1457 lines
55 KiB
Python
1457 lines
55 KiB
Python
import numpy as np
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from ._base import _fit_liblinear, BaseSVC, BaseLibSVM
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from ..base import BaseEstimator, RegressorMixin, OutlierMixin
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from ..linear_model._base import LinearClassifierMixin, SparseCoefMixin, \
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LinearModel
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from ..utils.validation import _num_samples
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from ..utils.validation import _deprecate_positional_args
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from ..utils.multiclass import check_classification_targets
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from ..utils.deprecation import deprecated
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class LinearSVC(LinearClassifierMixin,
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SparseCoefMixin,
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BaseEstimator):
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"""Linear Support Vector Classification.
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Similar to SVC with parameter kernel='linear', but implemented in terms of
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liblinear rather than libsvm, so it has more flexibility in the choice of
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penalties and loss functions and should scale better to large numbers of
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samples.
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This class supports both dense and sparse input and the multiclass support
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is handled according to a one-vs-the-rest scheme.
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Read more in the :ref:`User Guide <svm_classification>`.
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Parameters
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----------
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penalty : {'l1', 'l2'}, default='l2'
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Specifies the norm used in the penalization. The 'l2'
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penalty is the standard used in SVC. The 'l1' leads to ``coef_``
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vectors that are sparse.
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loss : {'hinge', 'squared_hinge'}, default='squared_hinge'
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Specifies the loss function. 'hinge' is the standard SVM loss
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(used e.g. by the SVC class) while 'squared_hinge' is the
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square of the hinge loss. The combination of ``penalty='l1'``
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and ``loss='hinge'`` is not supported.
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dual : bool, default=True
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Select the algorithm to either solve the dual or primal
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optimization problem. Prefer dual=False when n_samples > n_features.
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tol : float, default=1e-4
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Tolerance for stopping criteria.
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C : float, default=1.0
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Regularization parameter. The strength of the regularization is
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inversely proportional to C. Must be strictly positive.
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multi_class : {'ovr', 'crammer_singer'}, default='ovr'
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Determines the multi-class strategy if `y` contains more than
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two classes.
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``"ovr"`` trains n_classes one-vs-rest classifiers, while
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``"crammer_singer"`` optimizes a joint objective over all classes.
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While `crammer_singer` is interesting from a theoretical perspective
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as it is consistent, it is seldom used in practice as it rarely leads
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to better accuracy and is more expensive to compute.
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If ``"crammer_singer"`` is chosen, the options loss, penalty and dual
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will be ignored.
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fit_intercept : bool, default=True
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations
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(i.e. data is expected to be already centered).
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intercept_scaling : float, default=1
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When self.fit_intercept is True, instance vector x becomes
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``[x, self.intercept_scaling]``,
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i.e. a "synthetic" feature with constant value equals to
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intercept_scaling is appended to the instance vector.
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The intercept becomes intercept_scaling * synthetic feature weight
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Note! the synthetic feature weight is subject to l1/l2 regularization
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as all other features.
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To lessen the effect of regularization on synthetic feature weight
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(and therefore on the intercept) intercept_scaling has to be increased.
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class_weight : dict or 'balanced', default=None
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Set the parameter C of class i to ``class_weight[i]*C`` for
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SVC. If not given, all classes are supposed to have
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weight one.
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The "balanced" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies in the input data
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as ``n_samples / (n_classes * np.bincount(y))``.
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verbose : int, default=0
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Enable verbose output. Note that this setting takes advantage of a
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per-process runtime setting in liblinear that, if enabled, may not work
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properly in a multithreaded context.
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random_state : int, RandomState instance or None, default=None
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Controls the pseudo random number generation for shuffling the data for
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the dual coordinate descent (if ``dual=True``). When ``dual=False`` the
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underlying implementation of :class:`LinearSVC` is not random and
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``random_state`` has no effect on the results.
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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max_iter : int, default=1000
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The maximum number of iterations to be run.
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Attributes
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----------
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coef_ : ndarray of shape (1, n_features) if n_classes == 2 \
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else (n_classes, n_features)
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Weights assigned to the features (coefficients in the primal
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problem).
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``coef_`` is a readonly property derived from ``raw_coef_`` that
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follows the internal memory layout of liblinear.
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intercept_ : ndarray of shape (1,) if n_classes == 2 else (n_classes,)
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Constants in decision function.
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classes_ : ndarray of shape (n_classes,)
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The unique classes labels.
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n_iter_ : int
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Maximum number of iterations run across all classes.
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See Also
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--------
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SVC : Implementation of Support Vector Machine classifier using libsvm:
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the kernel can be non-linear but its SMO algorithm does not
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scale to large number of samples as LinearSVC does.
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Furthermore SVC multi-class mode is implemented using one
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vs one scheme while LinearSVC uses one vs the rest. It is
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possible to implement one vs the rest with SVC by using the
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:class:`~sklearn.multiclass.OneVsRestClassifier` wrapper.
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Finally SVC can fit dense data without memory copy if the input
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is C-contiguous. Sparse data will still incur memory copy though.
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sklearn.linear_model.SGDClassifier : SGDClassifier can optimize the same
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cost function as LinearSVC
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by adjusting the penalty and loss parameters. In addition it requires
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less memory, allows incremental (online) learning, and implements
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various loss functions and regularization regimes.
