hnishi: Doc: Fix variable name in example (#1200)

automl · Aug 4, 2021 · 7fa7fdc · 7fa7fdc
1 parent 58fd8be
commit 7fa7fdc
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diff --git a/development/_downloads/5a889c221f400053efc3d433ea6ac72b/example_pandas_train_test.py b/development/_downloads/5a889c221f400053efc3d433ea6ac72b/example_pandas_train_test.py
@@ -37,7 +37,7 @@
 
 
 def get_runhistory_models_performance(automl):
-    metric = cls.automl_._metric
+    metric = automl.automl_._metric
     data = automl.automl_.runhistory_.data
     performance_list = []
     for run_key, run_value in data.items():

diff --git a/development/_downloads/8fef80f116d77769427ecdd86b839143/example_pandas_train_test.ipynb b/development/_downloads/8fef80f116d77769427ecdd86b839143/example_pandas_train_test.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport sklearn.model_selection\nimport sklearn.datasets\nimport sklearn.metrics\n\nfrom smac.tae import StatusType\n\nimport autosklearn.classification\n\n\ndef get_runhistory_models_performance(automl):\n    metric = cls.automl_._metric\n    data = automl.automl_.runhistory_.data\n    performance_list = []\n    for run_key, run_value in data.items():\n        if run_value.status != StatusType.SUCCESS:\n            # Ignore crashed runs\n            continue\n        # Alternatively, it is possible to also obtain the start time with ``run_value.starttime``\n        endtime = pd.Timestamp(time.strftime('%Y-%m-%d %H:%M:%S',\n                                             time.localtime(run_value.endtime)))\n        val_score = metric._optimum - (metric._sign * run_value.cost)\n        test_score = metric._optimum - (metric._sign * run_value.additional_info['test_loss'])\n        train_score = metric._optimum - (metric._sign * run_value.additional_info['train_loss'])\n        performance_list.append({\n            'Timestamp': endtime,\n            'single_best_optimization_score': val_score,\n            'single_best_test_score': test_score,\n            'single_best_train_score': train_score,\n        })\n    return pd.DataFrame(performance_list)"
+        "import time\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport sklearn.model_selection\nimport sklearn.datasets\nimport sklearn.metrics\n\nfrom smac.tae import StatusType\n\nimport autosklearn.classification\n\n\ndef get_runhistory_models_performance(automl):\n    metric = automl.automl_._metric\n    data = automl.automl_.runhistory_.data\n    performance_list = []\n    for run_key, run_value in data.items():\n        if run_value.status != StatusType.SUCCESS:\n            # Ignore crashed runs\n            continue\n        # Alternatively, it is possible to also obtain the start time with ``run_value.starttime``\n        endtime = pd.Timestamp(time.strftime('%Y-%m-%d %H:%M:%S',\n                                             time.localtime(run_value.endtime)))\n        val_score = metric._optimum - (metric._sign * run_value.cost)\n        test_score = metric._optimum - (metric._sign * run_value.additional_info['test_loss'])\n        train_score = metric._optimum - (metric._sign * run_value.additional_info['train_loss'])\n        performance_list.append({\n            'Timestamp': endtime,\n            'single_best_optimization_score': val_score,\n            'single_best_test_score': test_score,\n            'single_best_train_score': train_score,\n        })\n    return pd.DataFrame(performance_list)"
       ]
     },
     {

diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/development/_images/sphx_glr_example_inspect_predictions_001.png b/development/_images/sphx_glr_example_inspect_predictions_001.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_002.png b/development/_images/sphx_glr_example_inspect_predictions_002.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_003.png b/development/_images/sphx_glr_example_inspect_predictions_003.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_thumb.png b/development/_images/sphx_glr_example_inspect_predictions_thumb.png
diff --git a/development/_images/sphx_glr_example_pandas_train_test_001.png b/development/_images/sphx_glr_example_pandas_train_test_001.png
diff --git a/development/_images/sphx_glr_example_pandas_train_test_thumb.png b/development/_images/sphx_glr_example_pandas_train_test_thumb.png
diff --git a/development/_sources/examples/20_basic/example_classification.rst.txt b/development/_sources/examples/20_basic/example_classification.rst.txt
diff --git a/development/_sources/examples/20_basic/example_multilabel_classification.rst.txt b/development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
@@ -157,7 +157,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type     cost  duration
     model_id                                                         
-    2            1              1.0  random_forest  0.44715  4.491632
+    2            1              1.0  random_forest  0.44715  4.304053
 
