2105 lines
80 KiB
Python
2105 lines
80 KiB
Python
"""
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Logistic Regression
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"""
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# Fabian Pedregosa <f@bianp.net>
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# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
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# Manoj Kumar <manojkumarsivaraj334@gmail.com>
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# Lars Buitinck
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# Simon Wu <s8wu@uwaterloo.ca>
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# Arthur Mensch <arthur.mensch@m4x.org
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import numbers
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from numbers import Integral, Real
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import warnings
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import numpy as np
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from scipy import optimize
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from joblib import effective_n_jobs
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from sklearn.metrics import get_scorer_names
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from ._base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator
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from ._linear_loss import LinearModelLoss
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from ._sag import sag_solver
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from ._glm.glm import NewtonCholeskySolver
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from .._loss.loss import HalfBinomialLoss, HalfMultinomialLoss
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from ..preprocessing import LabelEncoder, LabelBinarizer
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from ..svm._base import _fit_liblinear
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from ..utils import check_array, check_consistent_length, compute_class_weight
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from ..utils import check_random_state
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from ..utils.extmath import softmax
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from ..utils.extmath import row_norms
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from ..utils.optimize import _newton_cg, _check_optimize_result
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from ..utils.validation import check_is_fitted, _check_sample_weight
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from ..utils.multiclass import check_classification_targets
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from ..utils.parallel import delayed, Parallel
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from ..utils._param_validation import StrOptions, Interval
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from ..model_selection import check_cv
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from ..metrics import get_scorer
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_LOGISTIC_SOLVER_CONVERGENCE_MSG = (
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"Please also refer to the documentation for alternative solver options:\n"
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" https://scikit-learn.org/stable/modules/linear_model.html"
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"#logistic-regression"
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)
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def _check_solver(solver, penalty, dual):
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# TODO(1.4): Remove "none" option
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if solver not in ["liblinear", "saga"] and penalty not in ("l2", "none", None):
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raise ValueError(
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"Solver %s supports only 'l2' or 'none' penalties, got %s penalty."
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% (solver, penalty)
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)
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if solver != "liblinear" and dual:
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raise ValueError(
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"Solver %s supports only dual=False, got dual=%s" % (solver, dual)
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)
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if penalty == "elasticnet" and solver != "saga":
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raise ValueError(
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"Only 'saga' solver supports elasticnet penalty, got solver={}.".format(
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solver
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)
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)
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if solver == "liblinear" and penalty == "none":
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raise ValueError("penalty='none' is not supported for the liblinear solver")
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return solver
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def _check_multi_class(multi_class, solver, n_classes):
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"""Computes the multi class type, either "multinomial" or "ovr".
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For `n_classes` > 2 and a solver that supports it, returns "multinomial".
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For all other cases, in particular binary classification, return "ovr".
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"""
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if multi_class == "auto":
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if solver in ("liblinear", "newton-cholesky"):
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multi_class = "ovr"
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elif n_classes > 2:
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multi_class = "multinomial"
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else:
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multi_class = "ovr"
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if multi_class == "multinomial" and solver in ("liblinear", "newton-cholesky"):
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raise ValueError("Solver %s does not support a multinomial backend." % solver)
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return multi_class
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def _logistic_regression_path(
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X,
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y,
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pos_class=None,
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Cs=10,
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fit_intercept=True,
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max_iter=100,
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tol=1e-4,
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verbose=0,
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solver="lbfgs",
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coef=None,
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class_weight=None,
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dual=False,
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penalty="l2",
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intercept_scaling=1.0,
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multi_class="auto",
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random_state=None,
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check_input=True,
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max_squared_sum=None,
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sample_weight=None,
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l1_ratio=None,
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n_threads=1,
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):
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"""Compute a Logistic Regression model for a list of regularization
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parameters.
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This is an implementation that uses the result of the previous model
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to speed up computations along the set of solutions, making it faster
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than sequentially calling LogisticRegression for the different parameters.
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Note that there will be no speedup with liblinear solver, since it does
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not handle warm-starting.
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Read more in the :ref:`User Guide <logistic_regression>`.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Input data.
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y : array-like of shape (n_samples,) or (n_samples, n_targets)
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Input data, target values.
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pos_class : int, default=None
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The class with respect to which we perform a one-vs-all fit.
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If None, then it is assumed that the given problem is binary.
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Cs : int or array-like of shape (n_cs,), default=10
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List of values for the regularization parameter or integer specifying
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the number of regularization parameters that should be used. In this
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case, the parameters will be chosen in a logarithmic scale between
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1e-4 and 1e4.
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fit_intercept : bool, default=True
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Whether to fit an intercept for the model. In this case the shape of
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the returned array is (n_cs, n_features + 1).
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max_iter : int, default=100
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Maximum number of iterations for the solver.
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tol : float, default=1e-4
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Stopping criterion. For the newton-cg and lbfgs solvers, the iteration
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will stop when ``max{|g_i | i = 1, ..., n} <= tol``
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where ``g_i`` is the i-th component of the gradient.
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verbose : int, default=0
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For the liblinear and lbfgs solvers set verbose to any positive
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number for verbosity.
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solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, \
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default='lbfgs'
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Numerical solver to use.
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coef : array-like of shape (n_features,), default=None
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Initialization value for coefficients of logistic regression.
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Useless for liblinear solver.
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class_weight : dict or 'balanced', default=None
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Weights associated with classes in the form ``{class_label: weight}``.
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If not given, all classes are supposed to have weight one.
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The "balanced" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies in the input data
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as ``n_samples / (n_classes * np.bincount(y))``.
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Note that these weights will be multiplied with sample_weight (passed
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through the fit method) if sample_weight is specified.
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dual : bool, default=False
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Dual or primal formulation. Dual formulation is only implemented for
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l2 penalty with liblinear solver. Prefer dual=False when
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n_samples > n_features.
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penalty : {'l1', 'l2', 'elasticnet'}, default='l2'
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Used to specify the norm used in the penalization. The 'newton-cg',
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'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
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only supported by the 'saga' solver.
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intercept_scaling : float, default=1.
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Useful only when the solver 'liblinear' is used
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and self.fit_intercept is set to True. In this case, x becomes
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[x, self.intercept_scaling],
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i.e. a "synthetic" feature with constant value equal to
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intercept_scaling is appended to the instance vector.
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The intercept becomes ``intercept_scaling * synthetic_feature_weight``.
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Note! the synthetic feature weight is subject to l1/l2 regularization
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as all other features.
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To lessen the effect of regularization on synthetic feature weight
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(and therefore on the intercept) intercept_scaling has to be increased.
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multi_class : {'ovr', 'multinomial', 'auto'}, default='auto'
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If the option chosen is 'ovr', then a binary problem is fit for each
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label. For 'multinomial' the loss minimised is the multinomial loss fit
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across the entire probability distribution, *even when the data is
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binary*. 'multinomial' is unavailable when solver='liblinear'.
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'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
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and otherwise selects 'multinomial'.
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.. versionadded:: 0.18
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Stochastic Average Gradient descent solver for 'multinomial' case.
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.. versionchanged:: 0.22
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Default changed from 'ovr' to 'auto' in 0.22.
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random_state : int, RandomState instance, default=None
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Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
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data. See :term:`Glossary <random_state>` for details.
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check_input : bool, default=True
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If False, the input arrays X and y will not be checked.
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max_squared_sum : float, default=None
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Maximum squared sum of X over samples. Used only in SAG solver.
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If None, it will be computed, going through all the samples.
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The value should be precomputed to speed up cross validation.
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sample_weight : array-like of shape(n_samples,), default=None
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Array of weights that are assigned to individual samples.
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If not provided, then each sample is given unit weight.
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l1_ratio : float, default=None
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The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
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used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
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to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
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to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
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combination of L1 and L2.
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n_threads : int, default=1
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Number of OpenMP threads to use.
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Returns
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-------
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coefs : ndarray of shape (n_cs, n_features) or (n_cs, n_features + 1)
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List of coefficients for the Logistic Regression model. If
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fit_intercept is set to True then the second dimension will be
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n_features + 1, where the last item represents the intercept. For
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``multiclass='multinomial'``, the shape is (n_classes, n_cs,
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n_features) or (n_classes, n_cs, n_features + 1).
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Cs : ndarray
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Grid of Cs used for cross-validation.
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n_iter : array of shape (n_cs,)
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Actual number of iteration for each Cs.
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Notes
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-----
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You might get slightly different results with the solver liblinear than
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with the others since this uses LIBLINEAR which penalizes the intercept.
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.. versionchanged:: 0.19
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The "copy" parameter was removed.
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"""
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if isinstance(Cs, numbers.Integral):
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Cs = np.logspace(-4, 4, Cs)
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solver = _check_solver(solver, penalty, dual)
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# Preprocessing.
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if check_input:
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X = check_array(
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X,
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accept_sparse="csr",
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dtype=np.float64,
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accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
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)
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y = check_array(y, ensure_2d=False, dtype=None)
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check_consistent_length(X, y)
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n_samples, n_features = X.shape
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classes = np.unique(y)
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random_state = check_random_state(random_state)
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multi_class = _check_multi_class(multi_class, solver, len(classes))
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if pos_class is None and multi_class != "multinomial":
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if classes.size > 2:
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raise ValueError("To fit OvR, use the pos_class argument")
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# np.unique(y) gives labels in sorted order.
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pos_class = classes[1]
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# If sample weights exist, convert them to array (support for lists)
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# and check length
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# Otherwise set them to 1 for all examples
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sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype, copy=True)
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if solver == "newton-cholesky":
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# IMPORTANT NOTE: Rescaling of sample_weight:
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# Same as in _GeneralizedLinearRegressor.fit().
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# We want to minimize
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# obj = 1/(2*sum(sample_weight)) * sum(sample_weight * deviance)
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# + 1/2 * alpha * L2,
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# with
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# deviance = 2 * log_loss.