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Notes
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-----
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The underlying C implementation uses a random number generator to
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select features when fitting the model. It is thus not uncommon
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to have slightly different results for the same input data. If
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that happens, try with a smaller ``tol`` parameter.
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The underlying implementation, liblinear, uses a sparse internal
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representation for the data that will incur a memory copy.
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Predict output may not match that of standalone liblinear in certain
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cases. See :ref:`differences from liblinear <liblinear_differences>`
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in the narrative documentation.
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References
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----------
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`LIBLINEAR: A Library for Large Linear Classification
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<https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__
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Examples
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--------
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>>> from sklearn.svm import LinearSVC
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>>> from sklearn.pipeline import make_pipeline
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>>> from sklearn.preprocessing import StandardScaler
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>>> from sklearn.datasets import make_classification
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>>> X, y = make_classification(n_features=4, random_state=0)
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>>> clf = make_pipeline(StandardScaler(),
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... LinearSVC(random_state=0, tol=1e-5))
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>>> clf.fit(X, y)
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Pipeline(steps=[('standardscaler', StandardScaler()),
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('linearsvc', LinearSVC(random_state=0, tol=1e-05))])
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>>> print(clf.named_steps['linearsvc'].coef_)
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[[0.141... 0.526... 0.679... 0.493...]]
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>>> print(clf.named_steps['linearsvc'].intercept_)
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[0.1693...]
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>>> print(clf.predict([[0, 0, 0, 0]]))
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[1]
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"""
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@_deprecate_positional_args
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def __init__(self, penalty='l2', loss='squared_hinge', *, dual=True,
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tol=1e-4, C=1.0, multi_class='ovr', fit_intercept=True,
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intercept_scaling=1, class_weight=None, verbose=0,
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random_state=None, max_iter=1000):
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self.dual = dual
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self.tol = tol
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self.C = C
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self.multi_class = multi_class
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self.fit_intercept = fit_intercept
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self.intercept_scaling = intercept_scaling
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self.class_weight = class_weight
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self.verbose = verbose
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self.random_state = random_state
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self.max_iter = max_iter
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self.penalty = penalty
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self.loss = loss
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def fit(self, X, y, sample_weight=None):
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"""Fit the model according to the given training data.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Training vector, where n_samples in the number of samples and
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n_features is the number of features.
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y : array-like of shape (n_samples,)
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Target vector relative to X.
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sample_weight : array-like of shape (n_samples,), default=None
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Array of weights that are assigned to individual
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samples. If not provided,
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then each sample is given unit weight.
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.. versionadded:: 0.18
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Returns
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-------
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self : object
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An instance of the estimator.
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"""
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if self.C < 0:
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raise ValueError("Penalty term must be positive; got (C=%r)"
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% self.C)
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X, y = self._validate_data(X, y, accept_sparse='csr',
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dtype=np.float64, order="C",
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accept_large_sparse=False)
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check_classification_targets(y)
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self.classes_ = np.unique(y)
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self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
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X, y, self.C, self.fit_intercept, self.intercept_scaling,
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self.class_weight, self.penalty, self.dual, self.verbose,
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self.max_iter, self.tol, self.random_state, self.multi_class,
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self.loss, sample_weight=sample_weight)
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if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
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self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
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if self.fit_intercept:
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intercept = self.intercept_[1] - self.intercept_[0]
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self.intercept_ = np.array([intercept])
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return self
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def _more_tags(self):
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return {
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'_xfail_checks': {
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'check_sample_weights_invariance':
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'zero sample_weight is not equivalent to removing samples',
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}
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}
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class LinearSVR(RegressorMixin, LinearModel):
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"""Linear Support Vector Regression.
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Similar to SVR with parameter kernel='linear', but implemented in terms of
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liblinear rather than libsvm, so it has more flexibility in the choice of
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penalties and loss functions and should scale better to large numbers of
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samples.
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This class supports both dense and sparse input.
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Read more in the :ref:`User Guide <svm_regression>`.
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.. versionadded:: 0.16
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Parameters
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----------
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epsilon : float, default=0.0
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Epsilon parameter in the epsilon-insensitive loss function. Note
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that the value of this parameter depends on the scale of the target
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variable y. If unsure, set ``epsilon=0``.
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tol : float, default=1e-4
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Tolerance for stopping criteria.
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C : float, default=1.0
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Regularization parameter. The strength of the regularization is
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inversely proportional to C. Must be strictly positive.
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loss : {'epsilon_insensitive', 'squared_epsilon_insensitive'}, \
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default='epsilon_insensitive'
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Specifies the loss function. The epsilon-insensitive loss
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(standard SVR) is the L1 loss, while the squared epsilon-insensitive
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loss ('squared_epsilon_insensitive') is the L2 loss.
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fit_intercept : bool, default=True
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Whether to calculate the intercept for this model. If set
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to false, no intercept will be used in calculations
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(i.e. data is expected to be already centered).
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intercept_scaling : float, default=1.
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When self.fit_intercept is True, instance vector x becomes
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[x, self.intercept_scaling],
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|
i.e. a "synthetic" feature with constant value equals to
|
|
intercept_scaling is appended to the instance vector.
