From 16bba8ca6c5718ed1cebfd4f84833d6419753be7 Mon Sep 17 00:00:00 2001 From: W Potosnak Date: Wed, 30 Mar 2022 23:57:29 -0400 Subject: [PATCH 1/2] notebook and random seed updates --- auton_survival/estimators.py | 256 +++--- auton_survival/experiments.py | 30 +- auton_survival/metrics.py | 270 +++--- auton_survival/models/cmhe/__init__.py | 23 +- auton_survival/models/cmhe/cmhe_utilities.py | 8 +- auton_survival/models/cph/__init__.py | 36 +- auton_survival/models/cph/dcph_utilities.py | 6 +- auton_survival/models/dcm/__init__.py | 7 +- auton_survival/models/dsm/__init__.py | 50 +- auton_survival/models/dsm/utilities.py | 5 +- auton_survival/phenotyping.py | 68 +- auton_survival/preprocessing.py | 126 ++- examples/Demo of CMHE on Synthetic Data.ipynb | 346 ++++---- .../Phenotyping Censored Time-to-Events.ipynb | 566 +++++++++++++ ...vival Regression with Auton-Survival.ipynb | 784 ++++++++++++++++++ examples/estimators_demo_utils.py | 34 + examples/matplotlibrc | 42 + 17 files changed, 2116 insertions(+), 541 deletions(-) create mode 100644 examples/Phenotyping Censored Time-to-Events.ipynb create mode 100644 examples/Survival Regression with Auton-Survival.ipynb create mode 100644 examples/estimators_demo_utils.py create mode 100644 examples/matplotlibrc diff --git a/auton_survival/estimators.py b/auton_survival/estimators.py index 00e518f..0c83a7c 100644 --- a/auton_survival/estimators.py +++ b/auton_survival/estimators.py @@ -78,10 +78,7 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): A pandas dataframe with columns 'time' and 'event'. random_seed : int Controls the rproduecibility of fitted estimators. - hyperparams : dict - Optional kwarg arguments. - Keys correspond to parameter names as strings and items correspond - to parameter values. + hyperparams : Optional arguments Options include: - 'k' : int, default=3 Size of the underlying Cox mixtures. @@ -99,7 +96,6 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): Returns ----------- Trained instance of the Deep Cox Mixtures model. - """ from .models.dcm import DeepCoxMixtures @@ -120,7 +116,7 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): model.fit(features.values, outcomes.time.values, outcomes.event.values, iters=epochs, batch_size=batch_size, learning_rate=learning_rate) - + return model # if len(layers): model = DeepCoxMixture(k=k, inputdim=features.shape[1], hidden=layers[0]) @@ -147,35 +143,37 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): #return (model, breslow_splines, unique_times) -def _predict_dcm(model, features, times): +# THIS IS 1 OF 2 _PREDICT_DCM FUNCTIONS HERE BUT THIS ONE THROWS A BUG SO I USE _PREDICT_DCM FUNCTION BELOW +# def _predict_dcm(model, features, times): - """Predict survival probabilities at specified time(s) using the - Deep Cox Mixtures model. +# """Predict survival probabilities at specified time(s) using the +# Deep Cox Mixtures model. - Parameters - ----------- - model : Trained instance of the Deep Cox Mixtures model. - features : pd.DataFrame - A pandas dataframe with rows corresponding to individual - samples and columns as covariates. - times: float or list - A float or list of the times at which to compute - the survival probability. +# Parameters +# ----------- +# model : Trained instance of the Deep Cox Mixtures model. +# features : pd.DataFrame +# A pandas dataframe with rows corresponding to individual +# samples and columns as covariates. +# times: float or list +# A float or list of the times at which to compute +# the survival probability. - Returns - ----------- - np.array : A numpy array of the survival probabilites at each time point in times. +# Returns +# ----------- +# np.array : An array of the survival probabilites at each +# time point in times. - """ +# """ - #raise NotImplementedError() +# #raise NotImplementedError() - survival_predictions = model.predict_survival(features, times) - if len(times)>1: - survival_predictions = pd.DataFrame(survival_predictions, columns=times).T - return __interpolate_missing_times(survival_predictions, times) - else: - return survival_predictions +# survival_predictions = model.predict_survival(features, times) +# if len(times)>1: +# survival_predictions = pd.DataFrame(survival_predictions, columns=times).T +# return __interpolate_missing_times(survival_predictions, times) +# else: +# return survival_predictions def _fit_dcph(features, outcomes, random_seed, **hyperparams): @@ -199,30 +197,39 @@ def _fit_dcph(features, outcomes, random_seed, **hyperparams): A pandas dataframe with columns 'time' and 'event'. random_seed : int Controls the reproducibility of called functions. - hyperparams : dict - Optional arguments for the estimator stored in a python dictionary. - Keys correspond to parameter names as strings and items correspond - to parameter values. + hyperparams : Optional arguments Options include: - 'layers' : list, default=[100] A list consisting of the number of neurons in each hidden layer. - - 'lr' : float, default=1e-3 + - 'learning rate' : float, default=1e-3 Learning rate for the 'Adam' optimizer. - 'bs' : int, default=100 Learning is performed on mini-batches of input data. This parameter specifies the size of each mini-batch. - 'epochs' : int, default=50 Number of complete passes through the training data. - - 'activation' : str, default='relu' - Activation function - Options include: 'relu', 'relu6', 'tanh' - + Return: ----------- Trained instance of the Deep Cox Proportional Hazards model. - """ - raise NotImplementedError() + + from .models.cph import DeepCoxPH + + layers = hyperparams.get("layers", [100]) + learning_rate = hyperparams.get("learning_rate", 1e-3) + bs = hyperparams.get("bs", 100) + epochs = hyperparams.get("epochs", 50) + + model = DeepCoxPH(layers=layers, random_seed=random_seed) + + model.fit(features.values, outcomes.time.values, outcomes.event.values, + iters=epochs, learning_rate=learning_rate, batch_size=bs, + optimizer="Adam") + + return model + + #raise NotImplementedError() # import torch # import torchtuples as ttup @@ -279,13 +286,13 @@ def __interpolate_missing_times(survival_predictions, times): survival_predictions : pd.DataFrame A pandas dataframe of the survival probabilites at each time in times. - times: float or list + times : float or list A float or list of the times at which to compute the survival probability. Returns ----------- - pd.DataFrame : Survival probabilities interpolated using 'backfill' + np.array : An array of survival probabilities interpolated using 'backfill' method at missing time points. """ @@ -302,27 +309,25 @@ def _predict_dcph(model, features, times): Parameters ----------- - model: + model : Trained instance of the Deep Cox PH model. - features: pd.DataFrame + features : pd.DataFrame A pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list + times : float or list A float or list of the times at which to compute the survival probability. Returns ----------- - pd.DataFrame : A pandas dataframe of the survival probabilites at each + np.array : An array of the survival probabilites at each time point in times. """ + if isinstance(times, float) or isinstance(times, int): times = [float(times)] - survival_predictions = model.predict_surv_df(features.values.astype('float32')) - - return __interpolate_missing_times(survival_predictions, times) - + return model.predict_survival(x=features.values, t=times) def _fit_cph(features, outcomes, random_seed, **hyperparams): """Fit a linear Cox Proportional Hazards model to a given dataset. @@ -336,13 +341,10 @@ def _fit_cph(features, outcomes, random_seed, **hyperparams): A pandas dataframe with columns 'time' and 'event'. random_seed : int Controls the reproducibility of called functions. - hyperparams : dict - Optional arguments for the estimator stored in a python dictionary. - Keys correspond to parameter names as strings and items correspond - to parameter values. + hyperparams : Optional arguments Options include: - - 'lr' : float, default=1e-3 - Learning rate + - 'l2' : float, default=1e-3 + Penalizer Returns ----------- @@ -355,8 +357,8 @@ def _fit_cph(features, outcomes, random_seed, **hyperparams): data = outcomes.join(features) penalizer = hyperparams.get('l2', 1e-3) - return CoxPHFitter(penalizer=penalizer).fit(data, - duration_col='time', + return CoxPHFitter(penalizer=penalizer).fit(data, + duration_col='time', event_col='event') def _fit_rsf(features, outcomes, random_seed, **hyperparams): @@ -374,16 +376,13 @@ def _fit_rsf(features, outcomes, random_seed, **hyperparams): Parameters ----------- features : pd.DataFrame - A pandas dataframe with rows corresponding to individual samples and columns - as covariates. + A pandas dataframe with rows corresponding to individual samples and + columns as covariates. outcomes : pd.DataFrame A pandas dataframe with columns 'time' and 'event'. random_seed : int Controls the reproducibility of called functions. - hyperparams : dict - Optional arguments for the estimator stored in a python dictionary. - Keys correspond to parameter names as strings and items correspond - to parameter values. + hyperparams : Optional arguments Options include: - 'n_estimators' : int, default=50 Number of trees. @@ -405,7 +404,7 @@ def _fit_rsf(features, outcomes, random_seed, **hyperparams): max_depth = hyperparams.get("max_depth", 5) max_features = hyperparams.get("max_features", 'sqrt') - # Initialize an RSF model. + # Initialize an RSF model. rsf = RandomSurvivalForest(n_estimators=n_estimators, max_depth=max_depth, max_features=max_features, @@ -442,10 +441,7 @@ def _fit_dsm(features, outcomes, random_seed, **hyperparams): A pandas dataframe with columns 'time' and 'event'. random_seed : int Controls the reproducibility of called functions. - hyperparams : dict - Optional arguments for the estimator stored in a python dictionary. - Keys correspond to parameter names as strings and items correspond - to parameter values. + hyperparams : Optional arguments Options include: - 'layers' : list A list of integers describing the dimensionality of each hidden layer. @@ -473,7 +469,8 @@ def _fit_dsm(features, outcomes, random_seed, **hyperparams): model = DeepSurvivalMachines(k=k, layers=layers, distribution=distribution, - temp=temperature) + temp=temperature, + random_seed=random_seed) model.fit(features.values, outcomes['time'].values, @@ -492,16 +489,20 @@ def _predict_dsm(model, features, times): features : pd.DataFrame A pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list + times : float or list A float or list of the times at which to compute survival probability. Returns ----------- - np.array : numpy array of the survival probabilites at each point in times. + np.array : An array of the survival probabilites at each + time point in times. """ - return model.predict_survival(features.values, times) + survival_predictions = model.predict_survival(x=features.values, t=times) + survival_predictions = pd.DataFrame(survival_predictions, columns=times).T + + return __interpolate_missing_times(survival_predictions, times) def _predict_cph(model, features, times): @@ -512,18 +513,19 @@ def _predict_cph(model, features, times): ----------- model : Trained instance of the Cox Proportional Hazards model. features : pd.DataFrame - A pandas dataframe with rows corresponding to individual samples - and columns as covariates. - times: float or list + A pandas dataframe with rows corresponding to individual samples and + columns as covariates. + times : float or list A float or list of the times at which to compute the survival probability. Returns ----------- - np.array : numpy array of the survival probabilites at each time point in times. + np.array : An array of the survival probabilites at each + time point in times. """ - if isinstance(times, float): times = [times] + if isinstance(times, float): times = [times] return model.predict_survival_function(features, times=times).values.T def _predict_rsf(model, features, times): @@ -533,25 +535,28 @@ def _predict_rsf(model, features, times): Parameters ----------- - model: + model : Trained instance of the Random Survival Forests model. - features: pd.DataFrame - A pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list + features : pd.DataFrame + A pandas dataframe with rows corresponding to individual samples and + columns as covariates. + times : float or list A float or list of the times at which to compute the survival probability. Returns ----------- - pd.DataFrame : A pandas dataframe of the survival probabilites at each time point in times. - Probabilities are interpolated using 'backfill' method at missing time points. - + np.array : An array of the survival probabilites at each + time point in times. + """ if isinstance(times, float) or isinstance(times, int): times = [float(times)] - survival_predictions = model.predict_survival_function(features.values, return_array=True) - survival_predictions = pd.DataFrame(survival_predictions, columns=model.event_times_).T + survival_predictions = model.predict_survival_function(features.values, + return_array=True) + survival_predictions = pd.DataFrame(survival_predictions, + columns=model.event_times_).T return __interpolate_missing_times(survival_predictions, times) @@ -561,18 +566,18 @@ def _predict_dcm(model, features, times): Parameters ----------- - model: + model : Trained instance of the Deep Cox Mixtures model. - features: pd.DataFrame + features : pd.DataFrame A pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list + times : float or list A float or list of the times at which to compute the survival probability. Returns ----------- - pd.DataFrame : A pandas dataframe of the survival probabilites at each + np.array : An array of the survival probabilites at each time point in times. """ @@ -580,12 +585,7 @@ def _predict_dcm(model, features, times): if isinstance(times, float) or isinstance(times, int): times = [float(times)] - from .models.dcm.dcm_utilities import predict_scores - - import torch - x = torch.from_numpy(features.values.astype('float32')) - - survival_predictions = predict_scores(model, x, times) + survival_predictions = model.predict_survival(x=features.values, t=times) survival_predictions = pd.DataFrame(survival_predictions, columns=times).T return __interpolate_missing_times(survival_predictions, times) @@ -605,11 +605,9 @@ class SurvivalModel: - `dcm` : Deep Cox Mixtures [4] model - `rsf` : Random Survival Forests [1] model - `cph` : Cox Proportional Hazards [2] model - random_seed: int Controls the reproducibility of called functions. - References ----------- @@ -668,26 +666,37 @@ def fit(self, features, outcomes, """ if weights is not None: - assert len(weights) == features.shape[0], "Size of passed weights must match size of training data." + assert len(weights) == features.shape[0], "Size of passed weights \ + must match size of training data." assert (weights>0.).any(), "All weights must be positive." # assert ((weights>0.0)&(weights<=1.0)).all(), "Weights must be in the range (0,1]." # weights[weights>(1-weights_clip)] = 1-weights_clip # weights[weights<(weights_clip)] = weights_clip data = features.join(outcomes) - data_resampled = data.sample(weights = weights, + data_resampled = data.sample(weights = weights, frac = resample_size, replace = True, random_state = self.random_seed) features = data_resampled[features.columns] outcomes = data_resampled[outcomes.columns] - - if self.model == 'cph': self._model = _fit_cph(features, outcomes, self.random_seed, **self.hyperparams) - elif self.model == 'rsf': self._model = _fit_rsf(features, outcomes, self.random_seed, **self.hyperparams) - elif self.model == 'dsm': self._model = _fit_dsm(features, outcomes, self.random_seed, **self.hyperparams) - elif self.model == 'dcph': self._model = _fit_dcph(features, outcomes, self.random_seed, **self.hyperparams) - elif self.model == 'dcm': self._model = _fit_dcm(features, outcomes, self.random_seed, **self.hyperparams) + #linting + if self.model == 'cph': + self._model = _fit_cph(features, outcomes, self.random_seed, + **self.hyperparams) + elif self.model == 'rsf': + self._model = _fit_rsf(features, outcomes, self.random_seed, + **self.hyperparams) + elif self.model == 'dsm': + self._model = _fit_dsm(features, outcomes, self.random_seed, + **self.hyperparams) + elif self.model == 'dcph': + self._model = _fit_dcph(features, outcomes, self.random_seed, + **self.hyperparams) + elif self.model == 'dcm': + self._model = _fit_dcm(features, outcomes, self.random_seed, + **self.hyperparams) else : raise NotImplementedError() self.fitted = True @@ -699,19 +708,30 @@ def predict_survival(self, features, times): Parameters ----------- - features: pd.DataFrame + features : pd.DataFrame a pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list - a float or list of the times at which to compute the survival probability. + times : float or list + a float or list of the times at which to compute the survival + probability. - """ + Returns + ----------- + np.array : An array of the survival probabilites at each + time point in times. - if self.model == 'cph': return _predict_cph(self._model, features, times) - elif self.model == 'rsf': return _predict_rsf(self._model, features, times) - elif self.model == 'dsm': return _predict_dsm(self._model, features, times) - elif self.model == 'dcph': return _predict_dcph(self._model, features, times) - elif self.model == 'dcm': return _predict_dcm(self._model, features, times) + """ + #linting + if self.model == 'cph': + return _predict_cph(self._model, features, times) + elif self.model == 'rsf': + return _predict_rsf(self._model, features, times) + elif self.model == 'dsm': + return _predict_dsm(self._model, features, times) + elif self.model == 'dcph': + return _predict_dcph(self._model, features, times) + elif self.model == 'dcm': + return _predict_dcm(self._model, features, times) else : raise NotImplementedError() def predict_risk(self, features, times): @@ -723,7 +743,7 @@ def predict_risk(self, features, times): features : pd.DataFrame a pandas dataframe with rows corresponding to individual samples and columns as covariates. - times: float or list + times : float or list a float or list of the times at which to compute the risk. Returns @@ -737,7 +757,7 @@ def predict_risk(self, features, times): class CounterfactualSurvivalModel: - """Universal interface to train multiple different counterfactual + """Universal interface to train multiple different counterfactual survival models.""" def __init__(self, treated_model, control_model): diff --git a/auton_survival/experiments.py b/auton_survival/experiments.py index 687fa55..50b1cc3 100644 --- a/auton_survival/experiments.py +++ b/auton_survival/experiments.py @@ -1,5 +1,4 @@ import numpy as np -from sklearn.utils import shuffle from auton_survival.estimators import SurvivalModel, CounterfactualSurvivalModel from auton_survival.metrics import survival_regression_metric @@ -63,7 +62,8 @@ def __init__(self, model, cv_folds=5, random_seed=0, hyperparam_grid={}): def fit(self, features, outcomes, ret_trained_model=True): - r"""Fits the Survival Regression Model to the data in a Cross Validation fashion. + r"""Fits the Survival Regression Model to the data in a Cross + Validation fashion. Parameters ----------- @@ -76,14 +76,13 @@ def fit(self, features, outcomes, ret_trained_model=True): a column named 'event' that contains the censoring status. \( \delta_i = 1 \) if the event is observed. ret_trained_model : bool - If True, the trained model is returned. If False, the fit function returns - self. + If True, the trained model is returned. If False, the fit function + returns self. Returns ----------- auton_survival.estimators.SurvivalModel: The selected survival model based on lowest integrated brier score. - """ n = len(features) @@ -121,16 +120,23 @@ def fit(self, features, outcomes, ret_trained_model=True): fold_models = {} for fold in tqdm(range(self.cv_folds)): # Fit the model - fold_model = SurvivalModel(model=self.model, random_seed=self.random_seed, **hyper_param) + fold_model = SurvivalModel(model=self.model, random_seed=self.random_seed, **hyper_param) fold_model.fit(features.loc[folds!