sksurv.metrics.as_cumulative_dynamic_auc_scorer#

class sksurv.metrics.as_cumulative_dynamic_auc_scorer(estimator, times, tied_tol=1e-08)[source]#

Wraps an estimator to use cumulative_dynamic_auc() as score function.

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

Parameters

estimator (object) – Instance of an estimator.
times (array-like, shape = (n_times,)) – The time points for which the area under the time-dependent ROC curve is computed. Values must be within the range of follow-up times of the test data survival_test.
tied_tol (float, optional, default: 1e-8) – The tolerance value for considering ties. If the absolute difference between risk scores is smaller or equal than tied_tol, risk scores are considered tied.

estimator_#

Estimator that was fit.

Type: estimator

See also

cumulative_dynamic_auc

__init__(estimator, times, tied_tol=1e-08)[source]#

Methods

`__init__`(estimator, times[, tied_tol])
`fit`(X, y, **fit_params)
`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_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

sksurv.metrics.as_concordance_index_ipcw_scorer

sksurv.metrics.as_integrated_brier_score_scorer