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The intercept becomes intercept_scaling * synthetic feature weight
|
|
Note! the synthetic feature weight is subject to l1/l2 regularization
|
|
as all other features.
|
|
To lessen the effect of regularization on synthetic feature weight
|
|
(and therefore on the intercept) intercept_scaling has to be increased.
|
|
|
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dual : bool, default=True
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Select the algorithm to either solve the dual or primal
|
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optimization problem. Prefer dual=False when n_samples > n_features.
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verbose : int, default=0
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Enable verbose output. Note that this setting takes advantage of a
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per-process runtime setting in liblinear that, if enabled, may not work
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properly in a multithreaded context.
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random_state : int, RandomState instance or None, default=None
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Controls the pseudo random number generation for shuffling the data.
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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max_iter : int, default=1000
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The maximum number of iterations to be run.
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Attributes
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----------
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coef_ : ndarray of shape (n_features) if n_classes == 2 \
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else (n_classes, n_features)
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Weights assigned to the features (coefficients in the primal
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problem).
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`coef_` is a readonly property derived from `raw_coef_` that
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follows the internal memory layout of liblinear.
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intercept_ : ndarray of shape (1) if n_classes == 2 else (n_classes)
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Constants in decision function.
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n_iter_ : int
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Maximum number of iterations run across all classes.
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Examples
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--------
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>>> from sklearn.svm import LinearSVR
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>>> from sklearn.pipeline import make_pipeline
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>>> from sklearn.preprocessing import StandardScaler
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>>> from sklearn.datasets import make_regression
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>>> X, y = make_regression(n_features=4, random_state=0)
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>>> regr = make_pipeline(StandardScaler(),
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... LinearSVR(random_state=0, tol=1e-5))
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>>> regr.fit(X, y)
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Pipeline(steps=[('standardscaler', StandardScaler()),
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('linearsvr', LinearSVR(random_state=0, tol=1e-05))])
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>>> print(regr.named_steps['linearsvr'].coef_)
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[18.582... 27.023... 44.357... 64.522...]
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>>> print(regr.named_steps['linearsvr'].intercept_)
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[-4...]
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>>> print(regr.predict([[0, 0, 0, 0]]))
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[-2.384...]
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See Also
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--------
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LinearSVC : Implementation of Support Vector Machine classifier using the
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same library as this class (liblinear).
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|
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|
SVR : Implementation of Support Vector Machine regression using libsvm:
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|
the kernel can be non-linear but its SMO algorithm does not
|
|
scale to large number of samples as LinearSVC does.
|
|
|
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sklearn.linear_model.SGDRegressor : SGDRegressor can optimize the same cost
|
|
function as LinearSVR
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by adjusting the penalty and loss parameters. In addition it requires
|
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less memory, allows incremental (online) learning, and implements
|
|
various loss functions and regularization regimes.
|
|
"""
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@_deprecate_positional_args
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def __init__(self, *, epsilon=0.0, tol=1e-4, C=1.0,
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loss='epsilon_insensitive', fit_intercept=True,
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intercept_scaling=1., dual=True, verbose=0,
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random_state=None, max_iter=1000):
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self.tol = tol
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self.C = C
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self.epsilon = epsilon
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self.fit_intercept = fit_intercept
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self.intercept_scaling = intercept_scaling
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self.verbose = verbose
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self.random_state = random_state
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self.max_iter = max_iter
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self.dual = dual
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self.loss = loss
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def fit(self, X, y, sample_weight=None):
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"""Fit the model according to the given training data.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
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Training vector, where n_samples in the number of samples and
|
|
n_features is the number of features.
|
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|
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y : array-like of shape (n_samples,)
|
|
Target vector relative to X
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|
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sample_weight : array-like of shape (n_samples,), default=None
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|
Array of weights that are assigned to individual
|
|
samples. If not provided,
|
|
then each sample is given unit weight.
|
|
|
|
.. versionadded:: 0.18
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|
|
|
Returns
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-------
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self : object
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An instance of the estimator.
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"""
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if self.C < 0:
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raise ValueError("Penalty term must be positive; got (C=%r)"
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% self.C)
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X, y = self._validate_data(X, y, accept_sparse='csr',
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dtype=np.float64, order="C",
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accept_large_sparse=False)
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penalty = 'l2' # SVR only accepts l2 penalty
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self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
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X, y, self.C, self.fit_intercept, self.intercept_scaling,
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None, penalty, self.dual, self.verbose,
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self.max_iter, self.tol, self.random_state, loss=self.loss,
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epsilon=self.epsilon, sample_weight=sample_weight)
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self.coef_ = self.coef_.ravel()
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return self
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def _more_tags(self):
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return {
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|
'_xfail_checks': {
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|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
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|
}
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|
}
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|
|
|
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class SVC(BaseSVC):
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"""C-Support Vector Classification.
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|
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The implementation is based on libsvm. The fit time scales at least
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quadratically with the number of samples and may be impractical
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beyond tens of thousands of samples. For large datasets
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consider using :class:`~sklearn.svm.LinearSVC` or
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:class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a
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:class:`~sklearn.kernel_approximation.Nystroem` transformer.
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The multiclass support is handled according to a one-vs-one scheme.
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For details on the precise mathematical formulation of the provided
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kernel functions and how `gamma`, `coef0` and `degree` affect each
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other, see the corresponding section in the narrative documentation:
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:ref:`svm_kernels`.
