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Batuhan Berk Başoğlu 2020-11-12 11:05:57 -05:00
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import numpy as np
import scipy.sparse as sp
import warnings
from abc import ABCMeta, abstractmethod
# mypy error: error: Module 'sklearn.svm' has no attribute '_libsvm'
# (and same for other imports)
from . import _libsvm as libsvm # type: ignore
from .import _liblinear as liblinear # type: ignore
from . import _libsvm_sparse as libsvm_sparse # type: ignore
from ..base import BaseEstimator, ClassifierMixin
from ..preprocessing import LabelEncoder
from ..utils.multiclass import _ovr_decision_function
from ..utils import check_array, check_random_state
from ..utils import column_or_1d
from ..utils import compute_class_weight
from ..utils.extmath import safe_sparse_dot
from ..utils.validation import check_is_fitted, _check_large_sparse
from ..utils.validation import _num_samples
from ..utils.validation import _check_sample_weight, check_consistent_length
from ..utils.multiclass import check_classification_targets
from ..exceptions import ConvergenceWarning
from ..exceptions import NotFittedError
LIBSVM_IMPL = ['c_svc', 'nu_svc', 'one_class', 'epsilon_svr', 'nu_svr']
def _one_vs_one_coef(dual_coef, n_support, support_vectors):
"""Generate primal coefficients from dual coefficients
for the one-vs-one multi class LibSVM in the case
of a linear kernel."""
# get 1vs1 weights for all n*(n-1) classifiers.
# this is somewhat messy.
# shape of dual_coef_ is nSV * (n_classes -1)
# see docs for details
n_class = dual_coef.shape[0] + 1
# XXX we could do preallocation of coef but
# would have to take care in the sparse case
coef = []
sv_locs = np.cumsum(np.hstack([[0], n_support]))
for class1 in range(n_class):
# SVs for class1:
sv1 = support_vectors[sv_locs[class1]:sv_locs[class1 + 1], :]
for class2 in range(class1 + 1, n_class):
# SVs for class1:
sv2 = support_vectors[sv_locs[class2]:sv_locs[class2 + 1], :]
# dual coef for class1 SVs:
alpha1 = dual_coef[class2 - 1, sv_locs[class1]:sv_locs[class1 + 1]]
# dual coef for class2 SVs:
alpha2 = dual_coef[class1, sv_locs[class2]:sv_locs[class2 + 1]]
# build weight for class1 vs class2
coef.append(safe_sparse_dot(alpha1, sv1)
+ safe_sparse_dot(alpha2, sv2))
return coef
class BaseLibSVM(BaseEstimator, metaclass=ABCMeta):
"""Base class for estimators that use libsvm as backing library
This implements support vector machine classification and regression.
Parameter documentation is in the derived `SVC` class.
"""
# The order of these must match the integer values in LibSVM.
# XXX These are actually the same in the dense case. Need to factor
# this out.
_sparse_kernels = ["linear", "poly", "rbf", "sigmoid", "precomputed"]
@abstractmethod
def __init__(self, kernel, degree, gamma, coef0,
tol, C, nu, epsilon, shrinking, probability, cache_size,
class_weight, verbose, max_iter, random_state):
if self._impl not in LIBSVM_IMPL:
raise ValueError("impl should be one of %s, %s was given" % (
LIBSVM_IMPL, self._impl))
if gamma == 0:
msg = ("The gamma value of 0.0 is invalid. Use 'auto' to set"
" gamma to a value of 1 / n_features.")
raise ValueError(msg)
self.kernel = kernel
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
self.tol = tol
self.C = C
self.nu = nu
self.epsilon = epsilon
self.shrinking = shrinking
self.probability = probability
self.cache_size = cache_size
self.class_weight = class_weight
self.verbose = verbose
self.max_iter = max_iter
self.random_state = random_state
@property
def _pairwise(self):
# Used by cross_val_score.
return self.kernel == "precomputed"
def fit(self, X, y, sample_weight=None):
"""Fit the SVM model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) \
or (n_samples, n_samples)
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
For kernel="precomputed", the expected shape of X is
(n_samples, n_samples).
y : array-like of shape (n_samples,)
Target values (class labels in classification, real numbers in
regression)
sample_weight : array-like of shape (n_samples,), default=None
Per-sample weights. Rescale C per sample. Higher weights
force the classifier to put more emphasis on these points.
