text: 
  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 _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_))
  