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()
asscore
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
See also
Methods
__init__
(estimator, times)fit
(X, y, **fit_params)Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Call predict on the estimator.
Call predict_cumulative_hazard_function on the estimator.
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