599 lines
22 KiB
Python
599 lines
22 KiB
Python
import numpy as np
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import scipy.sparse as sp
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from scipy import linalg, optimize, sparse
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_greater
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_warns
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from sklearn.utils.testing import raises
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from sklearn.utils.testing import ignore_warnings
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from sklearn.utils import ConvergenceWarning
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from sklearn.linear_model.logistic import (
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LogisticRegression,
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logistic_regression_path, LogisticRegressionCV,
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_logistic_loss_and_grad, _logistic_loss_grad_hess,
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_multinomial_loss_grad_hess
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)
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from sklearn.cross_validation import StratifiedKFold
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from sklearn.datasets import load_iris, make_classification
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X = [[-1, 0], [0, 1], [1, 1]]
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X_sp = sp.csr_matrix(X)
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Y1 = [0, 1, 1]
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Y2 = [2, 1, 0]
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iris = load_iris()
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def check_predictions(clf, X, y):
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"""Check that the model is able to fit the classification data"""
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n_samples = len(y)
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classes = np.unique(y)
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n_classes = classes.shape[0]
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predicted = clf.fit(X, y).predict(X)
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assert_array_equal(clf.classes_, classes)
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assert_equal(predicted.shape, (n_samples,))
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assert_array_equal(predicted, y)
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probabilities = clf.predict_proba(X)
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assert_equal(probabilities.shape, (n_samples, n_classes))
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assert_array_almost_equal(probabilities.sum(axis=1), np.ones(n_samples))
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assert_array_equal(probabilities.argmax(axis=1), y)
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def test_predict_2_classes():
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"""Simple sanity check on a 2 classes dataset
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Make sure it predicts the correct result on simple datasets.
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"""
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check_predictions(LogisticRegression(random_state=0), X, Y1)
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check_predictions(LogisticRegression(random_state=0), X_sp, Y1)
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check_predictions(LogisticRegression(C=100, random_state=0), X, Y1)
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check_predictions(LogisticRegression(C=100, random_state=0), X_sp, Y1)
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check_predictions(LogisticRegression(fit_intercept=False,
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random_state=0), X, Y1)
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check_predictions(LogisticRegression(fit_intercept=False,
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random_state=0), X_sp, Y1)
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def test_error():
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"""Test for appropriate exception on errors"""
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assert_raises(ValueError, LogisticRegression(C=-1).fit, X, Y1)
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def test_predict_3_classes():
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check_predictions(LogisticRegression(C=10), X, Y2)
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check_predictions(LogisticRegression(C=10), X_sp, Y2)
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def test_predict_iris():
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"""Test logistic regression with the iris dataset"""
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n_samples, n_features = iris.data.shape
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target = iris.target_names[iris.target]
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# Test that both multinomial and OvR solvers handle
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# multiclass data correctly and give good accuracy
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# score (>0.95) for the training data.
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for clf in [LogisticRegression(C=len(iris.data)),
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LogisticRegression(C=len(iris.data), solver='lbfgs',
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multi_class='multinomial'),
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LogisticRegression(C=len(iris.data), solver='newton-cg',
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multi_class='multinomial')]:
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clf.fit(iris.data, target)
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assert_array_equal(np.unique(target), clf.classes_)
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pred = clf.predict(iris.data)
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assert_greater(np.mean(pred == target), .95)
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probabilities = clf.predict_proba(iris.data)
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assert_array_almost_equal(probabilities.sum(axis=1),
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np.ones(n_samples))
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pred = iris.target_names[probabilities.argmax(axis=1)]
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assert_greater(np.mean(pred == target), .95)
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def test_multinomial_validation():
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for solver in ['lbfgs', 'newton-cg']:
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lr = LogisticRegression(C=-1, solver=solver, multi_class='multinomial')
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assert_raises(ValueError, lr.fit, [[0, 1], [1, 0]], [0, 1])
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def test_multinomial_binary():
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"""Test multinomial LR on a binary problem."""
