projektAI/venv/Lib/site-packages/sklearn/cross_decomposition/tests/test_pls.py
2021-06-06 22:13:05 +02:00

583 lines
21 KiB
Python

import pytest
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_allclose)
from sklearn.datasets import load_linnerud
from sklearn.cross_decomposition._pls import (
_center_scale_xy,
_get_first_singular_vectors_power_method,
_get_first_singular_vectors_svd,
_svd_flip_1d
)
from sklearn.cross_decomposition import CCA
from sklearn.cross_decomposition import PLSSVD, PLSRegression, PLSCanonical
from sklearn.datasets import make_regression
from sklearn.utils import check_random_state
from sklearn.utils.extmath import svd_flip
from sklearn.exceptions import ConvergenceWarning
def assert_matrix_orthogonal(M):
K = np.dot(M.T, M)
assert_array_almost_equal(K, np.diag(np.diag(K)))
def test_pls_canonical_basics():
# Basic checks for PLSCanonical
d = load_linnerud()
X = d.data
Y = d.target
pls = PLSCanonical(n_components=X.shape[1])
pls.fit(X, Y)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
# Check X = TP' and Y = UQ'
T = pls._x_scores
P = pls.x_loadings_
U = pls._y_scores
Q = pls.y_loadings_
# Need to scale first
Xc, Yc, x_mean, y_mean, x_std, y_std = _center_scale_xy(
X.copy(), Y.copy(), scale=True)
assert_array_almost_equal(Xc, np.dot(T, P.T))
assert_array_almost_equal(Yc, np.dot(U, Q.T))
# Check that rotations on training data lead to scores
Xt = pls.transform(X)
assert_array_almost_equal(Xt, pls._x_scores)
Xt, Yt = pls.transform(X, Y)
assert_array_almost_equal(Xt, pls._x_scores)
assert_array_almost_equal(Yt, pls._y_scores)
# Check that inverse_transform works
X_back = pls.inverse_transform(Xt)
assert_array_almost_equal(X_back, X)
def test_sanity_check_pls_regression():
# Sanity check for PLSRegression
# The results were checked against the R-packages plspm, misOmics and pls
d = load_linnerud()
X = d.data
Y = d.target
pls = PLSRegression(n_components=X.shape[1])
pls.fit(X, Y)
expected_x_weights = np.array(
[[-0.61330704, -0.00443647, 0.78983213],
[-0.74697144, -0.32172099, -0.58183269],
[-0.25668686, 0.94682413, -0.19399983]])
expected_x_loadings = np.array(
[[-0.61470416, -0.24574278, 0.78983213],
[-0.65625755, -0.14396183, -0.58183269],
[-0.51733059, 1.00609417, -0.19399983]])
expected_y_weights = np.array(
[[+0.32456184, 0.29892183, 0.20316322],
[+0.42439636, 0.61970543, 0.19320542],
[-0.13143144, -0.26348971, -0.17092916]])
expected_y_loadings = np.array(
[[+0.32456184, 0.29892183, 0.20316322],
[+0.42439636, 0.61970543, 0.19320542],
[-0.13143144, -0.26348971, -0.17092916]])
assert_array_almost_equal(np.abs(pls.x_loadings_),
np.abs(expected_x_loadings))
assert_array_almost_equal(np.abs(pls.x_weights_),
np.abs(expected_x_weights))
assert_array_almost_equal(np.abs(pls.y_loadings_),
np.abs(expected_y_loadings))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_weights))
