113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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#
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# License: BSD 3 clause
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import numpy as np
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from scipy import sparse
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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.linear_model.base import LinearRegression
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from sklearn.utils import check_random_state
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from sklearn.datasets.samples_generator import make_sparse_uncorrelated
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from sklearn.datasets.samples_generator import make_regression
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def test_linear_regression():
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"""
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Test LinearRegression on a simple dataset.
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"""
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# a simple dataset
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X = [[1], [2]]
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Y = [1, 2]
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clf = LinearRegression()
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clf.fit(X, Y)
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assert_array_almost_equal(clf.coef_, [1])
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assert_array_almost_equal(clf.intercept_, [0])
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assert_array_almost_equal(clf.predict(X), [1, 2])
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# test it also for degenerate input
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X = [[1]]
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Y = [0]
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clf = LinearRegression()
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clf.fit(X, Y)
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assert_array_almost_equal(clf.coef_, [0])
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assert_array_almost_equal(clf.intercept_, [0])
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assert_array_almost_equal(clf.predict(X), [0])
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def test_fit_intercept():
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"""
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Test assertions on betas shape.
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"""
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X2 = np.array([[0.38349978, 0.61650022],
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[0.58853682, 0.41146318]])
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X3 = np.array([[0.27677969, 0.70693172, 0.01628859],
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[0.08385139, 0.20692515, 0.70922346]])
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y = np.array([1, 1])
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lr2_without_intercept = LinearRegression(fit_intercept=False).fit(X2, y)
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lr2_with_intercept = LinearRegression(fit_intercept=True).fit(X2, y)
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lr3_without_intercept = LinearRegression(fit_intercept=False).fit(X3, y)
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lr3_with_intercept = LinearRegression(fit_intercept=True).fit(X3, y)
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assert_equal(lr2_with_intercept.coef_.shape,
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lr2_without_intercept.coef_.shape)
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assert_equal(lr3_with_intercept.coef_.shape,
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lr3_without_intercept.coef_.shape)
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assert_equal(lr2_without_intercept.coef_.ndim,
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lr3_without_intercept.coef_.ndim)
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def test_linear_regression_sparse(random_state=0):
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"Test that linear regression also works with sparse data"
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random_state = check_random_state(random_state)
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n = 100
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X = sparse.eye(n, n)
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beta = random_state.rand(n)
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y = X * beta[:, np.newaxis]
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ols = LinearRegression()
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ols.fit(X, y.ravel())
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assert_array_almost_equal(beta, ols.coef_ + ols.intercept_)
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assert_array_almost_equal(ols.residues_, 0)
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def test_linear_regression_multiple_outcome(random_state=0):
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"Test multiple-outcome linear regressions"
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X, y = make_regression(random_state=random_state)
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Y = np.vstack((y, y)).T
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n_features = X.shape[1]
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clf = LinearRegression(fit_intercept=True)
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clf.fit((X), Y)
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assert_equal(clf.coef_.shape, (2, n_features))
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Y_pred = clf.predict(X)
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clf.fit(X, y)
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y_pred = clf.predict(X)
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assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
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def test_linear_regression_sparse_multiple_outcome(random_state=0):
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"Test multiple-outcome linear regressions with sparse data"
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random_state = check_random_state(random_state)
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X, y = make_sparse_uncorrelated(random_state=random_state)
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X = sparse.coo_matrix(X)
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Y = np.vstack((y, y)).T
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n_features = X.shape[1]
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ols = LinearRegression()
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ols.fit(X, Y)
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assert_equal(ols.coef_.shape, (2, n_features))
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Y_pred = ols.predict(X)
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ols.fit(X, y.ravel())
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y_pred = ols.predict(X)
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assert_array_almost_equal(np.vstack((y_pred, y_pred)).T, Y_pred, decimal=3)
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