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 highdimensional distribution.
The implementation is based on libsvm.
Read more in the User Guide.
 Parameters
 kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, 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.
 degreeint, default=3
Degree of the polynomial kernel function (‘poly’). 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.
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=1e3
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 perprocess 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
 class_weight_ndarray of shape (n_classes,)
Multipliers of parameter C for each class. Computed based on the
class_weight
parameter. coef_ndarray of 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 fromdual_coef_
andsupport_vectors_
. 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_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 for consistency with other outlier detection algorithms.New 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.
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
Signed distance to the separating hyperplane.
fit
(X[, y, sample_weight])Detects the soft boundary of the set of samples X.
fit_predict
(X[, y])Perform fit on X and returns labels for X.
get_params
([deep])Get parameters for this estimator.
predict
(X)Perform classification on samples in X.
Raw scoring function of the samples.
set_params
(**params)Set the parameters of this estimator.

decision_function
(X)[source]¶ Signed distance to the separating hyperplane.
Signed distance is positive for an inlier and negative for an outlier.
 Parameters
 Xarraylike 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, **params)[source]¶ Detects the soft boundary of the set of samples X.
 Parameters
 X{arraylike, 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.
 sample_weightarraylike of shape (n_samples,), default=None
Persample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
 yIgnored
not used, present for API consistency by convention.
 Returns
 selfobject
Notes
If X is not a Cordered contiguous array it is copied.

fit_predict
(X, y=None)[source]¶ Perform fit on X and returns labels for X.
Returns 1 for outliers and 1 for inliers.
 Parameters
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
 yIgnored
Not used, present for API consistency by convention.
 Returns
 yndarray of shape (n_samples,)
1 for inliers, 1 for outliers.

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 oneclass model, +1 or 1 is returned.
 Parameters
 X{arraylike, 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
 Xarraylike 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_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.