272 lines
9.9 KiB
Python
272 lines
9.9 KiB
Python
import numpy as np
|
|
|
|
from ..base import BaseLibSVM, BaseLibLinear, _get_class_weight
|
|
from . import libsvm
|
|
from .. import liblinear
|
|
|
|
|
|
class SparseBaseLibSVM(BaseLibSVM):
|
|
|
|
_kernel_types = ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']
|
|
_svm_types = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
|
|
|
|
def __init__(self, impl, kernel, degree, gamma, coef0, cache_size,
|
|
tol, C, nu, p, shrinking, probability):
|
|
|
|
assert impl in self._svm_types, \
|
|
"impl should be one of %s, %s was given" % (
|
|
self._svm_types, impl)
|
|
|
|
assert kernel in self._kernel_types, \
|
|
"kernel should be one of %s, "\
|
|
"%s was given." % (self._kernel_types, kernel)
|
|
|
|
self.kernel = kernel
|
|
self.impl = impl
|
|
self.degree = degree
|
|
self.gamma = gamma
|
|
self.coef0 = coef0
|
|
self.cache_size = cache_size
|
|
self.tol = tol
|
|
self.C = C
|
|
self.nu = nu
|
|
self.p = p
|
|
self.shrinking = shrinking
|
|
self.probability = probability
|
|
|
|
# container for when we call fit
|
|
self._support_data = np.empty(0, dtype=np.float64, order='C')
|
|
self._support_indices = np.empty(0, dtype=np.int32, order='C')
|
|
self._support_indptr = np.empty(0, dtype=np.int32, order='C')
|
|
|
|
# strictly speaking, dual_coef is not sparse (see Notes above)
|
|
self._dual_coef_data = np.empty(0, dtype=np.float64, order='C')
|
|
self._dual_coef_indices = np.empty(0, dtype=np.int32, order='C')
|
|
self._dual_coef_indptr = np.empty(0, dtype=np.int32, order='C')
|
|
self.intercept_ = np.empty(0, dtype=np.float64, order='C')
|
|
|
|
# only used in classification
|
|
self.n_support = np.empty(0, dtype=np.int32, order='C')
|
|
|
|
def fit(self, X, y, class_weight={}, sample_weight=[], **params):
|
|
"""
|
|
Fit the SVM model according to the given training data and
|
|
parameters.
|
|
|
|
Parameters
|
|
----------
|
|
X : sparse matrix, shape = [n_samples, n_features]
|
|
Training vectors, where n_samples is the number of samples and
|
|
n_features is the number of features.
|
|
|
|
y : array-like, shape = [n_samples]
|
|
Target values (integers in classification, real numbers in
|
|
regression)
|
|
|
|
class_weight : {dict, 'auto'}, optional
|
|
Weights associated with classes in the form
|
|
{class_label : weight}. If not given, all classes are
|
|
supposed to have weight one.
|
|
|
|
The 'auto' mode uses the values of y to automatically adjust
|
|
weights inversely proportional to class frequencies.
|
|
|
|
sample_weight : array-like, shape = [n_samples], optional
|
|
Weights applied to individual samples (1. for unweighted).
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Returns an instance of self.
|
|
|
|
Notes
|
|
-----
|
|
For maximum effiency, use a sparse matrix in csr format
|
|
(scipy.sparse.csr_matrix)
|
|
"""
|
|
self._set_params(**params)
|
|
|
|
import scipy.sparse
|
|
X = scipy.sparse.csr_matrix(X)
|
|
X.data = np.asanyarray(X.data, dtype=np.float64, order='C')
|
|
y = np.asanyarray(y, dtype=np.float64, order='C')
|
|
sample_weight = np.asanyarray(sample_weight, dtype=np.float64,
|
|
order='C')
|
|
|
|
solver_type = self._svm_types.index(self.impl)
|
|
kernel_type = self._kernel_types.index(self.kernel)
|
|
|
|
self.class_weight, self.class_weight_label = \
|
|
_get_class_weight(class_weight, y)
|
|
|
|
if (kernel_type in [1, 2]) and (self.gamma == 0):
|
|
# if custom gamma is not provided ...
|
|
self.gamma = 1.0 / X.shape[0]
|
|
|
|
self.label_, self.probA_, self.probB_ = libsvm.libsvm_sparse_train(
|
|
X.shape[1], X.data, X.indices, X.indptr, y,
|
|
solver_type, kernel_type, self.degree, self.gamma,
|
|
self.coef0, self.tol, self.C, self._support_data,
|
|
self._support_indices, self._support_indptr,
|
|
self._dual_coef_data, self.intercept_,
|
|
self.class_weight_label, self.class_weight, sample_weight,
|
|
self.n_support, self.nu, self.cache_size, self.p,
|
|
int(self.shrinking), int(self.probability))
|
|
|
|
n_class = len(self.label_) - 1
|
|
n_SV = self._support_indptr.size - 1
|
|
|
|
dual_coef_indices = np.tile(np.arange(n_SV), n_class)
|
|
dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
|
|
dual_coef_indices.size / n_class)
|
|
|
|
# this will fail if n_SV is zero. This is a limitation
|
|
# in scipy.sparse, which does not permit empty matrices
|
|
self.support_vectors_ = scipy.sparse.csr_matrix((self._support_data,
|
|
self._support_indices,
|
|
self._support_indptr),
|
|
(n_SV, X.shape[1]))
|
|
|
|
self.dual_coef_ = scipy.sparse.csr_matrix((self._dual_coef_data,
|
|
dual_coef_indices,
|
|
dual_coef_indptr),
|
|
(n_class, n_SV)
|
|
)
|
|
return self
|
|
|
|
def predict(self, T):
|
|
"""
|
|
This function does classification or regression on an array of
|
|
test vectors T.