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Read more in the :ref:`User Guide <svm_classification>`.
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|
Parameters
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----------
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C : float, default=1.0
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Regularization parameter. The strength of the regularization is
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inversely proportional to C. Must be strictly positive. The penalty
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is a squared l2 penalty.
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to pre-compute the kernel matrix from data matrices; that matrix
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should be an array of shape ``(n_samples, n_samples)``.
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degree : int, default=3
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Degree of the polynomial kernel function ('poly').
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Ignored by all other kernels.
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gamma : {'scale', 'auto'} or float, default='scale'
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Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
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- if ``gamma='scale'`` (default) is passed then it uses
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1 / (n_features * X.var()) as value of gamma,
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|
- if 'auto', uses 1 / n_features.
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|
|
.. versionchanged:: 0.22
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The default value of ``gamma`` changed from 'auto' to 'scale'.
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coef0 : float, default=0.0
|
|
Independent term in kernel function.
|
|
It is only significant in 'poly' and 'sigmoid'.
|
|
|
|
shrinking : bool, default=True
|
|
Whether to use the shrinking heuristic.
|
|
See the :ref:`User Guide <shrinking_svm>`.
|
|
|
|
probability : bool, default=False
|
|
Whether to enable probability estimates. This must be enabled prior
|
|
to calling `fit`, will slow down that method as it internally uses
|
|
5-fold cross-validation, and `predict_proba` may be inconsistent with
|
|
`predict`. Read more in the :ref:`User Guide <scores_probabilities>`.
|
|
|
|
tol : float, default=1e-3
|
|
Tolerance for stopping criterion.
|
|
|
|
cache_size : float, default=200
|
|
Specify the size of the kernel cache (in MB).
|
|
|
|
class_weight : dict or 'balanced', default=None
|
|
Set the parameter C of class i to class_weight[i]*C for
|
|
SVC. If not given, all classes are supposed to have
|
|
weight one.
|
|
The "balanced" mode uses the values of y to automatically adjust
|
|
weights inversely proportional to class frequencies in the input data
|
|
as ``n_samples / (n_classes * np.bincount(y))``
|
|
|
|
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.
|
|
|
|
decision_function_shape : {'ovo', 'ovr'}, default='ovr'
|
|
Whether to return a one-vs-rest ('ovr') decision function of shape
|
|
(n_samples, n_classes) as all other classifiers, or the original
|
|
one-vs-one ('ovo') decision function of libsvm which has shape
|
|
(n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one
|
|
('ovo') is always used as multi-class strategy. The parameter is
|
|
ignored for binary classification.
|
|
|
|
.. versionchanged:: 0.19
|
|
decision_function_shape is 'ovr' by default.
|
|
|
|
.. versionadded:: 0.17
|
|
*decision_function_shape='ovr'* is recommended.
|
|
|
|
.. versionchanged:: 0.17
|
|
Deprecated *decision_function_shape='ovo' and None*.
|
|
|
|
break_ties : bool, default=False
|
|
If true, ``decision_function_shape='ovr'``, and number of classes > 2,
|
|
:term:`predict` will break ties according to the confidence values of
|
|
:term:`decision_function`; otherwise the first class among the tied
|
|
classes is returned. Please note that breaking ties comes at a
|
|
relatively high computational cost compared to a simple predict.
|
|
|
|
.. versionadded:: 0.22
|
|
|
|
random_state : int, RandomState instance or None, default=None
|
|
Controls the pseudo random number generation for shuffling the data for
|
|
probability estimates. Ignored when `probability` is False.
|
|
Pass an int for reproducible output across multiple function calls.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
Attributes
|
|
----------
|
|
class_weight_ : ndarray of shape (n_classes,)
|
|
Multipliers of parameter C for each class.
|
|
Computed based on the ``class_weight`` parameter.
|
|
|
|
classes_ : ndarray of shape (n_classes,)
|
|
The classes labels.
|
|
|
|
coef_ : ndarray of shape (n_classes * (n_classes - 1) / 2, 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 a readonly property derived from `dual_coef_` and
|
|
`support_vectors_`.
|
|
|
|
dual_coef_ : ndarray of shape (n_classes -1, n_SV)
|
|
Dual coefficients of the support vector in the decision
|
|
function (see :ref:`sgd_mathematical_formulation`), multiplied by
|
|
their targets.
|
|
For multiclass, coefficient for all 1-vs-1 classifiers.
|
|
The layout of the coefficients in the multiclass case is somewhat
|
|
non-trivial. See the :ref:`multi-class section of the User Guide
|
|
<svm_multi_class>` for details.
|
|
|
|
fit_status_ : int
|
|
0 if correctly fitted, 1 otherwise (will raise warning)
|
|
|
|
intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
|
|
Constants in decision function.
|
|
|
|
support_ : ndarray of shape (n_SV)
|
|
Indices of support vectors.
|
|
|
|
support_vectors_ : ndarray of shape (n_SV, n_features)
|
|
Support vectors.
|
|
|
|
n_support_ : ndarray of shape (n_classes,), dtype=int32
|
|
Number of support vectors for each class.
|
|
|
|
probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
|
|
probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2)
|
|
If `probability=True`, it corresponds to the parameters learned in
|
|
Platt scaling to produce probability estimates from decision values.
