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)[source]¶ 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
. Ifrank_ratio = 1
, only ranking is performed, ifrank_ratio = 0
, only regression is performed. A nonzero 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 ifrank_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 solverspecific 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 nonzero 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
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)[source]¶ 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)[source]¶ Build a survival support vector machine model from training data.
 Parameters
X (arraylike, 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)[source]¶ Rank samples according to survival times
Lower ranks indicate shorter survival, higher ranks longer survival.
 Parameters
X (arraylike, shape = (n_samples, n_features)) – The input samples.
 Returns
y – Predicted ranks.
 Return type
ndarray, shape = (n_samples,)

score
(X, y)[source]¶ Returns the concordance index of the prediction.
 Parameters
X (arraylike, 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