=fold], outcomes.loc[folds!=fold]) fold_models[fold] = fold_model # Predict risk scores - predictions[folds==fold] = fold_model.predict_survival(features.loc[folds==fold], times=unique_times) - # Evaluate IBS + predictions[folds==fold] = fold_model.predict_survival(features.loc[folds==fold], + times=unique_times) + score_per_fold = [] for fold in range(self.cv_folds): - score = survival_regression_metric('ibs', predictions, outcomes, unique_times, folds, fold) + outcomes_train = outcomes.loc[folds!=fold] + outcomes_test = outcomes.loc[folds==fold] + predictions_test = predictions[folds==fold] + + # Compute IBS + score = survival_regression_metric('ibs', outcomes_train, outcomes_test, + predictions_test, unique_times) score_per_fold.append(score) current_score = np.mean(score_per_fold) @@ -147,7 +153,8 @@ def fit(self, features, outcomes, ret_trained_model=True): if ret_trained_model: - model = SurvivalModel(model=self.model, random_seed=self.random_seed, **self.best_hyperparameter) + model = SurvivalModel(model=self.model, random_seed=self.random_seed, + **self.best_hyperparameter) model.fit(features, outcomes) return model @@ -173,7 +180,8 @@ def evaluate(self, features, outcomes, metrics=['auc', 'ctd'], horizons=[]): for fold in range(self.cv_folds): fold_model = self.best_model_per_fold[fold] - fold_predictions = fold_model.predict(features.loc[self.folds==fold], times=horizons) + fold_predictions = fold_model.predict(features.loc[self.folds==fold], + times=horizons) for i, horizon in enumerate(horizons): for metric in metrics: diff --git a/auton_survival/metrics.py b/auton_survival/metrics.py index bc636d7..95e19b3 100644 --- a/auton_survival/metrics.py +++ b/auton_survival/metrics.py @@ -21,7 +21,8 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -"""Tools to compute metrics used to assess survival outcomes and survival model performance.""" +"""Tools to compute metrics used to assess survival outcomes and survival +model performance.""" from sksurv import metrics, util from lifelines import KaplanMeierFitter, CoxPHFitter @@ -37,15 +38,16 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, weights=None, horizon=None, interpolate=True, - weights_clip=1e-2, n_bootstrap=None, + weights_clip=1e-2, n_bootstrap=None, size_bootstrap=1.0, random_seed=0): - """Compute metrics for comparing population level survival outcomes across treatment arms. + """Compute metrics for comparing population level survival outcomes + across treatment arms. Parameters ---------- metric : str - The metric to evalute for comparing survival outcomes. + The metric to evalute for comparing survival outcomes. Options include: - `median` - `time_to` @@ -53,9 +55,11 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, - `restricted_mean` - `survival_at` outcomes : pd.DataFrame - A pandas dataframe with rows corresponding to individual samples and columns 'time' and 'event'. + A pandas dataframe with rows corresponding to individual samples + and columns 'time' and 'event'. treatment_indicator : np.array - Boolean numpy array of treatment indicators. True means individual was assigned a specific treatment. + Boolean numpy array of treatment indicators. True means individual + was assigned a specific treatment. weights : pd.Series, default=None Treatment assignment propensity scores, \( \widehat{\mathbb{P}}(A|X=x) \). If `None`, all weights are set to \( 0.5 \). Default is `None`. @@ -66,7 +70,8 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, interpolate : bool, default=True Whether to interpolate the survival curves. weights_clip : float - Weights below this value are clipped. This is to ensure IPTW estimation is numerically stable. + Weights below this value are clipped. This is to ensure IPTW + estimation is numerically stable. Large weights can result in estimator with high variance. n_bootstrap : int, default=None The number of bootstrap samples to use. @@ -75,21 +80,22 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, The fraction of the population to sample for each bootstrap sample. random_seed: int, default=0 Controls the reproducibility random sampling for bootstrapping. - + Returns ---------- float or list: The metric value(s) for the specified metric. - + """ - assert metric in ['median', 'hazard_ratio', 'restricted_mean', 'survival_at', 'time_to'] + assert metric in ['median', 'hazard_ratio', 'restricted_mean', + 'survival_at', 'time_to'] if metric in ['restricted_mean', 'survival_at', 'time_to']: assert horizon is not None, "Please specify Event Horizon" if metric == 'hazard_ratio': - Warning("WARNING: You are computing Hazard Ratios.\n Make sure you have tested the PH Assumptions.") - if (n_bootstrap is None) and (weights is not None): + warnings.warn("WARNING: You are computing Hazard Ratios.\n Make sure you have tested the PH Assumptions.") + if (n_bootstrap is None) and (weights is not None): Warning("Treatment Propensity weights would be ignored, Since no boostrapping is performed."+ "In order to incorporate IPTW weights please specify number of bootstrap iterations n_bootstrap>=1") # Bootstrapping ... @@ -113,10 +119,14 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, control_outcomes = outcomes[~treatment_indicator] if metric == 'survival_at': _metric = _survival_at_diff - elif metric == 'time_to': _metric = _time_to_diff - elif metric == 'restricted_mean': _metric = _restricted_mean_diff - elif metric == 'median': _metric = _time_to_diff - elif metric == 'hazard_ratio': _metric = _hazard_ratio + elif metric == 'time_to': + _metric = _time_to_diff + elif metric == 'restricted_mean': + _metric = _restricted_mean_diff + elif metric == 'median': + _metric = _time_to_diff + elif metric == 'hazard_ratio': + _metric = _hazard_ratio else: raise NotImplementedError() if n_bootstrap is None: @@ -136,110 +146,96 @@ def survival_diff_metric(metric, outcomes, treatment_indicator, size_bootstrap=size_bootstrap, random_seed=i) for i in range(n_bootstrap)] -def survival_regression_metric(metric, predictions, outcomes, times, - folds=None, fold=None): +def survival_regression_metric(metric, outcomes_train, outcomes_test, + predictions, times): """Compute metrics to assess survival model performance. - + Parameters ----------- metric: string Measure used to assess the survival regression model performance. - Options include: + Options include: - `brs` : brier score - `ibs` : integrated brier score - `auc`: cumulative dynamic area under the curve - - `ctd` : concordance index inverse probability of censoring weights (ipcw) + - `ctd` : concordance index inverse probability of censoring + weights (ipcw) predictions: np.array A numpy array of survival time predictions for the samples. - outcomes : pd.DataFrame - A pandas dataframe with rows corresponding to individual samples and columns 'time' and 'event'. + outcomes_train : pd.DataFrame + A pandas dataframe with rows corresponding to individual samples and + columns 'time' and 'event' for test data. + outcomes_test : pd.DataFrame + A pandas dataframe with rows corresponding to individual samples and + columns 'time' and 'event' for training data. times: np.array The time points at which to compute metric value(s). - folds: pd.DataFrame, default=None - A pandas dataframe of train and test folds. - fold: int, default=None - A specific fold number in the folds input. - + Returns ----------- float: The metric value for the specified metric. - - """ - - if folds is None: - - survival_train = util.Surv.from_dataframe('event', 'time', outcomes) - survival_test = survival_train - predictions_test = predictions - else: - - outcomes_train = outcomes.iloc[folds!=fold] - outcomes_test = outcomes.iloc[folds==fold] - predictions_test = predictions[folds==fold] - - te_valid_idx = outcomes_test['time']<= outcomes_train['time'].max() - - outcomes_test = outcomes_test[te_valid_idx] - predictions_test = predictions_test[te_valid_idx.values] - - te_min, te_max = outcomes_test['time'].min(), outcomes_test['time'].max() - - survival_train = util.Surv.from_dataframe('event', 'time', outcomes_train) - survival_test = util.Surv.from_dataframe('event', 'time', outcomes_test) - - unique_time_mask = (times>te_min)&(timesoutcomes_train['time'].min()) for phenotype in set(phenotypes_test): - assert phenotype in phenotypes_train, "Testing on Phenotypes not found in the Training set!!" + assert phenotype in phenotypes_train, "Testing on Phenotypes not found \ + in the Training set!!" survival_curves = {} for phenotype in set(phenotypes_train): @@ -281,12 +278,14 @@ def phenotype_purity(phenotypes, outcomes, predictions[phenotypes==phenotype] = float(survival_curves[phenotype].predict(times=time, interpolate=True)) if bootstrap is None: - return float(metrics.brier_score(survival_train, survival_test, predictions, time)[1]) + return float(metrics.brier_score(survival_train, survival_test, + predictions, time)[1]) else: scores = [] for i in tqdm(range(bootstrap)): idx = np.random.choice(n, size=n, replace=True) - score = float(metrics.brier_score(survival_train, survival_test[idx], predictions[idx], time)[1]) + score = float(metrics.brier_score(survival_train, survival_test[idx], + predictions[idx], time)[1]) scores.append(score) return scores @@ -319,9 +318,9 @@ def phenotype_purity(phenotypes, outcomes, raise NotImplementedError() def __get_restricted_area(km_estimate, horizon): - """Compute area under the Kaplan Meier curve (mean survival time) restricted by a specified - time horizion. - + """Compute area under the Kaplan Meier curve (mean survival time) restricted + by a specified time horizion. + Parameters ----------- km_estimate : Fitted Kaplan Meier estimator. @@ -329,11 +328,11 @@ def __get_restricted_area(km_estimate, horizon): The time horizon at which to compare the survival curves. Must be specified for metric 'restricted_mean' and 'survival_at'. For 'hazard_ratio' this is ignored. - + Returns ----------- float : Area under the Kaplan Meier curve (mean survival time). - + """ x = km_estimate.survival_function_.index.values @@ -349,44 +348,50 @@ def __get_restricted_area(km_estimate, horizon): def _restricted_mean_diff(treated_outcomes, control_outcomes, horizon, treated_weights, control_weights, size_bootstrap=1.0, random_seed=None, **kwargs): - """Compute the difference in the area under the Kaplan Meier curve (mean survival time) - between control and treatment groups. - + """Compute the difference in the area under the Kaplan Meier curve + (mean survival time) between control and treatment groups. + Parameters ----------- treated_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that received a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + received a specific treatment. control_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that did not receive a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + did not receive a specific treatment. horizon : float The time horizon at which to compare the survival curves. Must be specified for metric 'restricted_mean' and 'survival_at'. For 'hazard_ratio' this is ignored. treated_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that received a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that received a specific treatment. control_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that did not receive a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that did not receive a specific treatment. size_bootstrap : float, default=1.0 The fraction of the population to sample for each bootstrap sample. random_seed: int, default=None Controls the reproducibility random sampling for bootstrapping. kwargs : dict Additional arguments for the Kaplan Meier estimator?? - + Returns ----------- - float : The difference in the area under the Kaplan Meier curve (mean survival time). - between control and treatment groups. - + float : The difference in the area under the Kaplan Meier curve + (mean survival time) between control and treatment groups. + """ if random_seed is not None: treated_outcomes = treated_outcomes.sample(n=int(size_bootstrap*len(treated_outcomes)), weights=treated_weights, - random_state=random_seed, replace=True) + random_state=random_seed, + replace=True) control_outcomes = control_outcomes.sample(n=int(size_bootstrap*len(control_outcomes)), weights=control_weights, - random_state=random_seed, replace=True) + random_state=random_seed, + replace=True) treatment_survival = KaplanMeierFitter().fit(treated_outcomes['time'], treated_outcomes['event']) @@ -398,35 +403,40 @@ def _restricted_mean_diff(treated_outcomes, control_outcomes, horizon, def _survival_at_diff(treated_outcomes, control_outcomes, horizon, treated_weights, control_weights, interpolate=True, size_bootstrap=1.0, random_seed=None): - """Compute the difference in Kaplan Meier survival function estimates between the control and treatment - groups at a specified time horizon. - + """Compute the difference in Kaplan Meier survival function estimates + between the control and treatment groups at a specified time horizon. + Parameters ----------- treated_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that received a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + received a specific treatment. control_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that did not receive a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + did not receive a specific treatment. horizon : float The time horizon at which to compare the survival curves. Must be specified for metric 'restricted_mean' and 'survival_at'. For 'hazard_ratio' this is ignored. treated_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that received a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that received a specific treatment. control_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that did not receive a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that did not receive a specific treatment. interpolate : bool, default=True Whether to interpolate the survival curves. size_bootstrap : float, default=1.0 The fraction of the population to sample for each bootstrap sample. random_seed: int, default=None Controls the reproducibility random sampling for bootstrapping. - + Returns ----------- - pd.Series : A pandas series of the difference in Kaplan Meier survival estimates between - the control and treatment groups at a specified time horizon. - + pd.Series : A pandas series of the difference in Kaplan Meier survival + estimates between the control and treatment groups at a specified time + horizon. + """ if random_seed is not None: @@ -437,63 +447,73 @@ def _survival_at_diff(treated_outcomes, control_outcomes, horizon, weights=control_weights, random_state=random_seed, replace=True) - treatment_survival = KaplanMeierFitter().fit(treated_outcomes['time'], treated_outcomes['event']) - control_survival = KaplanMeierFitter().fit(control_outcomes['time'], control_outcomes['event']) + treatment_survival = KaplanMeierFitter().fit(treated_outcomes['time'], + treated_outcomes['event']) + control_survival = KaplanMeierFitter().fit(control_outcomes['time'], + control_outcomes['event']) return treatment_survival.predict(horizon, interpolate=interpolate) - control_survival.predict(horizon, interpolate=interpolate) def _time_to_diff(treated_outcomes, control_outcomes, horizon, interpolate=True): """Not implemented. - + Parameters ----------- treated_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that received a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + received a specific treatment. control_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that did not receive a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + did not receive a specific treatment. horizon : float The time horizon at which to compare the survival curves. Must be specified for metric 'restricted_mean' and 'survival_at'. For 'hazard_ratio' this is ignored. interpolate : bool, default=True Whether to interpolate the survival curves. - - """ + + """ raise NotImplementedError() - treatment_survival = KaplanMeierFitter().fit(treated_outcomes['time'], treated_outcomes['event']) - control_survival = KaplanMeierFitter().fit(control_outcomes['time'], control_outcomes['event']) + treatment_survival = KaplanMeierFitter().fit(treated_outcomes['time'], + treated_outcomes['event']) + control_survival = KaplanMeierFitter().fit(control_outcomes['time'], + control_outcomes['event']) def _hazard_ratio(treated_outcomes, control_outcomes, treated_weights, control_weights, size_bootstrap=1.0, random_seed=None, **kwargs): - """Train an instance of the Cox Proportional Hazards model and return the exp(coefficients) - (hazard ratios) of the model. - + """Train an instance of the Cox Proportional Hazards model and return the + exp(coefficients) (hazard ratios) of the model. + Parameters ----------- treated_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that received a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + received a specific treatment. control_outcomes : pd.DataFrame - A pandas dataframe with columns 'time' and 'event' for samples that did not receive a specific treatment. + A pandas dataframe with columns 'time' and 'event' for samples that + did not receive a specific treatment. treated_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that received a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that received a specific treatment. control_weights : pd.Series - A pandas series of the inverse probability of censoring weights for samples that did not receive a specific treatment. + A pandas series of the inverse probability of censoring weights for + samples that did not receive a specific treatment. size_bootstrap : float, default=1.0 The fraction of the population to sample for each bootstrap sample. random_seed: int, default=None Controls the reproducibility random sampling for bootstrapping. kwargs : dict Additional arguments for the Cox proportional hazards model. - Please include dictionary key and item pairs specified by the following module: - - lifelines.fitters.coxph_fitter.CoxPHFitters - + Please include dictionary key and item pairs specified by the following + module: lifelines.fitters.coxph_fitter.CoxPHFitters + Returns ----------- pd.Series : The exp(coefficients) (hazard ratios) of the Cox Proportional Hazards model. - + """ if random_seed is not None: diff --git a/auton_survival/models/cmhe/__init__.py b/auton_survival/models/cmhe/__init__.py index 267a508..1aa1c4e 100644 --- a/auton_survival/models/cmhe/__init__.py +++ b/auton_survival/models/cmhe/__init__.py @@ -107,6 +107,8 @@ class DeepCoxMixturesHeterogenousEffects: layers: list A list of integers consisting of the number of neurons in each hidden layer. + random_seed: int + Controls the reproducibility of called functions. Example ------- @@ -116,12 +118,13 @@ class DeepCoxMixturesHeterogenousEffects: """ - def __init__(self, k, g, layers=None): + def __init__(self, k, g, layers=None, random_seed=0): self.layers = layers self.fitted = False self.k = k self.g = g + self.random_seed = random_seed def __call__(self): if self.fitted: @@ -138,11 +141,11 @@ def _preprocess_test_data(self, x, a=None): return torch.from_numpy(x).float() def _preprocess_training_data(self, x, t, e, a, vsize, val_data, - random_state): + random_seed): idx = list(range(x.shape[0])) - np.random.seed(random_state) + np.random.seed(random_seed) np.random.shuffle(idx) x_tr, t_tr, e_tr, a_tr = x[idx], t[idx], e[idx], a[idx] @@ -176,13 +179,17 @@ def _preprocess_training_data(self, x, t, e, a, vsize, val_data, def _gen_torch_model(self, inputdim, optimizer): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepCMHETorch(self.k, self.g, inputdim, layers=self.layers, optimizer=optimizer) def fit(self, x, t, e, a, vsize=0.15, val_data=None, iters=1, learning_rate=1e-3, batch_size=100, - patience=2, optimizer="Adam", random_state=100): + patience=2, optimizer="Adam"): r"""This method is used to train an instance of the DSM model. @@ -212,13 +219,12 @@ def fit(self, x, t, e, a, vsize=0.15, val_data=None, optimizer: str The choice of the gradient based optimization method. One of 'Adam', 'RMSProp' or 'SGD'. - random_state: float - random seed that determines how the validation set is chosen. + """ processed_data = self._preprocess_training_data(x, t, e, a, vsize, val_data, - random_state) + self.random_seed) x_tr, t_tr, e_tr, a_tr, x_vl, t_vl, e_vl, a_vl = processed_data @@ -235,7 +241,8 @@ def fit(self, x, t, e, a, vsize=0.15, val_data=None, lr=learning_rate, bs=batch_size, patience=patience, - return_losses=True) + return_losses=True, + random_seed=self.random_seed) self.torch_model = (model[0].eval(), model[1]) self.fitted = True diff --git a/auton_survival/models/cmhe/cmhe_utilities.py b/auton_survival/models/cmhe/cmhe_utilities.py index 069e1da..be721ef 100644 --- a/auton_survival/models/cmhe/cmhe_utilities.py +++ b/auton_survival/models/cmhe/cmhe_utilities.py @@ -271,12 +271,12 @@ def test_step(model, x, t, e, a, breslow_splines, loss='q', typ='soft'): def train_cmhe(model, train_data, val_data, epochs=50, patience=2, vloss='q', bs=256, typ='soft', lr=1e-3, - use_posteriors=False, debug=False, random_state=0, + use_posteriors=False, debug=False, return_losses=False, update_splines_after=10, - smoothing_factor=1e-4): + smoothing_factor=1e-4, random_seed=0): - torch.manual_seed(random_state) - np.random.seed(random_state) + torch.manual_seed(random_seed) + np.random.seed(random_seed) if val_data is None: val_data = train_data diff --git a/auton_survival/models/cph/__init__.py b/auton_survival/models/cph/__init__.py index 642f131..db5c765 100644 --- a/auton_survival/models/cph/__init__.py +++ b/auton_survival/models/cph/__init__.py @@ -59,6 +59,8 @@ class DeepCoxPH: layers: list A list of integers consisting of the number of neurons in each hidden layer. + random_seed: int + Controls the reproducibility of called functions. Example ------- >>> from auton_survival import DeepCoxPH @@ -67,10 +69,11 @@ class DeepCoxPH: """ - def __init__(self, layers=None): + def __init__(self, layers=None, random_seed=0): self.layers = layers self.fitted = False + self.random_seed = random_seed def __call__(self): if self.fitted: @@ -83,11 +86,11 @@ def __call__(self): def _preprocess_test_data(self, x): return torch.from_numpy(x).float() - def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state): + def _preprocess_training_data(self, x, t, e, vsize, val_data, random_seed): idx = list(range(x.shape[0])) - np.random.seed(random_state) + np.random.seed(random_seed) np.random.shuffle(idx) x_train, t_train, e_train = x[idx], t[idx], e[idx] @@ -117,12 +120,16 @@ def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state): def _gen_torch_model(self, inputdim, optimizer): """Helper function to return a torch model.""" + # Add random seed to get the same results like in dcm __init__.py + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepCoxPHTorch(inputdim, layers=self.layers, optimizer=optimizer) def fit(self, x, t, e, vsize=0.15, val_data=None, iters=1, learning_rate=1e-3, batch_size=100, - optimizer="Adam", random_state=100): + optimizer="Adam"): r"""This method is used to train an instance of the DSM model. @@ -149,13 +156,12 @@ def fit(self, x, t, e, vsize=0.15, val_data=None, optimizer: str The choice of the gradient based optimization method. One of 'Adam', 'RMSProp' or 'SGD'. - random_state: float - random seed that determines how the validation set is chosen. + """ processed_data = self._preprocess_training_data(x, t, e, vsize, val_data, - random_state) + self.random_seed) x_train, t_train, e_train, x_val, t_val, e_val = processed_data @@ -171,7 +177,8 @@ def fit(self, x, t, e, vsize=0.15, val_data=None, epochs=iters, lr=learning_rate, bs=batch_size, - return_losses=True) + return_losses=True, + random_seed=self.random_seed) self.torch_model = (model[0].eval(), model[1]) self.fitted = True @@ -232,6 +239,8 @@ class DeepRecurrentCoxPH(DeepCoxPH): layers: list A list of integers consisting of the number of neurons in each hidden layer. + random_seed: int + Controls the reproducibility of called functions. Example ------- >>> from dsm.contrib import DeepRecurrentCoxPH @@ -240,12 +249,13 @@ class DeepRecurrentCoxPH(DeepCoxPH): """ - def __init__(self, layers=None, hidden=None, typ="LSTM"): + def __init__(self, layers=None, hidden=None, typ="LSTM", random_seed=0): super(DeepRecurrentCoxPH, self).__init__(layers=layers) self.typ = typ self.hidden = hidden + self.random_seed = random_seed def __call__(self): if self.fitted: @@ -257,6 +267,10 @@ def __call__(self): def _gen_torch_model(self, inputdim, optimizer): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepRecurrentCoxPHTorch(inputdim, layers=self.layers, hidden=self.hidden, optimizer=optimizer, typ=self.typ) @@ -264,11 +278,11 @@ def _gen_torch_model(self, inputdim, optimizer): def _preprocess_test_data(self, x): return torch.from_numpy(_get_padded_features(x)).float() - def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state): + def _preprocess_training_data(self, x, t, e, vsize, val_data, random_seed): """RNNs require different preprocessing for variable length sequences""" idx = list(range(x.shape[0])) - np.random.seed(random_state) + np.random.seed(random_seed) np.random.shuffle(idx) x = _get_padded_features(x) diff --git a/auton_survival/models/cph/dcph_utilities.py b/auton_survival/models/cph/dcph_utilities.py index 0660021..5a6cb35 100644 --- a/auton_survival/models/cph/dcph_utilities.py +++ b/auton_survival/models/cph/dcph_utilities.py @@ -75,10 +75,10 @@ def test_step(model, x, t, e): def train_dcph(model, train_data, val_data, epochs=50, patience=3, bs=256, lr=1e-3, debug=False, - random_state=0, return_losses=False): + random_seed=0, return_losses=False): - torch.manual_seed(random_state) - np.random.seed(random_state) + torch.manual_seed(random_seed) + np.random.seed(random_seed) if val_data is None: val_data = train_data diff --git a/auton_survival/models/dcm/__init__.py b/auton_survival/models/dcm/__init__.py index 4bff763..477c1cc 100644 --- a/auton_survival/models/dcm/__init__.py +++ b/auton_survival/models/dcm/__init__.py @@ -79,6 +79,9 @@ class DeepCoxMixtures: layers: list A list of integers consisting of the number of neurons in each hidden layer. + random_seed: int + Controls the reproducibility of called functions. + Example ------- >>> from auton_survival.models.dcm import DeepCoxMixtures @@ -97,7 +100,7 @@ def __init__(self, k=3, layers=None, gamma=10, self.gamma = gamma self.smoothing_factor = smoothing_factor self.use_activation = use_activation - self.random_seed = random_seed + self.random_seed = random_seed def __call__(self): if self.fitted: @@ -183,8 +186,6 @@ def fit(self, x, t, e, vsize=0.15, val_data=None, optimizer: str The choice of the gradient based optimization method. One of 'Adam', 'RMSProp' or 'SGD'. - random_seed: float - random seed that determines how the validation set is chosen. """ diff --git a/auton_survival/models/dsm/__init__.py b/auton_survival/models/dsm/__init__.py index 2baf75f..c2e94f2 100644 --- a/auton_survival/models/dsm/__init__.py +++ b/auton_survival/models/dsm/__init__.py @@ -181,16 +181,21 @@ class DSMBase(): """Base Class for all DSM models""" def __init__(self, k=3, layers=None, distribution="Weibull", - temp=1000., discount=1.0): + temp=1000., discount=1.0, random_seed=0): self.k = k self.layers = layers self.dist = distribution self.temp = temp self.discount = discount self.fitted = False + self.random_seed = random_seed def _gen_torch_model(self, inputdim, optimizer, risks): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepSurvivalMachinesTorch(inputdim, k=self.k, layers=self.layers, @@ -202,7 +207,7 @@ def _gen_torch_model(self, inputdim, optimizer, risks): def fit(self, x, t, e, vsize=0.15, val_data=None, iters=1, learning_rate=1e-3, batch_size=100, - elbo=True, optimizer="Adam", random_state=100): + elbo=True, optimizer="Adam"): r"""This method is used to train an instance of the DSM model. @@ -232,14 +237,12 @@ def fit(self, x, t, e, vsize=0.15, val_data=None, optimizer: str The choice of the gradient based optimization method. One of 'Adam', 'RMSProp' or 'SGD'. - random_state: float - random seed that determines how the validation set is chosen. """ processed_data = self._preprocess_training_data(x, t, e, vsize, val_data, - random_state) + self.random_seed) x_train, t_train, e_train, x_val, t_val, e_val = processed_data #Todo: Change this somehow. The base design shouldn't depend on child @@ -257,7 +260,8 @@ def fit(self, x, t, e, vsize=0.15, val_data=None, n_iter=iters, lr=learning_rate, elbo=elbo, - bs=batch_size) + bs=batch_size, + random_seed=self.random_seed) self.torch_model = model.eval() self.fitted = True @@ -302,10 +306,10 @@ def compute_nll(self, x, t, e): def _preprocess_test_data(self, x): return torch.from_numpy(x) - def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state): + def _preprocess_training_data(self, x, t, e, vsize, val_data, random_seed): idx = list(range(x.shape[0])) - np.random.seed(random_state) + np.random.seed(random_seed) np.random.shuffle(idx) x_train, t_train, e_train = x[idx], t[idx], e[idx] @@ -506,11 +510,18 @@ def __init__(self, k=3, layers=None, hidden=None, layers=layers, distribution=distribution, temp=temp, - discount=discount) + discount=discount, + random_seed=0) self.hidden = hidden self.typ = typ + self.random_seed = random_seed + def _gen_torch_model(self, inputdim, optimizer, risks): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepRecurrentSurvivalMachinesTorch(inputdim, k=self.k, layers=self.layers, @@ -525,11 +536,11 @@ def _gen_torch_model(self, inputdim, optimizer, risks): def _preprocess_test_data(self, x): return torch.from_numpy(_get_padded_features(x)) - def _preprocess_training_data(self, x, t, e, vsize, val_data, random_state): + def _preprocess_training_data(self, x, t, e, vsize, val_data, random_seed): """RNNs require different preprocessing for variable length sequences""" idx = list(range(x.shape[0])) - np.random.seed(random_state) + np.random.seed(random_seed) np.random.shuffle(idx) x = _get_padded_features(x) @@ -578,11 +589,18 @@ def __init__(self, k=3, layers=None, hidden=None, super(DeepConvolutionalSurvivalMachines, self).__init__(k=k, distribution=distribution, temp=temp, - discount=discount) + discount=discount, + random_seed=0) self.hidden = hidden self.typ = typ + self.random_seed = random_seed + def _gen_torch_model(self, inputdim, optimizer, risks): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepConvolutionalSurvivalMachinesTorch(inputdim, k=self.k, hidden=self.hidden, @@ -607,12 +625,18 @@ def __init__(self, k=3, layers=None, hidden=None, layers=layers, distribution=distribution, temp=temp, - discount=discount) + discount=discount, + random_seed=0) self.hidden = hidden self.typ = typ + self.random_seed = random_seed def _gen_torch_model(self, inputdim, optimizer, risks): """Helper function to return a torch model.""" + + np.random.seed(self.random_seed) + torch.manual_seed(self.random_seed) + return DeepCNNRNNSurvivalMachinesTorch(inputdim, k=self.k, layers=self.layers, diff --git a/auton_survival/models/dsm/utilities.py b/auton_survival/models/dsm/utilities.py index 9d94667..2bbe273 100644 --- a/auton_survival/models/dsm/utilities.py +++ b/auton_survival/models/dsm/utilities.py @@ -115,9 +115,12 @@ def train_dsm(model, x_train, t_train, e_train, x_valid, t_valid, e_valid, n_iter=10000, lr=1e-3, elbo=True, - bs=100): + bs=100, random_seed=0): """Function to train the torch instance of the model.""" + torch.manual_seed(random_seed) + np.random.seed(random_seed) + logging.info('Pretraining the Underlying Distributions...') # For padded variable length sequences we first unroll the input and # mask out the padded nans. diff --git a/auton_survival/phenotyping.py b/auton_survival/phenotyping.py index 6588da1..c1602a7 100644 --- a/auton_survival/phenotyping.py +++ b/auton_survival/phenotyping.py @@ -145,8 +145,8 @@ def phenotype(self, features): var_min, var_max = self.min_max[num_var] - features[num_var][features[num_var]>=var_max] = var_max - features[num_var][features[num_var]<=var_min] = var_min + features.loc[features[num_var]>=var_max, [num_var]] = var_max + features.loc[features[num_var]<=var_min, [num_var]] = var_min features[num_var] = pd.cut(features[num_var], self.cut_bins[num_var], include_lowest=True) @@ -167,7 +167,7 @@ def _rename(self, phenotypes): phenotypes : list List of lists containing all possible combinations of specified categorical and numerical variable values. - + Returns -------- list: @@ -178,10 +178,10 @@ def _rename(self, phenotypes): ft_names = self.cat_vars + self.num_vars renamed = [] for i in range(len(phenotypes)): - row = [] - for j in range(len(ft_names)): - row.append(ft_names[j]+":"+str(phenotypes[i][j])) - renamed.append(" & ".join(row)) + row = [] + for j in range(len(ft_names)): + row.append(ft_names[j]+":"+str(phenotypes[i][j])) + renamed.append(" & ".join(row)) return renamed def fit_phenotype(self, features): @@ -221,7 +221,8 @@ class ClusteringPhenotyper(Phenotyper): Options include: - `kmeans`: K-Means Clustering - - `dbscan`: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) + - `dbscan`: Density-Based Spatial Clustering of Applications with + Noise (DBSCAN) - `gmm`: Gaussian Mixture - `hierarchical`: Agglomerative Clustering dim_red_method: str, default=None @@ -236,7 +237,8 @@ class ClusteringPhenotyper(Phenotyper): Controls the randomness and reproducibility of called functions kwargs: dict Additional arguments for dimensionality reduction and clustering - Please include dictionary key and item pairs specified by the following scikit-learn modules: + Please include dictionary key and item pairs specified by the following + scikit-learn modules: - `pca` : sklearn.decomposition.PCA - `nnmf` : sklearn.decomposition.NMF @@ -251,7 +253,8 @@ class ClusteringPhenotyper(Phenotyper): _VALID_DIMRED_METHODS = ['pca', 'kpca', 'nnmf', None] _VALID_CLUSTERING_METHODS = ['kmeans', 'dbscan', 'gmm', 'hierarchical'] - def __init__(self, clustering_method = 'kmeans', dim_red_method = None, random_seed=0, **kwargs): + def __init__(self, clustering_method = 'kmeans', dim_red_method = None, + random_seed=0, **kwargs): assert clustering_method in ClusteringPhenotyper._VALID_CLUSTERING_METHODS, "Please specify a valid Clustering method" assert dim_red_method in ClusteringPhenotyper._VALID_DIMRED_METHODS, "Please specify a valid Dimensionality Reduction