Returns
-------
self : object
Notes
-----
If X and y are not C-ordered and contiguous arrays of np.float64 and
X is not a scipy.sparse.csr_matrix, X and/or y may be copied.
If X is a dense array, then the other methods will not support sparse
matrices as input.
"""
rnd = check_random_state(self.random_state)
sparse = sp.isspmatrix(X)
if sparse and self.kernel == "precomputed":
raise TypeError("Sparse precomputed kernels are not supported.")
self._sparse = sparse and not callable(self.kernel)
if hasattr(self, 'decision_function_shape'):
if self.decision_function_shape not in ('ovr', 'ovo'):
raise ValueError(
f"decision_function_shape must be either 'ovr' or 'ovo', "
f"got {self.decision_function_shape}."
)
if callable(self.kernel):
check_consistent_length(X, y)
else:
X, y = self._validate_data(X, y, dtype=np.float64,
order='C', accept_sparse='csr',
accept_large_sparse=False)
y = self._validate_targets(y)
sample_weight = np.asarray([]
if sample_weight is None
else sample_weight, dtype=np.float64)
solver_type = LIBSVM_IMPL.index(self._impl)
# input validation
n_samples = _num_samples(X)
if solver_type != 2 and n_samples != y.shape[0]:
raise ValueError("X and y have incompatible shapes.\n" +
"X has %s samples, but y has %s." %
(n_samples, y.shape[0]))
if self.kernel == "precomputed" and n_samples != X.shape[1]:
raise ValueError("Precomputed matrix must be a square matrix."
" Input is a {}x{} matrix."
.format(X.shape[0], X.shape[1]))
if sample_weight.shape[0] > 0 and sample_weight.shape[0] != n_samples:
raise ValueError("sample_weight and X have incompatible shapes: "
"%r vs %r\n"
"Note: Sparse matrices cannot be indexed w/"
"boolean masks (use `indices=True` in CV)."
% (sample_weight.shape, X.shape))
kernel = 'precomputed' if callable(self.kernel) else self.kernel
if kernel == 'precomputed':
# unused but needs to be a float for cython code that ignores
# it anyway
self._gamma = 0.
elif isinstance(self.gamma, str):
if self.gamma == 'scale':
# var = E[X^2] - E[X]^2 if sparse
X_var = ((X.multiply(X)).mean() - (X.mean()) ** 2
if sparse else X.var())
self._gamma = 1.0 / (X.shape[1] * X_var) if X_var != 0 else 1.0
elif self.gamma == 'auto':
self._gamma = 1.0 / X.shape[1]
else:
raise ValueError(
"When 'gamma' is a string, it should be either 'scale' or "
"'auto'. Got '{}' instead.".format(self.gamma)
)
else:
self._gamma = self.gamma
fit = self._sparse_fit if self._sparse else self._dense_fit
if self.verbose:
print('[LibSVM]', end='')
seed = rnd.randint(np.iinfo('i').max)
fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)
# see comment on the other call to np.iinfo in this file
self.shape_fit_ = X.shape if hasattr(X, "shape") else (n_samples, )
# In binary case, we need to flip the sign of coef, intercept and
# decision function. Use self._intercept_ and self._dual_coef_
# internally.
self._intercept_ = self.intercept_.copy()
self._dual_coef_ = self.dual_coef_
if self._impl in ['c_svc', 'nu_svc'] and len(self.classes_) == 2:
self.intercept_ *= -1
self.dual_coef_ = -self.dual_coef_
return self
def _validate_targets(self, y):
"""Validation of y and class_weight.
Default implementation for SVR and one-class; overridden in BaseSVC.
"""
# XXX this is ugly.
# Regression models should not have a class_weight_ attribute.
self.class_weight_ = np.empty(0)
return column_or_1d(y, warn=True).astype(np.float64, copy=False)
def _warn_from_fit_status(self):
assert self.fit_status_ in (0, 1)
if self.fit_status_ == 1:
warnings.warn('Solver terminated early (max_iter=%i).'
' Consider pre-processing your data with'
' StandardScaler or MinMaxScaler.'
% self.max_iter, ConvergenceWarning)
def _dense_fit(self, X, y, sample_weight, solver_type, kernel,
random_seed):
if callable(self.kernel):
# you must store a reference to X to compute the kernel in predict
# TODO: add keyword copy to copy on demand
self.__Xfit = X
X = self._compute_kernel(X)
if X.shape[0] != X.shape[1]:
raise ValueError("X.shape[0] should be equal to X.shape[1]")
libsvm.set_verbosity_wrap(self.verbose)
# we don't pass **self.get_params() to allow subclasses to
# add other parameters to __init__
self.support_, self.support_vectors_, self._n_support, \
self.dual_coef_, self.intercept_, self._probA, \
self._probB, self.fit_status_ = libsvm.fit(
X, y,
svm_type=solver_type, sample_weight=sample_weight,
class_weight=self.class_weight_, kernel=kernel, C=self.C,
nu=self.nu, probability=self.probability, degree=self.degree,
shrinking=self.shrinking, tol=self.tol,
cache_size=self.cache_size, coef0=self.coef0,
gamma=self._gamma, epsilon=self.epsilon,
max_iter=self.max_iter, random_seed=random_seed)
self._warn_from_fit_status()
def _sparse_fit(self, X, y, sample_weight, solver_type, kernel,
random_seed):
X.data = np.asarray(X.data, dtype=np.float64, order='C')
X.sort_indices()
kernel_type = self._sparse_kernels.index(kernel)
libsvm_sparse.set_verbosity_wrap(self.verbose)
self.support_, self.support_vectors_, dual_coef_data, \
self.intercept_, self._n_support, \
self._probA, self._probB, self.fit_status_ = \
libsvm_sparse.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.class_weight_,
sample_weight, self.nu, self.cache_size, self.epsilon,
int(self.shrinking), int(self.probability), self.max_iter,
random_seed)
self._warn_from_fit_status()
if hasattr(self, "classes_"):
n_class = len(self.classes_) - 1
else: # regression
n_class = 1
n_SV = self.support_vectors_.shape[0]
dual_coef_indices = np.tile(np.arange(n_SV), n_class)
if not n_SV:
self.dual_coef_ = sp.csr_matrix([])
else:
dual_coef_indptr = np.arange(0, dual_coef_indices.size + 1,
dual_coef_indices.size / n_class)
self.dual_coef_ = sp.csr_matrix(
(dual_coef_data, dual_coef_indices, dual_coef_indptr),
(n_class, n_SV))
def predict(self, X):
"""Perform regression on samples in X.
For an one-class model, +1 (inlier) or -1 (outlier) is returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
(n_samples_test, n_samples_train).
Returns
-------
y_pred : ndarray of shape (n_samples,)
"""
X = self._validate_for_predict(X)
predict = self._sparse_predict if self._sparse else self._dense_predict
return predict(X)
def _dense_predict(self, X):
X = self._compute_kernel(X)
if X.ndim == 1:
X = check_array(X, order='C', accept_large_sparse=False)
kernel = self.kernel
if callable(self.kernel):
kernel = 'precomputed'
if X.shape[1] != self.shape_fit_[0]:
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of samples at training time" %
(X.shape[1], self.shape_fit_[0]))
svm_type = LIBSVM_IMPL.index(self._impl)
return libsvm.predict(
X, self.support_, self.support_vectors_, self._n_support,
self._dual_coef_, self._intercept_,
self._probA, self._probB, svm_type=svm_type, kernel=kernel,
degree=self.degree, coef0=self.coef0, gamma=self._gamma,
cache_size=self.cache_size)
def _sparse_predict(self, X):
# Precondition: X is a csr_matrix of dtype np.float64.
kernel = self.kernel
if callable(kernel):
kernel = 'precomputed'
kernel_type = self._sparse_kernels.index(kernel)
C = 0.0 # C is not useful here
return libsvm_sparse.libsvm_sparse_predict(
X.data, X.indices, X.indptr,
self.support_vectors_.data,
self.support_vectors_.indices,
self.support_vectors_.indptr,
self._dual_coef_.data, self._intercept_,
LIBSVM_IMPL.index(self._impl), kernel_type,
self.degree, self._gamma, self.coef0, self.tol,
C, self.class_weight_,
self.nu, self.epsilon, self.shrinking,
self.probability, self._n_support,
self._probA, self._probB)
def _compute_kernel(self, X):
"""Return the data transformed by a callable kernel"""
if callable(self.kernel):
# in the case of precomputed kernel given as a function, we
# have to compute explicitly the kernel matrix
kernel = self.kernel(X, self.__Xfit)
if sp.issparse(kernel):
kernel = kernel.toarray()
X = np.asarray(kernel, dtype=np.float64, order='C')
return X
def _decision_function(self, X):
"""Evaluates the decision function for the samples in X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Returns
-------
X : array-like of shape (n_samples, n_class * (n_class-1) / 2)
Returns the decision function of the sample for each class
in the model.
