text: # Copyright (c) 2018-2019, NVIDIA CORPORATION.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import pytest
  from sklearn.manifold.t_sne import trustworthiness as sklearn_trustworthiness
  from cuml.metrics import trustworthiness as cuml_trustworthiness
  
  from sklearn.datasets.samples_generator import make_blobs
  from cuml.manifold import UMAP
  
  import cudf
  import numpy as np
  
  
  @pytest.mark.parametrize('input_type', ['ndarray'])
  @pytest.mark.parametrize('n_samples', [10, 100])
  @pytest.mark.parametrize('n_features', [10, 100])
  @pytest.mark.parametrize('n_components', [2, 10])
  def test_trustworthiness(input_type, n_samples, n_features, n_components):
      centers = round(n_samples*0.4)
      X, y = make_blobs(n_samples=n_samples, centers=centers,
                        n_features=n_features)
  
      X_embedded = UMAP(n_components=n_components).fit_transform(X)
      X = X.astype(np.float32)
      X_embedded = X_embedded.astype(np.float32)
  
      if input_type == 'dataframe':
          gdf = cudf.DataFrame()
          for i in range(X.shape[1]):
              gdf[str(i)] = np.asarray(X[:, i], dtype=np.float32)
  
          gdf_embedded = cudf.DataFrame()
          for i in range(X_embedded.shape[1]):
              gdf_embedded[str(i)] = np.asarray(X_embedded[:, i],
                                                dtype=np.float32)
  
          cu_score = cuml_trustworthiness(gdf, gdf_embedded)
      else:
          cu_score = cuml_trustworthiness(X, X_embedded)
  
      sk_score = sklearn_trustworthiness(X, X_embedded)
  
      eps = 0.0001
      assert (sk_score * (1 - eps) <= cu_score and
              cu_score <= sk_score * (1 + eps))
      # assert cu_score == sk_score ideally
  