survivalpredict.validation.sur_cross_validate

survivalpredict.validation.sur_cross_validate(estimator, X, times, events, *, strata=None, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, params=None, pre_dispatch='2*n_jobs', return_train_score=False, return_estimator=False, brier_score_max_time=None, return_parameters=None, return_n_test_samples=None, method=None, error_score=nan, times_start=None)

Evaluate survival metrics by cross-validation and also record fit/score times.

Parameters:

  • estimator (survivalpredict estimator) – Instance of a survivalpredict compatible estimator.

  • X (array-like of shape (n_samples, n_features)) – Training data.

  • times (array-like of shape (n_samples), dtype=np.int64) – Point in time last observed.

  • events (array-like of shape (n_samples), dtype=np.bool_) – Experianed event.

  • strata (array-like of shape (n_samples,), dtype=np.int64, default=None) – If passed in, associated strata for per observation.

  • groups (array-like of shape (n_samples,), default=None) – Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).

  • scoring ({"integrated_brier_score_administrative", "integrated_brier_score_ipcw"} , default="integrated_brier_score_administrative") – survival metrics

  • cv (int, cross-validation generator or an iterable, default=None) –

    Determines the cross-validation splitting strategy. Possible inputs for cv are:

    • None, to use the default 5-fold cross validation,

    • int, to specify the number of folds in a (Stratified)KFold,

    • CV splitter,

    • An iterable yielding (train, test) splits as arrays of indices.

    For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

  • n_jobs (int, default=None) – Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the cross-validation splits. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

  • verbose (int, default=0) – The verbosity level.

  • params (dict, default=None) – Parameters to pass to the underlying estimator’s fit, the scorer, and the CV splitter.

  • pre_dispatch (int or str, default='2*n_jobs') –

    Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:

    • An int, giving the exact number of total jobs that are spawned

    • A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’

  • return_train_score (bool, default=False) – Whether to include train scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance.

  • return_estimator (bool, default=False) – Whether to return the estimators fitted on each split.

  • brier_score_max_time (int, default=None) – Maximum time to evaluate survival curves. If None, will evaluate all times seen.

  • return_parameters (bool, default=False) – Return parameters that has been used for the estimator.

  • return_n_test_samples (bool, default=False) – Whether to return the n_test_samples.

  • method (str, default='predict') – Method used to predict for estimator.

  • error_score ('raise' or numeric, default=np.nan) – Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised.

  • times_start (array-like of shape (n_samples, dtype=np.int64), default=None) – Starting point for observation. If not passed in, all times_start times are assumed to be 0.

Returns:

Array of scores of the estimator for each run of the cross validation.

Return type:

dict of float arrays of shape (n_splits,)