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target = (iris.target > 0).astype(np.intp)
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target = np.array(["setosa", "not-setosa"])[target]
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for solver in ['lbfgs', 'newton-cg']:
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clf = LogisticRegression(solver=solver, multi_class='multinomial')
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clf.fit(iris.data, target)
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assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
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assert_equal(clf.intercept_.shape, (1,))
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assert_array_equal(clf.predict(iris.data), target)
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mlr = LogisticRegression(solver=solver, multi_class='multinomial',
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fit_intercept=False)
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mlr.fit(iris.data, target)
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pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data),
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axis=1)]
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assert_greater(np.mean(pred == target), .9)
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def test_sparsify():
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"""Test sparsify and densify members."""
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n_samples, n_features = iris.data.shape
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target = iris.target_names[iris.target]
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clf = LogisticRegression(random_state=0).fit(iris.data, target)
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pred_d_d = clf.decision_function(iris.data)
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clf.sparsify()
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assert_true(sp.issparse(clf.coef_))
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pred_s_d = clf.decision_function(iris.data)
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sp_data = sp.coo_matrix(iris.data)
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pred_s_s = clf.decision_function(sp_data)
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clf.densify()
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pred_d_s = clf.decision_function(sp_data)
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assert_array_almost_equal(pred_d_d, pred_s_d)
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assert_array_almost_equal(pred_d_d, pred_s_s)
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assert_array_almost_equal(pred_d_d, pred_d_s)
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def test_inconsistent_input():
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"""Test that an exception is raised on inconsistent input"""
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rng = np.random.RandomState(0)
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X_ = rng.random_sample((5, 10))
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y_ = np.ones(X_.shape[0])
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y_[0] = 0
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clf = LogisticRegression(random_state=0)
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# Wrong dimensions for training data
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y_wrong = y_[:-1]
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assert_raises(ValueError, clf.fit, X, y_wrong)
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# Wrong dimensions for test data
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assert_raises(ValueError, clf.fit(X_, y_).predict,
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rng.random_sample((3, 12)))
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def test_write_parameters():
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"""Test that we can write to coef_ and intercept_"""
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clf = LogisticRegression(random_state=0)
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clf.fit(X, Y1)
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clf.coef_[:] = 0
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clf.intercept_[:] = 0
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assert_array_almost_equal(clf.decision_function(X), 0)
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@raises(ValueError)
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def test_nan():
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"""Test proper NaN handling.
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Regression test for Issue #252: fit used to go into an infinite loop.
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"""
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Xnan = np.array(X, dtype=np.float64)
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Xnan[0, 1] = np.nan
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LogisticRegression(random_state=0).fit(Xnan, Y1)
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def test_consistency_path():
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"""Test that the path algorithm is consistent"""
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rng = np.random.RandomState(0)
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X = np.concatenate((rng.randn(100, 2) + [1, 1], rng.randn(100, 2)))
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y = [1] * 100 + [-1] * 100
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Cs = np.logspace(0, 4, 10)
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f = ignore_warnings
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# can't test with fit_intercept=True since LIBLINEAR
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# penalizes the intercept
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for method in ('lbfgs', 'newton-cg', 'liblinear'):
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coefs, Cs = f(logistic_regression_path)(
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X, y, Cs=Cs, fit_intercept=False, tol=1e-16, solver=method)
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for i, C in enumerate(Cs):
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lr = LogisticRegression(C=C, fit_intercept=False, tol=1e-16)
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lr.fit(X, y)
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lr_coef = lr.coef_.ravel()
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assert_array_almost_equal(lr_coef, coefs[i], decimal=4)
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# test for fit_intercept=True
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for method in ('lbfgs', 'newton-cg', 'liblinear'):
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Cs = [1e3]
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coefs, Cs = f(logistic_regression_path)(
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X, y, Cs=Cs, fit_intercept=True, tol=1e-4, solver=method)
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lr = LogisticRegression(C=Cs[0], fit_intercept=True, tol=1e-4,
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intercept_scaling=10000)
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lr.fit(X, y)
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lr_coef = np.concatenate([lr.coef_.ravel(), lr.intercept_])
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assert_array_almost_equal(lr_coef, coefs[0], decimal=4)
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def test_liblinear_random_state():
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X, y = make_classification(n_samples=20)
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lr1 = LogisticRegression(random_state=0)
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lr1.fit(X, y)
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lr2 = LogisticRegression(random_state=0)
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lr2.fit(X, y)
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assert_array_almost_equal(lr1.coef_, lr2.coef_)
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def test_logistic_loss_and_grad():
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X_ref, y = make_classification(n_samples=20)
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n_features = X_ref.shape[1]
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X_sp = X_ref.copy()
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X_sp[X_sp < .1] = 0
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X_sp = sp.csr_matrix(X_sp)
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for X in (X_ref, X_sp):
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w = np.zeros(n_features)
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# First check that our derivation of the grad is correct
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loss, grad = _logistic_loss_and_grad(w, X, y, alpha=1.)
