481 lines
17 KiB
Python
481 lines
17 KiB
Python
import numpy as np
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from . import libsvm, liblinear
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from ..base import BaseEstimator
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def _get_class_weight(class_weight, y):
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"""
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Estimate class weights for unbalanced datasets.
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"""
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if class_weight == 'auto':
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uy = np.unique(y)
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weight_label = np.asarray(uy, dtype=np.int32, order='C')
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weight = np.array([1.0 / np.sum(y == i) for i in uy],
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dtype=np.float64, order='C')
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weight *= uy.shape[0] / np.sum(weight)
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else:
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weight = np.asarray(class_weight.values(),
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dtype=np.float64, order='C')
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weight_label = np.asarray(class_weight.keys(),
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dtype=np.int32, order='C')
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return weight, weight_label
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class BaseLibSVM(BaseEstimator):
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"""
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Base class for classifiers that use libsvm as library for
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support vector machine classification and regression.
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Should not be used directly, use derived classes instead
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"""
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_kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
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_svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
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def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
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tol, C, nu, p, shrinking, probability):
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if not impl in self._svm_types:
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raise ValueError("impl should be one of %s, %s was given" % (
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self._svm_types, impl))
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if not (kernel in self._kernel_types or hasattr(kernel, '__call__')):
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raise ValueError("kernel should be one of %s or a callable, " \
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"%s was given." % (self._kernel_types, kernel))
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self.kernel = kernel
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self.impl = impl
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self.degree = degree
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self.gamma = gamma
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self.coef0 = coef0
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self.cache_size = cache_size
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self.tol = tol
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self.C = C
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self.nu = nu
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self.p = p
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self.shrinking = shrinking
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self.probability = probability
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def _get_kernel(self, X):
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""" Get the kernel type code as well as the data transformed by
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the kernel (if the kernel is a callable.
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"""
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if hasattr(self.kernel, '__call__'):
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# in the case of precomputed kernel given as a function, we
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# have to compute explicitly the kernel matrix
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_X = np.asanyarray(self.kernel(X, self.__Xfit),
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dtype=np.float64, order='C')
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kernel_type = 4
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else:
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kernel_type = self._kernel_types.index(self.kernel)
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_X = X
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return kernel_type, _X
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def fit(self, X, y, class_weight={}, sample_weight=[], **params):
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"""
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Fit the SVM model according to the given training data and
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parameters.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Training vectors, where n_samples is the number of samples
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and n_features is the number of features.
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y : array-like, shape = [n_samples]
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Target values (integers in classification, real numbers in
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regression)
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class_weight : {dict, 'auto'}, optional
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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. The 'auto' mode uses the values of y to
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automatically adjust weights inversely proportional to
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class frequencies.
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sample_weight : array-like, shape = [n_samples], optional
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Weights applied to individual samples (1. for unweighted).
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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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self._set_params(**params)
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X = np.asanyarray(X, dtype=np.float64, order='C')
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y = np.asanyarray(y, dtype=np.float64, order='C')
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sample_weight = np.asanyarray(sample_weight, dtype=np.float64,
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order='C')
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if hasattr(self.kernel, '__call__'):
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# you must store a reference to X to compute the kernel in predict
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# there's a way around this, but it involves patching libsvm
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# TODO: put keyword copy to copy on demand
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self.__Xfit = X
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kernel_type, _X = self._get_kernel(X)
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self.class_weight, self.class_weight_label = \
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_get_class_weight(class_weight, y)
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# check dimensions
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solver_type = self._svm_types.index(self.impl)
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if solver_type != 2 and _X.shape[0] != y.shape[0]:
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raise ValueError("X and y have incompatible shapes.\n" +
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"X has %s features, but y has %s." % \
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(_X.shape[0], y.shape[0]))
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if self.kernel == "precomputed" and X.shape[0] != X.shape[1]:
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raise ValueError("X.shape[0] should be equal to X.shape[1]")
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if (kernel_type in [1, 2]) and (self.gamma == 0):
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# if custom gamma is not provided ...
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self.gamma = 1.0 / _X.shape[0]
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self.shape_fit_ = X.shape
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self.support_, self.support_vectors_, self.n_support_, \
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self.dual_coef_, self.intercept_, self.label_, self.probA_, \
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self.probB_ = \
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libsvm.train(_X, y, solver_type, kernel_type, self.degree,
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self.gamma, self.coef0, self.tol, self.C,
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self.nu, self.cache_size, self.p,
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self.class_weight_label, self.class_weight,
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sample_weight, int(self.shrinking),
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int(self.probability))
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return self
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def predict(self, X):
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"""
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This function does classification or regression on an array of
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test vectors X.
