Toggle Menu
Prev Up Next

scikit-learn 1.3.2
Other versions

Please cite us if you use the software.

# `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)

  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.

    degree int, 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’.

    coef0 float, default=0.0

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

    tol float, default=1e-3

      Tolerance for stopping criterion.

    nu float, 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.

    shrinking bool, default=True

      Whether to use the shrinking heuristic. See the User Guide.

    cache_size float, default=200

      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, 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=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 of `intercept_` 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

  `decision_function`(X) | 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`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Perform classification on samples in X.
  `score_samples`(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)

    Signed distance to the separating hyperplane.

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

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

        The data matrix.

    Returns:
      dec ndarray of shape (n_samples,)

        Returns the decision function of the samples.

  fit(X, y=None, sample_weight=None)

    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 and `n_features` is the number of features.

      y Ignored

        Not used, present for API consistency by convention.

      sample_weight array-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:
      self object

        Fitted estimator.

    Notes

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

  fit_predict(X, y=None)

    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.

      y Ignored

        Not used, present for API consistency by convention.

    Returns:
      y ndarray of shape (n_samples,)

        1 for inliers, -1 for outliers.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

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

    Returns:
      params dict

        Parameter names mapped to their values.

  property n_support_

    Number of support vectors for each class.

  predict(X)

    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_pred ndarray of shape (n_samples,)

        Class labels for samples in X.

  score_samples(X)

    Raw scoring function of the samples.

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

        The data matrix.

    Returns:
      score_samples ndarray of shape (n_samples,)

        Returns the (unshifted) scoring function of the samples.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → OneClassSVM

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

## Examples using `sklearn.svm.OneClassSVM`

Libsvm GUI

Libsvm GUI

Outlier detection on a real data set

Outlier detection on a real data set

Species distribution modeling

Species distribution modeling

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)