sksurv.linear_model.CoxnetSurvivalAnalysis#

class sksurv.linear_model.CoxnetSurvivalAnalysis(*, n_alphas=100, alphas=None, alpha_min_ratio='auto', l1_ratio=0.5, penalty_factor=None, normalize=False, copy_X=True, tol=1e-07, max_iter=100000, verbose=False, fit_baseline_model=False)[source]#

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

See the User Guide and 1 for further description.

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 or { "auto" }, optional, default: "auto") –

    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).

    If set to “auto”, the value will depend on the sample size relative to the number of features. If n_samples > n_features, the default value is 0.0001 If n_samples <= n_features, 0.01 is the default value.

  • 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.

  • fit_baseline_model (bool, optional, default: False) – Whether to estimate baseline survival function and baseline cumulative hazard function for each alpha. If enabled, predict_cumulative_hazard_function() and predict_survival_function() can be used to obtain predicted cumulative hazard function and survival function.

alphas_#

The actual sequence of alpha values used.

Type

ndarray, shape=(n_alphas,)

alpha_min_ratio_#

The inferred value of alpha_min_ratio.

Type

float

penalty_factor_#

The actual penalty factors used.

Type

ndarray, shape=(n_features,)

coef_#

Matrix of coefficients.

Type

ndarray, shape=(n_features, n_alphas)

offset_#

Bias term to account for non-centered features.

Type

ndarray, shape=(n_alphas,)

deviance_ratio_#

The fraction of (null) deviance explained.

Type

ndarray, shape=(n_alphas,)

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_,)

event_times_#

Unique time points where events occurred.

Type

array of shape = (n_event_times,)

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.

__init__(*, n_alphas=100, alphas=None, alpha_min_ratio='auto', l1_ratio=0.5, penalty_factor=None, normalize=False, copy_X=True, tol=1e-07, max_iter=100000, verbose=False, fit_baseline_model=False)[source]#

Methods

__init__(*[, n_alphas, alphas, ...])

fit(X, y)

Fit estimator.

get_params([deep])

Get parameters for this estimator.

predict(X[, alpha])

The linear predictor of the model.

predict_cumulative_hazard_function(X[, ...])

Predict cumulative hazard function.

predict_survival_function(X[, alpha, ...])

Predict survival function.

score(X, y)

Returns the concordance index of the prediction.

set_params(**params)

Set the parameters of this estimator.

Attributes

event_times_

fit(X, y)[source]#

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.

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, alpha=None)[source]#

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.

Returns

T – The predicted decision function

Return type

array, shape = (n_samples,)

predict_cumulative_hazard_function(X, alpha=None, return_array=False)[source]#

Predict cumulative hazard function.

Only available if fit() has been called with fit_baseline_model = True.

The cumulative hazard function for an individual with feature vector \(x_\alpha\) is defined as

\[H(t \mid x_\alpha) = \exp(x_\alpha^\top \beta) H_0(t) ,\]

where \(H_0(t)\) is the baseline hazard function, estimated by Breslow’s estimator.

Parameters

  • X (array-like, shape = (n_samples, n_features)) – Data matrix.

  • alpha (float, optional) – Constant that multiplies the penalty terms. The same alpha as used during training must be specified. If set to None, the last alpha in the solution path is used.

  • return_array (boolean, default: False) – If set, return an array with the cumulative hazard rate for each self.event_times_, otherwise an array of sksurv.functions.StepFunction.

Returns

cum_hazard – If return_array is set, an array with the cumulative hazard rate for each self.event_times_, otherwise an array of length n_samples of sksurv.functions.StepFunction instances will be returned.

Return type

ndarray

Examples

>>> import matplotlib.pyplot as plt
>>> from sksurv.datasets import load_breast_cancer
>>> from sksurv.preprocessing import OneHotEncoder
>>> from sksurv.linear_model import CoxnetSurvivalAnalysis

Load and prepare the data.

>>> X, y = load_breast_cancer()
>>> X = OneHotEncoder().fit_transform(X)

Fit the model.

>>> estimator = CoxnetSurvivalAnalysis(l1_ratio=0.99, fit_baseline_model=True)
>>> estimator.fit(X, y)

Estimate the cumulative hazard function for one sample and the five highest alpha.

>>> chf_funcs = {}
>>> for alpha in estimator.alphas_[:5]:
...     chf_funcs[alpha] = estimator.predict_cumulative_hazard_function(
...         X.iloc[:1], alpha=alpha)
...

Plot the estimated cumulative hazard functions.

>>> for alpha, chf_alpha in chf_funcs.items():
...     for fn in chf_alpha:
...         plt.step(fn.x, fn(fn.x), where="post",
...                  label="alpha = {:.3f}".format(alpha))
...
>>> plt.ylim(0, 1)
>>> plt.legend()
>>> plt.show()
predict_survival_function(X, alpha=None, return_array=False)[source]#

Predict survival function.

Only available if fit() has been called with fit_baseline_model = True.

The survival function for an individual with feature vector \(x_\alpha\) is defined as

\[S(t \mid x_\alpha) = S_0(t)^{\exp(x_\alpha^\top \beta)} ,\]

where \(S_0(t)\) is the baseline survival function, estimated by Breslow’s estimator.

Parameters

  • X (array-like, shape = (n_samples, n_features)) – Data matrix.

  • alpha (float, optional) – Constant that multiplies the penalty terms. The same alpha as used during training must be specified. If set to None, the last alpha in the solution path is used.

  • return_array (boolean, default: False) – If set, return an array with the probability of survival for each self.event_times_, otherwise an array of sksurv.functions.StepFunction.

Returns

survival – If return_array is set, an array with the probability of survival for each self.event_times_, otherwise an array of length n_samples of sksurv.functions.StepFunction instances will be returned.

Return type

ndarray

Examples

>>> import matplotlib.pyplot as plt
>>> from sksurv.datasets import load_breast_cancer
>>> from sksurv.preprocessing import OneHotEncoder
>>> from sksurv.linear_model import CoxnetSurvivalAnalysis

Load and prepare the data.

>>> X, y = load_breast_cancer()
>>> X = OneHotEncoder().fit_transform(X)

Fit the model.

>>> estimator = CoxnetSurvivalAnalysis(l1_ratio=0.99, fit_baseline_model=True)
>>> estimator.fit(X, y)

Estimate the survival function for one sample and the five highest alpha.

>>> surv_funcs = {}
>>> for alpha in estimator.alphas_[:5]:
...     surv_funcs[alpha] = estimator.predict_survival_function(
...         X.iloc[:1], alpha=alpha)
...

Plot the estimated survival functions.

>>> for alpha, surv_alpha in surv_funcs.items():
...     for fn in surv_alpha:
...         plt.step(fn.x, fn(fn.x), where="post",
...                  label="alpha = {:.3f}".format(alpha))
...
>>> plt.ylim(0, 1)
>>> plt.legend()
>>> plt.show()
score(X, y)[source]#

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

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

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