import functools from typing import List, Any import numpy as np import scipy.sparse as sp import pytest from sklearn.metrics import euclidean_distances from sklearn.random_projection import johnson_lindenstrauss_min_dim from sklearn.random_projection import _gaussian_random_matrix from sklearn.random_projection import gaussian_random_matrix from sklearn.random_projection import _sparse_random_matrix from sklearn.random_projection import sparse_random_matrix from sklearn.random_projection import SparseRandomProjection from sklearn.random_projection import GaussianRandomProjection from sklearn.utils._testing import assert_raises from sklearn.utils._testing import assert_raise_message from sklearn.utils._testing import assert_array_equal from sklearn.utils._testing import assert_almost_equal from sklearn.utils._testing import assert_array_almost_equal from sklearn.utils._testing import assert_warns from sklearn.exceptions import DataDimensionalityWarning all_sparse_random_matrix: List[Any] = [_sparse_random_matrix] all_dense_random_matrix: List[Any] = [_gaussian_random_matrix] all_random_matrix = all_sparse_random_matrix + all_dense_random_matrix all_SparseRandomProjection: List[Any] = [SparseRandomProjection] all_DenseRandomProjection: List[Any] = [GaussianRandomProjection] all_RandomProjection = set(all_SparseRandomProjection + all_DenseRandomProjection) # Make some random data with uniformly located non zero entries with # Gaussian distributed values def make_sparse_random_data(n_samples, n_features, n_nonzeros): rng = np.random.RandomState(0) data_coo = sp.coo_matrix( (rng.randn(n_nonzeros), (rng.randint(n_samples, size=n_nonzeros), rng.randint(n_features, size=n_nonzeros))), shape=(n_samples, n_features)) return data_coo.toarray(), data_coo.tocsr() def densify(matrix): if not sp.issparse(matrix): return matrix else: return matrix.toarray() n_samples, n_features = (10, 1000) n_nonzeros = int(n_samples * n_features / 100.) data, data_csr = make_sparse_random_data(n_samples, n_features, n_nonzeros) ############################################################################### # test on JL lemma ############################################################################### def test_invalid_jl_domain(): assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=1.1) assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=0.0) assert_raises(ValueError, johnson_lindenstrauss_min_dim, 100, eps=-0.1) assert_raises(ValueError, johnson_lindenstrauss_min_dim, 0, eps=0.5) def test_input_size_jl_min_dim(): assert_raises(ValueError, johnson_lindenstrauss_min_dim, 3 * [100], eps=2 * [0.9]) assert_raises(ValueError, johnson_lindenstrauss_min_dim, 3 * [100], eps=2 * [0.9]) johnson_lindenstrauss_min_dim(np.random.randint(1, 10, size=(10, 10)), eps=np.full((10, 10), 0.5)) ############################################################################### # tests random matrix generation ############################################################################### def check_input_size_random_matrix(random_matrix): assert_raises(ValueError, random_matrix, 0, 0) assert_raises(ValueError, random_matrix, -1, 1) assert_raises(ValueError, random_matrix, 1, -1) assert_raises(ValueError, random_matrix, 1, 0) assert_raises(ValueError, random_matrix, -1, 0) def check_size_generated(random_matrix): assert random_matrix(1, 5).shape == (1, 5) assert random_matrix(5, 1).shape == (5, 1) assert random_matrix(5, 5).shape == (5, 5) assert random_matrix(1, 1).shape == (1, 1) def check_zero_mean_and_unit_norm(random_matrix): # All random matrix should produce a transformation matrix # with zero mean and unit norm for each columns A = densify(random_matrix(10000, 1, random_state=0)) assert_array_almost_equal(0, np.mean(A), 3) assert_array_almost_equal(1.0, np.linalg.norm(A), 1) def check_input_with_sparse_random_matrix(random_matrix): n_components, n_features = 5, 10 for density in [-1., 0.0, 1.1]: