sksurv.metrics.as_integrated_brier_score_scorer#

class sksurv.metrics.as_integrated_brier_score_scorer(estimator, times)[source]#

Wraps an estimator to use the negative of integrated_brier_score() as score function.

The estimator needs to be able to estimate survival functions via a predict_survival_function method.

See the User Guide for using it for hyper-parameter optimization.

Parameters:
  • estimator (object) – Instance of an estimator that provides predict_survival_function.

  • times (array-like, shape = (n_times,)) – The time points for which to estimate the Brier score. Values must be within the range of follow-up times of the test data survival_test.

estimator_#

Estimator that was fit.

Type:

estimator

__init__(estimator, times)[source]#

Methods

__init__(estimator, times)

fit(X, y, **fit_params)

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Call predict on the estimator.

predict_cumulative_hazard_function(X)

Call predict_cumulative_hazard_function on the estimator.

predict_survival_function(X)

Call predict_survival_function on the estimator.

score(X, y)

Returns the score on the given data.

set_params(**params)

Set the parameters of this estimator.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

predict(X)#

Call predict on the estimator.

Only available if estimator supports predict.

Parameters:

X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.

predict_cumulative_hazard_function(X)#

Call predict_cumulative_hazard_function on the estimator.

Only available if estimator supports predict_cumulative_hazard_function.

Parameters:

X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.

predict_survival_function(X)#

Call predict_survival_function on the estimator.

Only available if estimator supports predict_survival_function.

Parameters:

X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.

score(X, y)[source]#

Returns the score on the given data.

Parameters:
  • X (array-like of shape (n_samples, n_features)) – Input data, where n_samples is the number of samples and n_features is the number of features.

  • y (array-like of shape (n_samples,)) – Target relative to X for classification or regression; None for unsupervised learning.

Returns:

score

Return type:

float

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance