OneClassSVM#

class sklearn.svm.OneClassSVM(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False, max_iter=-1)[source]#

Unsupervised Outlier Detection.

Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm.

Read more in the User Guide.

Parameters:
kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’

Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

degreeint, default=3

Degree of the polynomial kernel function (‘poly’). Must be non-negative. Ignored by all other kernels.

gamma{‘scale’, ‘auto’} or float, default=’scale’

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

  • if ‘auto’, uses 1 / n_features

  • if float, must be non-negative.

Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

coef0float, default=0.0

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

tolfloat, default=1e-3

Tolerance for stopping criterion.

nufloat, default=0.5

An upper bound on the fraction of training errors and a lower bound of the fraction of support vectors. Should be in the interval (0, 1]. By default 0.5 will be taken.

shrinkingbool, default=True

Whether to use the shrinking heuristic. See the User Guide.

cache_sizefloat, default=200

Specify the size of the kernel cache (in MB).

verbosebool, default=False

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.

max_iterint, default=-1

Hard limit on iterations within solver, or -1 for no limit.

Attributes:
coef_ndarray of shape (1, n_features)

Weights assigned to the features when kernel="linear".

dual_coef_ndarray of shape (1, n_SV)

Coefficients of the support vectors in the decision function.

fit_status_int

0 if correctly fitted, 1 otherwise (will raise warning)

intercept_ndarray of shape (1,)

Constant in the decision function.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

n_iter_int

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

Added in version 1.1.

n_support_ndarray of shape (n_classes,), dtype=int32

Number of support vectors for each class.

offset_float

Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset_. The offset is the opposite of intercept_ and is provided for consistency with other outlier detection algorithms.

Added in version 0.20.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vector X.

support_ndarray of shape (n_SV,)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors.

See also

sklearn.linear_model.SGDOneClassSVM

Solves linear One-Class SVM using Stochastic Gradient Descent.

sklearn.neighbors.LocalOutlierFactor

Unsupervised Outlier Detection using Local Outlier Factor (LOF).

sklearn.ensemble.IsolationForest

Isolation Forest Algorithm.

Examples

>>> from sklearn.svm import OneClassSVM
>>> X = [[0], [0.44], [0.45], [0.46], [1]]
>>> clf = OneClassSVM(gamma='auto').fit(X)
>>> clf.predict(X)
array([-1,  1,  1,  1, -1])
>>> clf.score_samples(X)
array([1.7798, 2.0547, 2.0556, 2.0561, 1.7332])

For a more extended example, see Species distribution modeling

decision_function(X)[source]#

Signed distance to the separating hyperplane.

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

Parameters:
Xarray-like of shape (n_samples, n_features)

The data matrix.

Returns:
decndarray of shape (n_samples,)

Returns the decision function of the samples.

fit(X, y=None, sample_weight=None)[source]#

Detect the soft boundary of the set of samples X.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features)

Set of samples, where n_samples is the number of samples and n_features is the number of features.

yIgnored

Not used, present for API consistency by convention.

sample_weightarray-like of shape (n_samples,), default=None

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:
selfobject

Fitted estimator.

Notes

If X is not a C-ordered contiguous array it is copied.

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.

Added 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.

predict(X)[source]#

Perform classification on samples in X.

For a one-class model, +1 or -1 is returned.

Parameters:
X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train)

For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns:
y_predndarray of shape (n_samples,)

Class labels for samples in X.

score_samples(X)[source]#

Raw scoring function of the samples.

Parameters:
Xarray-like of shape (n_samples, n_features)

The data matrix.

Returns:
score_samplesndarray of shape (n_samples,)

Returns the (unshifted) scoring function of the samples.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OneClassSVM[source]#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

Added in version 1.3.

Parameters:
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.