"""
# NOTE: _validate_for_predict contains check for is_fitted
# hence must be placed before any other attributes are used.
X = self._validate_for_predict(X)
X = self._compute_kernel(X)
if self._sparse:
dec_func = self._sparse_decision_function(X)
else:
dec_func = self._dense_decision_function(X)
# In binary case, we need to flip the sign of coef, intercept and
# decision function.
if self._impl in ['c_svc', 'nu_svc'] and len(self.classes_) == 2:
return -dec_func.ravel()
return dec_func
def _dense_decision_function(self, X):
X = check_array(X, dtype=np.float64, order="C",
accept_large_sparse=False)
kernel = self.kernel
if callable(kernel):
kernel = 'precomputed'
return libsvm.decision_function(
X, self.support_, self.support_vectors_, self._n_support,
self._dual_coef_, self._intercept_,
self._probA, self._probB,
svm_type=LIBSVM_IMPL.index(self._impl),
kernel=kernel, degree=self.degree, cache_size=self.cache_size,
coef0=self.coef0, gamma=self._gamma)
def _sparse_decision_function(self, X):
X.data = np.asarray(X.data, dtype=np.float64, order='C')
kernel = self.kernel
if hasattr(kernel, '__call__'):
kernel = 'precomputed'
kernel_type = self._sparse_kernels.index(kernel)
return libsvm_sparse.libsvm_sparse_decision_function(
X.data, X.indices, X.indptr,
self.support_vectors_.data,
self.support_vectors_.indices,
self.support_vectors_.indptr,
self._dual_coef_.data, self._intercept_,
LIBSVM_IMPL.index(self._impl), kernel_type,
self.degree, self._gamma, self.coef0, self.tol,
self.C, self.class_weight_,
self.nu, self.epsilon, self.shrinking,
self.probability, self._n_support,
self._probA, self._probB)
def _validate_for_predict(self, X):
check_is_fitted(self)
if not callable(self.kernel):
X = check_array(X, accept_sparse='csr', dtype=np.float64,
order="C", accept_large_sparse=False)
if self._sparse and not sp.isspmatrix(X):
X = sp.csr_matrix(X)
if self._sparse:
X.sort_indices()
if sp.issparse(X) and not self._sparse and not callable(self.kernel):
raise ValueError(
"cannot use sparse input in %r trained on dense data"
% type(self).__name__)
if self.kernel == "precomputed":
if X.shape[1] != self.shape_fit_[0]:
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of samples at training time" %
(X.shape[1], self.shape_fit_[0]))
elif not callable(self.kernel) and X.shape[1] != self.shape_fit_[1]:
raise ValueError("X.shape[1] = %d should be equal to %d, "
"the number of features at training time" %
(X.shape[1], self.shape_fit_[1]))
return X
@property
def coef_(self):
if self.kernel != 'linear':
raise AttributeError('coef_ is only available when using a '
'linear kernel')
coef = self._get_coef()
# coef_ being a read-only property, it's better to mark the value as
# immutable to avoid hiding potential bugs for the unsuspecting user.
if sp.issparse(coef):
# sparse matrix do not have global flags
coef.data.flags.writeable = False
else:
# regular dense array
coef.flags.writeable = False
return coef
def _get_coef(self):
return safe_sparse_dot(self._dual_coef_, self.support_vectors_)
@property
def n_support_(self):
try:
check_is_fitted(self)
except NotFittedError:
raise AttributeError
svm_type = LIBSVM_IMPL.index(self._impl)
if svm_type in (0, 1):
return self._n_support
else:
# SVR and OneClass
# _n_support has size 2, we make it size 1
return np.array([self._n_support[0]])
class BaseSVC(ClassifierMixin, BaseLibSVM, metaclass=ABCMeta):
"""ABC for LibSVM-based classifiers."""
