sksurv.linear_model.IPCRidge

class sksurv.linear_model.IPCRidge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')

Accelerated failure time model with inverse probability of censoring weights.

This model assumes a regression model of the form

\[\log y = \beta_0 + \mathbf{X} \beta + \epsilon\]

L2-shrinkage is applied to the coefficients \(\beta\) and each sample is weighted by the inverse probability of censoring to account for right censoring (under the assumption that censoring is independent of the features, i.e., random censoring).

See [1] for further description.

Parameters:alpha (float, optional, default: 1.0) – Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates.
coef_

Weight vector.

Type:ndarray, shape = (n_features,)

References

[1]W. Stute, “Consistent estimation under random censorship when covariables are present”, Journal of Multivariate Analysis, vol. 45, no. 1, pp. 89-103, 1993. doi:10.1006/jmva.1993.1028.
__init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver='auto')

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

Methods

__init__([alpha, fit_intercept, normalize, …]) Initialize self.
fit(X, y) Build an accelerated failure time model.
predict(X) Predict using the linear accelerated failure time model.
score(X, y[, sample_weight]) Returns the concordance index of the prediction.
fit(X, y)

Build an accelerated failure time model.

Parameters:
  • X (array-like, shape = (n_samples, n_features)) – Data matrix.
  • y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.
Returns:

Return type:

self

predict(X)

Predict using the linear accelerated failure time model.

Parameters:X ({array-like, sparse matrix}, shape = (n_samples, n_features)) – Samples.
Returns:C – Returns predicted values on original scale (NOT log scale).
Return type:array, shape = (n_samples,)
score(X, y, sample_weight=None)

Returns the concordance index of the prediction.

Parameters:
  • X (array-like, shape = (n_samples, n_features)) – Test samples.
  • y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.
Returns:

cindex – Estimated concordance index.

Return type:

float