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# The objective is invariant to multiplying sample_weight by a constant. We
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# choose this constant such that sum(sample_weight) = 1. Thus, we end up with
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# obj = sum(sample_weight * loss) + 1/2 * alpha * L2.
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# Note that LinearModelLoss.loss() computes sum(sample_weight * loss).
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#
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# This rescaling has to be done before multiplying by class_weights.
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sw_sum = sample_weight.sum() # needed to rescale penalty, nasty matter!
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sample_weight = sample_weight / sw_sum
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# If class_weights is a dict (provided by the user), the weights
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# are assigned to the original labels. If it is "balanced", then
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# the class_weights are assigned after masking the labels with a OvR.
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le = LabelEncoder()
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if isinstance(class_weight, dict) or multi_class == "multinomial":
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class_weight_ = compute_class_weight(class_weight, classes=classes, y=y)
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sample_weight *= class_weight_[le.fit_transform(y)]
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# For doing a ovr, we need to mask the labels first. For the
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# multinomial case this is not necessary.
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if multi_class == "ovr":
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w0 = np.zeros(n_features + int(fit_intercept), dtype=X.dtype)
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mask = y == pos_class
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y_bin = np.ones(y.shape, dtype=X.dtype)
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if solver in ["lbfgs", "newton-cg", "newton-cholesky"]:
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# HalfBinomialLoss, used for those solvers, represents y in [0, 1] instead
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# of in [-1, 1].
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mask_classes = np.array([0, 1])
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y_bin[~mask] = 0.0
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else:
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mask_classes = np.array([-1, 1])
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y_bin[~mask] = -1.0
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# for compute_class_weight
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if class_weight == "balanced":
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class_weight_ = compute_class_weight(
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class_weight, classes=mask_classes, y=y_bin
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)
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sample_weight *= class_weight_[le.fit_transform(y_bin)]
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else:
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if solver in ["sag", "saga", "lbfgs", "newton-cg"]:
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# SAG, lbfgs and newton-cg multinomial solvers need LabelEncoder,
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# not LabelBinarizer, i.e. y as a 1d-array of integers.
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# LabelEncoder also saves memory compared to LabelBinarizer, especially
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# when n_classes is large.
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le = LabelEncoder()
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Y_multi = le.fit_transform(y).astype(X.dtype, copy=False)
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else:
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# For liblinear solver, apply LabelBinarizer, i.e. y is one-hot encoded.
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lbin = LabelBinarizer()
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Y_multi = lbin.fit_transform(y)
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if Y_multi.shape[1] == 1:
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Y_multi = np.hstack([1 - Y_multi, Y_multi])
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w0 = np.zeros(
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(classes.size, n_features + int(fit_intercept)), order="F", dtype=X.dtype
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)
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if coef is not None:
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# it must work both giving the bias term and not
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if multi_class == "ovr":
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if coef.size not in (n_features, w0.size):
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raise ValueError(
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"Initialization coef is of shape %d, expected shape %d or %d"
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% (coef.size, n_features, w0.size)
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)
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w0[: coef.size] = coef
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else:
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# For binary problems coef.shape[0] should be 1, otherwise it
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# should be classes.size.
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n_classes = classes.size
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if n_classes == 2:
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n_classes = 1
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if coef.shape[0] != n_classes or coef.shape[1] not in (
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n_features,
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n_features + 1,
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):
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raise ValueError(
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"Initialization coef is of shape (%d, %d), expected "
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"shape (%d, %d) or (%d, %d)"
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% (
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coef.shape[0],
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coef.shape[1],
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classes.size,
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n_features,
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classes.size,
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n_features + 1,
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)
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)
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if n_classes == 1:
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w0[0, : coef.shape[1]] = -coef
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w0[1, : coef.shape[1]] = coef
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else:
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w0[:, : coef.shape[1]] = coef
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if multi_class == "multinomial":
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if solver in ["lbfgs", "newton-cg"]:
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# scipy.optimize.minimize and newton-cg accept only ravelled parameters,
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# i.e. 1d-arrays. LinearModelLoss expects classes to be contiguous and
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# reconstructs the 2d-array via w0.reshape((n_classes, -1), order="F").
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# As w0 is F-contiguous, ravel(order="F") also avoids a copy.
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w0 = w0.ravel(order="F")
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loss = LinearModelLoss(
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base_loss=HalfMultinomialLoss(n_classes=classes.size),
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fit_intercept=fit_intercept,
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)
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target = Y_multi
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if solver in "lbfgs":
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func = loss.loss_gradient
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elif solver == "newton-cg":
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func = loss.loss
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grad = loss.gradient
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hess = loss.gradient_hessian_product # hess = [gradient, hessp]
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warm_start_sag = {"coef": w0.T}
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else:
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target = y_bin
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if solver == "lbfgs":
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loss = LinearModelLoss(
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base_loss=HalfBinomialLoss(), fit_intercept=fit_intercept
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)
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func = loss.loss_gradient
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elif solver == "newton-cg":
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loss = LinearModelLoss(
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base_loss=HalfBinomialLoss(), fit_intercept=fit_intercept
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)
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func = loss.loss
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grad = loss.gradient
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hess = loss.gradient_hessian_product # hess = [gradient, hessp]
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elif solver == "newton-cholesky":
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loss = LinearModelLoss(
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base_loss=HalfBinomialLoss(), fit_intercept=fit_intercept
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)
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warm_start_sag = {"coef": np.expand_dims(w0, axis=1)}
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coefs = list()
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n_iter = np.zeros(len(Cs), dtype=np.int32)
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for i, C in enumerate(Cs):
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if solver == "lbfgs":
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l2_reg_strength = 1.0 / C
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iprint = [-1, 50, 1, 100, 101][
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np.searchsorted(np.array([0, 1, 2, 3]), verbose)
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]
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opt_res = optimize.minimize(
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func,
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w0,
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method="L-BFGS-B",
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jac=True,
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args=(X, target, sample_weight, l2_reg_strength, n_threads),
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options={"iprint": iprint, "gtol": tol, "maxiter": max_iter},
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)
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n_iter_i = _check_optimize_result(
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solver,
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opt_res,
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max_iter,
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extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,
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)
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w0, loss = opt_res.x, opt_res.fun
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elif solver == "newton-cg":
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l2_reg_strength = 1.0 / C
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args = (X, target, sample_weight, l2_reg_strength, n_threads)
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w0, n_iter_i = _newton_cg(
|
|
hess, func, grad, w0, args=args, maxiter=max_iter, tol=tol
|
|
)
|
|
elif solver == "newton-cholesky":
|
|
# The division by sw_sum is a consequence of the rescaling of
|
|
# sample_weight, see comment above.
|
|
l2_reg_strength = 1.0 / C / sw_sum
|
|
sol = NewtonCholeskySolver(
|
|
coef=w0,
|
|
linear_loss=loss,
|
|
l2_reg_strength=l2_reg_strength,
|
|
tol=tol,
|
|
max_iter=max_iter,
|
|
n_threads=n_threads,
|
|
verbose=verbose,
|
|
)
|
|
w0 = sol.solve(X=X, y=target, sample_weight=sample_weight)
|
|
n_iter_i = sol.iteration
|
|
elif solver == "liblinear":
|
|
coef_, intercept_, n_iter_i, = _fit_liblinear(
|
|
X,
|
|
target,
|
|
C,
|
|
fit_intercept,
|
|
intercept_scaling,
|
|
None,
|
|
penalty,
|
|
dual,
|
|
verbose,
|
|
max_iter,
|
|
tol,
|
|
random_state,
|
|
sample_weight=sample_weight,
|
|
)
|
|
if fit_intercept:
|
|
w0 = np.concatenate([coef_.ravel(), intercept_])
|
|
else:
|
|
w0 = coef_.ravel()
|
|
|
|
elif solver in ["sag", "saga"]:
|
|
if multi_class == "multinomial":
|
|
target = target.astype(X.dtype, copy=False)
|
|
loss = "multinomial"
|
|
else:
|
|
loss = "log"
|
|
# alpha is for L2-norm, beta is for L1-norm
|
|
if penalty == "l1":
|
|
alpha = 0.0
|
|
beta = 1.0 / C
|
|
elif penalty == "l2":
|
|
alpha = 1.0 / C
|
|
beta = 0.0
|
|
else: # Elastic-Net penalty
|
|
alpha = (1.0 / C) * (1 - l1_ratio)
|
|
beta = (1.0 / C) * l1_ratio
|
|
|
|
w0, n_iter_i, warm_start_sag = sag_solver(
|
|
X,
|
|
target,
|
|
sample_weight,
|
|
loss,
|
|
alpha,
|
|
beta,
|
|
max_iter,
|
|
tol,
|
|
verbose,
|
|
random_state,
|
|
False,
|
|
max_squared_sum,
|
|
warm_start_sag,
|
|
is_saga=(solver == "saga"),
|
|
)
|
|
|
|
else:
|
|
raise ValueError(
|
|
"solver must be one of {'liblinear', 'lbfgs', "
|
|
"'newton-cg', 'sag'}, got '%s' instead" % solver
|
|
)
|
|
|
|
if multi_class == "multinomial":
|
|
n_classes = max(2, classes.size)
|
|
if solver in ["lbfgs", "newton-cg"]:
|
|
multi_w0 = np.reshape(w0, (n_classes, -1), order="F")
|
|
else:
|
|
multi_w0 = w0
|
|
if n_classes == 2:
|
|
multi_w0 = multi_w0[1][np.newaxis, :]
|
|
coefs.append(multi_w0.copy())
|
|
else:
|
|
coefs.append(w0.copy())
|
|
|
|
n_iter[i] = n_iter_i
|
|
|
|
return np.array(coefs), np.array(Cs), n_iter
|
|
|
|
|
|
# helper function for LogisticCV
|
|
def _log_reg_scoring_path(
|
|
X,
|
|
y,
|
|
train,
|
|
test,
|
|
pos_class=None,
|
|
Cs=10,
|
|
scoring=None,
|
|
fit_intercept=False,
|
|
max_iter=100,
|
|
tol=1e-4,
|
|
class_weight=None,
|
|
verbose=0,
|
|
solver="lbfgs",
|
|
penalty="l2",
|
|
dual=False,
|
|
intercept_scaling=1.0,
|
|
multi_class="auto",
|
|
random_state=None,
|
|
max_squared_sum=None,
|
|
sample_weight=None,
|
|
l1_ratio=None,
|
|
):
|
|
"""Computes scores across logistic_regression_path
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
|
Training data.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_targets)
|
|
Target labels.
