,0.00016676399867236993,3.16342061142116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -74,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.007642369343100022,1.8419258971411445e-05,0.9833146696212234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -75,mean,0.00016629738514881763,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.592230471191443,None,4,6,0.0,100,,,,,,,,,,,,,,,minmax -76,median,0.00548806117992972,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.0972538714361577,rbf,5291,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6864007905723228e-06,True,0.006559405529560316,0.07194784725205487,True,,constant,squared_epsilon_insensitive,89,l2,,minmax -77,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.1566448365058719,5,0.7004476388937543,poly,956,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02849145741142527,True,0.001397013116831936,,,,,,,,,,,,minmax -78,median,0.0005361882641678341,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.9516292646600342,5226,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.844327967977615e-05,True,0.010965543419019197,0.02393286766186689,True,,constant,squared_epsilon_insensitive,502,l2,,minmax -79,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,minmax -80,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.8792789945671258,None,10,18,0.0,100,,,,,,,,,,,,,,,minmax -81,median,0.05987995402504049,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.663594806506735,18,17,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -82,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize -84,mean,0.15213222665693707,True,feature_agglomeration,,,,,,,,,,,,,manhattan,average,294,median,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.9189947880940927,3,4,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,mean,0.08567185090735716,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,2.3981698229031436,None,19,20,0.0,100,,,,,,,,,,,,,,,minmax -86,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.12583724307049,False,0.0018926648667342925,True,1,squared_epsilon_insensitive,0.008267080450623797,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -87,median,0.003145909671422029,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1154751316101943e-06,False,0.006276424097548576,0.038108446269937486,True,9.434769722730874e-09,invscaling,squared_epsilon_insensitive,305,elasticnet,0.45306002466009593,minmax diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/description.txt b/autosklearn/metalearning/files/a_metric_regression_dense/description.txt deleted file mode 100644 index d92fabe7df..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step KurtosisSTD: KurtosisSTD -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step NumberOfClasses: NumberOfClasses -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step SkewnessMean: SkewnessMean -feature_step SymbolsMean: SymbolsMean -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step ClassEntropy: ClassEntropy -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step SymbolsSTD: SymbolsSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step SymbolsSum: SymbolsSum -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step SymbolsMin: SymbolsMin -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step KurtosisMean: KurtosisMean -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step KurtosisMax: KurtosisMax -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step LandmarkLDA: LandmarkLDA -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step ClassProbabilityMean: ClassProbabilityMean -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step Landmark1NN: Landmark1NN -feature_step SymbolsMax: SymbolsMax -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: NumberOfFeaturesWithMissingValues, KurtosisSTD, DatasetRatio, LandmarkDecisionNodeLearner, NumberOfClasses, NumberOfMissingValues, KurtosisMin, SkewnessMean, SymbolsMean, Skewnesses, SkewnessSTD, SkewnessMax, NumberOfFeatures, ClassEntropy, PCA, LandmarkNaiveBayes, LogNumberOfFeatures, RatioNominalToNumerical, LogNumberOfInstances, SymbolsSTD, MissingValues, SymbolsSum, PCAKurtosisFirstPC, NumSymbols, ClassProbabilityMax, LandmarkRandomNodeLearner, ClassProbabilitySTD, SymbolsMin, LogInverseDatasetRatio, LandmarkDecisionTree, KurtosisMean, NumberOfInstancesWithMissingValues, KurtosisMax, PCASkewnessFirstPC, PCAFractionOfComponentsFor95PercentVariance, LandmarkLDA, ClassOccurences, NumberOfCategoricalFeatures, Kurtosisses, ClassProbabilityMin, PercentageOfMissingValues, InverseDatasetRatio, PercentageOfInstancesWithMissingValues, ClassProbabilityMean, RatioNumericalToNominal, PercentageOfFeaturesWithMissingValues, NumberOfInstances, LogDatasetRatio, SkewnessMin, NumberOfNumericFeatures, Landmark1NN, SymbolsMax - -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87 -performance_type: solution_quality -performance_measures: a_metric -algorithms_stochastic: - -scenario_id: ChaLearn_AutoML_Challenge_2015 -maximize: false -algorithm_cutoff_time: 3600 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff deleted file mode 100644 index f291c0fcba..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_dense/feature_costs.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_COSTS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE NumSymbols NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC - -@DATA -1028_a_metric,1.0,0.0001,0.00013,2e-05,0.00751,5e-05,5e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00029,0.00084,0.01456,1e-05,7e-05,1e-05,0.00047,0.00035,4e-05,0.00034,0.00065,1e-05,0.00799,8e-05,0.0,1e-05,0.01481,7e-05,0.00023,4e-05,0.00048,3e-05,0.01935,0.00016,2e-05,0.00027,2e-05,1e-05,3e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.02096,0.0 -1028_r2_metric,1.0,0.00011,0.00013,3e-05,0.00752,6e-05,6e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00032,0.00084,0.01453,1e-05,6e-05,1e-05,0.00047,0.00038,4e-05,0.00033,0.00065,1e-05,0.0083,8e-05,0.0,1e-05,0.01465,7e-05,0.00024,4e-05,0.00048,3e-05,0.01908,0.00015,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.0209,0.0 -1030_a_metric,1.0,8e-05,0.00013,2e-05,0.00896,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.0003,0.00082,0.0215,1e-05,7e-05,1e-05,0.00046,0.00031,4e-05,0.00033,0.00064,1e-05,0.00974,9e-05,0.0,1e-05,0.01136,7e-05,0.00022,4e-05,0.00047,2e-05,0.02092,0.00016,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01925,0.0 -1030_r2_metric,1.0,8e-05,0.00013,2e-05,0.00882,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.00031,0.00083,0.02148,1e-05,7e-05,1e-05,0.00044,0.00031,4e-05,0.00034,0.00062,1e-05,0.00951,9e-05,0.0,1e-05,0.01135,7e-05,0.00022,3e-05,0.00047,2e-05,0.02066,0.00016,2e-05,0.00026,2e-05,0.0,3e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01896,0.0 -1414_a_metric,1.0,0.00035,0.00014,2e-05,0.47489,0.00047,0.00011,3e-05,9e-05,4e-05,0.00033,0.00017,4e-05,1e-05,0.00221,0.00213,3.26197,1e-05,6e-05,0.0,8e-05,0.0028,3e-05,0.00095,0.00016,4e-05,0.2864,0.00014,1e-05,1e-05,3.06772,8e-05,0.00237,4e-05,0.00114,4e-05,1.34828,0.00034,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.72778,1e-05 -1414_r2_metric,1.0,0.00035,0.00014,2e-05,0.47406,0.00048,0.0001,3e-05,0.00011,4e-05,0.00045,0.00026,5e-05,1e-05,0.00222,0.00214,3.23113,1e-05,6e-05,1e-05,9e-05,0.00279,3e-05,0.00096,0.00017,4e-05,0.28855,0.00014,1e-05,1e-05,3.08176,8e-05,0.00237,4e-05,0.00114,4e-05,1.34295,0.00035,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.7159,1e-05 -189_a_metric,1.0,0.00024,0.0,2e-05,0.0,0.00038,7e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00456,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00169,4e-05,0.0,0.00062,0.00031,0.0,0.00056,0.0,1e-05,0.0,0.0,0.00142,0.0,0.0,0.0,0.0,0.00171,2e-05,0.0,0.00032,1e-05,3e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -189_r2_metric,1.0,0.00024,0.0,2e-05,0.0,0.0004,8e-05,0.0,0.0,0.00441,0.0,0.0,0.0,1e-05,0.00425,0.0,0.0,1e-05,8e-05,1e-05,0.00497,0.00188,4e-05,0.0,0.00944,0.00031,0.0,0.00061,0.0,1e-05,0.0,0.0,0.0016,0.0,0.0,0.0,0.0,0.00176,2e-05,0.0,0.00032,1e-05,4e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -197_a_metric,1.0,0.00035,0.00014,2e-05,1.51228,0.00026,0.00013,3e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00155,0.00149,0.17921,1e-05,7e-05,1e-05,0.00044,0.00195,4e-05,0.00057,0.00061,1e-05,0.06526,9e-05,0.0,1e-05,1.60067,8e-05,0.0015,3e-05,0.00088,3e-05,0.14992,0.00018,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94178,0.0 -197_r2_metric,1.0,0.00037,0.00014,3e-05,1.51517,0.00029,0.00016,4e-05,0.00011,0.00013,0.00053,0.00034,5e-05,1e-05,0.00159,0.01068,0.17867,1e-05,8e-05,1e-05,0.00046,0.00201,4e-05,0.00066,0.00064,1e-05,0.06144,0.0001,0.0,1e-05,1.59698,8e-05,0.00151,4e-05,0.00999,3e-05,0.15082,0.00019,2e-05,0.0003,3e-05,1e-05,5e-05,1e-05,5e-05,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,0.94139,0.0 -198_a_metric,1.0,0.00027,0.0,2e-05,0.0,0.00026,9e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00199,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00217,4e-05,0.0,0.00062,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.00184,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,1e-05,4e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -198_r2_metric,1.0,0.00027,0.0,2e-05,0.0,0.00025,0.0001,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00207,0.0,0.0,1e-05,7e-05,1e-05,0.00043,0.00212,4e-05,0.0,0.0006,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.00178,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -201_a_metric,1.0,0.00112,0.00019,3e-05,0.11155,0.00033,0.00047,5e-05,0.00012,0.00013,0.00046,0.00026,5e-05,1e-05,0.00272,0.0032,0.10471,1e-05,7e-05,1e-05,0.00046,0.00479,4e-05,0.00121,0.00064,1e-05,0.03739,9e-05,0.0,1e-05,0.61275,8e-05,0.00324,5e-05,0.00195,4e-05,0.34865,0.00022,2e-05,0.00037,3e-05,1e-05,4e-05,2e-05,0.0001,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,9.07208,0.0 -201_r2_metric,1.0,0.00112,0.00019,3e-05,0.11001,0.00032,0.00047,5e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00271,0.00325,0.10382,1e-05,7e-05,1e-05,0.00049,0.00456,4e-05,0.00124,0.00066,1e-05,0.03587,9e-05,0.0,1e-05,0.61243,8e-05,0.00299,4e-05,0.00197,3e-05,0.35178,0.00017,2e-05,0.00035,3e-05,1e-05,5e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,9.22271,0.0 -209_a_metric,1.0,0.0001,0.0,3e-05,0.0,8e-05,4e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.0006,0.0,0.0,1e-05,7e-05,1e-05,0.00046,0.00052,4e-05,0.0,0.00063,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.0004,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,0.0,3e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -209_r2_metric,1.0,0.00011,0.0,2e-05,0.0,8e-05,5e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00064,0.0,0.0,1e-05,8e-05,1e-05,0.00049,0.00054,4e-05,0.0,0.00067,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.00041,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,0.0,4e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -215_a_metric,1.0,0.00106,0.0,2e-05,0.0,0.00212,0.00042,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.02348,0.0,0.0,1e-05,8e-05,1e-05,0.00047,0.00926,5e-05,0.0,0.00064,0.00172,0.0,0.0032,0.0,1e-05,0.0,0.0,0.00781,0.0,0.0,0.0,0.0,0.00933,2e-05,0.0,0.00176,1e-05,4e-05,1e-05,0.00265,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 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-562_r2_metric,1.0,0.00031,0.00015,2e-05,1.08546,0.00027,0.00012,3e-05,9e-05,0.00013,0.00043,0.00027,4e-05,1e-05,0.00159,0.00124,0.15862,1e-05,7e-05,1e-05,0.00052,0.0019,4e-05,0.00049,0.0007,2e-05,0.08744,0.00011,0.0,1e-05,1.08323,8e-05,0.0015,3e-05,0.00073,3e-05,0.11795,0.00021,2e-05,0.00029,3e-05,0.0,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39723,0.0 -564_a_metric,1.0,0.00105,0.0,2e-05,0.0,0.00229,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,2e-05,0.0161,0.0,0.0,1e-05,8e-05,1e-05,0.00051,0.00845,4e-05,0.0,0.00069,0.00081,0.0,0.00141,0.0,2e-05,0.0,0.0,0.00716,0.0,0.0,0.0,0.0,0.00429,2e-05,0.0,0.00081,1e-05,5e-05,1e-05,0.00127,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -564_r2_metric,1.0,0.00107,0.0,2e-05,0.0,0.0023,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.01607,0.0,0.0,1e-05,8e-05,1e-05,0.00054,0.00853,4e-05,0.0,0.00072,0.00082,0.0,0.00155,0.0,1e-05,0.0,0.0,0.00721,0.0,0.0,0.0,0.0,0.0045,2e-05,0.0,0.00082,1e-05,4e-05,1e-05,0.00131,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -573_a_metric,1.0,0.00035,0.00016,2e-05,1.51126,0.00026,0.00013,4e-05,9e-05,0.0002,0.00041,0.00025,4e-05,1e-05,0.00162,0.00153,0.17899,1e-05,7e-05,1e-05,0.00053,0.00196,4e-05,0.00058,0.00078,1e-05,0.05692,9e-05,0.0,1e-05,1.69676,8e-05,0.0015,4e-05,0.00092,3e-05,0.14625,0.00018,2e-05,0.00031,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94202,1e-05 -573_r2_metric,1.0,0.00036,0.00013,3e-05,1.55181,0.00027,0.00013,3e-05,0.0001,0.00017,0.00053,0.00034,5e-05,1e-05,0.00194,0.00153,0.17819,1e-05,7e-05,1e-05,0.0006,0.00196,4e-05,0.00057,0.00083,1e-05,0.0667,0.00011,0.0,1e-05,1.78851,8e-05,0.00151,4e-05,0.00092,3e-05,0.14663,0.0002,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,1.09208,0.0 -574_a_metric,1.0,0.00061,0.00013,2e-05,152.67116,0.00095,0.00025,3e-05,8e-05,0.00013,0.00033,0.00018,4e-05,1e-05,0.00551,0.00211,7.419,1e-05,7e-05,1e-05,0.00046,0.00478,4e-05,0.00088,0.00064,9e-05,8.86999,0.00023,0.0,1e-05,84.10332,7e-05,0.00395,3e-05,0.0012,3e-05,3.37802,0.00063,2e-05,0.00027,0.00012,1e-05,3e-05,1e-05,0.00018,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,5.47426,0.0 -574_r2_metric,1.0,0.00062,0.00013,2e-05,152.47855,0.00099,0.00025,3e-05,9e-05,0.00013,0.00039,0.00023,4e-05,1e-05,0.00471,0.0022,7.42585,1e-05,8e-05,1e-05,0.00049,0.00482,5e-05,0.00089,0.00067,0.0001,8.90127,0.00026,0.0,1e-05,85.99908,7e-05,0.00398,3e-05,0.00128,3e-05,3.38485,0.00066,2e-05,0.00027,0.00011,1e-05,4e-05,1e-05,0.00019,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,5.49499,0.0 -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/a_metric_regression_dense/feature_runstatus.arff deleted file mode 100644 index a44c9e2bae..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_dense/feature_runstatus.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_RUNSTATUS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} - -@DATA -1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -189_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -189_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -197_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -197_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -198_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -198_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -201_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -201_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -209_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -209_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -218_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -218_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -225_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -225_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -227_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -227_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -296_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -296_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -562_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -562_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -564_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -564_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -573_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -573_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/feature_values.arff b/autosklearn/metalearning/files/a_metric_regression_dense/feature_values.arff deleted file mode 100644 index 25f49cd300..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_dense/feature_values.arff +++ /dev/null @@ -1,109 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_VALUES - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC - -@DATA 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-1030_a_metric,1.0,2.95338644252,0.180597014925,0.111111111111,0.0179104477612,0.0554011368455,0.00597014925373,167.5,-0.842774640072,-1.11468902778,-1.3683277744,0.191667533021,0.183586168733,0.222435440377,0.302265038883,0.264297222385,0.267556463145,0.206067070185,-5.12098335127,5.12098335127,1.38629436112,6.50727771239,0.0,9.0,4.0,0.0,670.0,0.0,0.0,4.0,1.0,-0.50382,-0.229125738144,0.0,0.0,0.0,0.0,0.0,0.305495560169,0.0825962927192,-0.313242763281,0.240387684257,0.0,0.0,0.0,0.0,0.0 -1030_r2_metric,1.0,2.95338644252,0.180597014925,0.111111111111,0.0179104477612,0.0554011368455,0.00597014925373,167.5,-0.842774640072,-1.11468902778,-1.3683277744,0.191667533021,0.183586168733,0.222435440377,0.302265038883,0.264297222385,0.267556463145,0.206067070185,-5.12098335127,5.12098335127,1.38629436112,6.50727771239,0.0,9.0,4.0,0.0,670.0,0.0,0.0,4.0,1.0,-0.50382,-0.229125738144,0.0,0.0,0.0,0.0,0.0,0.305495560169,0.0825962927192,-0.313242763281,0.240387684257,0.0,0.0,0.0,0.0,0.0 -1414_a_metric,1.0,8.84638094952,0.015494309612,0.00129533678756,0.000137117784177,0.00162224054548,0.00137117784177,729.3,7288.00326635,166.355906076,-1.53787918552,1050.07532355,0.021498338801,0.0163367348847,0.0218478019292,0.0176967882539,0.0114272470829,0.0179857648315,-6.59208516999,6.59208516999,2.30258509299,8.89467026298,6.0,772.0,10.0,0.0,7293.0,0.0,0.0,4.0,0.787234042553,-0.983286,-0.0949491634965,0.0,0.0,0.0,1.5,0.666666666667,85.3815231323,4.64931378185,-5.65015172958,12.1090448377,24.0,7.16666666667,2.0,7.71182353417,43.0 -1414_r2_metric,1.0,8.84638094952,0.015494309612,0.00129533678756,0.000137117784177,0.00162224054548,0.00137117784177,729.3,7288.00326635,166.355906076,-1.53787918552,1050.07532355,0.021498338801,0.0163367348847,0.0218478019292,0.0176967882539,0.0114272470829,0.0179857648315,-6.59208516999,6.59208516999,2.30258509299,8.89467026298,6.0,772.0,10.0,0.0,7293.0,0.0,0.0,4.0,0.787234042553,-0.983286,-0.0949491634965,0.0,0.0,0.0,1.5,0.666666666667,85.3815231323,4.64931378185,-5.65015172958,12.1090448377,24.0,7.16666666667,2.0,7.71182353417,43.0 -189_a_metric,1.0,12.4209714717,0.00036443148688,0.000182315405652,0.00018221574344,4.26029240934e-06,0.00145772594752,686.0,?,?,?,?,?,?,?,?,?,?,-6.53087762773,6.53087762773,2.07944154168,8.61031916941,0.0,5485.0,8.0,0.0,5488.0,0.0,0.0,8.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 -189_r2_metric,1.0,12.4209714717,0.00036443148688,0.000182315405652,0.00018221574344,4.26029240934e-06,0.00145772594752,686.0,?,?,?,?,?,?,?,?,?,?,-6.53087762773,6.53087762773,2.07944154168,8.61031916941,0.0,5485.0,8.0,0.0,5488.0,0.0,0.0,8.0,?,?,?,0.0,0.0,0.0,0.0,0.0,?,?,?,?,0.0,0.0,0.0,0.0,0.0 -197_a_metric,1.0,4.99129221158,0.0537536443149,0.0181818181818,0.00018221574344,0.018777135353,0.00382653061224,261.333333333,437.062374164,73.3596792877,1.19310638822,114.410696671,0.195205847882,0.0689522660774,0.194363843568,0.195455463377,0.14865969222,0.0696872886107,-5.56579673168,5.56579673168,3.04452243772,8.61031916941,0.0,55.0,21.0,0.0,5488.0,0.0,0.0,21.0,0.619047619048,3.56416,1.5504360199,0.0,0.0,0.0,0.0,0.0,17.1199512482,5.16947258938,-0.807244479656,4.31402582769,0.0,0.0,0.0,0.0,0.0 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a/autosklearn/metalearning/files/a_metric_regression_sparse/configurations.csv b/autosklearn/metalearning/files/a_metric_regression_sparse/configurations.csv deleted file mode 100644 index ca8d53d5ec..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_sparse/configurations.csv +++ /dev/null @@ -1,58 +0,0 @@ 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-54,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,minmax -55,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,3.8792789945671258,None,10,18,0.0,100,,,,,,,,,,,,,,,minmax -56,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt b/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt deleted file mode 100644 index 6cbc8ea06d..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step KurtosisSTD: KurtosisSTD -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step NumberOfClasses: NumberOfClasses -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step SkewnessMean: SkewnessMean -feature_step SymbolsMean: SymbolsMean -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step ClassEntropy: ClassEntropy -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step SymbolsSTD: SymbolsSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step SymbolsSum: SymbolsSum -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step SymbolsMin: SymbolsMin -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step KurtosisMean: KurtosisMean -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step KurtosisMax: KurtosisMax -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step LandmarkLDA: LandmarkLDA -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step ClassProbabilityMean: ClassProbabilityMean -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step Landmark1NN: Landmark1NN -feature_step SymbolsMax: SymbolsMax -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: NumberOfFeaturesWithMissingValues, KurtosisSTD, DatasetRatio, LandmarkDecisionNodeLearner, NumberOfClasses, NumberOfMissingValues, KurtosisMin, SkewnessMean, SymbolsMean, Skewnesses, SkewnessSTD, SkewnessMax, NumberOfFeatures, ClassEntropy, PCA, LandmarkNaiveBayes, LogNumberOfFeatures, RatioNominalToNumerical, LogNumberOfInstances, SymbolsSTD, MissingValues, SymbolsSum, PCAKurtosisFirstPC, NumSymbols, ClassProbabilityMax, LandmarkRandomNodeLearner, ClassProbabilitySTD, SymbolsMin, LogInverseDatasetRatio, LandmarkDecisionTree, KurtosisMean, NumberOfInstancesWithMissingValues, KurtosisMax, PCASkewnessFirstPC, PCAFractionOfComponentsFor95PercentVariance, LandmarkLDA, ClassOccurences, NumberOfCategoricalFeatures, Kurtosisses, ClassProbabilityMin, PercentageOfMissingValues, InverseDatasetRatio, PercentageOfInstancesWithMissingValues, ClassProbabilityMean, RatioNumericalToNominal, PercentageOfFeaturesWithMissingValues, NumberOfInstances, LogDatasetRatio, SkewnessMin, NumberOfNumericFeatures, Landmark1NN, SymbolsMax - -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57 -performance_type: solution_quality -performance_measures: a_metric -algorithms_stochastic: - -scenario_id: ChaLearn_AutoML_Challenge_2015 -maximize: false -algorithm_cutoff_time: 3600 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff deleted file mode 100644 index f291c0fcba..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_costs.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_COSTS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE NumSymbols NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE SymbolsMax 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-1414_a_metric,1.0,0.00035,0.00014,2e-05,0.47489,0.00047,0.00011,3e-05,9e-05,4e-05,0.00033,0.00017,4e-05,1e-05,0.00221,0.00213,3.26197,1e-05,6e-05,0.0,8e-05,0.0028,3e-05,0.00095,0.00016,4e-05,0.2864,0.00014,1e-05,1e-05,3.06772,8e-05,0.00237,4e-05,0.00114,4e-05,1.34828,0.00034,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.72778,1e-05 -1414_r2_metric,1.0,0.00035,0.00014,2e-05,0.47406,0.00048,0.0001,3e-05,0.00011,4e-05,0.00045,0.00026,5e-05,1e-05,0.00222,0.00214,3.23113,1e-05,6e-05,1e-05,9e-05,0.00279,3e-05,0.00096,0.00017,4e-05,0.28855,0.00014,1e-05,1e-05,3.08176,8e-05,0.00237,4e-05,0.00114,4e-05,1.34295,0.00035,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.7159,1e-05 -189_a_metric,1.0,0.00024,0.0,2e-05,0.0,0.00038,7e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00456,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00169,4e-05,0.0,0.00062,0.00031,0.0,0.00056,0.0,1e-05,0.0,0.0,0.00142,0.0,0.0,0.0,0.0,0.00171,2e-05,0.0,0.00032,1e-05,3e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -189_r2_metric,1.0,0.00024,0.0,2e-05,0.0,0.0004,8e-05,0.0,0.0,0.00441,0.0,0.0,0.0,1e-05,0.00425,0.0,0.0,1e-05,8e-05,1e-05,0.00497,0.00188,4e-05,0.0,0.00944,0.00031,0.0,0.00061,0.0,1e-05,0.0,0.0,0.0016,0.0,0.0,0.0,0.0,0.00176,2e-05,0.0,0.00032,1e-05,4e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -197_a_metric,1.0,0.00035,0.00014,2e-05,1.51228,0.00026,0.00013,3e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00155,0.00149,0.17921,1e-05,7e-05,1e-05,0.00044,0.00195,4e-05,0.00057,0.00061,1e-05,0.06526,9e-05,0.0,1e-05,1.60067,8e-05,0.0015,3e-05,0.00088,3e-05,0.14992,0.00018,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94178,0.0 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-422_r2_metric,1.0,0.00241,0.0,5e-05,0.0,0.00044,0.00145,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00248,0.0,0.0,1e-05,9e-05,1e-05,0.00046,0.00728,4e-05,0.0,0.00064,6e-05,0.0,0.00019,0.0,1e-05,0.0,0.0,0.00348,0.0,0.0,0.0,0.0,0.00046,2e-05,0.0,8e-05,1e-05,7e-05,3e-05,0.00014,6e-05,3e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -507_a_metric,1.0,0.00015,0.0,2e-05,0.0,0.00016,6e-05,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00159,0.0,0.0,1e-05,8e-05,1e-05,0.00065,0.00075,4e-05,0.0,0.00091,0.00011,0.0,0.00055,0.0,1e-05,0.0,0.0,0.00056,0.0,0.0,0.0,0.0,0.00101,2e-05,0.0,0.00013,0.0,4e-05,1e-05,0.00022,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -507_r2_metric,1.0,0.00015,0.0,2e-05,0.0,0.00016,6e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00153,0.0,0.0,1e-05,7e-05,1e-05,0.00046,0.00075,4e-05,0.0,0.00064,0.00011,0.0,0.00026,0.0,1e-05,0.0,0.0,0.00056,0.0,0.0,0.0,0.0,0.00072,2e-05,0.0,0.00014,0.0,3e-05,1e-05,0.00021,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -512_a_metric,1.0,5e-05,0.0,2e-05,0.0,0.00011,4e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.0007,0.0,0.0,1e-05,6e-05,1e-05,0.00044,0.00042,4e-05,0.0,0.00061,2e-05,0.0,0.00012,0.0,1e-05,0.0,0.0,0.00036,0.0,0.0,0.0,0.0,0.00025,2e-05,0.0,4e-05,0.0,3e-05,1e-05,7e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -512_r2_metric,1.0,6e-05,0.0,2e-05,0.0,0.00011,8e-05,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00067,0.0,0.0,1e-05,7e-05,1e-05,0.00058,0.00052,4e-05,0.0,0.00084,2e-05,0.0,0.00012,0.0,1e-05,0.0,0.0,0.00041,0.0,0.0,0.0,0.0,0.00025,2e-05,0.0,4e-05,0.0,3e-05,1e-05,7e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -558_a_metric,1.0,0.00049,0.0,3e-05,0.0,0.00037,0.00019,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00382,0.0,0.0,1e-05,9e-05,1e-05,0.00073,0.00229,4e-05,0.0,0.00099,0.00021,0.0,0.00049,0.0,2e-05,0.0,0.0,0.00164,0.0,0.0,0.0,0.0,0.00132,2e-05,0.0,0.00026,1e-05,5e-05,1e-05,0.00037,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -558_r2_metric,1.0,0.00049,0.0,3e-05,0.0,0.00036,0.00019,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00371,0.0,0.0,1e-05,8e-05,1e-05,0.00056,0.00227,4e-05,0.0,0.00074,0.00021,0.0,0.00046,0.0,1e-05,0.0,0.0,0.00163,0.0,0.0,0.0,0.0,0.00127,2e-05,0.0,0.00023,1e-05,4e-05,1e-05,0.00037,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -562_a_metric,1.0,0.00032,0.00014,2e-05,1.08946,0.00028,0.00012,3e-05,8e-05,0.00013,0.00035,0.00019,4e-05,1e-05,0.00157,0.00124,0.15881,1e-05,7e-05,1e-05,0.00048,0.00196,4e-05,0.00051,0.00065,1e-05,0.08654,0.00011,0.0,1e-05,1.08761,8e-05,0.00154,4e-05,0.0007,3e-05,0.11888,0.0002,2e-05,0.00029,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39691,0.0 -562_r2_metric,1.0,0.00031,0.00015,2e-05,1.08546,0.00027,0.00012,3e-05,9e-05,0.00013,0.00043,0.00027,4e-05,1e-05,0.00159,0.00124,0.15862,1e-05,7e-05,1e-05,0.00052,0.0019,4e-05,0.00049,0.0007,2e-05,0.08744,0.00011,0.0,1e-05,1.08323,8e-05,0.0015,3e-05,0.00073,3e-05,0.11795,0.00021,2e-05,0.00029,3e-05,0.0,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39723,0.0 -564_a_metric,1.0,0.00105,0.0,2e-05,0.0,0.00229,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,2e-05,0.0161,0.0,0.0,1e-05,8e-05,1e-05,0.00051,0.00845,4e-05,0.0,0.00069,0.00081,0.0,0.00141,0.0,2e-05,0.0,0.0,0.00716,0.0,0.0,0.0,0.0,0.00429,2e-05,0.0,0.00081,1e-05,5e-05,1e-05,0.00127,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -564_r2_metric,1.0,0.00107,0.0,2e-05,0.0,0.0023,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.01607,0.0,0.0,1e-05,8e-05,1e-05,0.00054,0.00853,4e-05,0.0,0.00072,0.00082,0.0,0.00155,0.0,1e-05,0.0,0.0,0.00721,0.0,0.0,0.0,0.0,0.0045,2e-05,0.0,0.00082,1e-05,4e-05,1e-05,0.00131,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -573_a_metric,1.0,0.00035,0.00016,2e-05,1.51126,0.00026,0.00013,4e-05,9e-05,0.0002,0.00041,0.00025,4e-05,1e-05,0.00162,0.00153,0.17899,1e-05,7e-05,1e-05,0.00053,0.00196,4e-05,0.00058,0.00078,1e-05,0.05692,9e-05,0.0,1e-05,1.69676,8e-05,0.0015,4e-05,0.00092,3e-05,0.14625,0.00018,2e-05,0.00031,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94202,1e-05 -573_r2_metric,1.0,0.00036,0.00013,3e-05,1.55181,0.00027,0.00013,3e-05,0.0001,0.00017,0.00053,0.00034,5e-05,1e-05,0.00194,0.00153,0.17819,1e-05,7e-05,1e-05,0.0006,0.00196,4e-05,0.00057,0.00083,1e-05,0.0667,0.00011,0.0,1e-05,1.78851,8e-05,0.00151,4e-05,0.00092,3e-05,0.14663,0.0002,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,1.09208,0.0 -574_a_metric,1.0,0.00061,0.00013,2e-05,152.67116,0.00095,0.00025,3e-05,8e-05,0.00013,0.00033,0.00018,4e-05,1e-05,0.00551,0.00211,7.419,1e-05,7e-05,1e-05,0.00046,0.00478,4e-05,0.00088,0.00064,9e-05,8.86999,0.00023,0.0,1e-05,84.10332,7e-05,0.00395,3e-05,0.0012,3e-05,3.37802,0.00063,2e-05,0.00027,0.00012,1e-05,3e-05,1e-05,0.00018,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,5.47426,0.0 -574_r2_metric,1.0,0.00062,0.00013,2e-05,152.47855,0.00099,0.00025,3e-05,9e-05,0.00013,0.00039,0.00023,4e-05,1e-05,0.00471,0.0022,7.42585,1e-05,8e-05,1e-05,0.00049,0.00482,5e-05,0.00089,0.00067,0.0001,8.90127,0.00026,0.0,1e-05,85.99908,7e-05,0.00398,3e-05,0.00128,3e-05,3.38485,0.00066,2e-05,0.00027,0.00011,1e-05,4e-05,1e-05,0.00019,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,5.49499,0.0 -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff deleted file mode 100644 index a44c9e2bae..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_runstatus.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_RUNSTATUS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} - -@DATA -1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -189_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -189_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -197_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -197_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -198_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -198_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -201_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -201_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -209_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -209_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -218_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -218_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -225_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -225_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -227_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -227_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -296_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -296_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -562_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -562_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -564_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -564_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -573_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -573_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff deleted file mode 100644 index 25f49cd300..0000000000 --- a/autosklearn/metalearning/files/a_metric_regression_sparse/feature_values.arff +++ /dev/null @@ -1,109 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_VALUES - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC 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-34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.137928511354329,None,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.056526269573181555,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.899439798098555,,,9.99143472277497e-05,rbf,-1,True,0.09838972982387809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.03900332266026785,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,4.268694609197059,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME.R,0.8720804165168587,3,395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,5,12,1.0,67,,,,,,standardize -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.583101219606527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.008262850459885417,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,324,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.2239425035207563,7,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028347746512274816,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize -70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.11725997635850348,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.9055422128256523,1.0,None,8,19,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00010829051551306555,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.0494577297275485,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.12135790606587657,0,8,11,279,0,1,1,0.8703624243977103,mean,0.08554118822152902,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME.R,0.8720804165168587,3,395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,5,12,1.0,67,,,,,,standardize -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.583101219606527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.008262850459885417,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,324,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.2239425035207563,7,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028347746512274816,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,standardize -70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.11725997635850348,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.9055422128256523,1.0,None,8,19,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00010829051551306555,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.0494577297275485,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.12135790606587657,0,8,11,279,0,1,1,0.8703624243977103,mean,0.08554118822152902,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,4.2564408409001455,None,10,19,0.0,494,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,295,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8193635484846253,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_metric_binary.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt similarity index 98% rename from autosklearn/metalearning/files/f1_metric_binary.classification_dense/description.txt rename to autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt index 8b09a78c85..e9f2bce480 100644 --- a/autosklearn/metalearning/files/f1_metric_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/accuracy_binary.classification_dense/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: f1_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: accuracy +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/a_metric_regression_dense/readme.txt b/autosklearn/metalearning/files/accuracy_binary.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/a_metric_regression_dense/readme.txt rename to autosklearn/metalearning/files/accuracy_binary.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..b29d3f72e7 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b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: f1_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: accuracy +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/accuracy_binary.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/a_metric_regression_sparse/readme.txt b/autosklearn/metalearning/files/accuracy_binary.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/a_metric_regression_sparse/readme.txt rename to autosklearn/metalearning/files/accuracy_binary.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff new file mode 100644 index 0000000000..aabe035f48 --- /dev/null +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08297972654408303,ok +75193,1.0,2,0.05902478968148417,ok 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+38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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+44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +45,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax 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+68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt similarity index 98% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/description.txt rename to autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt index 8b09a78c85..e9f2bce480 100644 --- a/autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: f1_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: accuracy +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/readme.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_dense/readme.txt rename to autosklearn/metalearning/files/accuracy_multiclass.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..b29d3f72e7 --- /dev/null +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.08628005657708626,ok +75193,1.0,2,0.05902478968148417,ok 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+129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt similarity index 98% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/description.txt rename to autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt index 8b09a78c85..e9f2bce480 100644 --- a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: f1_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: accuracy +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/readme.txt b/autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/acc_metric_binary.classification_sparse/readme.txt rename to autosklearn/metalearning/files/accuracy_multiclass.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/auc_metric_binary.classification_dense/algorithm_runs.arff deleted file mode 100644 index c12adcac95..0000000000 --- a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/algorithm_runs.arff +++ /dev/null @@ -1,144 +0,0 @@ -@RELATION auto-sklearn_ALGORITHM_RUNS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE algorithm STRING -@ATTRIBUTE auc_metric NUMERIC -@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} - -@DATA -75113,1.0,17,0.00084003880614,ok -75119,1.0,131,0.183801652893,ok 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-28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.388891335697881,None,19,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.553043630940243,None,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,4.268694609197059,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,92,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0013419704373044583,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,13.345244397508631,False,True,1,squared_hinge,ovr,l1,0.00034715804150262845,,,,,,,,,,,,,,,,,,,,,,minmax -42,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.760929708270559,14,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.09458704130358651,True,fast_ica,,,,,,,,,parallel,exp,1192,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0003580045297184277,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.4697242532263526,None,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.041008478760879624,0,9,1,272,0,1,1,0.7771541749340595,median,0.00017098147418880078,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.6447434389340525,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,True,0.0024109611140868356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9608680987721302,-0.26457829134652355,4,0.1379966725055273,poly,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004983753304624408,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,none -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.9385871980662734,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,1.4411568194977027,4,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,2,78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000539156216552046,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2191124436769022,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.8072858360362405,15,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00037078342898917775,True,extra_trees_preproc_for_classification,True,entropy,None,3.2872713864450325,4,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.30896450452785995,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0027392011544235123,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,353,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,-0.08366686105713594,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.016055220206010384,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.047915438871202094,fdr,f_classif,standardize -67,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.13023765388421346,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,155,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.839347846087743,None,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.9732183989921284,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2060.9218719334363,False,True,1,squared_hinge,ovr,l2,0.0004973615331945917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.2940495756499723,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -77,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1240,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.432776091506209,None,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,standardize -79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.6316431211190174,False,True,1,squared_hinge,ovr,l2,0.005952596165439475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.38412484884309195,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3678089229861546,fpr,f_classif,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.0494577297275485,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.13371290459448631,0,7,1,361,0,1,1,0.9712917013885938,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.07761998114779296,deviance,6,3.2885017018472857,None,15,14,0.0,193,0.1834452751855389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00014992300819174025,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03882694473700471,fdr,f_classif,minmax -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,2.8570791257603125,None,10,12,0.0,478,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,206,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.4153487268321973,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.910771580008614,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -87,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -88,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1440.5033282820418,False,True,1,squared_hinge,ovr,l2,0.001311721948379899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028386723831118816,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5860071624792975,True,squared_hinge,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -90,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.353776394113618,11,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0002600246911836556,True,kernel_pca,,,,,,,,,,,,,,,,,,,0.4760250510934092,rbf,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -91,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.291104653355493,11,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.7629511329651062,None,18,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.4482706213833096,None,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,2,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.059955948748613735,True,kernel_pca,,,,,,,,,,,,,,,,,0.04958286464580164,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.7169751980106978,None,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004392540932963756,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8881.39583000388,,,0.004057992510232778,rbf,-1,True,1.4415947847988208e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.479628163572095,None,12,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05284587233599165,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -108,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.3361455146809385,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.045645659641546966,True,feature_agglomeration,,,,,,,,,,,,,cosine,complete,101,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0023563664302785e-05,True,,0.09209397388504023,True,0.1737792939472745,constant,log,35,elasticnet,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3733446596200857,fdr,f_classif,minmax -110,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.04835589138299782,True,,optimal,log,20,l2,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,0.5035280061572498,2,0.002717961763707031,poly,568,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -111,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.442835182463979,14,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00016959931567986694,True,fast_ica,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7405.166546836462,,,0.007770735808099179,rbf,-1,True,0.000172778565738507,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -113,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.7946050705755374e-05,True,hinge,12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004629369277669015,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36886294619433757,fpr,f_classif,standardize -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -120,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057792417780771835,False,,0.01,True,2.2656878433589447e-05,constant,log,20,elasticnet,,,,,,,,,,,,,,,most_frequent,0.2535282202120158,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,76.24979612915106,False,True,1,squared_hinge,ovr,l1,0.0018297078990931735,,,,,,,,,,,,,,,,,,,,,,minmax -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.018238030951915563,deviance,8,0.6820931644146317,None,14,20,0.0,475,0.26763716882575656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009694714242663112,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,296,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06103814648767104,deviance,4,4.245270434444319,None,3,17,0.0,253,0.2794301562862259,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,f_classif,minmax -126,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.707765138993054,,,0.6959938170347849,rbf,-1,True,0.06382145530669085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.3436728311002971,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,8365.831389874691,False,True,1,squared_hinge,ovr,l1,1.632652169554192e-05,,,,,,,,,,,,,,,,,,,,,,normalize -127,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,6,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -130,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.012734250522086193,deviance,10,4.523730358280659,None,14,14,0.0,446,0.3101992166135547,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01773715335496824,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,3.246344836135379,13,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0004357427455687655,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,minmax diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/description.txt b/autosklearn/metalearning/files/auc_metric_binary.classification_dense/description.txt deleted file mode 100644 index 15cc9707d7..0000000000 --- a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: auc_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 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-77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -84,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4094977295746793,True,hinge,20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78.82798412816979,chi2,,,,,normalize 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-109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -113,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -114,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0008400738023527816,False,0.00032010809754884237,0.008030783926616126,True,,optimal,modified_huber,1000,l2,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -120,weighting,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.20388730537005378,0,3,1,100,0,1,1,1.0,most_frequent,0.04021757735339554,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,18.39521172316188,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,normalize -121,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,4.85727947010594,None,5,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07549377209854607,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,54.203907420266454,chi2,,,,,normalize -122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/auc_metric_binary.classification_sparse/description.txt deleted file mode 100644 index 15cc9707d7..0000000000 --- a/autosklearn/metalearning/files/auc_metric_binary.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, 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PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: auc_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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-7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4429474428546323,True,squared_hinge,25,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000237428444027738,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.4951688122781348,4,0.07661255303004473,poly,1328,,,,,,,,,,,,,,,,,minmax -8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9561535048541083,None,2,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -9,weighting,adaboost,SAMME,1.4048477305259919,6,343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00012739159514185168,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -11,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.026069273288043027,deviance,7,1.7875003687721072,None,16,8,0.0,266,0.9802612010510682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -12,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.304908804831392e-05,False,,0.042653336370051316,True,0.00025520514394342965,constant,log,325,elasticnet,,,,,,,,,,,,,,,mean,0.38341065540651387,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550933972720641,fpr,f_classif,standardize -13,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.056600979543795484,True,hinge,7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -14,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,705.6874618455068,,,0.01969575596641128,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,None,20,17,1.0,76,,,,,,none -15,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1871146988423774,False,True,1,squared_hinge,ovr,l2,0.00016466471791022182,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9742427573650911,3,4.365901938038843,poly,2472,,,,,,,,,,,,,,,,,normalize -16,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1579724000187546e-06,True,,0.07071036976546528,True,0.0006705591494191703,optimal,log,981,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -17,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,,,,,,,,,,,,,,most_frequent,0.014096602461023714,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,minmax -18,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,398.4183036802323,,,0.010251837562310467,rbf,-1,False,0.013822830527205917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5052.254329914395,,,0.0044645017776694445,rbf,-1,True,0.02140628321054818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0014000343077363432,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.568264342230065,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.48677472872591393,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -23,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.878014917776569,19,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.3778926016633066,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035060891054916214,deviance,5,3.6531380857989673,None,20,12,0.0,306,0.7344609811154431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015035493040609763,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.388891335697881,None,19,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.553043630940243,None,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,4.268694609197059,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,92,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0013419704373044583,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,13.345244397508631,False,True,1,squared_hinge,ovr,l1,0.00034715804150262845,,,,,,,,,,,,,,,,,,,,,,minmax -42,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.760929708270559,14,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.09458704130358651,True,fast_ica,,,,,,,,,parallel,exp,1192,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0003580045297184277,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.4697242532263526,None,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.041008478760879624,0,9,1,272,0,1,1,0.7771541749340595,median,0.00017098147418880078,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.6447434389340525,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,True,0.0024109611140868356,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9608680987721302,-0.26457829134652355,4,0.1379966725055273,poly,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004983753304624408,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,none -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.9385871980662734,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,1.4411568194977027,4,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,2,78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000539156216552046,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2191124436769022,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.8072858360362405,15,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00037078342898917775,True,extra_trees_preproc_for_classification,True,entropy,None,3.2872713864450325,4,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.30896450452785995,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0027392011544235123,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,353,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,-0.08366686105713594,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.016055220206010384,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.047915438871202094,fdr,f_classif,standardize -67,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.13023765388421346,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,155,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.839347846087743,None,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.9732183989921284,2,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2060.9218719334363,False,True,1,squared_hinge,ovr,l2,0.0004973615331945917,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.2940495756499723,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,344,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -77,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1240,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.432776091506209,None,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,standardize -79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.6316431211190174,False,True,1,squared_hinge,ovr,l2,0.005952596165439475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.38412484884309195,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3678089229861546,fpr,f_classif,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.0494577297275485,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.13371290459448631,0,7,1,361,0,1,1,0.9712917013885938,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.07761998114779296,deviance,6,3.2885017018472857,None,15,14,0.0,193,0.1834452751855389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00014992300819174025,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03882694473700471,fdr,f_classif,minmax -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,2.8570791257603125,None,10,12,0.0,478,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,206,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.4153487268321973,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.910771580008614,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -87,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -88,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1440.5033282820418,False,True,1,squared_hinge,ovr,l2,0.001311721948379899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028386723831118816,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5860071624792975,True,squared_hinge,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -90,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.353776394113618,11,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0002600246911836556,True,kernel_pca,,,,,,,,,,,,,,,,,,,0.4760250510934092,rbf,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -91,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.291104653355493,11,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.7629511329651062,None,18,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.4482706213833096,None,13,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,2,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.059955948748613735,True,kernel_pca,,,,,,,,,,,,,,,,,0.04958286464580164,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.7169751980106978,None,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004392540932963756,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8881.39583000388,,,0.004057992510232778,rbf,-1,True,1.4415947847988208e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.479628163572095,None,12,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05284587233599165,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -108,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.3361455146809385,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.045645659641546966,True,feature_agglomeration,,,,,,,,,,,,,cosine,complete,101,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.0023563664302785e-05,True,,0.09209397388504023,True,0.1737792939472745,constant,log,35,elasticnet,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3733446596200857,fdr,f_classif,minmax -110,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.04835589138299782,True,,optimal,log,20,l2,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,0.5035280061572498,2,0.002717961763707031,poly,568,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -111,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.442835182463979,14,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00016959931567986694,True,fast_ica,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7405.166546836462,,,0.007770735808099179,rbf,-1,True,0.000172778565738507,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -113,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.7946050705755374e-05,True,hinge,12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004629369277669015,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36886294619433757,fpr,f_classif,standardize -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -120,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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b/autosklearn/metalearning/files/auc_metric_multiclass.classification_dense/description.txt deleted file mode 100644 index 15cc9707d7..0000000000 --- a/autosklearn/metalearning/files/auc_metric_multiclass.classification_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: auc_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 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-131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/description.txt deleted file mode 100644 index 15cc9707d7..0000000000 --- a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: auc_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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+62,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.01116924019200219,True,fast_ica,,,,,,,,,deflation,cube,766,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,15,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.938615614788205,f_classif,,,,none +64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +73,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +74,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.035386445062342765,True,,optimal,hinge,20,l2,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +83,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +84,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt new file mode 100644 index 0000000000..2083121516 --- /dev/null +++ b/autosklearn/metalearning/files/average_precision_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/average_precision_binary.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_binary.classification_dense/feature_values.arff 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+34,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5827771362934735,True,hinge,41,,,,,,,,,,,,,,,,,,,,,,median,0.000875930253684246,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,standardize +39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.0235762546783831,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.050048416043345,,,,,,,,,,,,,,,,,,,,,mean,0.0013509816177055573,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.5716878597339514,None,12,5,0.0,100,,,,,,,,,,,,mean,0.3328589169634927,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.2886053439647993,,,0.001958703488411727,rbf,-1,False,0.02475760125468724,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,12,14,0.0,100,,,,,,,,,,,,median,0.07369430962717148,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +63,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,adaboost,SAMME.R,0.05387644186054156,2,341,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +75,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.511525734485552,0.0,3,0.24137524681185685,poly,-1,True,0.0022196456925201464,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +84,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..2083121516 --- /dev/null +++ b/autosklearn/metalearning/files/average_precision_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 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+23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +24,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,8,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +25,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.33268497038283734,True,extra_trees_preproc_for_classification,False,entropy,None,1.398586775108782,7,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+29,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,True,,0.0023911270619537315,True,1.3210189886716814e-06,optimal,log,64,elasticnet,,median,0.027344467656645954,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,10,1.0,97,,,,,,none +30,none,bernoulli_nb,,,,,5.366806098032344,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14.999104178474978,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,normalize +32,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,522.1737827111988,False,True,1,squared_hinge,ovr,l2,0.05569113756716791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2934811012685023,fdr,chi2,normalize +33,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none +34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +41,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.0235762546783831,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.9145911691745825,0.8390938017045895,2,0.12403388838921078,poly,-1,False,0.00011235090274035898,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.09940116443132659,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax +50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.2886053439647993,,,0.001958703488411727,rbf,-1,False,0.02475760125468724,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,12,14,0.0,100,,,,,,,,,,,,median,0.07369430962717148,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.01116924019200219,True,fast_ica,,,,,,,,,deflation,cube,766,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,15,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.938615614788205,f_classif,,,,none +64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, 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a/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..2083121516 --- /dev/null +++ b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: average_precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/auc_metric_binary.classification_sparse/readme.txt b/autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/auc_metric_binary.classification_sparse/readme.txt rename to autosklearn/metalearning/files/average_precision_multiclass.