OneClassSVM#

classsklearn.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 theUser 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 isused 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’.

• ifgamma='scale' (default) is passed then it uses1 / (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 ofgamma 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 trainingerrors and a lower bound of the fraction of supportvectors. Should be in the interval (0, 1]. By default 0.5will be taken.

shrinkingbool, default=True

Whether to use the shrinking heuristic.See theUser 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 aper-process runtime setting in libsvm that, if enabled, may not workproperly 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 whenkernel="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 duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas 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 ofintercept_ and is provided forconsistency 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 vectorX.

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

>>>fromsklearn.svmimportOneClassSVM>>>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,seeSpecies 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, wheren_samples is the number of samples andn_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 weightsforce 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 tofit.

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 checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained 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 thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

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

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

• 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter infit.

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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

Gallery examples#

Outlier detection on a real data set

Outlier detection on a real data set

Species distribution modeling

Species distribution modeling

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

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

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)