survival.svm.FastKernelSurvivalSVM

class survival.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

[R15]

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

Attributes

coef_:	Coefficients of the features in the decision function.
fit_X_:	Training data.
optimizer_result_:	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.
get_params([deep])	Get parameters for this estimator.
predict(X)
score(X, y)
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.

Returns:

self

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self