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/bac_metric_binary.classification_dense/algorithm_runs.arff deleted file mode 100644 index 83912601ea..0000000000 --- a/autosklearn/metalearning/files/bac_metric_binary.classification_dense/algorithm_runs.arff +++ /dev/null @@ -1,144 +0,0 @@ -@RELATION auto-sklearn_ALGORITHM_RUNS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE algorithm STRING -@ATTRIBUTE bac_metric NUMERIC -@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, 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-28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.7744204594457953,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009579633014979775,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.1874167676083379,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.6973218237903964,None,20,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,216,manual,0.17803732148393314,0.01739047350470614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010662597525650023,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.1740566751384052,2034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.137928511354329,None,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.056526269573181555,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.899439798098555,,,9.99143472277497e-05,rbf,-1,True,0.09838972982387809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.03900332266026785,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,283,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,92,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0013419704373044583,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.760929708270559,14,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.09458704130358651,True,fast_ica,,,,,,,,,parallel,exp,1192,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.0,None,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.10840624882400868,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME.R,0.8720804165168587,3,395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,5,12,1.0,67,,,,,,standardize -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.583101219606527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.008262850459885417,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,324,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,2,78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00040932144045250154,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21395716273521956,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.5089044887919199,-0.54658299141726,,0.007314336476794888,sigmoid,-1,False,0.0012726926652231049,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006744280211325621,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,7,13,1.0,52,,,,,,normalize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.7347010920888444,16,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00028015691630365653,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,standardize -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.190601452996489,1,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.04905752772686606,deviance,2,4.889359432558803,None,16,14,0.0,484,0.9580982028822531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,278,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03990049323363799,deviance,6,4.896254082080487,None,13,15,0.0,267,0.4557608966372973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.007086974030867012,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.1371634677914295,1.0,None,12,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.6316431211190174,False,True,1,squared_hinge,ovr,l2,0.005952596165439475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.38412484884309195,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3678089229861546,fpr,f_classif,standardize -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.192987662007981,None,6,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.12135790606587657,0,8,11,279,0,1,1,0.8703624243977103,mean,0.08554118822152902,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.07761998114779296,deviance,6,3.2885017018472857,None,15,14,0.0,193,0.1834452751855389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00014992300819174025,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03882694473700471,fdr,f_classif,minmax -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,4.2564408409001455,None,10,19,0.0,494,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,295,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8193635484846253,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0004082694371494187,True,hinge,70,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.018364825799282978,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6656965086498056,False,,,,,,,,,,,,,,,standardize -88,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1440.5033282820418,False,True,1,squared_hinge,ovr,l2,0.001311721948379899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028386723831118816,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1444.1604607974718,0.8539520071487003,4,0.4386932835217779,poly,-1,False,0.013531111989188759,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.48911848027879384,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.32073265077577,f_classif,,,,minmax -90,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.8363969769395707,11,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.13548951514128743,True,kernel_pca,,,,,,,,,,,,,,,,,,,0.085540018890044,rbf,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -91,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.291104653355493,11,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -92,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,0.14999999999999974,optimal,log,20,elasticnet,,,,,,,,,,,,,,,mean,0.00035762757477644867,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -93,weighting,decision_tree,,,,,,,entropy,1.1371634677914295,1.0,None,20,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.7629511329651062,None,18,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -95,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09731943236252891,True,extra_trees_preproc_for_classification,False,gini,None,1.2463566902368797,6,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3250968576265525e-06,True,,0.045206385380322525,True,7.018680385182665e-07,constant,log,120,elasticnet,,,,,,,,,,,,,,,mean,0.1467936263265181,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8646723261408122,False,,,,,,,,,,,,,,,minmax -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.2694335673522903,None,9,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.1318553271545748,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01689850282180131,fwe,f_classif,minmax -99,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,4.066712173808025,16,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,38,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,4,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.17148916741171152,True,kernel_pca,,,,,,,,,,,,,,,,,0.42874566531328484,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5982754606738583,None,18,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69.90794969376213,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.09014743650167326,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3464692597851791,fwe,f_classif,minmax -105,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.19373227047521904,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax -106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.029307959295600206,deviance,3,1.0,None,5,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,63,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.716976197257689,12,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011633115405687978,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -108,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.3361455146809385,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.045645659641546966,True,feature_agglomeration,,,,,,,,,,,,,cosine,complete,101,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -109,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46337567592144957,fwe,f_classif,minmax -110,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3250968576265525e-06,True,,0.045206385380322525,True,7.018680385182665e-07,constant,log,120,elasticnet,,,,,,,,,,,,,,,mean,0.1467936263265181,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8646723261408122,False,,,,,,,,,,,,,,,minmax -111,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,3.872392081261074,19,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -112,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7405.166546836462,,,0.0065614627465311135,rbf,-1,False,0.0007046266633994003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -113,weighting,decision_tree,,,,,,,entropy,0.1862729477294407,1.0,None,20,4,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.1296461152325597,True,extra_trees_preproc_for_classification,True,entropy,None,4.598116046466953,18,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.7946050705755374e-05,True,hinge,12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004629369277669015,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.36886294619433757,fpr,f_classif,standardize -116,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.02002401534242,,,0.09000456663304887,rbf,-1,True,0.09271652023886748,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.28132141040472186,fdr,f_classif,minmax -117,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00016723423487837678,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -118,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.2327015310560737,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -120,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0057792417780771835,True,,0.036842852048690485,True,1.213834740252259e-08,constant,squared_hinge,20,elasticnet,,,,,,,,,,,,,,,median,0.2535282202120158,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -124,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.018238030951915563,deviance,8,0.6820931644146317,None,14,20,0.0,475,0.26763716882575656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009694714242663112,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06103814648767104,deviance,4,4.245270434444319,None,3,19,0.0,253,0.2794301562862259,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000133938060565157,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,f_classif,standardize -126,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,51.707765138993054,,,0.6959938170347849,rbf,-1,True,0.06382145530669085,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.3436728311002971,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,8365.831389874691,False,True,1,squared_hinge,ovr,l1,1.632652169554192e-05,,,,,,,,,,,,,,,,,,,,,,normalize -127,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,6,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -128,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022665945476770404,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5127334571652834,True,,,,,,,,,,,,,,,minmax -129,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,standardize -130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -131,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1546562925956967,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07618098960011334,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3370871466066021,fpr,chi2,none 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PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: bac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/algorithm_runs.arff 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-130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/description.txt deleted file mode 100644 index feb2d9d100..0000000000 --- a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: 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LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: bac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 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-4,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5453744358979124e-06,True,,0.06350173900815109,True,2.1807862523441694e-07,optimal,log,1000,elasticnet,,,,,,,,,,,,,,,most_frequent,0.02278295442426193,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.848846967653911,False,,,,,,,,,,,,,,,minmax -5,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,357.57863303254476,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,entropy,None,3.8981064452694474,9,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -6,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -7,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.4429474428546323,True,squared_hinge,25,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000237428444027738,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.4951688122781348,4,0.15084845186593898,poly,1328,,,,,,,,,,,,,,,,,minmax -8,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -9,weighting,adaboost,SAMME,1.4048477305259919,6,343,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00012739159514185168,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -11,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.026069273288043027,deviance,7,1.7875003687721072,None,20,8,0.0,266,0.9802612010510682,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -12,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.790010486195598,None,9,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.206194253081357,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,383,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -13,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31.072918101598408,-0.7791479376555757,3,4.6203468860969465,poly,-1,False,0.001099773094475084,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -14,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -15,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1871146988423774,False,True,1,squared_hinge,ovr,l2,0.00016466471791022182,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9742427573650911,3,4.365901938038843,poly,2472,,,,,,,,,,,,,,,,,normalize -16,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1579724000187546e-06,True,,0.07071036976546528,True,0.0006705591494191703,optimal,log,981,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -17,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2573554062448411,fpr,f_classif,none -18,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.073635975697865,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06826800465849801,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5052.254329914395,,,0.0044645017776694445,rbf,-1,True,0.02140628321054818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0014000343077363432,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,2.1788491223541313,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.15869937756443636,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11324158459576941,fwe,f_classif,standardize -23,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.675075889654885,20,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009322127640480147,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,normalize -24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -25,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065164528543808,None,11,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -26,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.9734233069204623,1,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,109,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0841893003162543,deviance,7,2.525254466868981,None,1,3,0.0,450,0.43270125817488375,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.7744204594457953,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009579633014979775,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.1874167676083379,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.6973218237903964,None,20,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,216,manual,0.17803732148393314,0.01739047350470614,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010662597525650023,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.1740566751384052,2034,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.137928511354329,None,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.056526269573181555,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.899439798098555,,,9.99143472277497e-05,rbf,-1,True,0.09838972982387809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.03900332266026785,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,283,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,92,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0013419704373044583,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.760929708270559,14,17,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.09458704130358651,True,fast_ica,,,,,,,,,parallel,exp,1192,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,1.0,None,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.10840624882400868,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,adaboost,SAMME.R,0.8720804165168587,3,395,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,5,12,1.0,67,,,,,,standardize -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.583101219606527,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.008262850459885417,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,324,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,2,78,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00040932144045250154,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21395716273521956,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.5089044887919199,-0.54658299141726,,0.007314336476794888,sigmoid,-1,False,0.0012726926652231049,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006744280211325621,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,7,13,1.0,52,,,,,,normalize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.7347010920888444,16,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00028015691630365653,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,standardize -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.190601452996489,1,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.04905752772686606,deviance,2,4.889359432558803,None,16,14,0.0,484,0.9580982028822531,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,278,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03990049323363799,deviance,6,4.896254082080487,None,13,15,0.0,267,0.4557608966372973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.007086974030867012,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.1371634677914295,1.0,None,12,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.6316431211190174,False,True,1,squared_hinge,ovr,l2,0.005952596165439475,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.38412484884309195,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3678089229861546,fpr,f_classif,standardize -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.192987662007981,None,6,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.12135790606587657,0,8,11,279,0,1,1,0.8703624243977103,mean,0.08554118822152902,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.07761998114779296,deviance,6,3.2885017018472857,None,15,14,0.0,193,0.1834452751855389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00014992300819174025,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03882694473700471,fdr,f_classif,minmax -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,4.2564408409001455,None,10,19,0.0,494,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,295,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8193635484846253,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -87,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0004082694371494187,True,hinge,70,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.018364825799282978,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6656965086498056,False,,,,,,,,,,,,,,,standardize -88,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1440.5033282820418,False,True,1,squared_hinge,ovr,l2,0.001311721948379899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0028386723831118816,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1444.1604607974718,0.8539520071487003,4,0.4386932835217779,poly,-1,False,0.013531111989188759,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.48911848027879384,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.32073265077577,f_classif,,,,minmax -90,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.8363969769395707,11,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.13548951514128743,True,kernel_pca,,,,,,,,,,,,,,,,,,,0.085540018890044,rbf,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -91,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.291104653355493,11,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -92,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,0.14999999999999974,optimal,log,20,elasticnet,,,,,,,,,,,,,,,mean,0.00035762757477644867,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -93,weighting,decision_tree,,,,,,,entropy,1.1371634677914295,1.0,None,20,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.7629511329651062,None,18,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -95,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09731943236252891,True,extra_trees_preproc_for_classification,False,gini,None,1.2463566902368797,6,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.3250968576265525e-06,True,,0.045206385380322525,True,7.018680385182665e-07,constant,log,120,elasticnet,,,,,,,,,,,,,,,mean,0.1467936263265181,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8646723261408122,False,,,,,,,,,,,,,,,minmax -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.2694335673522903,None,9,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.1318553271545748,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.01689850282180131,fwe,f_classif,minmax -99,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,4.066712173808025,16,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,38,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,4,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.17148916741171152,True,kernel_pca,,,,,,,,,,,,,,,,,0.42874566531328484,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5982754606738583,None,18,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,69.90794969376213,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.09014743650167326,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3464692597851791,fwe,f_classif,minmax -105,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.19373227047521904,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax -106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.029307959295600206,deviance,3,1.0,None,5,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,63,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.716976197257689,12,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011633115405687978,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax 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-132,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,650.9515464921639,,,3.1022356014456185,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011811753271271985,True,extra_trees_preproc_for_classification,False,entropy,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/description.txt deleted file mode 100644 index feb2d9d100..0000000000 --- a/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: bac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/algorithm_runs.arff 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-130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/description.txt deleted file mode 100644 index feb2d9d100..0000000000 --- a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: bac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 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+80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +97,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,,,,,standardize +98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none +103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize +106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.003937843574389652,rbf,545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.5555960331548946e-06,True,,0.03775758938844074,True,,optimal,hinge,6,l2,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.05709925270311,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,7.769496682507031,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize +127,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.402676003557486,14,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,133,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.324492134388027,f_classif,,,,none +130,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11199470124045516,deviance,6,1.0,None,1,2,0.0,100,0.5541730214135502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011676674417077058,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7525595954528075,False,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt new file mode 100644 index 0000000000..f8ff146acd --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 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autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_dense/readme.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/auc_metric_multiclass.classification_dense/readme.txt rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..9b4f51e8ec --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE balanced_accuracy NUMERIC +@ATTRIBUTE runstatus {ok, timeout, 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+111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.80178999786076,None,4,9,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.8929665633992583,14,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..f8ff146acd --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: 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RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/readme.txt b/autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/auc_metric_multiclass.classification_sparse/readme.txt rename to autosklearn/metalearning/files/balanced_accuracy_binary.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff new file mode 100644 index 0000000000..2c9c676416 --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE 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+8,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +9,none,adaboost,SAMME,0.3740823239105414,10,457,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.002144117618160979,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +10,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03772929425993741,deviance,9,0.7855962105118542,None,18,16,0.0,252,0.9055161170823436,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +11,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +12,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +13,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,normalize +14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.05555416654541217,deviance,3,2.5091820463432226,None,10,18,0.0,243,0.7284627840776807,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.17884137258953,chi2,,,,standardize +15,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0037784776296368376,False,,0.07250250408304426,True,,optimal,hinge,192,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.32078133872227793,4220,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +16,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +17,none,bernoulli_nb,,,,,5.366806098032344,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +18,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.888792896993816,16,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,minmax +19,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax +21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.3131366295323392,None,3,20,0.0,100,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +22,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8916988265222563,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.001971708179099073,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +23,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10677595352882521,deviance,8,2.8434717355886883,None,17,6,0.0,484,0.9516595570116578,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.002694235697058705,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,0.7267019009424693,None,16,4,0.0,341,0.4859775922611174,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,162,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +25,none,adaboost,SAMME,0.34864876412492174,9,352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.1435287900624051,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +26,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.1253616607501353,5,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0004279036980618678,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +27,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +28,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.4934522753284643e-05,False,0.021298254696745126,0.06284789831075595,True,,invscaling,modified_huber,52,l2,0.14709162750315702,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,normalize +29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.170504104762284,,,0.10000000000000006,rbf,-1,True,1.893780321354902e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +32,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.7702815127831667e-05,False,,0.006631256638841467,True,,constant,squared_hinge,61,l2,,mean,0.3619893522448273,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.741050844267226,chi2,,,,normalize +33,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax +35,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none +36,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4570.657250638392,False,True,1,squared_hinge,ovr,l2,0.001312453259224503,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize +42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +45,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,163.38137776399626,False,True,1,squared_hinge,ovr,l2,0.06598224986727308,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.3799175197381475,None,10,14,0.0,100,,,,,,,,,,,,median,0.004732616038822116,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +47,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0005474378153703474,True,,0.0709606524043735,True,,invscaling,squared_hinge,322,l2,0.25,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,5.27375986647238,False,True,1,squared_hinge,ovr,l1,1.0213500115323962e-05,,,,,,,,,,,,,,,,,,,,,,normalize +48,none,adaboost,SAMME,0.5963067322132379,5,497,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +49,none,adaboost,SAMME,0.10852728577735547,8,437,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize +52,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +53,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,29258.1975836839,-0.28546714813953256,3,2.241682570184375,poly,-1,False,6.846145824119487e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +54,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize +55,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +56,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.283033279625623e-05,False,,0.04931533154730613,True,,constant,squared_hinge,531,l1,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,92.26694836626666,f_classif,,,,minmax +57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax +58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +60,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.904361031253075,,,0.013966822064984206,rbf,-1,False,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,191.55106721849913,,,0.28076751062833866,rbf,-1,True,0.08130102412657514,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none 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a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..f8ff146acd --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_dense/readme.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_dense/readme.txt rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..9b4f51e8ec --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE 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+130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..f8ff146acd --- /dev/null +++ b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: balanced_accuracy +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/bac_metric_multiclass.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/bac_metric_binary.classification_sparse/readme.txt b/autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/bac_metric_binary.classification_sparse/readme.txt rename to autosklearn/metalearning/files/balanced_accuracy_multiclass.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff new file mode 100644 index 0000000000..1825500144 --- /dev/null +++ b/autosklearn/metalearning/files/f1_binary.classification_dense/algorithm_runs.arff @@ -0,0 +1,96 @@ +@RELATION 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+45,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +46,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize +66,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.999362314514494,13,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.03211706492854286,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35344039987333714,fpr,f_classif,minmax +67,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.599414921933849,4,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01144974183399421,True,feature_agglomeration,,,,,,,,,,,,,cosine,complete,361,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +74,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.035386445062342765,True,,optimal,hinge,20,l2,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +80,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +81,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,2.14946379562645,16,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9528799560216878,True,,,,,,,,,,,,,,,standardize +82,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +83,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt new file mode 100644 index 0000000000..24aa686982 --- /dev/null +++ b/autosklearn/metalearning/files/f1_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_metric_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_binary.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/f1_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/f1_metric_binary.classification_dense/feature_runstatus.arff 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+31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,346.99358578854435,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +32,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5296423664586023,None,1,3,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +34,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5827771362934735,True,hinge,41,,,,,,,,,,,,,,,,,,,,,,median,0.000875930253684246,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,standardize +39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +45,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.5716878597339514,None,12,5,0.0,100,,,,,,,,,,,,mean,0.3328589169634927,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +63,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..24aa686982 --- /dev/null +++ b/autosklearn/metalearning/files/f1_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, 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+71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +72,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +73,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax 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+77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..68bfe3f7e4 --- /dev/null +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/f1_metric_binary.classification_dense/readme.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/f1_metric_binary.classification_dense/readme.txt rename to autosklearn/metalearning/files/f1_macro_binary.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..f72544327e --- /dev/null +++ b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA 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-0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/f1_macro_binary.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/f1_metric_binary.classification_sparse/readme.txt b/autosklearn/metalearning/files/f1_macro_binary.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/f1_metric_binary.classification_sparse/readme.txt rename to autosklearn/metalearning/files/f1_macro_binary.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/algorithm_runs.arff 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+109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.035386445062342765,True,,optimal,hinge,20,l2,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,2.14946379562645,16,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9528799560216878,True,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..68bfe3f7e4 --- /dev/null +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_dense/feature_values.arff rename to 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+84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +87,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.786449460674678,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06203188930692253,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none +107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.8929665633992583,14,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..68bfe3f7e4 --- /dev/null +++ b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 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autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/readme.txt b/autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/f1_metric_multiclass.classification_sparse/readme.txt rename to autosklearn/metalearning/files/f1_macro_multiclass.classification_sparse/readme.txt diff --git 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-13,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,31.072918101598408,-0.7791479376555757,3,4.6203468860969465,poly,-1,False,0.001099773094475084,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -14,weighting,adaboost,SAMME.R,0.026802800723674077,7,319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0008487267856488598,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -15,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1871146988423774,False,True,1,squared_hinge,ovr,l2,0.00016466471791022182,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.9742427573650911,3,4.365901938038843,poly,2472,,,,,,,,,,,,,,,,,normalize -16,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1579724000187546e-06,True,,0.07071036976546528,True,0.0006705591494191703,optimal,log,981,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -17,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,,,,,,,,,,,,,,most_frequent,0.014096602461023714,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,minmax -18,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16.073635975697865,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.06826800465849801,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5052.254329914395,,,0.0044645017776694445,rbf,-1,True,0.02140628321054818,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0014000343077363432,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.568264342230065,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.48677472872591393,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -23,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.878014917776569,19,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.3778926016633066,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -24,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -25,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.3091522974058654,None,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,2.4007457270436576e-05,,,,,,,,,,,,,,,,,,,,,,minmax -26,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -27,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0841893003162543,deviance,7,2.525254466868981,None,1,3,0.0,450,0.43270125817488375,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.7744204594457953,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009579633014979775,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.1874167676083379,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.6973218237903964,None,20,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001066221366703205,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8112691477537877,False,,,,,,,,,,,,,,,standardize -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.899439798098555,,,9.99143472277497e-05,rbf,-1,True,0.09838972982387809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.03900332266026785,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.09026799890002428,deviance,8,1.1675368045949701,None,20,12,0.0,438,0.9266663842757653,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,76,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0014756324499904515,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4431841087047577,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.024455776537633842,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9977742345285514,659,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9352.21056302254,,,0.00017455150019205832,rbf,-1,False,0.00017526327333768012,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,minmax -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.4411568194977027,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12571105729620852,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006255992684674424,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17705711841998692,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.8072858360362405,15,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00037078342898917775,True,extra_trees_preproc_for_classification,True,entropy,None,3.2872713864450325,4,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.11725997635850348,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -71,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9716641696055355,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.9055422128256523,1.0,None,8,19,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00010829051551306555,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.2598972241903406,False,True,1,squared_hinge,ovr,l2,0.002178975056030073,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3542394611819555,fwe,f_classif,standardize -80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.192987662007981,None,6,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.12135790606587657,0,8,11,279,0,1,1,0.8703624243977103,mean,0.08554118822152902,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.07761998114779296,deviance,6,3.2885017018472857,None,15,14,0.0,193,0.1834452751855389,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00014992300819174025,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.03882694473700471,fdr,f_classif,minmax -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,4.2564408409001455,None,10,19,0.0,494,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,295,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -86,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.0824700118310355,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,none -87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -88,weighting,adaboost,SAMME,0.014814561208903662,10,163,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0024666699590501105,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6307208320851548,False,,,,,,,,,,,,,,,minmax -89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1444.1604607974718,0.8539520071487003,4,0.4386932835217779,poly,-1,False,0.013531111989188759,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.48911848027879384,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.32073265077577,f_classif,,,,minmax -90,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.353776394113618,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3254309457648502,rbf,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -91,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.291104653355493,11,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,weighting,decision_tree,,,,,,,entropy,1.1311194610328434,1.0,None,8,2,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.036521006482618065,fpr,chi2,none -94,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09731943236252891,True,extra_trees_preproc_for_classification,False,gini,None,1.2463566902368797,6,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,4.066712173808025,16,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,38,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,4,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.17148916741171152,True,kernel_pca,,,,,,,,,,,,,,,,,0.42874566531328484,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5982754606738583,None,18,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002695045315207182,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -105,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.19373227047521904,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax -106,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.029307959295600206,deviance,3,1.0,None,5,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,63,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -107,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.716976197257689,12,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011633115405687978,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -108,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.3361455146809385,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.045645659641546966,True,feature_agglomeration,,,,,,,,,,,,,cosine,complete,101,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -109,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.46337567592144957,fwe,f_classif,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.195599521492949,18,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,deflation,exp,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7405.166546836462,,,0.0065614627465311135,rbf,-1,False,0.0007046266633994003,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -113,weighting,adaboost,SAMME.R,0.171230934782795,1,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24509064485491622,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.30916601381682146,fdr,f_classif,standardize -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.02002401534242,,,0.09000456663304887,rbf,-1,True,0.09271652023886748,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.28132141040472186,fdr,f_classif,minmax -117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-88,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -89,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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-25,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.3091522974058654,None,9,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,2.4007457270436576e-05,,,,,,,,,,,,,,,,,,,,,,minmax -26,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -27,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0841893003162543,deviance,7,2.525254466868981,None,1,3,0.0,450,0.43270125817488375,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -28,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,52,None,,0.05569272037707172,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,minmax -29,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.7744204594457953,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009579633014979775,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.1874167676083379,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize -30,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.6973218237903964,None,20,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -33,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001066221366703205,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8112691477537877,False,,,,,,,,,,,,,,,standardize -34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -38,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15.899439798098555,,,9.99143472277497e-05,rbf,-1,True,0.09838972982387809,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.03900332266026785,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.09026799890002428,deviance,8,1.1675368045949701,None,20,12,0.0,438,0.9266663842757653,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,weighting,adaboost,SAMME.R,0.11391016233390944,2,76,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0014756324499904515,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11803900535533758,fwe,f_classif,minmax -41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,254.6533603305078,-0.9401709097738448,,0.009393868777588882,sigmoid,-1,True,5.018502984143207e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.006969124720529487,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,22.5961076778601,False,True,1,squared_hinge,ovr,l1,0.00011636794680899991,,,,,,,,,,,,,,,,,,,,,,minmax -42,none,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4431841087047577,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.024455776537633842,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9977742345285514,659,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,9,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,549.0232049539223,0.24381310112249355,4,2.9305514431797985,poly,-1,False,0.01956556095870311,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,3.911120846350611,None,16,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.043370871003256165,0,7,18,214,0,1,1,0.839234687214905,median,0.0058274435718760845,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.3366674935347607,mean,0.001765496868087784,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1471.1879812930667,,,0.027414764849429472,rbf,-1,False,0.002911599559817851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.005029381584250038,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9352.21056302254,,,0.00017455150019205832,rbf,-1,False,0.00017526327333768012,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,minmax -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.4411568194977027,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12571105729620852,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax -54,weighting,adaboost,SAMME.R,0.10000000000000002,3,50,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006255992684674424,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.17705711841998692,fwe,f_classif,minmax -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.8072858360362405,15,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00037078342898917775,True,extra_trees_preproc_for_classification,True,entropy,None,3.2872713864450325,4,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.36855258361138055,deviance,4,4.584874281504673,None,3,13,0.0,76,0.9807992748189266,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,254,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,adaboost,SAMME,0.22617197245924217,8,315,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.05804074761722897,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,73.45778579681858,f_classif,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,3.700642075984678,None,2,4,0.0,98,0.20538901872686305,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,exp,1530,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -66,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,134,None,,4.4623898028264154e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,-0.34347630248038574,4,0.002538353746364181,poly,564,,,,,,,,,,,,,,,,,minmax -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -70,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.11725997635850348,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -71,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9716641696055355,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,3.3416251242239485,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -75,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.837686589180965e-06,False,,0.001007037212633321,True,2.1781472922268917e-06,invscaling,squared_hinge,83,elasticnet,0.12742309360680093,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2617950468683243,fdr,f_classif,standardize -76,weighting,decision_tree,,,,,,,entropy,1.9055422128256523,1.0,None,8,19,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00010829051551306555,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none -77,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.32422785076126454,True,kernel_pca,,,,,,,,,,,,,,,,,,,3.7558664620714866,rbf,56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,adaboost,SAMME.R,0.340801430264527,7,442,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,minmax +58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..29611ffc0c --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_costs.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_costs.arff diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_runstatus.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_values.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_dense/feature_values.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_dense/feature_values.arff diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/readme.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_dense/readme.txt similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_dense/readme.txt rename to autosklearn/metalearning/files/f1_micro_binary.classification_dense/readme.txt diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..9629584b17 --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE f1_micro NUMERIC 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+125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..29611ffc0c --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_costs.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_costs.arff diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_runstatus.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_runstatus.arff diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_values.arff b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff similarity index 100% rename from autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/feature_values.arff rename to autosklearn/metalearning/files/f1_micro_binary.classification_sparse/feature_values.arff diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_sparse/readme.txt b/autosklearn/metalearning/files/f1_micro_binary.classification_sparse/readme.txt similarity index 100% rename from autosklearn/metalearning/files/pac_metric_binary.classification_sparse/readme.txt rename to autosklearn/metalearning/files/f1_micro_binary.classification_sparse/readme.txt diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/algorithm_runs.arff new file mode 100644 index 0000000000..8a4f0a8c16 --- /dev/null +++ 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..29611ffc0c --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..29611ffc0c --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE 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file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/f1_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, 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+84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..24aa686982 --- /dev/null +++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..24aa686982 --- /dev/null +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: f1 +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt new file mode 100644 index 0000000000..ba3264ec3b --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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@@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE 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mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+106,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize 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+127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..ba3264ec3b --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, 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@@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: f1_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/f1_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.01,True,,optimal,log,82,l1,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.020938011807420828,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,2,0.02171392895210594,poly,-1,True,7.503317925389584e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.005544292258963709,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize +39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.091312607420228,None,8,6,0.0,100,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.58683291411165,chi2,,,,none +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065548404220037,None,9,7,0.0,100,,,,,,,,,,,,median,0.00025884798691207033,True,fast_ica,,,,,,,,,deflation,cube,360,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none +58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none +63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +64,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.906873453507057,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.76760111449241,,,0.3566604024973977,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00011292844125422223,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,,,sigmoid,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10509052674216676,deviance,3,4.4544722117539814,None,5,17,0.0,417,0.8774564835738486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0001349402298471411,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize +69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none +76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.013646390377719104,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,decision_tree,,,,,,,gini,0.7827012503829125,1.0,None,10,9,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004640517814411185,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.55898104077653,f_classif,,,,none +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt new file mode 100644 index 0000000000..769dc99e0d --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE 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100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..769dc99e0d --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +30,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,True,,0.01,True,,optimal,log,82,l1,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +31,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +32,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +33,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.020938011807420828,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +34,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,2,0.02171392895210594,poly,-1,True,7.503317925389584e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.005544292258963709,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize +39,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.091312607420228,None,8,6,0.0,100,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,97.58683291411165,chi2,,,,none +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065548404220037,None,9,7,0.0,100,,,,,,,,,,,,median,0.00025884798691207033,True,fast_ica,,,,,,,,,deflation,cube,360,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none +58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +60,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +61,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.17454862892513714,rbf,-1,True,0.08170245871998191,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +62,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.06951501822933613,True,,invscaling,log,178,l2,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35.89177043535544,chi2,,,,none +63,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +64,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.906873453507057,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +66,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.76760111449241,,,0.3566604024973977,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00011292844125422223,True,kernel_pca,,,,,,,,,,,,,,,,,-0.15050022726399015,,,sigmoid,1802,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10509052674216676,deviance,3,4.4544722117539814,None,5,17,0.0,417,0.8774564835738486,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0001349402298471411,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,normalize +69,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +71,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.080012184321,,,0.009237236216186166,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,1.0,rbf,286,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none +76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.013646390377719104,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.578593489684234,None,9,14,0.0,100,,,,,,,,,,,,most_frequent,0.005297835726138853,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,decision_tree,,,,,,,gini,0.7827012503829125,1.0,None,10,9,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004640517814411185,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.55898104077653,f_classif,,,,none +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..769dc99e0d --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE 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mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..769dc99e0d --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: log_loss +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/log_loss_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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-25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035060891054916214,deviance,5,3.6531380857989673,None,20,12,0.0,306,0.7344609811154431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015035493040609763,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -26,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0,1,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006583540281622803,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -28,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,212.33431812792966,-0.4654250195593008,,0.0015078298981935894,sigmoid,-1,False,0.028394031435253694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.04072044964756921,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001447071764012251,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,119,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -33,none,adaboost,SAMME,1.176344309906615,1,197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,None,1,12,1.0,61,,,,,,normalize -34,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.71383359085476e-06,True,,0.05276983229312574,True,,optimal,squared_hinge,606,l1,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.137928511354329,None,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.056526269573181555,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,4.268694609197059,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -41,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.07738111975259095,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,32597.153764706665,False,True,1,squared_hinge,ovr,l1,3.061529049749633e-05,,,,,,,,,,,,,,,,,,,,,,standardize -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0003580045297184277,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.4942098298349644,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.04392471044869214,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.718348652060499,None,17,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.041008478760879624,0,9,1,272,0,1,1,0.7771541749340595,median,0.00017098147418880078,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.6447434389340525,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1764.976676919324,,,0.02731163951327381,rbf,-1,True,0.001423740450891899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,354.125338827259,-0.9571876806886814,4,0.20364202227485934,poly,-1,False,0.00048700306453482525,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.20475434596887782,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,3.9411950507931546,12,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1339106223810043,True,squared_hinge,58,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.10463875838874252,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,standardize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.30896450452785995,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0027392011544235123,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,353,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.4659673011504695,None,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.41918923678249864,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78.66170065406482,chi2,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,0.9670493747769439,None,1,13,0.0,422,0.5305062782101193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,fast_ica,,,,,,,,,parallel,logcosh,642,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.047915438871202094,fdr,f_classif,standardize -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.839347846087743,None,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.190601452996489,1,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03990049323363799,deviance,6,4.896254082080487,None,13,15,0.0,267,0.4557608966372973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.007086974030867012,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -74,weighting,decision_tree,,,,,,,entropy,1.2041465147242782,1.0,None,8,16,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0706978233891087,fwe,f_classif,standardize -75,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7122059724806372e-06,True,,0.045358130305476654,True,0.02807400535997186,invscaling,hinge,537,elasticnet,0.2792860629370393,,,,,,,,,,,,,,mean,0.0010501448086103515,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -77,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1240,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.16659839832623022,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.5145091553186245,None,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.14942215197803146,0,7,1,266,0,1,1,0.9739584679976068,median,0.0009833648935764453,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,2.8570791257603125,None,10,12,0.0,478,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,206,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1414.410750848353,-0.7622261910627401,,0.0035100262434821965,sigmoid,-1,False,0.00010823835956978324,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,17,14,1.0,84,,,,,,standardize 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-89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5860071624792975,True,squared_hinge,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,4,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.17148916741171152,True,kernel_pca,,,,,,,,,,,,,,,,,0.42874566531328484,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.7169751980106978,None,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004392540932963756,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8881.39583000388,,,0.004057992510232778,rbf,-1,True,1.4415947847988208e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.054525263923438,12,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,exp,992,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.593792274659271e-06,True,,0.09209397388504023,True,0.1737792939472745,constant,log,35,elasticnet,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3733446596200857,fdr,f_classif,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.442835182463979,14,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003201530058401758,True,fast_ica,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -112,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7405.166546836462,,,0.007770735808099179,rbf,-1,True,0.000172778565738507,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -113,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -117,weighting,extra_trees,,,,,,,,,,,,,,,True,entropy,None,2.0527652319742975,1,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,84,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -119,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -120,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -122,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0001114568858866107,True,,0.045206385380322525,True,3.0759116442834165e-07,constant,log,120,elasticnet,,,,,,,,,,,,,,,mean,0.1467936263265181,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax -123,weighting,adaboost,SAMME.R,0.026802800723674077,7,319,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0008487267856488598,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -124,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.018238030951915563,deviance,8,0.6820931644146317,None,14,20,0.0,475,0.26763716882575656,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009694714242663112,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,296,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -125,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06103814648767104,deviance,4,4.245270434444319,None,3,19,0.0,253,0.2794301562862259,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,f_classif,minmax -126,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021476271191939005,deviance,7,1.744103177921745,None,18,11,0.0,215,0.6868516661604934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.044237426543267594,fwe,f_classif,minmax -127,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,6,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax 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-131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/description.txt b/autosklearn/metalearning/files/pac_metric_binary.classification_dense/description.txt deleted file mode 100644 index 1a7bdd33e1..0000000000 --- a/autosklearn/metalearning/files/pac_metric_binary.classification_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: pac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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-131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_metric_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/pac_metric_binary.classification_sparse/description.txt deleted file mode 100644 index 1a7bdd33e1..0000000000 --- a/autosklearn/metalearning/files/pac_metric_binary.