# The R / Python difference in the signs should be consistent across
# loadings, weights, etc.
x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
def test_sanity_check_pls_regression_constant_column_Y():
# Check behavior when the first column of Y is constant
# The results are checked against a modified version of plsreg2
# from the R-package plsdepot
d = load_linnerud()
X = d.data
Y = d.target
Y[:, 0] = 1
pls = PLSRegression(n_components=X.shape[1])
pls.fit(X, Y)
expected_x_weights = np.array(
[[-0.6273573, 0.007081799, 0.7786994],
[-0.7493417, -0.277612681, -0.6011807],
[-0.2119194, 0.960666981, -0.1794690]])
expected_x_loadings = np.array(
[[-0.6273512, -0.22464538, 0.7786994],
[-0.6643156, -0.09871193, -0.6011807],
[-0.5125877, 1.01407380, -0.1794690]])
expected_y_loadings = np.array(
[[0.0000000, 0.0000000, 0.0000000],
[0.4357300, 0.5828479, 0.2174802],
[-0.1353739, -0.2486423, -0.1810386]])
assert_array_almost_equal(np.abs(expected_x_weights),
np.abs(pls.x_weights_))
assert_array_almost_equal(np.abs(expected_x_loadings),
np.abs(pls.x_loadings_))
# For the PLSRegression with default parameters, y_loadings == y_weights
assert_array_almost_equal(np.abs(pls.y_loadings_),
np.abs(expected_y_loadings))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_loadings))
x_loadings_sign_flip = np.sign(expected_x_loadings / pls.x_loadings_)
x_weights_sign_flip = np.sign(expected_x_weights / pls.x_weights_)
y_loadings_sign_flip = np.sign(expected_y_loadings / pls.y_loadings_)
assert_array_equal(x_loadings_sign_flip, x_weights_sign_flip)
assert_array_equal(x_loadings_sign_flip[1:], y_loadings_sign_flip[1:])
def test_sanity_check_pls_canonical():
# Sanity check for PLSCanonical
# The results were checked against the R-package plspm
d = load_linnerud()
X = d.data
Y = d.target
pls = PLSCanonical(n_components=X.shape[1])
pls .fit(X, Y)
expected_x_weights = np.array(
[[-0.61330704, 0.25616119, -0.74715187],
[-0.74697144, 0.11930791, 0.65406368],
[-0.25668686, -0.95924297, -0.11817271]])
expected_x_rotations = np.array(
[[-0.61330704, 0.41591889, -0.62297525],
[-0.74697144, 0.31388326, 0.77368233],
[-0.25668686, -0.89237972, -0.24121788]])
expected_y_weights = np.array(
[[+0.58989127, 0.7890047, 0.1717553],
[+0.77134053, -0.61351791, 0.16920272],
[-0.23887670, -0.03267062, 0.97050016]])
expected_y_rotations = np.array(
[[+0.58989127, 0.7168115, 0.30665872],
[+0.77134053, -0.70791757, 0.19786539],
[-0.23887670, -0.00343595, 0.94162826]])
assert_array_almost_equal(np.abs(pls.x_rotations_),
np.abs(expected_x_rotations))
assert_array_almost_equal(np.abs(pls.x_weights_),
np.abs(expected_x_weights))
assert_array_almost_equal(np.abs(pls.y_rotations_),
np.abs(expected_y_rotations))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_weights))
x_rotations_sign_flip = np.sign(pls.x_rotations_ / expected_x_rotations)
x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
y_rotations_sign_flip = np.sign(pls.y_rotations_ / expected_y_rotations)
y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
assert_array_almost_equal(x_rotations_sign_flip, x_weights_sign_flip)
assert_array_almost_equal(y_rotations_sign_flip, y_weights_sign_flip)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
def test_sanity_check_pls_canonical_random():
# Sanity check for PLSCanonical on random data
# The results were checked against the R-package plspm
n = 500
p_noise = 10
q_noise = 5
# 2 latents vars:
rng = check_random_state(11)
l1 = rng.normal(size=n)
l2 = rng.normal(size=n)
latents = np.array([l1, l1, l2, l2]).T
X = latents + rng.normal(size=4 * n).reshape((n, 4))
Y = latents + rng.normal(size=4 * n).reshape((n, 4))
X = np.concatenate(
(X, rng.normal(size=p_noise * n).reshape(n, p_noise)), axis=1)
Y = np.concatenate(
(Y, rng.normal(size=q_noise * n).reshape(n, q_noise)), axis=1)
pls = PLSCanonical(n_components=3)
pls.fit(X, Y)
expected_x_weights = np.array(
[[0.65803719, 0.19197924, 0.21769083],
[0.7009113, 0.13303969, -0.15376699],
[0.13528197, -0.68636408, 0.13856546],
[0.16854574, -0.66788088, -0.12485304],
[-0.03232333, -0.04189855, 0.40690153],
[0.1148816, -0.09643158, 0.1613305],
[0.04792138, -0.02384992, 0.17175319],
[-0.06781, -0.01666137, -0.18556747],
[-0.00266945, -0.00160224, 0.11893098],
[-0.00849528, -0.07706095, 0.1570547],
[-0.00949471, -0.02964127, 0.34657036],