@abstractmethod
def __init__(self, kernel, degree, gamma, coef0, tol, C, nu,
shrinking, probability, cache_size, class_weight, verbose,
max_iter, decision_function_shape, random_state,
break_ties):
self.decision_function_shape = decision_function_shape
self.break_ties = break_ties
super().__init__(
kernel=kernel, degree=degree, gamma=gamma,
coef0=coef0, tol=tol, C=C, nu=nu, epsilon=0., shrinking=shrinking,
probability=probability, cache_size=cache_size,
class_weight=class_weight, verbose=verbose, max_iter=max_iter,
random_state=random_state)
def _validate_targets(self, y):
y_ = column_or_1d(y, warn=True)
check_classification_targets(y)
cls, y = np.unique(y_, return_inverse=True)
self.class_weight_ = compute_class_weight(self.class_weight,
classes=cls, y=y_)
if len(cls) < 2:
raise ValueError(
"The number of classes has to be greater than one; got %d"
" class" % len(cls))
self.classes_ = cls
return np.asarray(y, dtype=np.float64, order='C')
def decision_function(self, X):
"""Evaluates the decision function for the samples in X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Returns
-------
X : ndarray of shape (n_samples, n_classes * (n_classes-1) / 2)
Returns the decision function of the sample for each class
in the model.
If decision_function_shape='ovr', the shape is (n_samples,
n_classes).
Notes
-----
If decision_function_shape='ovo', the function values are proportional
to the distance of the samples X to the separating hyperplane. If the
exact distances are required, divide the function values by the norm of
the weight vector (``coef_``). See also `this question
<https://stats.stackexchange.com/questions/14876/
interpreting-distance-from-hyperplane-in-svm>`_ for further details.
If decision_function_shape='ovr', the decision function is a monotonic
transformation of ovo decision function.
"""
dec = self._decision_function(X)
if self.decision_function_shape == 'ovr' and len(self.classes_) > 2:
return _ovr_decision_function(dec < 0, -dec, len(self.classes_))
return dec
def predict(self, X):
"""Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features) or \
(n_samples_test, n_samples_train)
For kernel="precomputed", the expected shape of X is
(n_samples_test, n_samples_train).
Returns
-------
y_pred : ndarray of shape (n_samples,)
Class labels for samples in X.
"""
check_is_fitted(self)
if self.break_ties and self.decision_function_shape == 'ovo':
raise ValueError("break_ties must be False when "
"decision_function_shape is 'ovo'")
if (self.break_ties
and self.decision_function_shape == 'ovr'
and len(self.classes_) > 2):
y = np.argmax(self.decision_function(X), axis=1)
else:
y = super().predict(X)
return self.classes_.take(np.asarray(y, dtype=np.intp))
# Hacky way of getting predict_proba to raise an AttributeError when
# probability=False using properties. Do not use this in new code; when
# probabilities are not available depending on a setting, introduce two
# estimators.
def _check_proba(self):
if not self.probability:
raise AttributeError("predict_proba is not available when "
" probability=False")
if self._impl not in ('c_svc', 'nu_svc'):
raise AttributeError("predict_proba only implemented for SVC"
" and NuSVC")
@property
def predict_proba(self):
"""Compute probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training
time: fit with attribute `probability` set to True.
Parameters
----------
X : array-like of shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
[n_samples_test, n_samples_train]
Returns
-------
T : ndarray of shape (n_samples, n_classes)
Returns the probability of the sample for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute :term:`classes_`.
Notes
-----
The probability model is created using cross validation, so
the results can be slightly different than those obtained by
predict. Also, it will produce meaningless results on very small
datasets.
"""
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
X = self._validate_for_predict(X)
if self.probA_.size == 0 or self.probB_.size == 0:
raise NotFittedError("predict_proba is not available when fitted "
"with probability=False")
pred_proba = (self._sparse_predict_proba
if self._sparse else self._dense_predict_proba)
return pred_proba(X)
@property
def predict_log_proba(self):
"""Compute log probabilities of possible outcomes for samples in X.
The model need to have probability information computed at training
time: fit with attribute `probability` set to True.
Parameters
----------
X : array-like of shape (n_samples, n_features) or \
(n_samples_test, n_samples_train)
For kernel="precomputed", the expected shape of X is
(n_samples_test, n_samples_train).
Returns
-------
T : ndarray of shape (n_samples, n_classes)
Returns the log-probabilities of the sample for each class in
the model. The columns correspond to the classes in sorted
order, as they appear in the attribute :term:`classes_`.
Notes
-----
The probability model is created using cross validation, so
the results can be slightly different than those obtained by
predict. Also, it will produce meaningless results on very small
datasets.
