sksurv.linear_model.IPCRidge#

class sksurv.linear_model.IPCRidge(alpha=1.0, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto')[source]#

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,)

n_features_in_#

Number of features seen during fit.

Type: int

feature_names_in_#

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type: ndarray of shape (n_features_in_,)

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='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto')[source]#

Methods

`__init__`([alpha, fit_intercept, normalize, ...])
`fit`(X, y)	Build an accelerated failure time model.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the linear accelerated failure time model.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y)[source]#

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.

Return type

self

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)[source]#

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)[source]#

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – \(R^2\) of self.predict(X) wrt. y.

Return type

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

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.linear_model.CoxPHSurvivalAnalysis

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