740 lines
25 KiB
Python
740 lines
25 KiB
Python
import warnings
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import numpy as np
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import pytest
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from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
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from sklearn.cross_decomposition import CCA, PLSSVD, PLSCanonical, PLSRegression
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from sklearn.cross_decomposition._pls import (
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_center_scale_xy,
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_get_first_singular_vectors_power_method,
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_get_first_singular_vectors_svd,
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_svd_flip_1d,
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)
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from sklearn.datasets import load_linnerud, make_regression
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from sklearn.ensemble import VotingRegressor
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from sklearn.exceptions import ConvergenceWarning
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from sklearn.linear_model import LinearRegression
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from sklearn.utils import check_random_state
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from sklearn.utils.extmath import svd_flip
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def assert_matrix_orthogonal(M):
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K = np.dot(M.T, M)
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assert_array_almost_equal(K, np.diag(np.diag(K)))
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def test_pls_canonical_basics():
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# Basic checks for PLSCanonical
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d = load_linnerud()
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X = d.data
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Y = d.target
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pls = PLSCanonical(n_components=X.shape[1])
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pls.fit(X, Y)
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assert_matrix_orthogonal(pls.x_weights_)
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assert_matrix_orthogonal(pls.y_weights_)
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assert_matrix_orthogonal(pls._x_scores)
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assert_matrix_orthogonal(pls._y_scores)
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# Check X = TP' and Y = UQ'
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T = pls._x_scores
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P = pls.x_loadings_
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U = pls._y_scores
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Q = pls.y_loadings_
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# Need to scale first
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Xc, Yc, x_mean, y_mean, x_std, y_std = _center_scale_xy(
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X.copy(), Y.copy(), scale=True
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)
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assert_array_almost_equal(Xc, np.dot(T, P.T))
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assert_array_almost_equal(Yc, np.dot(U, Q.T))
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# Check that rotations on training data lead to scores
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Xt = pls.transform(X)
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assert_array_almost_equal(Xt, pls._x_scores)
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Xt, Yt = pls.transform(X, Y)
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assert_array_almost_equal(Xt, pls._x_scores)
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assert_array_almost_equal(Yt, pls._y_scores)
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# Check that inverse_transform works
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X_back = pls.inverse_transform(Xt)
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assert_array_almost_equal(X_back, X)
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_, Y_back = pls.inverse_transform(Xt, Yt)
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assert_array_almost_equal(Y_back, Y)
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def test_sanity_check_pls_regression():
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# Sanity check for PLSRegression
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# The results were checked against the R-packages plspm, misOmics and pls
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d = load_linnerud()
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X = d.data
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Y = d.target
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pls = PLSRegression(n_components=X.shape[1])
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X_trans, _ = pls.fit_transform(X, Y)
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# FIXME: one would expect y_trans == pls.y_scores_ but this is not
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# the case.
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# xref: https://github.com/scikit-learn/scikit-learn/issues/22420
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assert_allclose(X_trans, pls.x_scores_)
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expected_x_weights = np.array(
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[
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[-0.61330704, -0.00443647, 0.78983213],
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[-0.74697144, -0.32172099, -0.58183269],
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[-0.25668686, 0.94682413, -0.19399983],
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]
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)
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expected_x_loadings = np.array(
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[
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[-0.61470416, -0.24574278, 0.78983213],
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[-0.65625755, -0.14396183, -0.58183269],
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[-0.51733059, 1.00609417, -0.19399983],
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]
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)
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expected_y_weights = np.array(
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[
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[+0.32456184, 0.29892183, 0.20316322],
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[+0.42439636, 0.61970543, 0.19320542],
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[-0.13143144, -0.26348971, -0.17092916],
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]
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)
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expected_y_loadings = np.array(
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[
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[+0.32456184, 0.29892183, 0.20316322],
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[+0.42439636, 0.61970543, 0.19320542],
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[-0.13143144, -0.26348971, -0.17092916],
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]
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)
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assert_array_almost_equal(np.abs(pls.x_loadings_), np.abs(expected_x_loadings))
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assert_array_almost_equal(np.abs(pls.x_weights_), np.abs(expected_x_weights))
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assert_array_almost_equal(np.abs(pls.y_loadings_), np.abs(expected_y_loadings))
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assert_array_almost_equal(np.abs(pls.y_weights_), np.abs(expected_y_weights))
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# The R / Python difference in the signs should be consistent across
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# loadings, weights, etc.
