617 lines
21 KiB
Python
617 lines
21 KiB
Python
from ..base import ClassifierMixin, RegressorMixin
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from ..feature_selection.selector_mixin import SelectorMixin
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from .base import BaseLibLinear, BaseLibSVM
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class LinearSVC(BaseLibLinear, ClassifierMixin, SelectorMixin):
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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.
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This class supports both dense and sparse input. Use C-ordered arrays or
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CSR matrices containing 64-bit floats for optimal performance; any other
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input format will be converted (and copied).
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Parameters
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----------
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C : float, optional (default=1.0)
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Penalty parameter C of the error term.
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loss : string, 'l1' or 'l2' (default='l2')
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Specifies the loss function. 'l1' is the hinge loss (standard SVM)
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while 'l2' is the squared hinge loss.
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penalty : string, 'l1' or '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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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, optional (default=1e-4)
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Tolerance for stopping criteria
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multi_class: boolean, optional (default=False)
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Perform multi-class SVM as per Cramer and Singer. If active,
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the options loss, penalty and dual will be ignored.
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fit_intercept : boolean, optional (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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(e.g. data is expected to be already centered).
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intercept_scaling : float, optional (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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scale_C : bool
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Scale C with number of samples. It makes the setting of C independent
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of the number of samples.
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Attributes
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----------
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`coef_` : array, shape = [n_features] if n_classes == 2 \
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else [n_classes, n_features]
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Weights asigned to the features (coefficients in the primal
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problem). This is only available in the case of linear kernel.
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`coef_` is readonly property derived from `raw_coef_` that \
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follows the internal memory layout of liblinear.
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`intercept_` : array, shape = [1] if n_classes == 2 else [n_classes]
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Constants in decision function.
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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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**References:**
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`LIBLINEAR: A Library for Large Linear Classification
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<http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__
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See also
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--------
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SVC
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"""
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# all the implementation is provided by the mixins
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pass
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class SVC(BaseLibSVM, ClassifierMixin):
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"""C-Support Vector Classification.
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Parameters
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----------
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C : float, optional (default=1.0)
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Penalty parameter C of the error term.
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kernel : string, optional (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'.
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If none is given, 'rbf' will be used.
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degree : int, optional (default=3)
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Degree of kernel function.
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It is significant only in 'poly' and 'sigmoid'.
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gamma : float, optional (default=0.0)
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Kernel coefficient for 'rbf' and 'poly'.
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If gamma is 0.0 then 1/n_features will be used instead.
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coef0 : float, optional (default=0.0)
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Independent term in kernel function.
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It is only significant in 'poly' and 'sigmoid'.
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probability: boolean, optional (default=False)
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Whether to enable probability estimates. This must be enabled prior
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to calling predict_proba.
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shrinking: boolean, optional (default=True)
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Whether to use the shrinking heuristic.
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tol: float, optional (default=1e-3)
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Tolerance for stopping criterion.
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cache_size: float, optional
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Specify the size of the kernel cache (in MB)
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scale_C : bool
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Scale C with number of samples. It makes the setting of C independant
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of the number of samples.
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Attributes
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----------
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`support_` : array-like, shape = [n_SV]
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Index of support vectors.
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`support_vectors_` : array-like, shape = [n_SV, n_features]
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Support vectors.
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`n_support_` : array-like, dtype=int32, shape = [n_class]
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number of support vector for each class.
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`dual_coef_` : array, shape = [n_class-1, n_SV]
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Coefficients of the support vector in the decision function.
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`coef_` : array, shape = [n_class-1, n_features]
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Weights asigned to the features (coefficients in the primal
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problem). This is only available in the case of linear kernel.
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`coef_` is readonly property derived from `dual_coef_` and
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`support_vectors_`
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`intercept_` : array, shape = [n_class * (n_class-1) / 2]
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Constants in decision function.
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Examples
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--------
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>>> import numpy as np
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>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
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>>> y = np.array([1, 1, 2, 2])
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>>> from sklearn.svm import SVC
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>>> clf = SVC()
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>>> clf.fit(X, y)
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SVC(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf',
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probability=False, scale_C=False, shrinking=True, tol=0.001)
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>>> print clf.predict([[-0.8, -1]])
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[ 1.]