 
 
@@ -265,7 +265,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  14.411 seconds)
+   **Total running time of the script:** ( 0 minutes  14.648 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

diff --git a/development/_sources/examples/20_basic/example_multioutput_regression.rst.txt b/development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
@@ -122,10 +122,9 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight         type      cost   duration
-    model_id                                                         
-    17           1              0.7  extra_trees  0.077008  10.480845
-    21           2              0.3  extra_trees  0.086712   2.570516
+              rank  ensemble_weight              type      cost  duration
+    model_id                                                             
+    14           1              1.0  gaussian_process  0.000041  4.039571
 
 
 
@@ -152,14 +151,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    [(0.700000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_regression', 'regressor:__choice__': 'extra_trees', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:extra_trees_preproc_for_regression:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_regression:criterion': 'mae', 'feature_preprocessor:extra_trees_preproc_for_regression:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_regression:max_features': 0.7605634701105549, 'feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf': 13, 'feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split': 2, 'feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_regression:n_estimators': 100, 'regressor:extra_trees:bootstrap': 'False', 'regressor:extra_trees:criterion': 'mae', 'regressor:extra_trees:max_depth': 'None', 'regressor:extra_trees:max_features': 0.9315905211305524, 'regressor:extra_trees:max_leaf_nodes': 'None', 'regressor:extra_trees:min_impurity_decrease': 0.0, 'regressor:extra_trees:min_samples_leaf': 1, 'regressor:extra_trees:min_samples_split': 2, 'regressor:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.002878753280772165, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 1661, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'normal'},
-    dataset_properties={
-      'task': 5,
-      'sparse': False,
-      'multioutput': True,
-      'target_type': 'regression',
-      'signed': False})),
-    (0.300000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'extra_trees', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'median', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'True', 'regressor:extra_trees:bootstrap': 'False', 'regressor:extra_trees:criterion': 'mse', 'regressor:extra_trees:max_depth': 'None', 'regressor:extra_trees:max_features': 0.9000068627721328, 'regressor:extra_trees:max_leaf_nodes': 'None', 'regressor:extra_trees:min_impurity_decrease': 0.0, 'regressor:extra_trees:min_samples_leaf': 3, 'regressor:extra_trees:min_samples_split': 2, 'regressor:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 365, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
+    [(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'median', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'power_transformer', 'feature_preprocessor:feature_agglomeration:affinity': 'manhattan', 'feature_preprocessor:feature_agglomeration:linkage': 'average', 'feature_preprocessor:feature_agglomeration:n_clusters': 365, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'regressor:gaussian_process:alpha': 0.0001892420474677165, 'regressor:gaussian_process:thetaL': 1.7549152191973897e-09, 'regressor:gaussian_process:thetaU': 2227.384462976473},
     dataset_properties={
       'task': 5,
       'sparse': False,
@@ -194,7 +186,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9237314051823002
+    R2 score: 0.9999535658893771
 
 
 
@@ -417,7 +409,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  4.849 seconds)
+   **Total running time of the script:** ( 1 minutes  57.411 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

diff --git a/development/_sources/examples/20_basic/example_regression.rst.txt b/development/_sources/examples/20_basic/example_regression.rst.txt
@@ -121,11 +121,11 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight              type      cost   duration
     model_id                                                              
-    31           1             0.42    ard_regression  0.449401   0.894925
-    6            2             0.34    ard_regression  0.455042   0.885143
-    5            3             0.02  gaussian_process  0.455084  24.584934
-    25           4             0.16               sgd  0.460012   0.844743
-    22           5             0.06        libsvm_svr  0.461373   0.863893
+    31           1             0.42    ard_regression  0.449401   0.803644
+    6            2             0.34    ard_regression  0.455042   0.810605
+    5            3             0.02  gaussian_process  0.455084  22.746987
+    25           4             0.16               sgd  0.460012   0.789462
+    22           5             0.06        libsvm_svr  0.461373   0.812813
 
 
 
@@ -266,7 +266,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  7.209 seconds)
+   **Total running time of the script:** ( 2 minutes  1.622 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py:

diff --git a/development/_sources/examples/20_basic/sg_execution_times.rst.txt b/development/_sources/examples/20_basic/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**06:25.822** total execution time for **examples_20_basic** files:
+**06:16.033** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:07.209 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:02.351 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 02:04.849 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:01.622 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:59.353 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:57.411 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.411 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.648 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+