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x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
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x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
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y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
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y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
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assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
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assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
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def test_sanity_check_pls_regression_constant_column_Y():
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# Check behavior when the first column of Y is constant
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# The results are checked against a modified version of plsreg2
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# from the R-package plsdepot
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d = load_linnerud()
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X = d.data
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Y = d.target
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Y[:, 0] = 1
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pls = PLSRegression(n_components=X.shape[1])
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pls.fit(X, Y)
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expected_x_weights = np.array(
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[
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[-0.6273573, 0.007081799, 0.7786994],
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[-0.7493417, -0.277612681, -0.6011807],
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[-0.2119194, 0.960666981, -0.1794690],
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]
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)
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expected_x_loadings = np.array(
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[
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[-0.6273512, -0.22464538, 0.7786994],
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[-0.6643156, -0.09871193, -0.6011807],
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[-0.5125877, 1.01407380, -0.1794690],
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]
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)
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expected_y_loadings = np.array(
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[
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[0.0000000, 0.0000000, 0.0000000],
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[0.4357300, 0.5828479, 0.2174802],
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[-0.1353739, -0.2486423, -0.1810386],
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]
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)
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assert_array_almost_equal(np.abs(expected_x_weights), np.abs(pls.x_weights_))
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assert_array_almost_equal(np.abs(expected_x_loadings), np.abs(pls.x_loadings_))
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# For the PLSRegression with default parameters, y_loadings == y_weights
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assert_array_almost_equal(np.abs(pls.y_loadings_), np.abs(expected_y_loadings))
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assert_array_almost_equal(np.abs(pls.y_weights_), np.abs(expected_y_loadings))
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x_loadings_sign_flip = np.sign(expected_x_loadings / pls.x_loadings_)
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x_weights_sign_flip = np.sign(expected_x_weights / pls.x_weights_)
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# we ignore the first full-zeros row for y
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y_loadings_sign_flip = np.sign(expected_y_loadings[1:] / pls.y_loadings_[1:])
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assert_array_equal(x_loadings_sign_flip, x_weights_sign_flip)
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assert_array_equal(x_loadings_sign_flip[1:], y_loadings_sign_flip)
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def test_sanity_check_pls_canonical():
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# Sanity check for PLSCanonical
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# The results were checked against the R-package plspm
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d = load_linnerud()
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X = d.data
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Y = d.target
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pls = PLSCanonical(n_components=X.shape[1])
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pls.fit(X, Y)
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expected_x_weights = np.array(
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[
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[-0.61330704, 0.25616119, -0.74715187],
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[-0.74697144, 0.11930791, 0.65406368],
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[-0.25668686, -0.95924297, -0.11817271],
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]
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)
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expected_x_rotations = np.array(
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[
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[-0.61330704, 0.41591889, -0.62297525],
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[-0.74697144, 0.31388326, 0.77368233],
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[-0.25668686, -0.89237972, -0.24121788],
