class_docstr: DiscriminationThreshold visualizer tests
class_docstr_tok: ["DiscriminationThreshold","visualizer","tests"]
docstr: Test with NumPy arrays
code: def test_numpy_integration(self):
      """
      Test with NumPy arrays
      """
      _, ax = plt.subplots()
  
      # Load the occupancy dataset from fixtures
      data = load_occupancy(return_dataset=True)
      X, y = data.to_numpy()
  
      classes = ["unoccupied", "occupied"]
  
      # Create the visualizer
      viz = DiscriminationThreshold(
          LogisticRegression(), ax=ax, classes=classes, random_state=193
      )
      viz.fit(X, y)
      viz.finalize()
  
      self.assert_images_similar(viz, tol=0.1)
  
imports: import sys
  import pytest
  import yellowbrick
  import matplotlib.pyplot
  from yellowbrick.classifier.threshold import *
  from yellowbrick.datasets import load_occupancy, load_spam
  from yellowbrick.utils import is_probabilistic, is_classifier
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from numpy.testing import assert_array_equal
  from sklearn.base import ClassifierMixin
  from sklearn.svm import LinearSVC, NuSVC
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import Pipeline
  from sklearn.datasets import make_classification
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.naive_bayes import BernoulliNB, GaussianNB
  from sklearn.linear_model import Ridge, LogisticRegression
  from sklearn.model_selection import StratifiedShuffleSplit
label_desc: Use image node_id 4 for calling the TestDiscriminationThreshold obj's underlying member method code with example usage: obj.test_numpy_integration() without return types