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See also
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--------
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SVR, LinearSVC
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"""
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def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=0.0,
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coef0=0.0, shrinking=True, probability=False,
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tol=1e-3, cache_size=200, scale_C=False):
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super(SVC, self).__init__('c_svc', kernel, degree, gamma, coef0, tol,
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C, 0., 0., shrinking, probability,
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cache_size, scale_C, sparse="auto")
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class NuSVC(BaseLibSVM, ClassifierMixin):
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"""Nu-Support Vector Classification.
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Parameters
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----------
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nu : float, optional (default=0.5)
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An upper bound on the fraction of training errors and a lower
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bound of the fraction of support vectors. Should be in the
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interval (0, 1].
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kernel : string, optional (default='rbf')
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Specifies the kernel type to be used in the algorithm.
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one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'.
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If none is given 'rbf' will be used.
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degree : int, optional (default=3)
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degree of kernel function
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is significant only in poly, rbf, sigmoid
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gamma : float, optional (default=0.0)
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kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features
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will be taken.
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coef0 : float, optional (default=0.0)
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independent term in kernel function. It is only significant
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in poly/sigmoid.
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probability: boolean, optional (default=False)
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Whether to enable probability estimates. This must be enabled prior
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to calling predict_proba.
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shrinking: boolean, optional (default=True)
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Whether to use the shrinking heuristic.
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tol: float, optional (default=1e-3)
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Tolerance for stopping criterion.
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cache_size: float, optional
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Specify the size of the kernel cache (in MB)
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Attributes
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----------
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`support_` : array-like, shape = [n_SV]
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Index of support vectors.
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`support_vectors_` : array-like, shape = [n_SV, n_features]
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Support vectors.
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`n_support_` : array-like, dtype=int32, shape = [n_class]
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number of support vector for each class.
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`dual_coef_` : array, shape = [n_classes-1, n_SV]
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Coefficients of the support vector in the decision function.
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`coef_` : array, shape = [n_classes-1, n_features]
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Weights asigned to the features (coefficients in the primal
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problem). This is only available in the case of linear kernel.
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`coef_` is readonly property derived from `dual_coef_` and
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`support_vectors_`
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`intercept_` : array, shape = [n_class * (n_class-1) / 2]
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Constants in decision function.
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Examples
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--------
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>>> import numpy as np
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>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
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>>> y = np.array([1, 1, 2, 2])
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>>> from sklearn.svm import NuSVC
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>>> clf = NuSVC()
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>>> clf.fit(X, y)
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NuSVC(cache_size=200, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', nu=0.5,
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probability=False, shrinking=True, tol=0.001)
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>>> print clf.predict([[-0.8, -1]])
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[ 1.]
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See also
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--------
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SVC, LinearSVC, SVR
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"""
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def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma=0.0,
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coef0=0.0, shrinking=True, probability=False,
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tol=1e-3, cache_size=200):
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super(NuSVC, self).__init__('nu_svc', kernel, degree, gamma, coef0,
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tol, 0., nu, 0., shrinking, probability,
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cache_size, scale_C=None, sparse="auto")
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class SVR(BaseLibSVM, RegressorMixin):
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"""epsilon-Support Vector Regression.
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The free parameters in the model are C and epsilon.
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Parameters
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----------
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C : float, optional (default=1.0)
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penalty parameter C of the error term.
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epsilon : float, optional (default=0.1)
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epsilon in the epsilon-SVR model. It specifies the epsilon-tube
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within which no penalty is associated in the training loss function
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with points predicted within a distance epsilon from the actual
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value.
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kernel : string, optional (default='rbf')
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Specifies the kernel type to be used in the algorithm.
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one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'.
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If none is given 'rbf' will be used.
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degree : int, optional (default=3)
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degree of kernel function
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is significant only in poly, rbf, sigmoid
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gamma : float, optional (default=0.0)
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kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features
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will be taken.
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coef0 : float, optional (default=0.0)
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independent term in kernel function. It is only significant
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in poly/sigmoid.
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probability: boolean, optional (default=False)
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Whether to enable probability estimates. This must be enabled prior
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to calling predict_proba.
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shrinking: boolean, optional (default=True)
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Whether to use the shrinking heuristic.
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tol: float, optional (default=1e-3)
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Tolerance for stopping criterion.
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cache_size: float, optional
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Specify the size of the kernel cache (in MB)
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scale_C : bool
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Scale C with number of samples. It makes the setting of C independant
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of the number of samples.
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Attributes
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----------
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`support_` : array-like, shape = [n_SV]
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Index of support vectors.
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`support_vectors_` : array-like, shape = [nSV, n_features]
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Support vectors.
