Uploaded Test files

venv/Lib/site-packages/sklearn/tests/test_random_projection.py

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+
+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)