content: """
  Testing for the approximate neighbor search using
  Locality Sensitive Hashing Forest module
  (sklearn.neighbors.LSHForest).
  """
  
  # Author: Maheshakya Wijewardena, Joel Nothman
  
  import numpy as np
  import scipy.sparse as sp
  
  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_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_array_less
  from sklearn.utils.testing import assert_greater
  from sklearn.utils.testing import assert_true
  from sklearn.utils.testing import assert_not_equal
  from sklearn.utils.testing import assert_warns_message
  from sklearn.utils.testing import ignore_warnings
  
  from sklearn.metrics.pairwise import pairwise_distances
  from sklearn.neighbors import LSHForest
  from sklearn.neighbors import NearestNeighbors
  
  
  def test_neighbors_accuracy_with_n_candidates():
      # Checks whether accuracy increases as `n_candidates` increases.
      n_candidates_values = np.array([.1, 50, 500])
      n_samples = 100
      n_features = 10
      n_iter = 10
      n_points = 5
      rng = np.random.RandomState(42)
      accuracies = np.zeros(n_candidates_values.shape[0], dtype=float)
      X = rng.rand(n_samples, n_features)
  
      for i, n_candidates in enumerate(n_candidates_values):
          lshf = LSHForest(n_candidates=n_candidates)
          lshf.fit(X)
          for j in range(n_iter):
              query = X[rng.randint(0, n_samples)].reshape(1, -1)
  
              neighbors = lshf.kneighbors(query, n_neighbors=n_points,
                                          return_distance=False)
              distances = pairwise_distances(query, X, metric='cosine')
              ranks = np.argsort(distances)[0, :n_points]
  
              intersection = np.intersect1d(ranks, neighbors).shape[0]
              ratio = intersection / float(n_points)
              accuracies[i] = accuracies[i] + ratio
  
          accuracies[i] = accuracies[i] / float(n_iter)
      # Sorted accuracies should be equal to original accuracies
      assert_true(np.all(np.diff(accuracies) >= 0),
                  msg="Accuracies are not non-decreasing.")
      # Highest accuracy should be strictly greater than the lowest
      assert_true(np.ptp(accuracies) > 0,
                  msg="Highest accuracy is not strictly greater than lowest.")
  
  
  def test_neighbors_accuracy_with_n_estimators():
      # Checks whether accuracy increases as `n_estimators` increases.
      n_estimators = np.array([1, 10, 100])
      n_samples = 100
      n_features = 10
      n_iter = 10
      n_points = 5
      rng = np.random.RandomState(42)
      accuracies = np.zeros(n_estimators.shape[0], dtype=float)
      X = rng.rand(n_samples, n_features)
  
      for i, t in enumerate(n_estimators):
          lshf = LSHForest(n_candidates=500, n_estimators=t)
          lshf.fit(X)
          for j in range(n_iter):
              query = X[rng.randint(0, n_samples)].reshape(1, -1)
              neighbors = lshf.kneighbors(query, n_neighbors=n_points,
                                          return_distance=False)
              distances = pairwise_distances(query, X, metric='cosine')
              ranks = np.argsort(distances)[0, :n_points]
  
              intersection = np.intersect1d(ranks, neighbors).shape[0]
              ratio = intersection / float(n_points)
              accuracies[i] = accuracies[i] + ratio
  
          accuracies[i] = accuracies[i] / float(n_iter)
      # Sorted accuracies should be equal to original accuracies
      assert_true(np.all(np.diff(accuracies) >= 0),
                  msg="Accuracies are not non-decreasing.")
      # Highest accuracy should be strictly greater than the lowest
      assert_true(np.ptp(accuracies) > 0,
                  msg="Highest accuracy is not strictly greater than lowest.")
  
  
  @ignore_warnings
  def test_kneighbors():
      # Checks whether desired number of neighbors are returned.
      # It is guaranteed to return the requested number of neighbors
      # if `min_hash_match` is set to 0. Returned distances should be
      # in ascending order.
      n_samples = 12
      n_features = 2
      n_iter = 10
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
  
      lshf = LSHForest(min_hash_match=0)
      # Test unfitted estimator
      assert_raises(ValueError, lshf.kneighbors, X[0])
  
      lshf.fit(X)
  
      for i in range(n_iter):
          n_neighbors = rng.randint(0, n_samples)
          query = X[rng.randint(0, n_samples)].reshape(1, -1)
          neighbors = lshf.kneighbors(query, n_neighbors=n_neighbors,
                                      return_distance=False)
          # Desired number of neighbors should be returned.
          assert_equal(neighbors.shape[1], n_neighbors)
  
