# sksurv.linear_model.CoxnetSurvivalAnalysis¶

class sksurv.linear_model.CoxnetSurvivalAnalysis(n_alphas=100, alphas=None, alpha_min_ratio=0.0001, l1_ratio=0.5, penalty_factor=None, normalize=False, copy_X=True, tol=1e-07, max_iter=100000, verbose=False)

Cox’s proportional hazard’s model with elastic net penalty.

Parameters: n_alphas : int, optional, default: 100 Number of alphas along the regularization path. alphas : array-like or None, optional List of alphas where to compute the models. If None alphas are set automatically. alpha_min_ratio : float, optional, default 0.0001 Determines minimum alpha of the regularization path if alphas is None. The smallest value for alpha is computed as the fraction of the data derived maximum alpha (i.e. the smallest value for which all coefficients are zero). l1_ratio : float, optional, 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. penalty_factor : array-like or None, optional Separate penalty factors can be applied to each coefficient. This is a number that multiplies alpha to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables. Note: the penalty factors are internally rescaled to sum to n_features, and the alphas sequence will reflect this change. normalize : boolean, optional, default: False If True, the features X will be normalized before optimization by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False. copy_X : boolean, optional, default: True If True, X will be copied; else, it may be overwritten. tol : float, optional, default: 1e-7 The tolerance for the optimization: optimization continues until all updates are smaller than tol. max_iter : int, optional, default: 100000 The maximum number of iterations. verbose : bool, optional, default: False Whether to print additional information during optimization.

References

 [1] Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. Journal of statistical software. 2011 Mar;39(5):1.
Attributes: alphas_ : ndarray, shape=(n_alphas,) The actual sequence of alpha values used. penalty_factor_ : ndarray, shape=(n_features,) The actual penalty factors used. coef_ : ndarray, shape=(n_features, n_alphas) Matrix of coefficients. deviance_ratio_ : ndarray, shape=(n_alphas,) The fraction of (null) deviance explained.
__init__(n_alphas=100, alphas=None, alpha_min_ratio=0.0001, l1_ratio=0.5, penalty_factor=None, normalize=False, copy_X=True, tol=1e-07, max_iter=100000, verbose=False)

Methods

 __init__([n_alphas, alphas, …]) fit(X, y) Fit estimator. predict(X[, alpha]) The linear predictor of the model. score(X, y)
fit(X, y)

Fit estimator.

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. self
predict(X, alpha=None)

The linear predictor of the model.

Parameters: X : array-like, shape = (n_samples, n_features) Test data of which to calculate log-likelihood from alpha : float, optional Constant that multiplies the penalty terms. If the same alpha was used during training, exact coefficients are used, otherwise coefficients are interpolated from the closest alpha values that were used during training. If set to None, the last alpha in the solution path is used. T : array, shape = (n_samples,) The predicted decision function