assert_raises(ValueError, random_matrix, n_components, n_features, density=density) @pytest.mark.parametrize("random_matrix", all_random_matrix) def test_basic_property_of_random_matrix(random_matrix): # Check basic properties of random matrix generation check_input_size_random_matrix(random_matrix) check_size_generated(random_matrix) check_zero_mean_and_unit_norm(random_matrix) @pytest.mark.parametrize("random_matrix", all_sparse_random_matrix) def test_basic_property_of_sparse_random_matrix(random_matrix): check_input_with_sparse_random_matrix(random_matrix) random_matrix_dense = functools.partial(random_matrix, density=1.0) check_zero_mean_and_unit_norm(random_matrix_dense) def test_gaussian_random_matrix(): # Check some statical properties of Gaussian random matrix # Check that the random matrix follow the proper distribution. # Let's say that each element of a_{ij} of A is taken from # a_ij ~ N(0.0, 1 / n_components). # n_components = 100 n_features = 1000 A = _gaussian_random_matrix(n_components, n_features, random_state=0) assert_array_almost_equal(0.0, np.mean(A), 2) assert_array_almost_equal(np.var(A, ddof=1), 1 / n_components, 1) def test_sparse_random_matrix(): # Check some statical properties of sparse random matrix n_components = 100 n_features = 500 for density in [0.3, 1.]: s = 1 / density A = _sparse_random_matrix(n_components, n_features, density=density, random_state=0) A = densify(A) # Check possible values values = np.unique(A) assert np.sqrt(s) / np.sqrt(n_components) in values assert - np.sqrt(s) / np.sqrt(n_components) in values if density == 1.0: assert np.size(values) == 2 else: assert 0. in values assert np.size(values) == 3 # Check that the random matrix follow the proper distribution. # Let's say that each element of a_{ij} of A is taken from # # - -sqrt(s) / sqrt(n_components) with probability 1 / 2s # - 0 with probability 1 - 1 / s # - +sqrt(s) / sqrt(n_components) with probability 1 / 2s # assert_almost_equal(np.mean(A == 0.0), 1 - 1 / s, decimal=2) assert_almost_equal(np.mean(A == np.sqrt(s) / np.sqrt(n_components)), 1 / (2 * s), decimal=2) assert_almost_equal(np.mean(A == - np.sqrt(s) / np.sqrt(n_components)), 1 / (2 * s), decimal=2) assert_almost_equal(np.var(A == 0.0, ddof=1), (1 - 1 / s) * 1 / s, decimal=2) assert_almost_equal(np.var(A == np.sqrt(s) / np.sqrt(n_components), ddof=1), (1 - 1 / (2 * s)) * 1 / (2 * s), decimal=2) assert_almost_equal(np.var(A == - np.sqrt(s) / np.sqrt(n_components), ddof=1), (1 - 1 / (2 * s)) * 1 / (2 * s), decimal=2) ############################################################################### # tests on random projection transformer ############################################################################### def test_sparse_random_projection_transformer_invalid_density(): for RandomProjection in all_SparseRandomProjection: assert_raises(ValueError, RandomProjection(density=1.1).fit, data) assert_raises(ValueError, RandomProjection(density=0).fit, data) assert_raises(ValueError, RandomProjection(density=-0.1).fit, data) def test_random_projection_transformer_invalid_input(): for RandomProjection in all_RandomProjection: assert_raises(ValueError, RandomProjection(n_components='auto').fit, [[0, 1, 2]]) assert_raises(ValueError, RandomProjection(n_components=-10).fit, data) def test_try_to_transform_before_fit(): for RandomProjection in all_RandomProjection: assert_raises(ValueError, RandomProjection(n_components='auto').transform, data) def test_too_many_samples_to_find_a_safe_embedding(): data, _ = make_sparse_random_data(1000, 100, 1000) for RandomProjection in all_RandomProjection: rp = RandomProjection(n_components='auto', eps=0.1) expected_msg = ( 'eps=0.100000 and n_samples=1000 lead to a target dimension' ' of 5920 which is larger than the original space with' ' n_features=100') assert_raise_message(ValueError, expected_msg, rp.fit, data) def test_random_projection_embedding_quality(): data, _ = make_sparse_random_data(8, 5000, 15000) eps = 0.2 original_distances = euclidean_distances(data, squared=True) original_distances = original_distances.ravel() non_identical = original_distances != 0.0 # remove 0 distances to avoid division by 0 original_distances = original_distances[non_identical] for RandomProjection in all_RandomProjection: rp = RandomProjection(n_components='auto', eps=eps, random_state=0) projected = rp.fit_transform(data) projected_distances = euclidean_distances(projected, squared=True) projected_distances = projected_distances.ravel() # remove 0 distances to avoid division by 0 projected_distances = projected_distances[non_identical] distances_ratio = projected_distances / original_distances # check that the automatically tuned values for the density respect the # contract for eps: pairwise distances are preserved according to the # Johnson-Lindenstrauss lemma assert distances_ratio.max() < 1 + eps assert 1 - eps < distances_ratio.min() def test_SparseRandomProjection_output_representation(): for SparseRandomProjection in all_SparseRandomProjection: # when using sparse input, the projected data can be forced to be a # dense numpy array rp = SparseRandomProjection(n_components=10, dense_output=True, random_state=0) rp.fit(data) assert isinstance(rp.transform(data), np.ndarray) sparse_data = sp.csr_matrix(data) assert isinstance(rp.transform(sparse_data), np.ndarray) # the output can be left to a sparse matrix instead rp = SparseRandomProjection(n_components=10, dense_output=False, random_state=0) rp = rp.fit(data) # output for dense input will stay dense: assert isinstance(rp.transform(data), np.ndarray) # output for sparse output will be sparse: assert sp.issparse(rp.transform(sparse_data)) def test_correct_RandomProjection_dimensions_embedding(): for RandomProjection in all_RandomProjection: rp = RandomProjection(n_components='auto', random_state=0, eps=0.5).fit(data) # the number of components is adjusted from the shape of the training # set assert rp.n_components == 'auto' assert rp.n_components_ == 110 if RandomProjection in all_SparseRandomProjection: assert rp.density == 'auto' assert_almost_equal(rp.density_, 0.03, 2) assert rp.components_.shape == (110, n_features) projected_1 = rp.transform(data) assert projected_1.shape == (n_samples, 110) # once the RP is 'fitted' the projection is always the same projected_2 = rp.transform(data) assert_array_equal(projected_1, projected_2) # fit transform with same random seed will lead to the same results rp2 = RandomProjection(random_state=0, eps=0.5) projected_3 = rp2.fit_transform(data) assert_array_equal(projected_1, projected_3) # Try to transform with an input X of size different from fitted. assert_raises(ValueError, rp.transform, data[:, 1:5]) # it is also possible to fix the number of components and the density # level if RandomProjection in all_SparseRandomProjection: rp = RandomProjection(n_components=100, density=0.001, random_state=0) projected = rp.fit_transform(data) assert projected.shape == (n_samples, 100) assert rp.components_.shape == (100, n_features) assert rp.components_.nnz < 115 # close to 1% density assert 85 < rp.components_.nnz # close to 1% density def test_warning_n_components_greater_than_n_features(): n_features = 20 data, _ = make_sparse_random_data(5, n_features, int(n_features / 4)) for RandomProjection in all_RandomProjection: assert_warns(DataDimensionalityWarning, RandomProjection(n_components=n_features + 1).fit, data) def test_works_with_sparse_data(): n_features = 20 data, _ = make_sparse_random_data(5, n_features, int(n_features / 4)) for RandomProjection in all_RandomProjection: rp_dense = RandomProjection(n_components=3, random_state=1).fit(data) rp_sparse = RandomProjection(n_components=3, random_state=1).fit(sp.csr_matrix(data)) assert_array_almost_equal(densify(rp_dense.components_), densify(rp_sparse.components_)) # TODO remove in 0.24 def test_deprecations(): with pytest.warns(FutureWarning, match="deprecated in 0.22"): gaussian_random_matrix(10, 100) with pytest.warns(FutureWarning, match="deprecated in 0.22"): sparse_random_matrix(10, 100)