|
|
If `probability=False`, it's an empty array. Platt scaling uses the
|
|
logistic function
|
|
``1 / (1 + exp(decision_value * probA_ + probB_))``
|
|
where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
|
|
more information on the multiclass case and training procedure see
|
|
section 8 of [1]_.
|
|
|
|
shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
|
|
Array dimensions of training vector ``X``.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.pipeline import make_pipeline
|
|
>>> from sklearn.preprocessing import StandardScaler
|
|
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
|
|
>>> y = np.array([1, 1, 2, 2])
|
|
>>> from sklearn.svm import SVC
|
|
>>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
|
|
>>> clf.fit(X, y)
|
|
Pipeline(steps=[('standardscaler', StandardScaler()),
|
|
('svc', SVC(gamma='auto'))])
|
|
|
|
>>> print(clf.predict([[-0.8, -1]]))
|
|
[1]
|
|
|
|
See Also
|
|
--------
|
|
SVR : Support Vector Machine for Regression implemented using libsvm.
|
|
|
|
LinearSVC : Scalable Linear Support Vector Machine for classification
|
|
implemented using liblinear. Check the See Also section of
|
|
LinearSVC for more comparison element.
|
|
|
|
References
|
|
----------
|
|
.. [1] `LIBSVM: A Library for Support Vector Machines
|
|
<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
|
|
|
|
.. [2] `Platt, John (1999). "Probabilistic outputs for support vector
|
|
machines and comparison to regularizedlikelihood methods."
|
|
<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
|
|
"""
|
|
|
|
_impl = 'c_svc'
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, C=1.0, kernel='rbf', degree=3, gamma='scale',
|
|
coef0=0.0, shrinking=True, probability=False,
|
|
tol=1e-3, cache_size=200, class_weight=None,
|
|
verbose=False, max_iter=-1, decision_function_shape='ovr',
|
|
break_ties=False,
|
|
random_state=None):
|
|
|
|
super().__init__(
|
|
kernel=kernel, degree=degree, gamma=gamma,
|
|
coef0=coef0, tol=tol, C=C, nu=0., shrinking=shrinking,
|
|
probability=probability, cache_size=cache_size,
|
|
class_weight=class_weight, verbose=verbose, max_iter=max_iter,
|
|
decision_function_shape=decision_function_shape,
|
|
break_ties=break_ties,
|
|
random_state=random_state)
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
'_xfail_checks': {
|
|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
|
|
}
|
|
}
|
|
|
|
|
|
class NuSVC(BaseSVC):
|
|
"""Nu-Support Vector Classification.
|
|
|
|
Similar to SVC but uses a parameter to control the number of support
|
|
vectors.
|
|
|
|
The implementation is based on libsvm.
|
|
|
|
Read more in the :ref:`User Guide <svm_classification>`.
|
|
|
|
Parameters
|
|
----------
|
|
nu : float, default=0.5
|
|
An upper bound on the fraction of margin errors (see :ref:`User Guide
|
|
<nu_svc>`) and a lower bound of the fraction of support vectors.
|
|
Should be in the interval (0, 1].
|
|
|
|
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.
|
|
|
|
degree : int, 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.
|
|
|
|
.. versionchanged:: 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'.
|
|
|
|
shrinking : bool, default=True
|
|
Whether to use the shrinking heuristic.
|
|
See the :ref:`User Guide <shrinking_svm>`.
|
|
|
|
probability : bool, default=False
|
|
Whether to enable probability estimates. This must be enabled prior
|
|
to calling `fit`, will slow down that method as it internally uses
|
|
5-fold cross-validation, and `predict_proba` may be inconsistent with
|
|
`predict`. Read more in the :ref:`User Guide <scores_probabilities>`.
|
|
|
|
tol : float, default=1e-3
|
|
Tolerance for stopping criterion.
|
|
|
|
cache_size : float, default=200
|
|
Specify the size of the kernel cache (in MB).
|
|
|
|
class_weight : {dict, 'balanced'}, default=None
|
|
Set the parameter C of class i to class_weight[i]*C for
|
|
SVC. If not given, all classes are supposed to have
|
|
weight one. The "balanced" mode uses the values of y to automatically
|
|
adjust weights inversely proportional to class frequencies as
|
|
``n_samples / (n_classes * np.bincount(y))``
|
|
|
|
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.
|
|
|
|
decision_function_shape : {'ovo', 'ovr'}, default='ovr'
|
|
Whether to return a one-vs-rest ('ovr') decision function of shape
|
|
(n_samples, n_classes) as all other classifiers, or the original
|
|
one-vs-one ('ovo') decision function of libsvm which has shape
|
|
(n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one
|
|
('ovo') is always used as multi-class strategy. The parameter is
|
|
ignored for binary classification.
|
|
|
|
.. versionchanged:: 0.19
|
|
decision_function_shape is 'ovr' by default.
|
|
|
|
.. versionadded:: 0.17
|
|
*decision_function_shape='ovr'* is recommended.
|
|
|
|
.. versionchanged:: 0.17
|
|
Deprecated *decision_function_shape='ovo' and None*.
