476 lines
17 KiB
Python
476 lines
17 KiB
Python
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# 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 scipy import linalg
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from itertools import product
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import ignore_warnings
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from sklearn.linear_model.base import LinearRegression
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from sklearn.linear_model.base import _preprocess_data
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from sklearn.linear_model.base import sparse_center_data, center_data
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from sklearn.linear_model.base import _rescale_data
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from sklearn.utils import check_random_state
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from sklearn.utils.testing import assert_greater
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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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rng = np.random.RandomState(0)
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def test_linear_regression():
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# Test LinearRegression on a simple dataset.
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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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reg = LinearRegression()
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reg.fit(X, Y)
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assert_array_almost_equal(reg.coef_, [1])
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assert_array_almost_equal(reg.intercept_, [0])
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assert_array_almost_equal(reg.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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reg = LinearRegression()
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reg.fit(X, Y)
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assert_array_almost_equal(reg.coef_, [0])
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assert_array_almost_equal(reg.intercept_, [0])
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assert_array_almost_equal(reg.predict(X), [0])
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def test_linear_regression_sample_weights():
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# TODO: loop over sparse data as well
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rng = np.random.RandomState(0)
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# It would not work with under-determined systems
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for n_samples, n_features in ((6, 5), ):
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y = rng.randn(n_samples)
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X = rng.randn(n_samples, n_features)
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sample_weight = 1.0 + rng.rand(n_samples)
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for intercept in (True, False):
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# LinearRegression with explicit sample_weight
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reg = LinearRegression(fit_intercept=intercept)
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reg.fit(X, y, sample_weight=sample_weight)
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coefs1 = reg.coef_
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inter1 = reg.intercept_
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assert_equal(reg.coef_.shape, (X.shape[1], )) # sanity checks
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assert_greater(reg.score(X, y), 0.5)
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# Closed form of the weighted least square
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# theta = (X^T W X)^(-1) * X^T W y
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W = np.diag(sample_weight)
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if intercept is False:
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X_aug = X
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else:
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dummy_column = np.ones(shape=(n_samples, 1))
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X_aug = np.concatenate((dummy_column, X), axis=1)
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coefs2 = linalg.solve(X_aug.T.dot(W).dot(X_aug),
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X_aug.T.dot(W).dot(y))
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if intercept is False:
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assert_array_almost_equal(coefs1, coefs2)
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else:
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assert_array_almost_equal(coefs1, coefs2[1:])
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assert_almost_equal(inter1, coefs2[0])
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def test_raises_value_error_if_sample_weights_greater_than_1d():
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# Sample weights must be either scalar or 1D
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n_sampless = [2, 3]
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n_featuress = [3, 2]
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for n_samples, n_features in zip(n_sampless, n_featuress):
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X = rng.randn(n_samples, n_features)
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y = rng.randn(n_samples)
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sample_weights_OK = rng.randn(n_samples) ** 2 + 1
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sample_weights_OK_1 = 1.
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sample_weights_OK_2 = 2.
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reg = LinearRegression()
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# make sure the "OK" sample weights actually work
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reg.fit(X, y, sample_weights_OK)
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reg.fit(X, y, sample_weights_OK_1)
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reg.fit(X, y, sample_weights_OK_2)
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def test_fit_intercept():
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# Test assertions on betas shape.