method" @@ -292,7 +295,8 @@ def __init__(self, clustering_method = 'kmeans', dim_red_method = None, random_s c_kwargs['covariance_type'] = 'diag' c_kwargs['n_components'] = c_kwargs.get('n_clusters', 3) - self.clustering_model = clustering_model(**c_kwargs) + self.clustering_model = clustering_model(random_state=random_seed, + **c_kwargs) if dim_red_method is not None: d_kwargs = _get_method_kwargs(dim_red_model, kwargs) if dim_red_method == 'kpca': @@ -301,7 +305,8 @@ def __init__(self, clustering_method = 'kmeans', dim_red_method = None, random_s d_kwargs['n_jobs'] = -1 d_kwargs['max_iter'] = 500 - self.dim_red_model = dim_red_model(**d_kwargs) + self.dim_red_model = dim_red_model(random_state=random_seed, + **d_kwargs) def fit(self, features): @@ -320,7 +325,7 @@ def fit(self, features): """ - if self.dim_red_method is not None: + if self.dim_red_method is not None: print("Fitting the following Dimensionality Reduction Model:\n", self.dim_red_model) self.dim_red_model = self.dim_red_model.fit(features) features = self.dim_red_model.transform(features) @@ -358,7 +363,7 @@ def _predict_proba_kmeans(self, features): negative_exp_distances = np.exp(-self.clustering_model.transform(features)) probs = negative_exp_distances/negative_exp_distances.sum(axis=1).reshape((-1, 1)) - + #assert int(np.sum(probs)) == len(probs), 'Not valid probabilities' return probs @@ -378,16 +383,18 @@ def phenotype(self, features): Returns ----------- np.array: - a numpy array of the probability estimates of sample association to learned subgroups. + a numpy array of the probability estimates of sample association to + learned subgroups. """ - assert self.fitted, "Phenotyper must be `fitted` before calling `phenotype`." - + assert self.fitted, "Phenotyper must be `fitted` before calling \ + `phenotype`." + if self.dim_red_method is not None: features = self.dim_red_model.transform(features) - if self.clustering_method == 'gmm': - return self.clustering_model.predict_proba(features) + if self.clustering_method == 'gmm': + return self.clustering_model.predict_proba(features) elif self.clustering_method == 'kmeans': return self._predict_proba_kmeans(features) @@ -404,7 +411,8 @@ def fit_phenotype(self, features): Returns ----------- np.array - a numpy array of the probability estimates of sample association to learned clusters. + a numpy array of the probability estimates of sample association + to learned clusters. """ @@ -414,7 +422,7 @@ class SurvivalVirtualTwinsPhenotyper(object): """"Not Yet Implemented""" - + _VALID_PHENO_METHODS = ['rsf'] _DEFAULT_PHENO_HYPERPARAMS = {} _DEFAULT_PHENO_HYPERPARAMS['rsf'] = {'n_estimators': 50, @@ -422,7 +430,7 @@ class SurvivalVirtualTwinsPhenotyper(object): def __init__(self, cf_method='dcph', - phenotyping_method='rsf', + phenotyping_method='rsf', cf_hyperparams=None, phenotyper_hyperparams=None, random_seed=0): @@ -441,7 +449,7 @@ def __init__(self, phenotyper_hyperparams = {} phenotyper_hyperparams = deepcopy(SurvivalVirtualTwinsPhenotyper._DEFAULT_PHENO_HYPERPARAMS[phenotyping_method]).update(phenotyper_hyperparams) - self.phenotyper_hyperparams = phenotyper_hyperparams + self.phenotyper_hyperparams = phenotyper_hyperparams cf_hyperparams = deepcopy(SurvivalVirtualTwinsPhenotyper._DEFAULT_PHENO_HYPERPARAMS[cf_method]).update(cf_hyperparams) self.cf_hyperparams = cf_hyperparams @@ -454,11 +462,13 @@ def fit(self, features, outcomes, interventions, horizon): cf_model = CounterfactualSurvivalRegressionCV(**self.cf_method_hyperparams) - self.cf_model = cf_model.fit(features, outcomes, interventions) + self.cf_model = cf_model.fit(features, outcomes, interventions) times = np.unique(outcomes.times.values) - cf_predictions = self.cf_model.predict_counterfactual_survival(features, interventions, times) - + cf_predictions = self.cf_model.predict_counterfactual_survival(features, + interventions, + times) + ite_estimates = cf_predictions[1] - cf_predictions[0] if self.phenotyping_method == 'rsf': @@ -476,8 +486,4 @@ def predict(self, features): phenotype_preds= self.pheno_model.predict(features) phenotype_preds = (phenotype_preds - phenotype_preds.min()) / (phenotype_preds.max() - phenotype_preds.min()) - return phenotype_preds - - - - + return phenotype_preds diff --git a/auton_survival/preprocessing.py b/auton_survival/preprocessing.py index e792f93..20aaf66 100644 --- a/auton_survival/preprocessing.py +++ b/auton_survival/preprocessing.py @@ -62,14 +62,14 @@ def __init__(self, cat_feat_strat='ignore', self.fitted = False - def fit(self, data, cat_feats=None, num_feats=None, + def fit(self, data, cat_feats=None, num_feats=None, fill_value=-1, n_neighbors=5, **kwargs): if cat_feats is None: cat_feats = [] if num_feats is None: num_feats = [] - assert (len(cat_feats + num_feats) != 0, - "Please specify categorical and numerical features.") + assert len(cat_feats + num_feats) != 0, "Please specify \ + categorical and numerical features." self._cat_feats = cat_feats self._num_feats = num_feats @@ -83,22 +83,22 @@ def fit(self, data, cat_feats=None, num_feats=None, df = df.drop(columns=list(remaining_feats)) ####### CAT VARIABLES - if len(cat_feats): + if self._cat_feats: if self.cat_feat_strat == 'replace': - self._cat_base_imputer = SimpleImputer(strategy='constant', + self._cat_base_imputer = SimpleImputer(strategy='constant', fill_value=fill_value).fit(df[cat_feats]) elif self.cat_feat_strat == 'mode': self._cat_base_imputer = SimpleImputer(strategy='most_frequent', fill_value=fill_value).fit(df[cat_feats]) ####### NUM VARIABLES - if len(num_feats): + if self._num_feats: if self.num_feat_strat == 'mean': self._num_base_imputer = SimpleImputer(strategy='mean').fit(df[num_feats]) elif self.num_feat_strat == 'median': self._num_base_imputer = SimpleImputer(strategy='median').fit(df[num_feats]) elif self.num_feat_strat == 'knn': - self._num_base_imputer = KNNImputer(n_neighbors=n_neighbors, + self._num_base_imputer = KNNImputer(n_neighbors=n_neighbors, **kwargs).fit(df[num_feats]) elif self.num_feat_strat == 'missforest': from missingpy import MissForest @@ -110,7 +110,8 @@ def fit(self, data, cat_feats=None, num_feats=None, def transform(self, data): all_feats = self._cat_feats + self._num_feats - assert len(set(data.columns)^set(all_feats)) == 0, "Passed columns don't match columns trained on !!! " + assert len(set(data.columns)^set(all_feats)) == 0, "Passed columns don't \ + match columns trained on !!! " assert self.fitted, "Model is not fitted yet !!!" df = data.copy() @@ -179,8 +180,66 @@ def __init__(self, scaling_strategy='standard'): self.scaling_strategy = scaling_strategy - def fit_transform(self, data, feats=[]): - """Scales dataset using the scaling strategy. + def fit(self, data, num_feats=None): + """Fits scaler to dataset using scaling strategy. + + Parameters + ---------- + data: pandas.DataFrame + Dataframe to be scaled. + feats: list + List of numerical/continuous features to be scaled. + **NOTE**: if left empty, all features are interpreted as numerical. + Returns: + Fitted instance of scaler. + """ + + self._num_feats = num_feats + + df = data.copy() + + if self.scaling_strategy == 'standard': + scaler = StandardScaler() + elif self.scaling_strategy == 'minmax': + scaler = MinMaxScaler() + else: + scaler = None + + if scaler: + if self._num_feats: + self.scaler = scaler.fit(df[self._num_feats]) + else: + self.scaler = scaler.fit(df) + + self.fitted = True + return self + + def transform(self, data): + """Scales data using scaling strategy. + + Parameters + ---------- + data: pandas.DataFrame + Dataframe to be scaled. + feats: list + List of numerical/continuous features to be scaled. + **NOTE**: if left empty, all features are interpreted as numerical. + Returns: + Fitted instance of scaler. + """ + + df = data.copy() + + if self._num_feats: + df[self._num_feats] = self.scaler.transform(df[self._num_feats]) + else: + df[df.columns] = self.scaler.transform(df) + + return df + + def fit_transform(self, data, num_feats=[]): + """Fits a scaler and rescales a dataset using a standard rescaling + strategy. Parameters ---------- @@ -206,7 +265,8 @@ def fit_transform(self, data, feats=[]): scaler = None if scaler is not None: - if feats: df[feats] = scaler.fit_transform(df[feats]) + if num_feats: + df[num_feats] = scaler.fit_transform(df[num_feats]) else: df[df.columns] = scaler.fit_transform(df) return df @@ -223,6 +283,8 @@ class Preprocessor: Strategy for imputing numerical/continuous features. scaling_strategy: str Strategy to use for scaling numerical/continuous data. + one_hot: bool + Whether to apply one hot encoding to the data. remaining: str Strategy for handling remaining columns. """ @@ -230,16 +292,50 @@ class Preprocessor: def __init__(self, cat_feat_strat='ignore', num_feat_strat='mean', scaling_strategy='standard', + one_hot=True, remaining='drop'): + self.one_hot = one_hot + self.imputer = Imputer(cat_feat_strat=cat_feat_strat, num_feat_strat=num_feat_strat, remaining=remaining) self.scaler = Scaler(scaling_strategy=scaling_strategy) - def fit_transform(self, data, cat_feats, num_feats, - one_hot=True, fill_value=-1, n_neighbors=5, **kwargs): + def fit(self, data, cat_feats, num_feats, + fill_value=-1, n_neighbors=5, **kwargs): + """Fit imputer and scaler to dataset.""" + + self._cat_feats = cat_feats + self._num_feats = num_feats + + self.imputer.fit(data, cat_feats=self._cat_feats, + num_feats=self._num_feats, fill_value=-1, + n_neighbors=5, **kwargs) + + data_imputed = self.imputer.transform(data) + + self.scaler.fit(data_imputed, num_feats=self._num_feats) + + self.fitted = True + return self + + def transform(self, data): + """Impute and scale the dataset.""" + + data_imputed = self.imputer.transform(data) + data_transformed = self.scaler.transform(data_imputed) + + if self.one_hot: + data_transformed[self._cat_feats] = data_transformed[self._cat_feats].astype('category') + data_transformed = pd.get_dummies(data_transformed, dummy_na=False, + drop_first=True) + + return data_transformed + + def fit_transform(self, data, cat_feats, num_feats, + fill_value=-1, n_neighbors=5, **kwargs): """Imputes and scales dataset. Parameters @@ -269,9 +365,9 @@ def fit_transform(self, data, cat_feats, num_feats, fill_value=fill_value, n_neighbors=n_neighbors, **kwargs) - output = self.scaler.fit_transform(imputer_output, feats=num_feats) + output = self.scaler.fit_transform(imputer_output, num_feats=num_feats) - if one_hot: + if self.one_hot: output[cat_feats] = output[cat_feats].astype('category') output = pd.get_dummies(output, dummy_na=False, drop_first=True) diff --git a/examples/Demo of CMHE on Synthetic Data.ipynb b/examples/Demo of CMHE on Synthetic Data.ipynb index ef3bd6d..207c412 100644 --- a/examples/Demo of CMHE on Synthetic Data.ipynb +++ b/examples/Demo of CMHE on Synthetic Data.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "18a9fda0", + "id": "4d03080b", "metadata": {}, "source": [ "# Deep Cox Mixtures with Heterogenous Effects (CMHE) Demo\n", @@ -23,9 +23,9 @@ "\n", "\n", "### 2. [Synthetic Data](#syndata) \n", - "####               1.1 [Generative Process for the Synthetic Dataset.](#gensyndata)\n", - "####               1.2 [Loading and Visualizing the Dataset.](#vissyndata)\n", - "####               1.2 [Split Dataset into Train and Test.](#splitdata)\n", + "####               2.1 [Generative Process for the Synthetic Dataset.](#gensyndata)\n", + "####               2.2 [Loading and Visualizing the Dataset.](#vissyndata)\n", + "####               2.2 [Split Dataset into Train and Test.](#splitdata)\n", "\n", " \n", "### 3. [Counterfactual Phenotyping](#phenotyping)\n", @@ -38,17 +38,17 @@ "\n", "### 4. [Factual Regression](#regression)\n", "\n", - "####               3.1 [Factual Regression with CMHE](#regcmhe)\n", + "####               4.1 [Factual Regression with CMHE](#regcmhe)\n", "\n", "\n", - "####               3.1 [Comparison with a Deep Cox Proportional Hazards Model](#deepcph)\n", + "####               4.1 [Comparison with a Deep Cox Proportional Hazards Model](#deepcph)\n", "\n", "
\n" ] }, { "cell_type": "markdown", - "id": "6ee01537", + "id": "e68d1255", "metadata": {}, "source": [ "\n", @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "8bfde8db", + "id": "e0637c88", "metadata": {}, "source": [ "\n", @@ -88,8 +88,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "a99d9a37", + "execution_count": 1, + "id": "b1c157e8", "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "4b10831b", + "id": "1d6cfe4b", "metadata": {}, "source": [ "\n", @@ -114,7 +114,7 @@ }, { "cell_type": "markdown", - "id": "8adc8551", + "id": "ddbbc1e6", "metadata": {}, "source": [ "1. Features $x_1$, $x_2$ and the base survival phenotypes $Z$ are sampled from $\\texttt{scikit-learn's make_blobs(...)}$ function which generates isotropic Gaussian blobs:\n", @@ -133,8 +133,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "121cd2b9", + "execution_count": 2, + "id": "4999dadd", "metadata": {}, "outputs": [ { @@ -244,7 +244,7 @@ "4 0.748930 " ] }, - "execution_count": 20, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -259,7 +259,7 @@ }, { "cell_type": "markdown", - "id": "fc875d77", + "id": "2cf92113", "metadata": {}, "source": [ "\n", @@ -268,22 +268,56 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "a3dee50a", - "metadata": { - "scrolled": false - }, + "execution_count": 3, + "id": "9f5fa4d9", + "metadata": {}, "outputs": [ + { + "ename": "RuntimeError", + "evalue": "Failed to process string with tex because latex could not be found", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[1;34m(self, command, tex)\u001b[0m\n\u001b[0;32m 274\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m report = subprocess.check_output(command,\n\u001b[0m\u001b[0;32m 276\u001b[0m \u001b[0mcwd\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtexcache\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[1;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 424\u001b[1;33m return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[0m\u001b[0;32m 425\u001b[0m **kwargs).stdout\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[0;32m 504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 505\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mPopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mprocess\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 506\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\subprocess.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, user, group, extra_groups, encoding, errors, text, umask)\u001b[0m\n\u001b[0;32m 950\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 951\u001b[1;33m self._execute_child(args, executable, preexec_fn, close_fds,\n\u001b[0m\u001b[0;32m 952\u001b[0m \u001b[0mpass_fds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcwd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\subprocess.py\u001b[0m in \u001b[0;36m_execute_child\u001b[1;34m(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, unused_restore_signals, unused_gid, unused_gids, unused_uid, unused_umask, unused_start_new_session)\u001b[0m\n\u001b[0;32m 1419\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1420\u001b[1;33m hp, ht, pid, tid = _winapi.CreateProcess(executable, args,\n\u001b[0m\u001b[0;32m 1421\u001b[0m \u001b[1;31m# no special security\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 2] The system cannot find the file specified", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m 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edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m 2191\u001b[0m else suppress())\n\u001b[0;32m 2192\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2193\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2195\u001b[0m bbox_inches = self.figure.get_tightbbox(\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m 39\u001b[0m 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40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 42\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in 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texmanager.get_text_width_height_descent(\n\u001b[0m\u001b[0;32m 228\u001b[0m s, fontsize, renderer=self)\n\u001b[0;32m 229\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[1;34m(self, tex, fontsize, renderer)\u001b[0m\n\u001b[0;32m 421\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 422\u001b[0m \u001b[1;31m# use dviread.