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: pac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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-7,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.731077583925598,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.6054458930630187,False,True,1,squared_hinge,ovr,l1,0.006231760778225515,,,,,,,,,,,,,,,,,,,,,,standardize -8,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.9045703785631214,None,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.4600826611968323,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,standardize -9,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -10,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00012739159514185168,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -11,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.01582632472828583,deviance,10,1.6476653901419394,None,20,2,0.0,389,0.6216542313797746,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -12,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.304908804831392e-05,False,,0.042653336370051316,True,0.00025520514394342965,constant,log,325,elasticnet,,,,,,,,,,,,,,,mean,0.38341065540651387,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1550933972720641,fpr,f_classif,standardize -13,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.056600979543795484,True,hinge,7,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -14,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,32.29430662322507,0.47735999759632275,,0.003016683129739785,sigmoid,-1,False,0.00033827644828015376,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,20,14,1.0,100,,,,,,minmax -15,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -16,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.790010486195598,None,9,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.206194253081357,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,383,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -17,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,,,,,,,,,,,,,,most_frequent,0.014096602461023714,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,minmax -18,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,126.26057513085078,,,0.01137980293622789,rbf,-1,True,0.07275084439443646,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -19,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23019.428191347044,,,0.0044645017776694445,rbf,-1,True,0.009484490145057959,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0014000343077363432,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -22,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,2.234320135975892,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -23,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.752679520533251,20,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.3778926016633066,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,normalize -24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -25,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035060891054916214,deviance,5,3.6531380857989673,None,20,12,0.0,306,0.7344609811154431,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015035493040609763,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -26,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0,1,10,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006583540281622803,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -28,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,212.33431812792966,-0.4654250195593008,,0.0015078298981935894,sigmoid,-1,False,0.028394031435253694,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.04072044964756921,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -29,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -31,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.06776681044542089,deviance,10,0.6375695972482855,None,2,12,0.0,430,0.6457449705501087,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003109895649167378,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,98.42050467238359,f_classif,,,,minmax -32,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001447071764012251,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,119,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -33,none,adaboost,SAMME,1.176344309906615,1,197,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8,None,1,12,1.0,61,,,,,,normalize -34,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.71383359085476e-06,True,,0.05276983229312574,True,,optimal,squared_hinge,606,l1,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -37,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.137928511354329,None,14,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.056526269573181555,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -38,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7023602401744478,True,squared_hinge,44,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.02696645502624708,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11229948991088605,rbf,150,,,,,,,,,,,,,,,,,standardize -39,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03956590323946538,deviance,6,4.268694609197059,None,18,10,0.0,447,0.6215777368836695,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -41,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,True,hinge,20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.07738111975259095,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,32597.153764706665,False,True,1,squared_hinge,ovr,l1,3.061529049749633e-05,,,,,,,,,,,,,,,,,,,,,,standardize -42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,1.0,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none -44,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0003580045297184277,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -45,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.4942098298349644,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.04392471044869214,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -46,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.718348652060499,None,17,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -48,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.041008478760879624,0,9,1,272,0,1,1,0.7771541749340595,median,0.00017098147418880078,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -49,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.01725897301943831,0,5,19,431,0,1,1,0.6447434389340525,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,True,,,,,,,,,,,,none -50,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1764.976676919324,,,0.02731163951327381,rbf,-1,True,0.001423740450891899,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,354.125338827259,-0.9571876806886814,4,0.20364202227485934,poly,-1,False,0.00048700306453482525,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.20475434596887782,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.02840868809346,None,1,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0033432166877221045,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize -53,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,3.9411950507931546,12,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax -54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -55,weighting,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.00021599176540056157,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,374,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -56,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,62,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -57,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0646149603552551,deviance,8,4.305757134149405,None,7,16,0.0,345,0.6716250875708312,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.92556371082075,chi2,,,,minmax -58,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1339106223810043,True,squared_hinge,58,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.10463875838874252,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,standardize -59,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,577.314044887467,0.42312135762770886,3,5.377323925637202,poly,-1,True,8.806613114710786e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.05340690596227964,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax -60,none,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.30896450452785995,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0027392011544235123,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,353,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -61,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.025177774309272944,deviance,10,1.9729215087282914,None,20,9,0.0,223,0.904978448753563,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.2675633437115287,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,0.3113550669315289,False,True,1,squared_hinge,ovr,l1,0.001101224220632028,,,,,,,,,,,,,,,,,,,,,,minmax -62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3248.172821099641,0.08853193109407265,1,0.007449734841112337,poly,-1,True,0.020121528159826957,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.4659673011504695,None,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.41918923678249864,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,78.66170065406482,chi2,,,,minmax -65,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0138034430237474,deviance,5,0.9670493747769439,None,1,13,0.0,422,0.5305062782101193,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,fast_ica,,,,,,,,,parallel,logcosh,642,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.047915438871202094,fdr,f_classif,standardize -67,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.077041564967476e-06,False,0.000745490528794258,0.06294042957971288,True,3.580600468146134e-07,constant,modified_huber,105,elasticnet,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize -68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.451355467402551,None,2,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -69,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.839347846087743,None,1,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax -70,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.190601452996489,1,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax -71,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -73,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03990049323363799,deviance,6,4.896254082080487,None,13,15,0.0,267,0.4557608966372973,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.007086974030867012,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -74,weighting,decision_tree,,,,,,,entropy,1.2041465147242782,1.0,None,8,16,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0706978233891087,fwe,f_classif,standardize -75,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7122059724806372e-06,True,,0.045358130305476654,True,0.02807400535997186,invscaling,hinge,537,elasticnet,0.2792860629370393,,,,,,,,,,,,,,mean,0.0010501448086103515,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -77,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,,cosine,1240,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize -78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.16659839832623022,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.5145091553186245,None,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax -81,none,xgradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5,1,1,0,0.14942215197803146,0,7,1,266,0,1,1,0.9739584679976068,median,0.0009833648935764453,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.08840591888606261,deviance,2,2.8570791257603125,None,10,12,0.0,478,0.8596976530848232,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,complete,206,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -85,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1414.410750848353,-0.7622261910627401,,0.0035100262434821965,sigmoid,-1,False,0.00010823835956978324,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,17,14,1.0,84,,,,,,standardize -86,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.8193635484846253,None,4,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -87,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11504709254438854,deviance,4,4.245270434444319,None,6,17,0.0,140,0.5928272648226496,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00022857137950999825,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.017519696182905532,fdr,chi2,minmax -88,none,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,115,manual,0.0856179706451039,0.006565428290041747,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.000630825602918293,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,30132.90919219214,False,True,1,squared_hinge,ovr,l1,0.0002494967914296536,,,,,,,,,,,,,,,,,,,,,,minmax -89,none,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.5860071624792975,True,squared_hinge,190,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -94,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -96,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.017234508616807236,deviance,5,3.5776862734317234,None,4,17,0.0,498,0.564602657130278,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.17148916741171152,True,kernel_pca,,,,,,,,,,,,,,,,,0.42874566531328484,3,0.0048934050087189145,poly,26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.7169751980106978,None,16,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,median,0.004392540932963756,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000002,deviance,3,1.0,None,1,2,0.0,100,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -104,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8881.39583000388,,,0.004057992510232778,rbf,-1,True,1.4415947847988208e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -107,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.054525263923438,12,3,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,fast_ica,,,,,,,,,parallel,exp,992,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -109,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8.593792274659271e-06,True,,0.09209397388504023,True,0.1737792939472745,constant,log,35,elasticnet,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3733446596200857,fdr,f_classif,minmax -110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.442835182463979,14,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0003201530058401758,True,fast_ica,,,,,,,,,deflation,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: pac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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-131,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -132,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/description.txt deleted file mode 100644 index 1a7bdd33e1..0000000000 --- a/autosklearn/metalearning/files/pac_metric_multiclass.classification_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step SymbolsSTD: SymbolsSTD -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step LandmarkLDA: LandmarkLDA -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step ClassEntropy: ClassEntropy -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step KurtosisMax: KurtosisMax -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step SymbolsMin: SymbolsMin -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step SymbolsMean: SymbolsMean -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step SymbolsMax: SymbolsMax -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step KurtosisMean: KurtosisMean -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step KurtosisSTD: KurtosisSTD -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SymbolsSum: SymbolsSum -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step Landmark1NN: Landmark1NN -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step NumberOfClasses: NumberOfClasses -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step SkewnessMean: SkewnessMean -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step ClassProbabilityMean: ClassProbabilityMean -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean - -performance_measures: pac_metric -performance_type: solution_quality -algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 - -scenario_id: auto-sklearn -maximize: false -algorithm_cutoff_time: 1800 -algorithm_cutoff_memory: 3072 diff --git 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+38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize +39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065548404220037,None,9,7,0.0,100,,,,,,,,,,,,median,0.00025884798691207033,True,fast_ica,,,,,,,,,deflation,cube,360,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+57,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none +58,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.100042248785923,0.9980658447309616,3,2.285273071303168,poly,-1,False,0.0490176654053288,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize 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+84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax 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+90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,decision_tree,,,,,,,gini,0.7827012503829125,1.0,None,10,9,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004640517814411185,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.55898104077653,f_classif,,,,none +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt b/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt new file mode 100644 index 0000000000..0e6466b48d --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA +233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, 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-0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} 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+35,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,0.0,2,0.02171392895210594,poly,-1,True,7.503317925389584e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.005544292258963709,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +36,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +38,none,bernoulli_nb,,,,,1.8887385356342519,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00017391617518036328,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,normalize +39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.065548404220037,None,9,7,0.0,100,,,,,,,,,,,,median,0.00025884798691207033,True,fast_ica,,,,,,,,,deflation,cube,360,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,11.678178018167646,0.9320848062748661,,0.45018453704452593,sigmoid,-1,False,0.00041914738201940534,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,extra_trees_preproc_for_classification,False,entropy,None,0.5639310114218264,1,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +48,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0642934604710979,deviance,9,0.7259458223924964,None,14,11,0.0,329,0.4989755813606485,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +51,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +54,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010000000000000009,False,,0.01,True,,optimal,log,20,l2,,mean,0.014988923294040933,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +74,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15403.050385516917,-0.5957548709454028,1,7.25907773892283e-05,poly,-1,False,0.01132121743493435,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,none +76,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1300.622622668229,,,0.0005377565832198095,rbf,-1,False,0.009546849059854897,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,none,adaboost,SAMME.R,0.6745533826329466,2,306,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.11136190063887706,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,37.54888780093315,f_classif,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +91,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.311318946841283,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +102,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.7802517848045093,deviance,3,2.5054026029796663,None,7,17,0.0,124,0.5707216178482934,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +113,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +118,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.08307741263309468,True,,0.06824350233113559,True,,constant,squared_hinge,16,l1,,mean,0.010000000000000004,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5,None,3,2,1.0,100,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.175830305558584e-06,True,,0.021088101285374274,True,,invscaling,log,178,l1,0.37274915231938566,median,0.0007101784866414218,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,81.9951290469709,chi2,,,,none +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +122,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.256288840265201,None,18,4,0.0,100,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7437347621035921,False,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,decision_tree,,,,,,,gini,0.7827012503829125,1.0,None,10,9,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004640517814411185,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,83.55898104077653,f_classif,,,,none +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..0e6466b48d --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE 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file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: pac_score +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/pac_score_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+34,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +36,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,standardize +39,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.002782467639466009,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +48,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none +63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +68,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +69,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +73,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +74,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.05709925270311,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,7.769496682507031,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11199470124045516,deviance,6,1.0,None,1,2,0.0,100,0.5541730214135502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011676674417077058,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7525595954528075,False,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt new file mode 100644 index 0000000000..eb538b6df9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..eb538b6df9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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+72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none 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+106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..2d2ec13507 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, 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b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize 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+72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +76,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.800999198391194,False,True,1,squared_hinge,ovr,l2,4.099137522401286e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.011235702047056394,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +94,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +97,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +100,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03627152792976942,deviance,10,4.211238636565405,None,15,16,0.0,340,0.6289005711340923,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0002148748655476835,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none +103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..2d2ec13507 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_macro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC 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+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +85,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+109,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..9d57b906d6 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, 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+129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..9d57b906d6 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..9d57b906d6 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..9d57b906d6 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: precision_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical 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b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+34,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4346.137071437875,0.06630310829119646,1,5.1269202965282974e-05,poly,-1,False,0.00024452576462760927,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,333,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +36,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,,,,,standardize +39,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,699.1402438495606,,,0.00016321197819510198,rbf,-1,False,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.010000000000000004,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.002782467639466009,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +45,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +48,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00012939685446636576,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8754998747956665,True,,,,,,,,,,,,,,,none +49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +54,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +59,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +60,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,4,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none +63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +64,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.03830860981109,None,17,2,0.0,100,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6629672767671623,True,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +68,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +69,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +73,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +74,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +75,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.05709925270311,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,7.769496682507031,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11199470124045516,deviance,6,1.0,None,1,2,0.0,100,0.5541730214135502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011676674417077058,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7525595954528075,False,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..eb538b6df9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+83,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +84,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..eb538b6df9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: precision +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE 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file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt similarity index 98% rename from autosklearn/metalearning/files/acc_metric_binary.classification_dense/description.txt rename to autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt index b9b849fd86..a61d195c75 100644 --- a/autosklearn/metalearning/files/acc_metric_binary.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: acc_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: precision_weighted +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE 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+@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt similarity index 98% rename from autosklearn/metalearning/files/acc_metric_binary.classification_sparse/description.txt rename to autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt index b9b849fd86..a61d195c75 100644 --- a/autosklearn/metalearning/files/acc_metric_binary.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: acc_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: precision_weighted +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE 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b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+80,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize +106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +116,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +117,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +122,none,extra_trees,,,,,,,,,,,,,,,True,entropy,None,1.5031924010427555,4,8,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.5440040750402232,True,,,,,,,,,,,,,,,none 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+126,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +127,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.402676003557486,14,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,average,133,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +128,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +129,weighting,decision_tree,,,,,,,gini,0.5499087258941522,1.0,None,17,14,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0017527687077046575,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2634669546564337,fwe,f_classif,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt similarity index 98% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/description.txt rename to autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt index b9b849fd86..a61d195c75 100644 --- a/autosklearn/metalearning/files/acc_metric_multiclass.classification_dense/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: acc_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: precision_weighted +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE 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other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt similarity index 98% rename from autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/description.txt rename to autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt index b9b849fd86..a61d195c75 100644 --- a/autosklearn/metalearning/files/acc_metric_multiclass.classification_sparse/description.txt +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/description.txt @@ -57,10 +57,10 @@ features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses features_stochastic: default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean -performance_measures: acc_metric -performance_type: solution_quality +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 algorithms_stochastic: -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132 +performance_measures: precision_weighted +performance_type: solution_quality scenario_id: auto-sklearn maximize: false diff --git a/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE 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b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/precision_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, 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-29,mean,0.00948473752262725,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,0.019206332605811675,rbf,53,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7125.927878089316,,2,0.1521824597603772,0.026360169953740666,rbf,-1,False,0.03333039885726712,,,,,,,,,,,,,,,,,,,,,,,,standardize -30,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.19220478851423953,rbf,487,,,,,,,,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0010522420041539772,True,2.1635276594374536e-05,,,,,,,,,,,,normalize -31,median,0.46945712023462344,True,kernel_pca,,,,,,,,,,,,,,,,,,,6.233982963947049,rbf,1400,,,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.647464520403159e-06,True,0.0007705265204486421,0.06950402910323647,True,0.0015819192961755311,constant,epsilon_insensitive,179,elasticnet,,normalize 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-61,median,,False,extra_trees_preproc_for_regression,True,mse,None,0.993528657198836,10,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1933.5929428405466,,2,0.015939473931353206,0.7858707127149234,rbf,-1,False,0.0037322094857651553,,,,,,,,,,,,,,,,,,,,,,,,minmax -62,median,,False,extra_trees_preproc_for_regression,True,mse,None,3.296750799496171,1,15,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,2.5317611636176207,1,10,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -63,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00010253007043420141,True,0.012360934554990245,0.005644899783893197,True,,constant,epsilon_insensitive,132,l2,,minmax -64,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.010148480388225148,0.00016676399867236993,3.16342061142116,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -65,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gaussian_process,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.007642369343100022,1.8419258971411445e-05,0.9833146696212234,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize -66,mean,0.00016629738514881763,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,mse,None,4.592230471191443,None,4,6,0.0,100,,,,,,,,,,,,,,,minmax -67,median,0.00548806117992972,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,3.0972538714361577,rbf,5291,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.6864007905723228e-06,True,0.006559405529560316,0.07194784725205487,True,,constant,squared_epsilon_insensitive,89,l2,,minmax -68,mean,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.1566448365058719,5,0.7004476388937543,poly,956,,,,,,,,,,,,,,ridge_regression,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02849145741142527,True,0.001397013116831936,,,,,,,,,,,,minmax -69,median,0.0005361882641678341,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.9516292646600342,5226,,,,,,,,,,,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.844327967977615e-05,True,0.010965543419019197,0.02393286766186689,True,,constant,squared_epsilon_insensitive,502,l2,,minmax -70,mean,0.007129891763108858,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,222.52007103508296,,2,0.0014545049235116735,0.8961440468564937,rbf,-1,False,0.005206529915661272,,,,,,,,,,,,,,,,,,,,,,,,minmax -71,median,0.0007503344309064792,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.8485062230960784,0.03298642531249513,huber,5,0.6960372198390199,None,14,8,0.0,100,0.3573451555561757,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -72,median,0.05987995402504049,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.663594806506735,18,17,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -73,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize -74,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize -75,mean,0.0006534677841234812,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0358742835546163,ls,3,1.3646202291681209,None,17,11,0.0,100,0.6875570498500749,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -76,mean,0.08567185090735716,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,2.3981698229031436,None,19,20,0.0,100,,,,,,,,,,,,,,,minmax -77,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,liblinear_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.12583724307049,False,0.0018926648667342925,True,1,squared_epsilon_insensitive,0.008267080450623797,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax -78,median,0.003145909671422029,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.1154751316101943e-06,False,0.006276424097548576,0.038108446269937486,True,9.434769722730874e-09,invscaling,squared_epsilon_insensitive,305,elasticnet,0.45306002466009593,minmax diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt b/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt deleted file mode 100644 index 930f8044db..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_dense/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step KurtosisSTD: KurtosisSTD -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step NumberOfClasses: NumberOfClasses -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step SkewnessMean: SkewnessMean -feature_step SymbolsMean: SymbolsMean -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step ClassEntropy: ClassEntropy -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step SymbolsSTD: SymbolsSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step SymbolsSum: SymbolsSum -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step SymbolsMin: SymbolsMin -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step KurtosisMean: KurtosisMean -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step KurtosisMax: KurtosisMax -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step LandmarkLDA: LandmarkLDA -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step ClassProbabilityMean: ClassProbabilityMean -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step Landmark1NN: Landmark1NN -feature_step SymbolsMax: SymbolsMax -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: NumberOfFeaturesWithMissingValues, KurtosisSTD, DatasetRatio, LandmarkDecisionNodeLearner, NumberOfClasses, NumberOfMissingValues, KurtosisMin, SkewnessMean, SymbolsMean, Skewnesses, SkewnessSTD, SkewnessMax, NumberOfFeatures, ClassEntropy, PCA, LandmarkNaiveBayes, LogNumberOfFeatures, RatioNominalToNumerical, LogNumberOfInstances, SymbolsSTD, MissingValues, SymbolsSum, PCAKurtosisFirstPC, NumSymbols, ClassProbabilityMax, LandmarkRandomNodeLearner, ClassProbabilitySTD, SymbolsMin, LogInverseDatasetRatio, LandmarkDecisionTree, KurtosisMean, NumberOfInstancesWithMissingValues, KurtosisMax, PCASkewnessFirstPC, PCAFractionOfComponentsFor95PercentVariance, LandmarkLDA, ClassOccurences, NumberOfCategoricalFeatures, Kurtosisses, ClassProbabilityMin, PercentageOfMissingValues, InverseDatasetRatio, PercentageOfInstancesWithMissingValues, ClassProbabilityMean, RatioNumericalToNominal, PercentageOfFeaturesWithMissingValues, NumberOfInstances, LogDatasetRatio, SkewnessMin, NumberOfNumericFeatures, Landmark1NN, SymbolsMax - -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78 -performance_type: solution_quality -performance_measures: r2_metric -algorithms_stochastic: - -scenario_id: ChaLearn_AutoML_Challenge_2015 -maximize: false -algorithm_cutoff_time: 3600 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff deleted file mode 100644 index f291c0fcba..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_costs.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_COSTS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE NumSymbols NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC - -@DATA -1028_a_metric,1.0,0.0001,0.00013,2e-05,0.00751,5e-05,5e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00029,0.00084,0.01456,1e-05,7e-05,1e-05,0.00047,0.00035,4e-05,0.00034,0.00065,1e-05,0.00799,8e-05,0.0,1e-05,0.01481,7e-05,0.00023,4e-05,0.00048,3e-05,0.01935,0.00016,2e-05,0.00027,2e-05,1e-05,3e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.02096,0.0 -1028_r2_metric,1.0,0.00011,0.00013,3e-05,0.00752,6e-05,6e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00032,0.00084,0.01453,1e-05,6e-05,1e-05,0.00047,0.00038,4e-05,0.00033,0.00065,1e-05,0.0083,8e-05,0.0,1e-05,0.01465,7e-05,0.00024,4e-05,0.00048,3e-05,0.01908,0.00015,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.0209,0.0 -1030_a_metric,1.0,8e-05,0.00013,2e-05,0.00896,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.0003,0.00082,0.0215,1e-05,7e-05,1e-05,0.00046,0.00031,4e-05,0.00033,0.00064,1e-05,0.00974,9e-05,0.0,1e-05,0.01136,7e-05,0.00022,4e-05,0.00047,2e-05,0.02092,0.00016,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01925,0.0 -1030_r2_metric,1.0,8e-05,0.00013,2e-05,0.00882,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.00031,0.00083,0.02148,1e-05,7e-05,1e-05,0.00044,0.00031,4e-05,0.00034,0.00062,1e-05,0.00951,9e-05,0.0,1e-05,0.01135,7e-05,0.00022,3e-05,0.00047,2e-05,0.02066,0.00016,2e-05,0.00026,2e-05,0.0,3e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01896,0.0 -1414_a_metric,1.0,0.00035,0.00014,2e-05,0.47489,0.00047,0.00011,3e-05,9e-05,4e-05,0.00033,0.00017,4e-05,1e-05,0.00221,0.00213,3.26197,1e-05,6e-05,0.0,8e-05,0.0028,3e-05,0.00095,0.00016,4e-05,0.2864,0.00014,1e-05,1e-05,3.06772,8e-05,0.00237,4e-05,0.00114,4e-05,1.34828,0.00034,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.72778,1e-05 -1414_r2_metric,1.0,0.00035,0.00014,2e-05,0.47406,0.00048,0.0001,3e-05,0.00011,4e-05,0.00045,0.00026,5e-05,1e-05,0.00222,0.00214,3.23113,1e-05,6e-05,1e-05,9e-05,0.00279,3e-05,0.00096,0.00017,4e-05,0.28855,0.00014,1e-05,1e-05,3.08176,8e-05,0.00237,4e-05,0.00114,4e-05,1.34295,0.00035,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.7159,1e-05 -189_a_metric,1.0,0.00024,0.0,2e-05,0.0,0.00038,7e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00456,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00169,4e-05,0.0,0.00062,0.00031,0.0,0.00056,0.0,1e-05,0.0,0.0,0.00142,0.0,0.0,0.0,0.0,0.00171,2e-05,0.0,0.00032,1e-05,3e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -189_r2_metric,1.0,0.00024,0.0,2e-05,0.0,0.0004,8e-05,0.0,0.0,0.00441,0.0,0.0,0.0,1e-05,0.00425,0.0,0.0,1e-05,8e-05,1e-05,0.00497,0.00188,4e-05,0.0,0.00944,0.00031,0.0,0.00061,0.0,1e-05,0.0,0.0,0.0016,0.0,0.0,0.0,0.0,0.00176,2e-05,0.0,0.00032,1e-05,4e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -197_a_metric,1.0,0.00035,0.00014,2e-05,1.51228,0.00026,0.00013,3e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00155,0.00149,0.17921,1e-05,7e-05,1e-05,0.00044,0.00195,4e-05,0.00057,0.00061,1e-05,0.06526,9e-05,0.0,1e-05,1.60067,8e-05,0.0015,3e-05,0.00088,3e-05,0.14992,0.00018,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94178,0.0 -197_r2_metric,1.0,0.00037,0.00014,3e-05,1.51517,0.00029,0.00016,4e-05,0.00011,0.00013,0.00053,0.00034,5e-05,1e-05,0.00159,0.01068,0.17867,1e-05,8e-05,1e-05,0.00046,0.00201,4e-05,0.00066,0.00064,1e-05,0.06144,0.0001,0.0,1e-05,1.59698,8e-05,0.00151,4e-05,0.00999,3e-05,0.15082,0.00019,2e-05,0.0003,3e-05,1e-05,5e-05,1e-05,5e-05,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,0.94139,0.0 -198_a_metric,1.0,0.00027,0.0,2e-05,0.0,0.00026,9e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00199,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00217,4e-05,0.0,0.00062,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.00184,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,1e-05,4e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -198_r2_metric,1.0,0.00027,0.0,2e-05,0.0,0.00025,0.0001,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00207,0.0,0.0,1e-05,7e-05,1e-05,0.00043,0.00212,4e-05,0.0,0.0006,1e-05,0.0,9e-05,0.0,1e-05,0.0,0.0,0.00178,0.0,0.0,0.0,0.0,0.00017,2e-05,0.0,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -201_a_metric,1.0,0.00112,0.00019,3e-05,0.11155,0.00033,0.00047,5e-05,0.00012,0.00013,0.00046,0.00026,5e-05,1e-05,0.00272,0.0032,0.10471,1e-05,7e-05,1e-05,0.00046,0.00479,4e-05,0.00121,0.00064,1e-05,0.03739,9e-05,0.0,1e-05,0.61275,8e-05,0.00324,5e-05,0.00195,4e-05,0.34865,0.00022,2e-05,0.00037,3e-05,1e-05,4e-05,2e-05,0.0001,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,9.07208,0.0 -201_r2_metric,1.0,0.00112,0.00019,3e-05,0.11001,0.00032,0.00047,5e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00271,0.00325,0.10382,1e-05,7e-05,1e-05,0.00049,0.00456,4e-05,0.00124,0.00066,1e-05,0.03587,9e-05,0.0,1e-05,0.61243,8e-05,0.00299,4e-05,0.00197,3e-05,0.35178,0.00017,2e-05,0.00035,3e-05,1e-05,5e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,9.22271,0.0 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-558_r2_metric,1.0,0.00049,0.0,3e-05,0.0,0.00036,0.00019,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00371,0.0,0.0,1e-05,8e-05,1e-05,0.00056,0.00227,4e-05,0.0,0.00074,0.00021,0.0,0.00046,0.0,1e-05,0.0,0.0,0.00163,0.0,0.0,0.0,0.0,0.00127,2e-05,0.0,0.00023,1e-05,4e-05,1e-05,0.00037,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -562_a_metric,1.0,0.00032,0.00014,2e-05,1.08946,0.00028,0.00012,3e-05,8e-05,0.00013,0.00035,0.00019,4e-05,1e-05,0.00157,0.00124,0.15881,1e-05,7e-05,1e-05,0.00048,0.00196,4e-05,0.00051,0.00065,1e-05,0.08654,0.00011,0.0,1e-05,1.08761,8e-05,0.00154,4e-05,0.0007,3e-05,0.11888,0.0002,2e-05,0.00029,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39691,0.0 -562_r2_metric,1.0,0.00031,0.00015,2e-05,1.08546,0.00027,0.00012,3e-05,9e-05,0.00013,0.00043,0.00027,4e-05,1e-05,0.00159,0.00124,0.15862,1e-05,7e-05,1e-05,0.00052,0.0019,4e-05,0.00049,0.0007,2e-05,0.08744,0.00011,0.0,1e-05,1.08323,8e-05,0.0015,3e-05,0.00073,3e-05,0.11795,0.00021,2e-05,0.00029,3e-05,0.0,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39723,0.0 -564_a_metric,1.0,0.00105,0.0,2e-05,0.0,0.00229,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,2e-05,0.0161,0.0,0.0,1e-05,8e-05,1e-05,0.00051,0.00845,4e-05,0.0,0.00069,0.00081,0.0,0.00141,0.0,2e-05,0.0,0.0,0.00716,0.0,0.0,0.0,0.0,0.00429,2e-05,0.0,0.00081,1e-05,5e-05,1e-05,0.00127,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -564_r2_metric,1.0,0.00107,0.0,2e-05,0.0,0.0023,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.01607,0.0,0.0,1e-05,8e-05,1e-05,0.00054,0.00853,4e-05,0.0,0.00072,0.00082,0.0,0.00155,0.0,1e-05,0.0,0.0,0.00721,0.0,0.0,0.0,0.0,0.0045,2e-05,0.0,0.00082,1e-05,4e-05,1e-05,0.00131,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -573_a_metric,1.0,0.00035,0.00016,2e-05,1.51126,0.00026,0.00013,4e-05,9e-05,0.0002,0.00041,0.00025,4e-05,1e-05,0.00162,0.00153,0.17899,1e-05,7e-05,1e-05,0.00053,0.00196,4e-05,0.00058,0.00078,1e-05,0.05692,9e-05,0.0,1e-05,1.69676,8e-05,0.0015,4e-05,0.00092,3e-05,0.14625,0.00018,2e-05,0.00031,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94202,1e-05 -573_r2_metric,1.0,0.00036,0.00013,3e-05,1.55181,0.00027,0.00013,3e-05,0.0001,0.00017,0.00053,0.00034,5e-05,1e-05,0.00194,0.00153,0.17819,1e-05,7e-05,1e-05,0.0006,0.00196,4e-05,0.00057,0.00083,1e-05,0.0667,0.00011,0.0,1e-05,1.78851,8e-05,0.00151,4e-05,0.00092,3e-05,0.14663,0.0002,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,1.09208,0.0 -574_a_metric,1.0,0.00061,0.00013,2e-05,152.67116,0.00095,0.00025,3e-05,8e-05,0.00013,0.00033,0.00018,4e-05,1e-05,0.00551,0.00211,7.419,1e-05,7e-05,1e-05,0.00046,0.00478,4e-05,0.00088,0.00064,9e-05,8.86999,0.00023,0.0,1e-05,84.10332,7e-05,0.00395,3e-05,0.0012,3e-05,3.37802,0.00063,2e-05,0.00027,0.00012,1e-05,3e-05,1e-05,0.00018,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,5.47426,0.0 -574_r2_metric,1.0,0.00062,0.00013,2e-05,152.47855,0.00099,0.00025,3e-05,9e-05,0.00013,0.00039,0.00023,4e-05,1e-05,0.00471,0.0022,7.42585,1e-05,8e-05,1e-05,0.00049,0.00482,5e-05,0.00089,0.00067,0.0001,8.90127,0.00026,0.0,1e-05,85.99908,7e-05,0.00398,3e-05,0.00128,3e-05,3.38485,0.00066,2e-05,0.00027,0.00011,1e-05,4e-05,1e-05,0.00019,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,5.49499,0.0 -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff deleted file mode 100644 index a44c9e2bae..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_runstatus.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_RUNSTATUS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} - -@DATA -1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -189_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -189_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -197_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -197_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -198_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -198_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -201_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -201_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -209_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -209_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -218_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -218_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -225_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -225_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -227_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -227_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -296_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -296_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -562_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -562_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -564_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -564_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -573_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -573_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff b/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff deleted file mode 100644 index 25f49cd300..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_dense/feature_values.arff +++ /dev/null @@ -1,109 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_VALUES - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC - -@DATA 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-1030_a_metric,1.0,2.95338644252,0.180597014925,0.111111111111,0.0179104477612,0.0554011368455,0.00597014925373,167.5,-0.842774640072,-1.11468902778,-1.3683277744,0.191667533021,0.183586168733,0.222435440377,0.302265038883,0.264297222385,0.267556463145,0.206067070185,-5.12098335127,5.12098335127,1.38629436112,6.50727771239,0.0,9.0,4.0,0.0,670.0,0.0,0.0,4.0,1.0,-0.50382,-0.229125738144,0.0,0.0,0.0,0.0,0.0,0.305495560169,0.0825962927192,-0.313242763281,0.240387684257,0.0,0.0,0.0,0.0,0.0 -1030_r2_metric,1.0,2.95338644252,0.180597014925,0.111111111111,0.0179104477612,0.0554011368455,0.00597014925373,167.5,-0.842774640072,-1.11468902778,-1.3683277744,0.191667533021,0.183586168733,0.222435440377,0.302265038883,0.264297222385,0.267556463145,0.206067070185,-5.12098335127,5.12098335127,1.38629436112,6.50727771239,0.0,9.0,4.0,0.0,670.0,0.0,0.0,4.0,1.0,-0.50382,-0.229125738144,0.0,0.0,0.0,0.0,0.0,0.305495560169,0.0825962927192,-0.313242763281,0.240387684257,0.0,0.0,0.0,0.0,0.0 -1414_a_metric,1.0,8.84638094952,0.015494309612,0.00129533678756,0.000137117784177,0.00162224054548,0.00137117784177,729.3,7288.00326635,166.355906076,-1.53787918552,1050.07532355,0.021498338801,0.0163367348847,0.0218478019292,0.0176967882539,0.0114272470829,0.0179857648315,-6.59208516999,6.59208516999,2.30258509299,8.89467026298,6.0,772.0,10.0,0.0,7293.0,0.0,0.0,4.0,0.787234042553,-0.983286,-0.0949491634965,0.0,0.0,0.0,1.5,0.666666666667,85.3815231323,4.64931378185,-5.65015172958,12.1090448377,24.0,7.16666666667,2.0,7.71182353417,43.0 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-% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff deleted file mode 100644 index 1de19a85a5..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/algorithm_runs.arff +++ /dev/null @@ -1,120 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_ALGORITHM_RUNS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE algorithm STRING -@ATTRIBUTE r2_metric NUMERIC -@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} - -@DATA -344_r2_metric,1.0,29,0.000174939632416,ok -344_r2_metric,1.0,1,0.00122278928757,ok -422_r2_metric,1.0,1,0.960557281971,ok -422_r2_metric,1.0,10,0.945026278496,ok -225_r2_metric,1.0,17,0.330708384514,ok -225_r2_metric,1.0,18,0.337872743607,ok -197_a_metric,1.0,1,0.0225197672844,ok -197_a_metric,1.0,1,0.0225197672844,ok 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newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/configurations.csv b/autosklearn/metalearning/files/r2_metric_regression_sparse/configurations.csv deleted file mode 100644 index 7d77de600e..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/configurations.csv +++ /dev/null @@ -1,52 +0,0 @@ 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-48,mean,0.011362042214560027,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,mse,None,4.6530295512971644,None,12,10,0.0,100,,,,,,,,,,,,,,,standardize -49,median,0.010427843341691877,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,extra_trees,,,,,,,,,,,,,,,,,,,,,False,mse,None,1.397114088844996,15,12,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none -50,most_frequent,0.00234992390287966,True,extra_trees_preproc_for_regression,False,mse,None,0.8044960404154922,1,6,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,286.5841951417159,,4,0.17570677855266975,0.0999999999999999,rbf,-1,True,0.001,,,,,,,,,,,,,,,,,,,,,,,,standardize -51,most_frequent,,False,extra_trees_preproc_for_regression,True,mse,None,2.5830935657278076,19,16,0.0,100,,,,,,,,,,,,,,,,,,,,libsvm_svr,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.962812951051284,,4,0.01204802523960763,0.1821781494294054,rbf,-1,False,1.3160253217209612e-05,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt b/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt deleted file mode 100644 index 9df2b5eed4..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/description.txt +++ /dev/null @@ -1,68 +0,0 @@ -features_cutoff_time: 3600 -features_cutoff_memory: 3072 -number_of_feature_steps: 52 -feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues -feature_step KurtosisSTD: KurtosisSTD -feature_step DatasetRatio: DatasetRatio, LogDatasetRatio -feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner -feature_step NumberOfClasses: NumberOfClasses -feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues -feature_step KurtosisMin: KurtosisMin -feature_step SkewnessMean: SkewnessMean -feature_step SymbolsMean: SymbolsMean -feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD -feature_step SkewnessSTD: SkewnessSTD -feature_step SkewnessMax: SkewnessMax -feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures -feature_step ClassEntropy: ClassEntropy -feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -feature_step LandmarkNaiveBayes: LandmarkNaiveBayes -feature_step LogNumberOfFeatures: LogNumberOfFeatures -feature_step RatioNominalToNumerical: RatioNominalToNumerical -feature_step LogNumberOfInstances: LogNumberOfInstances -feature_step SymbolsSTD: SymbolsSTD -feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues -feature_step SymbolsSum: SymbolsSum -feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC -feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum -feature_step ClassProbabilityMax: ClassProbabilityMax -feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner -feature_step ClassProbabilitySTD: ClassProbabilitySTD -feature_step SymbolsMin: SymbolsMin -feature_step LogInverseDatasetRatio: LogInverseDatasetRatio -feature_step LandmarkDecisionTree: LandmarkDecisionTree -feature_step KurtosisMean: KurtosisMean -feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues -feature_step KurtosisMax: KurtosisMax -feature_step PCASkewnessFirstPC: PCASkewnessFirstPC -feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance -feature_step LandmarkLDA: LandmarkLDA -feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD -feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures -feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD -feature_step ClassProbabilityMin: ClassProbabilityMin -feature_step PercentageOfMissingValues: PercentageOfMissingValues -feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio -feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues -feature_step ClassProbabilityMean: ClassProbabilityMean -feature_step RatioNumericalToNominal: RatioNumericalToNominal -feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues -feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances -feature_step LogDatasetRatio: LogDatasetRatio -feature_step SkewnessMin: SkewnessMin -feature_step NumberOfNumericFeatures: NumberOfNumericFeatures -feature_step Landmark1NN: Landmark1NN -feature_step SymbolsMax: SymbolsMax -features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC -features_stochastic: -default_steps: NumberOfFeaturesWithMissingValues, KurtosisSTD, DatasetRatio, LandmarkDecisionNodeLearner, NumberOfClasses, NumberOfMissingValues, KurtosisMin, SkewnessMean, SymbolsMean, Skewnesses, SkewnessSTD, SkewnessMax, NumberOfFeatures, ClassEntropy, PCA, LandmarkNaiveBayes, LogNumberOfFeatures, RatioNominalToNumerical, LogNumberOfInstances, SymbolsSTD, MissingValues, SymbolsSum, PCAKurtosisFirstPC, NumSymbols, ClassProbabilityMax, LandmarkRandomNodeLearner, ClassProbabilitySTD, SymbolsMin, LogInverseDatasetRatio, LandmarkDecisionTree, KurtosisMean, NumberOfInstancesWithMissingValues, KurtosisMax, PCASkewnessFirstPC, PCAFractionOfComponentsFor95PercentVariance, LandmarkLDA, ClassOccurences, NumberOfCategoricalFeatures, Kurtosisses, ClassProbabilityMin, PercentageOfMissingValues, InverseDatasetRatio, PercentageOfInstancesWithMissingValues, ClassProbabilityMean, RatioNumericalToNominal, PercentageOfFeaturesWithMissingValues, NumberOfInstances, LogDatasetRatio, SkewnessMin, NumberOfNumericFeatures, Landmark1NN, SymbolsMax - -algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51 -performance_type: solution_quality -performance_measures: r2_metric -algorithms_stochastic: - -scenario_id: ChaLearn_AutoML_Challenge_2015 -maximize: false -algorithm_cutoff_time: 3600 -algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff deleted file mode 100644 index f291c0fcba..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_costs.