sksurv.svm.FastKernelSurvivalSVM#

class sksurv.svm.FastKernelSurvivalSVM(alpha=1, *, rank_ratio=1.0, fit_intercept=False, kernel='rbf', gamma=None, degree=3, coef0=1, kernel_params=None, max_iter=20, verbose=False, tol=None, optimizer=None, random_state=None, timeit=False)[source]#

Efficient Training of kernel Survival Support Vector Machine.

See the User Guide and [1] for further description.

Parameters:
  • alpha (float, positive, default: 1) – Weight of penalizing the squared hinge loss in the objective function

  • rank_ratio (float, optional, default: 1.0) – Mixing parameter between regression and ranking objective with 0 <= rank_ratio <= 1. If rank_ratio = 1, only ranking is performed, if rank_ratio = 0, only regression is performed. A non-zero value is only allowed if optimizer is one of ‘avltree’, ‘PRSVM’, or ‘rbtree’.

  • fit_intercept (boolean, optional, default: False) – Whether to calculate an intercept for the regression model. If set to False, no intercept will be calculated. Has no effect if rank_ratio = 1, i.e., only ranking is performed.

  • kernel (str or callable, default: 'linear'.) – Kernel mapping used internally. This parameter is directly passed to sklearn.metrics.pairwise.pairwise_kernels(). If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS or “precomputed”. If kernel is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if kernel is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from sklearn.metrics.pairwise are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.

  • gamma (float, optional, default: None) – Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.

  • degree (int, default: 3) – Degree of the polynomial kernel. Ignored by other kernels.

  • coef0 (float, optional) – Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

  • kernel_params (mapping of string to any, optional) – Additional parameters (keyword arguments) for kernel function passed as callable object.

  • max_iter (int, optional, default: 20) – Maximum number of iterations to perform in Newton optimization

  • verbose (bool, optional, default: False) – Whether to print messages during optimization

  • tol (float or None, optional, default: None) – Tolerance for termination. For detailed control, use solver-specific options.

  • optimizer ({'avltree', 'rbtree'}, optional, default: 'rbtree') – Which optimizer to use.

  • random_state (int or numpy.random.RandomState instance, optional) – Random number generator (used to resolve ties in survival times).

  • timeit (False, int or None, default: None) – If non-zero value is provided the time it takes for optimization is measured. The given number of repetitions are performed. Results can be accessed from the optimizer_result_ attribute.

coef_#

Weights assigned to the samples in training data to represent the decision function in kernel space.

Type:

ndarray, shape = (n_samples,)

fit_X_#

Training data.

Type:

ndarray

optimizer_result_#

Stats returned by the optimizer. See scipy.optimize.OptimizeResult.

Type:

scipy.optimize.OptimizeResult

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_,)

n_iter_#

Number of iterations run by the optimization routine to fit the model.

Type:

int

See also

FastSurvivalSVM

Fast implementation for linear kernel.

References

__init__(alpha=1, *, rank_ratio=1.0, fit_intercept=False, kernel='rbf', gamma=None, degree=3, coef0=1, kernel_params=None, max_iter=20, verbose=False, tol=None, optimizer=None, random_state=None, timeit=False)[source]#

Methods

__init__([alpha, rank_ratio, fit_intercept, ...])

fit(X, y)

Build a survival support vector machine model from training data.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict(X)

Rank samples according to survival times

score(X, y)

Returns the concordance index of the prediction.

set_params(**params)

Set the parameters of this estimator.

Attributes

fit(X, y)[source]#

Build a survival support vector machine model from training data.

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_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]#

Rank samples according to survival times

Lower ranks indicate shorter survival, higher ranks longer survival.

Parameters:

X (array-like, shape = (n_samples, n_features)) – The input samples.

Returns:

y – Predicted ranks.

Return type:

ndarray, shape = (n_samples,)

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