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approx_grad = optimize.approx_fprime(
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w, lambda w: _logistic_loss_and_grad(w, X, y, alpha=1.)[0], 1e-3
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)
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assert_array_almost_equal(grad, approx_grad, decimal=2)
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# Second check that our intercept implementation is good
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w = np.zeros(n_features + 1)
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loss_interp, grad_interp = _logistic_loss_and_grad(
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w, X, y, alpha=1.
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)
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assert_array_almost_equal(loss, loss_interp)
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approx_grad = optimize.approx_fprime(
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w, lambda w: _logistic_loss_and_grad(w, X, y, alpha=1.)[0], 1e-3
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)
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assert_array_almost_equal(grad_interp, approx_grad, decimal=2)
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def test_logistic_loss_grad_hess():
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rng = np.random.RandomState(0)
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n_samples, n_features = 50, 5
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X_ref = rng.randn(n_samples, n_features)
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y = np.sign(X_ref.dot(5 * rng.randn(n_features)))
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X_ref -= X_ref.mean()
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X_ref /= X_ref.std()
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X_sp = X_ref.copy()
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X_sp[X_sp < .1] = 0
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X_sp = sp.csr_matrix(X_sp)
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for X in (X_ref, X_sp):
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w = .1 * np.ones(n_features)
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# First check that _logistic_loss_grad_hess is consistent
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# with _logistic_loss_and_grad
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loss, grad = _logistic_loss_and_grad(w, X, y, alpha=1.)
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loss_2, grad_2, hess = _logistic_loss_grad_hess(w, X, y, alpha=1.)
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assert_array_almost_equal(grad, grad_2)
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# Now check our hessian along the second direction of the grad
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vector = np.zeros_like(grad)
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vector[1] = 1
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hess_col = hess(vector)
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# Computation of the Hessian is particularly fragile to numerical
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# errors when doing simple finite differences. Here we compute the
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# grad along a path in the direction of the vector and then use a
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# least-square regression to estimate the slope
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e = 1e-3
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d_x = np.linspace(-e, e, 30)
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d_grad = np.array([
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_logistic_loss_and_grad(w + t * vector, X, y, alpha=1.)[1]
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for t in d_x
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])
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d_grad -= d_grad.mean(axis=0)
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approx_hess_col = linalg.lstsq(d_x[:, np.newaxis], d_grad)[0].ravel()
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assert_array_almost_equal(approx_hess_col, hess_col, decimal=3)
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# Second check that our intercept implementation is good
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w = np.zeros(n_features + 1)
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loss_interp, grad_interp = _logistic_loss_and_grad(
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w, X, y, alpha=1.
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)
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loss_interp_2, grad_interp_2, hess = \
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_logistic_loss_grad_hess(w, X, y, alpha=1.)
