289 lines
9.8 KiB
Python
289 lines
9.8 KiB
Python
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import numpy as np
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from scipy.sparse import csr_matrix
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import pytest
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal, assert_raises
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from sklearn.metrics.pairwise import kernel_metrics
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from sklearn.kernel_approximation import RBFSampler
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from sklearn.kernel_approximation import AdditiveChi2Sampler
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from sklearn.kernel_approximation import SkewedChi2Sampler
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from sklearn.kernel_approximation import Nystroem
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from sklearn.metrics.pairwise import polynomial_kernel, rbf_kernel, chi2_kernel
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# generate data
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rng = np.random.RandomState(0)
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X = rng.random_sample(size=(300, 50))
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Y = rng.random_sample(size=(300, 50))
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X /= X.sum(axis=1)[:, np.newaxis]
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Y /= Y.sum(axis=1)[:, np.newaxis]
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def _linear_kernel(X, Y):
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return np.dot(X, Y.T)
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def test_additive_chi2_sampler():
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# test that AdditiveChi2Sampler approximates kernel on random data
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# compute exact kernel
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# abbreviations for easier formula
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X_ = X[:, np.newaxis, :]
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Y_ = Y[np.newaxis, :, :]
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large_kernel = 2 * X_ * Y_ / (X_ + Y_)
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# reduce to n_samples_x x n_samples_y by summing over features
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kernel = (large_kernel.sum(axis=2))
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# approximate kernel mapping
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transform = AdditiveChi2Sampler(sample_steps=3)
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X_trans = transform.fit_transform(X)
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Y_trans = transform.transform(Y)
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kernel_approx = np.dot(X_trans, Y_trans.T)
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assert_array_almost_equal(kernel, kernel_approx, 1)
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X_sp_trans = transform.fit_transform(csr_matrix(X))
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Y_sp_trans = transform.transform(csr_matrix(Y))
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assert_array_equal(X_trans, X_sp_trans.A)
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assert_array_equal(Y_trans, Y_sp_trans.A)
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# test error is raised on negative input
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Y_neg = Y.copy()
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Y_neg[0, 0] = -1
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assert_raises(ValueError, transform.transform, Y_neg)
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# test error on invalid sample_steps
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transform = AdditiveChi2Sampler(sample_steps=4)
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assert_raises(ValueError, transform.fit, X)
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# test that the sample interval is set correctly
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sample_steps_available = [1, 2, 3]
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for sample_steps in sample_steps_available:
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# test that the sample_interval is initialized correctly
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transform = AdditiveChi2Sampler(sample_steps=sample_steps)
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assert transform.sample_interval is None
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# test that the sample_interval is changed in the fit method
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transform.fit(X)
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assert transform.sample_interval_ is not None
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# test that the sample_interval is set correctly
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sample_interval = 0.3
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transform = AdditiveChi2Sampler(sample_steps=4,
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sample_interval=sample_interval)
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assert transform.sample_interval == sample_interval
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transform.fit(X)
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assert transform.sample_interval_ == sample_interval
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def test_skewed_chi2_sampler():
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# test that RBFSampler approximates kernel on random data
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# compute exact kernel
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c = 0.03
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# set on negative component but greater than c to ensure that the kernel
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# approximation is valid on the group (-c; +\infty) endowed with the skewed
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# multiplication.
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Y[0, 0] = -c / 2.
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# abbreviations for easier formula
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X_c = (X + c)[:, np.newaxis, :]
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Y_c = (Y + c)[np.newaxis, :, :]
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# we do it in log-space in the hope that it's more stable
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# this array is n_samples_x x n_samples_y big x n_features
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log_kernel = ((np.log(X_c) / 2.) + (np.log(Y_c) / 2.) + np.log(2.) -
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np.log(X_c + Y_c))
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# reduce to n_samples_x x n_samples_y by summing over features in log-space
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kernel = np.exp(log_kernel.sum(axis=2))
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# approximate kernel mapping
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transform = SkewedChi2Sampler(skewedness=c, n_components=1000,
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random_state=42)
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X_trans = transform.fit_transform(X)
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Y_trans = transform.transform(Y)
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kernel_approx = np.dot(X_trans, Y_trans.T)
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assert_array_almost_equal(kernel, kernel_approx, 1)
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assert np.isfinite(kernel).all(), \
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'NaNs found in the Gram matrix'
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assert np.isfinite(kernel_approx).all(), \
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'NaNs found in the approximate Gram matrix'
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# test error is raised on when inputs contains values smaller than -c
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Y_neg = Y.copy()
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Y_neg[0, 0] = -c * 2.
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assert_raises(ValueError, transform.transform, Y_neg)
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def test_additive_chi2_sampler_exceptions():
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"""Ensures correct error message"""
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transformer = AdditiveChi2Sampler()
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X_neg = X.copy()
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X_neg[0, 0] = -1
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with pytest.raises(ValueError, match="X in AdditiveChi2Sampler.fit"):
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transformer.fit(X_neg)
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with pytest.raises(ValueError, match="X in AdditiveChi2Sampler.transform"):
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transformer.fit(X)
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transformer.transform(X_neg)
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def test_rbf_sampler():
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# test that RBFSampler approximates kernel on random data
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# compute exact kernel
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gamma = 10.
