class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Visual similarity of multiclass classifier with predict_proba function
docstr_tok: ["Visual","similarity","of","multiclass","classifier","with","predict_proba","function"]
code: def test_multiclass_probability(self):
      """
      Visual similarity of multiclass classifier with predict_proba function
      """
      # Create and fit the visualizer
      oz = PrecisionRecallCurve(
          GaussianNB(),
          per_class=True,
          micro=False,
          fill_area=False,
          iso_f1_curves=True,
          ap_score=False,
      )
      assert_not_fitted(oz)
  
      # Fit returns self
      assert (
          oz.fit(self.multiclass.X.train, self.multiclass.y.train) is oz
      )
  
      # Score the visualizer
      s = oz.score(self.multiclass.X.test, self.multiclass.y.test)
      assert_fitted(oz)
  
      # Score should be between 0 and 1
      assert 0.0 <= s <= 1.0
  
      # Check the multiclass classification properties
      assert oz.target_type_ == MULTICLASS
      assert isinstance(oz.score_, dict)
      assert oz.score_[MICRO] == s
      assert isinstance(oz.precision_, dict)
      assert isinstance(oz.recall_, dict)
      assert len(oz.score_) == len(oz.classes_) + 1
      assert len(oz.precision_) == len(oz.classes_) + 1
      assert len(oz.recall_) == len(oz.classes_) + 1
  
      # Compare the images
      oz.finalize()
      self.assert_images_similar(oz)
  
imports: import sys
  import pytest
  import matplotlib
  from yellowbrick.exceptions import *
  from yellowbrick.classifier.prcurve import *
  from yellowbrick.datasets import load_occupancy
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from .test_rocauc import FakeClassifier
  from sklearn.svm import LinearSVC, SVC
  from sklearn.naive_bayes import GaussianNB
  from sklearn.datasets import make_regression
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import RidgeClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 7 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_multiclass_probability() without return types