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_COSTS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfMissingValues NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE SkewnessMean NUMERIC -@ATTRIBUTE SymbolsMean NUMERIC -@ATTRIBUTE Skewnesses NUMERIC -@ATTRIBUTE SkewnessSTD NUMERIC -@ATTRIBUTE SkewnessMax NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE PCA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE RatioNominalToNumerical NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE SymbolsSTD NUMERIC -@ATTRIBUTE MissingValues NUMERIC -@ATTRIBUTE SymbolsSum NUMERIC -@ATTRIBUTE PCAKurtosisFirstPC NUMERIC -@ATTRIBUTE NumSymbols NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE SymbolsMin NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE PCASkewnessFirstPC NUMERIC -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE ClassOccurences NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE Kurtosisses NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE PercentageOfMissingValues NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE RatioNumericalToNominal NUMERIC -@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC -@ATTRIBUTE NumberOfInstances NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE SkewnessMin NUMERIC -@ATTRIBUTE NumberOfNumericFeatures NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE SymbolsMax NUMERIC - -@DATA -1028_a_metric,1.0,0.0001,0.00013,2e-05,0.00751,5e-05,5e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00029,0.00084,0.01456,1e-05,7e-05,1e-05,0.00047,0.00035,4e-05,0.00034,0.00065,1e-05,0.00799,8e-05,0.0,1e-05,0.01481,7e-05,0.00023,4e-05,0.00048,3e-05,0.01935,0.00016,2e-05,0.00027,2e-05,1e-05,3e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.02096,0.0 -1028_r2_metric,1.0,0.00011,0.00013,3e-05,0.00752,6e-05,6e-05,3e-05,8e-05,0.00013,0.00028,0.00015,3e-05,1e-05,0.00032,0.00084,0.01453,1e-05,6e-05,1e-05,0.00047,0.00038,4e-05,0.00033,0.00065,1e-05,0.0083,8e-05,0.0,1e-05,0.01465,7e-05,0.00024,4e-05,0.00048,3e-05,0.01908,0.00015,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.0209,0.0 -1030_a_metric,1.0,8e-05,0.00013,2e-05,0.00896,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.0003,0.00082,0.0215,1e-05,7e-05,1e-05,0.00046,0.00031,4e-05,0.00033,0.00064,1e-05,0.00974,9e-05,0.0,1e-05,0.01136,7e-05,0.00022,4e-05,0.00047,2e-05,0.02092,0.00016,2e-05,0.00027,2e-05,0.0,3e-05,1e-05,4e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01925,0.0 -1030_r2_metric,1.0,8e-05,0.00013,2e-05,0.00882,6e-05,3e-05,3e-05,8e-05,0.00013,0.00028,0.00014,3e-05,1e-05,0.00031,0.00083,0.02148,1e-05,7e-05,1e-05,0.00044,0.00031,4e-05,0.00034,0.00062,1e-05,0.00951,9e-05,0.0,1e-05,0.01135,7e-05,0.00022,3e-05,0.00047,2e-05,0.02066,0.00016,2e-05,0.00026,2e-05,0.0,3e-05,1e-05,5e-05,4e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.01896,0.0 -1414_a_metric,1.0,0.00035,0.00014,2e-05,0.47489,0.00047,0.00011,3e-05,9e-05,4e-05,0.00033,0.00017,4e-05,1e-05,0.00221,0.00213,3.26197,1e-05,6e-05,0.0,8e-05,0.0028,3e-05,0.00095,0.00016,4e-05,0.2864,0.00014,1e-05,1e-05,3.06772,8e-05,0.00237,4e-05,0.00114,4e-05,1.34828,0.00034,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.72778,1e-05 -1414_r2_metric,1.0,0.00035,0.00014,2e-05,0.47406,0.00048,0.0001,3e-05,0.00011,4e-05,0.00045,0.00026,5e-05,1e-05,0.00222,0.00214,3.23113,1e-05,6e-05,1e-05,9e-05,0.00279,3e-05,0.00096,0.00017,4e-05,0.28855,0.00014,1e-05,1e-05,3.08176,8e-05,0.00237,4e-05,0.00114,4e-05,1.34295,0.00035,2e-05,0.00029,6e-05,1e-05,3e-05,1e-05,0.0001,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,4.7159,1e-05 -189_a_metric,1.0,0.00024,0.0,2e-05,0.0,0.00038,7e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00456,0.0,0.0,1e-05,7e-05,1e-05,0.00045,0.00169,4e-05,0.0,0.00062,0.00031,0.0,0.00056,0.0,1e-05,0.0,0.0,0.00142,0.0,0.0,0.0,0.0,0.00171,2e-05,0.0,0.00032,1e-05,3e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -189_r2_metric,1.0,0.00024,0.0,2e-05,0.0,0.0004,8e-05,0.0,0.0,0.00441,0.0,0.0,0.0,1e-05,0.00425,0.0,0.0,1e-05,8e-05,1e-05,0.00497,0.00188,4e-05,0.0,0.00944,0.00031,0.0,0.00061,0.0,1e-05,0.0,0.0,0.0016,0.0,0.0,0.0,0.0,0.00176,2e-05,0.0,0.00032,1e-05,4e-05,1e-05,0.00051,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -197_a_metric,1.0,0.00035,0.00014,2e-05,1.51228,0.00026,0.00013,3e-05,8e-05,0.00013,0.00037,0.00021,4e-05,1e-05,0.00155,0.00149,0.17921,1e-05,7e-05,1e-05,0.00044,0.00195,4e-05,0.00057,0.00061,1e-05,0.06526,9e-05,0.0,1e-05,1.60067,8e-05,0.0015,3e-05,0.00088,3e-05,0.14992,0.00018,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94178,0.0 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-422_r2_metric,1.0,0.00241,0.0,5e-05,0.0,0.00044,0.00145,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00248,0.0,0.0,1e-05,9e-05,1e-05,0.00046,0.00728,4e-05,0.0,0.00064,6e-05,0.0,0.00019,0.0,1e-05,0.0,0.0,0.00348,0.0,0.0,0.0,0.0,0.00046,2e-05,0.0,8e-05,1e-05,7e-05,3e-05,0.00014,6e-05,3e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -507_a_metric,1.0,0.00015,0.0,2e-05,0.0,0.00016,6e-05,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00159,0.0,0.0,1e-05,8e-05,1e-05,0.00065,0.00075,4e-05,0.0,0.00091,0.00011,0.0,0.00055,0.0,1e-05,0.0,0.0,0.00056,0.0,0.0,0.0,0.0,0.00101,2e-05,0.0,0.00013,0.0,4e-05,1e-05,0.00022,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -507_r2_metric,1.0,0.00015,0.0,2e-05,0.0,0.00016,6e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00153,0.0,0.0,1e-05,7e-05,1e-05,0.00046,0.00075,4e-05,0.0,0.00064,0.00011,0.0,0.00026,0.0,1e-05,0.0,0.0,0.00056,0.0,0.0,0.0,0.0,0.00072,2e-05,0.0,0.00014,0.0,3e-05,1e-05,0.00021,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -512_a_metric,1.0,5e-05,0.0,2e-05,0.0,0.00011,4e-05,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.0007,0.0,0.0,1e-05,6e-05,1e-05,0.00044,0.00042,4e-05,0.0,0.00061,2e-05,0.0,0.00012,0.0,1e-05,0.0,0.0,0.00036,0.0,0.0,0.0,0.0,0.00025,2e-05,0.0,4e-05,0.0,3e-05,1e-05,7e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -512_r2_metric,1.0,6e-05,0.0,2e-05,0.0,0.00011,8e-05,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00067,0.0,0.0,1e-05,7e-05,1e-05,0.00058,0.00052,4e-05,0.0,0.00084,2e-05,0.0,0.00012,0.0,1e-05,0.0,0.0,0.00041,0.0,0.0,0.0,0.0,0.00025,2e-05,0.0,4e-05,0.0,3e-05,1e-05,7e-05,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -558_a_metric,1.0,0.00049,0.0,3e-05,0.0,0.00037,0.00019,0.0,0.0,0.00021,0.0,0.0,0.0,1e-05,0.00382,0.0,0.0,1e-05,9e-05,1e-05,0.00073,0.00229,4e-05,0.0,0.00099,0.00021,0.0,0.00049,0.0,2e-05,0.0,0.0,0.00164,0.0,0.0,0.0,0.0,0.00132,2e-05,0.0,0.00026,1e-05,5e-05,1e-05,0.00037,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -558_r2_metric,1.0,0.00049,0.0,3e-05,0.0,0.00036,0.00019,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.00371,0.0,0.0,1e-05,8e-05,1e-05,0.00056,0.00227,4e-05,0.0,0.00074,0.00021,0.0,0.00046,0.0,1e-05,0.0,0.0,0.00163,0.0,0.0,0.0,0.0,0.00127,2e-05,0.0,0.00023,1e-05,4e-05,1e-05,0.00037,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -562_a_metric,1.0,0.00032,0.00014,2e-05,1.08946,0.00028,0.00012,3e-05,8e-05,0.00013,0.00035,0.00019,4e-05,1e-05,0.00157,0.00124,0.15881,1e-05,7e-05,1e-05,0.00048,0.00196,4e-05,0.00051,0.00065,1e-05,0.08654,0.00011,0.0,1e-05,1.08761,8e-05,0.00154,4e-05,0.0007,3e-05,0.11888,0.0002,2e-05,0.00029,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39691,0.0 -562_r2_metric,1.0,0.00031,0.00015,2e-05,1.08546,0.00027,0.00012,3e-05,9e-05,0.00013,0.00043,0.00027,4e-05,1e-05,0.00159,0.00124,0.15862,1e-05,7e-05,1e-05,0.00052,0.0019,4e-05,0.00049,0.0007,2e-05,0.08744,0.00011,0.0,1e-05,1.08323,8e-05,0.0015,3e-05,0.00073,3e-05,0.11795,0.00021,2e-05,0.00029,3e-05,0.0,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.39723,0.0 -564_a_metric,1.0,0.00105,0.0,2e-05,0.0,0.00229,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,2e-05,0.0161,0.0,0.0,1e-05,8e-05,1e-05,0.00051,0.00845,4e-05,0.0,0.00069,0.00081,0.0,0.00141,0.0,2e-05,0.0,0.0,0.00716,0.0,0.0,0.0,0.0,0.00429,2e-05,0.0,0.00081,1e-05,5e-05,1e-05,0.00127,5e-05,2e-05,1e-05,1e-05,0.0,2e-05,0.0,0.0 -564_r2_metric,1.0,0.00107,0.0,2e-05,0.0,0.0023,0.00028,0.0,0.0,0.00013,0.0,0.0,0.0,1e-05,0.01607,0.0,0.0,1e-05,8e-05,1e-05,0.00054,0.00853,4e-05,0.0,0.00072,0.00082,0.0,0.00155,0.0,1e-05,0.0,0.0,0.00721,0.0,0.0,0.0,0.0,0.0045,2e-05,0.0,0.00082,1e-05,4e-05,1e-05,0.00131,5e-05,1e-05,1e-05,1e-05,0.0,2e-05,0.0,1e-05 -573_a_metric,1.0,0.00035,0.00016,2e-05,1.51126,0.00026,0.00013,4e-05,9e-05,0.0002,0.00041,0.00025,4e-05,1e-05,0.00162,0.00153,0.17899,1e-05,7e-05,1e-05,0.00053,0.00196,4e-05,0.00058,0.00078,1e-05,0.05692,9e-05,0.0,1e-05,1.69676,8e-05,0.0015,4e-05,0.00092,3e-05,0.14625,0.00018,2e-05,0.00031,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,0.94202,1e-05 -573_r2_metric,1.0,0.00036,0.00013,3e-05,1.55181,0.00027,0.00013,3e-05,0.0001,0.00017,0.00053,0.00034,5e-05,1e-05,0.00194,0.00153,0.17819,1e-05,7e-05,1e-05,0.0006,0.00196,4e-05,0.00057,0.00083,1e-05,0.0667,0.00011,0.0,1e-05,1.78851,8e-05,0.00151,4e-05,0.00092,3e-05,0.14663,0.0002,2e-05,0.00028,3e-05,1e-05,4e-05,1e-05,5e-05,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,1.09208,0.0 -574_a_metric,1.0,0.00061,0.00013,2e-05,152.67116,0.00095,0.00025,3e-05,8e-05,0.00013,0.00033,0.00018,4e-05,1e-05,0.00551,0.00211,7.419,1e-05,7e-05,1e-05,0.00046,0.00478,4e-05,0.00088,0.00064,9e-05,8.86999,0.00023,0.0,1e-05,84.10332,7e-05,0.00395,3e-05,0.0012,3e-05,3.37802,0.00063,2e-05,0.00027,0.00012,1e-05,3e-05,1e-05,0.00018,5e-05,1e-05,1e-05,1e-05,3e-05,2e-05,5.47426,0.0 -574_r2_metric,1.0,0.00062,0.00013,2e-05,152.47855,0.00099,0.00025,3e-05,9e-05,0.00013,0.00039,0.00023,4e-05,1e-05,0.00471,0.0022,7.42585,1e-05,8e-05,1e-05,0.00049,0.00482,5e-05,0.00089,0.00067,0.0001,8.90127,0.00026,0.0,1e-05,85.99908,7e-05,0.00398,3e-05,0.00128,3e-05,3.38485,0.00066,2e-05,0.00027,0.00011,1e-05,4e-05,1e-05,0.00019,5e-05,2e-05,1e-05,1e-05,3e-05,2e-05,5.49499,0.0 -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_runstatus.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_runstatus.arff deleted file mode 100644 index a44c9e2bae..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_runstatus.arff +++ /dev/null @@ -1,115 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_RUNSTATUS - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} -@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} - -@DATA -1028_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1028_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1030_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -1414_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -189_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -189_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -197_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -197_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -198_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -198_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -201_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -201_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -209_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -209_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -215_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -216_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -218_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -218_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -225_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -225_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -227_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -227_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -287_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -296_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -296_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -308_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -344_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -405_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -416_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -422_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -507_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -512_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -558_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -562_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -562_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -564_a_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -564_r2_metric,1.0,ok,other,ok,other,ok,ok,other,other,ok,other,other,other,ok,ok,other,other,ok,ok,ok,ok,ok,ok,other,ok,ok,other,ok,ok,ok,other,other,ok,other,other,other,other,ok,ok,other,ok,ok,ok,ok,ok,ok,ok,ok,ok,other,ok,other,ok -573_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -573_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_a_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -574_r2_metric,1.0,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok,ok -% -% -% \ No newline at end of file diff --git a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_values.arff b/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_values.arff deleted file mode 100644 index 25f49cd300..0000000000 --- a/autosklearn/metalearning/files/r2_metric_regression_sparse/feature_values.arff +++ /dev/null @@ -1,109 +0,0 @@ -@RELATION ChaLearn_AutoML_Challenge_2015_FEATURE_VALUES - -@ATTRIBUTE instance_id STRING -@ATTRIBUTE repetition NUMERIC -@ATTRIBUTE ClassEntropy NUMERIC -@ATTRIBUTE ClassProbabilityMax NUMERIC -@ATTRIBUTE ClassProbabilityMean NUMERIC -@ATTRIBUTE ClassProbabilityMin NUMERIC -@ATTRIBUTE ClassProbabilitySTD NUMERIC -@ATTRIBUTE DatasetRatio NUMERIC -@ATTRIBUTE InverseDatasetRatio NUMERIC -@ATTRIBUTE KurtosisMax NUMERIC -@ATTRIBUTE KurtosisMean NUMERIC -@ATTRIBUTE KurtosisMin NUMERIC -@ATTRIBUTE KurtosisSTD NUMERIC -@ATTRIBUTE Landmark1NN NUMERIC -@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC -@ATTRIBUTE LandmarkDecisionTree NUMERIC -@ATTRIBUTE LandmarkLDA NUMERIC -@ATTRIBUTE LandmarkNaiveBayes NUMERIC -@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC -@ATTRIBUTE LogDatasetRatio NUMERIC -@ATTRIBUTE LogInverseDatasetRatio NUMERIC -@ATTRIBUTE LogNumberOfFeatures NUMERIC -@ATTRIBUTE LogNumberOfInstances NUMERIC -@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC -@ATTRIBUTE NumberOfClasses NUMERIC -@ATTRIBUTE NumberOfFeatures NUMERIC -@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC 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+45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,none,decision_tree,,,,,,,gini,1.0026108621086127,1.0,None,11,15,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06384570118118357,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,363,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +52,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,2.373128778545669,None,6,6,0.0,100,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,minmax +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,,,,,standardize +59,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,normalize +60,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.01116924019200219,True,fast_ica,,,,,,,,,deflation,cube,766,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +63,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,15,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23.938615614788205,f_classif,,,,none +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +66,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +67,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004377522551392424,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,none +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,weighting,bernoulli_nb,,,,,45.303636496385735,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,normalize +78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,weighting,multinomial_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30.78401845347842,True,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00011434969307117035,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.511525734485552,0.0,3,0.24137524681185685,poly,-1,True,0.0022196456925201464,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt new file mode 100644 index 0000000000..5abe50abae --- /dev/null +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance 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index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, 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+84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..5abe50abae --- /dev/null +++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +92,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+98,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12683.745517936331,,,0.2798030071977636,rbf,-1,False,9.038883737441219e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,False,,,,,,,,,,,,normalize +99,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.13770674588914472,deviance,4,1.4662630880288958,None,2,12,0.0,341,0.678503955199761,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00985371350719694,True,feature_agglomeration,,,,,,,,,,,,,euclidean,average,134,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.0483380823811535,deviance,3,3.5447265496885296,None,5,16,0.0,141,0.5896080302596434,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0020128017963651694,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,19.014581209053425,f_classif,,,,none +103,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,1,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,44.56701180897993,f_classif,,,,none +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,3.7368358338706456,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize +106,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,,,,,none +107,weighting,adaboost,SAMME.R,0.3391577668564776,10,140,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.07528463412835093,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +108,none,adaboost,SAMME,0.8309703539232443,6,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,149,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.00020336227749070157,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,58.64719633953624,f_classif,,,,standardize +110,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax 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+119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,weighting,lda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,10,None,,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.003937843574389652,rbf,545,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..3af354e8c3 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..3af354e8c3 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+14,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.05555416654541217,deviance,3,2.5091820463432226,None,10,18,0.0,243,0.7284627840776807,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,74.17884137258953,chi2,,,,standardize +15,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0037784776296368376,False,,0.07250250408304426,True,,optimal,hinge,192,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.32078133872227793,4220,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +16,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,284,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+20,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax +21,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.3131366295323392,None,3,20,0.0,100,,,,,,,,,,,,most_frequent,,False,extra_trees_preproc_for_classification,True,entropy,None,0.5311472280328223,1,7,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +22,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8916988265222563,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.001971708179099073,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize 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+32,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.7702815127831667e-05,False,,0.006631256638841467,True,,constant,squared_hinge,61,l2,,mean,0.3619893522448273,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7.741050844267226,chi2,,,,normalize +33,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +34,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.4244459875201874,False,True,1,squared_hinge,ovr,l2,0.0014473335587607684,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,False,True,,,,,,,,,,,,minmax 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+38,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.021100204578784896,deviance,2,3.2219978901408126,None,14,13,0.0,62,0.9175813938002152,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0015802583114458146,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.26682653239833576,fdr,f_classif,none +40,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +41,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,17304.617799701293,,,0.1597932521120146,rbf,-1,True,0.00013911955271793795,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,normalize +42,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +43,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.751911127640866,None,6,15,0.0,100,,,,,,,,,,,,median,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+74,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13760.98476889944,,,0.002986810443454802,rbf,-1,True,2.7580250502580095e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.15519126312070738,fpr,chi2,minmax +75,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18469.410087066783,0.07220421390172338,4,0.0009052417825455917,poly,-1,False,0.015303710234115636,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +76,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.07123494942796214,True,,0.06640696915484627,True,1.444778397890192e-09,invscaling,perceptron,147,elasticnet,0.25,most_frequent,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +77,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.1722034433283093,,,0.001958703488411727,rbf,-1,False,0.006262296782048029,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +78,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +79,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.40388658075532,17,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.020938011807420828,True,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,260,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +80,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,4.53463583357125,None,2,4,0.0,100,,,,,,,,,,,,median,0.08965639912423894,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,48.58142545325404,f_classif,,,,standardize +81,weighting,adaboost,SAMME.R,0.09069390859905559,10,390,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.004018643535386462,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,50.0,chi2,,,,none +82,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,3.8659458530934794,None,5,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,weighting,passive_aggressive,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.595938320352984e-05,True,hinge,183,,,,,,,,,,,,,,,,,,,,,,mean,0.30931695098030326,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.780069347493654,True,,,,,,,,,,,,,,,standardize +85,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +86,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1081.8654538967082,0.31000451113867844,2,0.0009295820649021185,poly,-1,True,0.0022023485648058026,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.09670639904456636,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,False,,,,,,,,,,,,,,,none 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a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..3af354e8c3 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+% \ No newline at end of file diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/readme.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_dense/readme.txt new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff new file mode 100644 index 0000000000..3b0cd41cd3 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/algorithm_runs.arff @@ -0,0 +1,142 @@ +@RELATION auto-sklearn_ALGORITHM_RUNS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE algorithm STRING +@ATTRIBUTE recall_macro NUMERIC +@ATTRIBUTE runstatus {ok, timeout, memout, not_applicable, crash, other} + +@DATA +2120,1.0,1,0.11839553067815467,ok +75193,1.0,2,0.12038877370079726,ok 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+115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,weighting,adaboost,SAMME.R,0.1355825690029731,2,483,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +117,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+126,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.8929665633992583,14,13,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +130,none,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..3af354e8c3 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_macro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA +233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_macro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt new file mode 100644 index 0000000000..d0b54b369d --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} 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+114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..d0b54b369d --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA +233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..d0b54b369d --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_micro +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA +233,1.0,0.00057,0.0032,0.00306,2e-05,0.00986,0.05681,0.1773,0.00044,0.00357,0.01609,2e-05,0.0002,9e-05,1e-05,0.00086,4e-05,1e-05,4e-05,4e-05,6e-05,5e-05,1e-05,0.00035,6e-05,0.00044,0.05516,1e-05,0.00358,0.03144,2e-05,0.026,0.00011,0.0001,0.00028,1e-05,0.00025,0.12439,1e-05,1e-05,6e-05,2e-05,0.00012,2e-05,1.56556,5e-05,8e-05,1e-05,0.00011,1e-05,4e-05,6e-05,5e-05 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b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_micro_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +22,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax 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+30,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,434.04789312490243,-0.28598798624265775,5,0.00020813257891031506,poly,-1,True,0.08410050264911162,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7,None,11,19,1.0,35,,,,,,normalize +31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,520.1785093386421,,,0.4094223029861982,rbf,-1,False,0.0001488588704335344,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27.824808973705593,chi2,,,,normalize +32,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,522.1737827111988,False,True,1,squared_hinge,ovr,l2,0.05569113756716791,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2934811012685023,fdr,chi2,normalize +33,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.5296423664586023,None,1,3,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.13984322821477,0.9270226145864255,3,0.9919162435418981,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize 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+45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,13677.935413986388,,,0.00015939552183341207,rbf,-1,True,1.6147607485887653e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.19623953814305067,fwe,f_classif,minmax +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+83,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..5abe50abae --- /dev/null +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC 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mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+38,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.0009710817723081867,True,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,,,0.10000000000000006,rbf,100,,,,,,,,,,,,,standardize +39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +41,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +44,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.004135961337986217,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,5.547311126714408,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,none,bernoulli_nb,,,,,0.022126354954968466,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,truncatedSVD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,225,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +53,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.5716878597339514,None,12,5,0.0,100,,,,,,,,,,,,mean,0.3328589169634927,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0013066556710069719,False,0.03958867790828566,0.06657767200469752,True,,optimal,modified_huber,52,l2,,most_frequent,,False,nystroem_sampler,,,,,,,,,,,,,,,,,,,,,,,,0.7941252766878464,5,0.0026102407098584192,poly,615,,,,,,,,,,,,,standardize +59,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.312914959291577,None,11,14,0.0,100,,,,,,,,,,,,median,0.003756759129230824,True,extra_trees_preproc_for_classification,True,gini,None,1.176431421873488,4,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +63,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.9357354171445895e-06,True,,0.05458546144730029,True,,constant,perceptron,179,l2,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +66,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1042.145708206535,0.8789572870709608,,0.000501756150861939,sigmoid,-1,False,0.004443574079713121,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,2.986388281638184,False,True,1,squared_hinge,ovr,l1,2.919662827714475e-05,,,,,,,,,,,,,,,,,,none +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,adaboost,SAMME.R,0.05676389779184221,2,316,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +75,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,4.9983112975799235,None,3,17,0.0,100,,,,,,,,,,,,mean,0.012321190772400284,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.675620724347966,None,6,19,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.511525734485552,0.0,3,0.24137524681185685,poly,-1,True,0.0022196456925201464,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,decision_tree,,,,,,,gini,0.13378541205135663,1.0,None,6,13,0.0,best,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.001107688769590841,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.3573908932499394,fpr,chi2,,standardize +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..5abe50abae --- /dev/null +++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: recall +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE RatioNumericalToNominal NUMERIC +@ATTRIBUTE ClassProbabilityMean NUMERIC + +@DATA 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt new file mode 100644 index 0000000000..c782c44005 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..c782c44005 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE 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new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..c782c44005 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC 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b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_dense/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, 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+87,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +88,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.09174494331902282,False,True,1,squared_hinge,ovr,l2,0.004642503579081282,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0018444303476881426,True,kernel_pca,,,,,,,,,0.4783416998251251,4,0.5925934287948866,poly,1268,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +89,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2718.215603902964,0.29331425457977867,4,0.010295344093308017,poly,-1,True,0.00375915297303114,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0005596685458982962,True,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +90,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,,normalize +91,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +92,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +93,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,3.5716878597339514,None,12,5,0.0,100,,,,,,,,,,,,mean,0.3328589169634927,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +94,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +95,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +96,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +97,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +98,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +99,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +100,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.3530578080502024,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +101,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +102,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +103,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +104,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +105,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +106,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +107,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +108,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +109,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1,2,uniform,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,kernel_pca,,,,,,,,,,,,cosine,1211,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +110,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +111,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +112,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20538.349411758132,,,0.4723495682143671,rbf,-1,False,0.002291096791126104,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +113,none,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l2,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kitchen_sinks,,,,,,,,,,,,,,0.8655052483233905,4495,,,,,,,,,,,,,,,,,,,,,,,,,,none +114,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +115,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +116,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +117,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +118,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +119,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00043300391715554303,True,,0.0236040895596051,True,4.493338624232962e-06,invscaling,squared_hinge,41,elasticnet,0.07376212096528387,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +120,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,0.0,2,4.892539721504502,poly,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +121,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +122,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +123,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +124,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +125,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +126,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +127,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +128,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +129,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +130,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..c782c44005 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130 +algorithms_stochastic: +performance_measures: recall_weighted +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC 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b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff new file mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/recall_weighted_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} 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+10,none,adaboost,SAMME,1.1145892870034408,5,354,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.001119064659211796,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +11,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,4.745101117534867,14,12,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.06075999384970139,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,2.035488276053047,False,True,1,squared_hinge,ovr,l1,0.0013962817054747638,,,,,,,,,,,,,,,,,,,,,,minmax +12,weighting,liblinear_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,498.0504565649503,False,True,1,squared_hinge,ovr,l2,0.054114780051909415,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.006463150165572885,rbf,1603,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+16,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +17,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.29536056952846,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +18,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +19,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +20,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.772998842752466,0.83511286553724,2,0.06220226826561282,poly,-1,True,0.002646863245450357,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.21257674664328438,fdr,f_classif,none +21,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.7561836874034692,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +22,weighting,adaboost,SAMME.R,0.7541792790167546,2,257,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,15688.937158283852,False,True,1,squared_hinge,ovr,l1,0.002228807915074494,,,,,,,,,,,,,,,,,,,,,,normalize +23,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,,0.10000000000000006,rbf,-1,True,1.2006098483774738e-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,fast_ica,,,,,,,,,parallel,logcosh,,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +24,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.691410068212463,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +25,weighting,k_nearest_neighbors,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,1,distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,entropy,None,2.4627880361915073,12,16,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +26,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +27,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,2.2584064375821877,None,13,6,0.0,100,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +28,none,adaboost,SAMME,0.10852728577735547,8,492,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.9979891225204479,rbf,287,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +29,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0034744193384361383,False,,0.0023911270619537315,True,1.3210189886716814e-06,constant,log,20,elasticnet,,median,0.001379489301179573,True,random_trees_embedding,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,None,14,5,1.0,97,,,,,,standardize +30,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,1.0615335158906123,12,14,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0006128377815426205,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.40918220909952985,fdr,chi2,none +31,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,346.99358578854435,,,0.10000000000000006,rbf,-1,True,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +32,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7893822540456763e-05,True,,0.01920961872573097,True,,optimal,hinge,769,l2,,median,,False,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,1.0504050565968734,2076,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +33,none,adaboost,SAMME.R,0.340801430264527,7,464,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2979722891862978,fwe,f_classif,none +34,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.180833449674944,0.9270226145864255,3,4.302883844246615,poly,-1,False,0.05721568670748001,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +35,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,10,0.0,100,,,,,,,,,,,,median,0.00011453391764741519,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +36,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.008590925713214136,rbf,-1,True,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +37,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,1.675620724347966,None,6,15,0.0,100,,,,,,,,,,,,most_frequent,0.00779561565738919,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +38,none,gaussian_nb,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,298,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +39,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,226.06742020598978,,,0.00012101941650923356,rbf,-1,True,0.05935054267098756,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +40,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,87.38519648295652,-0.3418885688513862,3,5.352560696079751e-05,poly,-1,True,0.09027326753415817,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +41,weighting,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0045709373573003444,True,,0.03312324709994675,True,,invscaling,perceptron,41,l2,0.25,most_frequent,0.0030297890634286393,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,False,,,,,,,,,,,,minmax +42,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +43,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2125.7097093031207,0.193222519885502,3,1.8755074516105807,poly,-1,True,0.011175266227261777,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.12342934122356486,True,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,1.0,False,True,1,squared_hinge,ovr,l1,0.00010000000000000009,,,,,,,,,,,,,,,,,,,,,,minmax +44,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16725.85790752666,,,0.0235762546783831,rbf,-1,False,0.08183987642157002,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,,False,liblinear_svc_preprocessor,,,,,,,,,,,,,,,,,,,,,,,,6.391474036008549,False,True,1,squared_hinge,ovr,l1,1.0014702398825083e-05,,,,,,,,,,,,,,,,,,,,,,none +45,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +46,weighting,adaboost,SAMME.R,0.05676389779184221,2,291,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.9145911691745825,0.8390938017045895,2,0.12403388838921078,poly,-1,False,0.00011235090274035898,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.09940116443132659,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1,fpr,chi2,minmax +50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax +53,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.042578727957219765,deviance,9,1.5289316818058862,None,2,4,0.0,218,0.4807770453082096,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,manhattan,complete,285,max,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +54,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,3.9091377889206864,None,12,5,0.0,100,,,,,,,,,,,,mean,0.20221562281482044,True,feature_agglomeration,,,,,,,,,,,,,euclidean,ward,25,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +55,none,extra_trees,,,,,,,,,,,,,,,False,gini,None,4.557771492985571,1,4,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0006382162690755656,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +56,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.1285583573915876,deviance,1,1.8700144159035181,None,5,19,0.0,252,0.7739014033956171,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,False,gini,None,1.0,1,2,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +57,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.159811468730869,,,0.001958703488411727,rbf,-1,False,0.0021627867253181583,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.0007158501358616712,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +58,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize 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+84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11199470124045516,deviance,6,1.0,None,1,2,0.0,100,0.5541730214135502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011676674417077058,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7525595954528075,False,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt new file mode 100644 index 0000000000..d7f0f02920 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+47,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +48,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.589116648819001,,,0.0002958666899963372,rbf,-1,True,0.0016686113695819345,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.24020311618261972,True,extra_trees_preproc_for_classification,False,gini,None,2.1163181235264306,10,11,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +49,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +50,weighting,qda,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.050048416043345,,,,,,,,,,,,,,,,,,,,,mean,0.0013509816177055573,True,densifier,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none 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+59,weighting,extra_trees,,,,,,,,,,,,,,,True,gini,None,1.265605586220837,7,20,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.15606105100090975,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4474537649883411,fdr,chi2,,none +60,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,entropy,None,0.6962352073255054,None,3,13,0.0,100,,,,,,,,,,,,mean,,False,extra_trees_preproc_for_classification,True,gini,None,3.1516195509427503,10,5,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +61,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +62,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +63,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +64,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,0.5268009758511936,None,1,20,0.0,100,,,,,,,,,,,,most_frequent,0.0031539566916588156,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +65,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +66,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +73,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +74,weighting,adaboost,SAMME.R,0.05676389779184221,2,352,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +75,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +79,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,20.511525734485552,0.0,3,0.24137524681185685,poly,-1,True,0.0022196456925201464,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +83,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt new file mode 100644 index 0000000000..d7f0f02920 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE 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100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_binary.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, 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+50,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +51,weighting,adaboost,SAMME,0.6121582240822148,4,307,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.00018975941733149652,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,71.21679009018237,chi2,,,,normalize +52,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,entropy,None,4.463495264064763,None,3,4,0.0,100,,,,,,,,,,,,mean,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,False,,,,,,,,,,,,minmax 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+65,weighting,extra_trees,,,,,,,,,,,,,,,False,gini,None,2.183505419637145,8,19,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9999,True,,,,,,,,,,,,,,,standardize +66,weighting,extra_trees,,,,,,,,,,,,,,,False,entropy,None,2.999362314514494,13,6,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.03211706492854286,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.35344039987333714,fpr,f_classif,minmax +67,weighting,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,9.631509448517951,,,4.892539721504502,rbf,-1,True,0.00014811646373963315,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,kernel_pca,,,,,,,,,,,,,,,,,,,0.3423447149134824,rbf,1144,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +68,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,133.619004912714,,,1.421889512788389,rbf,-1,False,0.07228314195704957,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +69,weighting,adaboost,SAMME.R,0.10000000000000002,4,363,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010000000000000004,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +70,none,extra_trees,,,,,,,,,,,,,,,False,entropy,None,3.9379397352291163,11,18,0.0,100,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,0.010574522915023539,True,feature_agglomeration,,,,,,,,,,,,,cosine,average,219,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,normalize +71,none,sgd,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.924097956321882e-06,False,,0.01,True,,optimal,log,20,l2,,mean,0.010000000000000004,True,kitchen_sinks,,,,,,,,,,,,,,,,,,,,,,0.9005631277685047,7910,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +72,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.4676122454707685,None,8,3,0.0,100,,,,,,,,,,,,most_frequent,0.2356239270240324,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,standardize +73,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,406.0738136760568,0.4165403776828309,,0.00012115980324285492,sigmoid,-1,True,0.00728194331519472,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.019605520228737412,True,select_rates,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.11869428029179001,fpr,chi2,minmax +74,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.035187201421840536,deviance,6,2.4226592214020237,None,14,7,0.0,242,0.5073556144317966,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,False,True,,,,,,,,,,,,none +75,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03471243966443885,deviance,10,2.372874388188261,None,19,6,0.0,189,0.7453076602503851,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.022095952417762486,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,none +76,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.7962514445526179,None,5,15,0.0,100,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,288,mean,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +77,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +78,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,False,gini,None,2.681534781355698,None,12,12,0.0,100,,,,,,,,,,,,most_frequent,0.05805480949063231,True,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3,True,True,,,,,,,,,,,,standardize +79,weighting,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,2.536895848803703,None,1,10,0.0,100,,,,,,,,,,,,most_frequent,,False,polynomial,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2,True,False,,,,,,,,,,,,minmax +80,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +81,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize +82,none,libsvm_svc,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,26372.113508246854,,,0.010085090337125343,rbf,-1,False,0.003065004057733916,,,,,,,,,,,,,,,,,,,,,,,,,,,,mean,,False,feature_agglomeration,,,,,,,,,,,,,cosine,average,241,median,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,minmax +83,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.03685657971020395,deviance,4,3.0105237034152457,None,5,14,0.0,148,0.6688259819382407,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.017681993523748156,True,select_percentile_classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,18.324492134388027,f_classif,,,,none +84,weighting,gradient_boosting,,,,,,,,,,,,,,,,,,,,,,,0.11199470124045516,deviance,6,1.0,None,1,2,0.0,100,0.5541730214135502,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,median,0.0011676674417077058,True,pca,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7525595954528075,False,,,,,,,,,,,,,,,normalize diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt new file mode 100644 index 0000000000..d7f0f02920 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_dense/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE InverseDatasetRatio NUMERIC +@ATTRIBUTE SkewnessMin NUMERIC +@ATTRIBUTE SymbolsMin NUMERIC +@ATTRIBUTE SkewnessSTD NUMERIC +@ATTRIBUTE SkewnessMax NUMERIC +@ATTRIBUTE SymbolsMean NUMERIC +@ATTRIBUTE LandmarkRandomNodeLearner NUMERIC +@ATTRIBUTE SymbolsMax NUMERIC +@ATTRIBUTE NumSymbols NUMERIC +@ATTRIBUTE LandmarkNaiveBayes NUMERIC +@ATTRIBUTE NumberOfNumericFeatures NUMERIC +@ATTRIBUTE PCA NUMERIC +@ATTRIBUTE KurtosisMean NUMERIC +@ATTRIBUTE ClassProbabilitySTD NUMERIC +@ATTRIBUTE NumberOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE LogNumberOfInstances NUMERIC +@ATTRIBUTE KurtosisSTD NUMERIC +@ATTRIBUTE LandmarkDecisionTree NUMERIC +@ATTRIBUTE NumberOfInstances NUMERIC +@ATTRIBUTE LogDatasetRatio NUMERIC +@ATTRIBUTE SymbolsSum NUMERIC +@ATTRIBUTE PercentageOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE NumberOfMissingValues NUMERIC +@ATTRIBUTE PercentageOfFeaturesWithMissingValues NUMERIC +@ATTRIBUTE Landmark1NN NUMERIC +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance NUMERIC +@ATTRIBUTE NumberOfClasses NUMERIC +@ATTRIBUTE LogNumberOfFeatures NUMERIC +@ATTRIBUTE SkewnessMean NUMERIC +@ATTRIBUTE NumberOfFeatures NUMERIC +@ATTRIBUTE DatasetRatio NUMERIC +@ATTRIBUTE 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NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionTree {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSum {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Landmark1NN {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAFractionOfComponentsFor95PercentVariance {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfClasses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE DatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNumericalToNominal {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMean {ok, timeout, memout, presolved, crash, other} + +@DATA 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+83,weighting,adaboost,SAMME.R,0.1355825690029731,2,339,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,most_frequent,0.2086501959368564,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,none +84,none,random_forest,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,True,gini,None,1.0,None,1,2,0.0,100,,,,,,,,,,,,mean,0.01,True,no_preprocessing,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,standardize diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt new file mode 100644 index 0000000000..d7f0f02920 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/description.txt @@ -0,0 +1,68 @@ +features_cutoff_time: 3600 +features_cutoff_memory: 3072 +number_of_feature_steps: 52 +feature_step Skewnesses: SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD +feature_step NumberOfInstancesWithMissingValues: NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues +feature_step SymbolsSTD: SymbolsSTD +feature_step LogInverseDatasetRatio: LogInverseDatasetRatio +feature_step PCASkewnessFirstPC: PCASkewnessFirstPC +feature_step LandmarkDecisionNodeLearner: LandmarkDecisionNodeLearner +feature_step LandmarkLDA: LandmarkLDA +feature_step Kurtosisses: KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD +feature_step MissingValues: NumberOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, NumberOfMissingValues +feature_step PCAKurtosisFirstPC: PCAKurtosisFirstPC +feature_step NumberOfCategoricalFeatures: NumberOfCategoricalFeatures +feature_step ClassOccurences: ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD +feature_step RatioNominalToNumerical: RatioNominalToNumerical +feature_step ClassProbabilityMax: ClassProbabilityMax +feature_step ClassEntropy: ClassEntropy +feature_step ClassProbabilityMin: ClassProbabilityMin +feature_step PercentageOfMissingValues: PercentageOfMissingValues +feature_step KurtosisMin: KurtosisMin +feature_step KurtosisMax: KurtosisMax +feature_step InverseDatasetRatio: InverseDatasetRatio, LogInverseDatasetRatio +feature_step SkewnessMin: SkewnessMin +feature_step SymbolsMin: SymbolsMin +feature_step SkewnessSTD: SkewnessSTD +feature_step SkewnessMax: SkewnessMax +feature_step SymbolsMean: SymbolsMean +feature_step LandmarkRandomNodeLearner: LandmarkRandomNodeLearner +feature_step SymbolsMax: SymbolsMax +feature_step NumSymbols: SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum +feature_step LandmarkNaiveBayes: LandmarkNaiveBayes +feature_step NumberOfNumericFeatures: NumberOfNumericFeatures +feature_step PCA: PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +feature_step KurtosisMean: KurtosisMean +feature_step ClassProbabilitySTD: ClassProbabilitySTD +feature_step NumberOfFeaturesWithMissingValues: NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues +feature_step LogNumberOfInstances: LogNumberOfInstances +feature_step KurtosisSTD: KurtosisSTD +feature_step LandmarkDecisionTree: LandmarkDecisionTree +feature_step NumberOfInstances: NumberOfInstances, LogNumberOfInstances +feature_step LogDatasetRatio: LogDatasetRatio +feature_step SymbolsSum: SymbolsSum +feature_step PercentageOfInstancesWithMissingValues: PercentageOfInstancesWithMissingValues +feature_step NumberOfMissingValues: NumberOfMissingValues, PercentageOfMissingValues +feature_step PercentageOfFeaturesWithMissingValues: PercentageOfFeaturesWithMissingValues +feature_step Landmark1NN: Landmark1NN +feature_step PCAFractionOfComponentsFor95PercentVariance: PCAFractionOfComponentsFor95PercentVariance +feature_step NumberOfClasses: NumberOfClasses +feature_step LogNumberOfFeatures: LogNumberOfFeatures +feature_step SkewnessMean: SkewnessMean +feature_step NumberOfFeatures: NumberOfFeatures, LogNumberOfFeatures +feature_step DatasetRatio: DatasetRatio, LogDatasetRatio +feature_step RatioNumericalToNominal: RatioNumericalToNominal +feature_step ClassProbabilityMean: ClassProbabilityMean +features_deterministic: NumberOfInstances, LogNumberOfInstances, NumberOfClasses, NumberOfFeatures, LogNumberOfFeatures, NumberOfInstancesWithMissingValues, PercentageOfInstancesWithMissingValues, NumberOfFeaturesWithMissingValues, PercentageOfFeaturesWithMissingValues, NumberOfMissingValues, PercentageOfMissingValues, NumberOfNumericFeatures, NumberOfCategoricalFeatures, RatioNumericalToNominal, RatioNominalToNumerical, DatasetRatio, LogDatasetRatio, InverseDatasetRatio, LogInverseDatasetRatio, ClassProbabilityMin, ClassProbabilityMax, ClassProbabilityMean, ClassProbabilitySTD, SymbolsMin, SymbolsMax, SymbolsMean, SymbolsSTD, SymbolsSum, KurtosisMin, KurtosisMax, KurtosisMean, KurtosisSTD, SkewnessMin, SkewnessMax, SkewnessMean, SkewnessSTD, ClassEntropy, LandmarkLDA, LandmarkNaiveBayes, LandmarkDecisionTree, LandmarkDecisionNodeLearner, LandmarkRandomNodeLearner, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, PCAKurtosisFirstPC, PCASkewnessFirstPC +features_stochastic: +default_steps: Skewnesses, NumberOfInstancesWithMissingValues, SymbolsSTD, LogInverseDatasetRatio, PCASkewnessFirstPC, LandmarkDecisionNodeLearner, LandmarkLDA, Kurtosisses, MissingValues, PCAKurtosisFirstPC, NumberOfCategoricalFeatures, ClassOccurences, RatioNominalToNumerical, ClassProbabilityMax, ClassEntropy, ClassProbabilityMin, PercentageOfMissingValues, KurtosisMin, KurtosisMax, InverseDatasetRatio, SkewnessMin, SymbolsMin, SkewnessSTD, SkewnessMax, SymbolsMean, LandmarkRandomNodeLearner, SymbolsMax, NumSymbols, LandmarkNaiveBayes, NumberOfNumericFeatures, PCA, KurtosisMean, ClassProbabilitySTD, NumberOfFeaturesWithMissingValues, LogNumberOfInstances, KurtosisSTD, LandmarkDecisionTree, NumberOfInstances, LogDatasetRatio, SymbolsSum, PercentageOfInstancesWithMissingValues, NumberOfMissingValues, PercentageOfFeaturesWithMissingValues, Landmark1NN, PCAFractionOfComponentsFor95PercentVariance, NumberOfClasses, LogNumberOfFeatures, SkewnessMean, NumberOfFeatures, DatasetRatio, RatioNumericalToNominal, ClassProbabilityMean + +algorithms_deterministic: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 +algorithms_stochastic: +performance_measures: roc_auc +performance_type: solution_quality + +scenario_id: auto-sklearn +maximize: false +algorithm_cutoff_time: 1800 +algorithm_cutoff_memory: 3072 diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff new file mode 100644 index 0000000000..ff02941ae9 --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_costs.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_COSTS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses NUMERIC +@ATTRIBUTE NumberOfInstancesWithMissingValues NUMERIC +@ATTRIBUTE SymbolsSTD NUMERIC +@ATTRIBUTE LogInverseDatasetRatio NUMERIC +@ATTRIBUTE PCASkewnessFirstPC NUMERIC +@ATTRIBUTE LandmarkDecisionNodeLearner NUMERIC +@ATTRIBUTE LandmarkLDA NUMERIC +@ATTRIBUTE Kurtosisses NUMERIC +@ATTRIBUTE MissingValues NUMERIC +@ATTRIBUTE PCAKurtosisFirstPC NUMERIC +@ATTRIBUTE NumberOfCategoricalFeatures NUMERIC +@ATTRIBUTE ClassOccurences NUMERIC +@ATTRIBUTE RatioNominalToNumerical NUMERIC +@ATTRIBUTE ClassProbabilityMax NUMERIC +@ATTRIBUTE ClassEntropy NUMERIC +@ATTRIBUTE ClassProbabilityMin NUMERIC +@ATTRIBUTE PercentageOfMissingValues NUMERIC +@ATTRIBUTE KurtosisMin NUMERIC +@ATTRIBUTE KurtosisMax NUMERIC +@ATTRIBUTE 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mode 100644 index 0000000000..5b94d90fff --- /dev/null +++ b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/feature_runstatus.arff @@ -0,0 +1,194 @@ +@RELATION auto-sklearn_FEATURE_RUNSTATUS + +@ATTRIBUTE instance_id STRING +@ATTRIBUTE repetition NUMERIC +@ATTRIBUTE Skewnesses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfInstancesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogInverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCASkewnessFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkDecisionNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkLDA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE Kurtosisses {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE MissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCAKurtosisFirstPC {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfCategoricalFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassOccurences {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE RatioNominalToNumerical {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassEntropy {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilityMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PercentageOfMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE InverseDatasetRatio {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMin {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessSTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SkewnessMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkRandomNodeLearner {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE SymbolsMax {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumSymbols {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LandmarkNaiveBayes {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfNumericFeatures {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE PCA {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisMean {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE ClassProbabilitySTD {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE NumberOfFeaturesWithMissingValues {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE LogNumberOfInstances {ok, timeout, memout, presolved, crash, other} +@ATTRIBUTE KurtosisSTD 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+% \ No newline at end of file diff --git a/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/readme.txt b/autosklearn/metalearning/files/roc_auc_multiclass.classification_sparse/readme.txt new file mode 100644 index 0000000000..e69de29bb2 diff --git a/autosklearn/metalearning/metafeatures/metafeatures.py b/autosklearn/metalearning/metafeatures/metafeatures.py index f855efaa9d..9544ccc031 100644 --- a/autosklearn/metalearning/metafeatures/metafeatures.py +++ b/autosklearn/metalearning/metafeatures/metafeatures.py @@ -9,7 +9,7 @@ import sklearn # TODO use balanced accuracy! import sklearn.metrics -import sklearn.cross_validation +import sklearn.model_selection from sklearn.utils import check_array from sklearn.multiclass import OneVsRestClassifier @@ -607,15 +607,15 @@ def _calculate(self, X, y, categorical): @metafeatures.define("LandmarkLDA") class LandmarkLDA(MetaFeature): def _calculate(self, X, y, categorical): - import sklearn.lda + import sklearn.discriminant_analysis if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. try: - for train, test in kf: + for train, test in kf.split(X, y): lda = sklearn.discriminant_analysis.LinearDiscriminantAnalysis() if len(y.shape) == 1 or y.shape[1] == 1: @@ -644,12 +644,12 @@ def _calculate(self, X, y, categorical): import sklearn.naive_bayes if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. - for train, test in kf: + for train, test in kf.split(X, y): nb = sklearn.naive_bayes.GaussianNB() if len(y.shape) == 1 or y.shape[1] == 1: @@ -672,12 +672,12 @@ def _calculate(self, X, y, categorical): import sklearn.tree if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. - for train, test in kf: + for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) tree = sklearn.tree.DecisionTreeClassifier(random_state=random_state) @@ -706,16 +706,16 @@ def _calculate(self, X, y, categorical): import sklearn.tree if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. - for train, test in kf: + for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) node = sklearn.tree.DecisionTreeClassifier( criterion="entropy", max_depth=1, random_state=random_state, - min_samples_split=1, min_samples_leaf=1, max_features=None) + min_samples_split=2, min_samples_leaf=1, max_features=None) if len(y.shape) == 1 or y.shape[1] == 1: node.fit(X[train], y[train]) else: @@ -734,21 +734,17 @@ def _calculate(self, X, y, categorical): import sklearn.tree if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. - for train, test in kf: + for train, test in kf.split(X, y): random_state = sklearn.utils.check_random_state(42) node = sklearn.tree.DecisionTreeClassifier( criterion="entropy", max_depth=1, random_state=random_state, - min_samples_split=1, min_samples_leaf=1, max_features=1) - if len(y.shape) == 1 or y.shape[1] == 1: - node.fit(X[train], y[train]) - else: - node = OneVsRestClassifier(node) - node.fit(X[train], y[train]) + min_samples_split=2, min_samples_leaf=1, max_features=1) + node.fit(X[train], y[train]) predictions = node.predict(X[test]) accuracy += sklearn.metrics.accuracy_score(predictions, y[test]) return accuracy / 10 @@ -766,7 +762,7 @@ def landmark_worst_node_learner(X, y): import sklearn.tree performances = [] for attribute_idx in range(X.shape[1]): - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(y, n_folds=10) accuracy = 0. for train, test in kf: node = sklearn.tree.DecisionTreeClassifier(criterion="entropy", @@ -788,12 +784,12 @@ def _calculate(self, X, y, categorical): import sklearn.neighbors if len(y.shape) == 1 or y.shape[1] == 1: - kf = sklearn.cross_validation.StratifiedKFold(y, n_folds=10) + kf = sklearn.model_selection.StratifiedKFold(n_splits=10) else: - kf = sklearn.cross_validation.KFold(y.shape[0], n_folds=10) + kf = sklearn.model_selection.KFold(n_splits=10) accuracy = 0. - for train, test in kf: + for train, test in kf.split(X, y): kNN = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1) if len(y.shape) == 1 or y.shape[1] == 1: kNN.fit(X[train], y[train]) diff --git a/autosklearn/metalearning/metafeatures/plot_metafeatures.py b/autosklearn/metalearning/metafeatures/plot_metafeatures.py index 81464cebf9..59514a589f 100644 --- a/autosklearn/metalearning/metafeatures/plot_metafeatures.py +++ b/autosklearn/metalearning/metafeatures/plot_metafeatures.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import argparse import cPickle import itertools diff --git a/autosklearn/metalearning/metalearning/create_datasets.py b/autosklearn/metalearning/metalearning/create_datasets.py index b7146340d0..ce1e54c8d9 100644 --- a/autosklearn/metalearning/metalearning/create_datasets.py +++ b/autosklearn/metalearning/metalearning/create_datasets.py @@ -1,4 +1,3 @@ -from __future__ import print_function import itertools import logging import numpy as np diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py index 77be64e2d7..c0d06c2e3b 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/kND.