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assert_array_almost_equal(loss_interp, loss_interp_2)
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assert_array_almost_equal(grad_interp, grad_interp_2)
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def test_logistic_cv():
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"""test for LogisticRegressionCV object"""
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n_samples, n_features = 50, 5
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rng = np.random.RandomState(0)
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X_ref = rng.randn(n_samples, n_features)
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y = np.sign(X_ref.dot(5 * rng.randn(n_features)))
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X_ref -= X_ref.mean()
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X_ref /= X_ref.std()
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lr_cv = LogisticRegressionCV(Cs=[1.], fit_intercept=False,
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solver='liblinear')
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lr_cv.fit(X_ref, y)
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lr = LogisticRegression(C=1., fit_intercept=False)
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lr.fit(X_ref, y)
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assert_array_almost_equal(lr.coef_, lr_cv.coef_)
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assert_array_equal(lr_cv.coef_.shape, (1, n_features))
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assert_array_equal(lr_cv.classes_, [-1, 1])
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assert_equal(len(lr_cv.classes_), 2)
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coefs_paths = np.asarray(list(lr_cv.coefs_paths_.values()))
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assert_array_equal(coefs_paths.shape, (1, 3, 1, n_features))
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assert_array_equal(lr_cv.Cs_.shape, (1, ))
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scores = np.asarray(list(lr_cv.scores_.values()))
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assert_array_equal(scores.shape, (1, 3, 1))
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def test_logistic_cv_sparse():
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X, y = make_classification(n_samples=50, n_features=5,
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random_state=0)
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X[X < 1.0] = 0.0
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csr = sp.csr_matrix(X)
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clf = LogisticRegressionCV(fit_intercept=True)
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clf.fit(X, y)
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clfs = LogisticRegressionCV(fit_intercept=True)
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clfs.fit(csr, y)
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assert_array_almost_equal(clfs.coef_, clf.coef_)
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assert_array_almost_equal(clfs.intercept_, clf.intercept_)
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assert_equal(clfs.C_, clf.C_)
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def test_intercept_logistic_helper():
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n_samples, n_features = 10, 5
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X, y = make_classification(n_samples=n_samples, n_features=n_features,
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random_state=0)
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# Fit intercept case.
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alpha = 1.
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w = np.ones(n_features + 1)
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loss_interp, grad_interp, hess_interp = _logistic_loss_grad_hess(
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w, X, y, alpha)
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# Do not fit intercept. This can be considered equivalent to adding
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# a feature vector of ones, i.e column of one vectors.
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X_ = np.hstack((X, np.ones(10)[:, np.newaxis]))
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loss, grad, hess = _logistic_loss_grad_hess(w, X_, y, alpha)
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# In the fit_intercept=False case, the feature vector of ones is
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# penalized. This should be taken care of.
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assert_almost_equal(loss_interp + 0.5 * (w[-1] ** 2), loss)
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# Check gradient.
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assert_array_almost_equal(grad_interp[:n_features], grad[:n_features])
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assert_almost_equal(grad_interp[-1] + alpha * w[-1], grad[-1])
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rng = np.random.RandomState(0)
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grad = rng.rand(n_features + 1)
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hess_interp = hess_interp(grad)
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hess = hess(grad)
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assert_array_almost_equal(hess_interp[:n_features], hess[:n_features])
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assert_almost_equal(hess_interp[-1] + alpha * grad[-1], hess[-1])
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def test_ovr_multinomial_iris():
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"""Test that OvR and multinomial are correct using the iris dataset."""
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train, target = iris.data, iris.target
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n_samples, n_features = train.shape
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# Use pre-defined fold as folds generated for different y
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cv = StratifiedKFold(target, 3)
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clf = LogisticRegressionCV(cv=cv)
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clf.fit(train, target)
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clf1 = LogisticRegressionCV(cv=cv)
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target_copy = target.copy()
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target_copy[target_copy == 0] = 1
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clf1.fit(train, target_copy)
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assert_array_almost_equal(clf.scores_[2], clf1.scores_[2])
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assert_array_almost_equal(clf.intercept_[2:], clf1.intercept_)
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assert_array_almost_equal(clf.coef_[2][np.newaxis, :], clf1.coef_)
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# Test the shape of various attributes.