"""
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
def _dense_predict_proba(self, X):
X = self._compute_kernel(X)
kernel = self.kernel
if callable(kernel):
kernel = 'precomputed'
svm_type = LIBSVM_IMPL.index(self._impl)
pprob = libsvm.predict_proba(
X, self.support_, self.support_vectors_, self._n_support,
self._dual_coef_, self._intercept_,
self._probA, self._probB,
svm_type=svm_type, kernel=kernel, degree=self.degree,
cache_size=self.cache_size, coef0=self.coef0, gamma=self._gamma)
return pprob
def _sparse_predict_proba(self, X):
X.data = np.asarray(X.data, dtype=np.float64, order='C')
kernel = self.kernel
if callable(kernel):
kernel = 'precomputed'
kernel_type = self._sparse_kernels.index(kernel)
return libsvm_sparse.libsvm_sparse_predict_proba(
X.data, X.indices, X.indptr,
self.support_vectors_.data,
self.support_vectors_.indices,
self.support_vectors_.indptr,
self._dual_coef_.data, self._intercept_,
LIBSVM_IMPL.index(self._impl), kernel_type,
self.degree, self._gamma, self.coef0, self.tol,
self.C, self.class_weight_,
self.nu, self.epsilon, self.shrinking,
self.probability, self._n_support,
self._probA, self._probB)
def _get_coef(self):
if self.dual_coef_.shape[0] == 1:
# binary classifier
coef = safe_sparse_dot(self.dual_coef_, self.support_vectors_)
else:
# 1vs1 classifier
coef = _one_vs_one_coef(self.dual_coef_, self._n_support,
self.support_vectors_)
if sp.issparse(coef[0]):
coef = sp.vstack(coef).tocsr()
else:
coef = np.vstack(coef)
return coef
@property
def probA_(self):
return self._probA
@property
def probB_(self):
return self._probB
def _get_liblinear_solver_type(multi_class, penalty, loss, dual):
"""Find the liblinear magic number for the solver.
This number depends on the values of the following attributes:
- multi_class
- penalty
- loss
- dual
The same number is also internally used by LibLinear to determine
which solver to use.
"""
# nested dicts containing level 1: available loss functions,
# level2: available penalties for the given loss function,
# level3: whether the dual solver is available for the specified
# combination of loss function and penalty
_solver_type_dict = {
'logistic_regression': {
'l1': {False: 6},
'l2': {False: 0, True: 7}},
'hinge': {
'l2': {True: 3}},
'squared_hinge': {
'l1': {False: 5},
'l2': {False: 2, True: 1}},
'epsilon_insensitive': {
'l2': {True: 13}},
'squared_epsilon_insensitive': {
'l2': {False: 11, True: 12}},
'crammer_singer': 4
}
if multi_class == 'crammer_singer':
return _solver_type_dict[multi_class]
elif multi_class != 'ovr':
raise ValueError("`multi_class` must be one of `ovr`, "
"`crammer_singer`, got %r" % multi_class)
_solver_pen = _solver_type_dict.get(loss, None)
if _solver_pen is None:
error_string = ("loss='%s' is not supported" % loss)
else:
_solver_dual = _solver_pen.get(penalty, None)
if _solver_dual is None:
error_string = ("The combination of penalty='%s' "
"and loss='%s' is not supported"
% (penalty, loss))
else:
solver_num = _solver_dual.get(dual, None)
if solver_num is None:
error_string = ("The combination of penalty='%s' and "
"loss='%s' are not supported when dual=%s"
% (penalty, loss, dual))
else:
return solver_num
raise ValueError('Unsupported set of arguments: %s, '
'Parameters: penalty=%r, loss=%r, dual=%r'
% (error_string, penalty, loss, dual))
def _fit_liblinear(X, y, C, fit_intercept, intercept_scaling, class_weight,
penalty, dual, verbose, max_iter, tol,
random_state=None, multi_class='ovr',
loss='logistic_regression', epsilon=0.1,
sample_weight=None):
"""Used by Logistic Regression (and CV) and LinearSVC/LinearSVR.
Preprocessing is done in this function before supplying it to liblinear.
