old_contents: from umap import AlignedUMAP
  from sklearn.metrics import pairwise_distances
  from sklearn.cluster import KMeans
  import numpy as np
  from sklearn.metrics import adjusted_rand_score
  
  # ===============================
  # Test AlignedUMAP on sliced iris
  # ===============================
  
  
  def nn_accuracy(true_nn, embd_nn):
      num_correct = 0.0
      for i in range(true_nn.shape[0]):
          num_correct += np.sum(np.in1d(true_nn[i], embd_nn[i]))
      return num_correct / true_nn.size
  
  
  def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model):
      data, target = aligned_iris
      for i, slice in enumerate(data):
          data_dmat = pairwise_distances(slice)
          true_nn = np.argsort(data_dmat, axis=1)[:, :10]
          embd_dmat = pairwise_distances(aligned_iris_model.embeddings_[i])
          embd_nn = np.argsort(embd_dmat, axis=1)[:, :10]
          assert nn_accuracy(true_nn, embd_nn) >= 0.65
  
  
  def test_local_clustering(aligned_iris, aligned_iris_model):
      data, target = aligned_iris
  
      embd = aligned_iris_model.embeddings_[1]
      clusters = KMeans(n_clusters=2).fit_predict(embd)
      ari = adjusted_rand_score(target[1], clusters)
      assert ari >= 0.75
  
      embd = aligned_iris_model.embeddings_[3]
      clusters = KMeans(n_clusters=2).fit_predict(embd)
      ari = adjusted_rand_score(target[3], clusters)
      assert ari >= 0.40
  
  
  def test_aligned_update(aligned_iris, aligned_iris_relations):
      data, target = aligned_iris
      small_aligned_model = AlignedUMAP()
      small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2])
      small_aligned_model.update(data[3], relations=aligned_iris_relations[2])
      for i, slice in enumerate(data[:4]):
          data_dmat = pairwise_distances(slice)
          true_nn = np.argsort(data_dmat, axis=1)[:, :10]
          embd_dmat = pairwise_distances(small_aligned_model.embeddings_[i])
          embd_nn = np.argsort(embd_dmat, axis=1)[:, :10]
          assert nn_accuracy(true_nn, embd_nn) >= 0.65
  
new_contents: from umap import AlignedUMAP
  from sklearn.metrics import pairwise_distances
  from sklearn.cluster import KMeans
  import numpy as np
  from sklearn.metrics import adjusted_rand_score
  
  # ===============================
  # Test AlignedUMAP on sliced iris
  # ===============================
  
  
  def nn_accuracy(true_nn, embd_nn):
      num_correct = 0.0
      for i in range(true_nn.shape[0]):
          num_correct += np.sum(np.in1d(true_nn[i], embd_nn[i]))
      return num_correct / true_nn.size
  
  
  def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model):
      data, target = aligned_iris
      for i, slice in enumerate(data):
          data_dmat = pairwise_distances(slice)
          true_nn = np.argsort(data_dmat, axis=1)[:, :10]
          embd_dmat = pairwise_distances(aligned_iris_model.embeddings_[i])
          embd_nn = np.argsort(embd_dmat, axis=1)[:, :10]
          assert nn_accuracy(true_nn, embd_nn) >= 0.65
  
  
  def test_local_clustering(aligned_iris, aligned_iris_model):
      data, target = aligned_iris
  
      embd = aligned_iris_model.embeddings_[1]
      clusters = KMeans(n_clusters=2).fit_predict(embd)
      ari = adjusted_rand_score(target[1], clusters)
      assert ari >= 0.75
  
      embd = aligned_iris_model.embeddings_[3]
      clusters = KMeans(n_clusters=2).fit_predict(embd)
      ari = adjusted_rand_score(target[3], clusters)
      assert ari >= 0.40
  
  
  def test_aligned_update(aligned_iris, aligned_iris_relations):
      data, target = aligned_iris
      small_aligned_model = AlignedUMAP()
      small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2])
      small_aligned_model.update(data[3], relations=aligned_iris_relations[2])
      for i, slice in enumerate(data[:4]):
          data_dmat = pairwise_distances(slice)
          true_nn = np.argsort(data_dmat, axis=1)[:, :10]
          embd_dmat = pairwise_distances(small_aligned_model.embeddings_[i])
          embd_nn = np.argsort(embd_dmat, axis=1)[:, :10]
          assert nn_accuracy(true_nn, embd_nn) >= 0.45
  
subject: test fix