`sklearn.svm`.OneClassSVM¶

class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False, max_iter=-1, random_state=None)[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 : string, optional (default=’rbf’)

Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

nu : float, optional

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.

degree : int, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma : float, optional (default=’auto’)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.

coef0 : float, optional (default=0.0)

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

tol : float, optional

Tolerance for stopping criterion.

shrinking : boolean, optional

Whether to use the shrinking heuristic.

cache_size : float, optional

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

verbose : bool, 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_iter : int, optional (default=-1)

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

random_state : int seed, RandomState instance, or None (default)

The seed of the pseudo random number generator to use when shuffling the data for probability estimation.

Attributes:

support_ : array-like, shape = [n_SV]

Indices of support vectors.

support_vectors_ : array-like, shape = [nSV, n_features]

Support vectors.

dual_coef_ : array, shape = [n_classes-1, n_SV]

Coefficients of the support vectors in the decision function.

coef_ : array, shape = [n_classes-1, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_

intercept_ : array, shape = [n_classes-1]

Constants in decision function.

Methods

`decision_function`(X)	Distance of the samples X to the separating hyperplane.
`fit`(X[, y, sample_weight])	Detects the soft boundary of the set of samples X.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Perform regression on samples in X.
`set_params`(**params)	Set the parameters of this estimator.

__init__(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False, max_iter=-1, random_state=None)[source]¶

decision_function(X)[source]¶

Distance of the samples X to the separating hyperplane.

Parameters:

X : array-like, shape (n_samples, n_features)

Returns:

X : array-like, shape (n_samples,)

Returns the decision function of the samples.

fit(X, y=None, sample_weight=None, **params)[source]¶

Detects the soft boundary of the set of samples X.

Parameters:

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

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

sample_weight : array-like, shape (n_samples,)

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

Returns:

self : object

Returns self.

Notes

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

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

predict(X)[source]¶

Perform regression on samples in X.

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

Parameters:

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

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

Returns:

y_pred : array, shape (n_samples,)

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.