survivalpredict.estimators.CoxPHElasticNet

class survivalpredict.estimators.CoxPHElasticNet(*, alpha=0.0, l1_ratio=0.5, max_iter=100, tol=1e-09)

Cox Proportional Hazards with Elastic Net penalty and feature shrinkage.

A Cox Proportional Hazards model with Elastic Net penalty estimated via coordinate descent. The coordinate descent algorithm for Elastic Net/Lasso allows shrinking features asynchronously as the ‘alpha’ parameter increases, and the ‘l1_ratio’ is greater than 0. The raphson-newton-like for coordinate descent described in Simon et al. (2011)[1] is used.

Only ‘breslow’ ties are available; the literature is currently unclear on how to add stratification to Simon’s algorithm.

Parameters:

  • alpha (float, default=0.0) – Constant that multiplies the penalty terms. Used to penalize coefficients durring training.

  • l1_ratio (float, default=0.5) – The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

  • max_iter (int, default=100) – The maximum number of iterations.

  • tol (float, default=1e-9) – The tolerance for the optimization: if the updates are smaller or equal to tol, the optimization code checks the dual gap for optimality and continues until it is smaller or equal to tol.

References

[1] Simon N, Friedman J, Hastie T, Tibshirani R. Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent. J Stat Softw. 2011 Mar;39(5):1-13. doi: 10.18637/jss.v039.i05. PMID: 27065756; PMCID: PMC4824408.

Methods

fit(X, times, events[, check_input, times_start])

Fit model.

fit_predict(*args, **kwargs)

Fit model and Build survival curves.

predict(X[, max_time])

Build survival curves on an array of vectors X.

predict_risk(X)

Build relative risk on an array of vectors X.
__init__(*, alpha=0.0, l1_ratio=0.5, max_iter=100, tol=1e-09)
Parameters:

  • alpha (float)

  • l1_ratio (float)

  • max_iter (int | None)

  • tol (float)

Methods

__init__(*[, alpha, l1_ratio, max_iter, tol])

fit(X, times, events[, check_input, times_start])

Fit model.

fit_predict(*args, **kwargs)

Fit model and Build survival curves.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X[, max_time])

Build survival curves on an array of vectors X.

predict_risk(X)

Build relative risk on an array of vectors X.

set_fit_request(*[, check_input, events, ...])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_predict_request(*[, max_time])

Configure whether metadata should be requested to be passed to the predict method.
fit(X, times, events, check_input=True, times_start=None)

Fit model.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training data.

  • times (array-like of shape (n_samples), dtype=np.int64) – Point in time last observed.

  • events (array-like of shape (n_samples), dtype=np.bool_) – Experianed event.

  • check_input (bool, default=True) – If True, validates and casts inputs.

  • times_start (array-like of shape (n_samples, dtype=np.int64), default=None) – Starting point for observation. If not passed in, all times_start times are assumed to be 0.

Returns:

Fitted Estimator.

Return type:

object

fit_predict(*args, **kwargs)

Fit model and Build survival curves.

predict(X, max_time=None)

Build survival curves on an array of vectors X.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Predicting data.

  • max_time (int, default=None) – Maximum time of built survival curves. If none, maximum time is max time seen on training data.

Returns:

The estimated survival curves, the left-most column is the probability of survival at time 1, and the right-most column ends at max_time.

Return type:

ndarray of shape (n_samples, max_time), dtype=np.float64

predict_risk(X)

Build relative risk on an array of vectors X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Predicting data.

Returns:

The Relative risk of X, used under the hood for building survival curves. Relative risk is what ‘Concordance Index’ examines.

Return type:

ndarray of shape (n_samples), dtype=np.float64