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assert_equal(clf.coef_.shape, (3, n_features))
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assert_array_equal(clf.classes_, [0, 1, 2])
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coefs_paths = np.asarray(list(clf.coefs_paths_.values()))
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assert_array_almost_equal(coefs_paths.shape, (3, 3, 10, n_features + 1))
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assert_equal(clf.Cs_.shape, (10, ))
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scores = np.asarray(list(clf.scores_.values()))
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assert_equal(scores.shape, (3, 3, 10))
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# Test that for the iris data multinomial gives a better accuracy than OvR
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for solver in ['lbfgs', 'newton-cg']:
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clf_multi = LogisticRegressionCV(
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solver=solver, multi_class='multinomial', max_iter=15
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)
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clf_multi.fit(train, target)
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multi_score = clf_multi.score(train, target)
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ovr_score = clf.score(train, target)
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assert_greater(multi_score, ovr_score)
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# Test attributes of LogisticRegressionCV
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assert_equal(clf.coef_.shape, clf_multi.coef_.shape)
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assert_array_equal(clf_multi.classes_, [0, 1, 2])
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coefs_paths = np.asarray(list(clf_multi.coefs_paths_.values()))
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assert_array_almost_equal(coefs_paths.shape, (3, 3, 10,
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n_features + 1))
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assert_equal(clf_multi.Cs_.shape, (10, ))
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scores = np.asarray(list(clf_multi.scores_.values()))
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assert_equal(scores.shape, (3, 3, 10))
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def test_logistic_regression_solvers():
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X, y = make_classification(n_features=10, n_informative=5, random_state=0)
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clf_n = LogisticRegression(solver='newton-cg', fit_intercept=False)
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clf_n.fit(X, y)
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clf_lbf = LogisticRegression(solver='lbfgs', fit_intercept=False)
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clf_lbf.fit(X, y)
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clf_lib = LogisticRegression(fit_intercept=False)
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clf_lib.fit(X, y)
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assert_array_almost_equal(clf_n.coef_, clf_lib.coef_, decimal=3)
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assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=3)
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assert_array_almost_equal(clf_n.coef_, clf_lbf.coef_, decimal=3)
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|
|
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def test_logistic_regression_solvers_multiclass():
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X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
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n_classes=3, random_state=0)
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clf_n = LogisticRegression(solver='newton-cg', fit_intercept=False)
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clf_n.fit(X, y)
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clf_lbf = LogisticRegression(solver='lbfgs', fit_intercept=False)
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clf_lbf.fit(X, y)
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clf_lib = LogisticRegression(fit_intercept=False)
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clf_lib.fit(X, y)
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assert_array_almost_equal(clf_n.coef_, clf_lib.coef_, decimal=4)
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assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=4)
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assert_array_almost_equal(clf_n.coef_, clf_lbf.coef_, decimal=4)
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|
|
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def test_logistic_regressioncv_class_weights():
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X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
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n_classes=3, random_state=0)
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|
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# Test the liblinear fails when class_weight of type dict is
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# provided, when it is multiclass. However it can handle
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# binary problems.
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clf_lib = LogisticRegressionCV(class_weight={0: 0.1, 1: 0.2},
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solver='liblinear')
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assert_raises(ValueError, clf_lib.fit, X, y)
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y_ = y.copy()
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y_[y == 2] = 1
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clf_lib.fit(X, y_)
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assert_array_equal(clf_lib.classes_, [0, 1])
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# Test for class_weight=auto
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X, y = make_classification(n_samples=20, n_features=20, n_informative=10,
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random_state=0)
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clf_lbf = LogisticRegressionCV(solver='lbfgs', fit_intercept=False,
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class_weight='auto')
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clf_lbf.fit(X, y)
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clf_lib = LogisticRegressionCV(solver='liblinear', fit_intercept=False,
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class_weight='auto')
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clf_lib.fit(X, y)
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assert_array_almost_equal(clf_lib.coef_, clf_lbf.coef_, decimal=4)
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|
|
|
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def test_logistic_regression_convergence_warnings():
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"""Test that warnings are raised if model does not converge"""
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|
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X, y = make_classification(n_samples=20, n_features=20)
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clf_lib = LogisticRegression(solver='liblinear', max_iter=2, verbose=1)
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assert_warns(ConvergenceWarning, clf_lib.fit, X, y)
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assert_equal(clf_lib.n_iter_, 2)
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|
|
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def test_logistic_regression_multinomial():
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"""Tests for the multinomial option in logistic regression"""
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|
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# Some basic attributes of Logistic Regression
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n_samples, n_features, n_classes = 50, 20, 3
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X, y = make_classification(n_samples=50, n_features=20, n_informative=10,
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n_classes=3, random_state=0)
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clf_int = LogisticRegression(solver='lbfgs', multi_class='multinomial')
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|
clf_int.fit(X, y)
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|
assert_array_equal(clf_int.coef_.shape, (n_classes, n_features))
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|
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|
clf_wint = LogisticRegression(solver='lbfgs', multi_class='multinomial',
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|
fit_intercept=False)
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|
clf_wint.fit(X, y)
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|
assert_array_equal(clf_wint.coef_.shape, (n_classes, n_features))
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|
|
|
# Similar tests for newton-cg solver option
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|
clf_ncg_int = LogisticRegression(solver='newton-cg',
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|
multi_class='multinomial')
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|
clf_ncg_int.fit(X, y)
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|
assert_array_equal(clf_ncg_int.coef_.shape, (n_classes, n_features))
|
|
|
|
clf_ncg_wint = LogisticRegression(solver='newton-cg', fit_intercept=False,
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|
multi_class='multinomial')
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|
clf_ncg_wint.fit(X, y)
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|
assert_array_equal(clf_ncg_wint.coef_.shape, (n_classes, n_features))
|
|
|
|
# Compare solutions between lbfgs and newton-cg
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|
assert_almost_equal(clf_int.coef_, clf_ncg_int.coef_, decimal=3)
|
|
assert_almost_equal(clf_wint.coef_, clf_ncg_wint.coef_, decimal=3)
|
|
assert_almost_equal(clf_int.intercept_, clf_ncg_int.intercept_, decimal=3)