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]
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)
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expected_y_weights = np.array(
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[
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[+0.58989127, 0.7890047, 0.1717553],
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[+0.77134053, -0.61351791, 0.16920272],
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[-0.23887670, -0.03267062, 0.97050016],
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]
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)
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expected_y_rotations = np.array(
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[
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[+0.58989127, 0.7168115, 0.30665872],
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[+0.77134053, -0.70791757, 0.19786539],
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[-0.23887670, -0.00343595, 0.94162826],
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]
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)
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assert_array_almost_equal(np.abs(pls.x_rotations_), np.abs(expected_x_rotations))
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assert_array_almost_equal(np.abs(pls.x_weights_), np.abs(expected_x_weights))
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assert_array_almost_equal(np.abs(pls.y_rotations_), np.abs(expected_y_rotations))
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assert_array_almost_equal(np.abs(pls.y_weights_), np.abs(expected_y_weights))
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x_rotations_sign_flip = np.sign(pls.x_rotations_ / expected_x_rotations)
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x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
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y_rotations_sign_flip = np.sign(pls.y_rotations_ / expected_y_rotations)
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y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
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assert_array_almost_equal(x_rotations_sign_flip, x_weights_sign_flip)
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assert_array_almost_equal(y_rotations_sign_flip, y_weights_sign_flip)
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assert_matrix_orthogonal(pls.x_weights_)
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assert_matrix_orthogonal(pls.y_weights_)
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assert_matrix_orthogonal(pls._x_scores)
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assert_matrix_orthogonal(pls._y_scores)
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def test_sanity_check_pls_canonical_random():
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# Sanity check for PLSCanonical on random data
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# The results were checked against the R-package plspm
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n = 500
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p_noise = 10
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q_noise = 5
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# 2 latents vars:
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rng = check_random_state(11)
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l1 = rng.normal(size=n)
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l2 = rng.normal(size=n)
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latents = np.array([l1, l1, l2, l2]).T
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X = latents + rng.normal(size=4 * n).reshape((n, 4))
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Y = latents + rng.normal(size=4 * n).reshape((n, 4))
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X = np.concatenate((X, rng.normal(size=p_noise * n).reshape(n, p_noise)), axis=1)
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Y = np.concatenate((Y, rng.normal(size=q_noise * n).reshape(n, q_noise)), axis=1)
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pls = PLSCanonical(n_components=3)
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pls.fit(X, Y)
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expected_x_weights = np.array(
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[
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[0.65803719, 0.19197924, 0.21769083],
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[0.7009113, 0.13303969, -0.15376699],
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[0.13528197, -0.68636408, 0.13856546],
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[0.16854574, -0.66788088, -0.12485304],
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[-0.03232333, -0.04189855, 0.40690153],
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[0.1148816, -0.09643158, 0.1613305],
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[0.04792138, -0.02384992, 0.17175319],
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[-0.06781, -0.01666137, -0.18556747],
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[-0.00266945, -0.00160224, 0.11893098],
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[-0.00849528, -0.07706095, 0.1570547],
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[-0.00949471, -0.02964127, 0.34657036],
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[-0.03572177, 0.0945091, 0.3414855],
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[0.05584937, -0.02028961, -0.57682568],
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[0.05744254, -0.01482333, -0.17431274],
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]
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)
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expected_x_loadings = np.array(
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[
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[0.65649254, 0.1847647, 0.15270699],
