class_docstr: Test ROCAUC visualizer
class_docstr_tok: ["Test","ROCAUC","visualizer"]
docstr: Test that visualizer can handle sklearn pipeline as model input
  within a quickmethod
docstr_tok: ["Test","that","visualizer","can","handle","sklearn","pipeline","as","model","input","within","a","quickmethod"]
code: def test_pipeline_as_model_input_quickmethod(self):
      """
      Test that visualizer can handle sklearn pipeline as model input
      within a quickmethod
      """
      X, y = load_occupancy(return_dataset=True).to_pandas()
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=42
      )
  
      model = Pipeline(
          [("minmax", MinMaxScaler()), ("svc", SVC(random_state=42))]
      )
  
      oz = roc_auc(
          model,
          X_train,
          y_train,
          X_test,
          y_test,
          classes=["vacant", "occupied"],
          show=False,
          binary=True,
      )
      self.assert_images_similar(oz, tol=12)
  
imports: import pytest
  import numpy
  import numpy.testing
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from yellowbrick.classifier.rocauc import *
  from yellowbrick.exceptions import ModelError
  from yellowbrick.datasets import load_occupancy
  from sklearn.svm import LinearSVC, SVC
  from sklearn.naive_bayes import GaussianNB
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 28 for calling the TestROCAUC obj's underlying member method code with example usage: obj.test_pipeline_as_model_input_quickmethod() without return types