|
|
|
|
train : list of indices
|
|
The indices of the train set.
|
|
|
|
test : list of indices
|
|
The indices of the test set.
|
|
|
|
pos_class : int, default=None
|
|
The class with respect to which we perform a one-vs-all fit.
|
|
If None, then it is assumed that the given problem is binary.
|
|
|
|
Cs : int or list of floats, default=10
|
|
Each of the values in Cs describes the inverse of
|
|
regularization strength. If Cs is as an int, then a grid of Cs
|
|
values are chosen in a logarithmic scale between 1e-4 and 1e4.
|
|
If not provided, then a fixed set of values for Cs are used.
|
|
|
|
scoring : callable, default=None
|
|
A string (see model evaluation documentation) or
|
|
a scorer callable object / function with signature
|
|
``scorer(estimator, X, y)``. For a list of scoring functions
|
|
that can be used, look at :mod:`sklearn.metrics`. The
|
|
default scoring option used is accuracy_score.
|
|
|
|
fit_intercept : bool, default=False
|
|
If False, then the bias term is set to zero. Else the last
|
|
term of each coef_ gives us the intercept.
|
|
|
|
max_iter : int, default=100
|
|
Maximum number of iterations for the solver.
|
|
|
|
tol : float, default=1e-4
|
|
Tolerance for stopping criteria.
|
|
|
|
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.
|
|
|
|
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))``
|
|
|
|
Note that these weights will be multiplied with sample_weight (passed
|
|
through the fit method) if sample_weight is specified.
|
|
|
|
verbose : int, default=0
|
|
For the liblinear and lbfgs solvers set verbose to any positive
|
|
number for verbosity.
|
|
|
|
solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, \
|
|
default='lbfgs'
|
|
Decides which solver to use.
|
|
|
|
penalty : {'l1', 'l2', 'elasticnet'}, default='l2'
|
|
Used to specify the norm used in the penalization. The 'newton-cg',
|
|
'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
|
|
only supported by the 'saga' solver.
|
|
|
|
dual : bool, default=False
|
|
Dual or primal formulation. Dual formulation is only implemented for
|
|
l2 penalty with liblinear solver. Prefer dual=False when
|
|
n_samples > n_features.
|
|
|
|
intercept_scaling : float, default=1.
|
|
Useful only when the solver 'liblinear' is used
|
|
and self.fit_intercept is set to True. In this case, x becomes
|
|
[x, self.intercept_scaling],
|
|
i.e. a "synthetic" feature with constant value equals to
|
|
intercept_scaling is appended to the instance vector.
|
|
The intercept becomes intercept_scaling * synthetic feature weight
|
|
Note! the synthetic feature weight is subject to l1/l2 regularization
|
|
as all other features.
|
|
To lessen the effect of regularization on synthetic feature weight
|
|
(and therefore on the intercept) intercept_scaling has to be increased.
|
|
|
|
multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'
|
|
If the option chosen is 'ovr', then a binary problem is fit for each
|
|
label. For 'multinomial' the loss minimised is the multinomial loss fit
|
|
across the entire probability distribution, *even when the data is
|
|
binary*. 'multinomial' is unavailable when solver='liblinear'.
|
|
|
|
random_state : int, RandomState instance, default=None
|
|
Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
|
|
data. See :term:`Glossary <random_state>` for details.
|
|
|
|
max_squared_sum : float, default=None
|
|
Maximum squared sum of X over samples. Used only in SAG solver.
|
|
If None, it will be computed, going through all the samples.
|
|
The value should be precomputed to speed up cross validation.
|
|
|
|
sample_weight : array-like of shape(n_samples,), default=None
|
|
Array of weights that are assigned to individual samples.
|
|
If not provided, then each sample is given unit weight.
|
|
|
|
l1_ratio : float, default=None
|
|
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
|
|
used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
|
|
to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
|
|
to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
|
|
combination of L1 and L2.
|
|
|
|
Returns
|
|
-------
|
|
coefs : ndarray of shape (n_cs, n_features) or (n_cs, n_features + 1)
|
|
List of coefficients for the Logistic Regression model. If
|
|
fit_intercept is set to True then the second dimension will be
|
|
n_features + 1, where the last item represents the intercept.
|
|
|
|
Cs : ndarray
|
|
Grid of Cs used for cross-validation.
|
|
|
|
scores : ndarray of shape (n_cs,)
|
|
Scores obtained for each Cs.
|
|
|
|
n_iter : ndarray of shape(n_cs,)
|
|
Actual number of iteration for each Cs.
|
|
"""
|
|
X_train = X[train]
|
|
X_test = X[test]
|
|
y_train = y[train]
|
|
y_test = y[test]
|
|
|
|
if sample_weight is not None:
|
|
sample_weight = _check_sample_weight(sample_weight, X)
|
|
sample_weight = sample_weight[train]
|
|
|
|
coefs, Cs, n_iter = _logistic_regression_path(
|
|
X_train,
|
|
y_train,
|
|
Cs=Cs,
|
|
l1_ratio=l1_ratio,
|
|
fit_intercept=fit_intercept,
|
|
solver=solver,
|
|
max_iter=max_iter,
|
|
class_weight=class_weight,
|
|
pos_class=pos_class,
|
|
multi_class=multi_class,
|
|
tol=tol,
|
|
verbose=verbose,
|
|
dual=dual,
|
|
penalty=penalty,
|
|
intercept_scaling=intercept_scaling,
|
|
random_state=random_state,
|
|
check_input=False,
|
|
max_squared_sum=max_squared_sum,
|
|
sample_weight=sample_weight,
|
|
)
|
|
|
|
log_reg = LogisticRegression(solver=solver, multi_class=multi_class)
|
|
|
|
# The score method of Logistic Regression has a classes_ attribute.
|
|
if multi_class == "ovr":
|
|
log_reg.classes_ = np.array([-1, 1])
|
|
elif multi_class == "multinomial":
|
|
log_reg.classes_ = np.unique(y_train)
|
|
else:
|
|
raise ValueError(
|
|
"multi_class should be either multinomial or ovr, got %d" % multi_class
|
|
)
|
|
|
|
if pos_class is not None:
|
|
mask = y_test == pos_class
|
|
y_test = np.ones(y_test.shape, dtype=np.float64)
|
|
y_test[~mask] = -1.0
|
|
|
|
scores = list()
|
|
|
|
scoring = get_scorer(scoring)
|
|
for w in coefs:
|
|
if multi_class == "ovr":
|
|
w = w[np.newaxis, :]
|
|
if fit_intercept:
|
|
log_reg.coef_ = w[:, :-1]
|
|
log_reg.intercept_ = w[:, -1]
|
|
else:
|
|
log_reg.coef_ = w
|
|
log_reg.intercept_ = 0.0
|
|
|
|
if scoring is None:
|
|
scores.append(log_reg.score(X_test, y_test))
|
|
else:
|
|
scores.append(scoring(log_reg, X_test, y_test))
|
|
|
|
return coefs, Cs, np.array(scores), n_iter
|
|
|
|
|
|
class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
|
|
"""
|
|
Logistic Regression (aka logit, MaxEnt) classifier.
|
|
|
|
In the multiclass case, the training algorithm uses the one-vs-rest (OvR)
|
|
scheme if the 'multi_class' option is set to 'ovr', and uses the
|
|
cross-entropy loss if the 'multi_class' option is set to 'multinomial'.
|
|
(Currently the 'multinomial' option is supported only by the 'lbfgs',
|
|
'sag', 'saga' and 'newton-cg' solvers.)
|
|
|
|
This class implements regularized logistic regression using the
|
|
'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note
|
|
that regularization is applied by default**. It can handle both dense
|
|
and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit
|
|
floats for optimal performance; any other input format will be converted
|
|
(and copied).
|
|
|
|
The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization
|
|
with primal formulation, or no regularization. The 'liblinear' solver
|
|
supports both L1 and L2 regularization, with a dual formulation only for
|
|
the L2 penalty. The Elastic-Net regularization is only supported by the
|
|
'saga' solver.
|
|
|
|
Read more in the :ref:`User Guide <logistic_regression>`.
|
|
|
|
Parameters
|
|
----------
|
|
penalty : {'l1', 'l2', 'elasticnet', None}, default='l2'
|
|
Specify the norm of the penalty:
|
|
|
|
- `None`: no penalty is added;
|
|
- `'l2'`: add a L2 penalty term and it is the default choice;
|
|
- `'l1'`: add a L1 penalty term;
|
|
- `'elasticnet'`: both L1 and L2 penalty terms are added.
|
|
|
|
.. warning::
|
|
Some penalties may not work with some solvers. See the parameter
|
|
`solver` below, to know the compatibility between the penalty and
|
|
solver.
|
|
|
|
.. versionadded:: 0.19
|
|
l1 penalty with SAGA solver (allowing 'multinomial' + L1)
|
|
|
|
.. deprecated:: 1.2
|
|
The 'none' option was deprecated in version 1.2, and will be removed
|
|
in 1.4. Use `None` instead.
|
|
|
|
dual : bool, default=False
|
|
Dual or primal formulation. Dual formulation is only implemented for
|
|
l2 penalty with liblinear solver. Prefer dual=False when
|
|
n_samples > n_features.