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 423\u001b[1;33m \u001b[0mdvifile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_dvi\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_tex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 308\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtexfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 309\u001b[1;33m self._run_checked_subprocess(\n\u001b[0m\u001b[0;32m 310\u001b[0m [\"latex\", \"-interaction=nonstopmode\", \"--halt-on-error\",\n\u001b[0;32m 311\u001b[0m texfile], tex)\n", + "\u001b[1;32m~\\miniconda3\\envs\\localenv\\lib\\site-packages\\matplotlib\\texmanager.py\u001b[0m in \u001b[0;36m_run_checked_subprocess\u001b[1;34m(self, command, tex)\u001b[0m\n\u001b[0;32m 277\u001b[0m stderr=subprocess.STDOUT)\n\u001b[0;32m 278\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mFileNotFoundError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m raise RuntimeError(\n\u001b[0m\u001b[0;32m 280\u001b[0m \u001b[1;34m'Failed to process string with tex because {} could not be '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 281\u001b[0m 'found'.format(command[0])) from exc\n", + "\u001b[1;31mRuntimeError\u001b[0m: Failed to process string with tex because latex could not be found" + ] + }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -293,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "63fc4e40", + "id": "8089937e", "metadata": {}, "source": [ "\n", @@ -302,8 +336,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "5f7f70b5", + "execution_count": 3, + "id": "86e52537", "metadata": {}, "outputs": [ { @@ -357,8 +391,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "2f23bc61", + "execution_count": 4, + "id": "0bcca668", "metadata": {}, "outputs": [], "source": [ @@ -384,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "7806516d", + "id": "5246c210", "metadata": {}, "source": [ "\n", @@ -393,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "3ba93487", + "id": "ffcb6630", "metadata": {}, "source": [ "\n", @@ -402,8 +436,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "id": "a060b743", + "execution_count": 5, + "id": "256a7851", "metadata": {}, "outputs": [], "source": [ @@ -423,8 +457,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "id": "b5887db1", + "execution_count": 6, + "id": "60bf704d", "metadata": { "scrolled": true }, @@ -433,7 +467,13 @@ "name": "stderr", "output_type": "stream", "text": [ - " 88%|████████████████████████████████████▉ | 88/100 [00:41<00:05, 2.12it/s]\n" + " 0%| | 0/100 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -540,7 +580,7 @@ }, { "cell_type": "markdown", - "id": "a3e4a96c", + "id": "8d76fde5", "metadata": {}, "source": [ "\n", @@ -549,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "f62994a6", + "id": "43ee19ad", "metadata": {}, "source": [ "We compare the ability of CMHE against dimensionality reduction followed by clustering for counterfactual phenotyping. Specifically, we first perform dimensionality reduction of the input confounders, $\\mathbf{x}$, followed by clustering. Due to a small number of confounders in the synthetic data, in the following experiment, we directly perform clustering using a Gaussian Mixture Model (GMM) with 2 components and diagonal covariance matrices." @@ -557,8 +597,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "5dc0fa4d", + "execution_count": 9, + "id": "56513ea1", "metadata": {}, "outputs": [], "source": [ @@ -579,8 +619,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "5ca21a5a", + "execution_count": 10, + "id": "1f03edf9", "metadata": {}, "outputs": [ { @@ -590,8 +630,8 @@ "No Dimensionaity reduction specified...\n", " Proceeding to learn clusters with the raw features...\n", "Fitting the following Clustering Model:\n", - " GaussianMixture(covariance_type='diag', n_components=3)\n", - "Distribution of individuals in each treatement phenotype in the training data: [1438 1312 1149]\n", + " GaussianMixture(covariance_type='diag', n_components=3, random_state=0)\n", + "Distribution of individuals in each treatement phenotype in the training data: [1306 1162 1431]\n", "\n", "Group 2 has the maximum restricted mean survival time on the training data!\n" ] @@ -600,7 +640,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/chiragn/anaconda3/lib/python3.8/site-packages/lifelines/fitters/__init__.py:200: ApproximationWarning: Approximating using linear interpolation`.\n", + "C:\\Users\\Willa Potosnak\\miniconda3\\envs\\localenv\\lib\\site-packages\\lifelines\\fitters\\__init__.py:204: ApproximationWarning: Approximating using linear interpolation`.\n", + "\n", + " warnings.warn(\"Approximating using linear interpolation`.\\n\", exceptions.ApproximationWarning)\n", + "C:\\Users\\Willa Potosnak\\miniconda3\\envs\\localenv\\lib\\site-packages\\lifelines\\fitters\\__init__.py:204: ApproximationWarning: Approximating using linear interpolation`.\n", "\n", " warnings.warn(\"Approximating using linear interpolation`.\\n\", exceptions.ApproximationWarning)\n" ] @@ -619,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "e6dc8149", + "id": "b0e6383d", "metadata": {}, "source": [ "### Evaluate Clustering Phenotyper on Test Data" @@ -627,27 +670,25 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "e8bbd932", + "execution_count": 11, + "id": "30334e1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Distribution of individuals in each treatement phenotype in the test data: [393 380 328]\n" + "Distribution of individuals in each treatement phenotype in the test data: [380 330 391]\n" ] }, { "data": { - "image/png": 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RoPW6AWDcOHuTt6PmeRuUUg36lL16aAOUZdAWmYq2JGwbogxkKqWC+k06jfo6xMb5PUopzvUmoqjeeOMNiBSgs3MEqqvti8ORyRvQpuxZvK4BQIPJuRC0ha6IiCz5yle+gpUrP0BX1wjbZpoADiybEBHZafPmMejqOtvWmSYAkzcRkWW7d+/Go4/+J4BeW3vdAJM3EZFlL730Ejo770dlZaGtvW6AyZuIyLLXX38dhYWjUFR0kt2hMHkTEVn14ov/jb6+KRCxP3XaHwERkQMcPnwY27dvBXCy7fVugMmbiMiSjo4OKNWHM8/02F7vBhw8z5uIKJOqqqpQUbEUY8ZMtTsUAEzeRESWjBo1CiecMMPuMAawbEJEFIdSCkuXLsWBA112hzKAyZuIKI4PPvgA999/PwKBDrtDGcDkTUQUx/r16/WvTsmKmSYAa95ERHGtX78ehYWj8Y//WJUVM00A9ryJiOJ64YW2rLk5x5A9kRARZaGjR49i585PAEzLmpIJwLIJEVFMv/3tO+jpuRtz5nwua0omAHveREQxrVz5NoACXHdd9iRugMmbiCimrVt9mDZtQ1b1ugEmbyIiUz09Pdi161X09HxqdyjHYfImIjIRCATQ338UJ5xwut2hHIfJm4jIxLp16wCAyZuIyEmefvoPACZixAiX3aEch8mbiMjE7t2jAMzKqvndBs7zJiIyceqp38SppyLrZpoA7HkTEUX1i1+sx9q12+0OwxR73kREUfzbv90O4H+xaNF/2R1KVOx5ExFF8Pk6sXv3ZpSWTsvKkgnA5E1EdJxf/aoNwCFcdNGFdodiismbiCiM378J7e3a5gv33nutzdGYY/ImIgqjLUTVhfHjJ6OsrMzucExxwJKISOf3b8LatR+hvPxi1NefCRGxOyRTTN5ERDqt1w3ccstVuPba7ByoNLBsQkQUxuM5ioqKo1BK2R1KTLb2vEXkDqXUz5J8rBeAC0BI/xxUSrUMoa3w4tYepdSyZNoiImfbtu33uPrqX6Orq8vuUGJKKHmLyOUAFgJwAwgCeEYp9Z+JPqmI3AigEVrSTTh5i0gjIhKsiDSKiDvRpCsizQA2KKUawo+JiFcp5U80NiJyLqX6EAptweWXX2N3KHFZSt4iMg7AGgCesMMeAHUi0gWgVim1zUI78wD4oCV/AZDw3yUi4gbgVUqNDz+ulGoQkX0i4ldKhSy21Qhgb5SEXwOgKdHYiMjZDhx4H319h3Hhhdk7v9tgtecdADAdWsKNdCqADr3Xuz/ag0WkAlpPu8Y4pH9OpjSxBECbybkgAK+VdvU3gXoMLpcAACLfGIgo9/n9m7Bx418AAF/+8pdtjia+uAOWIvIojvWUWwDMBjBe//ysftl4AKuiPHaciDQB6ICWuEX/8AMYr5S6M4mYa6Al6WiCAGottrMEWp3crC0iyiPG/O4pU76A0tJSu8OJK2byFpEToCU5BaBRKXWlUmqjUupT/fMCAD+ElpBrReTL+uPGicgDAPYBqMOxpN0CoEwp9R2lVLKbwnkA7DE5FwRQabGdGmh/UUBEXCJSp/fGiSjPGPO758z5F7z8cnYuRBUpXs/bq38OmvWS9Xqx0Xv9oYjcAeB9aCUJI2kHAMzWk//7Qw87JpfF6zwA9opIHbRE3gbApQ9WMokT5RFjfvd1183CaaedZnM01sRL3rXQe91xrlsGLUnX6NeO1/8dhDaYWamU2jjEWCEiLv3L0FDb0pUACCmlWpRSIaVUAEADtBq+K9oDRMQrIu0i0t7d3Z2iMIjIbqeeugW7dr1odxiWxUveRgkiXl24NexrgZZcFyilTlVKrUkytrQJS8wepdSgwU+9Bh4EYPaXhl9/M6qcNGlSegMloozZsaMNL7/8st1hWBYvebv0z+2xLooohTQqpUqUUs+aPiBJYVMAXTEuS6SdgMklQWi1eiLKcVq9ewsOHdqOiy66yO5wLLN0e7zZFEATviRjSQUXrJdUQgD2mpzbC22GDRHlOK3e/S4AOCp5O3FhqjZEmZutK0GcvxLCBPXrzYQSiImIHKy0dDtEJuPss8+2OxTLnLgwVQDmvWI3gGaL7TRh8B2jke1YfRMgIocypgiKDMfll1+OggLnpETnRHqMD1HmcuuDkB5Ev1koWrL3A3CbnDNmzRBRDjOmCP7rv/5fPPLIIzZHkxira5ucAu2GGyumi4hZLfk4CdbToZQKiohfRBrDF5OClmwbItc1EZEOAB4Rma1PBTTaCYlIA7Q3g9qw630A/JGzUIgotxi97vPPL83aTYZjsVrztnILubHIVCJJTyUQw7EHaYtQeUWkHlptugxAq8mSsG0wGchUSi0TkZCesA0dXE2QKPcZve6PPnoI11/fhqeeesrmiBJjNXFm3V5AVhOs3jtviHGeiZooT5133ji89tpbWLz4CrtDSVi85L0RSSzbSkSUzYySyemnvw+lFC6++GK7Q0pYzOStlJqdqUCIiDLFKJkUFwdRWloKj8ds4ln2cuJsEyKiIbvgghPx3nuv4ZJLLnHUFEGDE2/SISJK2rFZJpPxs5/9DDNnzrQ7pKQweRNR3vD7N2HJEm0dvWuvPRvf/rbzpgganPe3AhFRkoxat89Xi9Gj38THH39sc0TJSzh5i8g8EblDRB7VP89LR2BERKlklEuqq6dg7tzRuP766/Hssylf/DRjLJdNRORyAI8jynKsIrIPwE1Kqf9MXWhERKlj9LoXLZqJF154AQDw9a9/3c6QhsRSz1vfj7IZWuKWKB8lAFpE5CfpCZOIaOiqq6fA652F559/HrNmzcIpp5xid0hJs7J7/E3Q7lAM30R4CbT1QJbo/4Z+rkFEvp2eUImIhm7Xrl1Yv3495s+fb3coQxKzbKLvHu+DdpdlEMCVEXtRrgHwuIgYq/m5AfhFpDnRBaeIiDJh/fr16O/vd3zytrp7PKDtSRl1E2F9tb7/E3boh0MNjIgoHebPn4+PP/4Ys2Y5d5ogED95L4TW616mlOqMdaG+ce/j0MontbGuJSKy04knngiRrFtvLyHxkrexUUGTxfaMjRC4/yMRZQ1jmmB39xu45JJL0N3dbXdIQxZvqqALx+rdVhjXuZKMh4go5YxpgqNHb0ZHx5soKYm1fa0zpHr3eMs76BARZYLR654zpxTvvLMel112GQoLC+0Oa8h4ezwR5azwtUzOOmsPDh06hCuucN7GC9EweRNRzgpfy+TTTzdg4sSJmDt3rr1BpQhXFSSinBS+lonXOwuffXYuysvLMWxYbqS9VO8ePz3iMXHn4iilPrASAxGRVeHlkkWLtPW6b731VjtDSrlU7h5vMPa8tLrjfG68DRJR1ggvl3i9s/Dqq69i1qxZKCoqsjmy1LFa8462GFWqPoiIUs4olxw6dAg1NTWor6+3O6SU4u7xRJTTXnzxRRw+fBhXXnml3aGkFHePJ6KctnLlSpx00kmYM2eO3aGkFKcKElFOMWaZAMCePXvw0ksv4aqrrnLkDvGx5NZ3Q0R5L3zHnNWrV6O3txfXXHONzVGlHmd6EFHOMQYrlTob5eXlKC8vtzuklIvZ8xaRJhH5o75/JRGRo4gIzj77bMcv/xpNvLJJLYAaaHtUEhE5xvLly3HjjTeir6/P7lDSgjVvIsoZxmClUgqPPPII/va3v+XECoLRMHkTUc4wBiu/9KUevPfee/jmN79pc0Tpw+RNRDmlunoKQqG/oKioKGeWf43GsbNNRMQLbceekP45qJRqSUG7rUop7sFJ5DDHNl2YhGeeeQZ1dXUYN26c3WGljSOTt4g0AtijlFoWfkxE3OHHkmi3DtoALRE5jFEyueyyafB4Fufc7fCRHJe8RcQNwKuUGh9+XCnVICL7RMSvlAol0a4LwMLURElEmRS+dvftt1cDqLY7pLRzYs17CYA2k3NBAN4k2/UCaErysURkI6PX/dWvluCVV16BUrm/np7V5O0Tkb40fPQmEXMNzNcKD0Kbm54QEfFAe0MIJREPEWUBbaDyv3HhhRdix44ddoeTdk5cz9sDYI/JuSCAyiTarFFKBZJ4HBFlif7+XvzmN7/BJZdcgs9//vN2h5N2TiybxONK5GJ91oo/PaEQUabs2dOBnTt34qabbrI7lIywmry9SqmCNHwkdOuTPqgIpKi8oQ9+7k1kgFNEvCLSLiLt3d3dqQiDiFJgx441mDp1Ki6++GK7Q8mIXOx5J6Iu0bnhSim/UqpSKVU5adKkdMVFRBZpM03+hoMHP4TX683Z2+EjOWqqoFIqpK8O5hpqW/qc7iHf1ENE9tJmmozC8uVtuP760+0OJ2MclbwtcMFCSUUvv5QopazscE9EWcjv34SVK9/Gxo2f4IILPo+bb05mroJzOTF5twEoMzlXAqDdQhteAFUiErlHpxsARMSn/7tZKWU2p5yIbOL3b8KSJa0AgC984V1s3rwMH388Ny9mmRicmLwD0KYLRuMG4DM5N8DsFnrj9nil1JLkwyOidApP3I89VoNHHvl/mDJlEk488USbI8ssJw5Y+hBlLrdeCvEAWBXlnDv9YRFRuoUnbp+vFqef/inefPNNfO9738vJ3XJicVzy1uvUfn1xqnCNABoip/2JSAeALv0uynhK9Me4UhAqEaWYcRu8z1cLr3cWli9fjgkTJmDRokU2R5Z58comHQDGA9ibgVgs0xeh8opIPbQByjIArSbT/toQZyBTL5csxLEVBdeISBDATcksckVE6WNsLvz+++/j+eefx1133YXRo0fbHVbGxUze2byutVLK0l2RSqkGAA1xrmkBpw0SZbXwlQMB4JRTTsELL7yA2bMj5x3kBycOWBJRHjGmBK5d+xEAYNGimQCAgoICXHrppXaGZismbyLKWuEDlNXVU7Bo0Ux4vbPwy1/+Eh9//DF+9KMfoaDAcUN3KcHkTURZKXJmidc7CwBw5MgR/OhHP0JFRUXeJm7AgbNNiCg/RM4sMfz2t7/Fzp070dAQcygr5zF5E1HWMmaWGHp7e/Hggw+iqqoKc+fOtS+wLMDkTURZx5hZEqmpqQldXV2466678u6mnEhM3kSUdYySiTGzxFBeXo5bb70VX//61+0IK6tIPmzUmS6VlZWqvd3KOlhElIi5c58BALzyylU2R2IPEelQSsVcJpGzTYjIdsZcbkNnZzcqKo5tdtLf34977rkHixcvxmmnnWZHiFmHZRMist3KlW+js/PYtoIVFZMGlUx+//vf4yc/+Qlee+01O8LLSux5E5Gtwm97j1Ym6e/vx3333YcZM2bg6quvtiHC7MTkTUS2MhucNKxatQqbN2/GypUrMWwYU5aBA5ZDwAFLoqGLNTjZ09ODM888EyNHjkRnZ2febC5sZcCSNW8iso3ZfG7DkSNHcNFFF+GBBx7Im8RtFf8GISLbxCuZFBcX4+GHH85kSI7BnjcR2SJ8oDL8FnjDihUrsG7dOhsicwYmbyKyRaxe9+7du3HzzTfjwQcfzHRYjsHkTUQZF6/XvXTpUhw6dAg//elPbYjOGZi8iSijwtfpjtbrfvvtt/HYY49hyZIlOOOMMzIdnmMweRNRxphtsGBQSuH222/HmDFjsHTpUhsidA7ONiGijIiXuAEtedfW1mL+/PmYNGnScefpGCZvIsoIs51xwhUUFOD222/PZFiOxbIJEaVdvAFKAHj44YexYsWKDEfmXEzeRJR28W7Gef/991FfX48XXnghk2E5GpM3EWWEWa9bKYWbb74Zw4YNw89//nMbInMm1ryJyFZNTU1YvXo1li9fjpNOOsnucByDPW8iSqtYi08dPHgQt912GyorK3HLLbdkODJnY8+biNIm3g05Y8eOxSOPPIKysjKuGpggJm8iSptY0wN7enowfPhwXH755XaE5ngsmxBRWsSaHrhv3z7MnDkTTz75pD3B5QAmbyJKi1jTA//pn/4JH3zwAc4666xMh5UzmLyJKG2i9bpbWlqwYsUK/Mu//AsqK2Pu9EUxMHkTUcqZzTDZvn07vF4vqqqqcPfdd9sQWe5w7ICliHgBuACE9M9BpVRLEu3UA5gAwKMf8iXTDhFpYs0wWbNmDfr6+rBixQoMHz7cjvByhiOTt4g0AtijlFoWfkxE3OHHLLbzgFIqpP/bA2CNiCxUSi1IddxE+SDWDJPFixfj0ksvxcSJE+0ILac4LnmLiBuAVyk1Pvy4UqpBRPaJiN9IxnHa8ULrZQ9cq5QKiMgCAK0iUsceOFFizGaYrFu3Dj09PZg3bx4Td4o4sea9BECbybkgAK/FdsqUUsHIg0qpNmilmCVJRUeUx6LNMNm5cycWLlyI7373u+jt7bUrtJzjxORdAy1JRxMEUGuxnXoRaTY51w6Aw+BECYjW6+7t7cXChQsRCoXwzDPPYNgwx/2xn7WcmLw9APaYnAvCetI1670DQAmAvYkERZTvovW6f/jDH2Lt2rXw+/2YNSv6Ot6UnFx8G3RZuUgpFauH7gHgT0k0RHkkvNe9bt06PPTQQ/jud7+La6+91ubIco+jet4i4tK/DKXxOYyaeaPZeRFpF5H27u7udIVB5Bh+/ybMnfsMOjsH/3+YM2cOnnrqKfz7v/+7TZHlNkcl73TT3xwaASyINpgJAEopv1KqUilVyQ1SibRySWdnNyoqJmHRopnYvn073n33XYgIrrvuOs7nThNHlU2UUiERASyWRpLQDKCBUwSJrAkfpHzllavw6aefYs6cOTh06BC2bt3KxJ1GjkreFriQZElFv2GnWSnFWjeRBZF3Uvb09GDBggV455138NJLLzFxp5kTk3cbgDKTcyXQpvklRL9FvouJm8i68Dspb7yxHNdffz1aW1vxxBNPoKamxubocp8Ta94BAG6Tc25opQ/LRKQOQCgycYsIf/uITETO6X700UexYsUK/PjHP8YNN9xgd3h5wYk9bx+AjsiD+mCjB8CqKOfc0QYgjQRt0uOuRey54ER5KdrCUzfccAOKioqwePFiGyPLL45L3kqpoIj4RaRRKdUQdqoR2mBjKPx6EekA4BGR2UqpQNhxD7Rb4Jv03rehBFrt3JWmb4HIscITt89Xi9Gj30QodApcLhe+9a1v2RxdfnFc8gYGFqHy6rXqELQaeKvJLJE2RB/IXKMfr0N0DSbHifJSZOI+fPi/sWTJ93Hvvffi/vvvtzm6/OPI5A2YljqiXdeAKIk4clVCIootfIBy//5W/PM//zOuuOIK3HPPPTZHlp8cm7yJKP38/k0DSbuzsxvV1VOwa9eLuOeee3DllVfi6aef5mJTNnHibBMiygCjTGJsZ1ZRMQnz55+EX/3qV/jmN7+JlStXci63jfiWSUSDGL1tI2n7fLX41rfOgIhg2LBhuOqq11BaWoqCAvb97MTkTUQAjk/a1dVTsGjRTCxaVIZvfOMbmDp1Knw+Hz73uc/ZHCkBTN5EpDMWmDKSttc7C9u3b8f555+PN998E4899pjdIVIYJm8iOm6BKQB444038I1vfAOHDh3CH/7wB3zlK1+xOUoKx+RNlOei3TH52Wef4Wtf+xrGjBmDtrY2nHnmmXaGSFEweRPlifBpf+EiByaVUhg9ejR+97vfoby8HFy3PjtxuJgoxxk73YRP+wtXXT0FPl8tvvrVEpx//vl49NFHAQDz5s1j4s5i7HkT5bhoA5GRmpubcfbZXvT19XE2iUMweRPloMg7IysqJg0MRIY7ePAgvve97+E3v/kNzj33XKxYsQJlZWbL5VM2YdmEKMdEuzPSGIiMtGHDBjz11FO4++67sW7dOiZuB2HPmyiHRK78F61EcuDAAaxZswbz58/Hl7/8Zbz33nuYPn16pkOlIWLyJsoB0W5pj5a4X3rpJXznO9/Bjh07EAwGMWXKFCZuh2LyJnIws1vaIxP3+++/j+9///t4/vnnMXPmTKxduxZTpkyxI2RKESZvIocKL5HEmkly4MABeDwe9PT04Kc//Sluu+02jBw5MtPhUooxeRM5ULzadl9fH1avXo2LL74YxcXFePzxx3HeeefhpJNOsiNcSgPONiFykPAbboDjE7dSCs899xzKy8tx6aWX4n/+538AAHV1dUzcOYbJm8ghwqcAGndFGolbKYU//elP+NKXvoQrrrgCSik8++yzmDNnjs1RU7qwbELkAPHKJEePHsX111+PESNG4Ne//jWuu+46bk+W4/jTJcpCkYtIRU4BPHLkCJ588km0tLRg9erVGDVqFP70pz9hxowZHIzME0zeRDaLttpf+NQ/4/OiRTNRWzsOd911F5544gns2rULVVVV2LFjB6ZOnYqzzz4747GTfZi8iWwQnrAjE7XxdeTUv/b2dpSVlUFEcOmll+K2227D3LlzISKZDZ6yApM3URrFW0O7unqK6Rzt9957D/fddx/GjBmD+vp6eDweLFu2DAsXLsTUqVMzEj9lLyZvoiEwS86GaL1q49/REva2bdvQ1NSEpqYmBAIBiAiuueYaAEBBQQHuuOOOFH8H5FRM3kQm4iVmwDw5G2Ld+Wj45JNPMHnyZIgIli5diieffBJf/OIX8dBDD2HBggXsZVNUopSyOwbHqqysVO3t7XaHQSkUrxYdTbzkHKmvrw8bNmzAH//4R6xevRqvv/46AoEAKioq0NXVBRGB2+1O/psgxxORDqVUZaxr2POmvGeWsK30mq3q7+9HQUEBAoEAampqsG/fPhQUFKCqqgr3338/SktLAYDraZNlTN6Ul9KdsLu7u/H6669j7dq1WL16NRYsWIB7770XM2bMwGWXXYaLLroINTU1KCkpGfL3QvmJyZvykrGvY0XFpCEn7KNHj2LHjh2YNm0alFI455xzsGnTJgDAiBEjcP755+O0004DAIwZMwZPPPFEyr4Pyl9M3pS3zPZ1jGfbtm34y1/+gtdffx2vvfYaOjs7ccYZZ2Djxo0QEcyfPx/XXnstzj33XMyePRtFRUVpiJ7yHZM3ZQUrMztSyeh1m+np6UEwGMTWrVvxzjvvYNu2bfjlL38JEUFDQwOamppQVFSEyspK3HrrrfiHf/iHgccuXbo0A98B5TvHzjYRES8AF4CQ/jmolGrJZDucbZI6c+c+EzehptqiRTNx+eUnYevWrdi6dSuuuuoqFBUV4cEHH8Rdd92F3t7egWtLS0vx7rvv4oQTTsBbb72Fvr4+nHXWWVz8idLCymwTRyZvEWkEsEcptSzWsXS3w+SdmFi9ayNxJ1PGMNPX14fdu3dj586d2LlzJ8455xxMnDgRa9aswb333outW7diz549A9d3dHTA4/Hg5Zdfxpo1azBjxoyBD5fLlbK4iOLJyamCIuIG4FVKjQ8/rpRqEJF9IuJXSoUy1Q5ZFz5IGKmiYhIWLZppqZ0jR45gy5Yt2LVr10Bi3rlzJ6666ipUVVVh/fr1uOyyy7B792709/cPPO6FF17A1772NYwYMQLDhw/HFVdcgRkzZuD000/HjBkzMG3aNADAvHnzMG/evJR8z0Tp4rjkDWAJgDaTc0EAXgBWet+paicrZbqGbIVZ7/ro0aPYuXPnwFKmBw4cwMMPPzwoMe/cuRM/+MEP8O1vfxtdXV2YPXv2oDaKiopwzjnnoKqqCieeeCLmz5+PyZMnD/ooLy8HAJx//vl45ZVXMvI9E6WLE5N3DWIn3VpYS7pDbqezs/O4P6e3b9+O4uJi3H333fiP//iPgeN//3sf/v73fsyZo00T+9vffotPPvnzoMcWFo7Ceedpj9m61Y/u7tcHnR8xwoUvfvEhAMCWLb/A3r2bBp0fPXoyZs/+CQBg7dp/BfA+CguPbZY0duzJqKi4T4/9fhw8+OGgx59wwgyUl9cDADo67sJnn+0cdL6kZBbOOON7AIA33vgB/v730KDzkyadixkzvACAV1/9Lvr6jgw6/7nPXYBFi34KAAOvW19fHw4ePAgAqK+vR2NjI/r7+3H33XejuLh4IPHOnDkTkyZpPfbp06fjueeeG5SYx44dO/A806dPh8/nA1Euc2Ly9gBoMjln9Jgz0s6ECRNw9dVXDzo2fPhwAEBVVRUWL148cLylZSu6uz8b+Pe4cV84rr2CguEDX59wwukoLBy8qH5h4bEpZ+PHn4kRI06IeO5xA1+feuq5OPnkc1BefqxEMWXKFNxxh9br/dnPPsJHH3006PGnnnoqbrlFO//jH3ehu7t70PkzzzwTN92knb/33i3Yv3//oPMejwfXX6+db2jYiKNHjw46f95552HhQm0utfHaiAgmTJiAyZMno7JSK/GNGzcOhw4dMp1iV1RUhMsuuyzqOaJ84bgBSxFRABqiDSjqg431Sqm4Cxynop3iYrcyerrxpGNAjohyk5UBS0dtQCwiLv3LkF3tiIhXRNpFpL2np8fy4xIZkCMiiseJZRNbKaX8APyANlWQPWkisoOjet5hU/dc2dAOEZFdHJW8LXBhiCWVFLdDRJQWTkzebQDMFj0uAWD1lsdUtUNElHFOTN4BAGbbjLgBNGe4HSKijHNi8vYBOG4KjT6DxANgVZRz0ZJ0wu0QEWULxyVvpVQQgF+fix2uEdq87VD4QRHpANAlIp6htENElE0cOVVQXzzKKyL10AYWywC0mizl2gaTAcgE2yEiyhqOu8Mym3BJWCJKh5y7w5KIiDRM3kREDsTkTUTkQKx5D4GIdAPYNsRmTgDwaYYeb+XaeNeYnY923MqxiQB2x4kplTL5elu9PtY1ibze0Y5Huy6Tr/lQX+9E20jX6212Ll2/46copWJv6KqU4oeNHwD8mXq8lWvjXWN2PtpxK8cAtOfq652K1zyR19vk9Y32M8jYaz7U1zvRNtL1esd4LW37HWfZxH4vZvDxVq6Nd43Z+WjHrR7LpEy+3lavj3VNIq93tONOf70TbSNdr7fZOdt+x1k2IVuJSLuKMyWKUouveWal6/Vmz5vs5rc7gDzE1zyz0vJ6s+dNjqTfFQvoK0MqpZbYGE7O09cHqgGwQClVa3c8uSLs7m4AcKko2zKaceTt8ZTfRKRRKdUQ9m+fiLQyqaSHvi5QJbRlJkrsjSZ3GIlbabtzQURqRMRntSPCnjc5ir7qYzO0HmBIP+YB0AGgTGkLjlEaiEgdgDuVUrPtjiUXiMg+ANNV2CJ4IqKUhQ3UAfa8KQl6Am1EnEW8RMSLY4uCuQAEY12fgEpoa64H9H8bCduVgrazTha83qRL1c9CL0O5VJTVS0WkRinVFi8WJm+yTER80P5s3gDAC6A1xrWNAPaE1/BEpFFE3InU9SLpv+zjIw7X6J9zqtedDa83adLwszDbCCYEi50Qlk0oKSKioJUujut96L2KDqVUZJKN+qdiCmLpAOAzaoe5KBteb5ZNNKn4WYhIDbTeu0Rc0wWg0crvMnvelA5LoK2jHk0QWs9lGTCQEBZaaLPJ5D9LI3I8cVuQsdeb4rL8szBheUCYyZvSoQaxf4Frof8C6wkiqSShJ6KuPE/cQIZeb7LE6s/CrMTninFuEN6kQ+ngAbDH5FwQUfYOTZT+ZyfCplm5TPYqzQdpf73JMks/C31WVCja76yVwUqAyZvs4RrKg/WpgR4AARFx6/++E8DeFMSWi1wpaodzvIfOFfb1Azg22G78JWn5r0iWTSil9KlUQJQ9Q1PY/hpo/wkGbR4dfuNOvkj3660/hxtaLbcGgEcfZ2C5KkKiPwul1DIRqdenFQLafQqW7xRm8iZHMZkqSGmk/4mfd2+MmTCUaZwsm1BKhU1Jc9kYRt7g6509Mv2zYPKmTHMhjX/i03Fc4OudLVxI4c+CyZvSoQ36an9RlABoz2As+YCvd/bI2M+CyZvSIQDz23/d0BaWotTh6509Mvaz4IAlpYMP2ip/g+ij8R4AqzIdUI7j6509MvazYM+bhiLqvF99doJfn1IWrhFAQyrXNckzfL2zh+0/Cy5MRZbpi8dXQetBuKENvrRDu3PsuF/MiGUxywBs4HoZ1vH1zh7Z+LNg8iYiciCWTYiIHIjJm4jIgZi8iYgciMmbiMiBmLyJiByIyZuIyIGYvImIHIi3x1Pe0HeZT2artDal1IIobXniPC4E7SaOJgB+K3fXJdBuO4BWq+1S7uFNOpQ3RKQLySfv2oi2rCTZSEvi7T6TZLv+RHZgodzAnjflqwYM3qU71v6M8XbzLtPXtBigL0TkBrAQQL1+2CciZQls12bWbqXedoP+2atvyDybvfD8wZ435Y2InvdxiTHBtsJ7yONjJU19g+TmsOdeYLbORSLt6tc34tibQ0ApNdvSN0COxwFLojRTSgUAhNfMH09h2w0AjDcCj74DOeUBJm+iDNATuJFkXXqZI1UeCPt6YQrbpSzG5E2UOa1hXyc6KBlLePknmQFZciAmb6LMCU+yZvscJiM8YSddxydnYfImypzwJNuVwnbvDPu6KYXtUhZj8ibKnPC54oFUNKjv8GIMUga5c07+4DxvogzQpwsaSTaklGobQltuADU4Ns8b0O66rDV7DOUeJm/KV24RiXvRUOaCG/Rk2xx26CaLD91nJUYAbdDmjocSDI0cjMmb8lVr/EsAETG9oSaMW0Qik3wJtF7xAgDesOPLUljaaAPQOJRePDkXkzfR0HVYvC7u2iYRanH87JElOHZHZSsTd/7i7fGUN9J4e7yZELTk2wbggSRWFYx6e7yI7APg0tufznJJfuJsE6KhK1NKSZSP8Uqp2UqphhQnWKNm7sLgaYKUR5i8iRxGr5kbfzXU6ysNUp5h8iZypvD1u1O20BU5B5M3kQPpA5XGjT51+jxyyiNM3kTOFT5fvNG2KMgWTN5EDhWxzGxNipeZpSzH5E3kbOFbqvlsi4IyjsmbyMH0uerGjT9uEfHGup5yB5M3kfOF975Z+84TTN5EDqffALRM/6dLXyaWchxvjyciciD2vImIHIjJm4jIgZi8iYgciMmbiMiBmLyJiByIyZuIyIGYvImIHIjJm4jIgZi8iYgciMmbiMiBmLyJiByIyZuIyIGYvImIHOj/A0u3JOq24NJiAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -667,7 +708,7 @@ }, { "cell_type": "markdown", - "id": "be165d69", + "id": "2251a0e1", "metadata": {}, "source": [ "\n", @@ -676,7 +717,7 @@ }, { "cell_type": "markdown", - "id": "7dd6b33d", + "id": "81a908ea", "metadata": {}, "source": [ "For completeness, we further evaluate the performance of CMHE in estimating factual risk over multiple time horizons using the standard survival analysis metrics, including: \n", @@ -695,36 +736,9 @@ "*We find that CMHE had similar or better discriminative performance than a simple Cox Model with MLP hazard functions. CMHE was also better calibrated as evidenced by overall lower Integrated Brier Score, suggesting utility for factual risk estimation.