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# `sklearn.linear_model`.SGDOneClassSVM

class sklearn.linear_model.SGDOneClassSVM(nu=0.5, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, warm_start=False, average=False)

  Solves linear One-Class SVM using Stochastic Gradient Descent.

  This implementation is meant to be used with a kernel approximation technique (e.g. `sklearn.kernel_approximation.Nystroem`) to obtain results similar to `sklearn.svm.OneClassSVM` which uses a Gaussian kernel by default.

  Read more in the User Guide.

  New in version 1.0.

  Parameters:
    nu float, default=0.5

      The nu parameter of the One Class SVM: 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.

    fit_intercept bool, default=True

      Whether the intercept should be estimated or not. Defaults to True.

    max_iter int, default=1000

      The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the `fit` method, and not the `partial_fit`. Defaults to 1000.

    tol float or None, default=1e-3

      The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to 1e-3.

    shuffle bool, default=True

      Whether or not the training data should be shuffled after each epoch. Defaults to True.

    verbose int, default=0

      The verbosity level.

    random_state int, RandomState instance or None, default=None

      The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.

    learning_rate{‘constant’, ‘optimal’, ‘invscaling’, ‘adaptive’}, default=’optimal’

      The learning rate schedule to use with `fit`. (If using `partial_fit`, learning rate must be controlled directly).

      - ‘constant’: `eta = eta0`

      - ‘optimal’: `eta = 1.0 / (alpha * (t + t0))` where t0 is chosen by a heuristic proposed by Leon Bottou.

      - ‘invscaling’: `eta = eta0 / pow(t, power_t)`

      - ‘adaptive’: eta = eta0, as long as the training keeps decreasing. Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5.

    eta0 float, default=0.0

      The initial learning rate for the ‘constant’, ‘invscaling’ or ‘adaptive’ schedules. The default value is 0.0 as eta0 is not used by the default schedule ‘optimal’.

    power_t float, default=0.5

      The exponent for inverse scaling learning rate [default 0.5].

    warm_start bool, default=False

      When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See the Glossary.

      Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled. If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling `fit` resets this counter, while `partial_fit` will result in increasing the existing counter.

    average bool or int, default=False

      When set to True, computes the averaged SGD weights and stores the result in the `coef_` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So `average=10` will begin averaging after seeing 10 samples.

  Attributes:
    coef_ndarray of shape (1, n_features)

      Weights assigned to the features.

    offset_ndarray of shape (1,)

      Offset used to define the decision function from the raw scores. We have the relation: decision_function = score_samples - offset.

    n_iter_int

      The actual number of iterations to reach the stopping criterion.

    t_int

      Number of weight updates performed during training. Same as `(n_iter_ * n_samples + 1)`.

    loss_function_concrete `LossFunction`
    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.

  See also

  `sklearn.svm.OneClassSVM`

    Unsupervised Outlier Detection.

  Notes

  This estimator has a linear complexity in the number of training samples and is thus better suited than the `sklearn.svm.OneClassSVM` implementation for datasets with a large number of training samples (say > 10,000).

  Examples

      >>> import numpy as np
      >>> from sklearn import linear_model
      >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
      >>> clf = linear_model.SGDOneClassSVM(random_state=42)
      >>> clf.fit(X)
      SGDOneClassSVM(random_state=42)

      >>> print(clf.predict([[4, 4]]))
      [1]

  Methods

  `decision_function`(X) | Signed distance to the separating hyperplane.
  `densify`() | Convert coefficient matrix to dense array format.
  `fit`(X[, y, coef_init, offset_init, ...]) | Fit linear One-Class SVM with Stochastic Gradient Descent.
  `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.
  `partial_fit`(X[, y, sample_weight]) | Fit linear One-Class SVM with Stochastic Gradient Descent.
  `predict`(X) | Return labels (1 inlier, -1 outlier) of the samples.
  `score_samples`(X) | Raw scoring function of the samples.
  `set_fit_request`(*[, coef_init, offset_init, ...]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.
  `set_partial_fit_request`(*[, sample_weight]) | Request metadata passed to the `partial_fit` method.
  `sparsify`() | Convert coefficient matrix to sparse format.

  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, sparse matrix}, shape (n_samples, n_features)

        Testing data.

    Returns:
      dec array-like, shape (n_samples,)

        Decision function values of the samples.

  densify()

    Convert coefficient matrix to dense array format.

    Converts the `coef_` member (back) to a numpy.ndarray. This is the default format of `coef_` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op.

    Returns:
      self

        Fitted estimator.

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

    Fit linear One-Class SVM with Stochastic Gradient Descent.

    This solves an equivalent optimization problem of the One-Class SVM primal optimization problem and returns a weight vector w and an offset rho such that the decision function is given by <w, x> - rho.

    Parameters:
      X{array-like, sparse matrix}, shape (n_samples, n_features)

        Training data.

      y Ignored

        Not used, present for API consistency by convention.

      coef_init array, shape (n_classes, n_features)

        The initial coefficients to warm-start the optimization.

      offset_init array, shape (n_classes,)

        The initial offset to warm-start the optimization.

      sample_weight array-like, shape (n_samples,), optional

        Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.

    Returns:
      self object

        Returns a fitted instance of self.

  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.

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

    Fit linear One-Class SVM with Stochastic Gradient Descent.

    Parameters:
      X{array-like, sparse matrix}, shape (n_samples, n_features)

        Subset of the training data.

      y Ignored

        Not used, present for API consistency by convention.

      sample_weight array-like, shape (n_samples,), optional

        Weights applied to individual samples. If not provided, uniform weights are assumed.

    Returns:
      self object

        Returns a fitted instance of self.

  predict(X)

    Return labels (1 inlier, -1 outlier) of the samples.

    Parameters:
      X{array-like, sparse matrix}, shape (n_samples, n_features)

        Testing data.

    Returns:
      y array, shape (n_samples,)

        Labels of the samples.

  score_samples(X)

    Raw scoring function of the samples.

    Parameters:
      X{array-like, sparse matrix}, shape (n_samples, n_features)

        Testing data.

    Returns:
      score_samples array-like, shape (n_samples,)

        Unshiffted scoring function values of the samples.

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

    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:
      coef_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

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

      offset_init str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

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

      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.

  set_partial_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SGDOneClassSVM

    Request metadata passed to the `partial_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 `partial_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 `partial_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 `partial_fit`.

    Returns:
      self object

        The updated object.

  sparsify()

    Convert coefficient matrix to sparse format.

    Converts the `coef_` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation.

    The `intercept_` member is not converted.

    Returns:
      self

        Fitted estimator.

    Notes

    For non-sparse models, i.e. when there are not many zeros in `coef_`, this may actually increase memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with `(coef_ == 0).sum()`, must be more than 50% for this to provide significant benefits.

    After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify.

## Examples using `sklearn.linear_model.SGDOneClassSVM`

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