      # Multiple points
      n_queries = 5
      queries = X[rng.randint(0, n_samples, n_queries)]
      distances, neighbors = lshf.kneighbors(queries,
                                             n_neighbors=1,
                                             return_distance=True)
      assert_equal(neighbors.shape[0], n_queries)
      assert_equal(distances.shape[0], n_queries)
      # Test only neighbors
      neighbors = lshf.kneighbors(queries, n_neighbors=1,
                                  return_distance=False)
      assert_equal(neighbors.shape[0], n_queries)
      # Test random point(not in the data set)
      query = rng.randn(n_features).reshape(1, -1)
      lshf.kneighbors(query, n_neighbors=1,
                      return_distance=False)
      # Test n_neighbors at initialization
      neighbors = lshf.kneighbors(query, return_distance=False)
      assert_equal(neighbors.shape[1], 5)
      # Test `neighbors` has an integer dtype
      assert_true(neighbors.dtype.kind == 'i',
                  msg="neighbors are not in integer dtype.")
  
  
  def test_radius_neighbors():
      # Checks whether Returned distances are less than `radius`
      # At least one point should be returned when the `radius` is set
      # to mean distance from the considering point to other points in
      # the database.
      # Moreover, this test compares the radius neighbors of LSHForest
      # with the `sklearn.neighbors.NearestNeighbors`.
      n_samples = 12
      n_features = 2
      n_iter = 10
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
  
      lshf = LSHForest()
      # Test unfitted estimator
      assert_raises(ValueError, lshf.radius_neighbors, X[0])
  
      lshf.fit(X)
  
      for i in range(n_iter):
          # Select a random point in the dataset as the query
          query = X[rng.randint(0, n_samples)].reshape(1, -1)
  
          # At least one neighbor should be returned when the radius is the
          # mean distance from the query to the points of the dataset.
          mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
          neighbors = lshf.radius_neighbors(query, radius=mean_dist,
                                            return_distance=False)
  
          assert_equal(neighbors.shape, (1,))
          assert_equal(neighbors.dtype, object)
          assert_greater(neighbors[0].shape[0], 0)
          # All distances to points in the results of the radius query should
          # be less than mean_dist
          distances, neighbors = lshf.radius_neighbors(query,
                                                       radius=mean_dist,
                                                       return_distance=True)
          assert_array_less(distances[0], mean_dist)
  
      # Multiple points
      n_queries = 5
      queries = X[rng.randint(0, n_samples, n_queries)]
      distances, neighbors = lshf.radius_neighbors(queries,
                                                   return_distance=True)
  
      # dists and inds should not be 1D arrays or arrays of variable lengths
      # hence the use of the object dtype.
      assert_equal(distances.shape, (n_queries,))
      assert_equal(distances.dtype, object)
      assert_equal(neighbors.shape, (n_queries,))
      assert_equal(neighbors.dtype, object)
  
      # Compare with exact neighbor search
      query = X[rng.randint(0, n_samples)].reshape(1, -1)
      mean_dist = np.mean(pairwise_distances(query, X, metric='cosine'))
      nbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
  
      distances_exact, _ = nbrs.radius_neighbors(query, radius=mean_dist)
      distances_approx, _ = lshf.radius_neighbors(query, radius=mean_dist)
  
      # Radius-based queries do not sort the result points and the order
      # depends on the method, the random_state and the dataset order. Therefore
      # we need to sort the results ourselves before performing any comparison.
      sorted_dists_exact = np.sort(distances_exact[0])
      sorted_dists_approx = np.sort(distances_approx[0])
  
      # Distances to exact neighbors are less than or equal to approximate
      # counterparts as the approximate radius query might have missed some
      # closer neighbors.
      assert_true(np.all(np.less_equal(sorted_dists_exact,
                                       sorted_dists_approx)))
  
  
  def test_radius_neighbors_boundary_handling():
      X = [[0.999, 0.001], [0.5, 0.5], [0, 1.], [-1., 0.001]]
      n_points = len(X)
  
      # Build an exact nearest neighbors model as reference model to ensure
      # consistency between exact and approximate methods
      nnbrs = NearestNeighbors(algorithm='brute', metric='cosine').fit(X)
  
      # Build a LSHForest model with hyperparameter values that always guarantee
      # exact results on this toy dataset.
      lsfh = LSHForest(min_hash_match=0, n_candidates=n_points).fit(X)
  
      # define a query aligned with the first axis
      query = [[1., 0.]]
  
      # Compute the exact cosine distances of the query to the four points of
      # the dataset
      dists = pairwise_distances(query, X, metric='cosine').ravel()
  