*" ] }, - { - "cell_type": "code", - "execution_count": 31, - "id": "9086369e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.73384696, 0.29896823, 0.08328623],\n", - " [0.9574444 , 0.83792984, 0.6892881 ],\n", - " [0.93582904, 0.7631118 , 0.56567705],\n", - " ...,\n", - " [0.97567105, 0.9040196 , 0.8069841 ],\n", - " [0.8633335 , 0.5535729 , 0.29013065],\n", - " [0.71951264, 0.28196204, 0.08862075]], dtype=float32)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict_survival(x_te, a_te, t=horizons)" - ] - }, { "cell_type": "markdown", - "id": "cae723dc", + "id": "24ac11ba", "metadata": {}, "source": [ "\n", @@ -734,18 +748,16 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "936afd66", - "metadata": { - "scrolled": false - }, + "execution_count": 13, + "id": "00931218", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Concordance Index (1 Year): 0.6894 (3 Year) 0.6978: (5 Year): 0.6987\n", - "Integrated Brier Score: 0.1524\n" + "Concordance Index (1 Year): 0.6545 (3 Year) 0.6652: (5 Year): 0.6662\n", + "Integrated Brier Score: 0.1597\n" ] } ], @@ -763,8 +775,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "id": "009bfae4", + "execution_count": 14, + "id": "215f805f", "metadata": {}, "outputs": [ { @@ -785,7 +797,7 @@ " 0.05497523, 0.5677395 ]], dtype=float32)" ] }, - "execution_count": 33, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -796,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "5d4662da", + "id": "5ec788f5", "metadata": {}, "source": [ "\n", @@ -805,100 +817,38 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "05fd05ea", - "metadata": {}, - "outputs": [], - "source": [ - "from auton_survival.estimators import SurvivalModel\n", - "\n", - "# Now let us train a Deep Cox-proportional Hazard model with two linear layers and tanh activations\n", - "random_seed = 0\n", - "hyperparams = {'layers':[[50, 50]], \n", - " 'lr':[1e-3],\n", - " 'bs':[128], \n", - " 'activation':['tanh']}" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "fa4a2017", + "execution_count": 15, + "id": "7784793f", "metadata": { "scrolled": true }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "0:\t[0s / 0s],\t\ttrain_loss: 3.4539,\tval_loss: 3.4712\n", - "1:\t[0s / 0s],\t\ttrain_loss: 3.3983,\tval_loss: 3.4378\n", - "2:\t[0s / 0s],\t\ttrain_loss: 3.3631,\tval_loss: 3.4173\n", - "3:\t[0s / 0s],\t\ttrain_loss: 3.3495,\tval_loss: 3.4090\n", - "4:\t[0s / 0s],\t\ttrain_loss: 3.3353,\tval_loss: 3.3997\n", - "5:\t[0s / 0s],\t\ttrain_loss: 3.3275,\tval_loss: 3.3949\n", - "6:\t[0s / 0s],\t\ttrain_loss: 3.3231,\tval_loss: 3.3874\n", - "7:\t[0s / 0s],\t\ttrain_loss: 3.3233,\tval_loss: 3.3898\n", - "8:\t[0s / 0s],\t\ttrain_loss: 3.3157,\tval_loss: 3.3892\n", - "9:\t[0s / 0s],\t\ttrain_loss: 3.3086,\tval_loss: 3.3821\n", - "10:\t[0s / 0s],\t\ttrain_loss: 3.2997,\tval_loss: 3.3888\n", - "11:\t[0s / 0s],\t\ttrain_loss: 3.2978,\tval_loss: 3.3864\n", - "12:\t[0s / 0s],\t\ttrain_loss: 3.2952,\tval_loss: 3.3854\n", - "13:\t[0s / 0s],\t\ttrain_loss: 3.3009,\tval_loss: 3.3910\n", - "14:\t[0s / 0s],\t\ttrain_loss: 3.2954,\tval_loss: 3.3866\n", - "15:\t[0s / 0s],\t\ttrain_loss: 3.2926,\tval_loss: 3.3890\n", - "16:\t[0s / 0s],\t\ttrain_loss: 3.2925,\tval_loss: 3.3853\n", - "17:\t[0s / 0s],\t\ttrain_loss: 3.2928,\tval_loss: 3.3838\n", - "18:\t[0s / 0s],\t\ttrain_loss: 3.2794,\tval_loss: 3.3780\n", - "19:\t[0s / 0s],\t\ttrain_loss: 3.2956,\tval_loss: 3.3802\n", - "20:\t[0s / 0s],\t\ttrain_loss: 3.2847,\tval_loss: 3.3787\n", - "21:\t[0s / 0s],\t\ttrain_loss: 3.2823,\tval_loss: 3.3785\n", - "22:\t[0s / 0s],\t\ttrain_loss: 3.2827,\tval_loss: 3.3784\n", - "23:\t[0s / 0s],\t\ttrain_loss: 3.2775,\tval_loss: 3.3852\n", - "24:\t[0s / 0s],\t\ttrain_loss: 3.2831,\tval_loss: 3.3793\n", - "25:\t[0s / 0s],\t\ttrain_loss: 3.2764,\tval_loss: 3.3689\n", - 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"42:\t[0s / 1s],\t\ttrain_loss: 3.2572,\tval_loss: 3.3597\n", - "43:\t[0s / 1s],\t\ttrain_loss: 3.2445,\tval_loss: 3.3635\n", - "44:\t[0s / 1s],\t\ttrain_loss: 3.2508,\tval_loss: 3.3605\n", - "45:\t[0s / 1s],\t\ttrain_loss: 3.2433,\tval_loss: 3.3623\n", - "46:\t[0s / 1s],\t\ttrain_loss: 3.2479,\tval_loss: 3.3560\n", - "47:\t[0s / 1s],\t\ttrain_loss: 3.2356,\tval_loss: 3.3593\n", - "48:\t[0s / 1s],\t\ttrain_loss: 3.2412,\tval_loss: 3.3614\n", - "49:\t[0s / 1s],\t\ttrain_loss: 3.2410,\tval_loss: 3.3589\n" + " 94%|█████████████████████████████████████████████████████████████████████████████ | 47/50 [00:03<00:00, 14.08it/s]\n" ] } ], "source": [ - "dcph_model = SurvivalModel(model='dcph', random_seed=0, hyperparams=hyperparams)\n", + "from auton_survival.estimators import SurvivalModel\n", + "\n", + "# Now let us train a Deep Cox-proportional Hazard model with two linear layers and tanh activations\n", + "dcph_model = SurvivalModel('dcph', random_seed=0, bs=100, learning_rate=1e-3, layers=[50, 50])\n", "\n", "interventions_tr.name, interventions_te.name = 'treat', 'treat'\n", - "features_tr_dcph = pd.concat([features_tr, interventions_tr], axis=1)\n", - "features_te_dcph = pd.concat([features_te, interventions_te], axis=1)\n", + "features_tr_dcph = pd.concat([features_tr, interventions_tr.astype('float64')], axis=1)\n", + "features_te_dcph = pd.concat([features_te, interventions_te.astype('float64')], axis=1)\n", + "outcomes_tr_dcph = pd.DataFrame(outcomes_tr, columns=['event', 'time']).astype('float64')\n", "\n", "# Train the DCPH model\n", - "dcph_model = dcph_model.fit(features_tr_dcph, outcomes_tr)" + "dcph_model = dcph_model.fit(features_tr_dcph, outcomes_tr_dcph)" ] }, { "cell_type": "markdown", - "id": "54c28db1", + "id": "b9cde19f", "metadata": {}, "source": [ "### Evaluate DCPH on Test Data" @@ -906,16 +856,16 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "a110aecb", + "execution_count": 16, + "id": "96b19d3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Concordance Index (1 Year): 0.6894 (3 Year) 0.6925: (5 Year): 0.6942\n", - "Integrated Brier Score: 0.1535\n" + "Concordance Index (1 Year): 0.6919 (3 Year) 0.6947: (5 Year): 0.6981\n", + "Integrated Brier Score: 0.153\n" ] } ], @@ -932,7 +882,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7dfd4090", + "id": "5029d649", "metadata": {}, "outputs": [], "source": [] @@ -940,7 +890,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a714d788", + "id": "8344a372", "metadata": {}, "outputs": [], "source": [] @@ -948,7 +898,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -962,7 +912,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/examples/Phenotyping Censored Time-to-Events.ipynb b/examples/Phenotyping Censored Time-to-Events.ipynb new file mode 100644 index 0000000..62ed487 --- /dev/null +++ b/examples/Phenotyping Censored Time-to-Events.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "67bd8372", + "metadata": {}, + "source": [ + "# Phenotyping Censored Survival Data\n", + "
\n", + "\n", + "Author: ***Willa Potosnak*** <wpotosna@andrew.cmu.edu>\n", + "\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "# Contents\n", + "\n", + "\n", + "### 1. [Introduction](#intro) \n", + "####               1.1 [The SUPPORT Dataset](#support)\n", + "####               1.2 [Preprocessing the Data](#preprocess)\n", + "\n", + "### 2. [Intersectional Phenotyping](#interpheno) \n", + "####               2.1 [Fitting the Intersectional Phenotyper](#fitinter)\n", + "####               2.2 [Plotting Survival Curves](#plotpheno)\n", + " \n", + "### 3. [Unsupervised Phenotyping](#clusterpheno)\n", + "####               3.1 [Fitting the Clustering Phenotyper](#fitcluster)\n", + "####               3.2 [Plotting Survival Curves](#clusterplot)\n", + "\n", + "### 4. [Supervised Phenotyping with Deep Cox Mixtures (DCM)](#DCM)\n", + "####               4.1 [Fitting the DCM Model](#fitDCM)\n", + "####               4.2 [Inferring Latent Phenotypes](#latentz)\n", + "####               4.3 [Plotting Survival Curves](#plotlatent)\n", + "\n", + "### 5. [Counterfactual Phenotyping](#countpheno)\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "074b3b68", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 1. Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "512b8278", + "metadata": {}, + "source": [ + "`auton-survival` offers utilities to phenotype, or group, samples for use in assessing differential survival probabilities across groups. Phenotyping can aid clinical decision makers by offering insight into groups of patients for which differential survival probabilities exist. This insight can influence clinical practices applied to these groups.\n", + " * Intersectional Phenotyping \n", + " - Identify phenotypes of samples from all possible combinations of user-specified categorical and numerical features.\n", + " * Unsupervised Phenotyping\n", + " - Identify phenotypes that group samples based on similarity in the feature space.\n", + " * Supervised Phenotyping\n", + " - Identify latent phenotypes $\\mathbf{P}(Z|X=\\mathbf{x})$ of samples from deep non-linear representations obtained from an encoder.\n", + " * Counterfactual Phenotyping\n", + " - Identify latent phenotypes $\\mathbf{P}(Z|X=\\mathbf{x})$ of samples that demonstrate heterogneous effects to an intervention from deep non-linear representations obtained from an encoder.\n" + ] + }, + { + "cell_type": "markdown", + "id": "ec05850a", + "metadata": {}, + "source": [ + "\n", + "\n", + "### 1.1. The SUPPORT Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "8a2fe8ac", + "metadata": {}, + "source": [ + "*For the original datasource, please refer to the following [website](https://biostat.app.vumc.org/wiki/Main/SupportDesc).*\n", + "\n", + "Data features, $\\mathbf{x}$, are stored in a pandas dataframe with rows corresponding to individual samples and columns as covariates. Data outcomes consists of 'time', $\\mathbf{t}$, and 'event', $\\mathbf{e}$, that correspond to the time to event and the censoring indicator, respectively. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2721914", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "from auton_survival.datasets import load_dataset" + ] + }, + { + "cell_type": "markdown", + "id": "ddfe2236", + "metadata": {}, + "source": [ + "\n", + "### 1.2. Preprocessing the Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67ba7253", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the SUPPORT dataset\n", + "outcomes, features = load_dataset(dataset='SUPPORT')\n", + "\n", + "# Identify categorical (cat_feats) and continuous (num_feats) features\n", + "cat_feats = ['sex', 'dzgroup', 'dzclass', 'income', 'race', 'ca']\n", + "num_feats = ['age', 'num.co', 'meanbp', 'wblc', 'hrt', 'resp', \n", + " 'temp', 'pafi', 'alb', 'bili', 'crea', 'sod', 'ph', \n", + " 'glucose', 'bun', 'urine', 'adlp', 'adls']\n", + "\n", + "# Let's take a look at the features\n", + "display(features.head(5))\n", + "\n", + "# Let's take a look at the outcomes\n", + "display(outcomes.head(5))" + ] + }, + { + "cell_type": "markdown", + "id": "9d47efcd", + "metadata": {}, + "source": [ + "Here we perform imputation and scaling on the entire dataset but in practice we recommend that preprocessing tools be fitted solely to training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9623c53d", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.preprocessing import Preprocessor\n", + "\n", + "preprocessor = Preprocessor(cat_feat_strat='ignore', num_feat_strat= 'mean') \n", + "x = preprocessor.fit_transform(features, cat_feats=cat_feats, num_feats=num_feats,\n", + " one_hot=True, fill_value=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "531a6f1c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Split the data into train and test sets\n", + "x_tr, x_te, y_tr, y_te = train_test_split(x, outcomes, test_size=0.2, random_state=1) \n", + "\n", + "print(f'Number of training data points: {len(x_tr)}')\n", + "print(f'Number of test data points: {len(x_te)}')" + ] + }, + { + "cell_type": "markdown", + "id": "217d0fb8", + "metadata": {}, + "source": [ + "\n", + "## 2. Intersectional Phenotyping" + ] + }, + { + "cell_type": "markdown", + "id": "7e62e88f", + "metadata": {}, + "source": [ + "The intersectional Phenotyper performs an exhaustive cartesian product on the user-specified set of categorical and numerical variables to obtain the phenotypes. Numeric variables are binned based on user-specified quantiles." + ] + }, + { + "cell_type": "markdown", + "id": "ceb67ada", + "metadata": {}, + "source": [ + "\n", + "### 2.1. Fitting the Intersectional Phenotyper" + ] + }, + { + "cell_type": "markdown", + "id": "5ca8abd6", + "metadata": {}, + "source": [ + "Here we fit the phenotyper on the entire dataset but in practice we recommend that the phenotyper be fitted solely to training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "319fdff9", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.phenotyping import IntersectionalPhenotyper\n", + "\n", + "# We create two bins based on the following quantiles of age groups\n", + "quantiles = (0, .5, 1.0)\n", + "\n", + "# 'ca' is cancer status\n", + "phenotyper = IntersectionalPhenotyper(cat_vars=['ca'], num_vars=['age'],\n", + " num_vars_quantiles=quantiles, random_seed=0)\n", + "phenotypes = phenotyper.fit_phenotype(features)\n", + "\n", + "# Let's look at the phenotypes for each sample\n", + "phenotypes " + ] + }, + { + "cell_type": "markdown", + "id": "f96c38d0", + "metadata": {}, + "source": [ + "\n", + "### 2.2. Plotting Survival Curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4958eed5", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival import reporting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n", + "reporting.plot_kaplanmeier(outcomes, phenotypes)\n", + "\n", + "plt.xlabel('Time (Days)')\n", + "plt.ylabel('Event-Free Survival Probability')\n", + "plt.legend(loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "51beb193", + "metadata": {}, + "source": [ + "As you can see, patients ages 18 to 64 without cancer have the highest survival rates. Alternatively, patients ages 64 to 101 with metastatic cancer have the lowest survival rates." + ] + }, + { + "cell_type": "markdown", + "id": "bc6bc99d", + "metadata": {}, + "source": [ + "\n", + "## 3. Unsupervised Phenotyping" + ] + }, + { + "cell_type": "markdown", + "id": "7d7012e9", + "metadata": {}, + "source": [ + "Dimensionality reduction of the input covariates, $\\mathbf{x}$, is performed followed by clustering. Learned clusters are considered phenotypes and used to group samples based on similarity in the covariate space. The estimated probability of sample cluster association is computed as the sample distance to a cluster center normalized by the sum of distances to other clusters.\n", + "\n", + "\\begin{align}\n", + "\\mathbf{P}(Z=k | X=\\mathbf{x}_i) = \\frac{\\mathbf{d}(x_i, x_c)}{\\sum_{j=1}^{K} \\mathbf{d}(x_i, x_j)}\n", + "\\end{align}\n", + "\n", + "Where $d_i$ is the distance to a cluster $k$ for i $\\in$ \\{1, 2, ..., $n$\\} where $i$ $\\not=$ $c$." + ] + }, + { + "cell_type": "markdown", + "id": "34dbb40a", + "metadata": {}, + "source": [ + "\n", + "### 3.1. Fitting the Clustering Phenotyper" + ] + }, + { + "cell_type": "markdown", + "id": "459e840c", + "metadata": {}, + "source": [ + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "970d276d", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.phenotyping import ClusteringPhenotyper\n", + "\n", + "# Perform dimensionality reduction using Principal Component Analysis (PCA)\n", + "dim_red_method = 'pca' \n", + "# We use a Gaussian Mixture Model with a diagonal covariance matrix\n", + "clustering_method = 'gmm'\n", + "n_components = 8 \n", + "n_clusters = 2 # Number of underlying phenotypes\n", + "\n", + "# Initialize and fit the clustering phenotyper\n", + "phenotyper = ClusteringPhenotyper(clustering_method=clustering_method, \n", + " dim_red_method=dim_red_method, \n", + " n_components=n_components, \n", + " n_clusters=n_clusters)\n", + "phenotypes = phenotyper.fit_phenotype(x_tr)\n", + "\n", + "# Let's look at the phenotypes\n", + "phenotypes" + ] + }, + { + "cell_type": "markdown", + "id": "be4e3f58", + "metadata": {}, + "source": [ + "\n", + "### 3.2. Plotting Survival Curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c3eef9d", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival import reporting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n", + "reporting.plot_kaplanmeier(outcomes.loc[x_tr.index], phenotypes.argmax(axis=1))\n", + "\n", + "plt.xlabel('Time (Days)')\n", + "plt.ylabel('Event-Free Survival Probability')\n", + "plt.xlabel('Time (Days)')\n", + "plt.legend(['Phenotype 1', 'Phenotype 2', 'Phenotype 3'], loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "69475a2e", + "metadata": {}, + "source": [ + "Intersecting survival rates indicate that the SUPPORT dataset follows non-proportional hazards which violates assumptions of the Cox Model." + ] + }, + { + "cell_type": "markdown", + "id": "4178b899", + "metadata": {}, + "source": [ + "\n", + "## 4. Supervised Phenotyping with Deep Cox Mixtures (DCM)" + ] + }, + { + "attachments": { + "5eea6305-e934-42d7-90c7-01eba7993e4e.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "d611f69e", + "metadata": {}, + "source": [ + "\n", + "\n", + "Deep Cox Mixtures (DCM) [2] generalizes the proportional hazards assumption via a mixture model, by assuming that there are latent groups and within each, the proportional hazards assumption holds. DCM allows the hazard ratio in each latent group, as well as the latent group membership, to be flexibly modeled by a deep neural network.\n", + "\n", + "![image.png](attachment:5eea6305-e934-42d7-90c7-01eba7993e4e.png)\n", + "\n", + "Figure A: DCM works by generating representation of the individual covariates, $x$, using an encoding neural network. The output representation, $xe$, then interacts with linear functions, $f$ and $g$, that determine the proportional hazards within each cluster $Z ∈ {1, 2, ...K}$ and the mixing weights $P(Z|X)$ respectively. For each cluster, baseline survival rates $Sk(t)$ are estimated non-parametrically. The final individual survival curve $S(t|x)$ is an average over the cluster specific individual survival curves weighted by the mixing probabilities $P(Z|X = x)$.\n", + "\n", + "\n", + "*For full details on Deep Cox Mixtures (DCM), please refer to the following paper*:\n", + "\n", + "[2] [Nagpal, C., Yadlowsky, S., Rostamzadeh, N., and Heller, K. (2021c). Deep cox mixtures for survival regression. In\n", + "Machine Learning for Healthcare Conference, pages 674–708. PMLR.](https://arxiv.org/abs/2101.06536)" + ] + }, + { + "cell_type": "markdown", + "id": "e0b928a9", + "metadata": {}, + "source": [ + "\n", + "### 4.1. Fitting the DCM Model" + ] + }, + { + "cell_type": "markdown", + "id": "a3a9c7a2", + "metadata": {}, + "source": [ + "Fit DCM model to training data. Perform hyperparameter tuning by selecting model parameters that minimize the brier score computed for the validation set.