|
|
|
|
For a classification model, the predicted class for each
|
|
sample in T is returned. For a regression model, the function
|
|
value of T calculated is returned.
|
|
|
|
For an one-class model, +1 or -1 is returned.
|
|
|
|
Parameters
|
|
----------
|
|
T : scipy.sparse.csr, shape = [n_samples, n_features]
|
|
|
|
Returns
|
|
-------
|
|
C : array, shape = [n_samples]
|
|
"""
|
|
import scipy.sparse
|
|
T = scipy.sparse.csr_matrix(T)
|
|
T.data = np.asanyarray(T.data, dtype=np.float64, order='C')
|
|
kernel_type = self._kernel_types.index(self.kernel)
|
|
|
|
return libsvm.libsvm_sparse_predict(T.data, T.indices, T.indptr,
|
|
self.support_vectors_.data,
|
|
self.support_vectors_.indices,
|
|
self.support_vectors_.indptr,
|
|
self.dual_coef_.data, self.intercept_,
|
|
self._svm_types.index(self.impl), kernel_type,
|
|
self.degree, self.gamma, self.coef0, self.tol,
|
|
self.C, self.class_weight_label, self.class_weight,
|
|
self.nu, self.cache_size, self.p, self.shrinking,
|
|
self.probability, self.n_support, self.label_,
|
|
self.probA_, self.probB_)
|
|
|
|
|
|
class SparseBaseLibLinear(BaseLibLinear):
|
|
|
|
def fit(self, X, y, class_weight={}, **params):
|
|
"""
|
|
Fit the model using X, y as training data.
|
|
|
|
Parameters
|
|
----------
|
|
X : sparse matrix, shape = [n_samples, n_features]
|
|
Training vector, where n_samples in the number of samples and
|
|
n_features is the number of features.
|
|
y : array, shape = [n_samples]
|
|
Target vector relative to X
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Returns an instance of self.
|
|
"""
|
|
self._set_params(**params)
|
|
|
|
import scipy.sparse
|
|
X = scipy.sparse.csr_matrix(X)
|
|
X.data = np.asanyarray(X.data, dtype=np.float64, order='C')
|
|
y = np.asanyarray(y, dtype=np.int32, order='C')
|
|
|
|
self.class_weight, self.class_weight_label = \
|
|
_get_class_weight(class_weight, y)
|
|
|
|
self.raw_coef_, self.label_ = \
|
|
liblinear.csr_train_wrap(X.shape[1], X.data, X.indices,
|
|
X.indptr, y,
|
|
self._get_solver_type(),
|
|
self.tol, self._get_bias(), self.C,
|
|
self.class_weight_label, self.class_weight)
|
|
|
|
return self
|
|
|
|
def predict(self, X):
|
|
"""
|
|
Predict target values of X according to the fitted model.
|
|
|
|
Parameters
|
|
----------
|
|
X : sparse matrix, shape = [n_samples, n_features]
|
|
|
|
Returns
|
|
-------
|
|
C : array, shape = [n_samples]
|
|
"""
|
|
import scipy.sparse
|
|
X = scipy.sparse.csr_matrix(X)
|
|
self._check_n_features(X)
|
|
X.data = np.asanyarray(X.data, dtype=np.float64, order='C')
|
|
|
|
return liblinear.csr_predict_wrap(X.shape[1], X.data,
|
|
X.indices, X.indptr,
|
|
self.raw_coef_,
|
|
self._get_solver_type(),
|
|
self.tol, self.C,
|
|
self.class_weight_label,
|
|
self.class_weight, self.label_,
|
|
self._get_bias())
|
|
|
|
def decision_function(self, X):
|
|
"""
|
|
Return the decision function of X according to the trained
|
|
model.
|
|
|
|
Parameters
|
|
----------
|
|
X : sparse matrix, shape = [n_samples, n_features]
|
|
|
|
Returns
|
|
-------
|
|
T : array-like, shape = [n_samples, n_class]
|
|
Returns the decision function of the sample for each class
|
|
in the model.
|
|
"""
|
|
import scipy.sparse
|
|
X = scipy.sparse.csr_matrix(X)
|
|
self._check_n_features(X)
|
|
X.data = np.asanyarray(X.data, dtype=np.float64, order='C')
|
|
|
|
dec_func = liblinear.csr_decision_function_wrap(
|
|
X.shape[1], X.data, X.indices, X.indptr, self.raw_coef_,
|
|
self._get_solver_type(), self.tol, self.C,
|
|
self.class_weight_label, self.class_weight, self.label_,
|
|
self._get_bias())
|
|
|
|
if len(self.label_) <= 2:
|
|
# in the two-class case, the decision sign needs be flipped
|
|
# due to liblinear's design
|
|
return -dec_func
|
|
else:
|
|
return dec_func
|
|
|
|
libsvm.set_verbosity_wrap(0)
|