|
|
|
|
tol : float, default=1e-4
|
|
Tolerance for stopping criteria.
|
|
|
|
C : float, default=1.0
|
|
Inverse of regularization strength; must be a positive float.
|
|
Like in support vector machines, smaller values specify stronger
|
|
regularization.
|
|
|
|
fit_intercept : bool, default=True
|
|
Specifies if a constant (a.k.a. bias or intercept) should be
|
|
added to the decision function.
|
|
|
|
intercept_scaling : float, default=1
|
|
Useful only when the solver 'liblinear' is used
|
|
and self.fit_intercept is set to True. In this case, x becomes
|
|
[x, self.intercept_scaling],
|
|
i.e. a "synthetic" feature with constant value equal to
|
|
intercept_scaling is appended to the instance vector.
|
|
The intercept becomes ``intercept_scaling * synthetic_feature_weight``.
|
|
|
|
Note! the synthetic feature weight is subject to l1/l2 regularization
|
|
as all other features.
|
|
To lessen the effect of regularization on synthetic feature weight
|
|
(and therefore on the intercept) intercept_scaling has to be increased.
|
|
|
|
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.
|
|
|
|
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))``.
|
|
|
|
Note that these weights will be multiplied with sample_weight (passed
|
|
through the fit method) if sample_weight is specified.
|
|
|
|
.. versionadded:: 0.17
|
|
*class_weight='balanced'*
|
|
|
|
random_state : int, RandomState instance, default=None
|
|
Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
|
|
data. See :term:`Glossary <random_state>` for details.
|
|
|
|
solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, \
|
|
default='lbfgs'
|
|
|
|
Algorithm to use in the optimization problem. Default is 'lbfgs'.
|
|
To choose a solver, you might want to consider the following aspects:
|
|
|
|
- For small datasets, 'liblinear' is a good choice, whereas 'sag'
|
|
and 'saga' are faster for large ones;
|
|
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and
|
|
'lbfgs' handle multinomial loss;
|
|
- 'liblinear' is limited to one-versus-rest schemes.
|
|
- 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
|
|
especially with one-hot encoded categorical features with rare
|
|
categories. Note that it is limited to binary classification and the
|
|
one-versus-rest reduction for multiclass classification. Be aware that
|
|
the memory usage of this solver has a quadratic dependency on
|
|
`n_features` because it explicitly computes the Hessian matrix.
|
|
|
|
.. warning::
|
|
The choice of the algorithm depends on the penalty chosen.
|
|
Supported penalties by solver:
|
|
|
|
- 'lbfgs' - ['l2', None]
|
|
- 'liblinear' - ['l1', 'l2']
|
|
- 'newton-cg' - ['l2', None]
|
|
- 'newton-cholesky' - ['l2', None]
|
|
- 'sag' - ['l2', None]
|
|
- 'saga' - ['elasticnet', 'l1', 'l2', None]
|
|
|
|
.. note::
|
|
'sag' and 'saga' fast convergence is only guaranteed on features
|
|
with approximately the same scale. You can preprocess the data with
|
|
a scaler from :mod:`sklearn.preprocessing`.
|
|
|
|
.. seealso::
|
|
Refer to the User Guide for more information regarding
|
|
:class:`LogisticRegression` and more specifically the
|
|
:ref:`Table <Logistic_regression>`
|
|
summarizing solver/penalty supports.
|
|
|
|
.. versionadded:: 0.17
|
|
Stochastic Average Gradient descent solver.
|
|
.. versionadded:: 0.19
|
|
SAGA solver.
|
|
.. versionchanged:: 0.22
|
|
The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
|
|
.. versionadded:: 1.2
|
|
newton-cholesky solver.
|
|
|
|
max_iter : int, default=100
|
|
Maximum number of iterations taken for the solvers to converge.
|
|
|
|
multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'
|
|
If the option chosen is 'ovr', then a binary problem is fit for each
|
|
label. For 'multinomial' the loss minimised is the multinomial loss fit
|
|
across the entire probability distribution, *even when the data is
|
|
binary*. 'multinomial' is unavailable when solver='liblinear'.
|
|
'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
|
|
and otherwise selects 'multinomial'.
|
|
|
|
.. versionadded:: 0.18
|
|
Stochastic Average Gradient descent solver for 'multinomial' case.
|
|
.. versionchanged:: 0.22
|
|
Default changed from 'ovr' to 'auto' in 0.22.
|
|
|
|
verbose : int, default=0
|
|
For the liblinear and lbfgs solvers set verbose to any positive
|
|
number for verbosity.
|
|
|
|
warm_start : bool, default=False
|
|
When set to True, reuse the solution of the previous call to fit as
|
|
initialization, otherwise, just erase the previous solution.
|
|
Useless for liblinear solver. See :term:`the Glossary <warm_start>`.
|
|
|
|
.. versionadded:: 0.17
|
|
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.
|
|
|
|
n_jobs : int, default=None
|
|
Number of CPU cores used when parallelizing over classes if
|
|
multi_class='ovr'". This parameter is ignored when the ``solver`` is
|
|
set to 'liblinear' regardless of whether 'multi_class' is specified or
|
|
not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
|
|
context. ``-1`` means using all processors.
|
|
See :term:`Glossary <n_jobs>` for more details.
|
|
|
|
l1_ratio : float, default=None
|
|
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
|
|
used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
|
|
to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
|
|
to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
|
|
combination of L1 and L2.
|
|
|
|
Attributes
|
|
----------
|
|
|
|
classes_ : ndarray of shape (n_classes, )
|
|
A list of class labels known to the classifier.
|
|
|
|
coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)
|
|
Coefficient of the features in the decision function.
|
|
|
|
`coef_` is of shape (1, n_features) when the given problem is binary.
|
|
In particular, when `multi_class='multinomial'`, `coef_` corresponds
|
|
to outcome 1 (True) and `-coef_` corresponds to outcome 0 (False).
|
|
|
|
intercept_ : ndarray of shape (1,) or (n_classes,)
|
|
Intercept (a.k.a. bias) added to the decision function.
|
|
|
|
If `fit_intercept` is set to False, the intercept is set to zero.
|
|
`intercept_` is of shape (1,) when the given problem is binary.
|
|
In particular, when `multi_class='multinomial'`, `intercept_`
|
|
corresponds to outcome 1 (True) and `-intercept_` corresponds to
|
|
outcome 0 (False).
|
|
|
|
n_features_in_ : int
|
|
Number of features seen during :term:`fit`.
|
|
|
|
.. versionadded:: 0.24
|
|
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
|
Names of features seen during :term:`fit`. Defined only when `X`
|
|
has feature names that are all strings.
|
|
|
|
.. versionadded:: 1.0
|
|
|
|
n_iter_ : ndarray of shape (n_classes,) or (1, )
|
|
Actual number of iterations for all classes. If binary or multinomial,
|
|
it returns only 1 element. For liblinear solver, only the maximum
|
|
number of iteration across all classes is given.
|
|
|
|
.. versionchanged:: 0.20
|
|
|
|
In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
|
|
``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
|
|
|
|
See Also
|
|
--------
|
|
SGDClassifier : Incrementally trained logistic regression (when given
|
|
the parameter ``loss="log"``).
|
|
LogisticRegressionCV : Logistic regression with built-in cross validation.
|
|
|
|
Notes
|
|
-----
|
|
The underlying C implementation uses a random number generator to
|
|
select features when fitting the model. It is thus not uncommon,
|
|
to have slightly different results for the same input data. If
|
|
that happens, try with a smaller tol parameter.
|
|
|
|
Predict output may not match that of standalone liblinear in certain
|
|
cases. See :ref:`differences from liblinear <liblinear_differences>`
|
|
in the narrative documentation.
|
|
|
|
References
|
|
----------
|
|
|
|
L-BFGS-B -- Software for Large-scale Bound-constrained Optimization
|
|
Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales.
|
|
http://users.iems.northwestern.edu/~nocedal/lbfgsb.html
|
|
|
|
LIBLINEAR -- A Library for Large Linear Classification
|
|
https://www.csie.ntu.edu.tw/~cjlin/liblinear/
|
|
|
|
SAG -- Mark Schmidt, Nicolas Le Roux, and Francis Bach
|
|
Minimizing Finite Sums with the Stochastic Average Gradient
|
|
https://hal.inria.fr/hal-00860051/document
|
|
|
|
SAGA -- Defazio, A., Bach F. & Lacoste-Julien S. (2014).
|
|
:arxiv:`"SAGA: A Fast Incremental Gradient Method With Support
|
|
for Non-Strongly Convex Composite Objectives" <1407.0202>`
|
|
|
|
Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descent
|
|
methods for logistic regression and maximum entropy models.
|
|
Machine Learning 85(1-2):41-75.
|
|
https://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.datasets import load_iris
|
|
>>> from sklearn.linear_model import LogisticRegression
|
|
>>> X, y = load_iris(return_X_y=True)
|
|
>>> clf = LogisticRegression(random_state=0).fit(X, y)
|
|
>>> clf.predict(X[:2, :])
|
|
array([0, 0])
|
|
>>> clf.predict_proba(X[:2, :])
|
|
array([[9.8...e-01, 1.8...e-02, 1.4...e-08],
|
|
[9.7...e-01, 2.8...e-02, ...e-08]])
|
|
>>> clf.score(X, y)
|
|
0.97...