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For a classification model, the predicted class for each
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sample in X is returned. For a regression model, the function
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value of X calculated is returned.
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For an one-class model, +1 or -1 is returned.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples]
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"""
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X = np.atleast_2d(np.asanyarray(X, dtype=np.float64, order='C'))
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n_samples, n_features = X.shape
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kernel_type, X = self._get_kernel(X)
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if self.kernel == "precomputed":
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if X.shape[1] != self.shape_fit_[0]:
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raise ValueError("X.shape[1] should be equal to the number of "
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"samples at training time!")
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elif n_features != self.shape_fit_[1]:
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raise ValueError("X.shape[1] should be equal to the number of "
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"features at training time!")
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return libsvm.predict( X, self.support_vectors_,
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self.dual_coef_, self.intercept_,
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self._svm_types.index(self.impl), kernel_type,
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self.degree, self.gamma, self.coef0, self.tol, self.C,
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self.nu, self.cache_size, self.p, self.n_support_,
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self.support_, self.label_, self.class_weight_label,
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self.class_weight, self.probA_, self.probB_,
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int(self.shrinking), int(self.probability))
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def predict_proba(self, T):
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"""
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This function does classification or regression on a test vector T
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given a model with probability information.
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Parameters
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----------
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T : array-like, shape = [n_samples, n_features]
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Returns
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-------
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T : array-like, shape = [n_samples, n_classes]
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Returns the probability of the sample for each class in
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the model, where classes are ordered by arithmetical
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order.
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Notes
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-----
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The probability model is created using cross validation, so
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the results can be slightly different than those obtained by
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predict. Also, it will meaningless results on very small
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datasets.
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"""
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if not self.probability:
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raise ValueError(
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"probability estimates must be enabled to use this method")
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T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
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kernel_type, T = self._get_kernel(T)
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if self.impl not in ('c_svc', 'nu_svc'):
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raise NotImplementedError
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pprob = libsvm.predict_proba(T, self.support_vectors_,
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self.dual_coef_, self.intercept_,
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self._svm_types.index(self.impl), kernel_type,
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self.degree, self.gamma, self.coef0, self.tol,
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self.C, self.nu, self.cache_size,
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self.p, self.n_support_,
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self.support_, self.label_,
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self.class_weight_label,
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self.class_weight,
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self.probA_, self.probB_, int(self.shrinking),
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int(self.probability))
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return pprob
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def predict_log_proba(self, T):
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"""
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This function does classification or regression on a test vector T
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given a model with probability information.
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Parameters
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----------
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T : array-like, shape = [n_samples, n_features]
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Returns
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-------
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T : array-like, shape = [n_samples, n_classes]
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Returns the log-probabilities of the sample for each class in
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the model, where classes are ordered by arithmetical
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order.
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Notes
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-----
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The probability model is created using cross validation, so
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the results can be slightly different than those obtained by
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predict. Also, it will meaningless results on very small
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datasets.
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"""
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return np.log(self.predict_proba(T))
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def decision_function(self, T):
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"""
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Calculate the distance of the samples T to the separating hyperplane.
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Parameters
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----------
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T : array-like, shape = [n_samples, n_features]
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Returns
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-------
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T : array-like, shape = [n_samples, n_class * (n_class-1) / 2]
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Returns the decision function of the sample for each class
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in the model.