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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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for i in range(10):
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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.predict(X) - y.ravel(), 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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reg = LinearRegression(fit_intercept=True)
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reg.fit((X), Y)
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assert_equal(reg.coef_.shape, (2, n_features))
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Y_pred = reg.predict(X)
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reg.fit(X, y)
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y_pred = reg.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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def test_preprocess_data():
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n_samples = 200
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n_features = 2
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples)
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expected_X_mean = np.mean(X, axis=0)
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expected_X_norm = np.std(X, axis=0) * np.sqrt(X.shape[0])
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expected_y_mean = np.mean(y, axis=0)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=False, normalize=False)
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assert_array_almost_equal(X_mean, np.zeros(n_features))
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assert_array_almost_equal(y_mean, 0)
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assert_array_almost_equal(X_norm, np.ones(n_features))
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assert_array_almost_equal(Xt, X)
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assert_array_almost_equal(yt, y)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=False)
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assert_array_almost_equal(X_mean, expected_X_mean)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(X_norm, np.ones(n_features))
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assert_array_almost_equal(Xt, X - expected_X_mean)
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assert_array_almost_equal(yt, y - expected_y_mean)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=True)
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assert_array_almost_equal(X_mean, expected_X_mean)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(X_norm, expected_X_norm)
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assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm)
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assert_array_almost_equal(yt, y - expected_y_mean)
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def test_preprocess_data_multioutput():
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n_samples = 200
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n_features = 3
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n_outputs = 2
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples, n_outputs)
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expected_y_mean = np.mean(y, axis=0)
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args = [X, sparse.csc_matrix(X)]
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for X in args:
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_, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=False,
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normalize=False)
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assert_array_almost_equal(y_mean, np.zeros(n_outputs))
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assert_array_almost_equal(yt, y)
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_, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True,
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normalize=False)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(yt, y - y_mean)
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_, yt, _, y_mean, _ = _preprocess_data(X, y, fit_intercept=True,
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normalize=True)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(yt, y - y_mean)
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def test_preprocess_data_weighted():
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n_samples = 200
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n_features = 2
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples)
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sample_weight = rng.rand(n_samples)
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expected_X_mean = np.average(X, axis=0, weights=sample_weight)
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expected_y_mean = np.average(y, axis=0, weights=sample_weight)
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# XXX: if normalize=True, should we expect a weighted standard deviation?
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# Currently not weighted, but calculated with respect to weighted mean
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expected_X_norm = (np.sqrt(X.shape[0]) *
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np.mean((X - expected_X_mean) ** 2, axis=0) ** .5)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=False,
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sample_weight=sample_weight)
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assert_array_almost_equal(X_mean, expected_X_mean)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(X_norm, np.ones(n_features))
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assert_array_almost_equal(Xt, X - expected_X_mean)
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assert_array_almost_equal(yt, y - expected_y_mean)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=True,
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sample_weight=sample_weight)
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assert_array_almost_equal(X_mean, expected_X_mean)
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assert_array_almost_equal(y_mean, expected_y_mean)
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assert_array_almost_equal(X_norm, expected_X_norm)