|
|
"""
|
|
|
|
_parameter_constraints: dict = {
|
|
# TODO(1.4): Remove "none" option
|
|
"penalty": [
|
|
StrOptions({"l1", "l2", "elasticnet", "none"}, deprecated={"none"}),
|
|
None,
|
|
],
|
|
"dual": ["boolean"],
|
|
"tol": [Interval(Real, 0, None, closed="left")],
|
|
"C": [Interval(Real, 0, None, closed="right")],
|
|
"fit_intercept": ["boolean"],
|
|
"intercept_scaling": [Interval(Real, 0, None, closed="neither")],
|
|
"class_weight": [dict, StrOptions({"balanced"}), None],
|
|
"random_state": ["random_state"],
|
|
"solver": [
|
|
StrOptions(
|
|
{"lbfgs", "liblinear", "newton-cg", "newton-cholesky", "sag", "saga"}
|
|
)
|
|
],
|
|
"max_iter": [Interval(Integral, 0, None, closed="left")],
|
|
"multi_class": [StrOptions({"auto", "ovr", "multinomial"})],
|
|
"verbose": ["verbose"],
|
|
"warm_start": ["boolean"],
|
|
"n_jobs": [None, Integral],
|
|
"l1_ratio": [Interval(Real, 0, 1, closed="both"), None],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
penalty="l2",
|
|
*,
|
|
dual=False,
|
|
tol=1e-4,
|
|
C=1.0,
|
|
fit_intercept=True,
|
|
intercept_scaling=1,
|
|
class_weight=None,
|
|
random_state=None,
|
|
solver="lbfgs",
|
|
max_iter=100,
|
|
multi_class="auto",
|
|
verbose=0,
|
|
warm_start=False,
|
|
n_jobs=None,
|
|
l1_ratio=None,
|
|
):
|
|
|
|
self.penalty = penalty
|
|
self.dual = dual
|
|
self.tol = tol
|
|
self.C = C
|
|
self.fit_intercept = fit_intercept
|
|
self.intercept_scaling = intercept_scaling
|
|
self.class_weight = class_weight
|
|
self.random_state = random_state
|
|
self.solver = solver
|
|
self.max_iter = max_iter
|
|
self.multi_class = multi_class
|
|
self.verbose = verbose
|
|
self.warm_start = warm_start
|
|
self.n_jobs = n_jobs
|
|
self.l1_ratio = l1_ratio
|
|
|
|
def fit(self, X, y, sample_weight=None):
|
|
"""
|
|
Fit the model according to the given training data.
|
|
|
|
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.
|
|
|
|
sample_weight : array-like of shape (n_samples,) default=None
|
|
Array of weights that are assigned to individual samples.
|
|
If not provided, then each sample is given unit weight.
|
|
|
|
.. versionadded:: 0.17
|
|
*sample_weight* support to LogisticRegression.
|
|
|
|
Returns
|
|
-------
|
|
self
|
|
Fitted estimator.
|
|
|
|
Notes
|
|
-----
|
|
The SAGA solver supports both float64 and float32 bit arrays.
|
|
"""
|
|
|
|
self._validate_params()
|
|
|
|
solver = _check_solver(self.solver, self.penalty, self.dual)
|
|
|
|
if self.penalty != "elasticnet" and self.l1_ratio is not None:
|
|
warnings.warn(
|
|
"l1_ratio parameter is only used when penalty is "
|
|
"'elasticnet'. Got "
|
|
"(penalty={})".format(self.penalty)
|
|
)
|
|
|
|
# TODO(1.4): Remove "none" option
|
|
if self.penalty == "none":
|
|
warnings.warn(
|
|
"`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4."
|
|
" To keep the past behaviour, set `penalty=None`.",
|
|
FutureWarning,
|
|
)
|
|
|
|
if self.penalty is None or self.penalty == "none":
|
|
if self.C != 1.0: # default values
|
|
warnings.warn(
|
|
"Setting penalty=None will ignore the C and l1_ratio parameters"
|
|
)
|
|
# Note that check for l1_ratio is done right above
|
|
C_ = np.inf
|
|
penalty = "l2"
|
|
else:
|
|
C_ = self.C
|
|
penalty = self.penalty
|
|
|
|
if solver == "lbfgs":
|
|
_dtype = np.float64
|
|
else:
|
|
_dtype = [np.float64, np.float32]
|
|
|
|
X, y = self._validate_data(
|
|
X,
|
|
y,
|
|
accept_sparse="csr",
|
|
dtype=_dtype,
|
|
order="C",
|
|
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
|
|
)
|
|
check_classification_targets(y)
|
|
self.classes_ = np.unique(y)
|
|
|
|
multi_class = _check_multi_class(self.multi_class, solver, len(self.classes_))
|
|
|
|
if solver == "liblinear":
|
|
if effective_n_jobs(self.n_jobs) != 1:
|
|
warnings.warn(
|
|
"'n_jobs' > 1 does not have any effect when"
|
|
" 'solver' is set to 'liblinear'. Got 'n_jobs'"
|
|
" = {}.".format(effective_n_jobs(self.n_jobs))
|
|
)
|
|
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
|
|
X,
|
|
y,
|
|
self.C,
|
|
self.fit_intercept,
|
|
self.intercept_scaling,
|
|
self.class_weight,
|
|
self.penalty,
|
|
self.dual,
|
|
self.verbose,
|
|
self.max_iter,
|
|
self.tol,
|
|
self.random_state,
|
|
sample_weight=sample_weight,
|
|
)
|
|
return self
|
|
|
|
if solver in ["sag", "saga"]:
|
|
max_squared_sum = row_norms(X, squared=True).max()
|
|
else:
|
|
max_squared_sum = None
|
|
|
|
n_classes = len(self.classes_)
|
|
classes_ = self.classes_
|
|
if n_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]
|
|
)
|
|
|
|
if len(self.classes_) == 2:
|
|
n_classes = 1
|
|
classes_ = classes_[1:]
|
|
|
|
if self.warm_start:
|
|
warm_start_coef = getattr(self, "coef_", None)
|
|
else:
|
|
warm_start_coef = None
|
|
if warm_start_coef is not None and self.fit_intercept:
|
|
warm_start_coef = np.append(
|
|
warm_start_coef, self.intercept_[:, np.newaxis], axis=1
|
|
)
|
|
|
|
# Hack so that we iterate only once for the multinomial case.
|
|
if multi_class == "multinomial":
|
|
classes_ = [None]
|
|
warm_start_coef = [warm_start_coef]
|
|
if warm_start_coef is None:
|
|
warm_start_coef = [None] * n_classes
|
|
|
|
path_func = delayed(_logistic_regression_path)
|
|
|
|
# The SAG solver releases the GIL so it's more efficient to use
|
|
# threads for this solver.
|
|
if solver in ["sag", "saga"]:
|
|
prefer = "threads"
|
|
else:
|
|
prefer = "processes"
|
|
|
|
# TODO: Refactor this to avoid joblib parallelism entirely when doing binary
|
|
# and multinomial multiclass classification and use joblib only for the
|
|
# one-vs-rest multiclass case.
|
|
if (
|
|
solver in ["lbfgs", "newton-cg", "newton-cholesky"]
|
|
and len(classes_) == 1
|
|
and effective_n_jobs(self.n_jobs) == 1
|
|
):
|
|
# In the future, we would like n_threads = _openmp_effective_n_threads()
|
|
# For the time being, we just do
|
|
n_threads = 1
|
|
else:
|
|
n_threads = 1
|
|
|
|
fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(
|
|
path_func(
|
|
X,
|
|
y,
|
|
pos_class=class_,
|
|
Cs=[C_],
|
|
l1_ratio=self.l1_ratio,
|
|
fit_intercept=self.fit_intercept,
|
|
tol=self.tol,
|
|
verbose=self.verbose,
|
|
solver=solver,
|
|
multi_class=multi_class,
|
|
max_iter=self.max_iter,
|
|
class_weight=self.class_weight,
|
|
check_input=False,
|
|
random_state=self.random_state,
|
|
coef=warm_start_coef_,
|
|
penalty=penalty,
|
|
max_squared_sum=max_squared_sum,
|
|
sample_weight=sample_weight,
|
|
n_threads=n_threads,
|
|
)
|
|
for class_, warm_start_coef_ in zip(classes_, warm_start_coef)
|
|
)
|
|
|
|
fold_coefs_, _, n_iter_ = zip(*fold_coefs_)
|
|
self.n_iter_ = np.asarray(n_iter_, dtype=np.int32)[:, 0]
|
|
|
|
n_features = X.shape[1]
|
|
if multi_class == "multinomial":
|
|
self.coef_ = fold_coefs_[0][0]
|
|
else:
|
|
self.coef_ = np.asarray(fold_coefs_)
|
|
self.coef_ = self.coef_.reshape(
|
|
n_classes, n_features + int(self.fit_intercept)
|
|
)
|
|
|
|
if self.fit_intercept:
|
|
self.intercept_ = self.coef_[:, -1]
|
|
self.coef_ = self.coef_[:, :-1]
|
|
else:
|
|
self.intercept_ = np.zeros(n_classes)
|
|
|
|
return self
|
|
|
|
def predict_proba(self, X):
|
|
"""
|
|
Probability estimates.
|
|
|
|
The returned estimates for all classes are ordered by the
|
|
label of classes.
|
|
|
|
For a multi_class problem, if multi_class is set to be "multinomial"
|
|
the softmax function is used to find the predicted probability of
|
|
each class.
|
|
Else use a one-vs-rest approach, i.e calculate the probability
|
|
of each class assuming it to be positive using the logistic function.
|
|
and normalize these values across all the classes.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Vector to be scored, where `n_samples` is the number of samples and
|
|
`n_features` is the number of features.
|
|
|
|
Returns
|
|
-------
|
|
T : array-like of shape (n_samples, n_classes)
|
|
Returns the probability of the sample for each class in the model,
|
|
where classes are ordered as they are in ``self.classes_``.