[-0.03572177, 0.0945091, 0.3414855],
[0.05584937, -0.02028961, -0.57682568],
[0.05744254, -0.01482333, -0.17431274]])
expected_x_loadings = np.array(
[[0.65649254, 0.1847647, 0.15270699],
[0.67554234, 0.15237508, -0.09182247],
[0.19219925, -0.67750975, 0.08673128],
[0.2133631, -0.67034809, -0.08835483],
[-0.03178912, -0.06668336, 0.43395268],
[0.15684588, -0.13350241, 0.20578984],
[0.03337736, -0.03807306, 0.09871553],
[-0.06199844, 0.01559854, -0.1881785],
[0.00406146, -0.00587025, 0.16413253],
[-0.00374239, -0.05848466, 0.19140336],
[0.00139214, -0.01033161, 0.32239136],
[-0.05292828, 0.0953533, 0.31916881],
[0.04031924, -0.01961045, -0.65174036],
[0.06172484, -0.06597366, -0.1244497]])
expected_y_weights = np.array(
[[0.66101097, 0.18672553, 0.22826092],
[0.69347861, 0.18463471, -0.23995597],
[0.14462724, -0.66504085, 0.17082434],
[0.22247955, -0.6932605, -0.09832993],
[0.07035859, 0.00714283, 0.67810124],
[0.07765351, -0.0105204, -0.44108074],
[-0.00917056, 0.04322147, 0.10062478],
[-0.01909512, 0.06182718, 0.28830475],
[0.01756709, 0.04797666, 0.32225745]])
expected_y_loadings = np.array(
[[0.68568625, 0.1674376, 0.0969508],
[0.68782064, 0.20375837, -0.1164448],
[0.11712173, -0.68046903, 0.12001505],
[0.17860457, -0.6798319, -0.05089681],
[0.06265739, -0.0277703, 0.74729584],
[0.0914178, 0.00403751, -0.5135078],
[-0.02196918, -0.01377169, 0.09564505],
[-0.03288952, 0.09039729, 0.31858973],
[0.04287624, 0.05254676, 0.27836841]])
assert_array_almost_equal(np.abs(pls.x_loadings_),
np.abs(expected_x_loadings))
assert_array_almost_equal(np.abs(pls.x_weights_),
np.abs(expected_x_weights))
assert_array_almost_equal(np.abs(pls.y_loadings_),
np.abs(expected_y_loadings))
assert_array_almost_equal(np.abs(pls.y_weights_),
np.abs(expected_y_weights))
x_loadings_sign_flip = np.sign(pls.x_loadings_ / expected_x_loadings)
x_weights_sign_flip = np.sign(pls.x_weights_ / expected_x_weights)
y_weights_sign_flip = np.sign(pls.y_weights_ / expected_y_weights)
y_loadings_sign_flip = np.sign(pls.y_loadings_ / expected_y_loadings)
assert_array_almost_equal(x_loadings_sign_flip, x_weights_sign_flip)
assert_array_almost_equal(y_loadings_sign_flip, y_weights_sign_flip)
assert_matrix_orthogonal(pls.x_weights_)
assert_matrix_orthogonal(pls.y_weights_)
assert_matrix_orthogonal(pls._x_scores)
assert_matrix_orthogonal(pls._y_scores)
def test_convergence_fail():
# Make sure ConvergenceWarning is raised if max_iter is too small
d = load_linnerud()
X = d.data
Y = d.target
pls_nipals = PLSCanonical(n_components=X.shape[1], max_iter=2)
with pytest.warns(ConvergenceWarning):
pls_nipals.fit(X, Y)
@pytest.mark.filterwarnings('ignore:.*scores_ was deprecated') # 1.1
@pytest.mark.parametrize('Est', (PLSSVD, PLSRegression, PLSCanonical))
def test_attibutes_shapes(Est):
# Make sure attributes are of the correct shape depending on n_components
d = load_linnerud()
X = d.data
Y = d.target
n_components = 2
pls = Est(n_components=n_components)
pls.fit(X, Y)
assert all(attr.shape[1] == n_components
for attr in (pls.x_scores_, pls.y_scores_, pls.x_weights_,
pls.y_weights_))
@pytest.mark.parametrize('Est', (PLSRegression, PLSCanonical, CCA))
def test_univariate_equivalence(Est):
# Ensure 2D Y with 1 column is equivalent to 1D Y
d = load_linnerud()
X = d.data
Y = d.target
est = Est(n_components=1)
one_d_coeff = est.fit(X, Y[:, 0]).coef_
two_d_coeff = est.fit(X, Y[:, :1]).coef_
assert one_d_coeff.shape == two_d_coeff.shape
assert_array_almost_equal(one_d_coeff, two_d_coeff)
@pytest.mark.parametrize('Est', (PLSRegression, PLSCanonical, CCA, PLSSVD))
def test_copy(Est):
# check that the "copy" keyword works
d = load_linnerud()
X = d.data
Y = d.target
X_orig = X.copy()
# copy=True won't modify inplace
pls = Est(copy=True).fit(X, Y)
assert_array_equal(X, X_orig)
# copy=False will modify inplace
with pytest.raises(AssertionError):
Est(copy=False).fit(X, Y)
assert_array_almost_equal(X, X_orig)
if Est is PLSSVD:
return # PLSSVD does not support copy param in predict or transform
X_orig = X.copy()
with pytest.raises(AssertionError):
pls.transform(X, Y, copy=False),
assert_array_almost_equal(X, X_orig)
X_orig = X.copy()
with pytest.raises(AssertionError):
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 constaint
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., 1.],
[1., 0., 0.],
[2., 2., 2.],
[3., 5., 4.]])
