271 lines
9.6 KiB
Python
271 lines
9.6 KiB
Python
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import numpy as np
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import pytest
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from sklearn.datasets import make_classification, make_regression
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from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
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from sklearn.feature_selection._mutual_info import _compute_mi
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import (
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assert_allclose,
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assert_array_equal,
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)
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from sklearn.utils.fixes import CSR_CONTAINERS
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def test_compute_mi_dd():
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# In discrete case computations are straightforward and can be done
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# by hand on given vectors.
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x = np.array([0, 1, 1, 0, 0])
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y = np.array([1, 0, 0, 0, 1])
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H_x = H_y = -(3 / 5) * np.log(3 / 5) - (2 / 5) * np.log(2 / 5)
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H_xy = -1 / 5 * np.log(1 / 5) - 2 / 5 * np.log(2 / 5) - 2 / 5 * np.log(2 / 5)
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I_xy = H_x + H_y - H_xy
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assert_allclose(_compute_mi(x, y, x_discrete=True, y_discrete=True), I_xy)
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def test_compute_mi_cc(global_dtype):
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# For two continuous variables a good approach is to test on bivariate
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# normal distribution, where mutual information is known.
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# Mean of the distribution, irrelevant for mutual information.
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mean = np.zeros(2)
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# Setup covariance matrix with correlation coeff. equal 0.5.
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sigma_1 = 1
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sigma_2 = 10
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corr = 0.5
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cov = np.array(
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[
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[sigma_1**2, corr * sigma_1 * sigma_2],
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[corr * sigma_1 * sigma_2, sigma_2**2],
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]
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)
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# True theoretical mutual information.
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I_theory = np.log(sigma_1) + np.log(sigma_2) - 0.5 * np.log(np.linalg.det(cov))
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rng = check_random_state(0)
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Z = rng.multivariate_normal(mean, cov, size=1000).astype(global_dtype, copy=False)
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x, y = Z[:, 0], Z[:, 1]
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# Theory and computed values won't be very close
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# We here check with a large relative tolerance
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for n_neighbors in [3, 5, 7]:
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I_computed = _compute_mi(
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x, y, x_discrete=False, y_discrete=False, n_neighbors=n_neighbors
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)
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assert_allclose(I_computed, I_theory, rtol=1e-1)
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def test_compute_mi_cd(global_dtype):
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# To test define a joint distribution as follows:
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# p(x, y) = p(x) p(y | x)
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# X ~ Bernoulli(p)
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# (Y | x = 0) ~ Uniform(-1, 1)
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# (Y | x = 1) ~ Uniform(0, 2)
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# Use the following formula for mutual information:
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# I(X; Y) = H(Y) - H(Y | X)
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# Two entropies can be computed by hand:
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# H(Y) = -(1-p)/2 * ln((1-p)/2) - p/2*log(p/2) - 1/2*log(1/2)
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# H(Y | X) = ln(2)
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# Now we need to implement sampling from out distribution, which is
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# done easily using conditional distribution logic.
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n_samples = 1000
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rng = check_random_state(0)
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for p in [0.3, 0.5, 0.7]:
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x = rng.uniform(size=n_samples) > p
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y = np.empty(n_samples, global_dtype)
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mask = x == 0
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y[mask] = rng.uniform(-1, 1, size=np.sum(mask))
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y[~mask] = rng.uniform(0, 2, size=np.sum(~mask))
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I_theory = -0.5 * (
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(1 - p) * np.log(0.5 * (1 - p)) + p * np.log(0.5 * p) + np.log(0.5)
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) - np.log(2)
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# Assert the same tolerance.
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for n_neighbors in [3, 5, 7]:
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I_computed = _compute_mi(
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x, y, x_discrete=True, y_discrete=False, n_neighbors=n_neighbors
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)
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assert_allclose(I_computed, I_theory, rtol=1e-1)
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def test_compute_mi_cd_unique_label(global_dtype):
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# Test that adding unique label doesn't change MI.
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n_samples = 100
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x = np.random.uniform(size=n_samples) > 0.5
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y = np.empty(n_samples, global_dtype)
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mask = x == 0
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y[mask] = np.random.uniform(-1, 1, size=np.sum(mask))
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y[~mask] = np.random.uniform(0, 2, size=np.sum(~mask))
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mi_1 = _compute_mi(x, y, x_discrete=True, y_discrete=False)
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x = np.hstack((x, 2))
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y = np.hstack((y, 10))
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mi_2 = _compute_mi(x, y, x_discrete=True, y_discrete=False)
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assert_allclose(mi_1, mi_2)
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# We are going test that feature ordering by MI matches our expectations.
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def test_mutual_info_classif_discrete(global_dtype):
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X = np.array(
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[[0, 0, 0], [1, 1, 0], [2, 0, 1], [2, 0, 1], [2, 0, 1]], dtype=global_dtype
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)
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y = np.array([0, 1, 2, 2, 1])
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# Here X[:, 0] is the most informative feature, and X[:, 1] is weakly
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# informative.
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mi = mutual_info_classif(X, y, discrete_features=True)
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assert_array_equal(np.argsort(-mi), np.array([0, 2, 1]))
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def test_mutual_info_regression(global_dtype):
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# We generate sample from multivariate normal distribution, using
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# transformation from initially uncorrelated variables. The zero
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# variables after transformation is selected as the target vector,
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# it has the strongest correlation with the variable 2, and
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# the weakest correlation with the variable 1.