|
|
|
|
break_ties : bool, default=False
|
|
If true, ``decision_function_shape='ovr'``, and number of classes > 2,
|
|
:term:`predict` will break ties according to the confidence values of
|
|
:term:`decision_function`; otherwise the first class among the tied
|
|
classes is returned. Please note that breaking ties comes at a
|
|
relatively high computational cost compared to a simple predict.
|
|
|
|
.. versionadded:: 0.22
|
|
|
|
random_state : int, RandomState instance or None, default=None
|
|
Controls the pseudo random number generation for shuffling the data for
|
|
probability estimates. Ignored when `probability` is False.
|
|
Pass an int for reproducible output across multiple function calls.
|
|
See :term:`Glossary <random_state>`.
|
|
|
|
Attributes
|
|
----------
|
|
class_weight_ : ndarray of shape (n_classes,)
|
|
Multipliers of parameter C of each class.
|
|
Computed based on the ``class_weight`` parameter.
|
|
|
|
classes_ : ndarray of shape (n_classes,)
|
|
The unique classes labels.
|
|
|
|
coef_ : ndarray of shape (n_classes * (n_classes -1) / 2, 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_`.
|
|
|
|
dual_coef_ : ndarray of shape (n_classes - 1, n_SV)
|
|
Dual coefficients of the support vector in the decision
|
|
function (see :ref:`sgd_mathematical_formulation`), multiplied by
|
|
their targets.
|
|
For multiclass, coefficient for all 1-vs-1 classifiers.
|
|
The layout of the coefficients in the multiclass case is somewhat
|
|
non-trivial. See the :ref:`multi-class section of the User Guide
|
|
<svm_multi_class>` for details.
|
|
|
|
fit_status_ : int
|
|
0 if correctly fitted, 1 if the algorithm did not converge.
|
|
|
|
intercept_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
|
|
Constants in decision function.
|
|
|
|
support_ : ndarray of shape (n_SV,)
|
|
Indices of support vectors.
|
|
|
|
support_vectors_ : ndarray of shape (n_SV, n_features)
|
|
Support vectors.
|
|
|
|
n_support_ : ndarray of shape (n_classes,), dtype=int32
|
|
Number of support vectors for each class.
|
|
|
|
fit_status_ : int
|
|
0 if correctly fitted, 1 if the algorithm did not converge.
|
|
|
|
probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
|
|
probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)
|
|
If `probability=True`, it corresponds to the parameters learned in
|
|
Platt scaling to produce probability estimates from decision values.
|
|
If `probability=False`, it's an empty array. Platt scaling uses the
|
|
logistic function
|
|
``1 / (1 + exp(decision_value * probA_ + probB_))``
|
|
where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
|
|
more information on the multiclass case and training procedure see
|
|
section 8 of [1]_.
|
|
|
|
shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
|
|
Array dimensions of training vector ``X``.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
|
|
>>> y = np.array([1, 1, 2, 2])
|
|
>>> from sklearn.pipeline import make_pipeline
|
|
>>> from sklearn.preprocessing import StandardScaler
|
|
>>> from sklearn.svm import NuSVC
|
|
>>> clf = make_pipeline(StandardScaler(), NuSVC())
|
|
>>> clf.fit(X, y)
|
|
Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
|
|
>>> print(clf.predict([[-0.8, -1]]))
|
|
[1]
|
|
|
|
See Also
|
|
--------
|
|
SVC : Support Vector Machine for classification using libsvm.
|
|
|
|
LinearSVC : Scalable linear Support Vector Machine for classification using
|
|
liblinear.
|
|
|
|
References
|
|
----------
|
|
.. [1] `LIBSVM: A Library for Support Vector Machines
|
|
<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
|
|
|
|
.. [2] `Platt, John (1999). "Probabilistic outputs for support vector
|
|
machines and comparison to regularizedlikelihood methods."
|
|
<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
|
|
"""
|
|
|
|
_impl = 'nu_svc'
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, nu=0.5, kernel='rbf', degree=3, gamma='scale',
|
|
coef0=0.0, shrinking=True, probability=False, tol=1e-3,
|
|
cache_size=200, class_weight=None, verbose=False, max_iter=-1,
|
|
decision_function_shape='ovr', break_ties=False,
|
|
random_state=None):
|
|
|
|
super().__init__(
|
|
kernel=kernel, degree=degree, gamma=gamma,
|
|
coef0=coef0, tol=tol, C=0., nu=nu, shrinking=shrinking,
|
|
probability=probability, cache_size=cache_size,
|
|
class_weight=class_weight, verbose=verbose, max_iter=max_iter,
|
|
decision_function_shape=decision_function_shape,
|
|
break_ties=break_ties,
|
|
random_state=random_state)
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
'_xfail_checks': {
|
|
'check_methods_subset_invariance':
|
|
'fails for the decision_function method',
|
|
'check_class_weight_classifiers': 'class_weight is ignored.',
|
|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
|
|
}
|
|
}
|
|
|
|
|
|
class SVR(RegressorMixin, BaseLibSVM):
|
|
"""Epsilon-Support Vector Regression.
|
|
|
|
The free parameters in the model are C and epsilon.
|
|
|
|
The implementation is based on libsvm. The fit time complexity
|
|
is more than quadratic with the number of samples which makes it hard
|
|
to scale to datasets with more than a couple of 10000 samples. For large
|
|
datasets consider using :class:`~sklearn.svm.LinearSVR` or
|
|
:class:`~sklearn.linear_model.SGDRegressor` instead, possibly after a
|
|
:class:`~sklearn.kernel_approximation.Nystroem` transformer.