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"""
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T = np.atleast_2d(np.asanyarray(T, dtype=np.float64, order='C'))
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kernel_type, T = self._get_kernel(T)
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dec_func = libsvm.decision_function(T, self.support_vectors_,
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self.dual_coef_, self.intercept_,
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self._svm_types.index(self.impl), kernel_type,
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self.degree, self.gamma, self.coef0, self.tol,
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self.C, self.class_weight_label,
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self.class_weight, self.nu, self.cache_size,
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self.p, int(self.shrinking),
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int(self.probability), self.n_support_,
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self.support_, self.label_, self.probA_,
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self.probB_)
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if self.impl != 'one_class':
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# libsvm has the convention of returning negative values for
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# rightmost labels, so we invert the sign since our label_ is
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# sorted by increasing order
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return -dec_func
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else:
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return dec_func
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@property
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def coef_(self):
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if self.kernel != 'linear':
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raise NotImplementedError('coef_ is only available when using a linear kernel')
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return np.dot(self.dual_coef_, self.support_vectors_)
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class BaseLibLinear(BaseEstimator):
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"""
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Base for classes binding liblinear (dense and sparse versions)
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"""
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_solver_type_dict = {
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'PL2_LLR_D0' : 0, # L2 penalty, logistic regression
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'PL2_LL2_D1' : 1, # L2 penalty, L2 loss, dual form
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'PL2_LL2_D0' : 2, # L2 penalty, L2 loss, primal form
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'PL2_LL1_D1' : 3, # L2 penalty, L1 Loss, dual form
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'MC_SVC' : 4, # Multi-class Support Vector Classification
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'PL1_LL2_D0' : 5, # L1 penalty, L2 Loss, primal form
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'PL1_LLR_D0' : 6, # L1 penalty, logistic regression
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'PL2_LLR_D1' : 7, # L2 penalty, logistic regression, dual form
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}
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def __init__(self, penalty='l2', loss='l2', dual=True, tol=1e-4, C=1.0,
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multi_class=False, fit_intercept=True, intercept_scaling=1):
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self.penalty = penalty
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self.loss = loss
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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.fit_intercept = fit_intercept
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self.intercept_scaling = intercept_scaling
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self.multi_class = multi_class
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# Check that the arguments given are valid:
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self._get_solver_type()
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def _get_solver_type(self):
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""" Return the magic number for the solver described by the
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settings.
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"""
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if self.multi_class:
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solver_type = 'MC_SVC'
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else:
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solver_type = "P%s_L%s_D%d" % (
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self.penalty.upper(), self.loss.upper(), int(self.dual))
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if not solver_type in self._solver_type_dict:
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raise ValueError('Not supported set of arguments: '
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+ solver_type)
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return self._solver_type_dict[solver_type]
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def fit(self, X, y, class_weight={}, **params):
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"""
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Fit the model according to the given training data and
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parameters.
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Parameters
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----------
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X : array-like, 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, shape = [n_samples]
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Target vector relative to X
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class_weight : {dict, 'auto'}, optional
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Weights associated with classes. If not given, all classes
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are supposed to have weight one.
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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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self._set_params(**params)
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self.class_weight, self.class_weight_label = \
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_get_class_weight(class_weight, y)
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X = np.asanyarray(X, dtype=np.float64, order='C')
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y = np.asanyarray(y, dtype=np.int32, order='C')
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self.raw_coef_, self.label_ = liblinear.train_wrap(X, y,
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self._get_solver_type(), self.tol,
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self._get_bias(), self.C,
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self.class_weight_label, self.class_weight)
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return self
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def predict(self, X):
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"""
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Predict target values of X according to the fitted model.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples]
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"""
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X = np.asanyarray(X, dtype=np.float64, order='C')
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self._check_n_features(X)
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coef = self.raw_coef_
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return liblinear.predict_wrap(X, coef,
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self._get_solver_type(),
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self.tol, self.C,
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self.class_weight_label,
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self.class_weight, self.label_,
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self._get_bias())
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def decision_function(self, X):
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"""
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Return the decision function of X according to the trained
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model.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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T : array-like, shape = [n_samples, n_class]
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Returns the decision function of the sample for each class
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in the model.
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"""
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X = np.atleast_2d(np.asanyarray(X, dtype=np.float64, order='C'))
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self._check_n_features(X)
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dec_func = liblinear.decision_function_wrap(
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X, self.raw_coef_, self._get_solver_type(), self.tol,
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self.C, self.class_weight_label, self.class_weight,
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self.label_, self._get_bias())
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if len(self.label_) <= 2:
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# in the two-class case, the decision sign needs be flipped
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# due to liblinear's design
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return -dec_func
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else:
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return dec_func
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def _check_n_features(self, X):
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n_features = self.raw_coef_.shape[1]
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if self.fit_intercept:
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n_features -= 1
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if X.shape[1] != n_features:
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raise ValueError("X.shape[1] should be %d, not %d." % (n_features,
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X.shape[1]))
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@property
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def intercept_(self):
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if self.fit_intercept:
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ret = self.intercept_scaling * self.raw_coef_[:, -1]
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if len(self.label_) <= 2:
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ret *= -1
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return ret
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return 0.0
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@property
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def coef_(self):
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if self.fit_intercept:
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ret = self.raw_coef_[:, : -1]
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else:
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ret = self.raw_coef_
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if len(self.label_) <= 2:
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return -ret
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else:
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return ret
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def predict_proba(self, T):
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# only available for logistic regression
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raise NotImplementedError(
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'liblinear does not provide this functionality')
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def _get_bias(self):
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if self.fit_intercept:
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return self.intercept_scaling
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else:
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return -1.0
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libsvm.set_verbosity_wrap(0)
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