`sklearn.linear_model`.SGDOneClassSVM¶

class sklearn.linear_model.SGDOneClassSVM(nu=0.5, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, warm_start=False, average=False)[source]¶

Solves linear One-Class SVM using Stochastic Gradient Descent.

This implementation is meant to be used with a kernel approximation technique (e.g. sklearn.kernel_approximation.Nystroem) to obtain results similar to sklearn.svm.OneClassSVM which uses a Gaussian kernel by default.

See also

sklearn.svm.OneClassSVM: Unsupervised Outlier Detection.

Notes

This estimator has a linear complexity in the number of training samples and is thus better suited than the sklearn.svm.OneClassSVM implementation for datasets with a large number of training samples (say > 10,000).

Examples

>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> clf = linear_model.SGDOneClassSVM(random_state=42)
>>> clf.fit(X)
SGDOneClassSVM(random_state=42)

>>> print(clf.predict([[4, 4]]))
[1]

Methods

`decision_function`(X)	Signed distance to the separating hyperplane.
`densify`()	Convert coefficient matrix to dense array format.
`fit`(X[, y, coef_init, offset_init, ...])	Fit linear One-Class SVM with Stochastic Gradient Descent.
`fit_predict`(X[, y])	Perform fit on X and returns labels for X.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`partial_fit`(X[, y, sample_weight])	Fit linear One-Class SVM with Stochastic Gradient Descent.
`predict`(X)	Return labels (1 inlier, -1 outlier) of the samples.
`score_samples`(X)	Raw scoring function of the samples.
`set_fit_request`(*[, coef_init, offset_init, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_partial_fit_request`(*[, sample_weight])	Request metadata passed to the `partial_fit` method.
`sparsify`()	Convert coefficient matrix to sparse format.

decision_function(X)[source]¶

Signed distance to the separating hyperplane.

Signed distance is positive for an inlier and negative for an outlier.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features): Testing data.

Returns:

decarray-like, shape (n_samples,): Decision function values of the samples.

densify()[source]¶

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:

self: Fitted estimator.

fit(X, y=None, coef_init=None, offset_init=None, sample_weight=None)[source]¶

Fit linear One-Class SVM with Stochastic Gradient Descent.

This solves an equivalent optimization problem of the One-Class SVM primal optimization problem and returns a weight vector w and an offset rho such that the decision function is given by <w, x> - rho.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features): Training data.
yIgnored: Not used, present for API consistency by convention.
coef_initarray, shape (n_classes, n_features): The initial coefficients to warm-start the optimization.
offset_initarray, shape (n_classes,): The initial offset to warm-start the optimization.
sample_weightarray-like, shape (n_samples,), optional: Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.

Returns:

selfobject: Returns a fitted instance of self.

fit_predict(X, y=None, **kwargs)[source]¶

Perform fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input samples.
yIgnored: Not used, present for API consistency by convention.
**kwargsdict: Arguments to be passed to fit.

New in version 1.4.

Returns:

yndarray of shape (n_samples,): 1 for inliers, -1 for outliers.

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

partial_fit(X, y=None, sample_weight=None)[source]¶

Fit linear One-Class SVM with Stochastic Gradient Descent.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features): Subset of the training data.
yIgnored: Not used, present for API consistency by convention.
sample_weightarray-like, shape (n_samples,), optional: Weights applied to individual samples. If not provided, uniform weights are assumed.

Returns:

selfobject: Returns a fitted instance of self.

predict(X)[source]¶

Return labels (1 inlier, -1 outlier) of the samples.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features): Testing data.

Returns:

yarray, shape (n_samples,): Labels of the samples.

score_samples(X)[source]¶

Raw scoring function of the samples.

Parameters:

X{array-like, sparse matrix}, shape (n_samples, n_features): Testing data.

Returns:

score_samplesarray-like, shape (n_samples,): Unshiffted scoring function values of the samples.

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:

coef_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for coef_init parameter in fit.
offset_initstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for offset_init parameter in fit.
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]¶

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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SGDOneClassSVM[source]¶

Request metadata passed to the partial_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 partial_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 partial_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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject: The updated object.

sparsify()[source]¶

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:

self: Fitted estimator.

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.

Examples using `sklearn.linear_model.SGDOneClassSVM`¶

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

Comparing anomaly detection algorithms for outlier detection on toy datasets

sklearn.linear_model.SGDOneClassSVM¶

Examples using sklearn.linear_model.SGDOneClassSVM¶

`sklearn.linear_model`.SGDOneClassSVM¶

Examples using `sklearn.linear_model.SGDOneClassSVM`¶