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[0.67554234, 0.15237508, -0.09182247],
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[0.19219925, -0.67750975, 0.08673128],
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[0.2133631, -0.67034809, -0.08835483],
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[-0.03178912, -0.06668336, 0.43395268],
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[0.15684588, -0.13350241, 0.20578984],
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[0.03337736, -0.03807306, 0.09871553],
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[-0.06199844, 0.01559854, -0.1881785],
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[0.00406146, -0.00587025, 0.16413253],
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[-0.00374239, -0.05848466, 0.19140336],
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[0.00139214, -0.01033161, 0.32239136],
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[-0.05292828, 0.0953533, 0.31916881],
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[0.04031924, -0.01961045, -0.65174036],
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[0.06172484, -0.06597366, -0.1244497],
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]
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)
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expected_y_weights = np.array(
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[
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[0.66101097, 0.18672553, 0.22826092],
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[0.69347861, 0.18463471, -0.23995597],
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[0.14462724, -0.66504085, 0.17082434],
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[0.22247955, -0.6932605, -0.09832993],
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[0.07035859, 0.00714283, 0.67810124],
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[0.07765351, -0.0105204, -0.44108074],
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[-0.00917056, 0.04322147, 0.10062478],
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[-0.01909512, 0.06182718, 0.28830475],
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[0.01756709, 0.04797666, 0.32225745],
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]
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)
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expected_y_loadings = np.array(
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[
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[0.68568625, 0.1674376, 0.0969508],
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[0.68782064, 0.20375837, -0.1164448],
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[0.11712173, -0.68046903, 0.12001505],
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[0.17860457, -0.6798319, -0.05089681],
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[0.06265739, -0.0277703, 0.74729584],
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[0.0914178, 0.00403751, -0.5135078],
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[-0.02196918, -0.01377169, 0.09564505],
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[-0.03288952, 0.09039729, 0.31858973],
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[0.04287624, 0.05254676, 0.27836841],
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]
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)
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assert_array_almost_equal(np.abs(pls.x_loadings_), np.abs(expected_x_loadings))
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assert_array_almost_equal(np.abs(pls.x_weights_), np.abs(expected_x_weights))
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assert_array_almost_equal(np.abs(pls.y_loadings_), np.abs(expected_y_loadings))
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assert_array_almost_equal(np.abs(pls.y_weights_), np.abs(expected_y_weights))
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x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
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x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
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y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
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y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
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assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
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assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
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assert_matrix_orthogonal(pls.x_weights_)
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assert_matrix_orthogonal(pls.y_weights_)
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assert_matrix_orthogonal(pls._x_scores)
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assert_matrix_orthogonal(pls._y_scores)
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def test_convergence_fail():
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# Make sure ConvergenceWarning is raised if max_iter is too small
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d = load_linnerud()
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X = d.data
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Y = d.target
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pls_nipals = PLSCanonical(n_components=X.shape[1], max_iter=2)
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with pytest.warns(ConvergenceWarning):
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pls_nipals.fit(X, Y)
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@pytest.mark.parametrize("Est", (PLSSVD, PLSRegression, PLSCanonical))
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def test_attibutes_shapes(Est):
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# Make sure attributes are of the correct shape depending on n_components
