class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Test using custom ISO F1 Values
docstr_tok: ["Test","using","custom","ISO","F1","Values"]
code: def test_custom_iso_f1_scores(self):
      """
      Test using custom ISO F1 Values
      """
      X, y = load_occupancy(return_dataset=True).to_numpy()
  
      vals = (0.1, 0.6, 0.3, 0.9, 0.9)
      viz = PrecisionRecallCurve(
          RandomForestClassifier(random_state=27),
          iso_f1_curves=True,
          iso_f1_values=vals,
      )
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=555
      )
  
      assert viz.fit(X_train, y_train) is viz
      viz.score(X_test, y_test)
      viz.finalize()
  
      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 12 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_custom_iso_f1_scores() without return types