|
|
|
|
Read more in the :ref:`User Guide <svm_regression>`.
|
|
|
|
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.
|
|
|
|
degree : int, 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.
|
|
|
|
.. versionchanged:: 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.
|
|
|
|
C : float, default=1.0
|
|
Regularization parameter. The strength of the regularization is
|
|
inversely proportional to C. Must be strictly positive.
|
|
The penalty is a squared l2 penalty.
|
|
|
|
epsilon : float, default=0.1
|
|
Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
|
|
within which no penalty is associated in the training loss function
|
|
with points predicted within a distance epsilon from the actual
|
|
value.
|
|
|
|
shrinking : bool, default=True
|
|
Whether to use the shrinking heuristic.
|
|
See the :ref:`User Guide <shrinking_svm>`.
|
|
|
|
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.
|
|
|
|
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 from `dual_coef_` and
|
|
`support_vectors_`.
|
|
|
|
dual_coef_ : ndarray of shape (1, n_SV)
|
|
Coefficients of the support vector in the decision function.
|
|
|
|
fit_status_ : int
|
|
0 if correctly fitted, 1 otherwise (will raise warning)
|
|
|
|
intercept_ : ndarray of shape (1,)
|
|
Constants in decision function.
|
|
|
|
n_support_ : ndarray of shape (n_classes,), dtype=int32
|
|
Number of support vectors for each class.
|
|
|
|
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 SVR
|
|
>>> from sklearn.pipeline import make_pipeline
|
|
>>> from sklearn.preprocessing import StandardScaler
|
|
>>> import numpy as np
|
|
>>> n_samples, n_features = 10, 5
|
|
>>> rng = np.random.RandomState(0)
|
|
>>> y = rng.randn(n_samples)
|
|
>>> X = rng.randn(n_samples, n_features)
|
|
>>> regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.2))
|
|
>>> regr.fit(X, y)
|
|
Pipeline(steps=[('standardscaler', StandardScaler()),
|
|
('svr', SVR(epsilon=0.2))])
|
|
|
|
See Also
|
|
--------
|
|
NuSVR : Support Vector Machine for regression implemented using libsvm
|
|
using a parameter to control the number of support vectors.
|
|
|
|
LinearSVR : Scalable Linear Support Vector Machine for regression
|
|
implemented using liblinear.
|
|
|
|
References
|
|
----------
|
|
.. [1] `LIBSVM: A Library for Support Vector Machines
|
|
<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
|
|
|
|
.. [2] `Platt, John (1999). "Probabilistic outputs for support vector
|
|
machines and comparison to regularizedlikelihood methods."
|
|
<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
|
|
"""
|
|
|
|
_impl = 'epsilon_svr'
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, kernel='rbf', degree=3, gamma='scale',
|
|
coef0=0.0, tol=1e-3, C=1.0, epsilon=0.1, shrinking=True,
|
|
cache_size=200, verbose=False, max_iter=-1):
|
|
|
|
super().__init__(
|
|
kernel=kernel, degree=degree, gamma=gamma,
|
|
coef0=coef0, tol=tol, C=C, nu=0., epsilon=epsilon, verbose=verbose,
|
|
shrinking=shrinking, probability=False, cache_size=cache_size,
|
|
class_weight=None, max_iter=max_iter, random_state=None)
|
|
|
|
# mypy error: Decorated property not supported
|
|
@deprecated( # type: ignore
|
|
"The probA_ attribute is deprecated in version 0.23 and will be "
|
|
"removed in version 1.0 (renaming of 0.25).")
|
|
@property
|
|
def probA_(self):
|
|
return self._probA
|
|
|
|
# mypy error: Decorated property not supported
|
|
@deprecated( # type: ignore
|
|
"The probB_ attribute is deprecated in version 0.23 and will be "
|
|
"removed in version 1.0 (renaming of 0.25).")
|
|
@property
|
|
def probB_(self):
|
|
return self._probB
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
'_xfail_checks': {
|
|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
|
|
}
|
|
}
|
|
|
|
|
|
class NuSVR(RegressorMixin, BaseLibSVM):
|
|
"""Nu Support Vector Regression.
|
|
|
|
Similar to NuSVC, for regression, uses a parameter nu to control
|
|
the number of support vectors. However, unlike NuSVC, where nu
|
|
replaces C, here nu replaces the parameter epsilon of epsilon-SVR.
|
|
|
|
The implementation is based on libsvm.
|
|
|
|
Read more in the :ref:`User Guide <svm_regression>`.
|
|
|
|
Parameters
|
|
----------
|
|
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.
|
|
|
|
C : float, default=1.0
|
|
Penalty parameter C of the error term.
|
|
|
|
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.
|
|
|
|
degree : int, 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.
|
|
|
|
.. versionchanged:: 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'.
|
|
|
|
shrinking : bool, default=True
|
|
Whether to use the shrinking heuristic.
|
|
See the :ref:`User Guide <shrinking_svm>`.
|
|
|
|
tol : float, default=1e-3
|
|
Tolerance for stopping criterion.
|
|
|
|
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.
|
|
|
|
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 from `dual_coef_` and
|
|
`support_vectors_`.