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d = load_linnerud()
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X = d.data
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Y = d.target
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n_components = 2
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pls = Est(n_components=n_components)
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pls.fit(X, Y)
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assert all(
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attr.shape[1] == n_components for attr in (pls.x_weights_, pls.y_weights_)
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)
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@pytest.mark.parametrize("Est", (PLSRegression, PLSCanonical, CCA))
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def test_univariate_equivalence(Est):
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# Ensure 2D Y with 1 column is equivalent to 1D Y
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d = load_linnerud()
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X = d.data
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Y = d.target
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est = Est(n_components=1)
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one_d_coeff = est.fit(X, Y[:, 0]).coef_
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two_d_coeff = est.fit(X, Y[:, :1]).coef_
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assert one_d_coeff.shape == two_d_coeff.shape
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assert_array_almost_equal(one_d_coeff, two_d_coeff)
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@pytest.mark.parametrize("Est", (PLSRegression, PLSCanonical, CCA, PLSSVD))
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def test_copy(Est):
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# check that the "copy" keyword works
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d = load_linnerud()
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X = d.data
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Y = d.target
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X_orig = X.copy()
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# copy=True won't modify inplace
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pls = Est(copy=True).fit(X, Y)
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assert_array_equal(X, X_orig)
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# copy=False will modify inplace
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with pytest.raises(AssertionError):
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Est(copy=False).fit(X, Y)
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assert_array_almost_equal(X, X_orig)
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if Est is PLSSVD:
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return # PLSSVD does not support copy param in predict or transform
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X_orig = X.copy()
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with pytest.raises(AssertionError):
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pls.transform(X, Y, copy=False),
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assert_array_almost_equal(X, X_orig)
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X_orig = X.copy()
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with pytest.raises(AssertionError):
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pls.predict(X, copy=False),
|
|
assert_array_almost_equal(X, X_orig)
|
|
|
|
# Make sure copy=True gives same transform and predictions as predict=False
|
|
assert_array_almost_equal(
|
|
pls.transform(X, Y, copy=True), pls.transform(X.copy(), Y.copy(), copy=False)
|
|
)
|
|
assert_array_almost_equal(
|
|
pls.predict(X, copy=True), pls.predict(X.copy(), copy=False)
|
|
)
|
|
|
|
|
|
def _generate_test_scale_and_stability_datasets():
|
|
"""Generate dataset for test_scale_and_stability"""
|
|
# dataset for non-regression 7818
|
|
rng = np.random.RandomState(0)
|
|
n_samples = 1000
|
|
n_targets = 5
|
|
n_features = 10
|
|
Q = rng.randn(n_targets, n_features)
|
|
Y = rng.randn(n_samples, n_targets)
|
|
X = np.dot(Y, Q) + 2 * rng.randn(n_samples, n_features) + 1
|
|
X *= 1000
|
|
yield X, Y
|
|
|
|
# Data set where one of the features is constraint
|
|
X, Y = load_linnerud(return_X_y=True)
|
|
# causes X[:, -1].std() to be zero
|
|
X[:, -1] = 1.0
|
|
yield X, Y
|
|
|
|
X = np.array([[0.0, 0.0, 1.0], [1.0, 0.0, 0.0], [2.0, 2.0, 2.0], [3.0, 5.0, 4.0]])
|
|
Y = np.array([[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]])
|
|
yield X, Y
|
|
|
|
# Seeds that provide a non-regression test for #18746, where CCA fails
|
|
seeds = [530, 741]
|
|
for seed in seeds:
|
|
rng = np.random.RandomState(seed)
|
|
X = rng.randn(4, 3)
|
|
Y = rng.randn(4, 2)
|
|
yield X, Y
|
|
|
|
|
|
@pytest.mark.parametrize("Est", (CCA, PLSCanonical, PLSRegression, PLSSVD))
|
|
@pytest.mark.parametrize("X, Y", _generate_test_scale_and_stability_datasets())
|
|
def test_scale_and_stability(Est, X, Y):
|
|
"""scale=True is equivalent to scale=False on centered/scaled data
|
|
This allows to check numerical stability over platforms as well"""
|
|
|
|
X_s, Y_s, *_ = _center_scale_xy(X, Y)
|
|
|
|
X_score, Y_score = Est(scale=True).fit_transform(X, Y)
|
|
X_s_score, Y_s_score = Est(scale=False).fit_transform(X_s, Y_s)
|
|
|
|
assert_allclose(X_s_score, X_score, atol=1e-4)
|
|
assert_allclose(Y_s_score, Y_score, atol=1e-4)
|
|
|
|
|
|
@pytest.mark.parametrize("Estimator", (PLSSVD, PLSRegression, PLSCanonical, CCA))
|
|
def test_n_components_upper_bounds(Estimator):
|
|
"""Check the validation of `n_components` upper bounds for `PLS` regressors."""
|
|
rng = np.random.RandomState(0)
|
|
X = rng.randn(10, 5)
|
|
Y = rng.randn(10, 3)
|
|
est = Estimator(n_components=10)
|
|
err_msg = "`n_components` upper bound is .*. Got 10 instead. Reduce `n_components`."