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('Est', (PLSSVD, PLSCanonical, CCA))
@pytest.mark.parametrize('n_components', (0, 4))
def test_n_components_bounds(Est, n_components):
# n_components should be in [1, min(n_samples, n_features, n_targets)]
# TODO: catch error instead of warning in 1.1
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
Y = rng.randn(10, 3)
est = Est(n_components=n_components)
with pytest.warns(FutureWarning,
match="n_components=3 will be used instead"):
est.fit(X, Y)
# make sure upper bound of rank is used as a fallback
assert est.transform(X).shape[1] == 3
@pytest.mark.parametrize('n_components', (0, 6))
def test_n_components_bounds_pls_regression(n_components):
# For PLSRegression, the upper bound for n_components is n_features
# TODO: catch error instead of warning in 1.1
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
Y = rng.randn(10, 3)
est = PLSRegression(n_components=n_components)
with pytest.warns(FutureWarning,
match="n_components=5 will be used instead"):
est.fit(X, Y)
# make sure upper bound of rank is used as a fallback
assert est.transform(X).shape[1] == 5
@pytest.mark.parametrize('Est', (PLSSVD, CCA, PLSCanonical))
def test_scores_deprecations(Est):
# Make sure x_scores_ and y_scores_ are deprecated.
# It's not deprecated for PLSRegression because y_score_ is different from
# transform(Y_train)
# TODO: remove attributes and test in 1.1
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
Y = rng.randn(10, 3)
est = Est().fit(X, Y)
with pytest.warns(FutureWarning, match="x_scores_ was deprecated"):
assert_allclose(est.x_scores_, est.transform(X))
with pytest.warns(FutureWarning, match="y_scores_ was deprecated"):
assert_allclose(est.y_scores_, est.transform(X, Y)[1])
@pytest.mark.parametrize('Est', (PLSRegression, PLSCanonical, CCA))
def test_norm_y_weights_deprecation(Est):
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
Y = rng.randn(10, 3)
est = Est().fit(X, Y)
with pytest.warns(FutureWarning, match="norm_y_weights was deprecated"):
est.norm_y_weights
# TODO: Remove test in 1.1
@pytest.mark.parametrize('Estimator',
(PLSRegression, PLSCanonical, CCA, PLSSVD))
@pytest.mark.parametrize('attribute',
("x_mean_", "y_mean_", "x_std_", "y_std_"))
def test_mean_and_std_deprecation(Estimator, attribute):
rng = np.random.RandomState(0)
X = rng.randn(10, 5)
Y = rng.randn(10, 3)
estimator = Estimator().fit(X, Y)
with pytest.warns(FutureWarning, match=f"{attribute} was deprecated"):
getattr(estimator, attribute)
@pytest.mark.parametrize('n_samples, n_features', [(100, 10), (100, 200)])
@pytest.mark.parametrize('seed', range(10))
def test_singular_value_helpers(n_samples, n_features, 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=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-1
assert_allclose(u1, u2, rtol=rtol)
assert_allclose(v1, v2, rtol=rtol)
def test_one_component_equivalence():
# PLSSVD, PLSRegression and PLSCanonical should all be equivalent when
# n_components is 1
X, Y = make_regression(100, 10, n_targets=5, random_state=0)
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)
assert_allclose(svd, reg, rtol=1e-2)
assert_allclose(svd, canonical, rtol=1e-2)
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():
"""Test that CCA converges. Non-regression test for #19549."""
X, y = make_regression(n_samples=200, n_features=20, n_targets=20,
random_state=20)
cca = CCA(n_components=10, max_iter=500)
with pytest.warns(None) as record:
cca.fit(X, y)
# ConvergenceWarning is not raised
assert not record
# 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)