sklearn.svm.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 : 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.

degree : int, optional (default=3)

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

gamma : {‘scale’, ‘auto’} or float, optional (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.

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

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.

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.

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.

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 = [1, n_SV]

Coefficients of the support vectors in the decision function.

coef_ : array, shape = [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 = [1,]

Constant in the decision function.

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.

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...])

Methods

decision_function(self, X) Signed distance to the separating hyperplane.
fit(self, X[, y, sample_weight]) Detects the soft boundary of the set of samples X.
fit_predict(self, X[, y]) Performs fit on X and returns labels for X.
get_params(self[, deep]) Get parameters for this estimator.
predict(self, X) Perform classification on samples in X.
score_samples(self, X) Raw scoring function of the samples.
set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, 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]
decision_function(self, X)[source]

Signed distance to the separating hyperplane.

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

Parameters:
X : array-like, shape (n_samples, n_features)
Returns:
dec : array-like, shape (n_samples,)

Returns the decision function of the samples.

fit(self, 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.

y : Ignored

not used, present for API consistency by convention.

Returns:
self : object

Notes

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

fit_predict(self, X, y=None)[source]

Performs fit on X and returns labels for X.

Returns -1 for outliers and 1 for inliers.

Parameters:
X : ndarray, shape (n_samples, n_features)

Input data.

y : Ignored

not used, present for API consistency by convention.

Returns:
y : ndarray, shape (n_samples,)

1 for inliers, -1 for outliers.

get_params(self, 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(self, X)[source]

Perform classification on samples in X.

For a 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,)

Class labels for samples in X.

score_samples(self, X)[source]

Raw scoring function of the samples.

Parameters:
X : array-like, shape (n_samples, n_features)
Returns:
score_samples : array-like, shape (n_samples,)

Returns the (unshifted) scoring function of the samples.

set_params(self, **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.

Returns:
self