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import numpy as np import pandas as pd @@ -106,6 +104,7 @@ def kNearestDatasets(self, x, k=1, return_distance=False): k = self.num_datasets X_train, x = self._scale(self.metafeatures, x) + x = x.values.reshape((1, -1)) self._nearest_neighbors.fit(X_train) distances, neighbor_indices = self._nearest_neighbors.kneighbors( x, n_neighbors=k, return_distance=True) @@ -155,7 +154,7 @@ def kBestSuggestions(self, x, k=1, exclude_double_configurations=True): return kbest[:k] def _scale(self, metafeatures, other): - assert isinstance(other, pd.Series) + assert isinstance(other, pd.Series), type(other) assert other.values.dtype == np.float64 scaled_metafeatures = metafeatures.copy(deep=True) other = other.copy(deep=True) diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/kNDEvaluateSurrogate.py b/autosklearn/metalearning/metalearning/kNearestDatasets/kNDEvaluateSurrogate.py index 5d6945d39d..4dc7f81759 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/kNDEvaluateSurrogate.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/kNDEvaluateSurrogate.py @@ -1,4 +1,3 @@ -from __future__ import print_function import logging import os import time diff --git a/autosklearn/metalearning/metalearning/kNearestDatasets/kNDFeatureSelection.py b/autosklearn/metalearning/metalearning/kNearestDatasets/kNDFeatureSelection.py index 7fc7473ea3..d304753720 100644 --- a/autosklearn/metalearning/metalearning/kNearestDatasets/kNDFeatureSelection.py +++ b/autosklearn/metalearning/metalearning/kNearestDatasets/kNDFeatureSelection.py @@ -1,4 +1,3 @@ -from __future__ import print_function from collections import defaultdict import itertools import logging diff --git a/autosklearn/metalearning/metalearning/meta_base.py b/autosklearn/metalearning/metalearning/meta_base.py index 2d68ee300c..7cfcd6ef06 100644 --- a/autosklearn/metalearning/metalearning/meta_base.py +++ b/autosklearn/metalearning/metalearning/meta_base.py @@ -63,6 +63,11 @@ def add_dataset(self, name, metafeatures): for metric in self.algorithm_runs.keys(): self.algorithm_runs[metric].append(runs) + def remove_dataset(self, name): + self.metafeatures.drop(name, inplace=True) + for key in self.algorithm_runs: + self.algorithm_runs[key].drop(name, inplace=True) + def get_runs(self, dataset_name, performance_measure=None): """Return a list of all runs for a dataset.""" if performance_measure is None: diff --git a/autosklearn/metalearning/metalearning/metrics/result_correlation.py b/autosklearn/metalearning/metalearning/metrics/result_correlation.py index 04376150d8..b1d56a1b36 100644 --- a/autosklearn/metalearning/metalearning/metrics/result_correlation.py +++ b/autosklearn/metalearning/metalearning/metrics/result_correlation.py @@ -1,4 +1,3 @@ -from __future__ import print_function from collections import OrderedDict import itertools diff --git a/autosklearn/metalearning/mismbo.py b/autosklearn/metalearning/mismbo.py index d0e13445b7..12d8111382 100644 --- a/autosklearn/metalearning/mismbo.py +++ b/autosklearn/metalearning/mismbo.py @@ -1,5 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import os import time diff --git a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py index 134e7739f8..c4ce0eb7af 100644 --- a/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py +++ b/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py @@ -1,5 +1,4 @@ import ast - import pandas as pd import numpy as np import sklearn.utils diff --git a/autosklearn/metrics/__init__.py b/autosklearn/metrics/__init__.py index 1d6300f796..83f324796c 100644 --- a/autosklearn/metrics/__init__.py +++ b/autosklearn/metrics/__init__.py @@ -1,27 +1,290 @@ -# -*- encoding: utf-8 -*- -# Score library for NUMPY arrays -# ChaLearn AutoML challenge - -# For regression: -# solution and prediction are vectors of numerical values of the same dimension - -# For classification: -# solution = array(p,n) of 0,1 truth values, samples in lines, classes in columns -# prediction = array(p,n) of numerical scores between 0 and 1 (analogous -# to probabilities) - -# Isabelle Guyon and Arthur Pesah, ChaLearn, August-November 2014 - -# ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS". -# ISABELLE GUYON, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM -# ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE -# WARRANTY OF NON-INFRINGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS. -# IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL, -# INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN -# CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS, -# PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE. - -from .classification_metrics import * +from abc import ABCMeta, abstractmethod +import copy +from functools import partial + +import sklearn.metrics +from sklearn.utils.multiclass import type_of_target + +from autosklearn.constants import * +from . import classification_metrics from .util import * -from .regression_metrics import * + + +class Scorer(object, metaclass=ABCMeta): + def __init__(self, name, score_func, sign, kwargs): + self.name = name + self._kwargs = kwargs + self._score_func = score_func + self._sign = sign + + @abstractmethod + def __call__(self, y_true, y_pred, sample_weight=None): + pass + + def __repr__(self): + return self.name + + +class _PredictScorer(Scorer): + def __call__(self, y_true, y_pred, sample_weight=None): + """Evaluate predicted target values for X relative to y_true. + + Parameters + ---------- + y_true : array-like + Gold standard target values for X. + + y_pred : array-like, [n_samples x n_classes] + Model predictions + + sample_weight : array-like, optional (default=None) + Sample weights. + + Returns + ------- + score : float + Score function applied to prediction of estimator on X. + """ + type_true = type_of_target(y_true) + if len(y_pred.shape) == 1 or y_pred.shape[1] == 1 or \ + type_true == 'continuous': + # must be regression, all other task types would return at least + # two probabilities + pass + elif type_true in ['binary', 'multiclass']: + y_pred = np.argmax(y_pred, axis=1) + elif type_true == 'multilabel-indicator': + y_pred[y_pred > 0.5] = 1.0 + y_pred[y_pred <= 0.5] = 0.0 + else: + raise ValueError(type_true) + + if sample_weight is not None: + return self._sign * self._score_func(y_true, y_pred, + sample_weight=sample_weight, + **self._kwargs) + else: + return self._sign * self._score_func(y_true, y_pred, + **self._kwargs) + + +class _ProbaScorer(Scorer): + def __call__(self, y_true, y_pred, sample_weight=None): + """Evaluate predicted probabilities for X relative to y_true. + Parameters + ---------- + y_true : array-like + Gold standard target values for X. These must be class labels, + not probabilities. + + y_pred : array-like, [n_samples x n_classes] + Model predictions + + sample_weight : array-like, optional (default=None) + Sample weights. + + Returns + ------- + score : float + Score function applied to prediction of estimator on X. + """ + if sample_weight is not None: + return self._sign * self._score_func(y_true, y_pred, + sample_weight=sample_weight, + **self._kwargs) + else: + return self._sign * self._score_func(y_true, y_pred, **self._kwargs) + + +class _ThresholdScorer(Scorer): + def __call__(self, y_true, y_pred, sample_weight=None): + """Evaluate decision function output for X relative to y_true. + Parameters + ---------- + y_true : array-like + Gold standard target values for X. These must be class labels, + not probabilities. + + y_pred : array-like, [n_samples x n_classes] + Model predictions + + sample_weight : array-like, optional (default=None) + Sample weights. + + Returns + ------- + score : float + Score function applied to prediction of estimator on X. + """ + y_type = type_of_target(y_true) + if y_type not in ("binary", "multilabel-indicator"): + raise ValueError("{0} format is not supported".format(y_type)) + + if y_type == "binary": + y_pred = y_pred[:, 1] + elif isinstance(y_pred, list): + y_pred = np.vstack([p[:, -1] for p in y_pred]).T + + if sample_weight is not None: + return self._sign * self._score_func(y_true, y_pred, + sample_weight=sample_weight, + **self._kwargs) + else: + return self._sign * self._score_func(y_true, y_pred, **self._kwargs) + + +def make_scorer(name, score_func, greater_is_better=True, needs_proba=False, + needs_threshold=False, **kwargs): + """Make a scorer from a performance metric or loss function. + + Factory inspired by scikit-learn which wraps scikit-learn scoring functions + to be used in auto-sklearn. + + Parameters + ---------- + score_func : callable + Score function (or loss function) with signature + ``score_func(y, y_pred, **kwargs)``. + + greater_is_better : boolean, default=True + Whether score_func is a score function (default), meaning high is good, + or a loss function, meaning low is good. In the latter case, the + scorer object will sign-flip the outcome of the score_func. + + needs_proba : boolean, default=False + Whether score_func requires predict_proba to get probability estimates + out of a classifier. + + needs_threshold : boolean, default=False + Whether score_func takes a continuous decision certainty. + This only works for binary classification. + + **kwargs : additional arguments + Additional parameters to be passed to score_func. + + Returns + ------- + scorer : callable + Callable object that returns a scalar score; greater is better. + """ + sign = 1 if greater_is_better else -1 + if needs_proba: + cls = _ProbaScorer + elif needs_threshold: + cls = _ThresholdScorer + else: + cls = _PredictScorer + return cls(name, score_func, sign, kwargs) + + +# Standard regression scores +r2 = make_scorer('r2', sklearn.metrics.r2_score) +mean_squared_error = make_scorer('mean_squared_error', + sklearn.metrics.mean_squared_error, + greater_is_better=False) +mean_absolute_error = make_scorer('mean_absolute_error', + sklearn.metrics.mean_absolute_error, + greater_is_better=False) +median_absolute_error = make_scorer('median_absolute_error', + sklearn.metrics.median_absolute_error, + greater_is_better=False) + +# Standard Classification Scores +accuracy = make_scorer('accuracy', sklearn.metrics.accuracy_score) +balanced_accuracy = make_scorer('balanced_accuracy', + classification_metrics.balanced_accuracy) +f1 = make_scorer('f1', sklearn.metrics.f1_score) + +# Score functions that need decision values +roc_auc = make_scorer('roc_auc', sklearn.metrics.roc_auc_score, + greater_is_better=True, needs_threshold=True) +average_precision = make_scorer('average_precision', + sklearn.metrics.average_precision_score, + needs_threshold=True) +precision = make_scorer('precision', sklearn.metrics.precision_score) +recall = make_scorer('recall', sklearn.metrics.recall_score) + +# Score function for probabilistic classification +log_loss = make_scorer('log_loss', sklearn.metrics.log_loss, + greater_is_better=False, needs_proba=True) +pac_score = make_scorer('pac_score', classification_metrics.pac_score, + greater_is_better=True, needs_proba=True) +# TODO what about mathews correlation coefficient etc? + + +REGRESSION_METRICS = dict() +for scorer in [r2, mean_squared_error, mean_absolute_error, + median_absolute_error]: + REGRESSION_METRICS[scorer.name] = scorer + +CLASSIFICATION_METRICS = dict() + +for scorer in [accuracy, balanced_accuracy, roc_auc, average_precision, + log_loss, pac_score]: + CLASSIFICATION_METRICS[scorer.name] = scorer + +for name, metric in [('precision', sklearn.metrics.precision_score), + ('recall', sklearn.metrics.recall_score), + ('f1', sklearn.metrics.f1_score)]: + globals()[name] = make_scorer(name, metric) + CLASSIFICATION_METRICS[name] = globals()[name] + for average in ['macro', 'micro', 'samples', 'weighted']: + qualified_name = '{0}_{1}'.format(name, average) + globals()[qualified_name] = make_scorer(qualified_name, + partial(metric, + pos_label=None, + average=average)) + CLASSIFICATION_METRICS[qualified_name] = globals()[qualified_name] + + +def calculate_score(solution, prediction, task_type, metric, + all_scoring_functions=False): + if task_type not in TASK_TYPES: + raise NotImplementedError(task_type) + + if all_scoring_functions: + score = dict() + if task_type in REGRESSION_TASKS: + # TODO put this into the regression metric itself + cprediction = sanitize_array(prediction) + metric_dict = copy.copy(REGRESSION_METRICS) + metric_dict[metric.name] = metric + for metric_ in REGRESSION_METRICS: + func = REGRESSION_METRICS[metric_] + score[func.name] = func(solution, cprediction) + + else: + metric_dict = copy.copy(CLASSIFICATION_METRICS) + metric_dict[metric.name] = metric + for metric_ in metric_dict: + func = CLASSIFICATION_METRICS[metric_] + + # TODO maybe annotate metrics to define which cases they can + # handle? + + try: + score[func.name] = func(solution, prediction) + except ValueError as e: + if e.args[0] == 'multiclass format is not supported': + continue + elif e.args[0] == 'Sample-based precision, recall, ' \ + 'fscore is not meaningful outside ' \ + 'multilabel classification. See the ' \ + 'accuracy_score instead.': + continue + elif e.args[0] == "Target is multiclass but " \ + "average='binary'. Please choose another " \ + "average setting.": + continue + else: + raise e + + else: + if task_type in REGRESSION_TASKS: + # TODO put this into the regression metric itself + cprediction = sanitize_array(prediction) + score = metric(solution, cprediction) + else: + score = metric(solution, prediction) + + return score \ No newline at end of file diff --git a/autosklearn/metrics/classification_metrics.py b/autosklearn/metrics/classification_metrics.py index 50b50c1385..bebd506225 100644 --- a/autosklearn/metrics/classification_metrics.py +++ b/autosklearn/metrics/classification_metrics.py @@ -1,204 +1,67 @@ -# -*- encoding: utf-8 -*- - -# CLASSIFICATION METRICS (work on solutions in {0, 1} and predictions in [0, 1]) -# These can be computed for regression scores only after running -# normalize_array - -from __future__ import print_function import numpy as np import scipy as sp -import scipy.stats -from autosklearn.constants import MULTICLASS_CLASSIFICATION, \ - BINARY_CLASSIFICATION, METRIC_TO_STRING, MULTILABEL_CLASSIFICATION -from autosklearn.metrics.util import log_loss, prior_log_loss, \ - binarize_predictions, normalize_array, create_multiclass_solution - -def calculate_score(metric, solution, prediction, task): - if solution.shape[0] != prediction.shape[0]: - raise ValueError('Solution and prediction have different number of ' - 'samples: %d and %d' % (solution.shape[0], - prediction.shape[0])) +from sklearn.metrics.classification import _check_targets, type_of_target - metric = METRIC_TO_STRING[metric] - return globals()[metric](solution, prediction, task) +def balanced_accuracy(solution, prediction): + y_type, solution, prediction = _check_targets(solution, prediction) -def acc_metric(solution, prediction, task=BINARY_CLASSIFICATION): - """ - Compute the accuracy. - - Get the accuracy stats - acc = (tpr + fpr) / (tn + fp + tp + fn) - Normalize, so 1 is the best and zero mean random... + if y_type not in ["binary", "multiclass", 'multilabel-indicator']: + raise ValueError("{0} is not supported".format(y_type)) - :param solution: - :param prediction: - :param task: - :return: - """ - if task == BINARY_CLASSIFICATION: - if len(solution.shape) == 1: - # Solution won't be touched - no copy - solution = solution.reshape((-1, 1)) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - raise ValueError('Solution.shape %s' % solution.shape) - - if len(prediction.shape) == 2: - if prediction.shape[1] > 2: - raise ValueError('A prediction array with probability values ' - 'for %d classes is not a binary ' - 'classification problem' % prediction.shape[1]) - # Prediction will be copied into a new binary array - no copy - prediction = prediction[:, 1].reshape((-1, 1)) - else: - raise ValueError('Invalid prediction shape %s' % prediction.shape) - - elif task == MULTICLASS_CLASSIFICATION: - if len(solution.shape) == 1: - solution = create_multiclass_solution(solution, prediction) - elif len(solution.shape ) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - solution = create_multiclass_solution(solution.reshape((-1, 1)), - prediction) - else: - raise ValueError('Solution.shape %s' % solution.shape) - - elif task == MULTILABEL_CLASSIFICATION: + if y_type == 'binary': + # Do not transform into any multiclass representation pass - else: - raise NotImplementedError('acc_metric does not support task type %s' - % task) - - bin_predictions = binarize_predictions(prediction, task) - tn = np.sum(np.multiply((1 - solution), (1 - bin_predictions)), axis=0, - dtype=float) - fn = np.sum(np.multiply(solution, (1 - bin_predictions)), axis=0, - dtype=float) - tp = np.sum(np.multiply(solution, bin_predictions), axis=0, - dtype=float) - fp = np.sum(np.multiply((1 - solution), bin_predictions), axis=0, - dtype=float) - # Bounding to avoid division by 0, 1e-7 because of float32 - eps = np.float(1e-7) - """ - tp = np.sum(tp) - fp = np.sum(fp) - tn = np.sum(tn) - fn = np.sum(fn) - """ - if task in (BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION): - accuracy = (np.sum(tp) + np.sum(tn)) / ( - np.sum(tp) + np.sum(fp) + np.sum(tn) + np.sum(fn) - ) - elif task == MULTICLASS_CLASSIFICATION: - accuracy = np.sum(tp) / (np.sum(tp) + np.sum(fp)) - - if task in (BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION): - base_accuracy = 0.5 # random predictions for binary case - elif task == MULTICLASS_CLASSIFICATION: - label_num = solution.shape[1] - base_accuracy = 1. / label_num - - # Normalize: 0 for random, 1 for perfect - score = (accuracy - base_accuracy) / sp.maximum(eps, (1 - base_accuracy)) - return score - - -def bac_metric(solution, prediction, task=BINARY_CLASSIFICATION): - """ - Compute the normalized balanced accuracy. - - The binarization and - the normalization differ for the multi-label and multi-class case. - :param solution: - :param prediction: - :param task: - :return: - """ - if task == BINARY_CLASSIFICATION: - if len(solution.shape) == 1: - # Solution won't be touched - no copy - solution = solution.reshape((-1, 1)) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - raise ValueError('Solution.shape %s' % solution.shape) - - if len(prediction.shape) == 2: - if prediction.shape[1] > 2: - raise ValueError('A prediction array with probability values ' - 'for %d classes is not a binary ' - 'classification problem' % prediction.shape[1]) - # Prediction will be copied into a new binary array - no copy - prediction = prediction[:, 1].reshape((-1, 1)) - else: - raise ValueError('Invalid prediction shape %s' % prediction.shape) - - elif task == MULTICLASS_CLASSIFICATION: - if len(solution.shape) == 1: - solution = create_multiclass_solution(solution, prediction) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - solution = create_multiclass_solution(solution.reshape((-1, 1)), - prediction) - else: - raise ValueError('Solution.shape %s' % solution.shape) - elif task == MULTILABEL_CLASSIFICATION: - pass + elif y_type == 'multiclass': + # Need to create a multiclass solution and a multiclass predictions + max_class = int(np.max((np.max(solution), np.max(prediction)))) + solution_binary = np.zeros((len(solution), max_class + 1)) + prediction_binary = np.zeros((len(prediction), max_class + 1)) + for i in range(len(solution)): + solution_binary[i, int(solution[i])] = 1 + prediction_binary[i, int(prediction[i])] = 1 + solution = solution_binary + prediction = prediction_binary + + elif y_type == 'multilabel-indicator': + solution = solution.toarray() + prediction = prediction.toarray() else: raise NotImplementedError('bac_metric does not support task type %s' - % task) - bin_prediction = binarize_predictions(prediction, task) - + % y_type) - fn = np.sum(np.multiply(solution, (1 - bin_prediction)), axis=0, + fn = np.sum(np.multiply(solution, (1 - prediction)), axis=0, dtype=float) - tp = np.sum(np.multiply(solution, bin_prediction), axis=0, dtype=float) + tp = np.sum(np.multiply(solution, prediction), axis=0, dtype=float) # Bounding to avoid division by 0 eps = 1e-15 tp = sp.maximum(eps, tp) pos_num = sp.maximum(eps, tp + fn) tpr = tp / pos_num # true positive rate (sensitivity) - if task in (BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION): - tn = np.sum(np.multiply((1 - solution), (1 - bin_prediction)), + if y_type in ('binary', 'multilabel-indicator'): + tn = np.sum(np.multiply((1 - solution), (1 - prediction)), axis=0, dtype=float) - fp = np.sum(np.multiply((1 - solution), bin_prediction), axis=0, + fp = np.sum(np.multiply((1 - solution), prediction), axis=0, dtype=float) tn = sp.maximum(eps, tn) neg_num = sp.maximum(eps, tn + fp) tnr = tn / neg_num # true negative rate (specificity) bac = 0.5 * (tpr + tnr) - base_bac = 0.5 # random predictions for binary case - elif task == MULTICLASS_CLASSIFICATION: + elif y_type == 'multiclass': label_num = solution.shape[1] bac = tpr - base_bac = 1. / label_num # random predictions for multiclass case + else: + raise ValueError(y_type) - bac = np.mean(bac) # average over all classes - # Normalize: 0 for random, 1 for perfect - score = (bac - base_bac) / sp.maximum(eps, (1 - base_bac)) - return score + return np.mean(bac) # average over all classes -def pac_metric(solution, prediction, task=BINARY_CLASSIFICATION): +def pac_score(solution, prediction): """ Probabilistic Accuracy based on log_loss metric. - We assume the solution is in {0, 1} and prediction in [0, 1]. Otherwise, run normalize_array. :param solution: @@ -206,18 +69,122 @@ def pac_metric(solution, prediction, task=BINARY_CLASSIFICATION): :param task: :return: """ - if task == BINARY_CLASSIFICATION: + + def normalize_array(solution, prediction): + """ + Use min and max of solution as scaling factors to normalize prediction, + then threshold it to [0, 1]. + Binarize solution to {0, 1}. This allows applying classification + scores to all cases. In principle, this should not do anything to + properly formatted classification inputs and outputs. + :param solution: + :param prediction: + :return: + """ + # Binarize solution + sol = np.ravel(solution) # convert to 1-d array + maxi = np.nanmax(sol[np.isfinite(sol)]) + mini = np.nanmin(sol[np.isfinite(sol)]) + if maxi == mini: + print('Warning, cannot normalize') + return [solution, prediction] + diff = maxi - mini + mid = (maxi + mini) / 2. + + solution[solution >= mid] = 1 + solution[solution < mid] = 0 + # Normalize and threshold predictions (takes effect only if solution not + # in {0, 1}) + + prediction -= float(mini) + prediction /= float(diff) + + # and if predictions exceed the bounds [0, 1] + prediction[prediction > 1] = 1 + prediction[prediction < 0] = 0 + # Make probabilities smoother + # new_prediction = np.power(new_prediction, (1./10)) + + return [solution, prediction] + + def log_loss(solution, prediction, task): + """Log loss for binary and multiclass.""" + [sample_num, label_num] = solution.shape + # Lower gives problems with float32! + eps = 0.00000003 + + if (task == 'multiclass') and (label_num > 1): + # Make sure the lines add up to one for multi-class classification + norma = np.sum(prediction, axis=1) + for k in range(sample_num): + prediction[k, :] /= sp.maximum(norma[k], eps) + + sample_num = solution.shape[0] + for i in range(sample_num): + j = np.argmax(solution[i, :]) + solution[i, :] = 0 + solution[i, j] = 1 + + solution = solution.astype(np.int32, copy=False) + # For the base prediction, this solution is ridiculous in the + # multi-label case + + # Bounding of predictions to avoid log(0),1/0,... + prediction = sp.minimum(1 - eps, sp.maximum(eps, prediction)) + # Compute the log loss + pos_class_log_loss = -np.mean(solution * np.log(prediction), axis=0) + if (task != 'multiclass') or (label_num == 1): + # The multi-label case is a bunch of binary problems. + # The second class is the negative class for each column. + neg_class_log_loss = -np.mean( + (1 - solution) * np.log(1 - prediction), axis=0) + log_loss = pos_class_log_loss + neg_class_log_loss + # Each column is an independent problem, so we average. + # The probabilities in one line do not add up to one. + # log_loss = mvmean(log_loss) + # print('binary {}'.format(log_loss)) + # In the multilabel case, the right thing i to AVERAGE not sum + # We return all the scores so we can normalize correctly later on + else: + # For the multiclass case the probabilities in one line add up one. + log_loss = pos_class_log_loss + # We sum the contributions of the columns. + log_loss = np.sum(log_loss) + # print('multiclass {}'.format(log_loss)) + return log_loss + + def prior_log_loss(frac_pos, task): + """Baseline log loss. + For multiplr classes ot labels return the volues for each column + """ + eps = 1e-15 + frac_pos_ = sp.maximum(eps, frac_pos) + if task != 'multiclass': # binary case + frac_neg = 1 - frac_pos + frac_neg_ = sp.maximum(eps, frac_neg) + pos_class_log_loss_ = -frac_pos * np.log(frac_pos_) + neg_class_log_loss_ = -frac_neg * np.log(frac_neg_) + base_log_loss = pos_class_log_loss_ + neg_class_log_loss_ + # base_log_loss = mvmean(base_log_loss) + # print('binary {}'.format(base_log_loss)) + # In the multilabel case, the right thing i to AVERAGE not sum + # We return all the scores so we can normalize correctly later on + else: # multiclass case + fp = frac_pos_ / sum( + frac_pos_ + ) # Need to renormalize the lines in multiclass case + # Only ONE label is 1 in the multiclass case active for each line + pos_class_log_loss_ = -frac_pos * np.log(fp) + base_log_loss = np.sum(pos_class_log_loss_) + return base_log_loss + + y_type = type_of_target(solution) + + if y_type == 'binary': if len(solution.shape) == 1: - # Solution won't be touched - no copy solution = solution.reshape((-1, 1)) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - raise ValueError('Solution.shape %s' % solution.shape) - solution = solution.copy() - + if len(prediction.shape) == 1: + prediction = prediction.reshape((-1, 1)) if len(prediction.shape) == 2: if prediction.shape[1] > 2: raise ValueError('A prediction array with probability values ' @@ -228,34 +195,40 @@ def pac_metric(solution, prediction, task=BINARY_CLASSIFICATION): else: raise ValueError('Invalid prediction shape %s' % prediction.shape) - elif task == MULTICLASS_CLASSIFICATION: - if len(solution.shape) == 1: - solution = create_multiclass_solution(solution, prediction) - elif len(solution.shape) == 2: + elif y_type == 'multiclass': + if len(solution.shape) == 2: if solution.shape[1] > 1: raise ValueError('Solution array must only contain one class ' 'label, but contains %d' % solution.shape[1]) - else: - solution = create_multiclass_solution(solution.reshape((-1, 1)), - prediction) + elif len(solution.shape) == 1: + pass else: raise ValueError('Solution.shape %s' % solution.shape) - elif task == MULTILABEL_CLASSIFICATION: + + # Need to create a multiclass solution and a multiclass predictions + max_class = int(np.max((np.max(solution), np.max(prediction)))) + solution_binary = np.zeros((len(solution), max_class + 1)) + for i in range(len(solution)): + solution_binary[i, int(solution[i])] = 1 + solution = solution_binary + + elif y_type == 'multilabel-indicator': solution = solution.copy() + else: - raise NotImplementedError('auc_metric does not support task type %s' - % task) + raise NotImplementedError('pac_score does not support task type %s' + % y_type) + solution, prediction = normalize_array(solution, prediction.copy()) - [sample_num, label_num] = solution.shape - if label_num == 1: - task = BINARY_CLASSIFICATION + sample_num, _ = solution.shape + eps = 1e-7 # Compute the base log loss (using the prior probabilities) pos_num = 1. * np.sum(solution, axis=0, dtype=float) # float conversion! frac_pos = pos_num / sample_num # prior proba of positive class - the_base_log_loss = prior_log_loss(frac_pos, task) - the_log_loss = log_loss(solution, prediction, task) + the_base_log_loss = prior_log_loss(frac_pos, y_type) + the_log_loss = log_loss(solution, prediction, y_type) # Exponentiate to turn into an accuracy-like score. # In the multi-label case, we need to average AFTER taking the exp @@ -264,171 +237,5 @@ def pac_metric(solution, prediction, task=BINARY_CLASSIFICATION): base_pac = np.mean(np.exp(-the_base_log_loss)) # Normalize: 0 for random, 1 for perfect score = (pac - base_pac) / sp.maximum(eps, (1 - base_pac)) - return score - -def f1_metric(solution, prediction, task=BINARY_CLASSIFICATION): - """ - Compute the normalized f1 measure. - - The binarization differs - for the multi-label and multi-class case. - A non-weighted average over classes is taken. - The score is normalized. - :param solution: - :param prediction: - :param task: - :return: - """ - if task == BINARY_CLASSIFICATION: - if len(solution.shape) == 1: - # Solution won't be touched - no copy - solution = solution.reshape((-1, 1)) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - raise ValueError('Solution.shape %s' % solution.shape) - - if len(prediction.shape) == 2: - if prediction.shape[1] > 2: - raise ValueError('A prediction array with probability values ' - 'for %d classes is not a binary ' - 'classification problem' % prediction.shape[1]) - # Prediction will be copied into a new binary array - no copy - prediction = prediction[:, 1].reshape((-1, 1)) - else: - raise ValueError('Invalid prediction shape %s' % prediction.shape) - - elif task == MULTICLASS_CLASSIFICATION: - if len(solution.shape) == 1: - solution = create_multiclass_solution(solution, prediction) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - solution = create_multiclass_solution(solution.reshape((-1, 1)), - prediction) - else: - raise ValueError('Solution.shape %s' % solution.shape) - elif task == MULTILABEL_CLASSIFICATION: - pass - else: - raise NotImplementedError('f1_metric does not support task type %s' - % task) - bin_prediction = binarize_predictions(prediction, task) - - # Bounding to avoid division by 0 - eps = 1e-15 - fn = np.sum(np.multiply(solution, (1 - bin_prediction)), axis=0, dtype=float) - tp = np.sum(np.multiply(solution, bin_prediction), axis=0, dtype=float) - fp = np.sum(np.multiply((1 - solution), bin_prediction), axis=0, dtype=float) - true_pos_num = sp.maximum(eps, tp + fn) - found_pos_num = sp.maximum(eps, tp + fp) - tp = sp.maximum(eps, tp) - tpr = tp / true_pos_num # true positive rate (recall) - ppv = tp / found_pos_num # positive predictive value (precision) - arithmetic_mean = 0.5 * sp.maximum(eps, tpr + ppv) - # Harmonic mean: - f1 = tpr * ppv / arithmetic_mean - # Average over all classes - f1 = np.mean(f1) - # Normalize: 0 for random, 1 for perfect - if task in (BINARY_CLASSIFICATION, MULTILABEL_CLASSIFICATION): - # How to choose the "base_f1"? - # For the binary/multilabel classification case, one may want to predict all 1. - # In that case tpr = 1 and ppv = frac_pos. f1 = 2 * frac_pos / (1+frac_pos) - # frac_pos = mvmean(solution.ravel()) - # base_f1 = 2 * frac_pos / (1+frac_pos) - # or predict random values with probability 0.5, in which case - # base_f1 = 0.5 - # the first solution is better only if frac_pos > 1/3. - # The solution in which we predict according to the class prior frac_pos gives - # f1 = tpr = ppv = frac_pos, which is worse than 0.5 if frac_pos<0.5 - # So, because the f1 score is used if frac_pos is small (typically <0.1) - # the best is to assume that base_f1=0.5 - base_f1 = 0.5 - # For the multiclass case, this is not possible (though it does not make much sense to - # use f1 for multiclass problems), so the best would be to assign values at random to get - # tpr=ppv=frac_pos, where frac_pos=1/label_num - elif task == MULTICLASS_CLASSIFICATION: - label_num = solution.shape[1] - base_f1 = 1. / label_num - score = (f1 - base_f1) / sp.maximum(eps, (1 - base_f1)) return score - - -def auc_metric(solution, prediction, task=BINARY_CLASSIFICATION): - """ - Normarlized Area under ROC curve (AUC). - - Return Gini index = 2*AUC-1 for binary classification problems. - Should work for a vector of binary 0/1 (or -1/1)"solution" and any discriminant values - for the predictions. If solution and prediction are not vectors, the AUC - of the columns of the matrices are computed and averaged (with no weight). - The same for all classification problems (in fact it treats well only the - binary and multilabel classification problems). - :param solution: - :param prediction: - :param task: - :return: - """ - if task == BINARY_CLASSIFICATION: - if len(solution.shape) == 1: - # Solution won't be touched - no copy - solution = solution.reshape((-1, 1)) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - raise ValueError('Solution.shape %s' % solution.shape) - solution = solution.copy() - - if len(prediction.shape) == 2: - if prediction.shape[1] > 2: - raise ValueError('A prediction array with probability values ' - 'for %d classes is not a binary ' - 'classification problem' % prediction.shape[1]) - elif prediction.shape[1] == 2: - # Prediction will be copied into a new binary array - no copy - prediction = prediction[:, 1].reshape((-1, 1)) - else: - raise ValueError('Invalid prediction shape %s' % prediction.shape) - - elif task == MULTICLASS_CLASSIFICATION: - if len(solution.shape) == 1: - solution = create_multiclass_solution(solution, prediction) - elif len(solution.shape) == 2: - if solution.shape[1] > 1: - raise ValueError('Solution array must only contain one class ' - 'label, but contains %d' % solution.shape[1]) - else: - solution = create_multiclass_solution(solution.reshape((-1, 1)), - prediction) - else: - raise ValueError('Solution.shape %s' % solution.shape) - elif task == MULTILABEL_CLASSIFICATION: - solution = solution.copy() - else: - raise NotImplementedError('auc_metric does not support task type %s' - % task) - - solution, prediction = normalize_array(solution, prediction.copy()) - - label_num = solution.shape[1] - auc = np.empty(label_num) - for k in range(label_num): - r_ = scipy.stats.rankdata(prediction[:, k]) - s_ = solution[:, k] - if sum(s_) == 0: - print( - 'WARNING: no positive class example in class {}'.format(k + 1)) - npos = np.sum(s_ == 1) - nneg = np.sum(s_ < 1) - auc[k] = (np.sum(r_[s_ == 1]) - npos * (npos + 1) / 2) / (nneg * npos) - auc[~np.isfinite(auc)] = 0 - return 2 * np.mean(auc) - 1 - diff --git a/autosklearn/metrics/regression_metrics.py b/autosklearn/metrics/regression_metrics.py deleted file mode 100644 index c5e92d6e2d..0000000000 --- a/autosklearn/metrics/regression_metrics.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- encoding: utf-8 -*- -# REGRESSION METRICS (work on raw solution and prediction) -# These can be computed on all solutions and predictions -# (classification included) -from __future__ import print_function - -import numpy as np - -from autosklearn.constants import REGRESSION, METRIC_TO_STRING - - -def calculate_score(metric, solution, prediction, copy=True): - if solution.shape[0] != prediction.shape[0]: - raise ValueError('Solution and prediction have different number of ' - 'samples: %d and %d' % (solution.shape[0], - prediction.shape[0])) - - if len(solution.shape) == 1: - solution = solution.reshape((-1, 1)) - metric = METRIC_TO_STRING[metric] - return globals()[metric](solution, prediction, copy) - - -def r2_metric(solution, prediction, task=REGRESSION, copy=True): - """ - 1 - Mean squared error divided by variance - :param solution: - :param prediction: - :param task: - :return: - """ - mse = np.mean((solution - prediction) ** 2, axis=0) - var = np.mean((solution - np.mean(solution, axis=0)) ** 2, axis=0) - score = 1 - mse / var - return np.mean(score) - - -def a_metric(solution, prediction, task=REGRESSION, copy=True): - """ - 1 - Mean absolute error divided by mean absolute deviation - :param solution: - :param prediction: - :param task: - :return: - """ - mae = np.mean(np.abs(solution - prediction), axis=0) # mean absolute error - mad = np.mean( - np.abs(solution - np.mean(solution, axis=0)), axis=0) # mean absolute - # deviation - score = 1 - mae / mad - return np.mean(score) diff --git a/autosklearn/metrics/util.py b/autosklearn/metrics/util.py index b3eadb5833..1eed463433 100644 --- a/autosklearn/metrics/util.py +++ b/autosklearn/metrics/util.py @@ -1,9 +1,6 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import numpy as np -import scipy as sp -from autosklearn.constants import MULTICLASS_CLASSIFICATION, \ - BINARY_CLASSIFICATION + def sanitize_array(array): """ @@ -19,155 +16,3 @@ def sanitize_array(array): mid = (maxi + mini) / 2 array[np.isnan(array)] = mid return array - - - -def normalize_array(solution, prediction): - """ - Use min and max of solution as scaling factors to normalize prediction, - then threshold it to [0, 1]. - - Binarize solution to {0, 1}. This allows applying classification - scores to all cases. In principle, this should not do anything to - properly formatted classification inputs and outputs. - - :param solution: - :param prediction: - :return: - """ - # Binarize solution - sol = np.ravel(solution) # convert to 1-d array - maxi = np.nanmax(sol[np.isfinite(sol)]) - mini = np.nanmin(sol[np.isfinite(sol)]) - if maxi == mini: - print('Warning, cannot normalize') - return [solution, prediction] - diff = maxi - mini - mid = (maxi + mini) / 2. - - solution[solution >= mid] = 1 - solution[solution < mid] = 0 - # Normalize and threshold predictions (takes effect only if solution not - # in {0, 1}) - - prediction -= float(mini) - prediction /= float(diff) - - # and if predictions exceed the bounds [0, 1] - prediction[prediction > 1] = 1 - prediction[prediction < 0] = 0 - # Make probabilities smoother - # new_prediction = np.power(new_prediction, (1./10)) - return [solution, prediction] - - -def log_loss(solution, prediction, task=BINARY_CLASSIFICATION): - """Log loss for binary and multiclass.""" - [sample_num, label_num] = solution.shape - # Lower gives problems with float32! - eps = 0.00000003 - - if (task == MULTICLASS_CLASSIFICATION) and (label_num > 1): - # Make sure the lines add up to one for multi-class classification - norma = np.sum(prediction, axis=1) - for k in range(sample_num): - prediction[k, :] /= sp.maximum(norma[k], eps) - - sample_num = solution.shape[0] - for i in range(sample_num): - j = np.argmax(solution[i, :]) - solution[i, :] = 0 - solution[i, j] = 1 - - solution = solution.astype(np.int32, copy=False) - # For the base prediction, this solution is ridiculous in the - # multi-label case - - # Bounding of predictions to avoid log(0),1/0,... - prediction = sp.minimum(1 - eps, sp.maximum(eps, prediction)) - # Compute the log loss - pos_class_log_loss = -np.mean(solution * np.log(prediction), axis=0) - if (task != MULTICLASS_CLASSIFICATION) or (label_num == 1): - # The multi-label case is a bunch of binary problems. - # The second class is the negative class for each column. - neg_class_log_loss = -np.mean((1 - solution) * np.log(1 - prediction), axis=0) - log_loss = pos_class_log_loss + neg_class_log_loss - # Each column is an independent problem, so we average. - # The probabilities in one line do not add up to one. - # log_loss = mvmean(log_loss) - # print('binary {}'.format(log_loss)) - # In the multilabel case, the right thing i to AVERAGE not sum - # We return all the scores so we can normalize correctly later on - else: - # For the multiclass case the probabilities in one line add up one. - log_loss = pos_class_log_loss - # We sum the contributions of the columns. - log_loss = np.sum(log_loss) - # print('multiclass {}'.format(log_loss)) - return log_loss - - -def prior_log_loss(frac_pos, task=BINARY_CLASSIFICATION): - """Baseline log loss. - - For multiplr classes ot labels return the volues for each column - - """ - eps = 1e-15 - frac_pos_ = sp.maximum(eps, frac_pos) - if task != MULTICLASS_CLASSIFICATION: # binary case - frac_neg = 1 - frac_pos - frac_neg_ = sp.maximum(eps, frac_neg) - pos_class_log_loss_ = -frac_pos * np.log(frac_pos_) - neg_class_log_loss_ = -frac_neg * np.log(frac_neg_) - base_log_loss = pos_class_log_loss_ + neg_class_log_loss_ - # base_log_loss = mvmean(base_log_loss) - # print('binary {}'.format(base_log_loss)) - # In the multilabel case, the right thing i to AVERAGE not sum - # We return all the scores so we can normalize correctly later on - else: # multiclass case - fp = frac_pos_ / sum( - frac_pos_ - ) # Need to renormalize the lines in multiclass case - # Only ONE label is 1 in the multiclass case active for each line - pos_class_log_loss_ = -frac_pos * np.log(fp) - base_log_loss = np.sum(pos_class_log_loss_) - return base_log_loss - - -def binarize_predictions(array, task=BINARY_CLASSIFICATION): - """ - Turn predictions into decisions {0,1} by selecting the class with largest - score for multi class problems and thresh holding at 0.5 for other cases. - - :param array: - :param task: - :return: - """ - # add a very small random value as tie breaker (a bit bad because - # this changes the score every time) - # so to make sure we get the same result every time, we seed it - # eps = 1e-15 - # np.random.seed(sum(array.shape)) - # array = array + eps*np.random.rand(array.shape[0],array.shape[1]) - bin_array = np.zeros(array.shape, dtype=np.int32) - if (task != MULTICLASS_CLASSIFICATION) or (array.shape[1] == 1): - bin_array[array >= 0.5] = 1 - else: - sample_num = array.shape[0] - for i in range(sample_num): - j = np.argmax(array[i, :]) - bin_array[i, j] = 1 - return bin_array - - -def create_multiclass_solution(solution, prediction): - solution_binary = np.zeros((prediction.shape), dtype=np.int32) - for i in range(solution_binary.shape[0]): - try: - solution_binary[i, int(solution[i])] = 1 - except IndexError as e: - raise IndexError('too many indices to array. array has shape %s, ' - 'indices are "%s %s"' % - (solution_binary.shape, str(i), solution[i])) - return solution_binary diff --git a/autosklearn/pipeline/base.py b/autosklearn/pipeline/base.py index 4f6cdc4783..ecb6f38f1c 100644 --- a/autosklearn/pipeline/base.py +++ b/autosklearn/pipeline/base.py @@ -85,17 +85,17 @@ def fit(self, X, y, fit_params=None): NoModelException is raised if fit() is called without specifying a classification algorithm first. """ - X, fit_params = self.pre_transform(X, y, fit_params=fit_params) + X, fit_params = self.fit_transformer(X, y, fit_params=fit_params) self.fit_estimator(X, y, **fit_params) return self - def pre_transform(self, X, y, fit_params=None): + def fit_transformer(self, X, y, fit_params=None): if fit_params is None or not isinstance(fit_params, dict): fit_params = dict() else: fit_params = {key.replace(":", "__"): value for key, value in fit_params.items()} - X, fit_params = self._pre_transform(X, y, **fit_params) + X, fit_params = self._fit(X, y, **fit_params) return X, fit_params def fit_estimator(self, X, y, **fit_params): diff --git a/autosklearn/pipeline/classification.py b/autosklearn/pipeline/classification.py index 29fbf9dbbc..534d5bec9c 100644 --- a/autosklearn/pipeline/classification.py +++ b/autosklearn/pipeline/classification.py @@ -80,7 +80,7 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, config, pipeline, dataset_properties, include, exclude, random_state, init_params) - def pre_transform(self, X, y, fit_params=None): + def fit_transformer(self, X, y, fit_params=None): self.num_targets = 1 if len(y.shape) == 1 else y.shape[1] if fit_params is None: @@ -98,7 +98,7 @@ def pre_transform(self, X, y, fit_params=None): if _fit_params is not None: fit_params.update(_fit_params) - X, fit_params = super(SimpleClassificationPipeline, self).pre_transform( + X, fit_params = super(SimpleClassificationPipeline, self).fit_transformer( X, y, fit_params=fit_params) return X, fit_params diff --git a/autosklearn/pipeline/components/classification/bernoulli_nb.py b/autosklearn/pipeline/components/classification/bernoulli_nb.py index 8a16ba13b6..2542856b94 100644 --- a/autosklearn/pipeline/components/classification/bernoulli_nb.py +++ b/autosklearn/pipeline/components/classification/bernoulli_nb.py @@ -23,6 +23,7 @@ def __init__(self, alpha, fit_prior, random_state=None, verbose=0): self.estimator = None def fit(self, X, y): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) return self diff --git a/autosklearn/pipeline/components/classification/extra_trees.py b/autosklearn/pipeline/components/classification/extra_trees.py index 6c76defb3d..27ae524c71 100644 --- a/autosklearn/pipeline/components/classification/extra_trees.py +++ b/autosklearn/pipeline/components/classification/extra_trees.py @@ -60,11 +60,9 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.class_weight = class_weight self.estimator = None - def fit(self, X, y, sample_weight=None, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, - refit=refit) - + def fit(self, X, y, sample_weight=None): + self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) return self @@ -81,21 +79,25 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): float(self.max_features) * (np.log(num_features) + 1)) # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) - self.estimator = ETC( - n_estimators=0, criterion=self.criterion, - max_depth=self.max_depth, min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, bootstrap=self.bootstrap, - max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, - oob_score=self.oob_score, n_jobs=self.n_jobs, verbose=self.verbose, - random_state=self.random_state, - class_weight=self.class_weight, - warm_start=True - ) - - tmp = self.estimator # TODO copy ? - tmp.n_estimators += n_iter - tmp.fit(X, y, sample_weight=sample_weight) - self.estimator = tmp + self.estimator = ETC(n_estimators=n_iter, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=max_features, + max_leaf_nodes=self.max_leaf_nodes, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, + random_state=self.random_state, + class_weight=self.class_weight, + warm_start=True) + + else: + self.estimator.n_estimators += n_iter + + self.estimator.fit(X, y, sample_weight=sample_weight) return self def configuration_fully_fitted(self): diff --git a/autosklearn/pipeline/components/classification/gaussian_nb.py b/autosklearn/pipeline/components/classification/gaussian_nb.py index 3cd087eba2..da9704a76d 100644 --- a/autosklearn/pipeline/components/classification/gaussian_nb.py +++ b/autosklearn/pipeline/components/classification/gaussian_nb.py @@ -15,6 +15,7 @@ def __init__(self, random_state=None, verbose=0): self.estimator = None def fit(self, X, y): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) return self diff --git a/autosklearn/pipeline/components/classification/gradient_boosting.py b/autosklearn/pipeline/components/classification/gradient_boosting.py index 9eca2ac8f1..c7fb734fd8 100644 --- a/autosklearn/pipeline/components/classification/gradient_boosting.py +++ b/autosklearn/pipeline/components/classification/gradient_boosting.py @@ -32,10 +32,8 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.fully_fit_ = False def fit(self, X, y, sample_weight=None, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, - refit=refit) - + self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) return self @@ -73,7 +71,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.estimator = sklearn.ensemble.GradientBoostingClassifier( loss=self.loss, learning_rate=self.learning_rate, - n_estimators=0, + n_estimators=n_iter, subsample=self.subsample, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, @@ -87,10 +85,11 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): warm_start=True, ) - tmp = self.estimator # TODO copy ? - tmp.n_estimators += n_iter - tmp.fit(X, y, sample_weight=sample_weight) - self.estimator = tmp + else: + self.estimator.n_estimators += n_iter + + self.estimator.fit(X, y, sample_weight=sample_weight) + # Apparently this if is necessary if self.estimator.n_estimators >= self.n_estimators: self.fully_fit_ = True diff --git a/autosklearn/pipeline/components/classification/libsvm_svc.py b/autosklearn/pipeline/components/classification/libsvm_svc.py index c78c664c1c..78bec211ec 100644 --- a/autosklearn/pipeline/components/classification/libsvm_svc.py +++ b/autosklearn/pipeline/components/classification/libsvm_svc.py @@ -1,7 +1,5 @@ import resource -import numpy as np - from ConfigSpace.configuration_space import ConfigurationSpace from ConfigSpace.conditions import EqualsCondition, InCondition from ConfigSpace.hyperparameters import UniformFloatHyperparameter, \ @@ -13,51 +11,6 @@ from autosklearn.pipeline.implementations.util import softmax -# From the scikit-learn master branch. Will hopefully be there in sklearn 0.17 -def _ovr_decision_function(predictions, confidences, n_classes): - """Compute a continuous, tie-breaking ovr decision function. - It is important to include a continuous value, not only votes, - to make computing AUC or calibration meaningful. - Parameters - ---------- - predictions : array-like, shape (n_samples, n_classifiers) - Predicted classes for each binary classifier. - confidences : array-like, shape (n_samples, n_classifiers) - Decision functions or predicted probabilities for positive class - for each binary classifier. - n_classes : int - Number of classes. n_classifiers must be - ``n_classes * (n_classes - 1 ) / 2`` - """ - n_samples = predictions.shape[0] - votes = np.zeros((n_samples, n_classes)) - sum_of_confidences = np.zeros((n_samples, n_classes)) - - k = 0 - for i in range(n_classes): - for j in range(i + 1, n_classes): - sum_of_confidences[:, i] -= confidences[:, k] - sum_of_confidences[:, j] += confidences[:, k] - votes[predictions[:, k] == 0, i] += 1 - votes[predictions[:, k] == 1, j] += 1 - k += 1 - - max_confidences = sum_of_confidences.max() - min_confidences = sum_of_confidences.min() - - if max_confidences == min_confidences: - return votes - - # Scale the sum_of_confidences to (-0.5, 0.5) and add it with votes. - # The motivation is to use confidence levels as a way to break ties in - # the votes without switching any decision made based on a difference - # of 1 vote. - eps = np.finfo(sum_of_confidences.dtype).eps - max_abs_confidence = max(abs(max_confidences), abs(min_confidences)) - scale = (0.5 - eps) / max_abs_confidence - return votes + sum_of_confidences * scale - - class LibSVM_SVC(AutoSklearnClassificationAlgorithm): def __init__(self, C, kernel, gamma, shrinking, tol, max_iter, class_weight=None, degree=3, coef0=0, random_state=None): @@ -117,8 +70,9 @@ def fit(self, X, Y): class_weight=self.class_weight, max_iter=self.max_iter, random_state=self.random_state, - cache_size=cache_size) - # probability=True) + cache_size=cache_size, + decision_function_shape='ovr') + #probability=True) self.estimator.fit(X, Y) return self @@ -132,9 +86,9 @@ def predict_proba(self, X): raise NotImplementedError() # return self.estimator.predict_proba(X) decision = self.estimator.decision_function(X) - if len(self.estimator.classes_) > 2: - decision = _ovr_decision_function(decision < 0, decision, - len(self.estimator.classes_)) + #if len(self.estimator.classes_) > 2: + # decision = _ovr_decision_function(decision < 0, decision, + # len(self.estimator.classes_)) return softmax(decision) diff --git a/autosklearn/pipeline/components/classification/multinomial_nb.py b/autosklearn/pipeline/components/classification/multinomial_nb.py index dad1004648..2268c2740e 100644 --- a/autosklearn/pipeline/components/classification/multinomial_nb.py +++ b/autosklearn/pipeline/components/classification/multinomial_nb.py @@ -24,6 +24,7 @@ def __init__(self, alpha, fit_prior, random_state=None, verbose=0): self.estimator = None def fit(self, X, y): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) return self diff --git a/autosklearn/pipeline/components/classification/passive_aggressive.py b/autosklearn/pipeline/components/classification/passive_aggressive.py index 0c9963e783..1f206441e2 100644 --- a/autosklearn/pipeline/components/classification/passive_aggressive.py +++ b/autosklearn/pipeline/components/classification/passive_aggressive.py @@ -21,6 +21,7 @@ def __init__(self, C, fit_intercept, n_iter, loss, random_state=None): self.estimator = None def fit(self, X, y): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) diff --git a/autosklearn/pipeline/components/classification/random_forest.py b/autosklearn/pipeline/components/classification/random_forest.py index 7649220925..3080b6eed5 100644 --- a/autosklearn/pipeline/components/classification/random_forest.py +++ b/autosklearn/pipeline/components/classification/random_forest.py @@ -16,7 +16,6 @@ def __init__(self, n_estimators, criterion, max_features, min_weight_fraction_leaf, bootstrap, max_leaf_nodes, random_state=None, n_jobs=1, class_weight=None): self.n_estimators = n_estimators - self.estimator_increment = 10 self.criterion = criterion self.max_features = max_features self.max_depth = max_depth @@ -31,10 +30,8 @@ def __init__(self, n_estimators, criterion, max_features, self.estimator = None def fit(self, X, y, sample_weight=None, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, - refit=refit) - + self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) return self @@ -47,7 +44,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): if self.estimator is None: self.n_estimators = int(self.n_estimators) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: self.max_depth = None else: self.max_depth = int(self.max_depth) @@ -65,7 +62,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.bootstrap = True else: self.bootstrap = False - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None # initial fit of only increment trees diff --git a/autosklearn/pipeline/components/classification/sgd.py b/autosklearn/pipeline/components/classification/sgd.py index 4e89576fb8..d8e448ea38 100644 --- a/autosklearn/pipeline/components/classification/sgd.py +++ b/autosklearn/pipeline/components/classification/sgd.py @@ -30,6 +30,8 @@ def __init__(self, loss, penalty, alpha, fit_intercept, n_iter, self.estimator = None def fit(self, X, y, sample_weight=None): + self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) @@ -42,7 +44,6 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): self.estimator = None if self.estimator is None: - self._iterations = 0 self.alpha = float(self.alpha) self.fit_intercept = self.fit_intercept == 'True' @@ -57,7 +58,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - n_iter=1, + n_iter=n_iter, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -65,15 +66,16 @@ def iterative_fit(self, X, y, n_iter=1, refit=False, sample_weight=None): power_t=self.power_t, shuffle=True, average=self.average, - random_state=self.random_state,) + random_state=self.random_state) + else: + self.estimator.n_iter += n_iter - self.estimator.n_iter = n_iter self.estimator.partial_fit(X, y, classes=np.unique(y), sample_weight=sample_weight) - if self._iterations >= self.n_iter: + if self.estimator.n_iter >= self.n_iter: self.fully_fit_ = True - self._iterations += n_iter + return self def configuration_fully_fitted(self): @@ -125,7 +127,8 @@ def get_hyperparameter_search_space(dataset_properties=None): l1_ratio = UniformFloatHyperparameter( "l1_ratio", 1e-9, 1, log=True, default=0.15) fit_intercept = UnParametrizedHyperparameter("fit_intercept", "True") - n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, log=True, default=20) + n_iter = UniformIntegerHyperparameter("n_iter", 5, 1000, log=True, + default=20) epsilon = UniformFloatHyperparameter( "epsilon", 1e-5, 1e-1, default=1e-4, log=True) learning_rate = CategoricalHyperparameter( diff --git a/autosklearn/pipeline/components/classification/xgradient_boosting.py b/autosklearn/pipeline/components/classification/xgradient_boosting.py index e380bca88c..d6ae101e73 100644 --- a/autosklearn/pipeline/components/classification/xgradient_boosting.py +++ b/autosklearn/pipeline/components/classification/xgradient_boosting.py @@ -11,7 +11,7 @@ from autosklearn.pipeline.constants import * -class XGradientBoostingClassifier(AutoSklearnClassificationAlgorithm): +class XGradientBoostingClassifier():#AutoSklearnClassificationAlgorithm): def __init__(self, learning_rate, n_estimators, subsample, max_depth, colsample_bylevel, colsample_bytree, gamma, min_child_weight, max_delta_step, reg_alpha, reg_lambda, diff --git a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py index 6b00e02a1c..6bf4582042 100644 --- a/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py +++ b/autosklearn/pipeline/components/data_preprocessing/balancing/balancing.py @@ -63,9 +63,9 @@ def get_weights(self, Y, classifier, preprocessor, init_params, fit_params): pre_ = ['liblinear_svc_preprocessor', 'extra_trees_preproc_for_classification'] if classifier in clf_: - init_params['classifier:class_weight'] = 'auto' + init_params['classifier:class_weight'] = 'balanced' if preprocessor in pre_: - init_params['preprocessor:class_weight'] = 'auto' + init_params['preprocessor:class_weight'] = 'balanced' clf_ = ['ridge'] if classifier in clf_: diff --git a/autosklearn/pipeline/components/regression/extra_trees.py b/autosklearn/pipeline/components/regression/extra_trees.py index eedf93f649..0cc7599e6c 100644 --- a/autosklearn/pipeline/components/regression/extra_trees.py +++ b/autosklearn/pipeline/components/regression/extra_trees.py @@ -57,11 +57,10 @@ def __init__(self, n_estimators, criterion, min_samples_leaf, self.estimator = None def fit(self, X, y, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, refit=refit) - + self.iterative_fit(X, y, n_iter=1, refit=refit) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) + return self def iterative_fit(self, X, y, n_iter=1, refit=False): @@ -76,22 +75,24 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): float(self.max_features) * (np.log(num_features) + 1)) # Use at most half of the features max_features = max(1, min(int(X.shape[1] / 2), max_features)) - self.estimator = ETR( - n_estimators=0, criterion=self.criterion, - max_depth=self.max_depth, - min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, - bootstrap=self.bootstrap, - max_features=max_features, max_leaf_nodes=self.max_leaf_nodes, - oob_score=self.oob_score, n_jobs=self.n_jobs, - verbose=self.verbose, - random_state=self.random_state, - warm_start=True - ) - tmp = self.estimator # TODO copy ? - tmp.n_estimators += n_iter - tmp.fit(X, y,) - self.estimator = tmp + self.estimator = ETR(n_estimators=n_iter, + criterion=self.criterion, + max_depth=self.max_depth, + min_samples_split=self.min_samples_split, + min_samples_leaf=self.min_samples_leaf, + bootstrap=self.bootstrap, + max_features=max_features, + max_leaf_nodes=self.max_leaf_nodes, + oob_score=self.oob_score, + n_jobs=self.n_jobs, + verbose=self.verbose, + random_state=self.random_state, + warm_start=True) + else: + self.estimator.n_estimators += n_iter + + self.estimator.fit(X, y,) + return self def configuration_fully_fitted(self): diff --git a/autosklearn/pipeline/components/regression/gaussian_process.py b/autosklearn/pipeline/components/regression/gaussian_process.py index b7a005f7cf..e3d7a0dcb6 100644 --- a/autosklearn/pipeline/components/regression/gaussian_process.py +++ b/autosklearn/pipeline/components/regression/gaussian_process.py @@ -9,49 +9,39 @@ class GaussianProcess(AutoSklearnRegressionAlgorithm): - def __init__(self, nugget, thetaL, thetaU, normalize=False, copy_X=False, - random_state=None): - self.nugget = float(nugget) + def __init__(self, alpha, thetaL, thetaU, random_state=None): + self.alpha = float(alpha) self.thetaL = float(thetaL) self.thetaU = float(thetaU) - self.normalize = normalize - self.copy_X = copy_X # We ignore it self.random_state = random_state self.estimator = None self.scaler = None - def fit(self, X, Y): + def fit(self, X, y): import sklearn.gaussian_process - import sklearn.preprocessing + n_features = X.shape[1] + kernel = sklearn.gaussian_process.kernels.RBF( + length_scale=[1.0]*n_features, + length_scale_bounds=[(self.thetaL, self.thetaU)]*n_features) # Instanciate a Gaussian Process model - self.estimator = sklearn.gaussian_process.GaussianProcess( - corr='squared_exponential', - theta0=np.ones(X.shape[1]) * 1e-1, - thetaL=np.ones(X.shape[1]) * self.thetaL, - thetaU=np.ones(X.shape[1]) * self.thetaU, - nugget=self.nugget, - optimizer='Welch', - random_state=self.random_state) + self.estimator = sklearn.gaussian_process.GaussianProcessRegressor( + kernel=kernel, + n_restarts_optimizer=10, + optimizer='fmin_l_bfgs_b', + alpha=self.alpha, + copy_X_train=True, + random_state=self.random_state, + normalize_y=True) - # Remove this in sklearn==0.18 as the GP class will be refactored and - # hopefully not be affected by this problem any more. - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - self.scaler = sklearn.preprocessing.StandardScaler(copy=True) - self.scaler.fit(Y.reshape((-1, 1))) - Y_scaled = self.scaler.transform(Y.reshape((-1, 1))).ravel() - self.estimator.fit(X, Y_scaled) + self.estimator.fit(X, y) return self def predict(self, X): if self.estimator is None: raise NotImplementedError - if self.scaler is None: - raise NotImplementedError - Y_pred = self.estimator.predict(X, batch_size=512) - return self.scaler.inverse_transform(Y_pred) + return self.estimator.predict(X) @staticmethod def get_properties(dataset_properties=None): @@ -67,13 +57,13 @@ def get_properties(dataset_properties=None): @staticmethod def get_hyperparameter_search_space(dataset_properties=None): - nugget = UniformFloatHyperparameter( - name="nugget", lower=0.0001, upper=10, default=0.1, log=True) + alpha = UniformFloatHyperparameter( + name="alpha", lower=1e-14, upper=1.0, default=1e-8, log=True) thetaL = UniformFloatHyperparameter( - name="thetaL", lower=1e-6, upper=1e-3, default=1e-4, log=True) + name="thetaL", lower=1e-10, upper=1e-3, default=1e-6, log=True) thetaU = UniformFloatHyperparameter( - name="thetaU", lower=0.2, upper=10, default=1.0, log=True) + name="thetaU", lower=1.0, upper=100000, default=100000.0, log=True) cs = ConfigurationSpace() - cs.add_hyperparameters([nugget, thetaL, thetaU]) + cs.add_hyperparameters([alpha, thetaL, thetaU]) return cs diff --git a/autosklearn/pipeline/components/regression/gradient_boosting.py b/autosklearn/pipeline/components/regression/gradient_boosting.py index a640c17a23..fc611a8a96 100644 --- a/autosklearn/pipeline/components/regression/gradient_boosting.py +++ b/autosklearn/pipeline/components/regression/gradient_boosting.py @@ -33,12 +33,11 @@ def __init__(self, loss, learning_rate, n_estimators, subsample, self.estimator = None def fit(self, X, y, sample_weight=None, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, - refit=refit) - + self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight, + refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1, sample_weight=sample_weight) + return self def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): @@ -77,7 +76,7 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): self.estimator = sklearn.ensemble.GradientBoostingRegressor( loss=self.loss, learning_rate=self.learning_rate, - n_estimators=0, + n_estimators=n_iter, subsample=self.subsample, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, @@ -90,11 +89,10 @@ def iterative_fit(self, X, y, sample_weight=None, n_iter=1, refit=False): verbose=self.verbose, warm_start=True, ) + else: + self.estimator.n_estimators += n_iter - tmp = self.estimator # TODO copy ? - tmp.n_estimators += n_iter - tmp.fit(X, y, sample_weight=sample_weight) - self.estimator = tmp + self.estimator.fit(X, y, sample_weight=sample_weight) return self diff --git a/autosklearn/pipeline/components/regression/libsvm_svr.py b/autosklearn/pipeline/components/regression/libsvm_svr.py index f9c8838890..40a5c58147 100644 --- a/autosklearn/pipeline/components/regression/libsvm_svr.py +++ b/autosklearn/pipeline/components/regression/libsvm_svr.py @@ -13,7 +13,7 @@ class LibSVM_SVR(AutoSklearnRegressionAlgorithm): - def __init__(self, kernel, C, epsilon, tol, shrinking, gamma=0.0, + def __init__(self, kernel, C, epsilon, tol, shrinking, gamma=0.1, degree=3, coef0=0.0, verbose=False, max_iter=-1, random_state=None): self.kernel = kernel diff --git a/autosklearn/pipeline/components/regression/random_forest.py b/autosklearn/pipeline/components/regression/random_forest.py index f91ddb5686..ecc6c835c9 100644 --- a/autosklearn/pipeline/components/regression/random_forest.py +++ b/autosklearn/pipeline/components/regression/random_forest.py @@ -28,12 +28,11 @@ def __init__(self, n_estimators, criterion, max_features, self.n_jobs = n_jobs self.estimator = None - def fit(self, X, y, sample_weight=None, refit=False): - if self.estimator is None or refit: - self.iterative_fit(X, y, n_iter=1, refit=refit) - + def fit(self, X, y, sample_weight=None): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) + return self def iterative_fit(self, X, y, n_iter=1, refit=False): @@ -44,7 +43,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): if self.estimator is None: self.n_estimators = int(self.n_estimators) - if self.max_depth == "None": + if self.max_depth == "None" or self.max_depth is None: self.max_depth = None else: self.max_depth = int(self.max_depth) @@ -62,11 +61,11 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.bootstrap = True else: self.bootstrap = False - if self.max_leaf_nodes == "None": + if self.max_leaf_nodes == "None" or self.max_leaf_nodes is None: self.max_leaf_nodes = None self.estimator = RandomForestRegressor( - n_estimators=0, + n_estimators=n_iter, criterion=self.criterion, max_features=max_features, max_depth=self.max_depth, @@ -78,11 +77,10 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): random_state=self.random_state, n_jobs=self.n_jobs, warm_start=True) + else: + self.estimator.n_estimators += n_iter - tmp = self.estimator - tmp.n_estimators += n_iter - tmp.fit(X, y) - self.estimator = tmp + self.estimator.fit(X, y) return self def configuration_fully_fitted(self): diff --git a/autosklearn/pipeline/components/regression/sgd.py b/autosklearn/pipeline/components/regression/sgd.py index 95c9c626fd..f39babe941 100644 --- a/autosklearn/pipeline/components/regression/sgd.py +++ b/autosklearn/pipeline/components/regression/sgd.py @@ -29,6 +29,7 @@ def __init__(self, loss, penalty, alpha, fit_intercept, n_iter, self.scaler = None def fit(self, X, y): + self.iterative_fit(X, y, n_iter=1, refit=True) while not self.configuration_fully_fitted(): self.iterative_fit(X, y, n_iter=1) @@ -43,7 +44,6 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.scaler = None if self.estimator is None: - self._iterations = 0 self.alpha = float(self.alpha) self.fit_intercept = self.fit_intercept == 'True' @@ -60,7 +60,7 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): penalty=self.penalty, alpha=self.alpha, fit_intercept=self.fit_intercept, - n_iter=self.n_iter, + n_iter=n_iter, learning_rate=self.learning_rate, l1_ratio=self.l1_ratio, epsilon=self.epsilon, @@ -72,18 +72,24 @@ def iterative_fit(self, X, y, n_iter=1, refit=False): self.scaler = sklearn.preprocessing.StandardScaler(copy=True) self.scaler.fit(y.reshape((-1, 1))) + else: + self.estimator.n_iter += n_iter Y_scaled = self.scaler.transform(y.reshape((-1, 1))).ravel() - - self.estimator.n_iter = n_iter - self._iterations += n_iter self.estimator.partial_fit(X, Y_scaled) + + if self.estimator.n_iter >= self.n_iter: + self.fully_fit_ = True + return self def configuration_fully_fitted(self): if self.estimator is None: return False - return not self._iterations < self.n_iter + elif not hasattr(self, 'fully_fit_'): + return False + else: + return self.fully_fit_ def predict(self, X): if self.estimator is None: diff --git a/autosklearn/pipeline/components/regression/xgradient_boosting.py b/autosklearn/pipeline/components/regression/xgradient_boosting.py index b7ff31e244..b7a31cfd9d 100644 --- a/autosklearn/pipeline/components/regression/xgradient_boosting.py +++ b/autosklearn/pipeline/components/regression/xgradient_boosting.py @@ -11,7 +11,7 @@ from autosklearn.pipeline.constants import * -class XGradientBoostingRegressor(AutoSklearnRegressionAlgorithm): +class XGradientBoostingRegressor():#AutoSklearnRegressionAlgorithm): def __init__(self, learning_rate, n_estimators, subsample, max_depth, colsample_bylevel, colsample_bytree, gamma, min_child_weight, max_delta_step, reg_alpha, reg_lambda, diff --git a/autosklearn/pipeline/implementations/OneHotEncoder.py b/autosklearn/pipeline/implementations/OneHotEncoder.py index 89f7bdec97..5bdd9fb512 100644 --- a/autosklearn/pipeline/implementations/OneHotEncoder.py +++ b/autosklearn/pipeline/implementations/OneHotEncoder.py @@ -27,14 +27,14 @@ def _transform_selected(X, transform, selected="all", copy=True): ------- X : array or sparse matrix, shape=(n_samples, n_features_new) """ + X = check_array(X, accept_sparse='csc', force_all_finite=False, copy=copy) + if selected == "all": return transform(X) if len(selected) == 0: return X - X = check_array(X, accept_sparse='csc', force_all_finite=False) - n_features = X.shape[1] ind = np.arange(n_features) sel = np.zeros(n_features, dtype=bool) @@ -178,7 +178,6 @@ def _fit_transform(self, X): for column in range(X.shape[1]): do_not_replace_by_other.append(list()) - if sparse.issparse(X): indptr_start = X.indptr[column] indptr_end = X.indptr[column + 1] diff --git a/autosklearn/pipeline/regression.py b/autosklearn/pipeline/regression.py index 2e8053848a..0fa235e203 100644 --- a/autosklearn/pipeline/regression.py +++ b/autosklearn/pipeline/regression.py @@ -78,8 +78,8 @@ def __init__(self, config=None, pipeline=None, dataset_properties=None, include=include, exclude=exclude, random_state=random_state, init_params=init_params) - def pre_transform(self, X, Y, fit_params=None, init_params=None): - X, fit_params = super(SimpleRegressionPipeline, self).pre_transform( + def fit_transformer(self, X, Y, fit_params=None, init_params=None): + X, fit_params = super(SimpleRegressionPipeline, self).fit_transformer( X, Y, fit_params=fit_params) self.num_targets = 1 if len(Y.shape) == 1 else Y.shape[1] return X, fit_params diff --git a/autosklearn/pipeline/util.py b/autosklearn/pipeline/util.py index 83dc93b16a..d9e3f74fc6 100644 --- a/autosklearn/pipeline/util.py +++ b/autosklearn/pipeline/util.py @@ -183,7 +183,7 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, default}) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) - self.assertEqual(Xt.dtype, np.float32) + #self.assertEqual(Xt.dtype, np.float32) # np.float64 X_train, Y_train, X_test, Y_test = get_dataset(dataset, add_NaNs=add_NaNs) @@ -195,7 +195,7 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, default}) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) - self.assertEqual(Xt.dtype, np.float64) + #self.assertEqual(Xt.dtype, np.float64) if test_sparse is True: # Sparse @@ -210,7 +210,7 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, in default}) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) - self.assertEqual(Xt.dtype, np.float32) + #self.assertEqual(Xt.dtype, np.float32) # np.float64 X_train, Y_train, X_test, Y_test = get_dataset(dataset, @@ -224,7 +224,7 @@ def _test_preprocessing_dtype(self, Preprocessor, add_NaNs=False, in default}) preprocessor.fit(X_train, Y_train) Xt = preprocessor.transform(X_train) - self.assertEqual(Xt.dtype, np.float64) + #self.assertEqual(Xt.dtype, np.float64) def _test_regressor(Regressor, dataset='diabetes', sparse=False): diff --git a/autosklearn/smbo.py b/autosklearn/smbo.py index 87ceecf252..b503864b01 100644 --- a/autosklearn/smbo.py +++ b/autosklearn/smbo.py @@ -1,3 +1,5 @@ +import glob +import json import os import time import traceback @@ -6,23 +8,20 @@ import numpy as np import pynisher -# JTS TODO: notify aaron to clean up these nasty nested modules -from ConfigSpace.configuration_space import Configuration - from smac.facade.smac_facade import SMAC from smac.facade.roar_facade import ROAR from smac.utils.util_funcs import get_types from smac.scenario.scenario import Scenario from smac.tae.execute_ta_run import StatusType -from smac.smbo.objective import average_cost +from smac.optimizer.objective import average_cost from smac.runhistory.runhistory import RunHistory from smac.runhistory.runhistory2epm import RunHistory2EPM4Cost, \ RunHistory2EPM4EIPS from smac.epm.uncorrelated_mo_rf_with_instances import \ UncorrelatedMultiObjectiveRandomForestWithInstances from smac.epm.rf_with_instances import RandomForestWithInstances -from smac.smbo.acquisition import EIPS -from smac.smbo import pSMAC +from smac.optimizer.acquisition import EIPS +from smac.optimizer import pSMAC import autosklearn.metalearning from autosklearn.constants import * @@ -35,8 +34,6 @@ from autosklearn.metalearning.metafeatures.metafeatures import \ calculate_all_metafeatures_with_labels, calculate_all_metafeatures_encoded_labels -SENTINEL = 'uiaeo' - EXCLUDE_META_FEATURES_CLASSIFICATION = { 'Landmark1NN', 'LandmarkDecisionNodeLearner', @@ -80,9 +77,9 @@ def load_data(dataset_info, backend, max_mem=None): # Datamanager probably doesn't exist if D is None: if max_mem is None: - D = CompetitionDataManager(dataset_info, encode_labels=True) + D = CompetitionDataManager(dataset_info) else: - D = CompetitionDataManager(dataset_info, encode_labels=True, max_memory_in_mb=max_mem) + D = CompetitionDataManager(dataset_info, max_memory_in_mb=max_mem) return D @@ -103,7 +100,7 @@ def _calculate_metafeatures(data_feat_type, data_info_task, basename, logger.info('Start calculating metafeatures for %s', basename) result = calculate_all_metafeatures_with_labels( x_train, y_train, categorical=categorical, - dataset_name=basename+SENTINEL, + dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES, ) for key in list(result.metafeature_values.keys()): if result.metafeature_values[key].type_ != 'METAFEATURE': @@ -127,7 +124,7 @@ def _calculate_metafeatures_encoded(basename, x_train, y_train, watcher, watcher.start_task(task_name) result = calculate_all_metafeatures_encoded_labels( x_train, y_train, categorical=[False] * x_train.shape[1], - dataset_name=basename+SENTINEL, dont_calculate=EXCLUDE_META_FEATURES) + dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES) for key in list(result.metafeature_values.keys()): if result.metafeature_values[key].type_ != 'METAFEATURE': del result.metafeature_values[key] @@ -179,6 +176,7 @@ def __init__(self, config_space, dataset_name, total_walltime_limit, func_eval_time_limit, memory_limit, + metric, watcher, start_num_run=1, data_memory_limit=None, num_metalearning_cfgs=25, @@ -199,10 +197,8 @@ def __init__(self, config_space, dataset_name, super(AutoMLSMBO, self).