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.

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:

Parameters:	X : array-like, shape (n_samples, n_features)
Returns:	X : array-like, shape (n_samples,) Returns the decision function of the samples.

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:

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.

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:

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.

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:

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,)

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 latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.