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
est.fit(X, Y)
|
|
|
|
|
|
@pytest.mark.parametrize("n_samples, n_features", [(100, 10), (100, 200)])
|
|
def test_singular_value_helpers(n_samples, n_features, global_random_seed):
|
|
# Make sure SVD and power method give approximately the same results
|
|
X, Y = make_regression(
|
|
n_samples, n_features, n_targets=5, random_state=global_random_seed
|
|
)
|
|
u1, v1, _ = _get_first_singular_vectors_power_method(X, Y, norm_y_weights=True)
|
|
u2, v2 = _get_first_singular_vectors_svd(X, Y)
|
|
|
|
_svd_flip_1d(u1, v1)
|
|
_svd_flip_1d(u2, v2)
|
|
|
|
rtol = 1e-3
|
|
# Setting atol because some coordinates are very close to zero
|
|
assert_allclose(u1, u2, atol=u2.max() * rtol)
|
|
assert_allclose(v1, v2, atol=v2.max() * rtol)
|
|
|
|
|
|
def test_one_component_equivalence(global_random_seed):
|
|
# PLSSVD, PLSRegression and PLSCanonical should all be equivalent when
|
|
# n_components is 1
|
|
X, Y = make_regression(100, 10, n_targets=5, random_state=global_random_seed)
|
|
svd = PLSSVD(n_components=1).fit(X, Y).transform(X)
|
|
reg = PLSRegression(n_components=1).fit(X, Y).transform(X)
|
|
canonical = PLSCanonical(n_components=1).fit(X, Y).transform(X)
|
|
|
|
rtol = 1e-3
|
|
# Setting atol because some entries are very close to zero
|
|
assert_allclose(svd, reg, atol=reg.max() * rtol)
|
|
assert_allclose(svd, canonical, atol=canonical.max() * rtol)
|
|
|
|
|
|
def test_svd_flip_1d():
|
|
# Make sure svd_flip_1d is equivalent to svd_flip
|
|
u = np.array([1, -4, 2])
|
|
v = np.array([1, 2, 3])
|
|
|
|
u_expected, v_expected = svd_flip(u.reshape(-1, 1), v.reshape(1, -1))
|
|
_svd_flip_1d(u, v) # inplace
|
|
|
|
assert_allclose(u, u_expected.ravel())
|
|
assert_allclose(u, [-1, 4, -2])
|
|
|
|
assert_allclose(v, v_expected.ravel())
|
|
assert_allclose(v, [-1, -2, -3])
|
|
|
|
|
|
def test_loadings_converges(global_random_seed):
|
|
"""Test that CCA converges. Non-regression test for #19549."""
|
|
X, y = make_regression(
|
|
n_samples=200, n_features=20, n_targets=20, random_state=global_random_seed
|
|
)
|
|
|
|
cca = CCA(n_components=10, max_iter=500)
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", ConvergenceWarning)
|
|
|
|
cca.fit(X, y)
|
|
|
|
# Loadings converges to reasonable values
|
|
assert np.all(np.abs(cca.x_loadings_) < 1)
|
|
|
|
|
|
def test_pls_constant_y():
|
|
"""Checks warning when y is constant. Non-regression test for #19831"""
|
|
rng = np.random.RandomState(42)
|
|
x = rng.rand(100, 3)
|
|
y = np.zeros(100)
|
|
|
|
pls = PLSRegression()
|
|
|
|
msg = "y residual is constant at iteration"
|
|
with pytest.warns(UserWarning, match=msg):
|
|
pls.fit(x, y)
|
|
|
|
assert_allclose(pls.x_rotations_, 0)
|
|
|
|
|
|
@pytest.mark.parametrize("PLSEstimator", [PLSRegression, PLSCanonical, CCA])
|
|
def test_pls_coef_shape(PLSEstimator):
|
|
"""Check the shape of `coef_` attribute.