|
|
"""
|
|
check_is_fitted(self)
|
|
|
|
ovr = self.multi_class in ["ovr", "warn"] or (
|
|
self.multi_class == "auto"
|
|
and (
|
|
self.classes_.size <= 2
|
|
or self.solver in ("liblinear", "newton-cholesky")
|
|
)
|
|
)
|
|
if ovr:
|
|
return super()._predict_proba_lr(X)
|
|
else:
|
|
decision = self.decision_function(X)
|
|
if decision.ndim == 1:
|
|
# Workaround for multi_class="multinomial" and binary outcomes
|
|
# which requires softmax prediction with only a 1D decision.
|
|
decision_2d = np.c_[-decision, decision]
|
|
else:
|
|
decision_2d = decision
|
|
return softmax(decision_2d, copy=False)
|
|
|
|
def predict_log_proba(self, X):
|
|
"""
|
|
Predict logarithm of probability estimates.
|
|
|
|
The returned estimates for all classes are ordered by the
|
|
label of classes.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Vector to be scored, where `n_samples` is the number of samples and
|
|
`n_features` is the number of features.
|
|
|
|
Returns
|
|
-------
|
|
T : array-like of shape (n_samples, n_classes)
|
|
Returns the log-probability of the sample for each class in the
|
|
model, where classes are ordered as they are in ``self.classes_``.
|
|
"""
|
|
return np.log(self.predict_proba(X))
|
|
|
|
|
|
class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstimator):
|
|
"""Logistic Regression CV (aka logit, MaxEnt) classifier.
|
|
|
|
See glossary entry for :term:`cross-validation estimator`.
|
|
|
|
This class implements logistic regression using liblinear, newton-cg, sag
|
|
of lbfgs optimizer. The newton-cg, sag and lbfgs solvers support only L2
|
|
regularization with primal formulation. The liblinear solver supports both
|
|
L1 and L2 regularization, with a dual formulation only for the L2 penalty.
|
|
Elastic-Net penalty is only supported by the saga solver.
|
|
|
|
For the grid of `Cs` values and `l1_ratios` values, the best hyperparameter
|
|
is selected by the cross-validator
|
|
:class:`~sklearn.model_selection.StratifiedKFold`, but it can be changed
|
|
using the :term:`cv` parameter. The 'newton-cg', 'sag', 'saga' and 'lbfgs'
|
|
solvers can warm-start the coefficients (see :term:`Glossary<warm_start>`).
|
|
|
|
Read more in the :ref:`User Guide <logistic_regression>`.
|
|
|
|
Parameters
|
|
----------
|
|
Cs : int or list of floats, default=10
|
|
Each of the values in Cs describes the inverse of regularization
|
|
strength. If Cs is as an int, then a grid of Cs values are chosen
|
|
in a logarithmic scale between 1e-4 and 1e4.
|
|
Like in support vector machines, smaller values specify stronger
|
|
regularization.
|
|
|
|
fit_intercept : bool, default=True
|
|
Specifies if a constant (a.k.a. bias or intercept) should be
|
|
added to the decision function.
|
|
|
|
cv : int or cross-validation generator, default=None
|
|
The default cross-validation generator used is Stratified K-Folds.
|
|
If an integer is provided, then it is the number of folds used.
|
|
See the module :mod:`sklearn.model_selection` module for the
|
|
list of possible cross-validation objects.
|
|
|
|
.. versionchanged:: 0.22
|
|
``cv`` default value if None changed from 3-fold to 5-fold.
|
|
|
|
dual : bool, default=False
|
|
Dual or primal formulation. Dual formulation is only implemented for
|
|
l2 penalty with liblinear solver. Prefer dual=False when
|
|
n_samples > n_features.
|
|
|
|
penalty : {'l1', 'l2', 'elasticnet'}, default='l2'
|
|
Specify the norm of the penalty:
|
|
|
|
- `'l2'`: add a L2 penalty term (used by default);
|
|
- `'l1'`: add a L1 penalty term;
|
|
- `'elasticnet'`: both L1 and L2 penalty terms are added.
|
|
|
|
.. warning::
|
|
Some penalties may not work with some solvers. See the parameter
|
|
`solver` below, to know the compatibility between the penalty and
|
|
solver.
|
|
|
|
scoring : str or callable, default=None
|
|
A string (see model evaluation documentation) or
|
|
a scorer callable object / function with signature
|
|
``scorer(estimator, X, y)``. For a list of scoring functions
|
|
that can be used, look at :mod:`sklearn.metrics`. The
|
|
default scoring option used is 'accuracy'.
|
|
|
|
solver : {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, \
|
|
default='lbfgs'
|
|
|
|
Algorithm to use in the optimization problem. Default is 'lbfgs'.
|
|
To choose a solver, you might want to consider the following aspects:
|
|
|
|
- For small datasets, 'liblinear' is a good choice, whereas 'sag'
|
|
and 'saga' are faster for large ones;
|
|
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and
|
|
'lbfgs' handle multinomial loss;
|
|
- 'liblinear' might be slower in :class:`LogisticRegressionCV`
|
|
because it does not handle warm-starting. 'liblinear' is
|
|
limited to one-versus-rest schemes.
|
|
- 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
|
|
especially with one-hot encoded categorical features with rare
|
|
categories. Note that it is limited to binary classification and the
|
|
one-versus-rest reduction for multiclass classification. Be aware that
|
|
the memory usage of this solver has a quadratic dependency on
|
|
`n_features` because it explicitly computes the Hessian matrix.
|
|
|
|
.. warning::
|
|
The choice of the algorithm depends on the penalty chosen.
|
|
Supported penalties by solver:
|
|
|
|
- 'lbfgs' - ['l2']
|
|
- 'liblinear' - ['l1', 'l2']
|
|
- 'newton-cg' - ['l2']
|
|
- 'newton-cholesky' - ['l2']
|
|
- 'sag' - ['l2']
|
|
- 'saga' - ['elasticnet', 'l1', 'l2']
|
|
|
|
.. note::
|
|
'sag' and 'saga' fast convergence is only guaranteed on features
|
|
with approximately the same scale. You can preprocess the data with
|
|
a scaler from :mod:`sklearn.preprocessing`.
|
|
|
|
.. versionadded:: 0.17
|
|
Stochastic Average Gradient descent solver.
|
|
.. versionadded:: 0.19
|
|
SAGA solver.
|
|
.. versionadded:: 1.2
|
|
newton-cholesky solver.
|
|
|
|
tol : float, default=1e-4
|
|
Tolerance for stopping criteria.
|
|
|
|
max_iter : int, default=100
|
|
Maximum number of iterations of the optimization algorithm.
|
|
|
|
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.
|
|
|
|
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))``.
|
|
|
|
Note that these weights will be multiplied with sample_weight (passed
|
|
through the fit method) if sample_weight is specified.
|
|
|
|
.. versionadded:: 0.17
|
|
class_weight == 'balanced'
|
|
|
|
n_jobs : int, default=None
|
|
Number of CPU cores used during the cross-validation loop.
|
|
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
|
|
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
|
|
for more details.
|
|
|
|
verbose : int, default=0
|
|
For the 'liblinear', 'sag' and 'lbfgs' solvers set verbose to any
|
|
positive number for verbosity.
|
|
|
|
refit : bool, default=True
|
|
If set to True, the scores are averaged across all folds, and the
|
|
coefs and the C that corresponds to the best score is taken, and a
|
|
final refit is done using these parameters.
|
|
Otherwise the coefs, intercepts and C that correspond to the
|
|
best scores across folds are averaged.
|
|
|
|
intercept_scaling : float, default=1
|
|
Useful only when the solver 'liblinear' is used
|
|
and self.fit_intercept is set to True. In this case, x becomes
|
|
[x, self.intercept_scaling],
|
|
i.e. a "synthetic" feature with constant value equal to
|
|
intercept_scaling is appended to the instance vector.
|
|
The intercept becomes ``intercept_scaling * synthetic_feature_weight``.
|
|
|
|
Note! the synthetic feature weight is subject to l1/l2 regularization
|
|
as all other features.
|
|
To lessen the effect of regularization on synthetic feature weight
|
|
(and therefore on the intercept) intercept_scaling has to be increased.
|
|
|
|
multi_class : {'auto, 'ovr', 'multinomial'}, default='auto'
|
|
If the option chosen is 'ovr', then a binary problem is fit for each
|
|
label. For 'multinomial' the loss minimised is the multinomial loss fit
|
|
across the entire probability distribution, *even when the data is
|
|
binary*. 'multinomial' is unavailable when solver='liblinear'.
|
|
'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
|
|
and otherwise selects 'multinomial'.
|
|
|
|
.. versionadded:: 0.18
|
|
Stochastic Average Gradient descent solver for 'multinomial' case.
|
|
.. versionchanged:: 0.22
|
|
Default changed from 'ovr' to 'auto' in 0.22.
|
|
|
|
random_state : int, RandomState instance, default=None
|
|
Used when `solver='sag'`, 'saga' or 'liblinear' to shuffle the data.
|
|
Note that this only applies to the solver and not the cross-validation
|
|
generator. See :term:`Glossary <random_state>` for details.
|
|
|
|
l1_ratios : list of float, default=None
|
|
The list of Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``.
|
|
Only used if ``penalty='elasticnet'``. A value of 0 is equivalent to
|
|
using ``penalty='l2'``, while 1 is equivalent to using
|
|
``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a combination
|
|
of L1 and L2.
|
|
|
|
Attributes
|
|
----------
|
|
classes_ : ndarray of shape (n_classes, )
|
|
A list of class labels known to the classifier.
|
|
|
|
coef_ : ndarray of shape (1, n_features) or (n_classes, n_features)
|
|
Coefficient of the features in the decision function.
|
|
|
|
`coef_` is of shape (1, n_features) when the given problem
|
|
is binary.
|
|
|
|
intercept_ : ndarray of shape (1,) or (n_classes,)
|
|
Intercept (a.k.a. bias) added to the decision function.
|
|
|
|
If `fit_intercept` is set to False, the intercept is set to zero.
|
|
`intercept_` is of shape(1,) when the problem is binary.
|
|
|
|
Cs_ : ndarray of shape (n_cs)
|
|
Array of C i.e. inverse of regularization parameter values used
|
|
for cross-validation.
