# sksurv.metrics.as_concordance_index_ipcw_scorer#

class sksurv.metrics.as_concordance_index_ipcw_scorer(estimator, tau=None, tied_tol=1e-08)[source]#

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

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

Parameters
• estimator (object) – Instance of an estimator.

• tau (float, optional) – Truncation time. The survival function for the underlying censoring time distribution $$D$$ needs to be positive at tau, i.e., tau should be chosen such that the probability of being censored after time tau is non-zero: $$P(D > \tau) > 0$$. If None, no truncation is performed.

• 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

__init__(estimator, tau=None, tied_tol=1e-08)[source]#

Methods

 __init__(estimator[, tau, tied_tol]) fit(X, y, **fit_params) get_params([deep]) Get parameters for this estimator. 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_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