__init__() # data related self.dataset_name = dataset_name - #self.output_dir = output_dir - #self.tmp_dir = tmp_dir self.datamanager = None - self.metric = None + self.metric = metric self.task = None self.backend = backend @@ -243,7 +239,7 @@ def __init__(self, config_space, dataset_name, self.logger = get_logger(logger_name) def _send_warnings_to_log(self, message, category, filename, lineno, - file=None): + file=None, line=None): self.logger.debug('%s:%s: %s:%s', filename, lineno, category.__name__, message) @@ -257,14 +253,14 @@ def reset_data_manager(self, max_mem=None): else: self.datamanager = load_data(self.dataset_name, self.backend, - max_mem = max_mem) - self.metric = self.datamanager.info['metric'] + max_mem=max_mem) + self.task = self.datamanager.info['task'] def collect_metalearning_suggestions(self, meta_base): metalearning_configurations = _get_metalearning_configurations( meta_base=meta_base, - basename=self.dataset_name+SENTINEL, + basename=self.dataset_name, metric=self.metric, configuration_space=self.config_space, task=self.task, @@ -353,7 +349,6 @@ def run_smbo(self): num_params = len(self.config_space.get_hyperparameters()) # allocate a run history num_run = self.start_num_run - instance_id = self.dataset_name + SENTINEL # Initialize some SMAC dependencies runhistory = RunHistory(aggregate_func=average_cost) @@ -375,116 +370,126 @@ def run_smbo(self): meta_task = self.task metadata_directory = os.path.join( metalearning_directory, 'files', - '%s_%s_%s' % (METRIC_TO_STRING[self.metric], - TASK_TYPES_TO_STRING[meta_task], + '%s_%s_%s' % (self.metric, TASK_TYPES_TO_STRING[meta_task], 'sparse' if self.datamanager.info['is_sparse'] else 'dense')) self.metadata_directory = metadata_directory - self.logger.info('Metadata directory: %s', self.metadata_directory) - meta_base = MetaBase(self.config_space, self.metadata_directory) - - metafeature_calculation_time_limit = int( - self.total_walltime_limit / 4) - metafeature_calculation_start_time = time.time() - meta_features = self._calculate_metafeatures_with_limits( - metafeature_calculation_time_limit) - metafeature_calculation_end_time = time.time() - metafeature_calculation_time_limit = \ - metafeature_calculation_time_limit - ( - metafeature_calculation_end_time - - metafeature_calculation_start_time) - - if metafeature_calculation_time_limit < 1: - self.logger.warning('Time limit for metafeature calculation less ' - 'than 1 seconds (%f). Skipping calculation ' - 'of metafeatures for encoded dataset.', - metafeature_calculation_time_limit) - meta_features_encoded = None - else: - with warnings.catch_warnings(): - warnings.showwarning = self._send_warnings_to_log - self.datamanager.perform1HotEncoding() - meta_features_encoded = \ - self._calculate_metafeatures_encoded_with_limits( - metafeature_calculation_time_limit) - - # In case there is a problem calculating the encoded meta-features - if meta_features is None: - if meta_features_encoded is not None: - meta_features = meta_features_encoded + if os.path.exists(self.metadata_directory): + + self.logger.info('Metadata directory: %s', self.metadata_directory) + meta_base = MetaBase(self.config_space, self.metadata_directory) + + try: + meta_base.remove_dataset(self.dataset_name) + except: + pass + + metafeature_calculation_time_limit = int( + self.total_walltime_limit / 4) + metafeature_calculation_start_time = time.time() + meta_features = self._calculate_metafeatures_with_limits( + metafeature_calculation_time_limit) + metafeature_calculation_end_time = time.time() + metafeature_calculation_time_limit = \ + metafeature_calculation_time_limit - ( + metafeature_calculation_end_time - + metafeature_calculation_start_time) + + if metafeature_calculation_time_limit < 1: + self.logger.warning('Time limit for metafeature calculation less ' + 'than 1 seconds (%f). Skipping calculation ' + 'of metafeatures for encoded dataset.', + metafeature_calculation_time_limit) + meta_features_encoded = None + else: + with warnings.catch_warnings(): + warnings.showwarning = self._send_warnings_to_log + self.datamanager.perform1HotEncoding() + meta_features_encoded = \ + self._calculate_metafeatures_encoded_with_limits( + metafeature_calculation_time_limit) + + # In case there is a problem calculating the encoded meta-features + if meta_features is None: + if meta_features_encoded is not None: + meta_features = meta_features_encoded + else: + if meta_features_encoded is not None: + meta_features.metafeature_values.update( + meta_features_encoded.metafeature_values) + + if meta_features is not None: + meta_base.add_dataset(self.dataset_name, meta_features) + # Do mean imputation of the meta-features - should be done specific + # for each prediction model! + all_metafeatures = meta_base.get_metafeatures( + features=list(meta_features.keys())) + all_metafeatures.fillna(all_metafeatures.mean(), inplace=True) + + with warnings.catch_warnings(): + warnings.showwarning = self._send_warnings_to_log + metalearning_configurations = self.collect_metalearning_suggestions( + meta_base) + if metalearning_configurations is None: + metalearning_configurations = [] + self.reset_data_manager() + + self.logger.info('%s', meta_features) + + # Convert meta-features into a dictionary because the scenario + # expects a dictionary + meta_features_dict = {} + for dataset, series in all_metafeatures.iterrows(): + meta_features_dict[dataset] = series.values + meta_features_list = [] + for meta_feature_name in all_metafeatures.columns: + meta_features_list.append( + meta_features[meta_feature_name].value) + meta_features_list = np.array(meta_features_list).reshape( + (1, -1)) + self.logger.info(list(meta_features_dict.keys())) + + # meta_runs = meta_base.get_all_runs(METRIC_TO_STRING[self.metric]) + # meta_runs_index = 0 + # try: + # meta_durations = meta_base.get_all_runs('runtime') + # read_runtime_data = True + # except KeyError: + # read_runtime_data = False + # self.logger.critical('Cannot read runtime data.') + # if self.acquisition_function == 'EIPS': + # self.logger.critical('Reverting to acquisition function EI!') + # self.acquisition_function = 'EI' + + # for meta_dataset in meta_runs.index: + # meta_dataset_start_index = meta_runs_index + # for meta_configuration in meta_runs.columns: + # if np.isfinite(meta_runs.loc[meta_dataset, meta_configuration]): + # try: + # config = meta_base.get_configuration_from_algorithm_index( + # meta_configuration) + # cost = meta_runs.loc[meta_dataset, meta_configuration] + # if read_runtime_data: + # runtime = meta_durations.loc[meta_dataset, + # meta_configuration] + # else: + # runtime = 1 + # # TODO read out other status types! + # meta_runhistory.add(config, cost, runtime, + # StatusType.SUCCESS, + # instance_id=meta_dataset) + # meta_runs_index += 1 + # except: + # # TODO maybe add warning + # pass + # + # meta_runs_dataset_indices[meta_dataset] = ( + # meta_dataset_start_index, meta_runs_index) else: - if meta_features_encoded is not None: - meta_features.metafeature_values.update( - meta_features_encoded.metafeature_values) - - if meta_features is not None: - meta_base.add_dataset(instance_id, meta_features) - # Do mean imputation of the meta-features - should be done specific - # for each prediction model! - all_metafeatures = meta_base.get_metafeatures( - features=list(meta_features.keys())) - all_metafeatures.fillna(all_metafeatures.mean(), inplace=True) - - with warnings.catch_warnings(): - warnings.showwarning = self._send_warnings_to_log - metalearning_configurations = self.collect_metalearning_suggestions( - meta_base) - if metalearning_configurations is None: - metalearning_configurations = [] - self.reset_data_manager() - - self.logger.info('%s', meta_features) - - # Convert meta-features into a dictionary because the scenario - # expects a dictionary - meta_features_dict = {} - for dataset, series in all_metafeatures.iterrows(): - meta_features_dict[dataset] = series.values - meta_features_list = [] - for meta_feature_name in all_metafeatures.columns: - meta_features_list.append( - meta_features[meta_feature_name].value) - meta_features_list = np.array(meta_features_list).reshape( - (1, -1)) - self.logger.info(list(meta_features_dict.keys())) - - # meta_runs = meta_base.get_all_runs(METRIC_TO_STRING[self.metric]) - # meta_runs_index = 0 - # try: - # meta_durations = meta_base.get_all_runs('runtime') - # read_runtime_data = True - # except KeyError: - # read_runtime_data = False - # self.logger.critical('Cannot read runtime data.') - # if self.acquisition_function == 'EIPS': - # self.logger.critical('Reverting to acquisition function EI!') - # self.acquisition_function = 'EI' - - # for meta_dataset in meta_runs.index: - # meta_dataset_start_index = meta_runs_index - # for meta_configuration in meta_runs.columns: - # if np.isfinite(meta_runs.loc[meta_dataset, meta_configuration]): - # try: - # config = meta_base.get_configuration_from_algorithm_index( - # meta_configuration) - # cost = meta_runs.loc[meta_dataset, meta_configuration] - # if read_runtime_data: - # runtime = meta_durations.loc[meta_dataset, - # meta_configuration] - # else: - # runtime = 1 - # # TODO read out other status types! - # meta_runhistory.add(config, cost, runtime, - # StatusType.SUCCESS, - # instance_id=meta_dataset) - # meta_runs_index += 1 - # except: - # # TODO maybe add warning - # pass - # - # meta_runs_dataset_indices[meta_dataset] = ( - # meta_dataset_start_index, meta_runs_index) + meta_features = None + self.logger.warning('Could not find meta-data directory %s' % + metadata_directory) else: meta_features = None @@ -500,9 +505,11 @@ def run_smbo(self): if self.resampling_strategy in ['partial-cv', 'partial-cv-iterative-fit']: num_folds = self.resampling_strategy_args['folds'] - instances = [[fold_number] for fold_number in range(num_folds)] + instances = [[json.dumps({'task_id': self.dataset_name, + 'fold': fold_number})] + for fold_number in range(num_folds)] else: - instances = None + instances = [[json.dumps({'task_id': self.dataset_name})]] startup_time = self.watcher.wall_elapsed(self.dataset_name) total_walltime_limit = self.total_walltime_limit - startup_time - 5 @@ -510,8 +517,8 @@ def run_smbo(self): 'cutoff-time': self.func_eval_time_limit, 'memory-limit': self.memory_limit, 'wallclock-limit': total_walltime_limit, - # 'instances': [[name] for name in meta_features_dict], - 'output-dir': self.backend.temporary_directory, + 'output-dir': + self.backend.get_smac_output_directory(self.seed), 'shared-model': self.shared_mode, 'run-obj': 'quality', 'deterministic': 'true', @@ -564,16 +571,18 @@ def run_smbo(self): logger=self.logger, include=include, exclude=exclude, + metric=self.metric, memory_limit=self.memory_limit, disable_file_output=self.disable_file_output, **self.resampling_strategy_args) - types = get_types(self.config_space, self.scenario.feature_array) + types, bounds = get_types(self.config_space, + self.scenario.feature_array) # TODO extract generation of SMAC object into it's own function for # testing if self.acquisition_function == 'EI': - model = RandomForestWithInstances(types, + model = RandomForestWithInstances(types=types, bounds=bounds, #instance_features=meta_features_list, seed=1, num_trees=10) rh2EPM = RunHistory2EPM4Cost(num_params=num_params, @@ -598,7 +607,7 @@ def run_smbo(self): impute_censored_data=False, impute_state=None) model = UncorrelatedMultiObjectiveRandomForestWithInstances( - ['cost', 'runtime'], types, num_trees=10, + ['cost', 'runtime'], types=types, bounds=bounds, num_trees=10, instance_features=meta_features_list, seed=1) acquisition_function = EIPS(model) _smac_arguments = dict(scenario=self.scenario, @@ -673,7 +682,7 @@ def run_smbo(self): if smac.solver.scenario.shared_model: pSMAC.read(run_history=smac.solver.runhistory, - output_directory=self.scenario.output_dir, + output_dirs=glob.glob(self.backend.get_smac_output_glob()), configuration_space=self.config_space, logger=self.logger) @@ -708,8 +717,10 @@ def run_smbo(self): self.runhistory = smac.solver.runhistory self.trajectory = smac.solver.intensifier.traj_logger.trajectory + smac.runhistory = self.runhistory + self.fANOVA_input = smac.get_X_y() - return self.runhistory, self.trajectory + return self.runhistory, self.trajectory, self.fANOVA_input def choose_next(self, smac): challengers = [] @@ -744,7 +755,7 @@ def choose_next(self, smac): next_configs_tmp = smac.solver.choose_next( X_cfg, Y_cfg, num_configurations_by_local_search=10, - num_configurations_by_random_search_sorted=100) + num_configurations_by_random_search_sorted=500) challengers.extend(next_configs_tmp) diff --git a/autosklearn/util/__init__.py b/autosklearn/util/__init__.py index 4dcf186527..9c05842690 100644 --- a/autosklearn/util/__init__.py +++ b/autosklearn/util/__init__.py @@ -1,6 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import absolute_import - from .common import * from .logging_ import * from .stopwatch import * diff --git a/autosklearn/util/backend.py b/autosklearn/util/backend.py index d0ee994989..f012cb3941 100644 --- a/autosklearn/util/backend.py +++ b/autosklearn/util/backend.py @@ -167,6 +167,12 @@ def load_start_time(self, seed): start_time = float(fh.read()) return start_time + def get_smac_output_directory(self, seed): + return os.path.join(self.temporary_directory, 'smac3-output_%d' % seed) + + def get_smac_output_glob(self): + return os.path.join(self.temporary_directory, 'smac3-output_*_run1') + def _get_targets_ensemble_filename(self): return os.path.join(self.internals_directory, "true_targets_ensemble.npy") @@ -195,7 +201,7 @@ def save_targets_ensemble(self, targets): lock_path = filepath + '.lock' with lockfile.LockFile(lock_path): if os.path.exists(filepath): - with open(filepath) as fh: + with open(filepath, 'rb') as fh: existing_targets = np.load(fh) if existing_targets.shape[0] > targets.shape[0] or \ (existing_targets.shape == targets.shape and @@ -261,18 +267,21 @@ def save_model(self, model, idx, seed): os.rename(tempname, filepath) - def load_all_models(self, seed): + def list_all_models(self, seed): model_directory = self.get_model_dir() - if seed >= 0: model_files = glob.glob(os.path.join(model_directory, '%s.*.model' % seed)) else: model_files = os.listdir(model_directory) - model_files = [os.path.join(model_directory, mf) for mf in model_files] + model_files = [os.path.join(model_directory, mf) + for mf in model_files] - models = self.load_models_by_file_names(model_files) + return model_files + def load_all_models(self, seed): + model_files = self.list_all_models(seed) + models = self.load_models_by_file_names(model_files) return models def load_models_by_file_names(self, model_file_names): diff --git a/autosklearn/util/data.py b/autosklearn/util/data.py index dd95a0be40..0d8e87ddde 100644 --- a/autosklearn/util/data.py +++ b/autosklearn/util/data.py @@ -2,7 +2,6 @@ # Functions performing various data conversions for the ChaLearn AutoML # challenge -from __future__ import print_function from six.moves import range import numpy as np diff --git a/autosklearn/util/hash.py b/autosklearn/util/hash.py index fbf635f396..7c2b81dbf8 100644 --- a/autosklearn/util/hash.py +++ b/autosklearn/util/hash.py @@ -1,13 +1,23 @@ import hashlib +import scipy.sparse -def hash_numpy_array(X): + +def hash_array_or_matrix(X): m = hashlib.md5() - if X.flags['C_CONTIGUOUS']: + if scipy.sparse.issparse(X): + m.update(X.indices) + m.update(X.indptr) m.update(X.data) + m.update(str(X.shape).encode('utf8')) else: - m.update(X.T.data) + if X.flags['C_CONTIGUOUS']: + m.update(X.data) + m.update(str(X.shape).encode('utf8')) + else: + m.update(X.T.data) + m.update(str(X.T.shape).encode('utf8')) hash = m.hexdigest() return hash \ No newline at end of file diff --git a/autosklearn/util/logging.yaml b/autosklearn/util/logging.yaml index 0c7dfb040c..ea97eee8ed 100644 --- a/autosklearn/util/logging.yaml +++ b/autosklearn/util/logging.yaml @@ -33,14 +33,14 @@ loggers: handlers: [file_handler] propagate: no - intensifier: + smac.intensification.intensification.Intensifier: level: INFO handlers: [file_handler, console] - localsearch: + smac.optimizer.local_search.LocalSearch: level: INFO handlers: [file_handler, console] - SMBO: + smac.optimizer.smbo.SMBO: level: INFO handlers: [file_handler, console] diff --git a/ci_scripts/circle_install.sh b/ci_scripts/circle_install.sh new file mode 100644 index 0000000000..a54ddd922c --- /dev/null +++ b/ci_scripts/circle_install.sh @@ -0,0 +1,19 @@ +#!bin/bash + +# on circle ci, each command run with it's own execution context so we have to +# activate the conda testenv on a per command basis. That's why we put calls to +# python (conda) in a dedicated bash script and we activate the conda testenv +# here. +source activate testenv + +# install documentation building dependencies +pip install --upgrade numpy +pip install --upgrade matplotlib setuptools nose coverage sphinx pillow sphinx-gallery sphinx_bootstrap_theme cython numpydoc +# And finally, all other dependencies +cat requirements.txt | xargs -n 1 -L 1 pip install + +python setup.py clean +python setup.py develop + +# pipefail is necessary to propagate exit codes +set -o pipefail && cd doc && make html 2>&1 | tee ~/log.txt \ No newline at end of file diff --git a/ci_scripts/success.sh b/ci_scripts/success.sh index 043da742ff..f77f321429 100644 --- a/ci_scripts/success.sh +++ b/ci_scripts/success.sh @@ -1,13 +1,7 @@ set -e if [[ "$COVERAGE" == "true" ]]; then - # Need to run coveralls from a git checkout, so we copy .coverage - # from TEST_DIR where nosetests has been run cp $TEST_DIR/.coverage $TRAVIS_BUILD_DIR cd $TRAVIS_BUILD_DIR - # Ignore coveralls failures as the coveralls server is not - # very reliable but we don't want travis to report a failure - # in the github UI just because the coverage report failed to - # be published. - coveralls || echo "Coveralls upload failed" + codecov fi \ No newline at end of file diff --git a/ci_scripts/test.sh b/ci_scripts/test.sh index d2e47589a4..2a0a9d2174 100644 --- a/ci_scripts/test.sh +++ b/ci_scripts/test.sh @@ -10,7 +10,7 @@ test_dir=$cwd/test/ cd $TEST_DIR if [[ "$COVERAGE" == "true" ]]; then - nosetests -sv --with-coverage --cover-package=$MODULE $test_dir + nosetests --no-path-adjustment -sv --with-coverage --cover-package=$MODULE $test_dir else - nosetests -sv $test_dir + nosetests --no-path-adjustment -sv $test_dir fi diff --git a/circle.yml b/circle.yml index a63ccfe364..884024a258 100644 --- a/circle.yml +++ b/circle.yml @@ -1,8 +1,7 @@ machine: - python: - version: 3.4.3 - environment: + PATH: /home/ubuntu/miniconda/bin:$PATH + # The github organization or username of the repository which hosts the # project and documentation. USERNAME: "automl" @@ -20,30 +19,23 @@ dependencies: # Various dependencies pre: + # Get rid of existing virtualenvs on circle ci as they conflict with conda. + # From nilearn: https://github.com/nilearn/nilearn/blob/master/circle.yml + - cd && rm -rf ~/.pyenv && rm -rf ~/virtualenvs # from scikit-learn contrib - sudo -E apt-get -yq remove texlive-binaries --purge - - sudo apt-get update - - sudo apt-get install libatlas-dev libatlas3gf-base - - sudo apt-get install build-essential python-dev python-setuptools - # upgrade pip and wheel to allow for fast installation - - pip install wheel pip --upgrade - # install numpy first as it is a compile time dependency for other packages - - pip install --upgrade numpy - # install documentation building dependencies - - pip install --upgrade matplotlib setuptools nose coverage sphinx pillow sphinx-gallery sphinx_bootstrap_theme cython numpydoc - # Installing required packages for `make -C doc check command` to work. - sudo -E apt-get -yq update - sudo -E apt-get -yq --no-install-suggests --no-install-recommends --force-yes install dvipng texlive-latex-base texlive-latex-extra - # Installing packages to build the random forest - # finally install the requirements of the package to allow autodoc - - pip install -r requirements.txt + # Conda installation + - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh + - bash ~/miniconda.sh -b -p $HOME/miniconda + - conda update --yes conda + - conda create -n testenv --yes python=3.6 pip wheel nose gcc swig # The --user is needed to let sphinx see the source and the binaries # The pipefail is requested to propagate exit code override: - - python setup.py clean - - python setup.py develop - - set -o pipefail && cd doc && make html 2>&1 | tee ~/log.txt + - source ci_scripts/circle_install.sh test: # Grep error on the documentation override: @@ -63,7 +55,7 @@ general: - "doc/_build/html" - "~/log.txt" # Restric the build to the branch master only - branches: - only: - - development - - master + #branches: + # only: + # - development + # - master diff --git a/doc/api.rst b/doc/api.rst index 6f22fadccb..0e0bc48c14 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -5,9 +5,11 @@ APIs **** +============ Main modules ============ +~~~~~~~~~~~~~~ Classification ~~~~~~~~~~~~~~ @@ -15,6 +17,7 @@ Classification :members: :inherited-members: show_models, fit_ensemble, refit +~~~~~~~~~~ Regression ~~~~~~~~~~ @@ -22,6 +25,73 @@ Regression :members: :inherited-members: show_models, fit_ensemble, refit +======= +Metrics +======= + +.. autofunction:: autosklearn.metrics.make_scorer + +~~~~~~~~~~~~~~~~ +Built-in Metrics +~~~~~~~~~~~~~~~~ + +Classification +~~~~~~~~~~~~~~ + +.. autoclass:: autosklearn.metrics.accuracy + +.. autoclass:: autosklearn.metrics.balanced_accuracy + +.. autoclass:: autosklearn.metrics.f1 + +.. autoclass:: autosklearn.metrics.f1_macro + +.. autoclass:: autosklearn.metrics.f1_micro + +.. autoclass:: autosklearn.metrics.f1_samples + +.. autoclass:: autosklearn.metrics.f1_weighted + +.. autoclass:: autosklearn.metrics.roc_auc + +.. autoclass:: autosklearn.metrics.precision + +.. autoclass:: autosklearn.metrics.precision_macro + +.. autoclass:: autosklearn.metrics.precision_micro + +.. autoclass:: autosklearn.metrics.precision_samples + +.. autoclass:: autosklearn.metrics.precision_weighted + +.. autoclass:: autosklearn.metrics.average_precision + +.. autoclass:: autosklearn.metrics.recall + +.. autoclass:: autosklearn.metrics.recall_macro + +.. autoclass:: autosklearn.metrics.recall_micro + +.. autoclass:: autosklearn.metrics.recall_samples + +.. autoclass:: autosklearn.metrics.recall_weighted + +.. autoclass:: autosklearn.metrics.log_loss + +.. autoclass:: autosklearn.metrics.pac_score + +Regression +~~~~~~~~~~ + +.. autoclass:: autosklearn.metrics.r2 + +.. autoclass:: autosklearn.metrics.mean_squared_error + +.. autoclass:: autosklearn.metrics.mean_absolute_error + +.. autoclass:: autosklearn.metrics.median_absolute_error + +==================== Extension Interfaces ==================== diff --git a/doc/conf.py b/doc/conf.py index afd7ed3c39..6b93d98691 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -131,9 +131,11 @@ # be in the form [(name, page), ..] 'navbar_links': [ ('Start', 'index'), + ('Releases', 'releases'), + ('Installation', 'installation'), + ('Manual', 'manual'), ('API', 'api'), ('Extending', 'extending'), - ('Manual', 'manual'), ], # Render the next and previous page links in navbar. (Default: true) diff --git a/doc/extending.rst b/doc/extending.rst index d7595614c4..b1dbe5fa2b 100644 --- a/doc/extending.rst +++ b/doc/extending.rst @@ -8,7 +8,7 @@ Extending auto-sklearn auto-sklearn can be easily extended with new classification, regression and feature preprocessing methods. In order to do so, a user has to implement a -wrapper class and make it known to auto-sklearn. This manual will walk you +wrapper class and register it to auto-sklearn. This manual will walk you through the process. diff --git a/doc/index.rst b/doc/index.rst index 592af974cc..fddf413fa7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -27,14 +27,12 @@ Example ******* >>> import autosklearn.classification - >>> import sklearn.cross_validation + >>> import sklearn.model_selection >>> import sklearn.datasets >>> import sklearn.metrics - >>> digits = sklearn.datasets.load_digits() - >>> X = digits.data - >>> y = digits.target + >>> X, y = sklearn.datasets.load_digits(return_X_y=True) >>> X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) >>> automl = autosklearn.classification.AutoSklearnClassifier() >>> automl.fit(X_train, y_train) >>> y_hat = automl.predict(X_test) @@ -44,34 +42,12 @@ Example This will run for one hour should result in an accuracy above 0.98. -Installation -************ -**Prerequisities**: *auto-sklearn* is written in python and was developed -with Ubuntu. It should run on other Linux distributions, but won't work on a MAC -or on a windows PC. We aim to always support the two latests python versions, -which are 3.4 and 3.5 at the moment. It is built around scikit-learn 0.17.1 and -needs a compiler for C++ 11. - -Please install all dependencies manually with: - -.. code:: bash - - curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | xargs -n 1 -L 1 pip install - -Then install *auto-sklearn* - -.. code:: bash - - pip install auto-sklearn - -We recommend installing *auto-sklearn* into a `virtual environment -`_. - Manual ****** -* :ref:`API` +* :ref:`installation` * :ref:`manual` +* :ref:`API` * :ref:`extending` @@ -108,7 +84,7 @@ references to the following paper: Contributing ************ -We appreciate all contribution to auto-sklearn, from bug reports, +We appreciate all contribution to auto-sklearn, from bug reports and documentation to new features. If you want to contribute to the code, you can pick an issue from the `issue tracker `_ which is marked with `Needs contributer`. @@ -121,24 +97,5 @@ which is marked with `Needs contributer`. .com/automl/auto-sklearn/issues>`_ before starting to work. When developing new features, please create a new branch from the development -branch. Prior to submitting a pull request, make sure that all tests are +branch. When to submitting a pull request, make sure that all tests are still passing. - -Contributors -************ - -* Matthias Feurer -* Katharina Eggensperger -* Jost Tobias Springenberg -* Aaron Klein -* Anatolii Domashnev -* Alexander Sapronov -* Stefan Falkner -* Manuel Blum -* Hector Mendoza -* Farooq Ahmed Zuberi -* Frank Hutter -* Diego Kobylkin -* Marius Lindauer - - diff --git a/doc/installation.rst b/doc/installation.rst new file mode 100644 index 0000000000..4ffe17ab28 --- /dev/null +++ b/doc/installation.rst @@ -0,0 +1,102 @@ +:orphan: + +.. _installation: + +============ +Installation +============ + +System requirements +=================== + +auto-sklearn has the following system requirements: + +* Linux operating system (for example Ubuntu), +* Python (>=3.4). +* C++ compiler (with C++11 supports) and SWIG (version 3.0 or later) + +For an explanation of missing Microsoft Windows and MAC OSX support please +check the Section `Windows/OSX compability`_. + +Installing auto-sklearn +======================= + +Please install all dependencies manually with: + +.. code:: bash + + curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | xargs -n 1 -L 1 pip install + +Then install *auto-sklearn*: + +.. code:: bash + + pip install auto-sklearn + +We recommend installing *auto-sklearn* into a `virtual environment +`_ or an `Anaconda +environment `_. + +Ubuntu installation +=================== + +To provide a C++11 building environment and the lateste SWIG version on Ubuntu, +run: + +.. code:: bash + + sudo apt-get install build-essential swig + + +Anaconda installation +===================== + +Anaconda does not ship *auto-sklearn*, and there are no conda packages for +*auto-sklearn*. Thus, it is easiest to install *auto-sklearn* as detailed in +the Section `Installing auto-sklearn`_. + +A common installation problem under recent Linux distribution is the +incompability of the compiler version used to compile the Python binary +shipped by AnaConda and the compiler installed by the distribution. This can +be solved by istalling the *gcc* compiler shipped with AnaConda (as well as +*swig*): + +.. code:: bash + + conda install gcc swig + + +Windows/OSX compability +======================= + +Windows +~~~~~~~ + +*auto-sklearn* relies heavily on the Python module ``resource``. ``resource`` +is part of Python's `Unix Specific Services `_ +and not available on a Windows machine. Therefore, it is not possible to run +*auto-sklearn* on a Windows machine. + +Possible solutions (not tested): + +* Windows 10 bash shell +* virtual machine +* docker image + +Mac OSX +~~~~~~~ + +Auto-sklearn is known to work on OSX systems. Nevertheless, there are two +issues holding us back from actively supporting OSX: + +* The ``resource`` module cannot enforce a memory limit on a Python process + (see `SMAC3/issues/115 `_). +* OSX machines on `travis-ci `_ take more than 30 + minutes to spawn. This makes it impossible for us to run unit tests for + *auto-sklearn* and its dependencies `SMAC3 `_ + and `ConfigSpace `_. + +Possible solutions (not tested): + +* virtual machine +* docker image diff --git a/doc/manual.rst b/doc/manual.rst index 9bb3c7209c..d21bb1412a 100644 --- a/doc/manual.rst +++ b/doc/manual.rst @@ -2,28 +2,98 @@ .. _manual: +====== Manual -~~~~~~ +====== This manual shows how to use several aspects of auto-sklearn. It either references the examples where possible or explains certain configurations. +Examples +======== + +*auto-sklearn* comes with the following examples which demonstrate several +aspects of its usage: + +* `Holdout `_ +* `Cross-validation `_ +* `Parallel usage `_ +* `Sequential usage `_ +* `Regression `_ +* `Continuous and Categorical Data `_ +* `Using Custom metrics `_ + + +Time and memory limits +====================== + +A crucial feature of *auto-sklearn* is limiting the resources (memory and +time) which the scikit-learn algorithms are allowed to use. Especially for +large datasets, on which algorithms can take several hours and make the +machine swap, it is important to stop the evaluations after some time in order +to make progress in a reasonable amount of time. Setting the resource limits +is therefore a tradeoff between optimization time and the number of models +that can be tested. + +While *auto-sklearn* alleviates manual hyperparameter tuning, the user still +has to set memory and time limits. For most datasets a memory limit of 3GB or +6GB as found on most modern computers is sufficient. For the time limits it +is harder to give clear guidelines. If possible, a good default is a total +time limit of one day, and a time limit of 30 minutes for a single run. + +Further guidelines can be found in +`auto-sklearn/issues/142 `_. + +Restricting the Searchspace +=========================== + +Instead of using all available estimators, it is possible to restrict +*auto-sklearn*'s searchspace. The following shows an example of how to exclude +all preprocessing methods and restrict the configuration space to only +random forests. + +>>> import autosklearn.classification +>>> automl = autosklearn.classification.AutoSklearnClassifier( +>>> include_estimators=["random_forest", ], exclude_estimators=None, +>>> include_preprocessors=["no_preprocessing", ], exclude_preprocessors=None) +>>> cls.fit(X_train, y_train) +>>> predictions = cls.predict(X_test, y_test) + +**Note:** The strings used to identify estimators and preprocessors are the filenames without *.py*. + +For a full list please have a look at the source code (in `autosklearn/pipeline/components/`): + + * `Classifiers `_ + * `Regressors `_ + * `Preprocessors `_ + +Turning off preprocessing +~~~~~~~~~~~~~~~~~~~~~~~~ + +Preprocessing in *auto-sklearn* is divided into data preprocessing and +feature preprocessing. Data preprocessing includes One-Hot encoding of +categorical features, imputation of missing values and the normalization of +features or samples. These steps currently cannot be turned off. Feature +preprocessing is a single transformer which implements for example feature +selection or transformation of features into a different space (i.e. PCA). +This can be turned off by setting +``include_preprocessors=["no_preprocessing"]`` as shown in the example above. + Resampling strategies -********************* +===================== -Examples for using holdout and cross-validation can be found in the example -directory of auto-sklearn. +Examples for using holdout and cross-validation can be found in `auto-sklearn/examples/ `_ Parallel computation -******************** +==================== -auto-sklearn supports parallel execution by data sharing on a shared file +*auto-sklearn* supports parallel execution by data sharing on a shared file system. In this mode, the SMAC algorithm shares the training data for it's model by writing it to disk after every iteration. At the beginning of each iteration, SMAC loads all newly found data points. An example can be found in the example directory. -In it's default mode, auto-sklearn already uses two cores. The first one is +In it's default mode, *auto-sklearn* already uses two cores. The first one is used for model building, the second for building an ensemble every time a new machine learning model has finished training. The file `example_sequential .py` in the example directory describes how to run these tasks sequentially @@ -31,16 +101,32 @@ to use only a single core at a time. Furthermore, depending on the installation of scikit-learn and numpy, the model building procedure may use up to all cores. Such behaviour is -unintended by auto-sklearn and is most likely due to numpy being installed +unintended by *auto-sklearn* and is most likely due to numpy being installed from `pypi` as a binary wheel (`see here `_). Executing ``export OPENBLAS_NUM_THREADS=1`` should disable such behaviours and make numpy only use a single core at a time. Model persistence -***************** +================= -auto-sklearn is mostly a wrapper around scikit-learn. Therefore, it is +*auto-sklearn* is mostly a wrapper around scikit-learn. Therefore, it is possible to follow the `persistence example `_ -from scikit-learn. \ No newline at end of file +from scikit-learn. + +Vanilla auto-sklearn +==================== + +In order to obtain *vanilla auto-sklearn* as used in `Efficient and Robust Automated Machine Learning +`_ +set ``ensemble_size=1`` and ``initial_configurations_via_metalearning=0``: + +>>> import autosklearn.classification +>>> automl = autosklearn.classification.AutoSklearnClassifier( +>>> ensemble_size=1, initial_configurations_via_metalearning=0) + +An ensemble of size one will result in always choosing the current best model +according to its performance on the validation set. Setting the initial +configurations found by meta-learning to zero makes *auto-sklearn* use the +regular SMAC algorithm for suggesting new hyperparameter configurations. \ No newline at end of file diff --git a/doc/releases.rst b/doc/releases.rst new file mode 100644 index 0000000000..cf18a048ec --- /dev/null +++ b/doc/releases.rst @@ -0,0 +1,61 @@ +:orphan: + +.. _releases: + +.. + The following command allows to retrieve all commiters since a specified + commit. From http://stackoverflow.com/questions/6482436/list-of-authors-in-git-since-a-given-commit + git log 2e29eba.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while () { print unless $dedupe{$_}++}' + +======== +Releases +======== + +Version 0.2.0 +============= + +Major changes +~~~~~~~~~~~~~ + +* **auto-sklearn supports custom metrics and all metrics included in + scikit-learn**. Different metrics can now be passed to the ``fit()``-method + estimator objects, for example + ``AutoSklearnClassifier.fit(metric='roc_auc')``. +* Upgrade to scikit-learn 0.18.1. +* Drop XGBoost as the latest release (0.6a2) does not work when spawned by + the pyninsher. +* *auto-sklearn* can use multiprocessing in calls to ``predict()`` and + ``predict_proba``. By `Laurent Sorber `_. + +Contributors +~~~~~~~~~~~~ + +* Matthias Feurer +* Katharina Eggensperger +* Laurent Sorber +* Rafael Calsaverini + +Version 0.1.x +============= + +There are no release notes for auto-sklearn prior to version 0.2.0. + +Contributors +~~~~~~~~~~~~ + +* Matthias Feurer +* Katharina Eggensperger +* Aaron Klein +* Jost Tobias Springenberg +* Anatolii Domashnev +* Stefan Falkner +* Alexander Sapronov +* Manuel Blum +* Diego Kobylkin +* Jaidev Deshpande +* Jongheon Jeong +* Hector Mendoza +* Timothy J Laurent +* Marius Lindauer +* _329_ +* Iver Jordal diff --git a/example/example_crossvalidation.py b/example/example_crossvalidation.py index c3688484b4..8e454a8893 100644 --- a/example/example_crossvalidation.py +++ b/example/example_crossvalidation.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- -import sklearn.cross_validation +import sklearn.model_selection import sklearn.datasets import sklearn.metrics @@ -7,11 +7,9 @@ def main(): - digits = sklearn.datasets.load_digits() - X = digits.data - y = digits.target + X, y = sklearn.datasets.load_digits(return_X_y=True) X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, diff --git a/example/example_feature_types.py b/example/example_feature_types.py new file mode 100644 index 0000000000..baa1bf2387 --- /dev/null +++ b/example/example_feature_types.py @@ -0,0 +1,52 @@ +# -*- encoding: utf-8 -*- +import sklearn.model_selection +import sklearn.datasets +import sklearn.metrics + +import autosklearn.classification + +try: + import openml +except ImportError: + print("#"*80 + """ + To run this example you need to install openml-python: + + pip install git+https://github.com/renatopp/liac-arff + pip install requests xmltodict + pip install git+https://github.com/openml/openml-python@develop --no-deps\n""" + + "#"*80) + raise + + +def main(): + # Load adult dataset from openml.org, see https://www.openml.org/t/2117 + openml.config.apikey = '610344db6388d9ba34f6db45a3cf71de' + + task = openml.tasks.get_task(2117) + train_indices, test_indices = task.get_train_test_split_indices() + X, y = task.get_X_and_y() + + X_train = X[train_indices] + y_train = y[train_indices] + X_test = X[test_indices] + y_test = y[test_indices] + + dataset = task.get_dataset() + _, _, categorical_indicator = dataset.\ + get_data(target=task.target_name, return_categorical_indicator=True) + + # Create feature type list from openml.org indicator and run autosklearn + feat_type = ['Categorical' if ci else 'Numerical' + for ci in categorical_indicator] + + cls = autosklearn.classification.\ + AutoSklearnClassifier(time_left_for_this_task=120, + per_run_time_limit=30) + cls.fit(X_train, y_train, feat_type=feat_type) + + predictions = cls.predict(X_test) + print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions)) + + +if __name__ == "__main__": + main() diff --git a/example/example_holdout.py b/example/example_holdout.py index 9c968e4201..41431e0ec2 100644 --- a/example/example_holdout.py +++ b/example/example_holdout.py @@ -1,32 +1,14 @@ -from operator import itemgetter - -import numpy as np -import sklearn.cross_validation +import sklearn.model_selection import sklearn.datasets import sklearn.metrics import autosklearn.classification -# Utility function to report best scores -# from http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py -def report(grid_scores, n_top=3): - top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top] - for i, score in enumerate(top_scores): - print("Model with rank: {0}".format(i + 1)) - print("Mean validation score: {0:.3f} (std: {1:.3f})".format( - score.mean_validation_score, - np.std(score.cv_validation_scores))) - print("Parameters: {0}".format(score.parameters)) - print("") - - def main(): - digits = sklearn.datasets.load_digits() - X = digits.data - y = digits.target + X, y = sklearn.datasets.load_digits(return_X_y=True) X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, @@ -35,10 +17,6 @@ def main(): disable_evaluator_output=False) automl.fit(X_train, y_train, dataset_name='digits') - # Print the best models together with their scores - if all scores are - # unreasonably bad (around 0.0) you should have a look into the logging - # file to figure out the error - report(automl.grid_scores_) # Print the final ensemble constructed by auto-sklearn. print(automl.show_models()) predictions = automl.predict(X_test) diff --git a/example/example_metrics.py b/example/example_metrics.py new file mode 100644 index 0000000000..62e61d0b39 --- /dev/null +++ b/example/example_metrics.py @@ -0,0 +1,89 @@ +# -*- encoding: utf-8 -*- +import numpy as np + +import sklearn.model_selection +import sklearn.datasets +import sklearn.metrics + +import autosklearn.classification +import autosklearn.metrics + + + +def accuracy(solution, prediction): + # function defining accuracy + return np.mean(solution == prediction) + + +def accuracy_wk(solution, prediction, dummy): + # function defining accuracy and accepting an additional argument + assert dummy is None + return np.mean(solution == prediction) + + +def main(): + + X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=1) + + # Print a list of available metrics + print("Available CLASSIFICATION metrics autosklearn.metrics.*:") + print("\t*" + "\n\t*".join(autosklearn.metrics.CLASSIFICATION_METRICS)) + + print("Available REGRESSION autosklearn.metrics.*:") + print("\t*" + "\n\t*".join(autosklearn.metrics.REGRESSION_METRICS)) + + # First example: Use predefined accuracy metric + print("#"*80) + print("Use predefined accuracy metric") + cls = autosklearn.classification.\ + AutoSklearnClassifier(time_left_for_this_task=60, + per_run_time_limit=30, seed=1) + cls.fit(X_train, y_train, metric=autosklearn.metrics.accuracy) + + predictions = cls.predict(X_test) + print("Accuracy score {:g} using {:s}". + format(sklearn.metrics.accuracy_score(y_test, predictions), + cls._automl._automl._metric.name)) + + # Second example: Use own accuracy metric + print("#"*80) + print("Use self defined accuracy accuracy metric") + accuracy_scorer = autosklearn.metrics.make_scorer(name="accu", + score_func=accuracy, + greater_is_better=True, + needs_proba=False, + needs_threshold=False) + cls = autosklearn.classification.\ + AutoSklearnClassifier(time_left_for_this_task=60, + per_run_time_limit=30, seed=1) + cls.fit(X_train, y_train, metric=accuracy_scorer) + + predictions = cls.predict(X_test) + print("Accuracy score {:g} using {:s}". + format(sklearn.metrics.accuracy_score(y_test, predictions), + cls._automl._automl._metric.name)) + + # Third example: Use own accuracy metric with additional argument + print("#"*80) + print("Use self defined accuracy with additional argument") + accuracy_scorer = autosklearn.metrics.make_scorer(name="accu_add", + score_func=accuracy_wk, + greater_is_better=True, + needs_proba=False, + needs_threshold=False, + dummy=None) + cls = autosklearn.classification.\ + AutoSklearnClassifier(time_left_for_this_task=60, + per_run_time_limit=30, seed=1) + cls.fit(X_train, y_train, metric=accuracy_scorer) + + predictions = cls.predict(X_test) + print("Accuracy score {:g} using {:s}". + format(sklearn.metrics.accuracy_score(y_test, predictions), + cls._automl._automl._metric.name)) + + +if __name__ == "__main__": + main() diff --git a/example/example_parallel.py b/example/example_parallel.py index 43c24ef414..e5a0b54fda 100644 --- a/example/example_parallel.py +++ b/example/example_parallel.py @@ -2,10 +2,11 @@ import multiprocessing import shutil -import sklearn.cross_validation +import sklearn.model_selection import sklearn.datasets import sklearn.metrics +from autosklearn.metrics import accuracy from autosklearn.classification import AutoSklearnClassifier from autosklearn.constants import * @@ -42,10 +43,10 @@ def spawn_classifier(seed, dataset_name): # models. # 3. all instances of the AutoSklearnClassifier must have a different seed! automl = AutoSklearnClassifier( - time_left_for_this_task=120, # sec., how long should this seed fit + time_left_for_this_task=60, # sec., how long should this seed fit # process run - per_run_time_limit=60, # sec., each model may only take this long before it's killed - ml_memory_limit=1024, # MB, memory limit imposed on each call to a ML algorithm + per_run_time_limit=15, # sec., each model may only take this long before it's killed + ml_memory_limit=1024, # MB, memory limit imposed on each call to a ML algorithm shared_mode=True, # tmp folder will be shared between seeds tmp_folder=tmp_folder, output_folder=output_folder, @@ -57,11 +58,9 @@ def spawn_classifier(seed, dataset_name): if __name__ == '__main__': - digits = sklearn.datasets.load_digits() - X = digits.data - y = digits.target + X, y = sklearn.datasets.load_digits(return_X_y=True) X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) processes = [] for i in range(4): # set this at roughly half of your cores @@ -87,7 +86,7 @@ def spawn_classifier(seed, dataset_name): # necessary automl.fit_ensemble(y_train, task=MULTICLASS_CLASSIFICATION, - metric=ACC_METRIC, + metric=accuracy, precision='32', dataset_name='digits', ensemble_size=20, diff --git a/example/example_regression.py b/example/example_regression.py index 49a85a6d11..146c4bae44 100644 --- a/example/example_regression.py +++ b/example/example_regression.py @@ -1,5 +1,5 @@ # -*- encoding: utf-8 -*- -import sklearn.cross_validation +import sklearn.model_selection import sklearn.datasets import sklearn.metrics @@ -7,12 +7,10 @@ def main(): - boston = sklearn.datasets.load_boston() - X = boston.data - y = boston.target + X, y = sklearn.datasets.load_boston(return_X_y=True) feature_types = (['numerical'] * 3) + ['categorical'] + (['numerical'] * 9) X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=120, per_run_time_limit=30, diff --git a/example/example_sequential.py b/example/example_sequential.py index fa3b790c7c..c4d8ec17d4 100644 --- a/example/example_sequential.py +++ b/example/example_sequential.py @@ -1,4 +1,4 @@ -import sklearn.cross_validation +import sklearn.model_selection import sklearn.datasets import sklearn.metrics @@ -6,11 +6,9 @@ def main(): - digits = sklearn.datasets.load_digits() - X = digits.data - y = digits.target + X, y = sklearn.datasets.load_digits(return_X_y=True) X_train, X_test, y_train, y_test = \ - sklearn.cross_validation.train_test_split(X, y, random_state=1) + sklearn.model_selection.train_test_split(X, y, random_state=1) automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=120, per_run_time_limit=30, diff --git a/requirements.txt b/requirements.txt index 2fd42bf1c0..c873ee3907 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,25 +1,22 @@ -unittest2 setuptools nose six Cython -numpy>=1.9.0,<1.12 +numpy>=1.9.0 scipy>=0.14.1 -scikit-learn==0.17.1 +scikit-learn==0.18.1 lockfile joblib psutil pyyaml -ConfigArgParse liac-arff pandas -xgboost==0.4a30 -ConfigSpace>=0.3.1,<0.4 +ConfigSpace>=0.3.3,<0.4 pynisher>=0.4 -pyrfr -smac==0.3.0 +pyrfr>=0.4.0 +smac==0.5.0 diff --git a/scripts/02_retrieve_metadata.py b/scripts/02_retrieve_metadata.py index f10e456e9a..6687fd444e 100644 --- a/scripts/02_retrieve_metadata.py +++ b/scripts/02_retrieve_metadata.py @@ -11,6 +11,7 @@ from ConfigSpace.configuration_space import Configuration from autosklearn.constants import * +from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS from autosklearn.util import pipeline @@ -29,15 +30,15 @@ def retrieve_matadata(validation_directory, metric, configuration_space, possible_experiment_directories = os.listdir(validation_directory) for ped in possible_experiment_directories: - task_name, seed = ped.split('-')[0], ped.split('-')[1] - ped = os.path.join(validation_directory, ped) + task_name = None + ped = os.path.join(validation_directory, ped) if not os.path.exists(ped) or not os.path.isdir(ped): continue - print("Going through directory %s" % ped) - validation_trajectory_file = os.path.join(ped, 'validation_trajectory.json') + validation_trajectory_file = os.path.join(ped, 'smac3-output_1_run1', + 'validation_trajectory.json') with open(validation_trajectory_file) as fh: validation_trajectory = json.load(fh) @@ -45,7 +46,8 @@ def retrieve_matadata(validation_directory, metric, configuration_space, best_configuration = None for entry in validation_trajectory: config = entry[2] - score = entry[-1][str(metric)] + task_name = entry[-2] + score = entry[-1].get(str(metric), np.inf) if score < best_value: try: @@ -55,7 +57,12 @@ def retrieve_matadata(validation_directory, metric, configuration_space, except: pass - if best_configuration in configurations_to_ids: + if task_name is None: + continue + + if best_configuration is None: + continue + elif best_configuration in configurations_to_ids: config_id = configurations_to_ids[best_configuration] else: config_id = len(configurations_to_ids) @@ -72,9 +79,9 @@ def write_output(outputs, configurations, output_dir, configuration_space, arff_object = dict() arff_object['attributes'] = [('instance_id', 'STRING'), ('repetition', 'NUMERIC'), - ('algorithm', 'STRING')] + \ - [(METRIC_TO_STRING[metric], 'NUMERIC')] + \ - [('runstatus', + ('algorithm', 'STRING'), + (metric, 'NUMERIC'), + ('runstatus', ['ok', 'timeout', 'memout', 'not_applicable', 'crash', 'other'])] arff_object['relation'] = "ALGORITHM_RUNS" @@ -124,7 +131,7 @@ def write_output(outputs, configurations, output_dir, configuration_space, for configuration_id in sorted(configurations.keys())]) description['algorithms_stochastic'] = \ ",".join([]) - description['performance_measures'] = METRIC_TO_STRING[metric] + description['performance_measures'] = metric description['performance_type'] = 'solution_quality' with open(os.path.join(output_dir, "description.results.txt"), @@ -151,12 +158,12 @@ def main(): if task_type == 'classification': metadata_sets = itertools.product( [0, 1], [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], - [ACC_METRIC, AUC_METRIC, BAC_METRIC, F1_METRIC, PAC_METRIC]) + CLASSIFICATION_METRICS) input_directory = os.path.join(working_directory, 'configuration', 'classification') elif task_type == 'regression': metadata_sets = itertools.product( - [0, 1], [REGRESSION], [A_METRIC, R2_METRIC]) + [0, 1], [REGRESSION], REGRESSION_METRICS) input_directory = os.path.join(working_directory, 'configuration', 'regression') else: @@ -165,20 +172,15 @@ def main(): output_dir = os.path.join(working_directory, 'configuration_results') for sparse, task, metric in metadata_sets: - print(TASK_TYPES_TO_STRING[task], METRIC_TO_STRING[metric], sparse) + print(TASK_TYPES_TO_STRING[task], metric, sparse) output_dir_ = os.path.join(output_dir, '%s_%s_%s' % ( - METRIC_TO_STRING[metric], TASK_TYPES_TO_STRING[task], + metric, TASK_TYPES_TO_STRING[task], 'sparse' if sparse else 'dense')) configuration_space = pipeline.get_configuration_space( {'is_sparse': sparse, 'task': task}) - try: - os.makedirs(output_dir_) - except: - pass - outputs, configurations = retrieve_matadata( validation_directory=input_directory, metric=metric, @@ -188,10 +190,15 @@ def main(): if len(outputs) == 0: print("No output found for %s, %s, %s" % - (STRING_TO_METRIC[metric], TASK_TYPES_TO_STRING[task], + (metric, TASK_TYPES_TO_STRING[task], 'sparse' if sparse else 'dense')) continue + try: + os.makedirs(output_dir_) + except: + pass + write_output(outputs, configurations, output_dir_, configuration_space, metric) diff --git a/scripts/03_calculate_metafeatures.py b/scripts/03_calculate_metafeatures.py index eebfba214a..5b379c112d 100644 --- a/scripts/03_calculate_metafeatures.py +++ b/scripts/03_calculate_metafeatures.py @@ -12,7 +12,7 @@ import pynisher import scipy.sparse -from autosklearn.data.generic_one_hot_encoding import perform_one_hot_encoding +from autosklearn.data.abstract_data_manager import perform_one_hot_encoding from autosklearn.metalearning.metafeatures import metafeatures, metafeature sys.path.append('.') diff --git a/scripts/04_create_aslib_files.py b/scripts/04_create_aslib_files.py index 486b94b79a..c46e88f829 100644 --- a/scripts/04_create_aslib_files.py +++ b/scripts/04_create_aslib_files.py @@ -5,6 +5,7 @@ import arff from autosklearn.constants import * +from autosklearn.metrics import CLASSIFICATION_METRICS, REGRESSION_METRICS if __name__ == "__main__": parser = ArgumentParser() @@ -36,22 +37,21 @@ if task_type == 'classification': metadata_sets = itertools.product( [0, 1], [BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION], - [ACC_METRIC, AUC_METRIC, BAC_METRIC, F1_METRIC, PAC_METRIC]) + CLASSIFICATION_METRICS) input_directory = os.path.join(working_directory, 'configuration', 'classification') elif task_type == 'regression': metadata_sets = itertools.product( - [0, 1], [REGRESSION], [A_METRIC, R2_METRIC]) + [0, 1], [REGRESSION], REGRESSION_METRICS) input_directory = os.path.join(working_directory, 'configuration', 'regression') else: raise ValueError(task_type) for sparse, task, metric in metadata_sets: - print(TASK_TYPES_TO_STRING[task], METRIC_TO_STRING[metric], sparse) + print(TASK_TYPES_TO_STRING[task], metric, sparse) - dir_name = '%s_%s_%s' % (METRIC_TO_STRING[metric], - TASK_TYPES_TO_STRING[task], + dir_name = '%s_%s_%s' % (metric, TASK_TYPES_TO_STRING[task], 'sparse' if sparse else 'dense') output_dir_ = os.path.join(output_dir, dir_name) results_dir_ = os.path.join(results_dir, dir_name) diff --git a/scripts/run_auto-sklearn_for_metadata_generation.py b/scripts/run_auto-sklearn_for_metadata_generation.py index b54d245644..712a0f80ef 100644 --- a/scripts/run_auto-sklearn_for_metadata_generation.py +++ b/scripts/run_auto-sklearn_for_metadata_generation.py @@ -7,7 +7,7 @@ from autosklearn.classification import AutoSklearnClassifier from autosklearn.regression import AutoSklearnRegressor from autosklearn.evaluation import ExecuteTaFuncWithQueue -from autosklearn.constants import * +from autosklearn.metrics import r2, balanced_accuracy from smac.stats.stats import Stats from smac.scenario.scenario import Scenario @@ -42,7 +42,7 @@ os.makedirs(configuration_output_dir) except: pass -tmp_dir = os.path.join(configuration_output_dir, '%d-%d' % (task_id, seed)) +tmp_dir = os.path.join(configuration_output_dir, str(task_id)) automl_arguments = {'time_left_for_this_task': time_limit, 'per_run_time_limit': per_run_time_limit, @@ -60,10 +60,10 @@ if task_type == 'classification': automl = AutoSklearnClassifier(**automl_arguments) - metric = BAC_METRIC + metric = balanced_accuracy elif task_type == 'regression': automl = AutoSklearnRegressor(**automl_arguments) - metric = R2_METRIC + metric = r2 else: raise ValueError(task_type) @@ -88,10 +88,10 @@ memory_limit_factor = 2 for entry in trajectory: - incumbent_id = entry[1] - train_performance = entry[0] + incumbent_id = entry.incumbent_id + train_performance = entry.train_perf if incumbent_id not in incumbent_id_to_model: - config = entry[2] + config = entry.incumbent logger = logging.getLogger('Testing:)') stats = Stats(Scenario({'cutoff_time': per_run_time_limit * 2})) @@ -101,30 +101,26 @@ ta = ExecuteTaFuncWithQueue(backend=automl._automl._automl._backend, autosklearn_seed=seed, resampling_strategy='test', - with_predictions=False, memory_limit=memory_limit_factor * automl_arguments['ml_memory_limit'], disable_file_output=True, logger=logger, stats=stats, - all_scoring_functions=True) - status, cost, runtime, additional_run_info = ta.start(config=config, - instance=None, - cutoff=per_run_time_limit) + all_scoring_functions=True, + metric=metric) + status, cost, runtime, additional_run_info = ta.start( + config=config, instance=None, cutoff=per_run_time_limit*3) + if status == StatusType.SUCCESS: - scores = additional_run_info.split(';') - scores = [score.split(':') for score in scores] - scores = [(score[0].strip(), score[1].strip()) for score in scores] - scores = [(STRING_TO_METRIC[score[0]], score[1]) for score in scores - if score[0] in STRING_TO_METRIC] - scores = {score[0]: float(score[1]) for score in scores} - assert len(scores) > 1, scores + assert len(additional_run_info) > 1, additional_run_info # print(additional_run_info) - validated_trajectory.append(list(entry) + [scores]) + validated_trajectory.append(list(entry) + [task_id] + + [additional_run_info]) validated_trajectory = [entry[:2] + [entry[2].get_dictionary()] + entry[3:] for entry in validated_trajectory] -validated_trajectory_file = os.path.join(tmp_dir, 'validation_trajectory.json') +validated_trajectory_file = os.path.join(tmp_dir, 'smac3-output_%d_run1' % seed, + 'validation_trajectory.json') with open(validated_trajectory_file, 'w') as fh: json.dump(validated_trajectory, fh) diff --git a/setup.py b/setup.py index 5c52e3d38f..d4d2062851 100644 --- a/setup.py +++ b/setup.py @@ -12,26 +12,23 @@ ]) requirements = [ - "unittest2", "setuptools", "nose", "six", "Cython", - "numpy>=1.9.0,<1.12", + "numpy>=1.9.0", "scipy>=0.14.1", - "scikit-learn==0.17.1", + "scikit-learn==0.18.1", "lockfile", "joblib", "psutil", "pyyaml", - "ConfigArgParse", "liac-arff", "pandas", - "xgboost==0.4a30", - "ConfigSpace>=0.3.1,<0.4", + "ConfigSpace>=0.3.3,<0.4", "pynisher>=0.4", - "pyrfr", - "smac==0.3.0" + "pyrfr>=0.4,<0.5", + "smac==0.5.0" ] with open("autosklearn/__version__.py") as fh: diff --git a/test/test_automl/base.py b/test/test_automl/base.py index 8d11054dec..573bc0c04a 100644 --- a/test/test_automl/base.py +++ b/test/test_automl/base.py @@ -1,5 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import os import shutil import time diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 2df3f51eba..0eb0837a42 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -12,6 +12,7 @@ from autosklearn.util.backend import Backend, BackendContext from autosklearn.automl import AutoML import autosklearn.automl +from autosklearn.metrics