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
. 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_
¶ 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 (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)¶ 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)¶ 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