188 lines
6.6 KiB
Python
188 lines
6.6 KiB
Python
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from ...base import ClassifierMixin, RegressorMixin
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from .base import SparseBaseLibSVM, SparseBaseLibLinear
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from ...linear_model.sparse.base import CoefSelectTransformerMixin
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class SVC(SparseBaseLibSVM, ClassifierMixin):
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"""SVC for sparse matrices (csr).
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See :class:`sklearn.svm.SVC` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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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.sparse 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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"""
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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)
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class NuSVC(SparseBaseLibSVM, ClassifierMixin):
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"""NuSVC for sparse matrices (csr).
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See :class:`sklearn.svm.NuSVC` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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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.sparse 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, 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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"""
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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, scale_C=False):
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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)
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class SVR(SparseBaseLibSVM, RegressorMixin):
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"""SVR for sparse matrices (csr)
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See :class:`sklearn.svm.SVR` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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Examples
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--------
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>>> from sklearn.svm.sparse 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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"""
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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)
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class NuSVR(SparseBaseLibSVM, RegressorMixin):
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"""NuSVR for sparse matrices (csr)
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See :class:`sklearn.svm.NuSVC` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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Examples
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--------
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>>> from sklearn.svm.sparse 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(nu=0.1, C=1.0)
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>>> clf.fit(X, y)
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NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.2,
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kernel='rbf', nu=0.1, probability=False, scale_C=False, shrinking=True,
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tol=0.001)
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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, epsilon=0.1,
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probability=False, tol=1e-3, cache_size=200, scale_C=False):
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super(NuSVR, self).__init__('nu_svr', kernel, degree, gamma, coef0,
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tol, C, nu, epsilon, shrinking,
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probability, cache_size, scale_C)
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class OneClassSVM(SparseBaseLibSVM):
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"""OneClassSVM for sparse matrices (csr)
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See :class:`sklearn.svm.OneClassSVM` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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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, nu=0.5, shrinking=True,
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probability=False, cache_size=200, scale_C=False):
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super(OneClassSVM, self).__init__('one_class', kernel, degree, gamma,
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coef0, tol, 0.0, nu, 0.0, shrinking,
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probability, cache_size, scale_C)
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def fit(self, X, class_weight=None, sample_weight=None):
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super(OneClassSVM, self).fit(
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X, [], class_weight=class_weight, sample_weight=sample_weight)
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class LinearSVC(SparseBaseLibLinear, ClassifierMixin,
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CoefSelectTransformerMixin):
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"""Linear Support Vector Classification, Sparse Version
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Similar to SVC with parameter kernel='linear', but uses internally
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liblinear rather than libsvm, so it has more flexibility in the
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choice of penalties and loss functions and should be faster for
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huge datasets.
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See :class:`sklearn.svm.SVC` for a complete list of parameters
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Notes
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-----
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For best results, this accepts a matrix in csr format
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(scipy.sparse.csr), but should be able to convert from any array-like
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object (including other sparse representations).
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"""
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pass
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