|
|
|
|
# Test that the path give almost the same results. However since in this
|
|
# case we take the average of the coefs after fitting across all the
|
|
# folds, it need not be exactly the same.
|
|
for solver in ['lbfgs', 'newton-cg']:
|
|
clf_path = LogisticRegressionCV(solver=solver,
|
|
multi_class='multinomial', Cs=[1.])
|
|
clf_path.fit(X, y)
|
|
assert_array_almost_equal(clf_path.coef_, clf_int.coef_, decimal=3)
|
|
assert_almost_equal(clf_path.intercept_, clf_int.intercept_, decimal=3)
|
|
|
|
|
|
def test_multinomial_loss_grad_hess():
|
|
rng = np.random.RandomState(0)
|
|
n_samples, n_features, n_classes = 100, 5, 3
|
|
X = rng.randn(n_samples, n_features)
|
|
w = rng.rand(n_classes, n_features)
|
|
Y = np.zeros((n_samples, n_classes))
|
|
ind = np.argmax(np.dot(X, w.T), axis=1)
|
|
Y[range(0, n_samples), ind] = 1
|
|
w = w.ravel()
|
|
sample_weights = np.ones(X.shape[0])
|
|
_, grad, hessp = _multinomial_loss_grad_hess(w, X, Y, alpha=1.,
|
|
sample_weight=sample_weights)
|
|
# extract first column of hessian matrix
|
|
vec = np.zeros(n_features * n_classes)
|
|
vec[0] = 1
|
|
hess_col = hessp(vec)
|
|
|
|
# Estimate hessian using least squares as done in
|
|
# test_logistic_loss_grad_hess
|
|
e = 1e-3
|
|
d_x = np.linspace(-e, e, 30)
|
|
d_grad = np.array([
|
|
_multinomial_loss_grad_hess(w + t * vec, X, Y, alpha=1.,
|
|
sample_weight=sample_weights)[1]
|
|
for t in d_x
|
|
])
|
|
d_grad -= d_grad.mean(axis=0)
|
|
approx_hess_col = linalg.lstsq(d_x[:, np.newaxis], d_grad)[0].ravel()
|
|
assert_array_almost_equal(hess_col, approx_hess_col)
|
|
|
|
|
|
def test_liblinear_decision_function_zero():
|
|
"""Test negative prediction when decision_function values are zero.
|
|
|
|
Liblinear predicts the positive class when decision_function values
|
|
are zero. This is a test to verify that we do not do the same.
|
|
See Issue: https://github.com/scikit-learn/scikit-learn/issues/3600
|
|
and the PR https://github.com/scikit-learn/scikit-learn/pull/3623
|
|
"""
|
|
rng = np.random.RandomState(0)
|
|
X, y = make_classification(n_samples=5, n_features=5)
|
|
clf = LogisticRegression(fit_intercept=False)
|
|
clf.fit(X, y)
|
|
|
|
# Dummy data such that the decision function becomes zero.
|
|
X = np.zeros((5, 5))
|
|
assert_array_equal(clf.predict(X), np.zeros(5))
|
|
|
|
|
|
def test_liblinear_logregcv_sparse():
|
|
"""Test LogRegCV with solver='liblinear' works for sparse matrices"""
|
|
|
|
X, y = make_classification(n_samples=10, n_features=5)
|
|
clf = LogisticRegressionCV(solver='liblinear')
|
|
clf.fit(sparse.csr_matrix(X), y)
|