|
|
|
|
l1_ratios_ : ndarray of shape (n_l1_ratios)
|
|
Array of l1_ratios used for cross-validation. If no l1_ratio is used
|
|
(i.e. penalty is not 'elasticnet'), this is set to ``[None]``
|
|
|
|
coefs_paths_ : ndarray of shape (n_folds, n_cs, n_features) or \
|
|
(n_folds, n_cs, n_features + 1)
|
|
dict with classes as the keys, and the path of coefficients obtained
|
|
during cross-validating across each fold and then across each Cs
|
|
after doing an OvR for the corresponding class as values.
|
|
If the 'multi_class' option is set to 'multinomial', then
|
|
the coefs_paths are the coefficients corresponding to each class.
|
|
Each dict value has shape ``(n_folds, n_cs, n_features)`` or
|
|
``(n_folds, n_cs, n_features + 1)`` depending on whether the
|
|
intercept is fit or not. If ``penalty='elasticnet'``, the shape is
|
|
``(n_folds, n_cs, n_l1_ratios_, n_features)`` or
|
|
``(n_folds, n_cs, n_l1_ratios_, n_features + 1)``.
|
|
|
|
scores_ : dict
|
|
dict with classes as the keys, and the values as the
|
|
grid of scores obtained during cross-validating each fold, after doing
|
|
an OvR for the corresponding class. If the 'multi_class' option
|
|
given is 'multinomial' then the same scores are repeated across
|
|
all classes, since this is the multinomial class. Each dict value
|
|
has shape ``(n_folds, n_cs)`` or ``(n_folds, n_cs, n_l1_ratios)`` if
|
|
``penalty='elasticnet'``.
|
|
|
|
C_ : ndarray of shape (n_classes,) or (n_classes - 1,)
|
|
Array of C that maps to the best scores across every class. If refit is
|
|
set to False, then for each class, the best C is the average of the
|
|
C's that correspond to the best scores for each fold.
|
|
`C_` is of shape(n_classes,) when the problem is binary.
|
|
|
|
l1_ratio_ : ndarray of shape (n_classes,) or (n_classes - 1,)
|
|
Array of l1_ratio that maps to the best scores across every class. If
|
|
refit is set to False, then for each class, the best l1_ratio is the
|
|
average of the l1_ratio's that correspond to the best scores for each
|
|
fold. `l1_ratio_` is of shape(n_classes,) when the problem is binary.
|
|
|
|
n_iter_ : ndarray of shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs)
|
|
Actual number of iterations for all classes, folds and Cs.
|
|
In the binary or multinomial cases, the first dimension is equal to 1.
|
|
If ``penalty='elasticnet'``, the shape is ``(n_classes, n_folds,
|
|
n_cs, n_l1_ratios)`` or ``(1, n_folds, n_cs, n_l1_ratios)``.
|
|
|
|
n_features_in_ : int
|
|
Number of features seen during :term:`fit`.
|
|
|
|
.. versionadded:: 0.24
|
|
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
|
Names of features seen during :term:`fit`. Defined only when `X`
|
|
has feature names that are all strings.
|
|
|
|
.. versionadded:: 1.0
|
|
|
|
See Also
|
|
--------
|
|
LogisticRegression : Logistic regression without tuning the
|
|
hyperparameter `C`.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.datasets import load_iris
|
|
>>> from sklearn.linear_model import LogisticRegressionCV
|
|
>>> X, y = load_iris(return_X_y=True)
|
|
>>> clf = LogisticRegressionCV(cv=5, random_state=0).fit(X, y)
|
|
>>> clf.predict(X[:2, :])
|
|
array([0, 0])
|
|
>>> clf.predict_proba(X[:2, :]).shape
|
|
(2, 3)
|
|
>>> clf.score(X, y)
|
|
0.98...
|
|
"""
|
|
|
|
_parameter_constraints: dict = {**LogisticRegression._parameter_constraints}
|
|
|
|
for param in ["C", "warm_start", "l1_ratio"]:
|
|
_parameter_constraints.pop(param)
|
|
|
|
_parameter_constraints.update(
|
|
{
|
|
"Cs": [Interval(Integral, 1, None, closed="left"), "array-like"],
|
|
"cv": ["cv_object"],
|
|
"scoring": [StrOptions(set(get_scorer_names())), callable, None],
|
|
"l1_ratios": ["array-like", None],
|
|
"refit": ["boolean"],
|
|
"penalty": [StrOptions({"l1", "l2", "elasticnet"})],
|
|
}
|
|
)
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
Cs=10,
|
|
fit_intercept=True,
|
|
cv=None,
|
|
dual=False,
|
|
penalty="l2",
|
|
scoring=None,
|
|
solver="lbfgs",
|
|
tol=1e-4,
|
|
max_iter=100,
|
|
class_weight=None,
|
|
n_jobs=None,
|
|
verbose=0,
|
|
refit=True,
|
|
intercept_scaling=1.0,
|
|
multi_class="auto",
|
|
random_state=None,
|
|
l1_ratios=None,
|
|
):
|
|
self.Cs = Cs
|
|
self.fit_intercept = fit_intercept
|
|
self.cv = cv
|
|
self.dual = dual
|
|
self.penalty = penalty
|
|
self.scoring = scoring
|
|
self.tol = tol
|
|
self.max_iter = max_iter
|
|
self.class_weight = class_weight
|
|
self.n_jobs = n_jobs
|
|
self.verbose = verbose
|
|
self.solver = solver
|
|
self.refit = refit
|
|
self.intercept_scaling = intercept_scaling
|
|
self.multi_class = multi_class
|
|
self.random_state = random_state
|
|
self.l1_ratios = l1_ratios
|
|
|
|
def fit(self, X, y, sample_weight=None):
|
|
"""Fit the model according to the given training data.
|
|
|
|
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.
|
|
|
|
sample_weight : array-like of shape (n_samples,) default=None
|
|
Array of weights that are assigned to individual samples.
|
|
If not provided, then each sample is given unit weight.
|
|
|
|
Returns
|
|
-------
|
|
self : object
|
|
Fitted LogisticRegressionCV estimator.
|
|
"""
|
|
|
|
self._validate_params()
|
|
|
|
solver = _check_solver(self.solver, self.penalty, self.dual)
|
|
|
|
if self.penalty == "elasticnet":
|
|
if (
|
|
self.l1_ratios is None
|
|
or len(self.l1_ratios) == 0
|
|
or any(
|
|
(
|
|
not isinstance(l1_ratio, numbers.Number)
|
|
or l1_ratio < 0
|
|
or l1_ratio > 1
|
|
)
|
|
for l1_ratio in self.l1_ratios
|
|
)
|
|
):
|
|
raise ValueError(
|
|
"l1_ratios must be a list of numbers between "
|
|
"0 and 1; got (l1_ratios=%r)"
|
|
% self.l1_ratios
|
|
)
|
|
l1_ratios_ = self.l1_ratios
|
|
else:
|
|
if self.l1_ratios is not None:
|
|
warnings.warn(
|
|
"l1_ratios parameter is only used when penalty "
|
|
"is 'elasticnet'. Got (penalty={})".format(self.penalty)
|
|
)
|
|
|
|
l1_ratios_ = [None]
|
|
|
|
X, y = self._validate_data(
|
|
X,
|
|
y,
|
|
accept_sparse="csr",
|
|
dtype=np.float64,
|
|
order="C",
|
|
accept_large_sparse=solver not in ["liblinear", "sag", "saga"],
|
|
)
|
|
check_classification_targets(y)
|
|
|
|
class_weight = self.class_weight
|
|
|
|
# Encode for string labels
|
|
label_encoder = LabelEncoder().fit(y)
|
|
y = label_encoder.transform(y)
|
|
if isinstance(class_weight, dict):
|
|
class_weight = {
|
|
label_encoder.transform([cls])[0]: v for cls, v in class_weight.items()
|
|
}
|
|
|
|
# The original class labels
|
|
classes = self.classes_ = label_encoder.classes_
|
|
encoded_labels = label_encoder.transform(label_encoder.classes_)
|
|
|
|
multi_class = _check_multi_class(self.multi_class, solver, len(classes))
|
|
|
|
if solver in ["sag", "saga"]:
|
|
max_squared_sum = row_norms(X, squared=True).max()
|
|
else:
|
|
max_squared_sum = None
|
|
|
|
# init cross-validation generator
|
|
cv = check_cv(self.cv, y, classifier=True)
|
|
folds = list(cv.split(X, y))
|
|
|
|
# Use the label encoded classes
|
|
n_classes = len(encoded_labels)
|
|
|
|
if n_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]
|
|
)
|
|
|
|
if n_classes == 2:
|
|
# OvR in case of binary problems is as good as fitting
|
|
# the higher label
|
|
n_classes = 1
|
|
encoded_labels = encoded_labels[1:]
|
|
classes = classes[1:]
|
|
|
|
# We need this hack to iterate only once over labels, in the case of
|
|
# multi_class = multinomial, without changing the value of the labels.
|
|
if multi_class == "multinomial":
|
|
iter_encoded_labels = iter_classes = [None]
|
|
else:
|
|
iter_encoded_labels = encoded_labels
|
|
iter_classes = classes
|
|
|
|
# compute the class weights for the entire dataset y
|
|
if class_weight == "balanced":
|
|
class_weight = compute_class_weight(
|
|
class_weight, classes=np.arange(len(self.classes_)), y=y
|
|
)
|
|
class_weight = dict(enumerate(class_weight))
|
|
|
|
path_func = delayed(_log_reg_scoring_path)
