class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Test the precision_recall_curve quick method with numpy arrays.
docstr_tok: ["Test","the","precision_recall_curve","quick","method","with","numpy","arrays","."]
code: def test_quick_method(self):
      """
      Test the precision_recall_curve quick method with numpy arrays.
      """
      X, y = load_occupancy(return_dataset=True).to_numpy()
      model = DecisionTreeClassifier(random_state=23, max_depth=2)
  
      oz = precision_recall_curve(
          model,
          X,
          y,
          per_class=False,
          micro=True,
          fill_area=False,
          iso_f1_curves=True,
          ap_score=False,
          show=False,
      )
  
      assert isinstance(oz, PrecisionRecallCurve)
      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 9 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_quick_method() without return types