|
|
|
|
Non-regression test for:
|
|
https://github.com/scikit-learn/scikit-learn/issues/12410
|
|
"""
|
|
d = load_linnerud()
|
|
X = d.data
|
|
Y = d.target
|
|
|
|
pls = PLSEstimator(copy=True).fit(X, Y)
|
|
|
|
n_targets, n_features = Y.shape[1], X.shape[1]
|
|
assert pls.coef_.shape == (n_targets, n_features)
|
|
|
|
|
|
@pytest.mark.parametrize("scale", [True, False])
|
|
@pytest.mark.parametrize("PLSEstimator", [PLSRegression, PLSCanonical, CCA])
|
|
def test_pls_prediction(PLSEstimator, scale):
|
|
"""Check the behaviour of the prediction function."""
|
|
d = load_linnerud()
|
|
X = d.data
|
|
Y = d.target
|
|
|
|
pls = PLSEstimator(copy=True, scale=scale).fit(X, Y)
|
|
Y_pred = pls.predict(X, copy=True)
|
|
|
|
y_mean = Y.mean(axis=0)
|
|
X_trans = X - X.mean(axis=0)
|
|
|
|
assert_allclose(pls.intercept_, y_mean)
|
|
assert_allclose(Y_pred, X_trans @ pls.coef_.T + pls.intercept_)
|
|
|
|
|
|
@pytest.mark.parametrize("Klass", [CCA, PLSSVD, PLSRegression, PLSCanonical])
|
|
def test_pls_feature_names_out(Klass):
|
|
"""Check `get_feature_names_out` cross_decomposition module."""
|
|
X, Y = load_linnerud(return_X_y=True)
|
|
|
|
est = Klass().fit(X, Y)
|
|
names_out = est.get_feature_names_out()
|
|
|
|
class_name_lower = Klass.__name__.lower()
|
|
expected_names_out = np.array(
|
|
[f"{class_name_lower}{i}" for i in range(est.x_weights_.shape[1])],
|
|
dtype=object,
|
|
)
|
|
assert_array_equal(names_out, expected_names_out)
|
|
|
|
|
|
@pytest.mark.parametrize("Klass", [CCA, PLSSVD, PLSRegression, PLSCanonical])
|
|
def test_pls_set_output(Klass):
|
|
"""Check `set_output` in cross_decomposition module."""
|
|
pd = pytest.importorskip("pandas")
|
|
X, Y = load_linnerud(return_X_y=True, as_frame=True)
|
|
|
|
est = Klass().set_output(transform="pandas").fit(X, Y)
|
|
X_trans, y_trans = est.transform(X, Y)
|
|
assert isinstance(y_trans, np.ndarray)
|
|
assert isinstance(X_trans, pd.DataFrame)
|
|
assert_array_equal(X_trans.columns, est.get_feature_names_out())
|
|
|
|
|
|
def test_pls_regression_fit_1d_y():
|
|
"""Check that when fitting with 1d `y`, prediction should also be 1d.
|
|
|
|
Non-regression test for Issue #26549.
|
|
"""
|
|
X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
|
|
y = np.array([2, 6, 12, 20, 30, 42])
|
|
expected = y.copy()
|
|
|
|
plsr = PLSRegression().fit(X, y)
|
|
y_pred = plsr.predict(X)
|
|
assert y_pred.shape == expected.shape
|
|
|
|
# Check that it works in VotingRegressor
|
|
lr = LinearRegression().fit(X, y)
|
|
vr = VotingRegressor([("lr", lr), ("plsr", plsr)])
|
|
y_pred = vr.fit(X, y).predict(X)
|
|
assert y_pred.shape == expected.shape
|
|
assert_allclose(y_pred, expected)
|
|
|
|
|
|
def test_pls_regression_scaling_coef():
|
|
"""Check that when using `scale=True`, the coefficients are using the std. dev. from
|
|
both `X` and `Y`.