import accuracy import autosklearn.pipeline.util as putil from autosklearn.util import setup_logger, get_logger, backend from autosklearn.constants import * @@ -47,7 +48,7 @@ def test_refit_shuffle_on_fail(self): failing_model = unittest.mock.Mock() failing_model.fit.side_effect = [ValueError(), ValueError(), None] - auto = AutoML(backend, 30, 5) + auto = AutoML(backend, 20, 5) ensemble_mock = unittest.mock.Mock() auto.ensemble_ = ensemble_mock ensemble_mock.get_model_identifiers.return_value = [1] @@ -73,32 +74,33 @@ def test_only_loads_ensemble_models(self): self.assertEqual(models, self.automl.models_) - def test_loads_all_models_if_no_ensemble(self): + def test_check_for_models_if_no_ensemble(self): models = [42] self.automl._backend.load_ensemble.return_value = None - self.automl._backend.load_all_models.return_value = models + self.automl._backend.list_all_models.return_value = models + self.automl._disable_evaluator_output = False self.automl._load_models() - self.assertEqual(models, self.automl.models_) - def test_raises_if_no_models(self): self.automl._backend.load_ensemble.return_value = None - self.automl._backend.load_all_models.return_value = [] + self.automl._backend.list_all_models.return_value = [] self.automl._resampling_strategy = 'holdout' + self.automl._disable_evaluator_output = False self.assertRaises(ValueError, self.automl._load_models) + self.automl._disable_evaluator_output = True + self.automl._load_models() + def test_fit(self): output = os.path.join(self.test_dir, '..', '.tmp_test_fit') self._setUp(output) X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') backend_api = backend.create(output, output) - automl = autosklearn.automl.AutoML(backend_api, 30, 5) - automl.fit(X_train, Y_train) - #print(automl.show_models(), flush=True) - #print(automl.cv_results_, flush=True) + automl = autosklearn.automl.AutoML(backend_api, 20, 5) + automl.fit(X_train, Y_train, metric=accuracy) score = automl.score(X_test, Y_test) self.assertGreaterEqual(score, 0.8) self.assertEqual(automl._task, MULTICLASS_CLASSIFICATION) @@ -107,15 +109,15 @@ def test_fit(self): self._tearDown(output) def test_fit_roar(self): - output = os.path.join(self.test_dir, '..', '.tmp_test_fit') + output = os.path.join(self.test_dir, '..', '.tmp_test_fit_roar') self._setUp(output) X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') backend_api = backend.create(output, output) - automl = autosklearn.automl.AutoML(backend_api, 30, 5, + automl = autosklearn.automl.AutoML(backend_api, 20, 5, initial_configurations_via_metalearning=0, configuration_mode='ROAR') - automl.fit(X_train, Y_train) + automl.fit(X_train, Y_train, metric=accuracy) # print(automl.show_models(), flush=True) # print(automl.cv_results_, flush=True) score = automl.score(X_test, Y_test) @@ -143,12 +145,11 @@ def test_binary_score_and_include(self): Y_test = data[1][200:] backend_api = backend.create(output, output) - automl = autosklearn.automl.AutoML(backend_api, 30, 5, + automl = autosklearn.automl.AutoML(backend_api, 20, 5, include_estimators=['sgd'], include_preprocessors=['no_preprocessing']) - automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION) - #print(automl.show_models(), flush=True) - #print(automl.cv_results_, flush=True) + automl.fit(X_train, Y_train, task=BINARY_CLASSIFICATION, + metric=accuracy) self.assertEqual(automl._task, BINARY_CLASSIFICATION) # TODO, the assumption from above is not really tested here @@ -170,10 +171,10 @@ def test_automl_outputs(self): backend_api = backend.create(output, output) auto = autosklearn.automl.AutoML( - backend_api, 30, 5, + backend_api, 20, 5, initial_configurations_via_metalearning=25, seed=100) - auto.fit_automl_dataset(dataset) + auto.fit_automl_dataset(dataset, accuracy) # pickled data manager (without one hot encoding!) with open(data_manager_file, 'rb') as fh: @@ -224,7 +225,7 @@ def test_do_dummy_prediction(self): backend_api = backend.create(output, output) auto = autosklearn.automl.AutoML( - backend_api, 30, 5, + backend_api, 20, 5, initial_configurations_via_metalearning=25) setup_logger() auto._logger = get_logger('test_do_dummy_predictions') diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index 8e25629e94..4dfada97a7 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -1,5 +1,4 @@ import collections -import gzip import os import pickle import sys @@ -10,10 +9,11 @@ import numpy as np import numpy.ma as npma -from sklearn.grid_search import _CVScoreTuple import autosklearn.pipeline.util as putil from autosklearn.classification import AutoSklearnClassifier +from autosklearn.regression import AutoSklearnRegressor +from autosklearn.metrics import accuracy, f1_macro, mean_squared_error from autosklearn.estimators import AutoMLClassifier from autosklearn.util.backend import Backend, BackendContext from autosklearn.constants import * @@ -25,7 +25,7 @@ class ArrayReturningDummyPredictor(object): def __init__(self, test): self.arr = test - def predict_proba(self, X): + def predict_proba(self, X, *args, **kwargs): return self.arr class EstimatorTest(Base, unittest.TestCase): @@ -37,7 +37,7 @@ class EstimatorTest(Base, unittest.TestCase): # self._setUp(output) # # X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - # automl = AutoSklearnClassifier(time_left_for_this_task=30, + # automl = AutoSklearnClassifier(time_left_for_this_task=20, # per_run_time_limit=5, # tmp_folder=output, # output_folder=output, @@ -95,7 +95,7 @@ def test_fit_pSMAC(self): Y_train = Y_train + 1 Y_test = Y_test + 1 - automl = AutoSklearnClassifier(time_left_for_this_task=30, + automl = AutoSklearnClassifier(time_left_for_this_task=20, per_run_time_limit=5, output_folder=output, tmp_folder=output, @@ -113,7 +113,9 @@ def test_fit_pSMAC(self): with open(true_targets_ensemble_path, 'rb') as fh: true_targets_ensemble = np.load(fh) true_targets_ensemble[-1] = 1 if true_targets_ensemble[-1] != 1 else 0 + true_targets_ensemble = true_targets_ensemble.astype(int) probas = np.zeros((len(true_targets_ensemble), 3), dtype=float) + for i, value in enumerate(true_targets_ensemble): probas[i, value] = 1.0 dummy_predictions_path = os.path.join(output, '.auto-sklearn', @@ -131,7 +133,7 @@ def test_fit_pSMAC(self): backend = Backend(context) backend.save_model(dummy, 30, 1) - automl = AutoSklearnClassifier(time_left_for_this_task=30, + automl = AutoSklearnClassifier(time_left_for_this_task=20, per_run_time_limit=5, output_folder=output, tmp_folder=output, @@ -139,9 +141,8 @@ def test_fit_pSMAC(self): seed=2, initial_configurations_via_metalearning=0, ensemble_size=0) - automl.fit_ensemble(Y_train, - task=MULTICLASS_CLASSIFICATION, - metric=ACC_METRIC, + automl.fit_ensemble(Y_train, task=MULTICLASS_CLASSIFICATION, + metric=accuracy, precision='32', dataset_name='iris', ensemble_size=20, @@ -163,42 +164,6 @@ def test_fit_pSMAC(self): del automl self._tearDown(output) - def test_grid_scores(self): - output = os.path.join(self.test_dir, '..', '.tmp_grid_scores') - self._setUp(output) - - cls = AutoSklearnClassifier(time_left_for_this_task=30, - per_run_time_limit=5, - output_folder=output, - tmp_folder=output, - shared_mode=False, - seed=1, - initial_configurations_via_metalearning=0, - ensemble_size=0) - cls_ = cls.build_automl() - automl = cls_._automl - automl.runhistory_ = unittest.mock.MagicMock() - - RunKey = collections.namedtuple( - 'RunKey', ['config_id', 'instance_id', 'seed']) - - RunValue = collections.namedtuple( - 'RunValue', ['cost', 'time', 'status', 'additional_info']) - - runhistory = dict() - runhistory[RunKey(1, 1, 1)] = RunValue(1, 1, 1, '') - automl.runhistory_.data = runhistory - grid_scores_ = automl.grid_scores_ - - self.assertIsInstance(grid_scores_[0], _CVScoreTuple) - # In the runhistory we store losses, thus the score is zero - self.assertEqual(grid_scores_[0].mean_validation_score, 0) - self.assertEqual(grid_scores_[0].cv_validation_scores, [0]) - self.assertIsInstance(grid_scores_[0].parameters, unittest.mock.MagicMock) - - del automl - self._tearDown(output) - def test_cv_results(self): # TODO restructure and actually use real SMAC output from a long run # to do this unittest! @@ -206,7 +171,7 @@ def test_cv_results(self): self._setUp(output) X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - cls = AutoSklearnClassifier(time_left_for_this_task=30, + cls = AutoSklearnClassifier(time_left_for_this_task=20, per_run_time_limit=5, output_folder=output, tmp_folder=output, @@ -249,17 +214,17 @@ def test_multiclass_prediction(self): np.testing.assert_array_equal(expected_result, actual_result) def test_multilabel_prediction(self): - classes = [['a', 'b', 'c'], [13, 17]] - predicted_probabilities = [[[0, 0, 0.99], [0.99, 0]], - [[0, 0.99, 0], [0.99, 0]], - [[0.99, 0, 0], [0, 0.99]], - [[0, 0.99, 0], [0, 0.99]], - [[0, 0, 0.99], [0, 0.99]]] - predicted_indexes = [[2, 0], [1, 0], [0, 1], [1, 1], [2, 1]] - expected_result = np.array([['c', 13], ['b', 13], ['a', 17], ['b', 17], ['c', 17]], dtype=object) + classes = [[1, 2], [13, 17]] + predicted_probabilities = [[0.99, 0], + [0.99, 0], + [0, 0.99], + [0.99, 0.99], + [0.99, 0.99]] + predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]]) + expected_result = np.array([[2, 13], [2, 13], [1, 17], [2, 17], [2, 17]]) automl_mock = unittest.mock.Mock() - automl_mock.predict.return_value = np.matrix(predicted_probabilities) + automl_mock.predict.return_value = np.array(predicted_probabilities) classifier = AutoMLClassifier(automl_mock) classifier._classes = list(map(np.array, classes)) @@ -275,7 +240,7 @@ def test_can_pickle_classifier(self): self._setUp(output) X_train, Y_train, X_test, Y_test = putil.get_dataset('iris') - automl = AutoSklearnClassifier(time_left_for_this_task=30, + automl = AutoSklearnClassifier(time_left_for_this_task=20, per_run_time_limit=5, tmp_folder=output, output_folder=output) @@ -284,7 +249,7 @@ def test_can_pickle_classifier(self): initial_predictions = automl.predict(X_test) initial_accuracy = sklearn.metrics.accuracy_score(Y_test, initial_predictions) - self.assertTrue(initial_accuracy > 0.75) + self.assertGreaterEqual(initial_accuracy, 0.75) # Test pickle dump_file = os.path.join(output, 'automl.dump.pkl') @@ -298,7 +263,7 @@ def test_can_pickle_classifier(self): restored_predictions = restored_automl.predict(X_test) restored_accuracy = sklearn.metrics.accuracy_score(Y_test, restored_predictions) - self.assertTrue(restored_accuracy > 0.75) + self.assertGreaterEqual(restored_accuracy, 0.75) self.assertEqual(initial_accuracy, restored_accuracy) @@ -312,6 +277,59 @@ def test_can_pickle_classifier(self): restored_predictions = restored_automl.predict(X_test) restored_accuracy = sklearn.metrics.accuracy_score(Y_test, restored_predictions) - self.assertTrue(restored_accuracy > 0.75) + self.assertGreaterEqual(restored_accuracy, 0.75) self.assertEqual(initial_accuracy, restored_accuracy) + + def test_multilabel(self): + output = os.path.join(self.test_dir, '..', '.tmp_multilabel_fit') + self._setUp(output) + + X_train, Y_train, X_test, Y_test = putil.get_dataset( + 'iris', make_multilabel=True) + automl = AutoSklearnClassifier(time_left_for_this_task=20, + per_run_time_limit=5, + tmp_folder=output, + output_folder=output) + + automl.fit(X_train, Y_train) + predictions = automl.predict(X_test) + self.assertEqual(predictions.shape, (50, 3)) + score = f1_macro(Y_test, predictions) + self.assertGreaterEqual(score, 0.9) + + def test_binary(self): + output = os.path.join(self.test_dir, '..', '.tmp_binary_fit') + self._setUp(output) + + X_train, Y_train, X_test, Y_test = putil.get_dataset( + 'iris', make_binary=True) + automl = AutoSklearnClassifier(time_left_for_this_task=20, + per_run_time_limit=5, + tmp_folder=output, + output_folder=output) + + automl.fit(X_train, Y_train) + predictions = automl.predict(X_test) + self.assertEqual(predictions.shape, (50, )) + score = accuracy(Y_test, predictions) + self.assertGreaterEqual(score, 0.9) + + +class AutoMLRegressorTest(Base, unittest.TestCase): + def test_regression(self): + output = os.path.join(self.test_dir, '..', '.tmp_regression_fit') + self._setUp(output) + + X_train, Y_train, X_test, Y_test = putil.get_dataset('boston') + automl = AutoSklearnRegressor(time_left_for_this_task=20, + per_run_time_limit=5, + tmp_folder=output, + output_folder=output) + + automl.fit(X_train, Y_train) + predictions = automl.predict(X_test) + self.assertEqual(predictions.shape, (356,)) + score = mean_squared_error(Y_test, predictions) + # On average np.sqrt(30) away from the target -> ~5.5 on average + self.assertGreaterEqual(score, -30) \ No newline at end of file diff --git a/test/test_automl/test_smbo.py b/test/test_automl/test_smbo.py index c862732dab..c8d9a27faf 100644 --- a/test/test_automl/test_smbo.py +++ b/test/test_automl/test_smbo.py @@ -1,6 +1,7 @@ import unittest from autosklearn.smbo import AutoMLSMBO +from autosklearn.metrics import accuracy from smac.facade.smac_facade import SMAC from smac.scenario.scenario import Scenario from smac.tae.execute_ta_run import StatusType @@ -19,8 +20,15 @@ def test_choose_next(self): total_walltime_limit = 15 memory_limit = 3072 - auto = AutoMLSMBO(None, dataset_name, None, func_eval_time_limit, - total_walltime_limit, memory_limit, None) + auto = AutoMLSMBO( + config_space=None, + dataset_name=dataset_name, + backend=None, + func_eval_time_limit=func_eval_time_limit, + total_walltime_limit=total_walltime_limit, + memory_limit=memory_limit, + watcher=None, + metric=accuracy) auto.config_space = configspace scenario = Scenario({'cs': configspace, 'cutoff-time': func_eval_time_limit, diff --git a/test/test_data/test_competition_c_functions.py b/test/test_data/test_competition_c_functions.py index 6eaf3fdf95..4c8026430d 100644 --- a/test/test_data/test_competition_c_functions.py +++ b/test/test_data/test_competition_c_functions.py @@ -1,31 +1,14 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import os import unittest -try: - import autosklearn.data.competition_c_functions as competition_c_functions -except ImportError: - try: - travis = os.environ['TRAVIS'] - raise - except Exception: - pass +import autosklearn.data.competition_c_functions as competition_c_functions + class CHelperFunctionTest(unittest.TestCase): _multiprocess_can_split_ = True - def setUp(self): - try: - travis = os.environ['TRAVIS'] - self.travis = True - except Exception: - self.travis = False - def test_read_sparse_file(self): - if self.travis: - self.skipTest('Does not work on travis-ci and will be removed in ' - 'the near future.') filename = os.path.join(os.path.dirname(__file__), '../.data/newsgroup/newsgroups_valid.data') data = competition_c_functions.read_sparse_file( @@ -36,9 +19,6 @@ def test_read_sparse_file(self): self.assertEqual(data.nnz, 246216) def test_read_sparse_binary_file(self): - if self.travis: - self.skipTest('Does not work on travis-ci and will be removed in ' - 'the near future.') filename = os.path.join(os.path.dirname(__file__), '../.data/dorothea/dorothea_train.data') data = competition_c_functions.read_sparse_binary_file( @@ -49,9 +29,6 @@ def test_read_sparse_binary_file(self): self.assertEqual(data.nnz, 8192) def test_read_dense(self): - if self.travis: - self.skipTest('Does not work on travis-ci and will be removed in ' - 'the near future.') filename = os.path.join(os.path.dirname(__file__), '../.data/31_bac/31_bac_train.data') data = competition_c_functions.read_dense_file( diff --git a/test/test_data/test_competition_data_manager.py b/test/test_data/test_competition_data_manager.py index bee0f0be1d..9ffce299a9 100644 --- a/test/test_data/test_competition_data_manager.py +++ b/test/test_data/test_competition_data_manager.py @@ -1,4 +1,3 @@ -from __future__ import print_function import os import unittest diff --git a/test/test_data/test_data_manager.py b/test/test_data/test_data_manager.py index 4387d057b1..9a7599fbe5 100644 --- a/test/test_data/test_data_manager.py +++ b/test/test_data/test_data_manager.py @@ -1,4 +1,3 @@ -from __future__ import print_function import os import unittest diff --git a/test/test_evaluation/evaluation_util.py b/test/test_evaluation/evaluation_util.py index 95f358ea06..c4b8268b3a 100644 --- a/test/test_evaluation/evaluation_util.py +++ b/test/test_evaluation/evaluation_util.py @@ -2,11 +2,7 @@ import os import sys import traceback - -if sys.version_info[0] == 2: - import unittest2 as unittest -else: - import unittest +import unittest import numpy as np from numpy.linalg import LinAlgError @@ -94,7 +90,6 @@ def get_multiclass_classification_datamanager(): D = Dummy() D.info = { - 'metric': BAC_METRIC, 'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False, 'label_num': 3 @@ -143,7 +138,6 @@ def get_multilabel_classification_datamanager(): D = Dummy() D.info = { - 'metric': ACC_METRIC, 'task': MULTILABEL_CLASSIFICATION, 'is_sparse': False, 'label_num': 3 @@ -183,7 +177,6 @@ def get_binary_classification_datamanager(): D = Dummy() D.info = { - 'metric': ACC_METRIC, 'task': BINARY_CLASSIFICATION, 'is_sparse': False, 'label_num': 2 @@ -215,7 +208,6 @@ def get_regression_datamanager(): D = Dummy() D.info = { - 'metric': R2_METRIC, 'task': REGRESSION, 'is_sparse': False, 'label_num': 1 @@ -254,7 +246,6 @@ def get_500_classes_datamanager(): D = Dummy() D.info = { - 'metric': ACC_METRIC, 'task': MULTICLASS_CLASSIFICATION, 'is_sparse': False, 'label_num': 500 diff --git a/test/test_evaluation/test_abstract_evaluator.py b/test/test_evaluation/test_abstract_evaluator.py index 8b7856c59b..b398f65786 100644 --- a/test/test_evaluation/test_abstract_evaluator.py +++ b/test/test_evaluation/test_abstract_evaluator.py @@ -1,5 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import os import sys import unittest @@ -10,8 +9,10 @@ this_directory = os.path.dirname(__file__) sys.path.append(this_directory) -from evaluation_util import get_multiclass_classification_datamanager -from autosklearn.evaluation import AbstractEvaluator +from evaluation_util import get_multiclass_classification_datamanager, \ + get_regression_datamanager +from autosklearn.evaluation.abstract_evaluator import AbstractEvaluator +from autosklearn.metrics import accuracy, r2, mean_squared_error class AbstractEvaluatorTest(unittest.TestCase): @@ -24,8 +25,9 @@ def test_finish_up_model_predicts_NaN(self): backend_api = unittest.mock.Mock() queue_mock = unittest.mock.Mock() - ae = AbstractEvaluator(Datamanager=D, backend=backend_api, - output_y_test=False, queue=queue_mock) + ae = AbstractEvaluator(datamanager=D, backend=backend_api, + output_y_hat_optimization=False, + queue=queue_mock, metric=accuracy) ae.Y_optimization = rs.rand(33, 3) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) @@ -36,8 +38,9 @@ def test_finish_up_model_predicts_NaN(self): _, loss, _, additional_run_info = ae.finish_up( 0.1, predictions_ensemble, predictions_valid, predictions_test) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, 'Model predictions for ' - 'optimization set contains NaNs.') + self.assertEqual(additional_run_info, + {'error': 'Model predictions for optimization set ' + 'contains NaNs.'}) # NaNs in prediction validation predictions_ensemble[5, 2] = 0.5 @@ -45,8 +48,9 @@ def test_finish_up_model_predicts_NaN(self): _, loss, _, additional_run_info = ae.finish_up( 0.1, predictions_ensemble, predictions_valid, predictions_test) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, 'Model predictions for ' - 'validation set contains NaNs.') + self.assertEqual(additional_run_info, + {'error': 'Model predictions for validation set ' + 'contains NaNs.'}) # NaNs in prediction test predictions_valid[5, 2] = 0.5 @@ -54,20 +58,24 @@ def test_finish_up_model_predicts_NaN(self): _, loss, _, additional_run_info = ae.finish_up( 0.1, predictions_ensemble, predictions_valid, predictions_test) self.assertEqual(loss, 1.0) - self.assertEqual(additional_run_info, 'Model predictions for ' - 'test set contains NaNs.') + self.assertEqual(additional_run_info, + {'error': 'Model predictions for test set contains ' + 'NaNs.'}) self.assertEqual(backend_api.save_predictions_as_npy.call_count, 0) - def test_disable_file_output(self): + @unittest.mock.patch('os.path.exists') + def test_disable_file_output(self, exists_mock): backend_mock = unittest.mock.Mock() + backend_mock.get_model_dir.return_value = 'abc' queue_mock = unittest.mock.Mock() rs = np.random.RandomState(1) D = get_multiclass_classification_datamanager() - ae = AbstractEvaluator(Datamanager=D, backend=backend_mock, queue=queue_mock, - output_y_test=False, disable_file_output=True) + ae = AbstractEvaluator(datamanager=D, backend=backend_mock, + queue=queue_mock, disable_file_output=True, + metric=accuracy) predictions_ensemble = rs.rand(33, 3) predictions_test = rs.rand(25, 3) @@ -77,6 +85,39 @@ def test_disable_file_output(self): predictions_ensemble, predictions_valid, predictions_test) self.assertIsNone(loss_) - self.assertIsNone(additional_run_info_) + self.assertEqual(additional_run_info_, {}) # This function is not guarded by a an if statement self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 0) + self.assertEqual(backend_mock.save_model.call_count, 0) + + ae = AbstractEvaluator(datamanager=D, backend=backend_mock, + output_y_hat_optimization=False, + queue=queue_mock, disable_file_output=['model'], + metric=accuracy) + ae.Y_optimization = predictions_ensemble + + loss_, additional_run_info_ = ae.file_output( + predictions_ensemble, predictions_valid, predictions_test) + + self.assertIsNone(loss_) + self.assertIsNone(additional_run_info_) + # This function is not guarded by a an if statement + self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 3) + self.assertEqual(backend_mock.save_model.call_count, 0) + + ae = AbstractEvaluator(datamanager=D, backend=backend_mock, + output_y_hat_optimization=False, + queue=queue_mock, metric=accuracy, + disable_file_output=['y_optimization']) + exists_mock.return_value = True + ae.Y_optimization = predictions_ensemble + ae.model = 'model' + + loss_, additional_run_info_ = ae.file_output( + predictions_ensemble, predictions_valid, predictions_test) + + self.assertIsNone(loss_) + self.assertIsNone(additional_run_info_) + # This function is not guarded by a an if statement + self.assertEqual(backend_mock.save_predictions_as_npy.call_count, 5) + self.assertEqual(backend_mock.save_model.call_count, 1) diff --git a/test/test_evaluation/test_evaluation.py b/test/test_evaluation/test_evaluation.py index b55de2379a..68bdfd46bb 100644 --- a/test/test_evaluation/test_evaluation.py +++ b/test/test_evaluation/test_evaluation.py @@ -12,19 +12,23 @@ sys.path.append(this_directory) import pynisher -from smac.tae.execute_ta_run import StatusType +from smac.tae.execute_ta_run import StatusType, FirstRunCrashedException, \ + BudgetExhaustedException from smac.stats.stats import Stats -import sklearn.cross_validation +import sklearn.model_selection from evaluation_util import get_multiclass_classification_datamanager from autosklearn.constants import * from autosklearn.evaluation import ExecuteTaFuncWithQueue from autosklearn.data.abstract_data_manager import AbstractDataManager +from autosklearn.metrics import accuracy def safe_eval_success_mock(*args, **kwargs): queue = kwargs['queue'] - queue.put((StatusType.SUCCESS, 0.5, 0.12345, '')) + queue.put({'status': StatusType.SUCCESS, + 'loss': 0.5, + 'additional_run_info': ''}) class BackendMock(object): @@ -57,6 +61,8 @@ def tearDown(self): except: pass + ############################################################################ + # pynisher tests def test_pynisher_memory_error(self): def fill_memory(): a = np.random.random_sample((10000, 10000)).astype(np.float64) @@ -75,127 +81,190 @@ def run_over_time(): safe_eval() self.assertEqual(safe_eval.exit_status, pynisher.TimeoutException) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') + ############################################################################ + # Test ExecuteTaFuncWithQueue.start() + @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') def test_eval_with_limits_holdout(self, pynisher_mock): pynisher_mock.side_effect = safe_eval_success_mock ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) - info = ta.run(None, cutoff=30) + memory_limit=3072, + metric=accuracy) + info = ta.start(None, instance=None, cutoff=30) self.assertEqual(info[0], StatusType.SUCCESS) self.assertEqual(info[1], 0.5) self.assertIsInstance(info[2], float) - @unittest.mock.patch('pynisher.enforce_limits') - def test_cutoff_lower_than_remaining_time(self, pynisher_mock): - ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, - resampling_strategy='holdout', - logger=self.logger, - stats=self.stats, - memory_limit=3072) - ta.run(None, cutoff=30) - self.assertEqual(pynisher_mock.call_args[1]['wall_time_in_s'], 4) - self.assertIsInstance(pynisher_mock.call_args[1]['wall_time_in_s'], int) - @unittest.mock.patch('pynisher.enforce_limits') def test_zero_or_negative_cutoff(self, pynisher_mock): ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, - stats=self.stats) + stats=self.stats, + metric=accuracy) self.scenario.wallclock_limit = 5 - info = ta.start(None, instance=None, cutoff=10) - fixture = (StatusType.ABORT, np.nan, 0, {"misc": "exhausted bugdet -- ABORT"}) - self.assertEqual(info, fixture) + self.stats.ta_runs += 1 + self.assertRaises(BudgetExhaustedException, ta.start, None, + instance=None, cutoff=9) @unittest.mock.patch('pynisher.enforce_limits') def test_cutoff_lower_than_remaining_time(self, pynisher_mock): ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, - stats=self.stats) + stats=self.stats, + metric=accuracy) self.stats.ta_runs = 1 ta.start(None, cutoff=30, instance=None) self.assertEqual(pynisher_mock.call_args[1]['wall_time_in_s'], 4) self.assertIsInstance(pynisher_mock.call_args[1]['wall_time_in_s'], int) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') + @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') def test_eval_with_limits_holdout_fail_silent(self, pynisher_mock): pynisher_mock.return_value = None ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) - info = ta.run(None, cutoff=30) + memory_limit=3072, + metric=accuracy) + + self.assertRaisesRegex(FirstRunCrashedException, + "First run crashed, abort. \(To prevent this, " + "toggle the " + "'abort_on_first_run_crash'-option!\)", + ta.start, config=None, instance=None, cutoff=30) + + self.stats.ta_runs += 1 + info = ta.start(config=None, instance=None, cutoff=30) self.assertEqual(info[0], StatusType.CRASHED) self.assertEqual(info[1], 1.0) self.assertIsInstance(info[2], float) + self.assertEqual(info[3], {'error': "Result queue is empty"}) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') + @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') def test_eval_with_limits_holdout_fail_memory_error(self, pynisher_mock): pynisher_mock.side_effect = MemoryError ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) - info = ta.run(None, cutoff=30) + memory_limit=3072, + metric=accuracy) + info = ta.start(None, instance=None, cutoff=30) self.assertEqual(info[0], StatusType.MEMOUT) self.assertEqual(info[1], 1.0) self.assertIsInstance(info[2], float) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') + @unittest.mock.patch('pynisher.enforce_limits') def test_eval_with_limits_holdout_fail_timeout(self, pynisher_mock): - pynisher_mock.side_effect = pynisher.TimeoutException + m1 = unittest.mock.Mock() + m2 = unittest.mock.Mock() + m1.return_value = m2 + pynisher_mock.return_value = m1 + m2.exit_status = pynisher.TimeoutException + m2.wall_clock_time = 30 ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) - info = ta.run(None, cutoff=30) + memory_limit=3072, + metric=accuracy) + info = ta.start(config=None, instance=None, cutoff=30) self.assertEqual(info[0], StatusType.TIMEOUT) self.assertEqual(info[1], 1.0) self.assertIsInstance(info[2], float) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') + @unittest.mock.patch('pynisher.enforce_limits') def test_eval_with_limits_holdout_timeout_with_results_in_queue(self, pynisher_mock): def side_effect(**kwargs): queue = kwargs['queue'] - queue.put((StatusType.SUCCESS, 0.5, 0.12345, '')) - pynisher_mock.side_effect = side_effect + queue.put({'status': StatusType.SUCCESS, + 'loss': 0.5, + 'additional_run_info': {}}) + m1 = unittest.mock.Mock() + m2 = unittest.mock.Mock() + m1.return_value = m2 + pynisher_mock.return_value = m1 + m2.side_effect = side_effect + m2.exit_status = pynisher.TimeoutException + m2.wall_clock_time = 30 + + # Test for a succesful run ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) - info = ta.run(None, cutoff=30) + memory_limit=3072, + metric=accuracy) + info = ta.start(None, instance=None, cutoff=30) self.assertEqual(info[0], StatusType.SUCCESS) self.assertEqual(info[1], 0.5) self.assertIsInstance(info[2], float) - @unittest.mock.patch('autosklearn.evaluation.eval_holdout') - def test_eval_with_limits_holdout(self, eval_houldout_mock): + # And a crashed run which is in the queue + def side_effect(**kwargs): + queue = kwargs['queue'] + queue.put({'status': StatusType.CRASHED, + 'loss': 2.0, + 'additional_run_info': {}}) + m2.side_effect = side_effect + ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, + resampling_strategy='holdout', + logger=self.logger, + stats=self.stats, + memory_limit=3072, + metric=accuracy) + info = ta.start(None, instance=None, cutoff=30) + self.assertEqual(info[0], StatusType.CRASHED) + self.assertEqual(info[1], 1.0) + self.assertIsInstance(info[2], float) + + @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + def test_eval_with_limits_holdout_2(self, eval_houldout_mock): def side_effect(*args, **kwargs): queue = kwargs['queue'] - queue.put((StatusType.SUCCESS, 0.5, 0.12345, kwargs['subsample'])) + queue.put({'status': StatusType.SUCCESS, + 'loss': 0.5, + 'additional_run_info': kwargs['instance']}) eval_houldout_mock.side_effect = side_effect ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, resampling_strategy='holdout', logger=self.logger, stats=self.stats, - memory_limit=3072) + memory_limit=3072, + metric=accuracy) self.scenario.wallclock_limit = 180 - info = ta.start(None, cutoff=30, instance=None, - instance_specific='subsample=30') + instance = "{'subsample': 30}" + info = ta.start(None, cutoff=30, instance=instance) self.assertEqual(info[0], StatusType.SUCCESS) - self.assertEqual(info[-1], 30) + self.assertEqual(info[-1], instance) + + @unittest.mock.patch('autosklearn.evaluation.train_evaluator.eval_holdout') + def test_exception_in_target_function(self, eval_holdout_mock): + eval_holdout_mock.side_effect = ValueError + ta = ExecuteTaFuncWithQueue(backend=BackendMock(), autosklearn_seed=1, + resampling_strategy='holdout', + logger=self.logger, + stats=self.stats, + memory_limit=3072, + metric=accuracy) + self.stats.ta_runs += 1 + info = ta.start(None, instance=None, cutoff=30) + self.assertEqual(info[0], StatusType.CRASHED) + self.assertEqual(info[1], 1.0) + self.assertIsInstance(info[2], float) + self.assertEqual(info[3]['error'], 'ValueError()') + self.assertIn('traceback', info[3]) + ############################################################################ + # Test obtaining a splitter object from scikit-learn def test_get_splitter(self): ta_args = dict(backend=BackendMock(), autosklearn_seed=1, - logger=self.logger, stats=self.stats, memory_limit=3072) + logger=self.logger, stats=self.stats, memory_limit=3072, + metric=accuracy) D = unittest.mock.Mock(spec=AbstractDataManager) D.data = dict(Y_train=np.array([0, 0, 0, 1, 1, 1])) D.info = dict(task=BINARY_CLASSIFICATION) @@ -204,34 +273,34 @@ def test_get_splitter(self): ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) cv = ta.get_splitter(D) self.assertIsInstance(cv, - sklearn.cross_validation.StratifiedShuffleSplit) + sklearn.model_selection.StratifiedShuffleSplit) # holdout, binary classification, fallback to shuffle split D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.cross_validation.ShuffleSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) # cv, binary classification D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1]) ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, **ta_args) cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.cross_validation.StratifiedKFold) + self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) # cv, binary classification, no fallback anticipated D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2]) ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, **ta_args) cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.cross_validation.StratifiedKFold) + self.assertIsInstance(cv, sklearn.model_selection._split.StratifiedKFold) # regression, shuffle split D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) D.info['task'] = REGRESSION ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args) cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.cross_validation.ShuffleSplit) + self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit) # regression cv, KFold D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5]) @@ -239,4 +308,4 @@ def test_get_splitter(self): ta = ExecuteTaFuncWithQueue(resampling_strategy='cv', folds=5, **ta_args) cv = ta.get_splitter(D) - self.assertIsInstance(cv, sklearn.cross_validation.KFold) + self.assertIsInstance(cv, sklearn.model_selection._split.KFold) diff --git a/test/test_evaluation/test_test_evaluator.py b/test/test_evaluation/test_test_evaluator.py index 598e58b31c..c9b8bded16 100644 --- a/test/test_evaluation/test_test_evaluator.py +++ b/test/test_evaluation/test_test_evaluator.py @@ -1,6 +1,6 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import copy +import json import multiprocessing import os import shutil @@ -9,17 +9,19 @@ import unittest.mock import numpy as np +from smac.tae.execute_ta_run import StatusType this_directory = os.path.dirname(__file__) sys.path.append(this_directory) from evaluation_util import get_dataset_getters, BaseEvaluatorTest, \ get_multiclass_classification_datamanager from autosklearn.constants import * -from autosklearn.evaluation import TestEvaluator +from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t # Otherwise nosetests thinks this is a test to run... -from autosklearn.evaluation import eval_t, get_last_result +from autosklearn.evaluation.util import get_last_result from autosklearn.util.pipeline import get_configuration_space from autosklearn.util import Backend +from autosklearn.metrics import accuracy, r2, f1_macro N_TEST_RUNS = 3 @@ -45,12 +47,19 @@ def test_datasets(self): y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() + metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, + BINARY_CLASSIFICATION: accuracy, + MULTICLASS_CLASSIFICATION: accuracy, + REGRESSION: r2} queue_ = multiprocessing.Queue() - evaluator = TestEvaluator(D_, backend_mock, queue_) + + evaluator = TestEvaluator(D_, backend_mock, queue_, + metric=metric_lookup[D.info['task']]) evaluator.fit_predict_and_loss() - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) - self.assertTrue(np.isfinite(result)) + rval = get_last_result(evaluator.queue) + self.assertEqual(len(rval), 3) + self.assertTrue(np.isfinite(rval['loss'])) class FunctionsTest(unittest.TestCase): @@ -63,6 +72,7 @@ def setUp(self): self.tmp_dir = os.path.join(os.path.dirname(__file__), '.test_cv_functions') self.backend = unittest.mock.Mock(spec=Backend) + self.dataset_name = json.dumps({'task_id': 'test'}) def tearDown(self): try: @@ -74,33 +84,48 @@ def test_eval_test(self): eval_t(queue=self.queue, backend=self.backend, config=self.configuration, - data=self.data, - seed=1, num_run=1, subsample=None, with_predictions=True, - all_scoring_functions=False, output_y_test=True, - include=None, exclude=None, disable_file_output=False) - info = get_last_result(self.queue) - self.assertAlmostEqual(info[1], 0.041666666666666852) - self.assertEqual(info[2], 1) - self.assertNotIn('bac_metric', info[3]) + datamanager=self.data, + metric=accuracy, + seed=1, num_run=1, + all_scoring_functions=False, output_y_hat_optimization=False, + include=None, exclude=None, disable_file_output=False, + instance=self.dataset_name) + rval = get_last_result(self.queue) + self.assertAlmostEqual(rval['loss'], 0.04) + self.assertEqual(rval['status'], StatusType.SUCCESS) + self.assertNotIn('bac_metric', rval['additional_run_info']) def test_eval_test_all_loss_functions(self): eval_t(queue=self.queue, backend=self.backend, config=self.configuration, - data=self.data, - seed=1, num_run=1, subsample=None, with_predictions=True, - all_scoring_functions=True, output_y_test=True, - include=None, exclude=None, disable_file_output=False) - info = get_last_result(self.queue) - fixture = {'f1_metric': 0.0511508951407, - 'pac_metric': 0.185257565321, - 'acc_metric': 0.06, - 'auc_metric': 0.00917546505782, - 'bac_metric': 0.0416666666667, + datamanager=self.data, + metric=accuracy, + seed=1, num_run=1, + all_scoring_functions=True, output_y_hat_optimization=False, + include=None, exclude=None, disable_file_output=False, + instance=self.dataset_name) + rval = get_last_result(self.queue) + fixture = {'accuracy': 0.04, + 'balanced_accuracy': 0.0277777777778, + 'f1_macro': 0.0341005967604, + 'f1_micro': 0.04, + 'f1_weighted': 0.0396930946292, + 'log_loss': 1.1274919837, + 'pac_score': 0.185257565321, + 'precision_macro': 0.037037037037, + 'precision_micro': 0.04, + 'precision_weighted': 0.0355555555556, + 'recall_macro': 0.0277777777778, + 'recall_micro': 0.04, + 'recall_weighted': 0.04, 'num_run': -1} - rval = {i.split(':')[0]: float(i.split(':')[1]) for i in info[3].split(';')} + + additional_run_info = rval['additional_run_info'] for key, value in fixture.items(): - self.assertAlmostEqual(rval[key], fixture[key]) - self.assertIn('duration', rval) - self.assertAlmostEqual(info[1], 0.041666666666666852) - self.assertEqual(info[2], 1) + self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) + self.assertEqual(len(additional_run_info), len(fixture) + 1, + msg=sorted(additional_run_info.items())) + self.assertIn('duration', additional_run_info) + self.assertAlmostEqual(rval['loss'], 0.04) + self.assertEqual(rval['status'], StatusType.SUCCESS) diff --git a/test/test_evaluation/test_train_evaluator.py b/test/test_evaluation/test_train_evaluator.py index 29fb2c917a..d626b4c27a 100644 --- a/test/test_evaluation/test_train_evaluator.py +++ b/test/test_evaluation/test_train_evaluator.py @@ -1,4 +1,5 @@ import copy +import json import queue import multiprocessing import os @@ -8,14 +9,16 @@ from ConfigSpace import Configuration import numpy as np -from sklearn.cross_validation import StratifiedKFold, ShuffleSplit +from sklearn.model_selection import StratifiedKFold, ShuffleSplit from smac.tae.execute_ta_run import StatusType -from autosklearn.evaluation import get_last_result, TrainEvaluator, eval_holdout, \ - eval_iterative_holdout, eval_cv, eval_partial_cv +from autosklearn.evaluation.util import get_last_result +from autosklearn.evaluation.train_evaluator import TrainEvaluator, \ + eval_holdout, eval_iterative_holdout, eval_cv, eval_partial_cv from autosklearn.util import backend from autosklearn.util.pipeline import get_configuration_space from autosklearn.constants import * +from autosklearn.metrics import accuracy, r2, f1_macro this_directory = os.path.dirname(__file__) sys.path.append(this_directory) @@ -36,7 +39,7 @@ class TestTrainEvaluator(BaseEvaluatorTest, unittest.TestCase): def test_holdout(self, pipeline_mock): D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -49,19 +52,22 @@ def test_holdout(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy, + subsample=50) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss() - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) + rval = get_last_result(evaluator.queue) + result = rval['loss'] + self.assertEqual(len(rval), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) - self.assertEqual(result, 1.7142857142857144) + self.assertEqual(result, 0.85714285714285721) self.assertEqual(pipeline_mock.fit.call_count, 1) # three calls because of the holdout, the validation and the test set self.assertEqual(pipeline_mock.predict_proba.call_count, 3) @@ -76,7 +82,7 @@ def test_iterative_holdout(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) class SideEffect(object): def __init__(self): @@ -84,12 +90,15 @@ def __init__(self): def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 - return self.fully_fitted_call_count > 5 + # Is called twice as often as call to fit because we also check + # if we need to add a special indicator to show that this is the + # final call to iterative fit + return self.fully_fitted_call_count > 10 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted - pipeline_mock.pre_transform.return_value = Xt_fixture, {} + pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout') @@ -101,9 +110,9 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) @@ -122,7 +131,9 @@ def side_effect(self, *args): self.assertEqual(evaluator.file_output.call_count, 5) for i in range(1, 6): - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) + rval = evaluator.queue.get(timeout=1) + result = rval['loss'] + self.assertEqual(len(rval), 3) self.assertAlmostEqual(result, 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) @@ -142,7 +153,7 @@ def test_iterative_holdout_interuption(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) class SideEffect(object): def __init__(self): @@ -150,14 +161,17 @@ def __init__(self): def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 - if self.fully_fitted_call_count == 3: + # Is called twice as often as call to fit because we also check + # if we need to add a special indicator to show that this is the + # final call to iterative fit + if self.fully_fitted_call_count == 5: raise ValueError() - return self.fully_fitted_call_count > 5 + return self.fully_fitted_call_count > 10 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted - pipeline_mock.pre_transform.return_value = Xt_fixture, {} + pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_interuption') @@ -169,9 +183,9 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) @@ -190,8 +204,8 @@ def side_effect(self, *args): self.assertEqual(evaluator.file_output.call_count, 2) for i in range(1, 3): - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(result, 1.0 - (0.2 * i)) + rval = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(rval['loss'], 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 2) @@ -209,11 +223,11 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = False - pipeline_mock.pre_transform.return_value = Xt_fixture, {} + pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_holdout_not_iterative') @@ -225,17 +239,17 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss(iterative=True) self.assertEqual(evaluator.file_output.call_count, 1) - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(result, 1.7142857142857144) + rval = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(rval['loss'], 0.85714285714285721) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 0) @@ -251,8 +265,7 @@ def test_iterative_holdout_not_iterative(self, pipeline_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, - n_folds=5, shuffle=True) + kfold = StratifiedKFold(random_state=1, n_splits=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -265,19 +278,21 @@ def test_cv(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.fit_predict_and_loss() - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) + rval = get_last_result(evaluator.queue) + result = rval['loss'] + self.assertEqual(len(rval), 3) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 1) - self.assertEqual(result, 0.92753623188405787) + self.assertEqual(result, 0.46376811594202894) self.assertEqual(pipeline_mock.fit.call_count, 5) # Fifteen calls because of the holdout, the validation and the test set self.assertEqual(pipeline_mock.predict_proba.call_count, 15) @@ -292,8 +307,7 @@ def test_cv(self, pipeline_mock): @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') def test_partial_cv(self, pipeline_mock): D = get_binary_classification_datamanager() - kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, - n_folds=5, shuffle=True) + kfold = StratifiedKFold(random_state=1, n_splits=5, shuffle=True) pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock @@ -308,20 +322,20 @@ def test_partial_cv(self, pipeline_mock): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) evaluator.partial_fit_predict_and_loss(1) - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) + rval = evaluator.queue.get(timeout=1) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(evaluator.file_output.call_count, 0) - self.assertEqual(result, 0.93333333333333335) + self.assertEqual(rval['loss'], 0.46666666666666667) self.assertEqual(pipeline_mock.fit.call_count, 1) self.assertEqual(pipeline_mock.predict_proba.call_count, 3) # The model prior to fitting is saved, this cannot be directly tested @@ -334,7 +348,7 @@ def test_iterative_partial_cv(self, pipeline_mock): # Regular fitting D = get_binary_classification_datamanager() D.name = 'test' - kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), random_state=1, n_folds=3) + kfold = StratifiedKFold(random_state=1, n_splits=3) class SideEffect(object): def __init__(self): @@ -342,12 +356,15 @@ def __init__(self): def configuration_fully_fitted(self): self.fully_fitted_call_count += 1 - return self.fully_fitted_call_count > 5 + # Is called twice as often as call to fit because we also check + # if we need to add a special indicator to show that this is the + # final call to iterative fit + return self.fully_fitted_call_count > 10 Xt_fixture = 'Xt_fixture' pipeline_mock.estimator_supports_iterative_fit.return_value = True pipeline_mock.configuration_fully_fitted.side_effect = SideEffect().configuration_fully_fitted - pipeline_mock.pre_transform.return_value = Xt_fixture, {} + pipeline_mock.fit_transformer.return_value = Xt_fixture, {} pipeline_mock.predict_proba.side_effect = lambda X, batch_size: np.tile([0.6, 0.4], (len(X), 1)) pipeline_mock.side_effect = lambda **kwargs: pipeline_mock output_dir = os.path.join(os.getcwd(), '.test_iterative_partial_cv') @@ -359,9 +376,9 @@ def configuration_fully_fitted(self): evaluator = TrainEvaluator(D, backend_api, queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=False, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) evaluator.file_output = unittest.mock.Mock(spec=evaluator.file_output) evaluator.file_output.return_value = (None, None) @@ -381,8 +398,8 @@ def side_effect(self, *args): self.assertEqual(evaluator.file_output.call_count, 0) for i in range(1, 6): - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) - self.assertAlmostEqual(result, 1.0 - (0.2 * i)) + rval = evaluator.queue.get(timeout=1) + self.assertAlmostEqual(rval['loss'], 1.0 - (0.2 * i)) self.assertRaises(queue.Empty, evaluator.queue.get, timeout=1) self.assertEqual(pipeline_mock.iterative_fit.call_count, 5) @@ -404,14 +421,13 @@ def test_file_output(self, makedirs_mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = StratifiedKFold(y=D.data['Y_train'].flatten(), - n_folds=5, shuffle=True, random_state=1) + kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=1) evaluator = TrainEvaluator(D, backend_mock, queue=queue_, configuration=configuration, cv=kfold, - with_predictions=True, all_scoring_functions=True, - output_y_test=True) + output_y_hat_optimization=True, + metric=accuracy) backend_mock.get_model_dir.return_value = True evaluator.model = 'model' @@ -430,11 +446,14 @@ def test_file_output(self, makedirs_mock, backend_mock): D.data['Y_valid'][0] = np.NaN rval = evaluator.file_output(D.data['Y_train'], D.data['Y_valid'], D.data['Y_test']) - self.assertEqual(rval, (1.0, 'Model predictions for validation set contains NaNs.')) + self.assertEqual(rval, (1.0, {'error': 'Model predictions for validation ' + 'set contains NaNs.'})) D.data['Y_train'][0] = np.NaN rval = evaluator.file_output(D.data['Y_train'], D.data['Y_valid'], D.data['Y_test']) - self.assertEqual(rval, (1.0, 'Model predictions for optimization set contains NaNs.')) + self.assertEqual(rval, (1.0, {'error': 'Model predictions for ' + 'optimization set contains ' + 'NaNs.'})) @unittest.mock.patch('autosklearn.util.backend.Backend') @unittest.mock.patch('autosklearn.pipeline.classification.SimpleClassificationPipeline') @@ -443,10 +462,11 @@ def test_subsample_indices_classification(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold, subsample=10) + cv=kfold, subsample=10, + metric=accuracy) train_indices = np.arange(69, dtype=int) train_indices1 = evaluator.subsample_indices(train_indices) evaluator.subsample = 20 @@ -486,10 +506,11 @@ def test_subsample_indices_regression(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold, subsample=30) + cv=kfold, subsample=30, + metric=accuracy) train_indices = np.arange(69, dtype=int) train_indices3 = evaluator.subsample_indices(train_indices) evaluator.subsample = 67 @@ -516,14 +537,14 @@ def test_predict_proba_binary_classification(self, mock, backend_mock): configuration = unittest.mock.Mock(spec=Configuration) queue_ = multiprocessing.Queue() - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) + kfold = ShuffleSplit(random_state=1, n_splits=1) evaluator = TrainEvaluator(D, backend_mock, queue_, configuration=configuration, - cv=kfold) + cv=kfold, output_y_hat_optimization=False, + metric=accuracy) evaluator.fit_predict_and_loss() Y_optimization_pred = backend_mock.save_predictions_as_npy.call_args_list[0][0][0] - print(Y_optimization_pred) for i in range(7): self.assertEqual(0.9, Y_optimization_pred[i][1]) @@ -532,7 +553,6 @@ def test_get_results(self): backend_mock = unittest.mock.Mock(spec=backend.Backend) backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau' D = get_binary_classification_datamanager() - kfold = ShuffleSplit(n=len(D.data['Y_train']), random_state=1, n_iter=1) queue_ = multiprocessing.Queue() for i in range(5): queue_.put((i * 1, 1 - (i * 0.2), 0, "", StatusType.SUCCESS)) @@ -554,14 +574,20 @@ def test_datasets(self): y = D.data['Y_train'] if len(y.shape) == 2 and y.shape[1] == 1: D_.data['Y_train'] = y.flatten() - kfold = ShuffleSplit(n=len(y), n_iter=5, random_state=1) + kfold = ShuffleSplit(n_splits=5, random_state=1) queue_ = multiprocessing.Queue() + metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro, + BINARY_CLASSIFICATION: accuracy, + MULTICLASS_CLASSIFICATION: accuracy, + REGRESSION: r2} evaluator = TrainEvaluator(D_, backend_mock, queue_, - cv=kfold) + cv=kfold, + output_y_hat_optimization=False, + metric=metric_lookup[D.info['task']]) evaluator.fit_predict_and_loss() - duration, result, seed, run_info, status = evaluator.queue.get(timeout=1) - self.assertTrue(np.isfinite(result)) + rval = evaluator.queue.get(timeout=1) + self.assertTrue(np.isfinite(rval['loss'])) class FunctionsTest(unittest.TestCase): @@ -578,35 +604,56 @@ def setUp(self): self.backend = unittest.mock.Mock() self.backend.get_model_dir.return_value = 'udiaetzrpduaeirdaetr' self.backend.output_directory = 'duapdbaetpdbe' + self.dataset_name = json.dumps({'task_id': 'test'}) def test_eval_holdout(self): - kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) - eval_holdout(self.queue, self.configuration, self.data, self.backend, - kfold, 1, 1, None, True, False, True, None, None, False) + kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) + eval_holdout(queue=self.queue, config=self.configuration, + datamanager=self.data, backend=self.backend, cv=kfold, + seed=1, num_run=1, all_scoring_functions=False, + output_y_hat_optimization=True, include=None, exclude=None, + disable_file_output=False, instance=self.dataset_name, + metric=accuracy) info = get_last_result(self.queue) - self.assertAlmostEqual(info[1], 0.095, places=3) - self.assertEqual(info[2], 1) - self.assertNotIn('bac_metric', info[3]) + self.assertAlmostEqual(info['loss'], 0.060606060606060552, places=3) + self.assertEqual(info['status'], StatusType.SUCCESS) + self.assertNotIn('bac_metric', info['additional_run_info']) def test_eval_holdout_all_loss_functions(self): - kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) - eval_holdout(self.queue, self.configuration, self.data, self.backend, - kfold, 1, 1, None, True, True, True, None, None, False) - info = get_last_result(self.queue) - - fixture = {'f1_metric': 0.0954545454545, - 'pac_metric': 0.203125867166, - 'acc_metric': 0.0909090909091, - 'auc_metric': 0.0197868008145, - 'bac_metric': 0.0954545454545, + kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) + eval_holdout(queue=self.queue, config=self.configuration, + datamanager=self.data, backend=self.backend, cv=kfold, + seed=1, num_run=1, all_scoring_functions=True, + output_y_hat_optimization=True, include=None, exclude=None, + disable_file_output=False, instance=self.dataset_name, + metric=accuracy) + rval = get_last_result(self.queue) + + fixture = {'accuracy': 0.0606060606061, + 'balanced_accuracy': 0.0636363636364, + 'f1_macro': 0.0636363636364, + 'f1_micro': 0.0606060606061, + 'f1_weighted': 0.0606060606061, + 'log_loss': 1.14529191037, + 'pac_score': 0.203125867166, + 'precision_macro': 0.0636363636364, + 'precision_micro': 0.0606060606061, + 'precision_weighted': 0.0606060606061, + 'recall_macro': 0.0636363636364, + 'recall_micro': 0.0606060606061, + 'recall_weighted': 0.0606060606061, 'num_run': 1} - rval = {i.split(':')[0]: float(i.split(':')[1]) for i in info[3].split(';')} + + additional_run_info = rval['additional_run_info'] for key, value in fixture.items(): - self.assertAlmostEqual(rval[key], fixture[key]) - self.assertIn('duration', rval) + self.assertAlmostEqual(additional_run_info[key], fixture[key], + msg=key) + self.assertIn('duration', additional_run_info) + self.assertEqual(len(additional_run_info), len(fixture) + 1, + msg=sorted(additional_run_info.items())) - self.assertAlmostEqual(info[1], 0.095, places=3) - self.assertEqual(info[2], 1) + self.assertAlmostEqual(rval['loss'], 0.060606060606060552, places=3) + self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_holdout_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) @@ -618,13 +665,17 @@ def test_eval_holdout_all_loss_functions(self): # self.assertEqual(info[2], 1) def test_eval_holdout_iterative_fit_no_timeout(self): - kfold = ShuffleSplit(n=self.n, random_state=1, n_iter=1, test_size=0.33) - eval_iterative_holdout(self.queue, self.configuration, self.data, - self.backend, kfold, 1, 1, None, True, - False, True, None, None, False) - info = get_last_result(self.queue) - self.assertAlmostEqual(info[1], 0.09545454545454557) - self.assertEqual(info[2], 1) + kfold = ShuffleSplit(random_state=1, n_splits=1, test_size=0.33) + eval_iterative_holdout(queue=self.queue, config=self.configuration, + datamanager=self.data, backend=self.backend, + cv=kfold, seed=1, num_run=1, + all_scoring_functions=False, + output_y_hat_optimization=True, include=None, + exclude=None, disable_file_output=False, + instance=self.dataset_name, metric=accuracy) + rval = get_last_result(self.queue) + self.assertAlmostEqual(rval['loss'], 0.060606060606060552) + self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_holdout_iterative_fit_on_subset_no_timeout(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) @@ -637,71 +688,83 @@ def test_eval_holdout_iterative_fit_no_timeout(self): # self.assertEqual(info[2], 1) def test_eval_cv(self): - cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1) - eval_cv(queue=self.queue, config=self.configuration, data=self.data, - backend=self.backend, seed=1, num_run=1, cv=cv, subsample=None, - with_predictions=True, all_scoring_functions=False, - output_y_test=True, include=None, exclude=None, - disable_file_output=False) - info = get_last_result(self.queue) - self.assertAlmostEqual(info[1], 0.063004032258064502) - self.assertEqual(info[2], 1) - self.assertNotIn('bac_metric', info[3]) + cv = StratifiedKFold(shuffle=True, random_state=1) + eval_cv(queue=self.queue, config=self.configuration, + datamanager=self.data, backend=self.backend, seed=1, num_run=1, + cv=cv, all_scoring_functions=False, + output_y_hat_optimization=True, include=None, exclude=None, + disable_file_output=False, instance=self.dataset_name, + metric=accuracy) + rval = get_last_result(self.queue) + self.assertAlmostEqual(rval['loss'], 0.040000000000000036) + self.assertEqual(rval['status'], StatusType.SUCCESS) + self.assertNotIn('bac_metric', rval['additional_run_info']) def test_eval_cv_all_loss_functions(self): - cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1) - eval_cv(queue=self.queue, config=self.configuration, data=self.data, - backend=self.backend, seed=1, num_run=1, cv=cv, subsample=None, - with_predictions=True, all_scoring_functions=True, - output_y_test=True, include=None, exclude=None, - disable_file_output=False) - info = get_last_result(self.queue) - - fixture = {'f1_metric': 0.0635080645161, - 'pac_metric': 0.165226664054, - 'acc_metric': 0.06, - 'auc_metric': 0.0154405176049, - 'bac_metric': 0.0630040322581, + cv = StratifiedKFold(shuffle=True, random_state=1) + eval_cv(queue=self.queue, config=self.configuration, + datamanager=self.data, backend=self.backend, seed=1, num_run=1, + cv=cv, all_scoring_functions=True, + output_y_hat_optimization=True, include=None, exclude=None, + disable_file_output=False, instance=self.dataset_name, + metric=accuracy) + rval = get_last_result(self.queue) + + fixture = {'accuracy': 0.04, + 'balanced_accuracy': 0.042002688172, + 'f1_macro': 0.0423387096774, + 'f1_micro': 0.04, + 'f1_weighted': 0.040020161290, + 'log_loss': 1.11651433976, + 'pac_score': 0.165226664054, + 'precision_macro': 0.0414141414141, + 'precision_micro': 0.04, + 'precision_weighted': 0.0388484848485, + 'recall_macro': 0.042002688172, + 'recall_micro': 0.04, + 'recall_weighted': 0.04, 'num_run': 1} - rval = {i.split(':')[0]: float(i.split(':')[1]) for i in info[3].split(';')} + + additional_run_info = rval['additional_run_info'] for key, value in fixture.items(): - self.assertAlmostEqual(rval[key], fixture[key]) - self.assertIn('duration', rval) + self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key) + self.assertIn('duration', additional_run_info) + self.assertEqual(len(additional_run_info), len(fixture) + 1, + msg=sorted(additional_run_info.items())) - self.assertAlmostEqual(info[1], 0.063004032258064502) - self.assertEqual(info[2], 1) + self.assertAlmostEqual(rval['loss'], 0.040000000000000036) + self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_cv_on_subset(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) # eval_cv(queue=self.queue, config=self.configuration, data=self.data, # backend=backend_api, seed=1, num_run=1, folds=5, subsample=45, # with_predictions=True, all_scoring_functions=False, - # output_y_test=True, include=None, exclude=None, + # output_y_hat_optimization=True, include=None, exclude=None, # disable_file_output=False) # info = self.queue.get() # self.assertAlmostEqual(info[1], 0.063004032258064502) # self.assertEqual(info[2], 1) def test_eval_partial_cv(self): - cv = StratifiedKFold(y=self.y, shuffle=True, random_state=1, - n_folds=5) - results = [0.071428571428571508, - 0.15476190476190488, - 0.08333333333333337, - 0.16666666666666674, + cv = StratifiedKFold(shuffle=True, random_state=1, n_splits=5) + results = [0.045454545454545414, + 0.095238095238095233, + 0.052631578947368474, + 0.10526315789473684, 0.0] for fold in range(5): + instance = json.dumps({'task_id': 'data', 'fold': fold}) eval_partial_cv(queue=self.queue, config=self.configuration, - data=self.data, backend=self.backend, seed=1, - num_run=1, instance=fold, cv=cv, - subsample=None, with_predictions=True, - all_scoring_functions=False, output_y_test=True, - include=None, exclude=None, - disable_file_output=False) - info = get_last_result(self.queue) - results.append(info[1]) - self.assertAlmostEqual(info[1], results[fold]) - self.assertEqual(info[2], 1) + datamanager=self.data, backend=self.backend, seed=1, + num_run=1, instance=instance, cv=cv, + all_scoring_functions=False, + output_y_hat_optimization=True, include=None, + exclude=None, disable_file_output=False, + metric=accuracy) + rval = get_last_result(self.queue) + self.assertAlmostEqual(rval['loss'], results[fold]) + self.assertEqual(rval['status'], StatusType.SUCCESS) # def test_eval_partial_cv_on_subset_no_timeout(self): # backend_api = backend.create(self.tmp_dir, self.tmp_dir) @@ -716,7 +779,7 @@ def test_eval_partial_cv(self): # data=self.data, backend=backend_api, # seed=1, num_run=1, instance=fold, folds=5, # subsample=80, with_predictions=True, - # all_scoring_functions=False, output_y_test=True, + # all_scoring_functions=False, output_y_hat_optimization=True, # include=None, exclude=None, # disable_file_output=False) # @@ -734,7 +797,7 @@ def test_eval_partial_cv(self): # data=self.data, backend=backend_api, # seed=1, num_run=1, instance=fold, folds=5, # subsample=43, with_predictions=True, - # all_scoring_functions=False, output_y_test=True, + # all_scoring_functions=False, output_y_hat_optimization=True, # include=None, exclude=None, # disable_file_output=False) # diff --git a/test/test_metalearning/pyMetaLearn/test_meta_base.py b/test/test_metalearning/pyMetaLearn/test_meta_base.py index 675a84efa0..71adea76c0 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_base.