\n", + "\n", + "$\\textbf{Brier Score} \\ (\\textrm{BS})$: Defined as the Mean Squared Error (MSE) around the probabilistic prediction at a certain time horizon.\n", + "\\begin{align}\n", + "\\text{BS}(t) = \\mathop{\\mathbf{E}}_{x\\sim\\mathcal{D}}\\big[ ||\\mathbf{1}\\{ T > t \\} - \\widehat{\\mathbf{P}}(T>t|X)\\big)||_{_\\textbf{2}}^\\textbf{2} \\big]\n", + "\\end{align}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe4f1c0f", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.models.dcm import DeepCoxMixtures\n", + "from sklearn.model_selection import ParameterGrid\n", + "from sksurv.metrics import brier_score\n", + "\n", + "param_grid = {'k' : [3],\n", + " 'learning_rate' : [1e-3],\n", + " 'layers' : [[100]]\n", + " }\n", + "\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "for param in params:\n", + " model = DeepCoxMixtures(k = param['k'],\n", + " layers = param['layers'],\n", + " random_seed=0)\n", + " \n", + "# The fit method is called to train the model\n", + "model.fit(x_tr.values, y_tr.time.values, y_tr.event.values, iters = 100, learning_rate = param['learning_rate'])" + ] + }, + { + "cell_type": "markdown", + "id": "15eba080", + "metadata": {}, + "source": [ + "\n", + "### 4.2. Inferring latent Phenotypes\n", + "\n", + "The mixing probabilities $P(Z|X = x)$ estimate individual sample association to the latent phenotypes mediated by an encoder gating function \n", + "$g(.)$. $P(Z|X = x)$ is used to weight the cluster specific individual survival curves for computing the final individual survival curve $S(t|x)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6836ef3", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.models.dcm.dcm_utilities import predict_latent_z\n", + "\n", + "latent_z_prob = model.predict_latent_z(x_tr.values)\n", + "\n", + "# Let's look at the latent group probabilities\n", + "latent_z_prob" + ] + }, + { + "cell_type": "markdown", + "id": "d01440df", + "metadata": {}, + "source": [ + "\n", + "### 4.3. Plotting Survival Curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d5c0fa0", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival import reporting\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n", + "reporting.plot_kaplanmeier(outcomes.loc[x_tr.index], latent_z_prob.argmax(axis=1))\n", + "\n", + "plt.xlabel('Time (Days)')\n", + "plt.ylabel('Event-Free Survival Probability')\n", + "plt.legend(['Phenotype 1', 'Phenotype 2', 'Phenotype 3'], loc=\"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d9f1ea09", + "metadata": {}, + "source": [ + "Intersecting survival rates indicate that the SUPPORT dataset follows non-proportional hazards which violates assumptions of the Cox Model." + ] + }, + { + "cell_type": "markdown", + "id": "960e4de2", + "metadata": {}, + "source": [ + "\n", + "## 5. Counterfactual Phenotyping" + ] + }, + { + "cell_type": "markdown", + "id": "c631a6d2", + "metadata": {}, + "source": [ + "*For examples of counterfactual phenotyping with Deep Cox Mixtures with Heterogeneous Effects (CMHE) [1], please refer to the following paper and example jupyter notebook*:\n", + "\n", + "[1] [Counterfactual Phenotyping with Censored Time-to-Events, arXiv preprint, C. Nagpal, M. Goswami, K. Dufendach, A. Dubrawski](https://arxiv.org/abs/2202.11089)\n", + "\n", + "[Demo of CMHE on Synthetic Data.ipynb](https://github.com/autonlab/auton-survival/blob/master/examples/Demo%20of%20CMHE%20on%20Synthetic%20Data.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18cafacb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Survival Regression with Auton-Survival.ipynb b/examples/Survival Regression with Auton-Survival.ipynb new file mode 100644 index 0000000..aa318bb --- /dev/null +++ b/examples/Survival Regression with Auton-Survival.ipynb @@ -0,0 +1,784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2b6d4722", + "metadata": {}, + "source": [ + "# Survival Regression with `estimators.SurvivalModel`\n", + "
\n", + "\n", + "Author: ***Willa Potosnak*** <wpotosna@andrew.cmu.edu>\n", + "\n", + "
\n", + "\n", + "\n", + "
\n", + "\n", + "# Contents\n", + "\n", + "\n", + "### 1. [Introduction](#introduction) \n", + "####               1.1 [The SUPPORT Dataset](#support)\n", + "####               1.2 [Preprocessing the Data](#preprocess)\n", + "\n", + "### 2. [Cox Proportional Hazards (CPH)](#cph) \n", + "####               2.1 [Fit CPH Model](#fitcph)\n", + "####               2.2 [Evaluate CPH Model](#evalcph)\n", + "\n", + "### 3. [Deep Cox Proportional Hazards (DCPH)](#fsn) \n", + "####               3.1 [Fit DCPH Model](#fitfsn)\n", + "####               3.2 [Evaluate DCPH Model](#evalfsn)\n", + "\n", + "### 4. [Deep Survival Machines (DSM)](#dsm) \n", + "####               4.1 [Fit DSM Model](#fitdsm)\n", + "####               4.2 [Evaluate DSM Model](#evaldsm)\n", + "\n", + "### 5. [Deep Cox Mixtures (DCM)](#dcm)\n", + "####               5.1 [Fit DCM Model](#fitdcm)\n", + "####               5.2 [Evaluate DCM Model](#evaldcm)\n", + "\n", + "### 6. [Random Survival Forests (RSF)](#rsf)\n", + "####               6.1 [Fit RSF Model](#fitrsf)\n", + "####               6.2 [Evaluate RSF Model](#evalrsf)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3e230629", + "metadata": {}, + "source": [ + "\n", + "\n", + "## 1. Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "dbef236c", + "metadata": {}, + "source": [ + "The `SurvivalModels` class offers a steamlined approach to train two `auton-survival` models and three baseline survival models for right-censored time-to-event data. The fit method requires the same inputs across all five models, however, model parameter types vary and must be defined and tuned for the specified model.\n", + "\n", + "### Native `auton-survival` Models\n", + "\n", + "* **Faraggi-Simon Net (FSN)/DeepSurv**\n", + "* **Deep Survival Machines (DSM)** \n", + "* **Deep Cox Mixtures (DCM)**\n", + "\n", + "### External Models\n", + "\n", + "* **Random survival Forests (RSF)**\n", + "* **Cox Proportional Hazards (CPH)**\n", + "\n", + " \n", + "$\\textbf{Hyperparameter tuning}$ and $\\textbf{model evaluation}$ can be performed using the following metrics, among others.\n", + "\n", + "* $\\textbf{Brier Score (BS)}$: the Mean Squared Error (MSE) around the probabilistic prediction at a certain time horizon. The Brier Score can be decomposed into components that measure both discriminative performance and calibration.\n", + "\n", + "\\begin{align}\n", + "\\text{BS}(t) = \\mathop{\\mathbf{E}}_{x\\sim\\mathcal{D}}\\big[ ||\\mathbf{1}\\{ T > t \\} - \\widehat{\\mathbf{P}}(T>t|X)\\big)||_{_\\textbf{2}}^\\textbf{2} \\big]\n", + "\\end{align}\n", + "\n", + "* $\\textbf{Integrated Brier Score (IBS)}$: the integral of the time-dependent $\\textbf{BS}$ over the interval $[t_1; t_{max}]$ where the weighting function is $w(t)= \\frac{t}{t_{max}}$.\n", + "\n", + "\\begin{align}\n", + "\\text{IBS} = \\int_{t_1}^{t_{max}} \\mathrm{BS}^{c}(t)dw(t)\n", + "\\end{align}\n", + "\n", + "* $\\textbf{Area under ROC Curve (ROC-AUC)}$: survival model evaluation can be treated as binary classification to compute the **True Positive Rate (TPR)** and **False Positive Rate (FPR)** dependent on time, $t$. ROC-AUC is used to assess how well the model can distinguish samples that fail by a given time, $t$ from those that fail after this time.\n", + "\n", + "\\begin{align}\n", + "\\widehat{AUC}(t) = \\frac{\\sum_{i=1}^{n} \\sum_{j=1}^{n}I(y_j>t)I(y_i \\leq t)w_iI(\\hat{f}(x_j) \\leq \\hat{f}(x_i))}{(\\sum_{i=1}^{n} I(y_i > t))(\\sum_{i=1}^{n}I(y_i \\leq t)w_i)}\n", + "\\end{align}\n", + "\n", + "* $\\textbf{Time Dependent Concordance Index (C$^{td}$)}$: estimates ranking ability by exhaustively comparing relative risks across all pairs of individuals in the test set. We employ the ‘Time Dependent’ variant of Concordance Index that truncates the pairwise comparisons to the events occurring within a fixed time horizon. \n", + "\n", + "\\begin{align}\n", + "C^{td}(t) = P(\\hat{F}(t|x_i) > \\hat{F} (t|x_j)|\\delta_i = 1, T_i < T_j, T_i \\leq t)\n", + "\\end{align}\n" + ] + }, + { + "cell_type": "markdown", + "id": "504c17c1", + "metadata": {}, + "source": [ + "\n", + "\n", + "### 1.1. The SUPPORT Dataset" + ] + }, + { + "cell_type": "markdown", + "id": "98053699", + "metadata": {}, + "source": [ + "*For the original datasource, please refer to the following [website](https://biostat.app.vumc.org/wiki/Main/SupportDesc).*\n", + "\n", + "Data features $x$ are stored in a pandas dataframe with rows corresponding to individual samples and columns as covariates. Data outcome consists of 'time', $t$, and 'event', $e$, that correspond to the time to event and the censoring indicator, respectively. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cdcd385", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "from auton_survival.datasets import load_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfa73b92", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the SUPPORT dataset\n", + "outcomes, features = load_dataset(dataset='SUPPORT')\n", + "\n", + "# Identify categorical (cat_feats) and continuous (num_feats) features\n", + "cat_feats = ['sex', 'dzgroup', 'dzclass', 'income', 'race', 'ca']\n", + "num_feats = ['age', 'num.co', 'meanbp', 'wblc', 'hrt', 'resp', \n", + " 'temp', 'pafi', 'alb', 'bili', 'crea', 'sod', 'ph', \n", + " 'glucose', 'bun', 'urine', 'adlp', 'adls']\n", + "\n", + "# Let's take a look at the features\n", + "display(features.head(5))\n", + "\n", + "# Let's take a look at the outcomes\n", + "display(outcomes.head(5))" + ] + }, + { + "cell_type": "markdown", + "id": "fa12ceeb", + "metadata": {}, + "source": [ + "\n", + "### 1.2. Preprocess the Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a29c403b", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Split the SUPPORT data into training, validation, and test data\n", + "x_tr, x_te, y_tr, y_te = train_test_split(features, outcomes, test_size=0.2, random_state=1)\n", + "x_tr, x_val, y_tr, y_val = train_test_split(x_tr, y_tr, test_size=0.25, random_state=1) \n", + "\n", + "print(f'Number of training data points: {len(x_tr)}')\n", + "print(f'Number of validation data points: {len(x_val)}')\n", + "print(f'Number of test data points: {len(x_te)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1adc2af0", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.preprocessing import Preprocessor\n", + "\n", + "# Fit the imputer and scaler to the training data and transform the training, validation and test data\n", + "preprocessor = Preprocessor(cat_feat_strat='ignore', num_feat_strat= 'mean') \n", + "transformer = preprocessor.fit(features, cat_feats=cat_feats, num_feats=num_feats,\n", + " one_hot=True, fill_value=-1)\n", + "x_tr = transformer.transform(x_tr)\n", + "x_val = transformer.transform(x_val)\n", + "x_te = transformer.transform(x_te)" + ] + }, + { + "cell_type": "markdown", + "id": "9abc7fbe", + "metadata": {}, + "source": [ + "\n", + "## 2. Cox Proportional Hazards (CPH) " + ] + }, + { + "cell_type": "markdown", + "id": "f3a4af29", + "metadata": {}, + "source": [ + "CPH [2] model assumes that individuals across the population have constant proportional hazards overtime. In this model, the estimator of the survival function conditional on $X, S(·|X) , P(T > t|X)$, is assumed to have constant proportional hazard. Thus, the relative proportional hazard between individuals is constant across time.\n", + "\n", + "*For full details on CPH, please refer to the following paper*:\n", + "\n", + "[2] [Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological).](https://www.jstor.org/stable/2985181)" + ] + }, + { + "cell_type": "markdown", + "id": "e5e5e064", + "metadata": {}, + "source": [ + "\n", + "### 2.1. Fit CPH Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e17b408", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.estimators import SurvivalModel\n", + "from auton_survival.metrics import survival_regression_metric\n", + "from sklearn.model_selection import ParameterGrid\n", + "\n", + "# Define parameters for tuning the model\n", + "param_grid = {'l2' : [1e-3, 1e-4]}\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "# Define the times for tuning the model hyperparameters and for evaluating the model\n", + "times = np.quantile(y_tr['time'][y_tr['event']==1], np.linspace(0.1, 1, 10)).tolist()\n", + "\n", + "# Perform hyperparameter tuning \n", + "models = []\n", + "for param in params:\n", + " model = SurvivalModel('cph', random_seed=2, l2=param['l2'])\n", + " \n", + " # The fit method is called to train the model\n", + " model.fit(x_tr, y_tr)\n", + "\n", + " # Obtain survival probabilities for validation set and compute the Integrated Brier Score \n", + " predictions_val = model.predict_survival(x_val, times)\n", + " metric_val = survival_regression_metric('ibs', y_tr, y_val, predictions_val, times)\n", + " models.append([metric_val, model])\n", + " \n", + "# Select the best model based on the mean metric value computed for the validation set\n", + "metric_vals = [i[0] for i in models]\n", + "first_min_idx = metric_vals.index(min(metric_vals))\n", + "model = models[first_min_idx][1]" + ] + }, + { + "cell_type": "markdown", + "id": "487acea1", + "metadata": {}, + "source": [ + "\n", + "### 2.2. Evaluate CPH Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4dc0287", + "metadata": {}, + "outputs": [], + "source": [ + "from estimators_demo_utils import plot_performance_metrics\n", + "\n", + "# Obtain survival probabilities for test set\n", + "predictions_te = model.predict_survival(x_te, times)\n", + "\n", + "# Compute the Brier Score and time-dependent concordance index for the test set to assess model performance\n", + "results = dict()\n", + "results['Brier Score'] = survival_regression_metric('brs', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "results['Concordance Index'] = survival_regression_metric('ctd', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "plot_performance_metrics(results, times)" + ] + }, + { + "cell_type": "markdown", + "id": "5d421868", + "metadata": {}, + "source": [ + "\n", + "## 3. Deep Cox Proportional Hazards (DCPH)" + ] + }, + { + "cell_type": "markdown", + "id": "510b7693", + "metadata": {}, + "source": [ + "DCPH [2], [3] is an extension to the CPH model. DCPH involves modeling the proportional hazard ratios over the individuals with Deep Neural Networks allowing the ability to learn non linear hazard ratios.\n", + "\n", + "*For full details on DCPH models, Faraggi-Simon Net (FSN) and DeepSurv, please refer to the following papers*:\n", + "\n", + "[2] [Faraggi, David, and Richard Simon. \"A neural network model for survival data.\" Statistics in medicine 14.1 (1995): 73-82.](https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.4780140108)\n", + "\n", + "[3] [Katzman, Jared L., et al. \"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.\" BMC medical research methodology 18.1 (2018): 1-12.](https://arxiv.org/abs/1606.00931v3)" + ] + }, + { + "cell_type": "markdown", + "id": "d758f5e4", + "metadata": {}, + "source": [ + "\n", + "### 3.1. Fit DCPH Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3615982d", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.estimators import SurvivalModel\n", + "from auton_survival.metrics import survival_regression_metric\n", + "from sklearn.model_selection import ParameterGrid\n", + "\n", + "# Define parameters for tuning the model\n", + "param_grid = {'bs' : [100, 200],\n", + " 'learning_rate' : [ 1e-4, 1e-3],\n", + " 'layers' : [ [100], [100, 100] ]\n", + " }\n", + "\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "# Define the times for tuning the model hyperparameters and for evaluating the model\n", + "times = np.quantile(y_tr['time'][y_tr['event']==1], np.linspace(0.1, 1, 10)).tolist()\n", + "\n", + "# Perform hyperparameter tuning \n", + "models = []\n", + "for param in params:\n", + " model = SurvivalModel('dcph', random_seed=0, bs=param['bs'], learning_rate=param['learning_rate'], layers=param['layers'])\n", + " \n", + " # The fit method is called to train the model\n", + " model.fit(x_tr, y_tr)\n", + "\n", + " # Obtain survival probabilities for validation set and compute the Integrated Brier Score \n", + " predictions_val = model.predict_survival(x_val, times)\n", + " metric_val = survival_regression_metric('ibs', y_tr, y_val, predictions_val, times)\n", + " models.append([metric_val, model])\n", + " \n", + "# Select the best model based on the mean metric value computed for the validation set\n", + "metric_vals = [i[0] for i in models]\n", + "first_min_idx = metric_vals.index(min(metric_vals))\n", + "model = models[first_min_idx][1]" + ] + }, + { + "cell_type": "markdown", + "id": "1d04655e", + "metadata": {}, + "source": [ + "\n", + "### 3.2. Evaluate DCPH Model" + ] + }, + { + "cell_type": "markdown", + "id": "6e1e7afe", + "metadata": {}, + "source": [ + "Compute the Brier Score and time-dependent concordance index for the test set. See notebook introduction for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0484b26", + "metadata": {}, + "outputs": [], + "source": [ + "from estimators_demo_utils import plot_performance_metrics\n", + "\n", + "# Obtain survival probabilities for test set\n", + "predictions_te = model.predict_survival(x_te, times)\n", + "\n", + "# Compute the Brier Score and time-dependent concordance index for the test set to assess model performance\n", + "results = dict()\n", + "results['Brier Score'] = survival_regression_metric('brs', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "results['Concordance Index'] = survival_regression_metric('ctd', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "plot_performance_metrics(results, times)" + ] + }, + { + "attachments": { + "117f4303-396d-4535-a735-f59d72213396.PNG": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "5c8e05d8", + "metadata": {}, + "source": [ + "\n", + "## 4. Deep Survival Machines (DSM)\n", + "\n", + "DSM [5] is a fully parametric approach to modeling the event time distribution as a fixed size mixture over Weibull or Log-Normal distributions. The individual mixture distributions are parametrized with neural networks to learn complex non-linear representations of the data.