class_docstr: K-Elbow Visualizer Tests
class_docstr_tok: ["K-Elbow","Visualizer","Tests"]
docstr: Test the silhouette metric of the k-elbow visualizer
code: def test_set_colors_manually(self):
      """
      Test the silhouette metric of the k-elbow visualizer
      """
      oz = KElbowVisualizer(
          KMeans(random_state=0),
          k=5,
      )
  
      oz.metric_color = "r"
      oz.timing_color = "y"
      oz.vline_color = "c"
  
      # Create artificial "fit" data for testing purposes
      oz.k_values_ = [1, 2, 3, 4, 5, 6, 7, 8]
      oz.k_timers_ = [6.2, 8.3, 10.1, 15.8, 21.2, 27.9, 38.2, 44.9]
      oz.k_scores_ = [0.8, 0.7, 0.55, 0.48, 0.40, 0.38, 0.35, 0.30]
      oz.elbow_value_ = 5
      oz.elbow_score_ = 0.40
  
      # Execute drawing
      oz.draw()
      oz.finalize()
      self.assert_images_similar(oz, tol=3.2)
  
imports: import sys
  import pytest
  import numpy
  import matplotlib.pyplot
  from scipy.sparse import csc_matrix, csr_matrix
  from numpy.testing import assert_array_almost_equal
  from sklearn.datasets import make_blobs
  from sklearn.cluster import KMeans, MiniBatchKMeans
  from sklearn.feature_extraction.text import TfidfVectorizer
  from tests.fixtures import Dataset
  from tests.base import VisualTestCase
  from yellowbrick.datasets import load_hobbies
  from yellowbrick.cluster.elbow import distortion_score
  from yellowbrick.cluster.elbow import KElbowVisualizer, kelbow_visualizer
  from yellowbrick.exceptions import YellowbrickValueError, YellowbrickWarning
  from tests.base import IS_WINDOWS_OR_CONDA
label_desc: Use image node_id 18 for calling the TestKElbowVisualizer obj's underlying member method code with example usage: obj.test_set_colors_manually() without return types