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kernel = rbf_kernel(X, Y, gamma=gamma)
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# approximate kernel mapping
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rbf_transform = RBFSampler(gamma=gamma, n_components=1000, random_state=42)
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X_trans = rbf_transform.fit_transform(X)
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Y_trans = rbf_transform.transform(Y)
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kernel_approx = np.dot(X_trans, Y_trans.T)
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error = kernel - kernel_approx
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assert np.abs(np.mean(error)) <= 0.01 # close to unbiased
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np.abs(error, out=error)
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assert np.max(error) <= 0.1 # nothing too far off
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assert np.mean(error) <= 0.05 # mean is fairly close
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def test_input_validation():
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# Regression test: kernel approx. transformers should work on lists
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# No assertions; the old versions would simply crash
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X = [[1, 2], [3, 4], [5, 6]]
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AdditiveChi2Sampler().fit(X).transform(X)
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SkewedChi2Sampler().fit(X).transform(X)
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RBFSampler().fit(X).transform(X)
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X = csr_matrix(X)
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RBFSampler().fit(X).transform(X)
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def test_nystroem_approximation():
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# some basic tests
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rnd = np.random.RandomState(0)
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X = rnd.uniform(size=(10, 4))
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# With n_components = n_samples this is exact
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X_transformed = Nystroem(n_components=X.shape[0]).fit_transform(X)
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K = rbf_kernel(X)
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assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
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trans = Nystroem(n_components=2, random_state=rnd)
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X_transformed = trans.fit(X).transform(X)
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assert X_transformed.shape == (X.shape[0], 2)
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# test callable kernel
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trans = Nystroem(n_components=2, kernel=_linear_kernel, random_state=rnd)
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X_transformed = trans.fit(X).transform(X)
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assert X_transformed.shape == (X.shape[0], 2)
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# test that available kernels fit and transform
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kernels_available = kernel_metrics()
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for kern in kernels_available:
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trans = Nystroem(n_components=2, kernel=kern, random_state=rnd)
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X_transformed = trans.fit(X).transform(X)
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assert X_transformed.shape == (X.shape[0], 2)
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def test_nystroem_default_parameters():
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rnd = np.random.RandomState(42)
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X = rnd.uniform(size=(10, 4))
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# rbf kernel should behave as gamma=None by default
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# aka gamma = 1 / n_features
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nystroem = Nystroem(n_components=10)
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X_transformed = nystroem.fit_transform(X)
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K = rbf_kernel(X, gamma=None)
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K2 = np.dot(X_transformed, X_transformed.T)
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assert_array_almost_equal(K, K2)
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# chi2 kernel should behave as gamma=1 by default
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nystroem = Nystroem(kernel='chi2', n_components=10)
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X_transformed = nystroem.fit_transform(X)
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K = chi2_kernel(X, gamma=1)
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K2 = np.dot(X_transformed, X_transformed.T)
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assert_array_almost_equal(K, K2)
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def test_nystroem_singular_kernel():
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# test that nystroem works with singular kernel matrix
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rng = np.random.RandomState(0)
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X = rng.rand(10, 20)
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X = np.vstack([X] * 2) # duplicate samples
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gamma = 100
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N = Nystroem(gamma=gamma, n_components=X.shape[0]).fit(X)
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X_transformed = N.transform(X)
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K = rbf_kernel(X, gamma=gamma)
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assert_array_almost_equal(K, np.dot(X_transformed, X_transformed.T))
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assert np.all(np.isfinite(Y))
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def test_nystroem_poly_kernel_params():
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# Non-regression: Nystroem should pass other parameters beside gamma.
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rnd = np.random.RandomState(37)
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X = rnd.uniform(size=(10, 4))
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K = polynomial_kernel(X, degree=3.1, coef0=.1)
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nystroem = Nystroem(kernel="polynomial", n_components=X.shape[0],
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degree=3.1, coef0=.1)
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X_transformed = nystroem.fit_transform(X)
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assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
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def test_nystroem_callable():
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# Test Nystroem on a callable.
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rnd = np.random.RandomState(42)
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n_samples = 10
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X = rnd.uniform(size=(n_samples, 4))
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def logging_histogram_kernel(x, y, log):
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"""Histogram kernel that writes to a log."""
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log.append(1)
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return np.minimum(x, y).sum()
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kernel_log = []
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X = list(X) # test input validation
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Nystroem(kernel=logging_histogram_kernel,
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n_components=(n_samples - 1),
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kernel_params={'log': kernel_log}).fit(X)
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assert len(kernel_log) == n_samples * (n_samples - 1) / 2
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# if degree, gamma or coef0 is passed, we raise a warning
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msg = "Don't pass gamma, coef0 or degree to Nystroem"
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params = ({'gamma': 1}, {'coef0': 1}, {'degree': 2})
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for param in params:
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ny = Nystroem(kernel=_linear_kernel, **param)
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with pytest.raises(ValueError, match=msg):
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ny.fit(X)
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def test_nystroem_precomputed_kernel():
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# Non-regression: test Nystroem on precomputed kernel.
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# PR - 14706
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rnd = np.random.RandomState(12)
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X = rnd.uniform(size=(10, 4))
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K = polynomial_kernel(X, degree=2, coef0=.1)
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nystroem = Nystroem(kernel='precomputed', n_components=X.shape[0])
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X_transformed = nystroem.fit_transform(K)
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assert_array_almost_equal(np.dot(X_transformed, X_transformed.T), K)
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# if degree, gamma or coef0 is passed, we raise a ValueError
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msg = "Don't pass gamma, coef0 or degree to Nystroem"
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params = ({'gamma': 1}, {'coef0': 1}, {'degree': 2})
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for param in params:
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ny = Nystroem(kernel='precomputed', n_components=X.shape[0],
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**param)
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with pytest.raises(ValueError, match=msg):
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ny.fit(K)
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