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assert_array_almost_equal(Xt, (X - expected_X_mean) / expected_X_norm)
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assert_array_almost_equal(yt, y - expected_y_mean)
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def test_sparse_preprocess_data_with_return_mean():
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n_samples = 200
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n_features = 2
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# random_state not supported yet in sparse.rand
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X = sparse.rand(n_samples, n_features, density=.5) # , random_state=rng
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X = X.tolil()
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y = rng.rand(n_samples)
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XA = X.toarray()
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expected_X_norm = np.std(XA, axis=0) * np.sqrt(X.shape[0])
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=False, normalize=False,
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return_mean=True)
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assert_array_almost_equal(X_mean, np.zeros(n_features))
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assert_array_almost_equal(y_mean, 0)
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assert_array_almost_equal(X_norm, np.ones(n_features))
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assert_array_almost_equal(Xt.A, XA)
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assert_array_almost_equal(yt, y)
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=False,
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return_mean=True)
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assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
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assert_array_almost_equal(y_mean, np.mean(y, axis=0))
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assert_array_almost_equal(X_norm, np.ones(n_features))
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assert_array_almost_equal(Xt.A, XA)
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assert_array_almost_equal(yt, y - np.mean(y, axis=0))
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Xt, yt, X_mean, y_mean, X_norm = \
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_preprocess_data(X, y, fit_intercept=True, normalize=True,
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return_mean=True)
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assert_array_almost_equal(X_mean, np.mean(XA, axis=0))
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assert_array_almost_equal(y_mean, np.mean(y, axis=0))
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assert_array_almost_equal(X_norm, expected_X_norm)
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assert_array_almost_equal(Xt.A, XA / expected_X_norm)
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assert_array_almost_equal(yt, y - np.mean(y, axis=0))
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def test_csr_preprocess_data():
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# Test output format of _preprocess_data, when input is csr
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X, y = make_regression()
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X[X < 2.5] = 0.0
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csr = sparse.csr_matrix(X)
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csr_, y, _, _, _ = _preprocess_data(csr, y, True)
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assert_equal(csr_.getformat(), 'csr')
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def test_dtype_preprocess_data():
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n_samples = 200
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n_features = 2
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples)
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X_32 = np.asarray(X, dtype=np.float32)
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y_32 = np.asarray(y, dtype=np.float32)
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X_64 = np.asarray(X, dtype=np.float64)
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y_64 = np.asarray(y, dtype=np.float64)
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for fit_intercept in [True, False]:
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for normalize in [True, False]:
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Xt_32, yt_32, X_mean_32, y_mean_32, X_norm_32 = _preprocess_data(
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X_32, y_32, fit_intercept=fit_intercept, normalize=normalize,
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return_mean=True)
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Xt_64, yt_64, X_mean_64, y_mean_64, X_norm_64 = _preprocess_data(
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X_64, y_64, fit_intercept=fit_intercept, normalize=normalize,
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return_mean=True)
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Xt_3264, yt_3264, X_mean_3264, y_mean_3264, X_norm_3264 = (
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_preprocess_data(X_32, y_64, fit_intercept=fit_intercept,
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normalize=normalize, return_mean=True))
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Xt_6432, yt_6432, X_mean_6432, y_mean_6432, X_norm_6432 = (
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_preprocess_data(X_64, y_32, fit_intercept=fit_intercept,
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normalize=normalize, return_mean=True))
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assert_equal(Xt_32.dtype, np.float32)
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assert_equal(yt_32.dtype, np.float32)
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assert_equal(X_mean_32.dtype, np.float32)
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assert_equal(y_mean_32.dtype, np.float32)
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assert_equal(X_norm_32.dtype, np.float32)
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assert_equal(Xt_64.dtype, np.float64)
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assert_equal(yt_64.dtype, np.float64)
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assert_equal(X_mean_64.dtype, np.float64)
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assert_equal(y_mean_64.dtype, np.float64)
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assert_equal(X_norm_64.dtype, np.float64)
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assert_equal(Xt_3264.dtype, np.float32)
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assert_equal(yt_3264.dtype, np.float32)
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assert_equal(X_mean_3264.dtype, np.float32)
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assert_equal(y_mean_3264.dtype, np.float32)
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assert_equal(X_norm_3264.dtype, np.float32)
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assert_equal(Xt_6432.dtype, np.float64)
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assert_equal(yt_6432.dtype, np.float64)
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assert_equal(X_mean_6432.dtype, np.float64)
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assert_equal(y_mean_6432.dtype, np.float64)
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assert_equal(X_norm_6432.dtype, np.float64)
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assert_equal(X_32.dtype, np.float32)
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assert_equal(y_32.dtype, np.float32)
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assert_equal(X_64.dtype, np.float64)
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assert_equal(y_64.dtype, np.float64)
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assert_array_almost_equal(Xt_32, Xt_64)
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assert_array_almost_equal(yt_32, yt_64)
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assert_array_almost_equal(X_mean_32, X_mean_64)
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assert_array_almost_equal(y_mean_32, y_mean_64)
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assert_array_almost_equal(X_norm_32, X_norm_64)
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def test_rescale_data():
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n_samples = 200
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n_features = 2
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sample_weight = 1.0 + rng.rand(n_samples)
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X = rng.rand(n_samples, n_features)
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y = rng.rand(n_samples)
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rescaled_X, rescaled_y = _rescale_data(X, y, sample_weight)
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rescaled_X2 = X * np.sqrt(sample_weight)[:, np.newaxis]
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rescaled_y2 = y * np.sqrt(sample_weight)
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assert_array_almost_equal(rescaled_X, rescaled_X2)
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assert_array_almost_equal(rescaled_y, rescaled_y2)
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@ignore_warnings # all deprecation warnings
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|
|
def test_deprecation_center_data():
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n_samples = 200
|
||
|
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n_features = 2
|
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|
|
|
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w = 1.0 + rng.rand(n_samples)
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X = rng.rand(n_samples, n_features)
|
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y = rng.rand(n_samples)
|
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|
|
|
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param_grid = product([True, False], [True, False], [True, False],
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|
|
[None, w])
|
||
|
|
|
||
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for (fit_intercept, normalize, copy, sample_weight) in param_grid:
|
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|
|
|
||
|
|
XX = X.copy() # such that we can try copy=False as well
|
||
|
|
|
||
|
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X1, y1, X1_mean, X1_var, y1_mean = \
|
||
|
|
center_data(XX, y, fit_intercept=fit_intercept,
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||
|
|
normalize=normalize, copy=copy,
|
||
|
|
sample_weight=sample_weight)
|
||
|
|
|
||
|
|
XX = X.copy()
|
||
|
|
|
||
|
|
X2, y2, X2_mean, X2_var, y2_mean = \
|
||
|
|
_preprocess_data(XX, y, fit_intercept=fit_intercept,
|
||
|
|
normalize=normalize, copy=copy,
|
||
|
|
sample_weight=sample_weight)
|
||
|
|
|
||
|
|
assert_array_almost_equal(X1, X2)
|
||
|
|
assert_array_almost_equal(y1, y2)
|
||
|
|
assert_array_almost_equal(X1_mean, X2_mean)
|
||
|
|
assert_array_almost_equal(X1_var, X2_var)
|
||
|
|
assert_array_almost_equal(y1_mean, y2_mean)
|
||
|
|
|
||
|
|
# Sparse cases
|
||
|
|
X = sparse.csr_matrix(X)
|
||
|
|
|
||
|
|
for (fit_intercept, normalize, copy, sample_weight) in param_grid:
|
||
|
|
|
||
|
|
X1, y1, X1_mean, X1_var, y1_mean = \
|
||
|
|
center_data(X, y, fit_intercept=fit_intercept, normalize=normalize,
|
||
|
|
copy=copy, sample_weight=sample_weight)
|
||
|
|
|
||
|
|
X2, y2, X2_mean, X2_var, y2_mean = \
|
||
|
|
_preprocess_data(X, y, fit_intercept=fit_intercept,
|
||
|
|
normalize=normalize, copy=copy,
|
||
|
|
sample_weight=sample_weight, return_mean=False)
|
||
|
|
|
||
|
|
assert_array_almost_equal(X1.toarray(), X2.toarray())
|
||
|
|
assert_array_almost_equal(y1, y2)
|
||
|
|
assert_array_almost_equal(X1_mean, X2_mean)
|
||
|
|
assert_array_almost_equal(X1_var, X2_var)
|
||
|
|
assert_array_almost_equal(y1_mean, y2_mean)
|
||
|
|
|
||
|
|
for (fit_intercept, normalize) in product([True, False], [True, False]):
|
||
|
|
|
||
|
|
X1, y1, X1_mean, X1_var, y1_mean = \
|
||
|
|
sparse_center_data(X, y, fit_intercept=fit_intercept,
|
||
|
|
normalize=normalize)
|
||
|
|
|
||
|
|
X2, y2, X2_mean, X2_var, y2_mean = \
|
||
|
|
_preprocess_data(X, y, fit_intercept=fit_intercept,
|
||
|
|
normalize=normalize, return_mean=True)
|
||
|
|
|
||
|
|
assert_array_almost_equal(X1.toarray(), X2.toarray())
|
||
|
|
assert_array_almost_equal(y1, y2)
|
||
|
|
assert_array_almost_equal(X1_mean, X2_mean)
|
||
|
|
assert_array_almost_equal(X1_var, X2_var)
|
||
|
|
assert_array_almost_equal(y1_mean, y2_mean)
|