|
|
|
|
dual_coef_ : ndarray of shape (1, n_SV)
|
|
Coefficients of the support vector in the decision function.
|
|
|
|
fit_status_ : int
|
|
0 if correctly fitted, 1 otherwise (will raise warning)
|
|
|
|
intercept_ : ndarray of shape (1,)
|
|
Constants in decision function.
|
|
|
|
n_support_ : ndarray of shape (n_classes,), dtype=int32
|
|
Number of support vectors for each class.
|
|
|
|
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 NuSVR
|
|
>>> from sklearn.pipeline import make_pipeline
|
|
>>> from sklearn.preprocessing import StandardScaler
|
|
>>> import numpy as np
|
|
>>> n_samples, n_features = 10, 5
|
|
>>> np.random.seed(0)
|
|
>>> y = np.random.randn(n_samples)
|
|
>>> X = np.random.randn(n_samples, n_features)
|
|
>>> regr = make_pipeline(StandardScaler(), NuSVR(C=1.0, nu=0.1))
|
|
>>> regr.fit(X, y)
|
|
Pipeline(steps=[('standardscaler', StandardScaler()),
|
|
('nusvr', NuSVR(nu=0.1))])
|
|
|
|
See Also
|
|
--------
|
|
NuSVC : Support Vector Machine for classification implemented with libsvm
|
|
with a parameter to control the number of support vectors.
|
|
|
|
SVR : Epsilon Support Vector Machine for regression implemented with
|
|
libsvm.
|
|
|
|
References
|
|
----------
|
|
.. [1] `LIBSVM: A Library for Support Vector Machines
|
|
<http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_
|
|
|
|
.. [2] `Platt, John (1999). "Probabilistic outputs for support vector
|
|
machines and comparison to regularizedlikelihood methods."
|
|
<http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
|
|
"""
|
|
|
|
_impl = 'nu_svr'
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, nu=0.5, C=1.0, kernel='rbf', degree=3,
|
|
gamma='scale', coef0=0.0, shrinking=True,
|
|
tol=1e-3, cache_size=200, verbose=False, max_iter=-1):
|
|
|
|
super().__init__(
|
|
kernel=kernel, degree=degree, gamma=gamma, coef0=coef0,
|
|
tol=tol, C=C, nu=nu, epsilon=0., shrinking=shrinking,
|
|
probability=False, cache_size=cache_size, class_weight=None,
|
|
verbose=verbose, max_iter=max_iter, random_state=None)
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
'_xfail_checks': {
|
|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
|
|
}
|
|
}
|
|
|
|
|
|
class OneClassSVM(OutlierMixin, BaseLibSVM):
|
|
"""Unsupervised Outlier Detection.
|
|
|
|
Estimate the support of a high-dimensional distribution.
|
|
|
|
The implementation is based on libsvm.
|
|
|
|
Read more in the :ref:`User Guide <outlier_detection>`.
|
|
|
|
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.
|
|
|
|
degree : int, 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.
|
|
|
|
.. versionchanged:: 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 :ref:`User Guide <shrinking_svm>`.
|
|
|
|
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.
|
|
|
|
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 from `dual_coef_` and
|
|
`support_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 of `intercept_` and is provided for
|
|
consistency with other outlier detection algorithms.
|
|
|
|
.. versionadded:: 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...])
|
|
"""
|
|
|
|
_impl = 'one_class'
|
|
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, kernel='rbf', degree=3, gamma='scale',
|
|
coef0=0.0, tol=1e-3, nu=0.5, shrinking=True, cache_size=200,
|
|
verbose=False, max_iter=-1):
|
|
|
|
super().__init__(
|
|
kernel, degree, gamma, coef0, tol, 0., nu, 0.,
|
|
shrinking, False, cache_size, None, verbose, max_iter,
|
|
random_state=None)
|
|
|
|
def fit(self, X, y=None, sample_weight=None, **params):
|
|
"""Detects 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.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
"""
|
|
super().fit(X, np.ones(_num_samples(X)),
|
|
sample_weight=sample_weight, **params)
|
|
self.offset_ = -self._intercept_
|
|
return self
|
|
|
|
def decision_function(self, 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.
|
|
"""
|
|
dec = self._decision_function(X).ravel()
|
|
return dec
|
|
|
|
def score_samples(self, 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.
|
|
"""
|
|
return self.decision_function(X) + self.offset_
|
|
|
|
def predict(self, 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.
|
|
"""
|
|
y = super().predict(X)
|
|
return np.asarray(y, dtype=np.intp)
|
|
|
|
# mypy error: Decorated property not supported
|
|
@deprecated( # type: ignore
|
|
"The probA_ attribute is deprecated in version 0.23 and will be "
|
|
"removed in version 1.0.")
|
|
@property
|
|
def probA_(self):
|
|
return self._probA
|
|
|
|
# mypy error: Decorated property not supported
|
|
@deprecated( # type: ignore
|
|
"The probB_ attribute is deprecated in version 0.23 and will be "
|
|
"removed in version 1.0.")
|
|
@property
|
|
def probB_(self):
|
|
return self._probB
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
'_xfail_checks': {
|
|
'check_sample_weights_invariance':
|
|
'zero sample_weight is not equivalent to removing samples',
|
|
}
|
|
}
|