# sksurv.svm.NaiveSurvivalSVM#

class sksurv.svm.NaiveSurvivalSVM(penalty='l2', loss='squared_hinge', *, dual=False, tol=0.0001, alpha=1.0, verbose=0, random_state=None, max_iter=1000)[source]#

Naive version of linear Survival Support Vector Machine.

Uses regular linear support vector classifier (liblinear). A new set of samples is created by building the difference between any two feature vectors in the original data, thus this version requires $$O(\text{n_samples}^2)$$ space.

See sksurv.svm.HingeLossSurvivalSVM for the kernel naive survival SVM.

\begin{align}\begin{aligned}\begin{split}\min_{\mathbf{w}}\quad \frac{1}{2} \lVert \mathbf{w} \rVert_2^2 + \gamma \sum_{i = 1}^n \xi_i \\ \text{subject to}\quad \mathbf{w}^\top \mathbf{x}_i - \mathbf{w}^\top \mathbf{x}_j \geq 1 - \xi_{ij},\quad \forall (i, j) \in \mathcal{P}, \\ \xi_i \geq 0,\quad \forall (i, j) \in \mathcal{P}.\end{split}\\\mathcal{P} = \{ (i, j) \mid y_i > y_j \land \delta_j = 1 \}_{i,j=1,\dots,n}.\end{aligned}\end{align}

See ,  for further description.

Parameters:
• alpha (float, positive, default: 1.0) – Weight of penalizing the squared hinge loss in the objective function.

• loss ({'hinge', 'squared_hinge'}, default: 'squared_hinge') – Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss.

• penalty ({'l1', 'l2'}, default: 'l2') – Specifies the norm used in the penalization. The ‘l2’ penalty is the standard used in SVC. The ‘l1’ leads to coef_ vectors that are sparse.

• dual (bool, default: True) – Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.

• tol (float, optional, default: 1e-4) – Tolerance for stopping criteria.

• verbose (int, default: 0) – Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.

• random_state (int seed, RandomState instance, or None, default: None) – The seed of the pseudo random number generator to use when shuffling the data.

• max_iter (int, default: 1000) – The maximum number of iterations to be run.

n_iter_#

Number of iterations run by the optimization routine to fit the model.

Type:

int

sksurv.svm.FastSurvivalSVM

Alternative implementation with reduced time complexity for training.

References

__init__(penalty='l2', loss='squared_hinge', *, dual=False, tol=0.0001, alpha=1.0, verbose=0, random_state=None, max_iter=1000)[source]#

Methods

 __init__([penalty, loss, dual, tol, alpha, ...]) Predict confidence scores for samples. Convert coefficient matrix to dense array format. fit(X, y[, sample_weight]) Build a survival support vector machine model from training data. Get metadata routing of this object. get_params([deep]) Get parameters for this estimator. Rank samples according to survival times score(X, y) Returns the concordance index of the prediction. set_fit_request(*[, sample_weight]) Request metadata passed to the fit method. set_params(**params) Set the parameters of this estimator. set_score_request(*[, sample_weight]) Request metadata passed to the score method. Convert coefficient matrix to sparse format.
decision_function(X)#

Predict confidence scores for samples.

The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.

Parameters:

X ({array-like, sparse matrix} of shape (n_samples, n_features)) – The data matrix for which we want to get the confidence scores.

Returns:

scores – Confidence scores per (n_samples, n_classes) combination. In the binary case, confidence score for self.classes_ where >0 means this class would be predicted.

Return type:

ndarray of shape (n_samples,) or (n_samples, n_classes)

densify()#

Convert coefficient matrix to dense array format.

Converts the coef_ member (back) to a numpy.ndarray. This is the default format of coef_ and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

Returns:

Fitted estimator.

Return type:

self

fit(X, y, sample_weight=None)[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.

• sample_weight (array-like, shape = (n_samples,), optional) – Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Return type:

self

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:

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_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') #

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

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

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') #

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

sparsify()#

Convert coefficient matrix to sparse format.

Converts the coef_ member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns:

Fitted estimator.

Return type:

self

Notes

For non-sparse models, i.e. when there are not many zeros in coef_, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with (coef_ == 0).sum(), must be more than 50% for this to provide significant benefits.

After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.