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T = np.array([[1, 0.5, 2, 1], [0, 1, 0.1, 0.0], [0, 0.1, 1, 0.1], [0, 0.1, 0.1, 1]])
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cov = T.dot(T.T)
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mean = np.zeros(4)
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rng = check_random_state(0)
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Z = rng.multivariate_normal(mean, cov, size=1000).astype(global_dtype, copy=False)
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X = Z[:, 1:]
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y = Z[:, 0]
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mi = mutual_info_regression(X, y, random_state=0)
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assert_array_equal(np.argsort(-mi), np.array([1, 2, 0]))
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# XXX: should mutual_info_regression be fixed to avoid
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# up-casting float32 inputs to float64?
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assert mi.dtype == np.float64
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def test_mutual_info_classif_mixed(global_dtype):
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# Here the target is discrete and there are two continuous and one
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# discrete feature. The idea of this test is clear from the code.
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rng = check_random_state(0)
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X = rng.rand(1000, 3).astype(global_dtype, copy=False)
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X[:, 1] += X[:, 0]
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y = ((0.5 * X[:, 0] + X[:, 2]) > 0.5).astype(int)
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X[:, 2] = X[:, 2] > 0.5
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mi = mutual_info_classif(X, y, discrete_features=[2], n_neighbors=3, random_state=0)
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assert_array_equal(np.argsort(-mi), [2, 0, 1])
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for n_neighbors in [5, 7, 9]:
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mi_nn = mutual_info_classif(
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X, y, discrete_features=[2], n_neighbors=n_neighbors, random_state=0
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)
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# Check that the continuous values have an higher MI with greater
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# n_neighbors
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assert mi_nn[0] > mi[0]
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assert mi_nn[1] > mi[1]
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# The n_neighbors should not have any effect on the discrete value
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# The MI should be the same
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assert mi_nn[2] == mi[2]
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_mutual_info_options(global_dtype, csr_container):
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X = np.array(
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[[0, 0, 0], [1, 1, 0], [2, 0, 1], [2, 0, 1], [2, 0, 1]], dtype=global_dtype
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)
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y = np.array([0, 1, 2, 2, 1], dtype=global_dtype)
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X_csr = csr_container(X)
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for mutual_info in (mutual_info_regression, mutual_info_classif):
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with pytest.raises(ValueError):
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mutual_info(X_csr, y, discrete_features=False)
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with pytest.raises(ValueError):
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mutual_info(X, y, discrete_features="manual")
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with pytest.raises(ValueError):
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mutual_info(X_csr, y, discrete_features=[True, False, True])
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with pytest.raises(IndexError):
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mutual_info(X, y, discrete_features=[True, False, True, False])
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with pytest.raises(IndexError):
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mutual_info(X, y, discrete_features=[1, 4])
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mi_1 = mutual_info(X, y, discrete_features="auto", random_state=0)
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mi_2 = mutual_info(X, y, discrete_features=False, random_state=0)
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mi_3 = mutual_info(X_csr, y, discrete_features="auto", random_state=0)
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mi_4 = mutual_info(X_csr, y, discrete_features=True, random_state=0)
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mi_5 = mutual_info(X, y, discrete_features=[True, False, True], random_state=0)
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mi_6 = mutual_info(X, y, discrete_features=[0, 2], random_state=0)
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assert_allclose(mi_1, mi_2)
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assert_allclose(mi_3, mi_4)
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assert_allclose(mi_5, mi_6)
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assert not np.allclose(mi_1, mi_3)
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@pytest.mark.parametrize("correlated", [True, False])
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def test_mutual_information_symmetry_classif_regression(correlated, global_random_seed):
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"""Check that `mutual_info_classif` and `mutual_info_regression` are
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symmetric by switching the target `y` as `feature` in `X` and vice
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versa.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/23720
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"""
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rng = np.random.RandomState(global_random_seed)
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n = 100
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d = rng.randint(10, size=n)
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if correlated:
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c = d.astype(np.float64)
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else:
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c = rng.normal(0, 1, size=n)
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mi_classif = mutual_info_classif(
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c[:, None], d, discrete_features=[False], random_state=global_random_seed
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)
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mi_regression = mutual_info_regression(
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d[:, None], c, discrete_features=[True], random_state=global_random_seed
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)
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assert mi_classif == pytest.approx(mi_regression)
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def test_mutual_info_regression_X_int_dtype(global_random_seed):
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"""Check that results agree when X is integer dtype and float dtype.
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Non-regression test for Issue #26696.
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"""
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rng = np.random.RandomState(global_random_seed)
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X = rng.randint(100, size=(100, 10))
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X_float = X.astype(np.float64, copy=True)
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y = rng.randint(100, size=100)
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expected = mutual_info_regression(X_float, y, random_state=global_random_seed)
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result = mutual_info_regression(X, y, random_state=global_random_seed)
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assert_allclose(result, expected)
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@pytest.mark.parametrize(
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"mutual_info_func, data_generator",
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[
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(mutual_info_regression, make_regression),
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(mutual_info_classif, make_classification),
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],
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)
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def test_mutual_info_n_jobs(global_random_seed, mutual_info_func, data_generator):
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"""Check that results are consistent with different `n_jobs`."""
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X, y = data_generator(random_state=global_random_seed)
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single_job = mutual_info_func(X, y, random_state=global_random_seed, n_jobs=1)
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multi_job = mutual_info_func(X, y, random_state=global_random_seed, n_jobs=2)
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assert_allclose(single_job, multi_job)
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