class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Test quick method when both train and test data is supplied
docstr_tok: ["Test","quick","method","when","both","train","and","test","data","is","supplied"]
code: def test_quick_method_with_test_set(self):
      """
      Test quick method when both train and test data is supplied
      """
      X, y = load_occupancy(return_dataset=True).to_numpy()
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=555
      )
  
      viz = precision_recall_curve(
          RandomForestClassifier(random_state=72),
          X_train,
          y_train,
          X_test,
          y_test,
          show=False,
      )
      self.assert_images_similar(viz)
  
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 13 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_quick_method_with_test_set() without return types