      # The first point is almost aligned with the query (very small angle),
      # the cosine distance should therefore be almost null:
      assert_almost_equal(dists[0], 0, decimal=5)
  
      # The second point form an angle of 45 degrees to the query vector
      assert_almost_equal(dists[1], 1 - np.cos(np.pi / 4))
  
      # The third point is orthogonal from the query vector hence at a distance
      # exactly one:
      assert_almost_equal(dists[2], 1)
  
      # The last point is almost colinear but with opposite sign to the query
      # therefore it has a cosine 'distance' very close to the maximum possible
      # value of 2.
      assert_almost_equal(dists[3], 2, decimal=5)
  
      # If we query with a radius of one, all the samples except the last sample
      # should be included in the results. This means that the third sample
      # is lying on the boundary of the radius query:
      exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1)
      approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1)
  
      assert_array_equal(np.sort(exact_idx[0]), [0, 1, 2])
      assert_array_equal(np.sort(approx_idx[0]), [0, 1, 2])
      assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-1])
      assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-1])
  
      # If we perform the same query with a slighltly lower radius, the third
      # point of the dataset that lay on the boundary of the previous query
      # is now rejected:
      eps = np.finfo(np.float64).eps
      exact_dists, exact_idx = nnbrs.radius_neighbors(query, radius=1 - eps)
      approx_dists, approx_idx = lsfh.radius_neighbors(query, radius=1 - eps)
  
      assert_array_equal(np.sort(exact_idx[0]), [0, 1])
      assert_array_equal(np.sort(approx_idx[0]), [0, 1])
      assert_array_almost_equal(np.sort(exact_dists[0]), dists[:-2])
      assert_array_almost_equal(np.sort(approx_dists[0]), dists[:-2])
  
  
  def test_distances():
      # Checks whether returned neighbors are from closest to farthest.
      n_samples = 12
      n_features = 2
      n_iter = 10
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
  
      lshf = LSHForest()
      lshf.fit(X)
  
      for i in range(n_iter):
          n_neighbors = rng.randint(0, n_samples)
          query = X[rng.randint(0, n_samples)].reshape(1, -1)
          distances, neighbors = lshf.kneighbors(query,
                                                 n_neighbors=n_neighbors,
                                                 return_distance=True)
  
          # Returned neighbors should be from closest to farthest, that is
          # increasing distance values.
          assert_true(np.all(np.diff(distances[0]) >= 0))
  
          # Note: the radius_neighbors method does not guarantee the order of
          # the results.
  
  
  def test_fit():
      # Checks whether `fit` method sets all attribute values correctly.
      n_samples = 12
      n_features = 2
      n_estimators = 5
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
  
      lshf = LSHForest(n_estimators=n_estimators)
      lshf.fit(X)
  
      # _input_array = X
      assert_array_equal(X, lshf._fit_X)
      # A hash function g(p) for each tree
      assert_equal(n_estimators, len(lshf.hash_functions_))
      # Hash length = 32
      assert_equal(32, lshf.hash_functions_[0].components_.shape[0])
      # Number of trees_ in the forest
      assert_equal(n_estimators, len(lshf.trees_))
      # Each tree has entries for every data point
      assert_equal(n_samples, len(lshf.trees_[0]))
      # Original indices after sorting the hashes
      assert_equal(n_estimators, len(lshf.original_indices_))
      # Each set of original indices in a tree has entries for every data point
      assert_equal(n_samples, len(lshf.original_indices_[0]))
  
  
  def test_partial_fit():
      # Checks whether inserting array is consitent with fitted data.
      # `partial_fit` method should set all attribute values correctly.
      n_samples = 12
      n_samples_partial_fit = 3
      n_features = 2
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
      X_partial_fit = rng.rand(n_samples_partial_fit, n_features)
  
      lshf = LSHForest()
  
      # Test unfitted estimator
      lshf.partial_fit(X)
      assert_array_equal(X, lshf._fit_X)
  
      lshf.fit(X)
  
      # Insert wrong dimension
      assert_raises(ValueError, lshf.partial_fit,
                    np.random.randn(n_samples_partial_fit, n_features - 1))
  
      lshf.partial_fit(X_partial_fit)
  