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`dual_coef_` : array, shape = [n_classes-1, n_SV]
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Coefficients of the support vector in the decision function.
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`coef_` : array, shape = [n_classes-1, n_features]
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Weights asigned to the features (coefficients in the primal
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problem). This is only available in the case of linear kernel.
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`coef_` is readonly property derived from `dual_coef_` and
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`support_vectors_`
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`intercept_` : array, shape = [n_class * (n_class-1) / 2]
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Constants in decision function.
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Examples
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--------
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>>> from sklearn.svm import SVR
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>>> import numpy as np
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>>> n_samples, n_features = 10, 5
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>>> np.random.seed(0)
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>>> y = np.random.randn(n_samples)
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>>> X = np.random.randn(n_samples, n_features)
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>>> clf = SVR(C=1.0, epsilon=0.2)
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>>> clf.fit(X, y)
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SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.2, gamma=0.2,
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kernel='rbf', probability=False, scale_C=False, shrinking=True,
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tol=0.001)
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See also
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--------
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NuSVR
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"""
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def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,
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tol=1e-3, C=1.0, epsilon=0.1, shrinking=True,
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probability=False, cache_size=200, scale_C=False):
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super(SVR, self).__init__('epsilon_svr', kernel, degree, gamma, coef0,
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tol, C, 0., epsilon, shrinking, probability,
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cache_size, scale_C, sparse="auto")
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def fit(self, X, y, sample_weight=None, **params):
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"""
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Fit the SVM model according to the given training data and parameters.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vector, where n_samples is the number of samples and
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n_features is the number of features.
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y : array, shape = [n_samples]
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Target values. Array of floating-point numbers.
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cache_size: float, optional
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Specify the size of the cache (in MB)
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Returns
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-------
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self : object
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Returns self.
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"""
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# we copy this method because SVR does not accept class_weight
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return super(SVR, self).fit(X, y, sample_weight=sample_weight,
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**params)
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class NuSVR(BaseLibSVM, RegressorMixin):
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"""Nu Support Vector Regression.
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Similar to NuSVC, for regression, uses a parameter nu to control
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the number of support vectors. However, unlike NuSVC, where nu
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replaces C, here nu replaces with the parameter epsilon of SVR.
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Parameters
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----------
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C : float, optional (default=1.0)
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penalty parameter C of the error term.
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nu : float, optional
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An upper bound on the fraction of training errors and a lower bound of
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the fraction of support vectors. Should be in the interval (0, 1]. By
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default 0.5 will be taken. Only available if impl='nu_svc'.
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kernel : string, optional (default='rbf')
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Specifies the kernel type to be used in the algorithm.
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one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'.
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If none is given 'rbf' will be used.
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degree : int, optional (default=3)
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degree of kernel function
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is significant only in poly, rbf, sigmoid
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gamma : float, optional (default=0.0)
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kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features
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will be taken.
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coef0 : float, optional (default=0.0)
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independent term in kernel function. It is only significant
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in poly/sigmoid.
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probability: boolean, optional (default=False)
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Whether to enable probability estimates. This must be enabled prior
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to calling predict_proba.
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shrinking: boolean, optional (default=True)
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Whether to use the shrinking heuristic.
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tol: float, optional (default=1e-3)
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Tolerance for stopping criterion.
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cache_size: float, optional
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Specify the size of the kernel cache (in MB)
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scale_C : bool
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Scale C with number of samples. It makes the setting of C independant
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of the number of samples.
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Attributes
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----------
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`support_` : array-like, shape = [n_SV]
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Index of support vectors.
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`support_vectors_` : array-like, shape = [nSV, n_features]
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Support vectors.
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`dual_coef_` : array, shape = [n_classes-1, n_SV]
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Coefficients of the support vector in the decision function.
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`coef_` : array, shape = [n_classes-1, n_features]
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Weights asigned to the features (coefficients in the primal
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problem). This is only available in the case of linear kernel.
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`coef_` is readonly property derived from `dual_coef_` and
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`support_vectors_`
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`intercept_` : array, shape = [n_class * (n_class-1) / 2]
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Constants in decision function.
