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{‘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 is used 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’.
if
gamma='scale'
(default) is passed then it uses 1 / (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 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=1e-3
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 per-process 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.Deprecated since version 1.2:
class_weight_
was deprecated in version 1.2 and will be removed in 1.4.coef_
ndarray of shape (1, n_features)Weights assigned to the features when
kernel="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 during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- n_iter_int
Number of iterations run by the optimization routine to fit the model.
New in version 1.1.
n_support_
ndarray of shape (n_classes,), dtype=int32Number 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.
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
>>> 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])Detect the soft boundary of the set of samples X.
fit_predict
(X[, y])Perform fit on X and returns labels for X.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Perform classification on samples in X.
Raw scoring function of the samples.
set_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
- property coef_¶
Weights assigned to the features when
kernel="linear"
.- Returns:
- ndarray of shape (n_features, n_classes)
- 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, where
n_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 weights force 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 to
fit
.New 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 check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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.
- property n_support_¶
Number of support vectors for each class.
- 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: Union[bool, None, str] = '$UNCHANGED$') OneClassSVM [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism 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 the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_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 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.
Examples using sklearn.svm.OneClassSVM
¶
Outlier detection on a real data set
One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
Comparing anomaly detection algorithms for outlier detection on toy datasets
One-class SVM with non-linear kernel (RBF)