new_contents: from sklearn.manifold import trustworthiness
  from umap import UMAP
  from sklearn.datasets import make_blobs
  from nose.tools import assert_greater_equal
  import numpy as np
  
  
  # ===================================================
  #  UMAP Trustworthiness Test cases
  # ===================================================
  
  
  def test_umap_sparse_trustworthiness(sparse_test_data):
      embedding = UMAP(n_neighbors=10).fit_transform(sparse_test_data[:100])
      trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, 10)
      assert_greater_equal(
          trust,
          0.91,
          "Insufficiently trustworthy embedding for"
          "sparse test dataset: {}".format(trust),
      )
  
  
  def test_umap_trustworthiness_fast_approx(nn_data):
      data = nn_data[:50]
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          random_state=42,
          n_epochs=100,
          force_approximation_algorithm=True,
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.75,
          "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust),
      )
  
  
  def test_umap_trustworthiness_random_init(nn_data):
      data = nn_data[:50]
      embedding = UMAP(
          n_neighbors=10, min_dist=0.01, random_state=42, init="random"
      ).fit_transform(data)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.75,
          "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust),
      )
  
  
  def test_supervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(
          data, labels
      )
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.97,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
  
  def test_semisupervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      labels[10:30] = -1
      embedding = UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit_transform(
          data, labels
      )
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.97,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
  
  def test_metric_supervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          target_metric="l1",
          target_weight=0.8,
          n_epochs=100,
          random_state=42,
      ).fit_transform(data, labels)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.95,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
  
  def test_string_metric_supervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      labels = np.array(["this", "that", "other"])[labels]
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          target_metric="string",
          target_weight=0.8,
          n_epochs=100,
          random_state=42,
      ).fit_transform(data, labels)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.95,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
  
  def test_discrete_metric_supervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          target_metric="ordinal",
          target_weight=0.8,
          n_epochs=100,
          random_state=42,
      ).fit_transform(data, labels)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.95,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
  
  def test_count_metric_supervised_umap_trustworthiness():
      data, labels = make_blobs(50, cluster_std=0.5, random_state=42)
      labels = (labels ** 2) + 2 * labels
      embedding = UMAP(
          n_neighbors=10,
          min_dist=0.01,
          target_metric="count",
          target_weight=0.8,
          n_epochs=100,
          random_state=42,
      ).fit_transform(data, labels)
      trust = trustworthiness(data, embedding, 10)
      assert_greater_equal(
          trust,
          0.95,
          "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust),
      )
  
subject: Test module for UMAP trustworthiness (on spatial random data)