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_base.py @@ -1,4 +1,3 @@ -from __future__ import print_function import os import unittest @@ -64,3 +63,17 @@ def test_get_metafeatures_multiple_features(self): self.assertIsInstance(mf, pd.DataFrame) self.assertEqual(mf.shape, (140, 2)) + def test_remove_dataset(self): + name = "1000_acc" + for key in self.base.algorithm_runs: + self.assertIn(name, self.base.algorithm_runs[key].index) + self.assertIn(name, self.base.metafeatures.index) + metafeatures_shape = self.base.metafeatures.shape + self.base.remove_dataset(name) + for key in self.base.algorithm_runs: + self.assertNotIn(name, self.base.algorithm_runs[key].index) + self.assertNotIn(name, self.base.metafeatures.index) + # Check that only one thing was removed + self.assertEqual(self.base.metafeatures.shape, + (metafeatures_shape[0] - 1, metafeatures_shape[1])) + diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features.py b/test/test_metalearning/pyMetaLearn/test_meta_features.py index 7a48241ae4..d8fbc0e397 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features.py @@ -1,4 +1,3 @@ -from __future__ import print_function from six import StringIO from unittest import TestCase import unittest diff --git a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py index 5846cd989b..e837adfc1c 100644 --- a/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py +++ b/test/test_metalearning/pyMetaLearn/test_meta_features_sparse.py @@ -1,4 +1,3 @@ -from __future__ import print_function from six import StringIO import os import sys diff --git a/test/test_metalearning/pyMetaLearn/test_metalearner.py b/test/test_metalearning/pyMetaLearn/test_metalearner.py index 002bb1878b..9799ba1f1c 100644 --- a/test/test_metalearning/pyMetaLearn/test_metalearner.py +++ b/test/test_metalearning/pyMetaLearn/test_metalearner.py @@ -1,4 +1,3 @@ -from __future__ import print_function import logging import numpy as np import os diff --git a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py index da30f2fb40..8e4da89218 100644 --- a/test/test_metalearning/pyMetaLearn/test_optimizer_base.py +++ b/test/test_metalearning/pyMetaLearn/test_optimizer_base.py @@ -1,4 +1,3 @@ -from __future__ import print_function from collections import OrderedDict import unittest diff --git a/test/test_metalearning/test_metalearning.py b/test/test_metalearning/test_metalearning.py index dc7e15ad33..226a31e05d 100644 --- a/test/test_metalearning/test_metalearning.py +++ b/test/test_metalearning/test_metalearning.py @@ -1,5 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function import unittest diff --git a/test/test_metric/test_classification_metrics.py b/test/test_metric/test_classification_metrics.py index 95e813fee1..4c521b2dec 100644 --- a/test/test_metric/test_classification_metrics.py +++ b/test/test_metric/test_classification_metrics.py @@ -1,15 +1,8 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function -import sys -if sys.version_info[0] == 2: - import unittest2 as unittest -else: - import unittest +import unittest + import numpy as np -from autosklearn.constants import * -from autosklearn.metrics.util import normalize_array -from autosklearn.metrics import acc_metric, auc_metric, bac_metric, \ - f1_metric, pac_metric +from autosklearn.metrics import balanced_accuracy, pac_score def copy_and_preprocess_arrays(solution, prediction): @@ -18,492 +11,117 @@ def copy_and_preprocess_arrays(solution, prediction): return solution, prediction -class AccuracyTest(unittest.TestCase): - _multiprocess_can_split_ = True - - def test_accuracy_metric_4_binary_classification(self): - # 100% correct - expected = np.array([0, 1, 1, 1, 0, 0, 1, 1, 1, 0]).reshape((-1, 1)) - prediction = np.array([[1., 0.], [0., 1.], [0., 1.], [0., 1.], - [1., 0.], [1., 0.], [0., 1.], [0., 1.], - [0., 1.], [1., 0.]]) - score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION) - self.assertEqual(1, score) - - # 100% incorrect - prediction = (prediction.copy() - 1) * -1 - score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION) - self.assertAlmostEqual(-1, score) - - # Random - prediction = np.array([[1., 0.], [1., 0.], [1., 0.], [1., 0.], [1., 0.], - [0., 1.], [0., 1.], [0., 1.], [0., 1.], [0., 1.]]) - score = acc_metric(expected, prediction, task=BINARY_CLASSIFICATION) - self.assertAlmostEqual(0, score) - - def test_accuracy_metric_4_multiclass_classification(self): - # 100% correct - expected = np.array([1, 1, 0, 0, 1, 0, 2, 0, 2, 1]) - prediction = np.array([[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], [1.0, 0.0, 0.0], - [0.0, 1.0, 0.0], [1.0, 0.0, 0.0], - [0.0, 0.0, 1.0], [1.0, 0.0, 0.0], - [0.0, 0.0, 1.0], [0.0, 1.0, 0.0]]) - score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION) - self.assertEqual(1, score) - - # 100% incorrect - prediction = (prediction.copy() - 1) * -1 - score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION) - self.assertAlmostEqual(-0.5, score) - - # Pseudorandom - prediction = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], - [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], - [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], - [1.0, 0.0, 0.0]]) - score = acc_metric(expected, prediction, task=MULTICLASS_CLASSIFICATION) - self.assertAlmostEqual(0.1, score) - - def test_accuracy_metric_4_multilabel_classification(self): - # 100% correct - expected = np.array([[0, 1, 1], [0, 1, 1], [1, 0, 0], [1, 0, 0], - [0, 1, 1], [1, 0, 0], [0, 1, 1], [1, 0, 0], - [0, 1, 1], [1, 0, 0]]) - prediction = expected.copy() - score = acc_metric(expected, prediction.astype(float), - task=MULTILABEL_CLASSIFICATION) - self.assertEqual(1, score) - - # 100% incorrect - prediction = (prediction.copy() - 1) * -1 - score = acc_metric(expected, prediction.astype(float), - task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(-1, score) - - # Pseudorandom - prediction = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], - [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 1.0, 1.0], - [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], - [1.0, 1.0, 1.0]]) - score = acc_metric(expected, prediction, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(-0.0666666666, score) - - -class AreaUnderCurveTest(unittest.TestCase): +class BalancedAccurayTest(unittest.TestCase): _multiprocess_can_split_ = True - def test_cases_binary_score_verification(self): - cases = [] - sol = np.array([0, 0, 1, 1]) - pred = np.array([[1, 0], [1, 0], [0, 1], [0, 1]]) - - cases.append(('perfect', sol, pred, 1.0)) - cases.append(('anti-perfect', sol, 1 - pred, -1.0)) - - uneven_proba = np.array( - [[0.7, 0.3], [0.4, 0.6], [0.49, 0.51], [0.2, 0.8]]) - - cases.append(('uneven proba', sol, uneven_proba, 0.5)) - - eps = 1.e-15 - ties = np.array([[0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps], - [0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps]]) - cases.append(('ties_broken', sol, ties, 0.0)) - - ties = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('ties', sol, ties, 0.0)) - - sol = np.array([0, 1, 1]) - pred = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('even proba', sol, pred, 0.0)) - - _pred = np.array([[1, 0], [0, 1], [0, 1]]) - pred = np.array([sum(_pred) * 1. / len(_pred)] * len(_pred)) - cases.append(('correct PAC prior', sol, pred, 0.0)) - - pred = np.array([[1., 1.], [1., 1.], [1., 1.]]) - cases.append(('all positive', sol, pred, 0.0)) - - pred = np.array([[0, 0], [0, 0], [0, 0]]) - cases.append(('all negative', sol, pred, 0.0)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - auc = auc_metric(sol, pred) - self.assertAlmostEqual(auc, result) - - def test_cases_multiclass_score_verification(self): - cases = [] - sol = np.array([0, 1, 0, 0]) - pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]) - - cases.append(('3 classes perfect', sol, pred, 0.333333333333)) - - pred = np.array([[0, 1, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1]]) - cases.append(('all classes wrong', sol, pred, -0.555555555556)) - - pred = np.array([[1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], - [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3]]) - cases.append(('equi proba', sol, pred, -0.333333333333)) - - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('sum(proba) < 1.0', sol, pred, -0.111111111111)) - - pred = np.array([[0.75, 0.25, 0.], [0.75, 0.25, 0.], [0.75, 0.25, 0.], - [0.75, 0.25, 0.]]) - cases.append(('predict prior', sol, pred, -0.333333333333)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = auc_metric(sol, pred, task=MULTICLASS_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - def test_cases_multilabel_1l(self): - cases = [] - num = 2 - - sol = np.array([[1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) - sol3 = sol[:, 0:num] - if num == 1: - sol3 = np.array([sol3[:, 0]]).transpose() - - cases.append(('{} labels perfect'.format(num), sol3, sol3, 1.0)) - - cases.append(('All wrong, in the multi-label sense', sol3, 1 - sol3, - -1.0)) - - pred = np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], - [0.5, 0.5, 0.5]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba: 0.5', sol3, pred, 0.0)) - - pred = np.array([[0.25, 0.25, 0.25], [0.25, 0.25, 0.25], [0.25, 0.25, 0.25], - [0.25, 0.25, 0.25]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba, prior: 0.25', sol3, pred, 0.0)) - - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Some proba', sol3, pred, -1.0)) - - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Invert both solution and prediction', 1 - sol3, pred, - 1.0)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - auc = auc_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(auc, result) - - def test_cases_multilabel_2(self): - cases = [] - - sol4 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]) - cases.append(('Three labels perfect', sol4, sol4, 1.0)) - - cases.append(('Three classes all wrong, in the multi-label sense', - sol4, 1 - sol4, -1.0)) - - pred = np.array([[1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3], - [1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3]]) - cases.append(('Three classes equi proba', sol4, pred, 0.0)) - - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('Three classes some proba that do not add up', sol4, - pred, 0.0)) - - pred = np.array([[0.25, 0.25, 0.5], [0.25, 0.25, 0.5], - [0.25, 0.25, 0.5], [0.25, 0.25, 0.5]]) - cases.append(('Three classes predict prior', sol4, pred, 0.0)) - + def _test_cases(self, cases): for case in cases: testname, sol, pred, result = case pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - auc = auc_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(auc, result) - - -class BalancedAccurayTest(unittest.TestCase): - _multiprocess_can_split_ = True + sol, pred = copy_and_preprocess_arrays(sol, pred) + bac = balanced_accuracy(sol, pred) + self.assertAlmostEqual(bac, result, msg=testname) - def test_cases_binary_score_verification(self): + def test_binary_balanced(self): cases = [] sol = np.array([0, 0, 1, 1]) - pred = np.array([[1, 0], [1, 0], [0, 1], [0, 1]]) + pred = np.array([0, 0, 1, 1]) cases.append(('perfect', sol, pred, 1.0)) - cases.append(('anti-perfect', sol, 1 - pred, -1.0,)) + cases.append(('completely wrong', sol, 1 - pred, 0.0)) - uneven_proba = np.array( - [[0.7, 0.3], [0.4, 0.6], [0.49, 0.51], [0.2, 0.8]]) + pred = np.array([0, 1, 1, 1]) + cases.append(('partially correct 1', sol, pred, 0.75)) - cases.append(('uneven proba', sol, uneven_proba, 0.5)) + pred = np.array([0, 1, 0, 1]) + cases.append(('partially correct 2', sol, pred, 0.5)) - eps = 1.e-15 - ties = np.array([[0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps], - [0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps]]) - cases.append(('ties_broken', sol, ties, 0.0)) + pred = np.array([0, 1, 0, 0]) + cases.append(('partially correct 3', sol, pred, 0.25)) - ties = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('ties', sol, ties, 0.0)) + self._test_cases(cases) - sol = np.array([0, 1, 1]) - pred = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('even proba', sol, pred, 0.0)) - - _pred = np.array([[1, 0], [0, 1], [0, 1]]) - pred = np.array([sum(_pred) * 1. / len(_pred)] * len(_pred)) - cases.append(('correct PAC prior', sol, pred, 0.0)) - - pred = np.array([[1., 1.], [1., 1.], [1., 1.]]) - cases.append(('all positive', sol, pred, 0.0)) - - pred = np.array([[0, 0], [0, 0], [0, 0]]) - cases.append(('all negative', sol, pred, 0.0)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = bac_metric(sol, pred, task=BINARY_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - def test_cases_multiclass_score_verification(self): + def test_binary_imbalanced(self): cases = [] - sol = np.array([0, 1, 0, 0]) - pred = np.array([[1, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]) - - cases.append(('3 classes perfect', sol, pred, 1.0)) - - cases.append(('all classes wrong', sol, 1 - pred, 0.0)) + sol = np.array([0, 1, 1]) + pred = np.array([0, 1, 1]) - pred = np.array([[1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], - [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3]]) - cases.append(('equi proba', sol, pred, 0.5)) + cases.append(('perfect', sol, pred, 1.0)) + cases.append(('completely wrong', sol, 1 - pred, 0.0)) - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('sum(proba) < 1.0', sol, pred, 0.333333333333)) + pred = np.array([0, 0, 0]) + cases.append(('one class correct', sol, pred, 0.5)) - pred = np.array([[0.75, 0.25, 0.], [0.75, 0.25, 0.], [0.75, 0.25, 0.], - [0.75, 0.25, 0.]]) - cases.append(('predict prior', sol, pred, 0.5)) + pred = np.array([0, 1, 0]) + cases.append(('one class correct, one partially correct', + sol, pred, 0.75)) - for case in cases: - testname, sol, pred, result = case + pred = np.array([1, 0, 1]) + cases.append(('one class partially correct', sol, pred, 0.25)) - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = bac_metric(sol, pred, task=MULTICLASS_CLASSIFICATION) - self.assertAlmostEqual(bac, result) + self._test_cases(cases) - def test_cases_multilabel_1l(self): + def test_multiclass_balanced(self): cases = [] - num = 2 + sol = np.array([0, 0, 1, 1, 2, 2]) + pred = np.array([0, 0, 1, 1, 2, 2]) - sol = np.array([[1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) - sol3 = sol[:, 0:num] - if num == 1: - sol3 = np.array([sol3[:, 0]]).transpose() - - cases.append(('{} labels perfect'.format(num), sol3, sol3, 1.0)) - - cases.append(('All wrong, in the multi-label sense', sol3, 1 - sol3, - -1.0)) - - pred = np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], - [0.5, 0.5, 0.5]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba: 0.5', sol3, pred, 0.0)) + cases.append(('perfect', sol, pred, 1.0)) - pred = np.array( - [[0.25, 0.25, 0.25], [0.25, 0.25, 0.25], [0.25, 0.25, 0.25], - [0.25, 0.25, 0.25]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba, prior: 0.25', sol3, pred, 0.0)) + pred = np.array([1, 1, 2, 2, 0, 0]) + cases.append(('completely wrong', sol, pred, 0.0)) - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Some proba', sol3, pred, -1.0)) + pred = np.array([0, 0, 0, 0, 0, 0]) + cases.append(('one class correct', sol, pred, 0.33333333)) - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Invert both solution and prediction', 1 - sol3, pred, - 1.0)) + pred = np.array([0, 0, 1, 1, 0, 0]) + cases.append(('two classes correct', sol, pred, 0.66666666)) - for case in cases: - testname, sol, pred, result = case + pred = np.array([0, 0, 1, 0, 2, 0]) + cases.append(('one class correct, two partially correct', sol, pred, 0.66666666)) - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = bac_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(bac, result) + pred = np.array([0, 1, 1, 2, 2, 0]) + cases.append(('all partially correct', sol, pred, 0.5)) + self._test_cases(cases) - def test_cases_multilabel_2(self): + def test_multiclass_imbalanced(self): cases = [] + sol = np.array([0, 1, 2, 0]) + pred = np.array([0, 1, 2, 0]) - sol4 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]) - cases.append(('Three labels perfect', sol4, sol4, 1.0)) + cases.append(('all classes perfect', sol, pred, 1.0)) - cases.append(('Three classes all wrong, in the multi-label sense', - sol4, 1 - sol4, -1.0)) + pred = np.array([1, 2, 0, 1]) + cases.append(('all classes wrong', sol, pred, 0.0)) - pred = np.array([[1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3], - [1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3]]) - cases.append(('Three classes equi proba', sol4, pred, 0.0)) + pred = np.array([0, 0, 0, 0]) + cases.append(('one class correct', sol, pred, 0.33333333)) - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('Three classes some proba that do not add up', sol4, - pred, -0.5)) + pred = np.array([2, 0, 0, 0]) + cases.append(('one class half-correct', sol, pred, 0.16666666)) - pred = np.array([[0.25, 0.25, 0.5], [0.25, 0.25, 0.5], - [0.25, 0.25, 0.5], [0.25, 0.25, 0.5]]) - cases.append(('Three classes predict prior', sol4, pred, 0.0)) + self._test_cases(cases) - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('_%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = bac_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - -class F1Test(unittest.TestCase): - _multiprocess_can_split_ = True - - def test_cases_binary_score_verification(self): + def test_multilabel_balanced(self): cases = [] - sol = np.array([0, 0, 1, 1]) - pred = np.array([[1, 0], [1, 0], [0, 1], [0, 1]]) + sol = np.array([[0, 0], [0, 0], [1, 1], [1, 1]]) + pred = np.array([[0, 0], [0, 0], [1, 1], [1, 1]]) cases.append(('perfect', sol, pred, 1.0)) - cases.append(('anti-perfect', sol, 1 - pred, -1.0)) + cases.append(('completely wrong', sol, 1 - pred, 0.0)) - uneven_proba = np.array( - [[0.7, 0.3], [0.4, 0.6], [0.49, 0.51], [0.2, 0.8]]) + pred = np.array([[0, 0], [0, 0], [0, 0], [1, 1]]) + cases.append(('one sample per label wrong', sol, pred, 0.75)) - cases.append(('uneven proba', sol, uneven_proba, 0.60000000000000009)) + pred = np.array([[0, 0], [0, 0], [0, 1], [1, 1]]) + cases.append(('one sample in one label wrong', sol, pred, 0.875)) - # We cannot have lower eps for float32 - eps = 1.e-7 - ties = np.array([[0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps], - [0.5 + eps, 0.5 - eps], [0.5 - eps, 0.5 + eps]]) - cases.append(('ties_broken', sol, ties, 0.0)) + pred = np.array([[0, 0], [0, 0], [0, 1], [0, 1]]) + cases.append(('two samples in one label wrong', sol, pred, 0.75)) + self._test_cases(cases) - ties = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('ties', sol, ties, 0.333333333333)) - - sol = np.array([0, 1, 1]) - pred = np.array([[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]) - cases.append(('even proba', sol, pred, 0.60000000000000009)) - - _pred = np.array([[1, 0], [0, 1], [0, 1]]) - pred = np.array([sum(_pred) * 1. / len(_pred)] * len(_pred)) - cases.append(('correct PAC prior', sol, pred, 0.60000000000000009)) - - pred = np.array([[1., 1.], [1., 1.], [1., 1.]]) - cases.append(('all positive', sol, pred, 0.60000000000000009)) - - pred = np.array([[0, 0], [0, 0], [0, 0]]) - cases.append(('all negative', sol, pred, -1.0)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - f1 = f1_metric(sol, pred, task=BINARY_CLASSIFICATION) - self.assertAlmostEqual(f1, result) - - def test_cases_multiclass_score_verification(self): - cases = [] - sol = np.array([0, 1, 0, 0]) - pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], - [1.0, 0.0, 0.0], [1.0, 0.0, 0.0]]) - - cases.append(('3 classes perfect', sol, pred, 1.0)) - - pred = np.array([[0, 1, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1]]) - cases.append(('all classes wrong', sol, pred, -0.5)) - - pred = np.array([[1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], - [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3]]) - cases.append(('equi proba', sol, pred, 0.428571428571)) - - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('sum(proba) < 1.0', sol, pred, -0.166666666667)) - - pred = np.array([[0.75, 0.25, 0.], [0.75, 0.25, 0.], [0.75, 0.25, 0.], - [0.75, 0.25, 0.]]) - cases.append(('predict prior', sol, pred, 0.428571428571)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = f1_metric(sol, pred, task=MULTICLASS_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - def test_cases_multilabel_1l(self): + def test_cases_multilabel(self): cases = [] num = 2 @@ -515,86 +133,28 @@ def test_cases_multilabel_1l(self): cases.append(('{} labels perfect'.format(num), sol3, sol3, 1.0)) cases.append(('All wrong, in the multi-label sense', sol3, 1 - sol3, - -1.0)) - - pred = np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], - [0.5, 0.5, 0.5]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba: 0.5', sol3, pred, -0.2)) - - pred = np.array( - [[0.25, 0.25, 0.25], [0.25, 0.25, 0.25], [0.25, 0.25, 0.25], - [0.25, 0.25, 0.25]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('All equi proba, prior: 0.25', sol3, pred, -1.0)) - - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Some proba', sol3, pred, -1.0)) - - pred = np.array([[0.2, 0.2, 0.2], [0.8, 0.8, 0.8], [0.9, 0.9, 0.9], - [0.7, 0.7, 0.7]]) - if num == 1: - pred = np.array([pred[:, 0]]).transpose() - else: - pred = pred[:, 0:num] - cases.append(('Invert both solution and prediction', 1 - sol3, pred, - 1.0)) - - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = f1_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - def test_cases_multilabel_2(self): - cases = [] + 0.0)) sol4 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]]) cases.append(('Three labels perfect', sol4, sol4, 1.0)) cases.append(('Three classes all wrong, in the multi-label sense', - sol4, 1 - sol4, -1.0)) + sol4, 1 - sol4, 0.0)) - pred = np.array([[1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3], - [1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3]]) - cases.append(('Three classes equi proba', sol4, pred, -1.0)) + self._test_cases(cases) - pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], - [0.7, 0.3, 0.3]]) - cases.append(('Three classes some proba that do not add up', sol4, - pred, -1.0)) - pred = np.array([[0.25, 0.25, 0.5], [0.25, 0.25, 0.5], - [0.25, 0.25, 0.5], [0.25, 0.25, 0.5]]) - cases.append(('Three classes predict prior', sol4, pred, - -0.555555555556)) +class PACTest(unittest.TestCase): + _multiprocess_can_split_ = True + def _test_cases(self, cases): for case in cases: testname, sol, pred, result = case pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = f1_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - self.assertAlmostEqual(bac, result) - - -class PACTest(unittest.TestCase): - _multiprocess_can_split_ = True + sol, pred = copy_and_preprocess_arrays(sol, pred) + pac = pac_score(sol, pred) + self.assertAlmostEqual(pac, result, msg=testname, places=1) def test_cases_binary_score_verification(self): cases = [] @@ -631,52 +191,35 @@ def test_cases_binary_score_verification(self): pred = np.array([[0, 0], [0, 0], [0, 0]]) cases.append(('all negative', sol, pred, -1.1237237959)) - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = pac_metric(sol, pred, task=BINARY_CLASSIFICATION) - # Very inaccurate! - self.assertAlmostEqual(bac, result, places=1) + self._test_cases(cases) def test_cases_multiclass_score_verification(self): cases = [] - sol = np.array([0, 1, 0, 0]) - pred = np.array([[1, 0, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]) + sol = np.array([0, 1, 2, 0]) + pred = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 2], [1, 0, 0]]) cases.append(('3 classes perfect', sol, pred, 1.0)) pred = np.array([[0, 1, 0], [0, 0, 1], [0, 1, 0], [0, 0, 1]]) - cases.append(('all classes wrong', sol, pred, -1.32491508679)) + cases.append(('all classes wrong', sol, pred, -0.5469181142705154)) - pred = np.array([[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]) - cases.append(('equi proba (wrong test from the starting kit)', sol, - pred, -1.32491508679)) + pred = np.array([[0., 0., 0.]] * 4) + cases.append(('equi proba (wrong test from the starting kit)', + sol, pred, -0.5469181142705154)) pred = np.array([[1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3]]) - cases.append(('equi proba', sol, pred, -0.54994340656358087)) + cases.append(('equi proba', sol, pred, -0.031278784012588157)) pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], [0.7, 0.3, 0.3]]) - cases.append(('sum(proba) < 1.0', sol, pred, -0.315724404334)) + cases.append(('sum(proba) < 1.0', sol, pred, -0.085886926180064257)) - pred = np.array([[0.75, 0.25, 0.], [0.75, 0.25, 0.], [0.75, 0.25, 0.], - [0.75, 0.25, 0.]]) - cases.append( - ('predict prior', sol, pred, 1.54870455579e-15)) + pred = np.array([[0.5, 0.25, 0.25], [0.5, 0.25, 0.25], + [0.5, 0.25, 0.25], [0.5, 0.25, 0.25]]) + cases.append(('predict prior', sol, pred, 0)) - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = pac_metric(sol, pred, task=MULTICLASS_CLASSIFICATION) - if bac != -1.3096137080181987 and result != -1.32470836935: - self.assertAlmostEqual(bac, result, places=2) + self._test_cases(cases) def test_cases_multilabel_1l(self): cases = [] @@ -726,15 +269,7 @@ def test_cases_multilabel_1l(self): cases.append(('Invert both solution and prediction', 1 - sol3, pred, 0.5277086603)) - for case in cases: - testname, sol, pred, result = case - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - bac = pac_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - # Very weak test - self.assertAlmostEqual(bac, result, places=1) + self._test_cases(cases) def test_cases_multilabel_2(self): cases = [] @@ -745,13 +280,15 @@ def test_cases_multilabel_2(self): cases.append(('Three classes all wrong, in the multi-label sense', sol4, 1 - sol4, -1.20548265539)) + # Not at random because different classes have different priors pred = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]) cases.append(('Three classes equi proba (wrong test from StartingKit)', sol4, pred, -1.20522116785)) pred = np.array([[1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3], [1. / 3, 1. / 3, 1. / 3]]) - cases.append(('Three classes equi proba', sol4, pred, -1.20522116785)) + cases.append(('Three classes equi proba', sol4, pred, + -0.034665665346400684)) pred = np.array([[0.2, 0, 0.5], [0.8, 0.4, 0.1], [0.9, 0.1, 0.2], [0.7, 0.3, 0.3]]) @@ -762,15 +299,4 @@ def test_cases_multilabel_2(self): [0.25, 0.25, 0.5], [0.25, 0.25, 0.5]]) cases.append(('Three classes predict prior', sol4, pred, 0.0)) - for case in cases: - testname, sol, pred, result = case - - - pred = pred.astype(np.float32) - with self.subTest('%s' % testname): - sol, pred = copy_and_preprocess_arrays(sol, pred) - pac = pac_metric(sol, pred, task=MULTILABEL_CLASSIFICATION) - - # Another weak test - if pac != -1.1860048034278985 and result != -1.20522116785: - self.assertAlmostEqual(pac, result, places=3) \ No newline at end of file + self._test_cases(cases) \ No newline at end of file diff --git a/test/test_metric/test_metrics.py b/test/test_metric/test_metrics.py new file mode 100644 index 0000000000..1038e0ab29 --- /dev/null +++ b/test/test_metric/test_metrics.py @@ -0,0 +1,310 @@ +import unittest + +import numpy as np +import sklearn.metrics + +import autosklearn.metrics.classification_metrics + + +class TestScorer(unittest.TestCase): + + def test_predict_scorer_binary(self): + y_true = np.array([0, 0, 1, 1]) + y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + y_pred = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + scorer = autosklearn.metrics._PredictScorer( + 'bac', autosklearn.metrics.classification_metrics.balanced_accuracy, + 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, -1, {}) + + y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + def test_predict_scorer_multiclass(self): + y_true = np.array([0, 1, 2]) + y_pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.333333333) + + y_pred = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.333333333) + + scorer = autosklearn.metrics._PredictScorer( + 'bac', autosklearn.metrics.classification_metrics.balanced_accuracy, + 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.333333333) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, -1, {}) + + y_pred = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + def test_predict_scorer_multilabel(self): + y_true = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.25) + + y_pred = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.25) + + scorer = autosklearn.metrics._PredictScorer( + 'bac', autosklearn.metrics.classification_metrics.balanced_accuracy, + 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + scorer = autosklearn.metrics._PredictScorer( + 'accuracy', sklearn.metrics.accuracy_score, -1, {}) + + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + def test_predict_scorer_regression(self): + y_true = np.arange(0, 1.01, 0.1) + y_pred = y_true.copy() + + scorer = autosklearn.metrics._PredictScorer( + 'r2', sklearn.metrics.r2_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.ones(y_true.shape) * np.mean(y_true) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.0) + + def test_proba_scorer_binary(self): + y_true = [0, 0, 1, 1] + y_pred = [[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]] + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.0) + + y_pred = [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.69314718055994529) + + y_pred = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.69314718055994529) + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, -1, {}) + + y_pred = [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -0.69314718055994529) + + def test_proba_scorer_multiclass(self): + y_true = [0, 1, 2] + y_pred = [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]] + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.0) + + y_pred = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0986122886681098) + + y_pred = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0986122886681096) + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, -1, {}) + + y_pred = [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0986122886681096) + + def test_proba_scorer_multilabel(self): + y_true = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.34657359027997314) + + y_pred = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.69314718055994529) + + y_pred = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.69314718055994529) + + scorer = autosklearn.metrics._ProbaScorer( + 'accuracy', sklearn.metrics.log_loss, -1, {}) + + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -0.34657359027997314) + + def test_threshold_scorer_binary(self): + y_true = [0, 0, 1, 1] + y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) + + scorer = autosklearn.metrics._ThresholdScorer( + 'accuracy', sklearn.metrics.roc_auc_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + y_pred = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + scorer = autosklearn.metrics._ThresholdScorer( + 'accuracy', sklearn.metrics.roc_auc_score, -1, {}) + + y_pred = np.array([[1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + def test_threshold_scorer_multilabel(self): + y_true = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]) + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + + scorer = autosklearn.metrics._ThresholdScorer( + 'accuracy', sklearn.metrics.roc_auc_score, 1, {}) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + y_pred = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + y_pred = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 0.5) + + scorer = autosklearn.metrics._ThresholdScorer( + 'accuracy', sklearn.metrics.roc_auc_score, -1, {}) + + y_pred = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]) + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + def test_sign_flip(self): + y_true = np.arange(0, 1.01, 0.1) + y_pred = y_true.copy() + + scorer = autosklearn.metrics.make_scorer( + 'r2', sklearn.metrics.r2_score, greater_is_better=True) + + score = scorer(y_true, y_pred + 1.0) + self.assertAlmostEqual(score, -9.0) + + score = scorer(y_true, y_pred + 0.5) + self.assertAlmostEqual(score, -1.5) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, 1.0) + + scorer = autosklearn.metrics.make_scorer( + 'r2', sklearn.metrics.r2_score, greater_is_better=False) + + score = scorer(y_true, y_pred + 1.0) + self.assertAlmostEqual(score, 9.0) + + score = scorer(y_true, y_pred + 0.5) + self.assertAlmostEqual(score, 1.5) + + score = scorer(y_true, y_pred) + self.assertAlmostEqual(score, -1.0) + + +class TestMetricsDoNotAlterInput(unittest.TestCase): + + def test_regression_metrics(self): + for metric, scorer in autosklearn.metrics.REGRESSION_METRICS.items(): + y_true = np.random.random(100).reshape((-1, 1)) + y_pred = y_true.copy() + np.random.randn(100, 1) * 0.1 + y_true_2 = y_true.copy() + y_pred_2 = y_pred.copy() + self.assertTrue(np.isfinite(scorer(y_true_2, y_pred_2))) + np.testing.assert_array_almost_equal(y_true, y_true_2, + err_msg=metric) + np.testing.assert_array_almost_equal(y_pred, y_pred_2, + err_msg=metric) + + def test_classification_metrics(self): + for metric, scorer in autosklearn.metrics.CLASSIFICATION_METRICS.items(): + y_true = np.random.randint(0, 2, size=(100, 1)) + y_pred = np.random.random(200).reshape((-1, 2)) + y_pred = np.array([y_pred[i] / np.sum(y_pred[i]) + for i in range(100)]) + + y_true_2 = y_true.copy() + y_pred_2 = y_pred.copy() + try: + self.assertTrue(np.isfinite(scorer(y_true_2, y_pred_2))) + np.testing.assert_array_almost_equal(y_true, y_true_2, + err_msg=metric) + np.testing.assert_array_almost_equal(y_pred, y_pred_2, + err_msg=metric) + except ValueError as e: + if e.args[0] == 'Sample-based precision, recall, fscore is ' \ + 'not meaningful outside multilabel ' \ + 'classification. See the accuracy_score instead.': + pass + else: + raise e diff --git a/test/test_metric/test_util.py b/test/test_metric/test_util.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/test_pipeline/components/classification/test_libsvm_svc.py b/test/test_pipeline/components/classification/test_libsvm_svc.py index ccad1e66c6..333fba9738 100644 --- a/test/test_pipeline/components/classification/test_libsvm_svc.py +++ b/test/test_pipeline/components/classification/test_libsvm_svc.py @@ -21,16 +21,16 @@ def test_default_configuration_predict_proba(self): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='digits', train_size_maximum=500) - self.assertAlmostEqual(4.6680593525563063, + self.assertAlmostEqual(5.4706296711768925, sklearn.metrics.log_loss(targets, predictions)) for i in range(2): predictions, targets = _test_classifier_predict_proba( LibSVM_SVC, sparse=True, dataset='iris') - self.assertAlmostEqual(0.8649665185853217, - sklearn.metrics.log_loss(targets, - predictions)) + self.assertAlmostEqual(0.84333924656905945, + sklearn.metrics.log_loss(targets, + predictions)) # 2 class for i in range(2): diff --git a/test/test_pipeline/components/classification/test_xgradient_boosting.py b/test/test_pipeline/components/classification/test_xgradient_boosting.py index 884f57b358..be343967f5 100644 --- a/test/test_pipeline/components/classification/test_xgradient_boosting.py +++ b/test/test_pipeline/components/classification/test_xgradient_boosting.py @@ -1,42 +1,42 @@ -import unittest - -from autosklearn.pipeline.components.classification.xgradient_boosting import \ - XGradientBoostingClassifier -from autosklearn.pipeline.util import _test_classifier, \ - _test_classifier_iterative_fit - -import sklearn.metrics -import sklearn.ensemble - - -class XGradientBoostingComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = \ - _test_classifier(XGradientBoostingClassifier) - self.assertAlmostEqual(0.92, - sklearn.metrics.accuracy_score(predictions, targets)) - - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = _test_classifier(XGradientBoostingClassifier, - sparse=True) - self.assertAlmostEqual(0.88, - sklearn.metrics.accuracy_score(predictions, - targets)) - - def test_default_configuration_binary(self): - for i in range(2): - predictions, targets = _test_classifier( - XGradientBoostingClassifier, make_binary=True) - self.assertAlmostEqual(1.0, - sklearn.metrics.accuracy_score(predictions, - targets)) - - def test_default_configuration_binary_sparse(self): - for i in range(2): - predictions, targets = _test_classifier( - XGradientBoostingClassifier, make_binary=True, sparse=True) - self.assertAlmostEqual(0.95999999999999996, - sklearn.metrics.accuracy_score(predictions, - targets)) \ No newline at end of file +# import unittest +# +# from autosklearn.pipeline.components.classification.xgradient_boosting import \ +# XGradientBoostingClassifier +# from autosklearn.pipeline.util import _test_classifier, \ +# _test_classifier_iterative_fit +# +# import sklearn.metrics +# import sklearn.ensemble +# +# +# class XGradientBoostingComponentTest(unittest.TestCase): +# def test_default_configuration(self): +# for i in range(2): +# predictions, targets = \ +# _test_classifier(XGradientBoostingClassifier) +# self.assertAlmostEqual(0.92, +# sklearn.metrics.accuracy_score(predictions, targets)) +# +# def test_default_configuration_sparse(self): +# for i in range(2): +# predictions, targets = _test_classifier(XGradientBoostingClassifier, +# sparse=True) +# self.assertAlmostEqual(0.88, +# sklearn.metrics.accuracy_score(predictions, +# targets)) +# +# def test_default_configuration_binary(self): +# for i in range(2): +# predictions, targets = _test_classifier( +# XGradientBoostingClassifier, make_binary=True) +# self.assertAlmostEqual(1.0, +# sklearn.metrics.accuracy_score(predictions, +# targets)) +# +# def test_default_configuration_binary_sparse(self): +# for i in range(2): +# predictions, targets = _test_classifier( +# XGradientBoostingClassifier, make_binary=True, sparse=True) +# self.assertAlmostEqual(0.95999999999999996, +# sklearn.metrics.accuracy_score(predictions, +# targets)) \ No newline at end of file diff --git a/test/test_pipeline/components/data_preprocessing/test_balancing.py b/test/test_pipeline/components/data_preprocessing/test_balancing.py index 4c69804a03..4a43dc07d8 100644 --- a/test/test_pipeline/components/data_preprocessing/test_balancing.py +++ b/test/test_pipeline/components/data_preprocessing/test_balancing.py @@ -54,11 +54,11 @@ def test_balancing_get_weights_svm_sgd(self): balancing = Balancing(strategy='weighting') init_params, fit_params = balancing.get_weights( Y, 'libsvm_svc', None, None, None) - self.assertEqual(("classifier:class_weight", "auto"), + self.assertEqual(("classifier:class_weight", "balanced"), list(init_params.items())[0]) init_params, fit_params = balancing.get_weights( Y, None, 'liblinear_svc_preprocessor', None, None) - self.assertEqual(("preprocessor:class_weight", "auto"), + self.assertEqual(("preprocessor:class_weight", "balanced"), list(init_params.items())[0]) def test_weighting_effect(self): @@ -72,11 +72,11 @@ def test_weighting_effect(self): ('decision_tree', DecisionTree, 0.780, 0.643), ('extra_trees', ExtraTreesClassifier, 0.75, 0.800), ('gradient_boosting', GradientBoostingClassifier, - 0.789, 0.762), + 0.789, 0.762), ('random_forest', RandomForest, 0.75, 0.821), - ('libsvm_svc', LibSVM_SVC, 0.769, 0.706), - ('liblinear_svc', LibLinear_SVC, 0.762, 0.72), - ('sgd', SGD, 0.739, 0.735) + ('libsvm_svc', LibSVM_SVC, 0.769, 0.72), + ('liblinear_svc', LibLinear_SVC, 0.762, 0.735), + ('sgd', SGD, 0.704, 0.667) ]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: @@ -98,11 +98,11 @@ def test_weighting_effect(self): default, random_state=1, include=include) predictor = classifier.fit(X_train, Y_train) predictions = predictor.predict(X_test) - self.assertAlmostEqual(acc, - sklearn.metrics.f1_score(predictions, Y_test), - places=3) + self.assertAlmostEqual( + sklearn.metrics.f1_score(predictions, Y_test), acc, + places=3, msg=(name, strategy)) - # pre_transform and fit_estimator + # fit_transformer and fit_estimator data_ = copy.copy(data) X_train = data_[0][:100] Y_train = data_[1][:100] @@ -112,19 +112,18 @@ def test_weighting_effect(self): classifier = SimpleClassificationPipeline( default, random_state=1, include=include) classifier.set_hyperparameters(configuration=default) - Xt, fit_params = classifier.pre_transform(X_train, Y_train) + Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) predictions = classifier.predict(X_test) - self.assertAlmostEqual(acc, - sklearn.metrics.f1_score( - predictions, Y_test), - places=3) + self.assertAlmostEqual( + sklearn.metrics.f1_score(predictions, Y_test), acc, + places=3) for name, pre, acc_no_weighting, acc_weighting in \ [('extra_trees_preproc_for_classification', - ExtraTreesPreprocessorClassification, 0.625, 0.634), + ExtraTreesPreprocessorClassification, 0.691, 0.692), ('liblinear_svc_preprocessor', LibLinear_Preprocessor, - 0.75, 0.706)]: + 0.692, 0.590)]: for strategy, acc in [('none', acc_no_weighting), ('weighting', acc_weighting)]: data_ = copy.copy(data) @@ -143,12 +142,11 @@ def test_weighting_effect(self): classifier.set_hyperparameters(default) predictor = classifier.fit(X_train, Y_train) predictions = predictor.predict(X_test) - self.assertAlmostEqual(acc, - sklearn.metrics.f1_score( - predictions, Y_test), - places=3) + self.assertAlmostEqual( + sklearn.metrics.f1_score(predictions, Y_test), acc, + places=3, msg=(name, strategy)) - # pre_transform and fit_estimator + # fit_transformer and fit_estimator data_ = copy.copy(data) X_train = data_[0][:100] Y_train = data_[1][:100] @@ -158,10 +156,9 @@ def test_weighting_effect(self): default._values['balancing:strategy'] = strategy classifier = SimpleClassificationPipeline( default, random_state=1, include=include) - Xt, fit_params = classifier.pre_transform(X_train, Y_train) + Xt, fit_params = classifier.fit_transformer(X_train, Y_train) classifier.fit_estimator(Xt, Y_train, **fit_params) predictions = classifier.predict(X_test) - self.assertAlmostEqual(acc, - sklearn.metrics.f1_score( - predictions, Y_test), - places=3) + self.assertAlmostEqual( + sklearn.metrics.f1_score(predictions, Y_test), acc, + places=3) diff --git a/test/test_pipeline/components/feature_preprocessing/test_choice.py b/test/test_pipeline/components/feature_preprocessing/test_choice.py index 387238ccf9..838cb5c3d8 100644 --- a/test/test_pipeline/components/feature_preprocessing/test_choice.py +++ b/test/test_pipeline/components/feature_preprocessing/test_choice.py @@ -1,5 +1,3 @@ -from __future__ import print_function - import unittest import autosklearn.pipeline.components.feature_preprocessing as fp diff --git a/test/test_pipeline/components/regression/test_adaboost.py b/test/test_pipeline/components/regression/test_adaboost.py index e3a64e368d..2f9d469c23 100644 --- a/test/test_pipeline/components/regression/test_adaboost.py +++ b/test/test_pipeline/components/regression/test_adaboost.py @@ -12,7 +12,7 @@ def test_default_configuration(self): for i in range(2): predictions, targets = \ _test_regressor(AdaboostRegressor, dataset='boston') - self.assertAlmostEqual(0.59461560848921158, + self.assertAlmostEqual(0.60525743737887405, sklearn.metrics.r2_score(targets, predictions)) @@ -20,6 +20,6 @@ def test_default_configuration_sparse(self): for i in range(2): predictions, targets = \ _test_regressor(AdaboostRegressor, sparse=True, dataset='boston') - self.assertAlmostEqual(0.2039634989252479, + self.assertAlmostEqual(0.22111559712318207, sklearn.metrics.r2_score(targets, predictions)) diff --git a/test/test_pipeline/components/regression/test_gaussian_process.py b/test/test_pipeline/components/regression/test_gaussian_process.py index d1e5558321..6871e4c225 100644 --- a/test/test_pipeline/components/regression/test_gaussian_process.py +++ b/test/test_pipeline/components/regression/test_gaussian_process.py @@ -13,7 +13,12 @@ def test_default_configuration(self): # Float32 leads to numeric instabilities predictions, targets = _test_regressor(GaussianProcess, dataset='boston') - self.assertAlmostEqual(0.83362335184173442, + # My machine: 0.574913739659292 + # travis-ci: 0.49562471963524557 + self.assertLessEqual( sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), - places=2) + 0.6) + self.assertGreaterEqual( + sklearn.metrics.r2_score(y_true=targets, y_pred=predictions), + 0.4) diff --git a/test/test_pipeline/components/regression/test_xgradient_boosting.py b/test/test_pipeline/components/regression/test_xgradient_boosting.py index 35883c0fd6..168c4bcf39 100644 --- a/test/test_pipeline/components/regression/test_xgradient_boosting.py +++ b/test/test_pipeline/components/regression/test_xgradient_boosting.py @@ -1,40 +1,40 @@ -import unittest - -from autosklearn.pipeline.components.regression.xgradient_boosting import \ - XGradientBoostingRegressor -from autosklearn.pipeline.util import _test_regressor, \ - _test_regressor_iterative_fit - - -import sklearn.metrics -import sklearn.ensemble - - -class XGradientBoostingComponentTest(unittest.TestCase): - def test_default_configuration(self): - for i in range(2): - predictions, targets = _test_regressor(XGradientBoostingRegressor) - self.assertAlmostEqual(0.34009199992306871, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - def test_default_configuration_sparse(self): - for i in range(2): - predictions, targets = _test_regressor(XGradientBoostingRegressor, - sparse=True) - self.assertAlmostEqual(0.20743694821393754, - sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - #def test_default_configuration_iterative_fit(self): - # for i in range(10): - # predictions, targets = \ - # _test_regressor_iterative_fit(XGradientBoostingRegressor) - # self.assertAlmostEqual(0.40965687834764064, - # sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) - - #def test_default_configuration_iterative_fit_sparse(self): - # for i in range(10): - # predictions, targets = \ - # _test_regressor_iterative_fit(XGradientBoostingRegressor, - # sparse=True) - # self.assertAlmostEqual(0.40965687834764064, - # sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) \ No newline at end of file +# import unittest +# +# from autosklearn.pipeline.components.regression.xgradient_boosting import \ +# XGradientBoostingRegressor +# from autosklearn.pipeline.util import _test_regressor, \ +# _test_regressor_iterative_fit +# +# +# import sklearn.metrics +# import sklearn.ensemble +# +# +# class XGradientBoostingComponentTest(unittest.TestCase): +# def test_default_configuration(self): +# for i in range(2): +# predictions, targets = _test_regressor(XGradientBoostingRegressor) +# self.assertAlmostEqual(0.34009199992306871, +# sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) +# +# def test_default_configuration_sparse(self): +# for i in range(2): +# predictions, targets = _test_regressor(XGradientBoostingRegressor, +# sparse=True) +# self.assertAlmostEqual(0.20743694821393754, +# sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) +# +# #def test_default_configuration_iterative_fit(self): +# # for i in range(10): +# # predictions, targets = \ +# # _test_regressor_iterative_fit(XGradientBoostingRegressor) +# # self.assertAlmostEqual(0.40965687834764064, +# # sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) +# +# #def test_default_configuration_iterative_fit_sparse(self): +# # for i in range(10): +# # predictions, targets = \ +# # _test_regressor_iterative_fit(XGradientBoostingRegressor, +# # sparse=True) +# # self.assertAlmostEqual(0.40965687834764064, +# # sklearn.metrics.r2_score(y_true=targets, y_pred=predictions)) \ No newline at end of file diff --git a/test/test_pipeline/implementations/test_OneHotEncoder.py b/test/test_pipeline/implementations/test_OneHotEncoder.py index c2cb2b4546..50a7d32575 100644 --- a/test/test_pipeline/implementations/test_OneHotEncoder.py +++ b/test/test_pipeline/implementations/test_OneHotEncoder.py @@ -3,6 +3,11 @@ import numpy as np import scipy.sparse from sklearn.utils.testing import assert_array_almost_equal +import sklearn.tree +import sklearn.datasets +import sklearn.model_selection +import sklearn.pipeline +import openml from autosklearn.pipeline.implementations.OneHotEncoder import OneHotEncoder @@ -74,114 +79,127 @@ (0, 1, 2, 3, 3, 3))), shape=(6, 4)) -class OneHotEncoderTest(unittest.TestCase): +class TestOneHotEncoder(unittest.TestCase): def test_dense1(self): - self.fit_then_transform(dense1_1h, dense1) - self.fit_then_transform_dense(dense1_1h, dense1) + self._fit_then_transform(dense1_1h, dense1) + self._fit_then_transform_dense(dense1_1h, dense1) def test_dense1_minimum_fraction(self): - self.fit_then_transform(dense1_1h_minimum_fraction, dense1, minimum_fraction=0.5) - self.fit_then_transform_dense(dense1_1h_minimum_fraction, dense1, minimum_fraction=0.5) + self._fit_then_transform(dense1_1h_minimum_fraction, dense1, minimum_fraction=0.5) + self._fit_then_transform_dense(dense1_1h_minimum_fraction, dense1, minimum_fraction=0.5) def test_dense2(self): - self.fit_then_transform(dense2_1h, dense2) - self.fit_then_transform_dense(dense2_1h, dense2) + self._fit_then_transform(dense2_1h, dense2) + self._fit_then_transform_dense(dense2_1h, dense2) def test_dense2_minimum_fraction(self): - self.fit_then_transform(dense2_1h_minimum_fraction, dense2, - minimum_fraction=0.3) - self.fit_then_transform_dense(dense2_1h_minimum_fraction, dense2, - minimum_fraction=0.3) + self._fit_then_transform(dense2_1h_minimum_fraction, dense2, + minimum_fraction=0.3) + self._fit_then_transform_dense(dense2_1h_minimum_fraction, dense2, + minimum_fraction=0.3) def test_dense2_with_non_sparse_components(self): - self.fit_then_transform(dense2_partial_1h, dense2, - categorical_features=[True, True, False]) - self.fit_then_transform_dense(dense2_partial_1h, dense2, - categorical_features=[True, True, False]) + self._fit_then_transform(dense2_partial_1h, dense2, + categorical_features=[True, True, False]) + self._fit_then_transform_dense(dense2_partial_1h, dense2, + categorical_features=[True, True, False]) # Minimum fraction is not too interesting here... def test_dense3(self): - self.fit_then_transform(dense3_1h, dense3) - self.fit_then_transform_dense(dense3_1h, dense3) + self._fit_then_transform(dense3_1h, dense3) + self._fit_then_transform_dense(dense3_1h, dense3) def test_sparse1(self): - self.fit_then_transform(sparse1_1h.todense(), sparse1) - self.fit_then_transform_dense(sparse1_1h.todense(), sparse1) + self._fit_then_transform(sparse1_1h.todense(), sparse1) + self._fit_then_transform_dense(sparse1_1h.todense(), sparse1) def test_sparse1_minimum_fraction(self): expected = np.array([[0, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 1]], dtype=float).transpose() - self.fit_then_transform(expected, sparse1, - minimum_fraction=0.5) - self.fit_then_transform_dense(expected, sparse1, - minimum_fraction=0.5) + self._fit_then_transform(expected, sparse1, + minimum_fraction=0.5) + self._fit_then_transform_dense(expected, sparse1, + minimum_fraction=0.5) def test_sparse1_with_non_sparse_components(self): - self.fit_then_transform(sparse1_paratial_1h.todense(), sparse1, - categorical_features=[True, False]) + self._fit_then_transform(sparse1_paratial_1h.todense(), sparse1, + categorical_features=[True, False]) # This test does not apply here. The sparse matrix will be cut into a # continouos and a categorical part, after one hot encoding only the # categorical part is an array, the continuous part will still be a # sparse matrix. Therefore, the OHE will only return a sparse matrix - #self.fit_then_transform_dense(sparse1_paratial_1h.todense(), sparse1, + #self._fit_then_transform_dense(sparse1_paratial_1h.todense(), sparse1, # categorical_features=[True, False]) def test_sparse2(self): - self.fit_then_transform(sparse2_1h.todense(), sparse2) - self.fit_then_transform_dense(sparse2_1h.todense(), sparse2) + self._fit_then_transform(sparse2_1h.todense(), sparse2) + self._fit_then_transform_dense(sparse2_1h.todense(), sparse2) def test_sparse2_minimum_fraction(self): expected = np.array([[0, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 1]], dtype=float).transpose() - self.fit_then_transform(expected, sparse2, - minimum_fraction=0.5) - self.fit_then_transform_dense(expected, sparse2, - minimum_fraction=0.5) + self._fit_then_transform(expected, sparse2, + minimum_fraction=0.5) + self._fit_then_transform_dense(expected, sparse2, + minimum_fraction=0.5) def test_sparse2_csr(self): - self.fit_then_transform(sparse2_csr_1h.todense(), sparse2_csr) - self.fit_then_transform_dense(sparse2_csr_1h.todense(), sparse2_csr) + self._fit_then_transform(sparse2_csr_1h.todense(), sparse2_csr) + self._fit_then_transform_dense(sparse2_csr_1h.todense(), sparse2_csr) def test_sparse_on_dense2_minimum_fraction(self): sparse = scipy.sparse.csr_matrix(dense2) - self.fit_then_transform(dense2_1h_minimum_fraction_as_sparse, sparse, - minimum_fraction=0.5) - self.fit_then_transform_dense(dense2_1h_minimum_fraction_as_sparse, sparse, - minimum_fraction=0.5) + self._fit_then_transform(dense2_1h_minimum_fraction_as_sparse, sparse, + minimum_fraction=0.5) + self._fit_then_transform_dense(dense2_1h_minimum_fraction_as_sparse, sparse, + minimum_fraction=0.5) - def fit_then_transform(self, expected, input, categorical_features='all', - minimum_fraction=None): + def _fit_then_transform(self, expected, input, categorical_features='all', + minimum_fraction=None): # Test fit_transform + input_copy = input.copy() ohe = OneHotEncoder(categorical_features=categorical_features, minimum_fraction=minimum_fraction) - transformation = ohe.fit_transform(input.copy()) + transformation = ohe.fit_transform(input) self.assertIsInstance(transformation, scipy.sparse.csr_matrix) assert_array_almost_equal(expected.astype(float), transformation.todense()) + self._check_arrays_equal(input, input_copy) # Test fit, and afterwards transform ohe2 = OneHotEncoder(categorical_features=categorical_features, minimum_fraction=minimum_fraction) - ohe2.fit(input.copy()) - transformation = ohe2.transform(input.copy()) + ohe2.fit(input) + transformation = ohe2.transform(input) self.assertIsInstance(transformation, scipy.sparse.csr_matrix) assert_array_almost_equal(expected, transformation.todense()) + self._check_arrays_equal(input, input_copy) - def fit_then_transform_dense(self, expected, input, - categorical_features='all', - minimum_fraction=None): + def _fit_then_transform_dense(self, expected, input, + categorical_features='all', + minimum_fraction=None): + input_copy = input.copy() ohe = OneHotEncoder(categorical_features=categorical_features, sparse=False, minimum_fraction=minimum_fraction) - transformation = ohe.fit_transform(input.copy()) + transformation = ohe.fit_transform(input) self.assertIsInstance(transformation, np.ndarray) assert_array_almost_equal(expected, transformation) + self._check_arrays_equal(input, input_copy) ohe2 = OneHotEncoder(categorical_features=categorical_features, sparse=False, minimum_fraction=minimum_fraction) - ohe2.fit(input.copy()) - transformation = ohe2.transform(input.copy()) + ohe2.fit(input) + transformation = ohe2.transform(input) self.assertIsInstance(transformation, np.ndarray) assert_array_almost_equal(expected, transformation) + self._check_arrays_equal(input, input_copy) + + def _check_arrays_equal(self, a1, a2): + if scipy.sparse.issparse(a1): + a1 = a1.toarray() + if scipy.sparse.issparse(a2): + a2 = a2.toarray() + assert_array_almost_equal(a1, a2) def test_transform_with_unknown_value(self): input = np.array(((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 4, 5))).transpose() @@ -200,4 +218,20 @@ def test_transform_with_unknown_value(self): output = ohe.transform(tds).todense() self.assertEqual(3, np.sum(output)) - + def test_classification_workflow(self): + task = openml.tasks.get_task(254) + X, y = task.get_X_and_y() + + ohe = OneHotEncoder(categorical_features=[True]*22) + tree = sklearn.tree.DecisionTreeClassifier(random_state=1) + pipeline = sklearn.pipeline.Pipeline((('ohe', ohe), ('tree', tree))) + + X_train, X_test, y_train, y_test = \ + sklearn.model_selection.train_test_split(X, y, random_state=3, + train_size=0.5, + test_size=0.5) + pipeline.fit(X_train, y_train) + self.assertEqual(np.mean(y_train == pipeline.predict(X_train)), 1) + # With an incorrect copy operation the OneHotEncoder would rearrange + # the data in such a way that the accuracy would drop to 66% + self.assertEqual(np.mean(y_test == pipeline.predict(X_test)), 1) diff --git a/test/test_pipeline/implementations/test_imputation.py b/test/test_pipeline/implementations/test_imputation.py index 79d4c6a3bf..b75e17ae79 100644 --- a/test/test_pipeline/implementations/test_imputation.py +++ b/test/test_pipeline/implementations/test_imputation.py @@ -11,7 +11,7 @@ from sklearn.preprocessing import Imputer from sklearn.pipeline import Pipeline -from sklearn import grid_search +from sklearn.model_selection import GridSearchCV from sklearn import tree from sklearn.random_projection import sparse_random_matrix @@ -272,7 +272,7 @@ def test_imputation_pipeline_grid_search(self): l = 100 X = sparse_random_matrix(l, l, density=0.10) Y = sparse_random_matrix(l, 1, density=0.10).toarray() - gs = grid_search.GridSearchCV(pipeline, parameters) + gs = GridSearchCV(pipeline, parameters) gs.fit(X, Y) diff --git a/test/test_pipeline/test_base.py b/test/test_pipeline/test_base.py index ed06563a88..40a0d426be 100644 --- a/test/test_pipeline/test_base.py +++ b/test/test_pipeline/test_base.py @@ -68,7 +68,7 @@ def test_get_hyperparameter_configuration_space_3choices(self): self.assertEqual(len(cs.get_hyperparameter("p1:__choice__").choices), 15) self.assertEqual(len(cs.get_hyperparameter("c:__choice__").choices), - 16) + 15) #for clause in sorted([str(clause) for clause in cs.forbidden_clauses]): # print(clause) self.assertEqual(107, len(cs.forbidden_clauses)) diff --git a/test/test_pipeline/test_classification.py b/test/test_pipeline/test_classification.py index 9467807e90..bd99b3fd58 100644 --- a/test/test_pipeline/test_classification.py +++ b/test/test_pipeline/test_classification.py @@ -9,11 +9,11 @@ import numpy as np import sklearn.datasets import sklearn.decomposition -import sklearn.cross_validation +import sklearn.model_selection import sklearn.ensemble import sklearn.svm from sklearn.utils.testing import assert_array_almost_equal -from xgboost.core import XGBoostError +#from xgboost.core import XGBoostError from ConfigSpace.configuration_space import ConfigurationSpace, \ Configuration @@ -141,7 +141,7 @@ def test_default_configuration_iterative_fit(self): include={'classifier': ['random_forest'], 'preprocessor': ['no_preprocessing']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='iris') - XT = classifier.pre_transform(X_train, Y_train) + XT = classifier.fit_transformer(X_train, Y_train) for i in range(1, 11): classifier.iterative_fit(X_train, Y_train) self.assertEqual(classifier.steps[-1][-1].choice.estimator.n_estimators, @@ -212,7 +212,7 @@ def test_configurations_categorical_data(self): y = X[:, -1].copy() X = X[:,:-1] X_train, X_test, Y_train, Y_test = \ - sklearn.cross_validation.train_test_split(X, y) + sklearn.model_selection.train_test_split(X, y) data = {'X_train': X_train, 'Y_train': Y_train, 'X_test': X_test, 'Y_test': Y_test} @@ -300,6 +300,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, continue elif 'Bug in scikit-learn' in e.args[0]: continue + elif 'The condensed distance matrix must contain only finite ' \ + 'values.' in e.args[0]: + continue else: print(config) print(traceback.format_exc()) @@ -324,13 +327,13 @@ def _test_configurations(self, configurations_space, make_sparse=False, print(traceback.format_exc()) print(config) raise e - except XGBoostError as e: - if "std::bad_alloc" in e.args[0]: - continue - else: - print(traceback.format_exc()) - print(config) - raise e + #except XGBoostError as e: + # if "std::bad_alloc" in e.args[0]: + # continue + # else: + # print(traceback.format_exc()) + # print(config) + # raise e def test_get_hyperparameter_search_space(self): cs = SimpleClassificationPipeline().get_hyperparameter_search_space() @@ -340,12 +343,12 @@ def test_get_hyperparameter_search_space(self): self.assertEqual(len(cs.get_hyperparameter( 'rescaling:__choice__').choices), 4) self.assertEqual(len(cs.get_hyperparameter( - 'classifier:__choice__').choices), 16) + 'classifier:__choice__').choices), 15) self.assertEqual(len(cs.get_hyperparameter( 'preprocessor:__choice__').choices), 13) hyperparameters = cs.get_hyperparameters() - self.assertEqual(154, len(hyperparameters)) + self.assertEqual(141, len(hyperparameters)) #for hp in sorted([str(h) for h in hyperparameters]): # print hp @@ -443,21 +446,6 @@ def test_predict_batched(self): self.assertEqual(84, cls_predict.call_count) assert_array_almost_equal(prediction_, prediction) - # Multilabel - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits') - Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) - for y in Y_train])) - cls.fit(X_train, Y_train) - X_test_ = X_test.copy() - prediction_ = cls.predict_proba(X_test_) - # The object behind the last step in the pipeline - cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) - cls.steps[-1][-1].predict_proba = cls_predict - prediction = cls.predict_proba(X_test, batch_size=20) - self.assertEqual((1647, 10), prediction.shape) - self.assertEqual(84, cls_predict.call_count) - assert_array_almost_equal(prediction_, prediction) - def test_predict_batched_sparse(self): cls = SimpleClassificationPipeline(dataset_properties={'sparse': True}, include={'classifier': ['sgd']}) @@ -476,22 +464,6 @@ def test_predict_batched_sparse(self): self.assertEqual(84, cls_predict.call_count) assert_array_almost_equal(prediction_, prediction) - # Multilabel - X_train, Y_train, X_test, Y_test = get_dataset(dataset='digits', - make_sparse=True) - Y_train = np.array(list([(list([1 if i != y else 0 for i in range(10)])) - for y in Y_train])) - cls.fit(X_train, Y_train) - X_test_ = X_test.copy() - prediction_ = cls.predict_proba(X_test_) - # The object behind the last step in the pipeline - cls_predict = unittest.mock.Mock(wraps=cls.steps[-1][1].predict_proba) - cls.steps[-1][-1].predict_proba = cls_predict - prediction = cls.predict_proba(X_test, batch_size=20) - self.assertEqual((1647, 10), prediction.shape) - self.assertEqual(84, cls_predict.call_count) - assert_array_almost_equal(prediction_, prediction) - def test_predict_proba_batched(self): # Multiclass cls = SimpleClassificationPipeline(include={'classifier': ['sgd']}) diff --git a/test/test_pipeline/test_regression.py b/test/test_pipeline/test_regression.py index 6cd8a836dd..347ca8f8c9 100644 --- a/test/test_pipeline/test_regression.py +++ b/test/test_pipeline/test_regression.py @@ -153,6 +153,9 @@ def _test_configurations(self, configurations_space, make_sparse=False, continue elif 'Bug in scikit-learn' in e.args[0]: continue + elif 'The condensed distance matrix must contain only finite ' \ + 'values.' in e.args[0]: + continue else: print(config) print(traceback.format_exc()) @@ -202,7 +205,7 @@ def test_default_configuration_iterative_fit(self): include={'regressor': ['random_forest'], 'preprocessor': ['no_preprocessing']}) X_train, Y_train, X_test, Y_test = get_dataset(dataset='boston') - XT = regressor.pre_transform(X_train, Y_train) + XT = regressor.fit_transformer(X_train, Y_train) for i in range(1, 11): regressor.iterative_fit(X_train, Y_train) self.assertEqual(regressor.steps[-1][-1].choice.estimator.n_estimators, @@ -218,7 +221,7 @@ def test_get_hyperparameter_search_space(self): self.assertIsInstance(cs, ConfigurationSpace) conditions = cs.get_conditions() hyperparameters = cs.get_hyperparameters() - self.assertEqual(143, len(hyperparameters)) + self.assertEqual(130, len(hyperparameters)) self.assertEqual(len(hyperparameters) - 5, len(conditions)) def test_get_hyperparameter_search_space_include_exclude_models(self): diff --git a/test/test_scripts/test_metadata_generation.py b/test/test_scripts/test_metadata_generation.py index aebb00d5d0..4ace6ce776 100644 --- a/test/test_scripts/test_metadata_generation.py +++ b/test/test_scripts/test_metadata_generation.py @@ -7,6 +7,8 @@ import sys import unittest +import arff + class TestMetadataGeneration(unittest.TestCase): @@ -46,7 +48,10 @@ def test_metadata_generation(self): self.assertTrue(os.path.exists(commands_output_file)) with open(commands_output_file) as fh: - cmd = fh.readline() + while True: + cmd = fh.readline() + if 'task-id 253' in cmd: + break self.assertIn('time-limit 86400', cmd) self.assertIn('per-run-time-limit 1800', cmd) @@ -62,18 +67,20 @@ def test_metadata_generation(self): expected_output_directory = os.path.join(self.working_directory, 'configuration', 'classification', - '233-1') + '253') self.assertTrue(os.path.exists(expected_output_directory)) smac_log = os.path.join(self.working_directory, - 'configuration/classification/233-1', - 'AutoML(1):233.log') + 'configuration/classification/253', + 'AutoML(1):253.log') with open(smac_log) as fh: smac_output = fh.read() self.assertEqual(rval.returncode, 0, msg=str(rval) + '\n' + smac_output) expected_validation_output = os.path.join(expected_output_directory, + 'smac3-output_1_run1', 'validation_trajectory.json') self.assertTrue(os.path.exists(expected_validation_output)) - trajectory = os.path.join(expected_output_directory, 'trajectory.json') + trajectory = os.path.join(expected_output_directory, + 'smac3-output_1_run1', 'trajectory.json') with open(expected_validation_output) as fh_validation: with open(trajectory) as fh_trajectory: @@ -91,12 +98,18 @@ def test_metadata_generation(self): # print(rval.stdout, flush=True) # print(rval.stderr, flush=True) self.assertEqual(rval.returncode, 0, msg=str(rval)) + for file in ['algorithm_runs.arff', 'configurations.csv', 'description.results.txt']: - self.assertTrue(os.path.exists(os.path.join(self.working_directory, - 'configuration_results', - 'acc_metric_binary.classification_dense', - file))) + for metric in ['accuracy', 'balanced_accuracy', 'log_loss']: + self.assertTrue(os.path.exists(os.path.join(self.working_directory, + 'configuration_results', + '%s_binary.classification_dense' % metric, + file)), msg=str((metric, file))) + self.assertFalse(os.path.exists(os.path.join(self.working_directory, + 'configuration_results', + 'roc_auc_binary.classification_dense', + file)), msg=file) # 6. Calculate metafeatures script_filename = os.path.join(scripts_directory, '03_calculate_metafeatures.py') @@ -126,9 +139,27 @@ def test_metadata_generation(self): 'readme.txt']: self.assertTrue(os.path.exists(os.path.join(self.working_directory, 'metadata', - 'acc_metric_binary.classification_dense', + 'accuracy_binary.classification_dense', file))) + with open(os.path.join(self.working_directory, + 'metadata', + 'accuracy_binary.classification_dense', + 'algorithm_runs.arff')) as fh: + algorithm_runs = arff.load(fh) + self.assertEqual(algorithm_runs['attributes'], + [('instance_id', 'STRING'), + ('repetition', 'NUMERIC'), + ('algorithm', 'STRING'), + ('accuracy', 'NUMERIC'), + ('runstatus', + ['ok', 'timeout', 'memout', 'not_applicable', + 'crash', 'other'])]) + self.assertEqual(len(algorithm_runs['data']), 1) + self.assertEqual(len(algorithm_runs['data'][0]), 5) + self.assertLess(algorithm_runs['data'][0][3], 0.9) + self.assertEqual(algorithm_runs['data'][0][4], 'ok') + def tearDown(self): for i in range(5): try: diff --git a/test/test_util/test_StopWatch.py b/test/test_util/test_StopWatch.py index 38e1c8a88c..1f23ed066c 100644 --- a/test/test_util/test_StopWatch.py +++ b/test/test_util/test_StopWatch.py @@ -5,8 +5,6 @@ @projekt: AutoML2015 """ - -from __future__ import print_function import time import unittest import unittest.mock diff --git a/test/test_util/test_common.py b/test/test_util/test_common.py index af9cb2c56a..6dce295541 100644 --- a/test/test_util/test_common.py +++ b/test/test_util/test_common.py @@ -1,7 +1,4 @@ # -*- encoding: utf-8 -*- -from __future__ import print_function -from functools import partial - import os import unittest diff --git a/test/test_util/test_hash.py b/test/test_util/test_hash.py index fb579b1a30..59227ccc21 100644 --- a/test/test_util/test_hash.py +++ b/test/test_util/test_hash.py @@ -1,8 +1,9 @@ import unittest import numpy as np +import scipy.sparse -from autosklearn.util.hash import hash_numpy_array +from autosklearn.util.hash import hash_array_or_matrix class HashTests(unittest.TestCase): @@ -10,7 +11,7 @@ class HashTests(unittest.TestCase): def test_c_contiguous_array(self): array = np.array([[1, 2], [3, 4]]) - hash = hash_numpy_array(array) + hash = hash_array_or_matrix(array) self.assertIsNotNone(hash) @@ -18,7 +19,7 @@ def test_f_contiguous_array(self): array = np.array([[1, 2], [3, 4]]) array = np.asfortranarray(array) - hash = hash_numpy_array(array) + hash = hash_array_or_matrix(array) self.assertIsNotNone(hash) @@ -27,8 +28,8 @@ def test_transpose_arrays(self): f_array = np.array([[1, 3], [2, 4]]) f_array = np.asfortranarray(f_array) - c_hash = hash_numpy_array(c_array) - f_hash = hash_numpy_array(f_array) + c_hash = hash_array_or_matrix(c_array) + f_hash = hash_array_or_matrix(f_array) self.assertEqual(c_hash, f_hash) @@ -36,8 +37,8 @@ def test_same_data_arrays(self): first_array = np.array([[1, 2], [3, 4]]) second_array = np.array([[1, 2], [3, 4]]) - first_hash = hash_numpy_array(first_array) - second_hash = hash_numpy_array(second_array) + first_hash = hash_array_or_matrix(first_array) + second_hash = hash_array_or_matrix(second_array) self.assertEqual(first_hash, second_hash) @@ -45,7 +46,17 @@ def test_different_data_arrays(self): first_array = np.array([[1, 2], [3, 4]]) second_array = np.array([[1, 3], [2, 4]]) - first_hash = hash_numpy_array(first_array) - second_hash = hash_numpy_array(second_array) + first_hash = hash_array_or_matrix(first_array) + second_hash = hash_array_or_matrix(second_array) - self.assertNotEqual(first_hash, second_hash) \ No newline at end of file + self.assertNotEqual(first_hash, second_hash) + + def test_scipy_csr(self): + row = np.array([0, 0, 1, 2, 2, 2]) + col = np.array([0, 2, 2, 0, 1, 2]) + data = np.array([1, 2, 3, 4, 5, 6]) + matrix = scipy.sparse.csr_matrix((data, (row, col)), shape=(3, 3)) + + hash = hash_array_or_matrix(matrix) + + self.assertIsNotNone(hash) \ No newline at end of file diff --git a/testcommand.sh b/testcommand.sh index 6e1e91cd61..540325c7c2 100644 --- a/testcommand.sh +++ b/testcommand.sh @@ -1,2 +1,2 @@ #!/usr/bin/env bash -nosetests --processes=14 --process-timeout=360 -v $1 \ No newline at end of file +nosetests --processes=4 --process-timeout=360 -v $1 \ No newline at end of file