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.

See also

__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