Parameters
----------
X : {array-like, sparse matrix} of 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-like of shape (n_samples,)
Target vector relative to X
C : float
Inverse of cross-validation parameter. Lower the C, the more
the penalization.
fit_intercept : bool
Whether or not to fit the intercept, that is to add a intercept
term to the decision function.
intercept_scaling : float
LibLinear internally penalizes the intercept and this term is subject
to regularization just like the other terms of the feature vector.
In order to avoid this, one should increase the intercept_scaling.
such that the feature vector becomes [x, intercept_scaling].
class_weight : dict or 'balanced', default=None
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
penalty : {'l1', 'l2'}
The norm of the penalty used in regularization.
dual : bool
Dual or primal formulation,
verbose : int
Set verbose to any positive number for verbosity.
max_iter : int
Number of iterations.
tol : float
Stopping condition.
random_state : int or RandomState instance, default=None
Controls the pseudo random number generation for shuffling the data.
Pass an int for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
multi_class : {'ovr', 'crammer_singer'}, default='ovr'
`ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer`
optimizes a joint objective over all classes.
While `crammer_singer` is interesting from an theoretical perspective
as it is consistent it is seldom used in practice and rarely leads to
better accuracy and is more expensive to compute.
If `crammer_singer` is chosen, the options loss, penalty and dual will
be ignored.
loss : {'logistic_regression', 'hinge', 'squared_hinge', \
'epsilon_insensitive', 'squared_epsilon_insensitive}, \
default='logistic_regression'
The loss function used to fit the model.
epsilon : float, default=0.1
Epsilon parameter in the epsilon-insensitive loss function. Note
that the value of this parameter depends on the scale of the target
variable y. If unsure, set epsilon=0.
sample_weight : array-like of shape (n_samples,), default=None
Weights assigned to each sample.
Returns
-------
coef_ : ndarray of shape (n_features, n_features + 1)
The coefficient vector got by minimizing the objective function.
intercept_ : float
The intercept term added to the vector.
n_iter_ : int
Maximum number of iterations run across all classes.
"""
if loss not in ['epsilon_insensitive', 'squared_epsilon_insensitive']:
enc = LabelEncoder()
y_ind = enc.fit_transform(y)
classes_ = enc.classes_
if len(classes_) < 2:
raise ValueError("This solver needs samples of at least 2 classes"
" in the data, but the data contains only one"
" class: %r" % classes_[0])
class_weight_ = compute_class_weight(class_weight, classes=classes_,
y=y)
else:
class_weight_ = np.empty(0, dtype=np.float64)
y_ind = y
liblinear.set_verbosity_wrap(verbose)
rnd = check_random_state(random_state)
if verbose:
print('[LibLinear]', end='')
# LinearSVC breaks when intercept_scaling is <= 0
bias = -1.0
if fit_intercept:
if intercept_scaling <= 0:
raise ValueError("Intercept scaling is %r but needs to be greater "
"than 0. To disable fitting an intercept,"
" set fit_intercept=False." % intercept_scaling)
else:
bias = intercept_scaling
libsvm.set_verbosity_wrap(verbose)
libsvm_sparse.set_verbosity_wrap(verbose)
liblinear.set_verbosity_wrap(verbose)
# Liblinear doesn't support 64bit sparse matrix indices yet
if sp.issparse(X):
_check_large_sparse(X)
# LibLinear wants targets as doubles, even for classification
y_ind = np.asarray(y_ind, dtype=np.float64).ravel()
y_ind = np.require(y_ind, requirements="W")
sample_weight = _check_sample_weight(sample_weight, X,
dtype=np.float64)
solver_type = _get_liblinear_solver_type(multi_class, penalty, loss, dual)
raw_coef_, n_iter_ = liblinear.train_wrap(
X, y_ind, sp.isspmatrix(X), solver_type, tol, bias, C,
class_weight_, max_iter, rnd.randint(np.iinfo('i').max),
epsilon, sample_weight)
# Regarding rnd.randint(..) in the above signature:
# seed for srand in range [0..INT_MAX); due to limitations in Numpy
# on 32-bit platforms, we can't get to the UINT_MAX limit that
# srand supports
n_iter_ = max(n_iter_)
if n_iter_ >= max_iter:
warnings.warn("Liblinear failed to converge, increase "
"the number of iterations.", ConvergenceWarning)
if fit_intercept:
coef_ = raw_coef_[:, :-1]
intercept_ = intercept_scaling * raw_coef_[:, -1]
else:
coef_ = raw_coef_
intercept_ = 0.
return coef_, intercept_, n_iter_