class_docstr: Test ROCAUC visualizer
class_docstr_tok: ["Test","ROCAUC","visualizer"]
docstr: Test ROCAUC without a macro or micro average
docstr_tok: ["Test","ROCAUC","without","a","macro","or","micro","average"]
code: def test_rocauc_no_macro_no_micro(self):
      """
      Test ROCAUC without a macro or micro average
      """
      # Create and fit the visualizer
      visualizer = ROCAUC(
          LogisticRegression(), macro=False, micro=False
      )
      visualizer.fit(self.binary.X.train, self.binary.y.train)
  
      # Score the visualizer (should be the F1 score)
      s = visualizer.score(self.binary.X.test, self.binary.y.test)
      assert s == pytest.approx(0.8)
  
      # Assert that there is no macro score
      assert "macro" not in visualizer.fpr
      assert "macro" not in visualizer.tpr
      assert "macro" not in visualizer.roc_auc
  
      # Assert that there is no micro score
      assert "micro" not in visualizer.fpr
      assert "micro" not in visualizer.tpr
      assert "micro" not in visualizer.roc_auc
  
      # Compare the images
      visualizer.finalize()
      self.assert_images_similar(visualizer, tol=0.1, windows_tol=10)
  
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 15 for calling the TestROCAUC obj's underlying member method code with example usage: obj.test_rocauc_no_macro_no_micro() without return types