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 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 ifrank_ratio = 1
, i.e., only ranking is performed.kernel (str or callable, default: 'linear'.) – Kernel mapping used internally. This parameter is directly passed to
sklearn.metrics.pairwise.pairwise_kernels()
. If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS or “precomputed”. If kernel is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if kernel is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables fromsklearn.metrics.pairwise
are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.gamma (float, optional, default: None) – Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for
sklearn.metrics.pairwise
. Ignored by other kernels.degree (int, default: 3) – Degree of the polynomial kernel. Ignored by other kernels.
coef0 (float, optional) – Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
kernel_params (mapping of string to any, optional) – Additional parameters (keyword arguments) for kernel function passed as callable object.
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 or None, optional, default: None) – 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, int or None, default: None) – 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.
- 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.OptimizeResult
.
- n_features_in_#
Number of features seen during
fit
.- Type:
int
- feature_names_in_#
Names of features seen during
fit
. Defined only when X has feature names that are all strings.- Type:
ndarray of shape (n_features_in_,)
- n_iter_#
Number of iterations run by the optimization routine to fit the model.
- Type:
int
See also
FastSurvivalSVM
Fast implementation for linear kernel.
References
- __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]#
Methods
__init__
([alpha, rank_ratio, fit_intercept, ...])fit
(X, y)Build a survival support vector machine model from training data.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Rank samples according to survival times
score
(X, y)Returns the concordance index of the prediction.
set_params
(**params)Set the parameters of this estimator.
Attributes
- fit(X, y)[source]#
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.
- Return type:
self
- get_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters:
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
params – Parameter names mapped to their values.
- Return type:
dict
- predict(X)[source]#
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 – Predicted ranks.
- Return type:
ndarray, shape = (n_samples,)
- score(X, y)[source]#
Returns the concordance index of the prediction.
- Parameters:
X (array-like, 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
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance