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 [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_

Coefficients of the features in the decision function.

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

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)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([alpha, rank_ratio, fit_intercept, …]) Initialize self.
fit(X, y) Build a survival support vector machine model from training data.
predict(X) Rank samples according to survival times
score(X, y) Returns the concordance index of the prediction.
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.
Returns:

Return type:

self

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