sksurv.linear_model.IPCRidge#

class sksurv.linear_model.IPCRidge(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None)[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. alpha must be a non-negative float i.e. in [0, inf).

For numerical reasons, using alpha = 0 is not advised.
fit_intercept (bool, default: True) – Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. X and y are expected to be centered).
copy_X (bool, default: True) – If True, X will be copied; else, it may be overwritten.
max_iter (int, default: None) – Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000. For ‘lbfgs’ solver, the default value is 15000.
tol (float, default: 1e-3) – Precision of the solution. Note that tol has no effect for solvers ‘svd’ and ‘cholesky’.
solver ({'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', ) –
‘sag’, ‘saga’, ‘lbfgs’}, default: ‘auto’ Solver to use in the computational routines:
- ’auto’ chooses the solver automatically based on the type of data.
- ’svd’ uses a Singular Value Decomposition of X to compute the Ridge coefficients. It is the most stable solver, in particular more stable for singular matrices than ‘cholesky’ at the cost of being slower.
- ’cholesky’ uses the standard scipy.linalg.solve function to obtain a closed-form solution.
- ’sparse_cg’ uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than ‘cholesky’ for large-scale data (possibility to set tol and max_iter).
- ’lsqr’ uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure.
- ’sag’ uses a Stochastic Average Gradient descent, and ‘saga’ uses its improved, unbiased version named SAGA. Both methods also use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that ‘sag’ and ‘saga’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing.
- ’lbfgs’ uses L-BFGS-B algorithm implemented in scipy.optimize.minimize. It can be used only when positive is True.
All solvers except ‘svd’ support both dense and sparse data. However, only ‘lsqr’, ‘sag’, ‘sparse_cg’, and ‘lbfgs’ support sparse input when fit_intercept is True.
positive (bool, default: False) – When set to True, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case.
random_state (int, RandomState instance, default: None) – Used when solver == ‘sag’ or ‘saga’ to shuffle the data.

coef_#

Weight vector.

Type:: ndarray, shape = (n_features,)

intercept_#

Independent term in decision function. Set to 0.0 if fit_intercept = False.

Type:: float or ndarray, shape = (n_targets,)

n_iter_#

Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.

Type:: None or ndarray, shape = (n_targets,)

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, shape = (n_features_in_,)

References

__init__(alpha=1.0, *, fit_intercept=True, copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None)[source]#

Methods

`__init__`([alpha, fit_intercept, copy_X, ...])
`fit`(X, y)	Build an accelerated failure time model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the linear accelerated failure time model.
`score`(X, y[, sample_weight])	Returns the concordance index of the prediction.
`set_fit_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

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 with two fields. The first field is a boolean where True indicates an event and False indicates right-censoring. The second field is a float with the time of event or time of censoring.

Return type:

self

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

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:: y_pred – Returns predicted values on original scale (NOT log scale).
Return type:: array, shape = (n_samples,)

score(X, y, sample_weight=None)[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