|
|
|
|
Non-regression test for:
|
|
https://github.com/scikit-learn/scikit-learn/issues/27964
|
|
"""
|
|
# handcrafted data where we can predict Y from X with an additional scaling factor
|
|
rng = np.random.RandomState(0)
|
|
coef = rng.uniform(size=(3, 5))
|
|
X = rng.normal(scale=10, size=(30, 5)) # add a std of 10
|
|
Y = X @ coef.T
|
|
|
|
# we need to make sure that the dimension of the latent space is large enough to
|
|
# perfectly predict `Y` from `X` (no information loss)
|
|
pls = PLSRegression(n_components=5, scale=True).fit(X, Y)
|
|
assert_allclose(pls.coef_, coef)
|
|
|
|
# we therefore should be able to predict `Y` from `X`
|
|
assert_allclose(pls.predict(X), Y)
|
|
|
|
|
|
# TODO(1.7): Remove
|
|
@pytest.mark.parametrize("Klass", [PLSRegression, CCA, PLSSVD, PLSCanonical])
|
|
def test_pls_fit_warning_on_deprecated_Y_argument(Klass):
|
|
# Test warning message is shown when using Y instead of y
|
|
|
|
d = load_linnerud()
|
|
X = d.data
|
|
Y = d.target
|
|
y = d.target
|
|
|
|
msg = "`Y` is deprecated in 1.5 and will be removed in 1.7. Use `y` instead."
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
Klass().fit(X=X, Y=Y)
|
|
|
|
err_msg1 = "Cannot use both `y` and `Y`. Use only `y` as `Y` is deprecated."
|
|
with (
|
|
pytest.warns(FutureWarning, match=msg),
|
|
pytest.raises(ValueError, match=err_msg1),
|
|
):
|
|
Klass().fit(X, y, Y)
|
|
|
|
err_msg2 = "y is required."
|
|
with pytest.raises(ValueError, match=err_msg2):
|
|
Klass().fit(X)
|
|
|
|
|
|
# TODO(1.7): Remove
|
|
@pytest.mark.parametrize("Klass", [PLSRegression, CCA, PLSSVD, PLSCanonical])
|
|
def test_pls_transform_warning_on_deprecated_Y_argument(Klass):
|
|
# Test warning message is shown when using Y instead of y
|
|
|
|
d = load_linnerud()
|
|
X = d.data
|
|
Y = d.target
|
|
y = d.target
|
|
|
|
plsr = Klass().fit(X, y)
|
|
msg = "`Y` is deprecated in 1.5 and will be removed in 1.7. Use `y` instead."
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
plsr.transform(X=X, Y=Y)
|
|
|
|
err_msg1 = "Cannot use both `y` and `Y`. Use only `y` as `Y` is deprecated."
|
|
with (
|
|
pytest.warns(FutureWarning, match=msg),
|
|
pytest.raises(ValueError, match=err_msg1),
|
|
):
|
|
plsr.transform(X, y, Y)
|
|
|
|
|
|
# TODO(1.7): Remove
|
|
@pytest.mark.parametrize("Klass", [PLSRegression, CCA, PLSCanonical])
|
|
def test_pls_inverse_transform_warning_on_deprecated_Y_argument(Klass):
|
|
# Test warning message is shown when using Y instead of y
|
|
|
|
d = load_linnerud()
|
|
X = d.data
|
|
y = d.target
|
|
|
|
plsr = Klass().fit(X, y)
|
|
X_transformed, y_transformed = plsr.transform(X, y)
|
|
|
|
msg = "`Y` is deprecated in 1.5 and will be removed in 1.7. Use `y` instead."
|
|
with pytest.warns(FutureWarning, match=msg):
|
|
plsr.inverse_transform(X=X_transformed, Y=y_transformed)
|
|
|
|
err_msg1 = "Cannot use both `y` and `Y`. Use only `y` as `Y` is deprecated."
|
|
with (
|
|
pytest.warns(FutureWarning, match=msg),
|
|
pytest.raises(ValueError, match=err_msg1),
|
|
):
|
|
plsr.inverse_transform(X=X_transformed, y=y_transformed, Y=y_transformed)
|