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)

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 ("linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed") – Kernel. Default: “linear”

• degree (int, default: 3) – Degree for poly kernels. Ignored by other kernels.

• gamma (float, optional) – Kernel coefficient for rbf and poly kernels. Default: 1/n_features. Ignored by other kernels.

• coef0 (float, optional) – Independent term in poly and sigmoid kernels. Ignored by other kernels.

• kernel_params (mapping of string to any, optional) – Parameters (keyword arguments) and values for kernel passed as call

• 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, optional) – 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 or int) – 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.optimize.OptimizeResult.

Type

scipy.optimize.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_,)

See also

FastSurvivalSVM

Fast implementation for linear kernel.

References

1

Pölsterl, S., Navab, N., and Katouzian, A., An Efficient Training Algorithm for Kernel Survival Support Vector Machines 4th Workshop on Machine Learning in Life Sciences, 23 September 2016, Riva del Garda, Italy. arXiv:1611.07054

__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)

Methods

__init__([alpha, rank_ratio, fit_intercept, ...])
fit(X, y)	Build a survival support vector machine model from training data.
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.

fit(X, y)

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_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)

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)

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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