class_docstr: DiscriminationThreshold visualizer tests
class_docstr_tok: ["DiscriminationThreshold","visualizer","tests"]
docstr: Test splitter random state is modified
code: def test_splitter_random_state(self):
      """
      Test splitter random state is modified
      """
      viz = DiscriminationThreshold(NuSVC(), random_state=None)
      assert viz._check_cv(None, random_state=None).random_state is None
      assert viz._check_cv(None, random_state=42).random_state == 42
  
      splits = StratifiedShuffleSplit(n_splits=1, random_state=None)
      assert (
          viz._check_cv(splits, random_state=None).random_state is None
      )
      assert viz._check_cv(splits, random_state=23).random_state == 23
  
      splits = StratifiedShuffleSplit(n_splits=1, random_state=181)
      assert (
          viz._check_cv(splits, random_state=None).random_state == 181
      )
      assert viz._check_cv(splits, random_state=72).random_state == 72
  
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 15 for calling the TestDiscriminationThreshold obj's underlying member method code with example usage: obj.test_splitter_random_state() without return types