115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
"""
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Sparse Logistic Regression module
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This module has the same API as sklearn.linear_model.logistic, but is
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designed to handle efficiently data in sparse matrix format.
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"""
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import numpy as np
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import scipy.sparse as sp
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from ...base import ClassifierMixin
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from ...svm.sparse.base import SparseBaseLibLinear
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from ...linear_model.sparse.base import CoefSelectTransformerMixin
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from ...svm.liblinear import csr_predict_prob
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class LogisticRegression(SparseBaseLibLinear, ClassifierMixin,
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CoefSelectTransformerMixin):
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"""
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Logistic Regression.
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Implements L1 and L2 regularized logistic regression.
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Parameters
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----------
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penalty : string, 'l1' or 'l2'
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Used to specify the norm used in the penalization
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dual : boolean
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Dual or primal formulation. Dual formulation is only
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implemented for l2 penalty.
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C : float
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Specifies the strength of the regularization. The smaller it is
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the bigger in the regularization.
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fit_intercept : bool, default: True
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Specifies if a constant (a.k.a. bias or intercept) should be
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added the decision function
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intercept_scaling : float, default: 1
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when self.fit_intercept is True, instance vector x becomes
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[x, self.intercept_scaling],
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i.e. a "synthetic" feature with constant value equals 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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tol: float, optional
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tolerance for stopping criteria
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Attributes
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----------
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`coef_` : array, shape = [n_classes-1, n_features]
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Coefficient of the features in the decision function.
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`intercept_` : array, shape = [n_classes-1]
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intercept (a.k.a. bias) added to the decision function.
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It is available only when parameter intercept is set to True
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See also
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--------
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LinearSVC
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Notes
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-----
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The underlying C implementation uses a random number generator to
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select features when fitting the model. It is thus not uncommon,
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to have slightly different results for the same input data. If
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that happens, try with a smaller tol parameter.
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References
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----------
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LIBLINEAR -- A Library for Large Linear Classification
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http://www.csie.ntu.edu.tw/~cjlin/liblinear/
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"""
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def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0,
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fit_intercept=True, intercept_scaling=1):
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super(LogisticRegression, self).__init__ (penalty=penalty,
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dual=dual, loss='lr', tol=tol, C=C,
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fit_intercept=fit_intercept, intercept_scaling=intercept_scaling)
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def predict_proba(self, X):
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"""
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Probability estimates.
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The returned estimates for all classes are ordered by the
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label of classes.
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"""
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X = sp.csr_matrix(X)
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X.data = np.asanyarray(X.data, dtype=np.float64, order='C')
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probas = csr_predict_prob(X.shape[1], X.data, X.indices,
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X.indptr, self.raw_coef_,
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self._get_solver_type(),
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self.tol, self.C,
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self.class_weight_label,
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self.class_weight, self.label_,
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self._get_bias())
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return probas[:,np.argsort(self.label_)]
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def predict_log_proba(self, T):
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"""
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Log of Probability estimates.
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The returned estimates for all classes are ordered by the
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label of classes.
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"""
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return np.log(self.predict_proba(T))
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