\n", + "\n", + "![dsm_pipeline.PNG](attachment:117f4303-396d-4535-a735-f59d72213396.PNG)\n", + "\n", + "\n", + "Figure A: DSM works by modeling the conditional distribution $P(T |X = x)$ as a mixture over $k$ well-defined, parametric distributions. DSM generates representation of the individual covariates, $x$, using a deep multilayer perceptron followed by a softmax over mixture size, $k$. This representation then interacts with the additional set of parameters, to determine the mixture weights $w$ and the parameters of each of $k$ underlying survival distributions $\\{\\eta_k, \\beta_k\\}^K_{k=1}$. The final individual survival distribution for the event time, $T$, is a weighted average over these $K$ distributions.\n", + "\n", + "\n", + "*For full details on Deep Survival Machines (DSM), please refer to the following paper*:\n", + "\n", + "[5] [Chirag Nagpal, Xinyu Li, and Artur Dubrawski. Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks. 2020.](https://arxiv.org/abs/2003.01176)\n" + ] + }, + { + "cell_type": "markdown", + "id": "e12670aa", + "metadata": {}, + "source": [ + "\n", + "### 4.1. Fit DSM Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25db9823", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.estimators import SurvivalModel\n", + "from auton_survival.metrics import survival_regression_metric\n", + "from sklearn.model_selection import ParameterGrid\n", + "\n", + "# Define parameters for tuning the model\n", + "param_grid = {'layers' : [[100], [100, 100], [200]],\n", + " 'distribution' : ['Weibull', 'LogNormal'],\n", + " 'max_features' : ['sqrt', 'log2']\n", + " }\n", + "\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "# Define the times for tuning the model hyperparameters and for evaluating the model\n", + "times = np.quantile(y_tr['time'][y_tr['event']==1], np.linspace(0.1, 1, 10)).tolist()\n", + "\n", + "# Perform hyperparameter tuning \n", + "models = []\n", + "for param in params:\n", + " model = SurvivalModel('dsm', random_seed=0, layers=param['layers'], distribution=param['distribution'], max_features=param['max_features'])\n", + " \n", + " # The fit method is called to train the model\n", + " model.fit(x_tr, y_tr)\n", + "\n", + " # Obtain survival probabilities for validation set and compute the Integrated Brier Score \n", + " predictions_val = model.predict_survival(x_val, times)\n", + " metric_val = survival_regression_metric('ibs', y_tr, y_val, predictions_val, times)\n", + " models.append([metric_val, model])\n", + " \n", + "# Select the best model based on the mean metric value computed for the validation set\n", + "metric_vals = [i[0] for i in models]\n", + "first_min_idx = metric_vals.index(min(metric_vals))\n", + "model = models[first_min_idx][1]" + ] + }, + { + "cell_type": "markdown", + "id": "2d91e86f", + "metadata": {}, + "source": [ + "\n", + "### 4.2. Evaluate DSM Model" + ] + }, + { + "cell_type": "markdown", + "id": "7090258c", + "metadata": {}, + "source": [ + "Compute the Brier Score and time-dependent concordance index for the test set. See notebook introduction for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21b945e1", + "metadata": {}, + "outputs": [], + "source": [ + "from estimators_demo_utils import plot_performance_metrics\n", + "\n", + "# Obtain survival probabilities for test set\n", + "predictions_te = model.predict_survival(x_te, times)\n", + "\n", + "# Compute the Brier Score and time-dependent concordance index for the test set to assess model performance\n", + "results = dict()\n", + "results['Brier Score'] = survival_regression_metric('brs', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "results['Concordance Index'] = survival_regression_metric('ctd', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "plot_performance_metrics(results, times)" + ] + }, + { + "cell_type": "markdown", + "id": "c84feb42", + "metadata": {}, + "source": [ + "\n", + "## 5. Deep Cox Mixtures (DCM)" + ] + }, + { + "attachments": { + "5fb9b5ae-9d4a-442a-a396-c713d744e57b.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "id": "cf7bbe74", + "metadata": {}, + "source": [ + "DCM [2] generalizes the proportional hazards assumption via a mixture model, by assuming that there are latent groups and within each, the proportional hazards assumption holds. DCM allows the hazard ratio in each latent group, as well as the latent group membership, to be flexibly modeled by a deep neural network.\n", + "\n", + "![image.png](attachment:5fb9b5ae-9d4a-442a-a396-c713d744e57b.png)\n", + "\n", + "Figure B: DCM works by generating representation of the individual covariates, $x$, using an encoding neural network. The output representation, $xe$, then interacts with linear functions, $f$ and $g$, that determine the proportional hazards within each cluster $Z ∈ {1, 2, ...K}$ and the mixing weights $P(Z|X)$ respectively. For each cluster, baseline survival rates $Sk(t)$ are estimated non-parametrically. The final individual survival curve $S(t|x)$ is an average over the cluster specific individual survival curves weighted by the mixing probabilities $P(Z|X = x)$.\n", + "\n", + "\n", + "*For full details on Deep Cox Mixtures (DCM), please refer to the following paper*:\n", + "\n", + "[2] [Nagpal, C., Yadlowsky, S., Rostamzadeh, N., and Heller, K. (2021c). Deep cox mixtures for survival regression. In\n", + "Machine Learning for Healthcare Conference, pages 674–708. PMLR.](https://arxiv.org/abs/2101.06536)" + ] + }, + { + "cell_type": "markdown", + "id": "6c26d8ad", + "metadata": {}, + "source": [ + "\n", + "### 5.1. Fit DCM Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2886bc08", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.estimators import SurvivalModel\n", + "from auton_survival.metrics import survival_regression_metric\n", + "from sklearn.model_selection import ParameterGrid\n", + "\n", + "# Define parameters for tuning the model\n", + "param_grid = {'k' : [2, 3],\n", + " 'learning_rate' : [1e-3, 1e-4],\n", + " 'layers' : [[100], [100, 100]]\n", + " }\n", + "\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "# Define the times for tuning the model hyperparameters and for evaluating the model\n", + "times = np.quantile(y_tr['time'][y_tr['event']==1], np.linspace(0.1, 1, 10)).tolist()\n", + "\n", + "# Perform hyperparameter tuning \n", + "models = []\n", + "for param in params:\n", + " model = SurvivalModel('dcm', random_seed=7, k=param['k'], learning_rate=param['learning_rate'], layers=param['layers'])\n", + " \n", + " # The fit method is called to train the model\n", + " model.fit(x_tr, y_tr)\n", + "\n", + " # Obtain survival probabilities for validation set and compute the Integrated Brier Score \n", + " predictions_val = model.predict_survival(x_val, times)\n", + " metric_val = survival_regression_metric('ibs', y_tr, y_val, predictions_val, times)\n", + " models.append([metric_val, model])\n", + " \n", + "# Select the best model based on the mean metric value computed for the validation set\n", + "metric_vals = [i[0] for i in models]\n", + "first_min_idx = metric_vals.index(min(metric_vals))\n", + "model = models[first_min_idx][1]" + ] + }, + { + "cell_type": "markdown", + "id": "0941a352", + "metadata": {}, + "source": [ + "\n", + "### 5.2. Evaluate DCM Model" + ] + }, + { + "cell_type": "markdown", + "id": "ae8c5be0", + "metadata": {}, + "source": [ + "Compute the Brier Score and time-dependent concordance index for the test set. See notebook introduction for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbff7827", + "metadata": {}, + "outputs": [], + "source": [ + "from estimators_demo_utils import plot_performance_metrics\n", + "\n", + "# Obtain survival probabilities for test set\n", + "predictions_te = model.predict_survival(x_te, times)\n", + "\n", + "# Compute the Brier Score and time-dependent concordance index for the test set to assess model performance\n", + "results = dict()\n", + "results['Brier Score'] = survival_regression_metric('brs', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "results['Concordance Index'] = survival_regression_metric('ctd', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "plot_performance_metrics(results, times)" + ] + }, + { + "cell_type": "markdown", + "id": "3a067364", + "metadata": {}, + "source": [ + "\n", + "## 6. Random Survival Forests (RSF)\n", + "\n", + "RSF [4] is an extension of Random Forests to the survival settings where risk scores are computed by creating Nelson-Aalen estimators in the splits induced by the Random Forest.\n", + "\n", + "We observe that performance of the Random Survival Forest model, especially in terms of calibration is strongly influenced by the choice for the hyperparameters for the number of features considered at each split and the minimum number of data samples to continue growing a tree. We thus advise carefully tuning these hyper-parameters while benchmarking RSF.\n", + "\n", + "*For full details on Random Survival Forests (RSF), please refer to the following paper*:\n", + "\n", + "[4] [Hemant Ishwaran et al. Random survival forests. The annals of applied statistics, 2(3):841–860, 2008.](https://arxiv.org/abs/0811.1645)" + ] + }, + { + "cell_type": "markdown", + "id": "c423c9e1", + "metadata": {}, + "source": [ + "\n", + "### 6.1. Fit RSF Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86fdcf6c", + "metadata": {}, + "outputs": [], + "source": [ + "from auton_survival.estimators import SurvivalModel\n", + "from auton_survival.metrics import survival_regression_metric\n", + "from sklearn.model_selection import ParameterGrid\n", + "\n", + "# Define parameters for tuning the model\n", + "param_grid = {'n_estimators' : [100, 300],\n", + " 'max_depth' : [3, 5],\n", + " 'max_features' : ['sqrt', 'log2']\n", + " }\n", + "\n", + "params = ParameterGrid(param_grid)\n", + "\n", + "# Define the times for tuning the model hyperparameters and for evaluating the model\n", + "times = np.quantile(y_tr['time'][y_tr['event']==1], np.linspace(0.1, 1, 10)).tolist()\n", + "\n", + "# Perform hyperparameter tuning \n", + "models = []\n", + "for param in params:\n", + " model = SurvivalModel('rsf', random_seed=8, n_estimators=param['n_estimators'], max_depth=param['max_depth'], max_features=param['max_features'])\n", + " \n", + " # The fit method is called to train the model\n", + " model.fit(x_tr, y_tr)\n", + "\n", + " # Obtain survival probabilities for validation set and compute the Integrated Brier Score \n", + " predictions_val = model.predict_survival(x_val, times)\n", + " metric_val = survival_regression_metric('ibs', y_tr, y_val, predictions_val, times)\n", + " models.append([metric_val, model])\n", + " \n", + "# Select the best model based on the mean metric value computed for the validation set\n", + "metric_vals = [i[0] for i in models]\n", + "first_min_idx = metric_vals.index(min(metric_vals))\n", + "model = models[first_min_idx][1]" + ] + }, + { + "cell_type": "markdown", + "id": "e04d8197", + "metadata": {}, + "source": [ + "\n", + "### 6.2. Evaluate RSF Model" + ] + }, + { + "cell_type": "markdown", + "id": "894d3f64", + "metadata": {}, + "source": [ + "Compute the Brier Score and time-dependent concordance index for the test set. See notebook introduction for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b6eb74e", + "metadata": {}, + "outputs": [], + "source": [ + "from estimators_demo_utils import plot_performance_metrics\n", + "\n", + "# Obtain survival probabilities for test set\n", + "predictions_te = model.predict_survival(x_te, times)\n", + "\n", + "# Compute the Brier Score and time-dependent concordance index for the test set to assess model performance\n", + "results = dict()\n", + "results['Brier Score'] = survival_regression_metric('brs', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "results['Concordance Index'] = survival_regression_metric('ctd', outcomes_train=y_tr, outcomes_test=y_te, \n", + " predictions=predictions_te, times=times)\n", + "plot_performance_metrics(results, times)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dc394b6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/estimators_demo_utils.py b/examples/estimators_demo_utils.py new file mode 100644 index 0000000..8f6e431 --- /dev/null +++ b/examples/estimators_demo_utils.py @@ -0,0 +1,34 @@ +import matplotlib.pylab as plt +import matplotlib.gridspec as gridspec + +def plot_performance_metrics(results, times): + """Plot Brier Score, ROC-AUC, and time-dependent concordance index + for survival model evaluation. + + Parameters + ----------- + results : dict + Python dict with key as the evaulation metric + times : float or list + A float or list of the times at which to compute + the survival probability. + + Returns + ----------- + matplotlib subplots + + """ + + colors = ['blue', 'purple', 'orange', 'green'] + gs = gridspec.GridSpec(1, len(results), wspace=0.3) + + for fi, result in enumerate(results.keys()): + val = results[result] + x = [str(round(t, 1)) for t in times] + ax = plt.subplot(gs[0, fi]) # row 0, col 0 + ax.set_xlabel('Time') + ax.set_ylabel(result) + ax.set_ylim(0, 1) + ax.bar(x, val, color=colors[fi]) + plt.xticks(rotation=30) + plt.show() diff --git a/examples/matplotlibrc b/examples/matplotlibrc new file mode 100644 index 0000000..195c867 --- /dev/null +++ b/examples/matplotlibrc @@ -0,0 +1,42 @@ +### Font +font.family : Serif +font.size : 16.0 + +### Lines +lines.linewidth : 0.6 +lines.antialiased : True + +### Axes settings +axes.facecolor : fafafa +axes.edgecolor : black +axes.linewidth : 0.6 +axes.labelsize : 12.0 +axes.axisbelow : True + +### Ticks +xtick.major.size : 5 # major tick size in points +xtick.color : black # color of the tick labels +xtick.labelsize : 10.0 # fontsize of the tick labels +xtick.direction : out # direction: in or out +ytick.major.size : 5 # major tick size in points +ytick.color : black # color of the tick labels +ytick.labelsize : 10.0 # fontsize of the tick labels +ytick.direction : out # direction: in or out + +### Grid settings +axes.grid : True +grid.alpha : 0.4 +grid.linewidth : 0.5 + +### Legend +legend.fancybox : True +legend.fontsize : 10.0 +legend.facecolor : fdfdfd + +### Figure +figure.figsize : 10.0, 4.0 +figure.facecolor : white +figure.edgecolor : black + +### Bar plots +hatch.linewidth : 0.1 From 750939ea5145b097c4c43fa5de5bd4c5ead6aafe Mon Sep 17 00:00:00 2001 From: Chirag Nagpal Date: Thu, 31 Mar 2022 01:02:30 -0400 Subject: [PATCH 2/2] modified: estimators.py modified: preprocessing.py --- auton_survival/estimators.py | 19 +++++++++---------- auton_survival/preprocessing.py | 16 ++++++++++------ 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/auton_survival/estimators.py b/auton_survival/estimators.py index 0c83a7c..0a5a309 100644 --- a/auton_survival/estimators.py +++ b/auton_survival/estimators.py @@ -80,18 +80,18 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): Controls the rproduecibility of fitted estimators. hyperparams : Optional arguments Options include: - - 'k' : int, default=3 + - `k` : int, default=3 Size of the underlying Cox mixtures. - - 'layers' : list, default=[100] + - `layers` : list, default=[100] A list consisting of the number of neurons in each hidden layer. - - 'batch_size' : int, default=128 + - `batch_size` : int, default=128 Learning is performed on mini-batches of input data. This parameter specifies the size of each mini-batch. - - 'lr' : float, default=1e-3 + - `lr` : float, default=1e-3 Learning rate for the 'Adam' optimizer. - - 'epochs' : int, default=50 + - `epochs` : int, default=50 Number of complete passes through the training data. - -'smoothing_factor' : float, default=1e-4 + -`smoothing_factor` : float, default=1e-4 Returns ----------- @@ -113,7 +113,6 @@ def _fit_dcm(features, outcomes, random_seed, **hyperparams): gamma=gamma, smoothing_factor=smoothing_factor, random_seed=random_seed) - model.fit(features.values, outcomes.time.values, outcomes.event.values, iters=epochs, batch_size=batch_size, learning_rate=learning_rate) @@ -276,7 +275,7 @@ def _fit_dcph(features, outcomes, random_seed, **hyperparams): # val_batch_size=bs) # model.compute_baseline_hazards() - return model + # return model def __interpolate_missing_times(survival_predictions, times): """Interpolate survival probabilities at missing time points. @@ -681,7 +680,6 @@ def fit(self, features, outcomes, features = data_resampled[features.columns] outcomes = data_resampled[outcomes.columns] - #linting if self.model == 'cph': self._model = _fit_cph(features, outcomes, self.random_seed, **self.hyperparams) @@ -732,7 +730,8 @@ def predict_survival(self, features, times): return _predict_dcph(self._model, features, times) elif self.model == 'dcm': return _predict_dcm(self._model, features, times) - else : raise NotImplementedError() + else : + raise NotImplementedError() def predict_risk(self, features, times): diff --git a/auton_survival/preprocessing.py b/auton_survival/preprocessing.py index 20aaf66..cb14a36 100644 --- a/auton_survival/preprocessing.py +++ b/auton_survival/preprocessing.py @@ -310,9 +310,12 @@ def fit(self, data, cat_feats, num_feats, self._cat_feats = cat_feats self._num_feats = num_feats - self.imputer.fit(data, cat_feats=self._cat_feats, - num_feats=self._num_feats, fill_value=-1, - n_neighbors=5, **kwargs) + self.imputer.fit(data, + cat_feats=self._cat_feats, + num_feats=self._num_feats, + fill_value=fill_value, + n_neighbors=n_neighbors, + **kwargs) data_imputed = self.imputer.transform(data) @@ -329,9 +332,9 @@ def transform(self, data): if self.one_hot: data_transformed[self._cat_feats] = data_transformed[self._cat_feats].astype('category') - data_transformed = pd.get_dummies(data_transformed, dummy_na=False, + data_transformed = pd.get_dummies(data_transformed, + dummy_na=False, drop_first=True) - return data_transformed def fit_transform(self, data, cat_feats, num_feats, @@ -360,7 +363,8 @@ def fit_transform(self, data, cat_feats, num_feats, pandas.DataFrame: Imputed and scaled dataset. """ - imputer_output = self.imputer.fit_transform(data, cat_feats=cat_feats, + imputer_output = self.imputer.fit_transform(data, + cat_feats=cat_feats, num_feats=num_feats, fill_value=fill_value, n_neighbors=n_neighbors,