class_docstr: DiscriminationThreshold visualizer tests
class_docstr_tok: ["DiscriminationThreshold","visualizer","tests"]
docstr: Test that visualizer can be accessed within a sklearn pipeline
code: def test_within_pipeline(self):
      """
      Test that visualizer can be accessed within a sklearn pipeline
      """
      X, y = load_spam()
  
      model = Pipeline(
          [
              (
                  "imputer",
                  SimpleImputer(missing_values=np.nan, strategy="mean"),
              ),
              (
                  "dt",
                  DiscriminationThreshold(
                      LogisticRegression(
                          multi_class="auto", solver="liblinear"
                      ),
                      random_state=42,
                  ),
              ),
          ]
      )
  
      model.fit(X, y)
      model["dt"].finalize()
      self.assert_images_similar(model["dt"], tol=15)
  
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 19 for calling the TestDiscriminationThreshold obj's underlying member method code with example usage: obj.test_within_pipeline() without return types