# 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]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(estimator[, tau, tied_tol]) Initialize self. fit(X, y, **fit_params) score(X, y) Returns the score on the given data.

Attributes

 predict mock imports predict_cumulative_hazard_function mock imports predict_survival_function mock imports
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