# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import warnings from abc import ABCMeta, abstractmethod from numbers import Integral, Real import numpy as np import scipy.sparse as sp from ..base import BaseEstimator, ClassifierMixin, _fit_context from ..exceptions import ConvergenceWarning, NotFittedError from ..preprocessing import LabelEncoder from ..utils import check_array, check_random_state, column_or_1d, compute_class_weight from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import safe_sparse_dot from ..utils.metaestimators import available_if from ..utils.multiclass import _ovr_decision_function, check_classification_targets from ..utils.validation import ( _check_large_sparse, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted, validate_data, ) from . import _liblinear as liblinear # type: ignore # mypy error: error: Module 'sklearn.svm' has no attribute '_libsvm' # (and same for other imports) from . import _libsvm as libsvm # type: ignore from . import _libsvm_sparse as libsvm_sparse # type: ignore 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. """ _parameter_constraints: dict = { "kernel": [ StrOptions({"linear", "poly", "rbf", "sigmoid", "precomputed"}), callable, ], "degree": [Interval(Integral, 0, None, closed="left")], "gamma": [ StrOptions({"scale", "auto"}), Interval(Real, 0.0, None, closed="left"), ], "coef0": [Interval(Real, None, None, closed="neither")], "tol": [Interval(Real, 0.0, None, closed="neither")], "C": [Interval(Real, 0.0, None, closed="right")], "nu": [Interval(Real, 0.0, 1.0, closed="right")], "epsilon": [Interval(Real, 0.0, None, closed="left")], "shrinking": ["boolean"], "probability": ["boolean"], "cache_size": [Interval(Real, 0, None, closed="neither")], "class_weight": [StrOptions({"balanced"}), dict, None], "verbose": ["verbose"], "max_iter": [Interval(Integral, -1, None, closed="left")], "random_state": ["random_state"], } # 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) ) 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 def __sklearn_tags__(self): tags = super().__sklearn_tags__() # Used by cross_val_score. tags.input_tags.pairwise = self.kernel == "precomputed" tags.input_tags.sparse = self.kernel != "precomputed" return tags @_fit_context(prefer_skip_nested_validation=True) 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 Fitted estimator. 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.issparse(X) if sparse and self.kernel == "precomputed": raise TypeError("Sparse precomputed kernels are not supported.") self._sparse = sparse and not callable(self.kernel) if callable(self.kernel): check_consistent_length(X, y) else: X, y = validate_data( self, 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 = 0.0 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] elif isinstance(self.gamma, Real): 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_ dual_coef = self._dual_coef_.data if self._sparse else self._dual_coef_ intercept_finiteness = np.isfinite(self._intercept_).all() dual_coef_finiteness = np.isfinite(dual_coef).all() if not (intercept_finiteness and dual_coef_finiteness): raise ValueError( "The dual coefficients or intercepts are not finite." " The input data may contain large values and need to be" " preprocessed." ) # Since, in the case of SVC and NuSVC, the number of models optimized by # libSVM could be greater than one (depending on the input), `n_iter_` # stores an ndarray. # For the other sub-classes (SVR, NuSVR, and OneClassSVM), the number of # models optimized by libSVM is always one, so `n_iter_` stores an # integer. if self._impl in ["c_svc", "nu_svc"]: self.n_iter_ = self._num_iter else: self.n_iter_ = self._num_iter.item() return self def _validate_targets(self, y): """Validation of y and class_weight. Default implementation for SVR and one-class; overridden in BaseSVC. """ 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_, self._num_iter, ) = libsvm.fit( X, y, svm_type=solver_type, sample_weight=sample_weight, class_weight=getattr(self, "class_weight_", np.empty(0)), 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_, self._num_iter, ) = 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, getattr(self, "class_weight_", np.empty(0)), 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,) The predicted values. """ 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, getattr(self, "class_weight_", np.empty(0)), 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, getattr(self, "class_weight_", np.empty(0)), 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 = validate_data( self, X, accept_sparse="csr", dtype=np.float64, order="C", accept_large_sparse=False, reset=False, ) if self._sparse and not sp.issparse(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]) ) # Fixes https://nvd.nist.gov/vuln/detail/CVE-2020-28975 # Check that _n_support is consistent with support_vectors sv = self.support_vectors_ if not self._sparse and sv.size > 0 and self.n_support_.sum() != sv.shape[0]: raise ValueError( f"The internal representation of {self.__class__.__name__} was altered" ) return X @property def coef_(self): """Weights assigned to the features when `kernel="linear"`. Returns ------- ndarray of shape (n_features, n_classes) """ 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): """Number of support vectors for each class.""" 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.""" _parameter_constraints: dict = { **BaseLibSVM._parameter_constraints, "decision_function_shape": [StrOptions({"ovr", "ovo"})], "break_ties": ["boolean"], } for unused_param in ["epsilon", "nu"]: _parameter_constraints.pop(unused_param) @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=0.0, 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): """Evaluate the decision function for the samples in X. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. 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 `_ 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") return True @available_if(_check_proba) def predict_proba(self, X): """Compute probabilities of possible outcomes for samples in X. The model needs 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. """ 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) @available_if(_check_proba) def predict_log_proba(self, X): """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. """ 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, getattr(self, "class_weight_", np.empty(0)), 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): """Parameter learned in Platt scaling when `probability=True`. Returns ------- ndarray of shape (n_classes * (n_classes - 1) / 2) """ return self._probA @property def probB_(self): """Parameter learned in Platt scaling when `probability=True`. Returns ------- ndarray of shape (n_classes * (n_classes - 1) / 2) """ return self._probB def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = self.kernel != "precomputed" return tags 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` is 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. The lower the C, the higher the penalization. fit_intercept : bool Whether or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: ``[x_1, ..., x_n, 1]``, where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered). intercept_scaling : float Liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the `intercept_scaling` parameter can be set to a value greater than 1; the higher the value of `intercept_scaling`, the lower the impact of regularization on it. Then, the weights become `[w_x_1, ..., w_x_n, w_intercept*intercept_scaling]`, where `w_x_1, ..., w_x_n` represent the feature weights and the intercept weight is scaled by `intercept_scaling`. This scaling allows the intercept term to have a different regularization behavior compared to the other features. 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, RandomState instance or None, 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 `. 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_ : array of int Number of iterations run across for each class. """ 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.issparse(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 = max(n_iter_) if n_iter_max >= 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_ = 0.0 return coef_, intercept_, n_iter_