Efficient Training of kernel Survival Support Vector Machine.
See the User Guide and 1 for further description.
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’,
0 <= rank_ratio <= 1
rank_ratio = 1
rank_ratio = 0
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.
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
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
Weights assigned to the samples in training data to represent
the decision function in kernel space.
ndarray, shape = (n_samples,)
Stats returned by the optimizer. See scipy.optimize.optimize.OptimizeResult.
Fast implementation for linear kernel.
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
Initialize self. See help(type(self)) for accurate signature.
__init__([alpha, rank_ratio, fit_intercept, …])
Build a survival support vector machine model from training data.
Rank samples according to survival times
Returns the concordance index of the prediction.
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
Lower ranks indicate shorter survival, higher ranks longer survival.
X (array-like, shape = (n_samples, n_features)) – The input samples.
y – Predicted ranks.
X (array-like, shape = (n_samples, n_features)) – Test samples.
cindex – Estimated concordance index.