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.

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.

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
Attributes:
coef_ : ndarray, shape = (n_samples,)

Coefficients of the features in the decision function.

fit_X_ : ndarray

Training data.

optimizer_result_ : scipy.optimize.optimize.OptimizeResult

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

__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.
predict(X) Rank samples according to survival times
score(X, y)
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:
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 : ndarray, shape = (n_samples,)

Predicted ranks.