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Examples
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--------
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>>> from sklearn.svm import NuSVR
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>>> import numpy as np
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>>> n_samples, n_features = 10, 5
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>>> np.random.seed(0)
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>>> y = np.random.randn(n_samples)
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>>> X = np.random.randn(n_samples, n_features)
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>>> clf = NuSVR(C=1.0, nu=0.1)
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>>> clf.fit(X, y)
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NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.2, kernel='rbf',
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nu=0.1, probability=False, scale_C=False, shrinking=True, tol=0.001)
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See also
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--------
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NuSVC, SVR
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"""
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def __init__(self, nu=0.5, C=1.0, kernel='rbf', degree=3,
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gamma=0.0, coef0=0.0, shrinking=True,
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probability=False, tol=1e-3, cache_size=200,
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scale_C=False):
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super(NuSVR, self).__init__('nu_svr', kernel, degree, gamma, coef0,
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tol, C, nu, 0., shrinking, probability,
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cache_size, scale_C, sparse="auto")
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def fit(self, X, y, sample_weight=None, **params):
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"""
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Fit the SVM model according to the given training data and parameters.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vector, where n_samples is the number of samples and
|
|
n_features is the number of features.
|
|
y : array, shape = [n_samples]
|
|
Target values. Array of floating-point numbers.
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Returns self.
|
|
"""
|
|
# we copy this method because SVR does not accept class_weight
|
|
return super(NuSVR, self).fit(X, y, sample_weight=[], **params)
|
|
|
|
|
|
class OneClassSVM(BaseLibSVM):
|
|
"""Unsupervised Outliers Detection.
|
|
|
|
Estimate the support of a high-dimensional distribution.
|
|
|
|
Parameters
|
|
----------
|
|
kernel : string, optional
|
|
Specifies the kernel type to be used in
|
|
the algorithm. Can be one of 'linear', 'poly', 'rbf', 'sigmoid',
|
|
'precomputed'. If none is given 'rbf' will be used.
|
|
|
|
nu : float, optional
|
|
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.
|
|
|
|
degree : int, optional
|
|
Degree of kernel function. Significant only in poly, rbf, sigmoid.
|
|
|
|
gamma : float, optional (default=0.0)
|
|
kernel coefficient for rbf and poly, if gamma is 0.0 then 1/n_features
|
|
will be taken.
|
|
|
|
coef0 : float, optional
|
|
Independent term in kernel function. It is only significant in
|
|
poly/sigmoid.
|
|
|
|
tol: float, optional
|
|
Tolerance for stopping criterion.
|
|
|
|
shrinking: boolean, optional
|
|
Whether to use the shrinking heuristic.
|
|
|
|
cache_size: float, optional
|
|
Specify the size of the kernel cache (in MB)
|
|
|
|
scale_C : bool
|
|
Scale C with number of samples. It makes the setting of C independant
|
|
of the number of samples.
|
|
|
|
Attributes
|
|
----------
|
|
`support_` : array-like, shape = [n_SV]
|
|
Index of support vectors.
|
|
|
|
`support_vectors_` : array-like, shape = [nSV, n_features]
|
|
Support vectors.
|
|
|
|
`dual_coef_` : array, shape = [n_classes-1, n_SV]
|
|
Coefficient of the support vector in the decision function.
|
|
|
|
`coef_` : array, shape = [n_classes-1, n_features]
|
|
Weights asigned to the features (coefficients in the primal
|
|
problem). This is only available in the case of linear kernel.
|
|
|
|
`coef_` is readonly property derived from `dual_coef_` and
|
|
`support_vectors_`
|
|
|
|
`intercept_` : array, shape = [n_classes-1]
|
|
Constants in decision function.
|
|
|
|
"""
|
|
def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, tol=1e-3,
|
|
nu=0.5, shrinking=True, cache_size=200):
|
|
|
|
super(OneClassSVM, self).__init__('one_class', kernel, degree, gamma,
|
|
coef0, tol, 0., nu, 0., shrinking,
|
|
False, cache_size, scale_C=None,
|
|
sparse="auto")
|
|
|
|
def fit(self, X, class_weight={}, sample_weight=None, **params):
|
|
"""
|
|
Detects the soft boundary of the set of samples X.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
|
|
Set of samples, where n_samples is the number of samples and
|
|
n_features is the number of features.
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Returns self.
|
|
|
|
Notes
|
|
-----
|
|
If X is not a C-ordered contiguous array or a scipy.sparse.csr_matrix,
|
|
it is copied.
|
|
|
|
"""
|
|
super(OneClassSVM, self).fit(
|
|
X, [], class_weight=class_weight, sample_weight=sample_weight,
|
|
**params)
|
|
return self
|