|
|
|
|
# The SAG solver releases the GIL so it's more efficient to use
|
|
# threads for this solver.
|
|
if self.solver in ["sag", "saga"]:
|
|
prefer = "threads"
|
|
else:
|
|
prefer = "processes"
|
|
|
|
fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, prefer=prefer)(
|
|
path_func(
|
|
X,
|
|
y,
|
|
train,
|
|
test,
|
|
pos_class=label,
|
|
Cs=self.Cs,
|
|
fit_intercept=self.fit_intercept,
|
|
penalty=self.penalty,
|
|
dual=self.dual,
|
|
solver=solver,
|
|
tol=self.tol,
|
|
max_iter=self.max_iter,
|
|
verbose=self.verbose,
|
|
class_weight=class_weight,
|
|
scoring=self.scoring,
|
|
multi_class=multi_class,
|
|
intercept_scaling=self.intercept_scaling,
|
|
random_state=self.random_state,
|
|
max_squared_sum=max_squared_sum,
|
|
sample_weight=sample_weight,
|
|
l1_ratio=l1_ratio,
|
|
)
|
|
for label in iter_encoded_labels
|
|
for train, test in folds
|
|
for l1_ratio in l1_ratios_
|
|
)
|
|
|
|
# _log_reg_scoring_path will output different shapes depending on the
|
|
# multi_class param, so we need to reshape the outputs accordingly.
|
|
# Cs is of shape (n_classes . n_folds . n_l1_ratios, n_Cs) and all the
|
|
# rows are equal, so we just take the first one.
|
|
# After reshaping,
|
|
# - scores is of shape (n_classes, n_folds, n_Cs . n_l1_ratios)
|
|
# - coefs_paths is of shape
|
|
# (n_classes, n_folds, n_Cs . n_l1_ratios, n_features)
|
|
# - n_iter is of shape
|
|
# (n_classes, n_folds, n_Cs . n_l1_ratios) or
|
|
# (1, n_folds, n_Cs . n_l1_ratios)
|
|
coefs_paths, Cs, scores, n_iter_ = zip(*fold_coefs_)
|
|
self.Cs_ = Cs[0]
|
|
if multi_class == "multinomial":
|
|
coefs_paths = np.reshape(
|
|
coefs_paths,
|
|
(len(folds), len(l1_ratios_) * len(self.Cs_), n_classes, -1),
|
|
)
|
|
# equiv to coefs_paths = np.moveaxis(coefs_paths, (0, 1, 2, 3),
|
|
# (1, 2, 0, 3))
|
|
coefs_paths = np.swapaxes(coefs_paths, 0, 1)
|
|
coefs_paths = np.swapaxes(coefs_paths, 0, 2)
|
|
self.n_iter_ = np.reshape(
|
|
n_iter_, (1, len(folds), len(self.Cs_) * len(l1_ratios_))
|
|
)
|
|
# repeat same scores across all classes
|
|
scores = np.tile(scores, (n_classes, 1, 1))
|
|
else:
|
|
coefs_paths = np.reshape(
|
|
coefs_paths,
|
|
(n_classes, len(folds), len(self.Cs_) * len(l1_ratios_), -1),
|
|
)
|
|
self.n_iter_ = np.reshape(
|
|
n_iter_, (n_classes, len(folds), len(self.Cs_) * len(l1_ratios_))
|
|
)
|
|
scores = np.reshape(scores, (n_classes, len(folds), -1))
|
|
self.scores_ = dict(zip(classes, scores))
|
|
self.coefs_paths_ = dict(zip(classes, coefs_paths))
|
|
|
|
self.C_ = list()
|
|
self.l1_ratio_ = list()
|
|
self.coef_ = np.empty((n_classes, X.shape[1]))
|
|
self.intercept_ = np.zeros(n_classes)
|
|
for index, (cls, encoded_label) in enumerate(
|
|
zip(iter_classes, iter_encoded_labels)
|
|
):
|
|
|
|
if multi_class == "ovr":
|
|
scores = self.scores_[cls]
|
|
coefs_paths = self.coefs_paths_[cls]
|
|
else:
|
|
# For multinomial, all scores are the same across classes
|
|
scores = scores[0]
|
|
# coefs_paths will keep its original shape because
|
|
# logistic_regression_path expects it this way
|
|
|
|
if self.refit:
|
|
# best_index is between 0 and (n_Cs . n_l1_ratios - 1)
|
|
# for example, with n_cs=2 and n_l1_ratios=3
|
|
# the layout of scores is
|
|
# [c1, c2, c1, c2, c1, c2]
|
|
# l1_1 , l1_2 , l1_3
|
|
best_index = scores.sum(axis=0).argmax()
|
|
|
|
best_index_C = best_index % len(self.Cs_)
|
|
C_ = self.Cs_[best_index_C]
|
|
self.C_.append(C_)
|
|
|
|
best_index_l1 = best_index // len(self.Cs_)
|
|
l1_ratio_ = l1_ratios_[best_index_l1]
|
|
self.l1_ratio_.append(l1_ratio_)
|
|
|
|
if multi_class == "multinomial":
|
|
coef_init = np.mean(coefs_paths[:, :, best_index, :], axis=1)
|
|
else:
|
|
coef_init = np.mean(coefs_paths[:, best_index, :], axis=0)
|
|
|
|
# Note that y is label encoded and hence pos_class must be
|
|
# the encoded label / None (for 'multinomial')
|
|
w, _, _ = _logistic_regression_path(
|
|
X,
|
|
y,
|
|
pos_class=encoded_label,
|
|
Cs=[C_],
|
|
solver=solver,
|
|
fit_intercept=self.fit_intercept,
|
|
coef=coef_init,
|
|
max_iter=self.max_iter,
|
|
tol=self.tol,
|
|
penalty=self.penalty,
|
|
class_weight=class_weight,
|
|
multi_class=multi_class,
|
|
verbose=max(0, self.verbose - 1),
|
|
random_state=self.random_state,
|
|
check_input=False,
|
|
max_squared_sum=max_squared_sum,
|
|
sample_weight=sample_weight,
|
|
l1_ratio=l1_ratio_,
|
|
)
|
|
w = w[0]
|
|
|
|
else:
|
|
# Take the best scores across every fold and the average of
|
|
# all coefficients corresponding to the best scores.
|
|
best_indices = np.argmax(scores, axis=1)
|
|
if multi_class == "ovr":
|
|
w = np.mean(
|
|
[coefs_paths[i, best_indices[i], :] for i in range(len(folds))],
|
|
axis=0,
|
|
)
|
|
else:
|
|
w = np.mean(
|
|
[
|
|
coefs_paths[:, i, best_indices[i], :]
|
|
for i in range(len(folds))
|
|
],
|
|
axis=0,
|
|
)
|
|
|
|
best_indices_C = best_indices % len(self.Cs_)
|
|
self.C_.append(np.mean(self.Cs_[best_indices_C]))
|
|
|
|
if self.penalty == "elasticnet":
|
|
best_indices_l1 = best_indices // len(self.Cs_)
|
|
self.l1_ratio_.append(np.mean(l1_ratios_[best_indices_l1]))
|
|
else:
|
|
self.l1_ratio_.append(None)
|
|
|
|
if multi_class == "multinomial":
|
|
self.C_ = np.tile(self.C_, n_classes)
|
|
self.l1_ratio_ = np.tile(self.l1_ratio_, n_classes)
|
|
self.coef_ = w[:, : X.shape[1]]
|
|
if self.fit_intercept:
|
|
self.intercept_ = w[:, -1]
|
|
else:
|
|
self.coef_[index] = w[: X.shape[1]]
|
|
if self.fit_intercept:
|
|
self.intercept_[index] = w[-1]
|
|
|
|
self.C_ = np.asarray(self.C_)
|
|
self.l1_ratio_ = np.asarray(self.l1_ratio_)
|
|
self.l1_ratios_ = np.asarray(l1_ratios_)
|
|
# if elasticnet was used, add the l1_ratios dimension to some
|
|
# attributes
|
|
if self.l1_ratios is not None:
|
|
# with n_cs=2 and n_l1_ratios=3
|
|
# the layout of scores is
|
|
# [c1, c2, c1, c2, c1, c2]
|
|
# l1_1 , l1_2 , l1_3
|
|
# To get a 2d array with the following layout
|
|
# l1_1, l1_2, l1_3
|
|
# c1 [[ . , . , . ],
|
|
# c2 [ . , . , . ]]
|
|
# We need to first reshape and then transpose.
|
|
# The same goes for the other arrays
|
|
for cls, coefs_path in self.coefs_paths_.items():
|
|
self.coefs_paths_[cls] = coefs_path.reshape(
|
|
(len(folds), self.l1_ratios_.size, self.Cs_.size, -1)
|
|
)
|
|
self.coefs_paths_[cls] = np.transpose(
|
|
self.coefs_paths_[cls], (0, 2, 1, 3)
|
|
)
|
|
for cls, score in self.scores_.items():
|
|
self.scores_[cls] = score.reshape(
|
|
(len(folds), self.l1_ratios_.size, self.Cs_.size)
|
|
)
|
|
self.scores_[cls] = np.transpose(self.scores_[cls], (0, 2, 1))
|
|
|
|
self.n_iter_ = self.n_iter_.reshape(
|
|
(-1, len(folds), self.l1_ratios_.size, self.Cs_.size)
|
|
)
|
|
self.n_iter_ = np.transpose(self.n_iter_, (0, 1, 3, 2))
|
|
|
|
return self
|
|
|
|
def score(self, X, y, sample_weight=None):
|
|
"""Score using the `scoring` option on the given test data and labels.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Test samples.
|
|
|
|
y : array-like of shape (n_samples,)
|
|
True labels for X.
|
|
|
|
sample_weight : array-like of shape (n_samples,), default=None
|
|
Sample weights.
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
Score of self.predict(X) w.r.t. y.
|
|
"""
|
|
scoring = self.scoring or "accuracy"
|
|
scoring = get_scorer(scoring)
|
|
|
|
return scoring(self, X, y, sample_weight=sample_weight)
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
"_xfail_checks": {
|
|
"check_sample_weights_invariance": (
|
|
"zero sample_weight is not equivalent to removing samples"
|
|
),
|
|
}
|
|
}
|