      # size of _input_array = samples + 1 after insertion
      assert_equal(lshf._fit_X.shape[0],
                   n_samples + n_samples_partial_fit)
      # size of original_indices_[1] = samples + 1
      assert_equal(len(lshf.original_indices_[0]),
                   n_samples + n_samples_partial_fit)
      # size of trees_[1] = samples + 1
      assert_equal(len(lshf.trees_[1]),
                   n_samples + n_samples_partial_fit)
  
  
  def test_hash_functions():
      # Checks randomness of hash functions.
      # Variance and mean of each hash function (projection vector)
      # should be different from flattened array of hash functions.
      # If hash functions are not randomly built (seeded with
      # same value), variances and means of all functions are equal.
      n_samples = 12
      n_features = 2
      n_estimators = 5
      rng = np.random.RandomState(42)
      X = rng.rand(n_samples, n_features)
  
      lshf = LSHForest(n_estimators=n_estimators,
                       random_state=rng.randint(0, np.iinfo(np.int32).max))
      lshf.fit(X)
  
      hash_functions = []
      for i in range(n_estimators):
          hash_functions.append(lshf.hash_functions_[i].components_)
  
      for i in range(n_estimators):
          assert_not_equal(np.var(hash_functions),
                           np.var(lshf.hash_functions_[i].components_))
  
      for i in range(n_estimators):
          assert_not_equal(np.mean(hash_functions),
                           np.mean(lshf.hash_functions_[i].components_))
  
  
  def test_candidates():
      # Checks whether candidates are sufficient.
      # This should handle the cases when number of candidates is 0.
      # User should be warned when number of candidates is less than
      # requested number of neighbors.
      X_train = np.array([[5, 5, 2], [21, 5, 5], [1, 1, 1], [8, 9, 1],
                          [6, 10, 2]], dtype=np.float32)
      X_test = np.array([7, 10, 3], dtype=np.float32).reshape(1, -1)
  
      # For zero candidates
      lshf = LSHForest(min_hash_match=32)
      lshf.fit(X_train)
  
      message = ("Number of candidates is not sufficient to retrieve"
                 " %i neighbors with"
                 " min_hash_match = %i. Candidates are filled up"
                 " uniformly from unselected"
                 " indices." % (3, 32))
      assert_warns_message(UserWarning, message, lshf.kneighbors,
                           X_test, n_neighbors=3)
      distances, neighbors = lshf.kneighbors(X_test, n_neighbors=3)
      assert_equal(distances.shape[1], 3)
  
      # For candidates less than n_neighbors
      lshf = LSHForest(min_hash_match=31)
      lshf.fit(X_train)
  
      message = ("Number of candidates is not sufficient to retrieve"
                 " %i neighbors with"
                 " min_hash_match = %i. Candidates are filled up"
                 " uniformly from unselected"
                 " indices." % (5, 31))
      assert_warns_message(UserWarning, message, lshf.kneighbors,
                           X_test, n_neighbors=5)
      distances, neighbors = lshf.kneighbors(X_test, n_neighbors=5)
      assert_equal(distances.shape[1], 5)
  
  
  def test_graphs():
      # Smoke tests for graph methods.
      n_samples_sizes = [5, 10, 20]
      n_features = 3
      rng = np.random.RandomState(42)
  
      for n_samples in n_samples_sizes:
          X = rng.rand(n_samples, n_features)
          lshf = LSHForest(min_hash_match=0)
          lshf.fit(X)
  
          kneighbors_graph = lshf.kneighbors_graph(X)
          radius_neighbors_graph = lshf.radius_neighbors_graph(X)
  
          assert_equal(kneighbors_graph.shape[0], n_samples)
          assert_equal(kneighbors_graph.shape[1], n_samples)
          assert_equal(radius_neighbors_graph.shape[0], n_samples)
          assert_equal(radius_neighbors_graph.shape[1], n_samples)
  
  
  def test_sparse_input():
      # note: Fixed random state in sp.rand is not supported in older scipy.
      #       The test should succeed regardless.
      X1 = sp.rand(50, 100)
      X2 = sp.rand(10, 100)
      forest_sparse = LSHForest(radius=1, random_state=0).fit(X1)
      forest_dense = LSHForest(radius=1, random_state=0).fit(X1.A)
  
      d_sparse, i_sparse = forest_sparse.kneighbors(X2, return_distance=True)
      d_dense, i_dense = forest_dense.kneighbors(X2.A, return_distance=True)
  
      assert_almost_equal(d_sparse, d_dense)
      assert_almost_equal(i_sparse, i_dense)
  
      d_sparse, i_sparse = forest_sparse.radius_neighbors(X2,
                                                          return_distance=True)
      d_dense, i_dense = forest_dense.radius_neighbors(X2.A,
                                                       return_distance=True)
      assert_equal(d_sparse.shape, d_dense.shape)
      for a, b in zip(d_sparse, d_dense):
          assert_almost_equal(a, b)
      for a, b in zip(i_sparse, i_dense):
          assert_almost_equal(a, b)
  