class_docstr: Test the PrecisionRecallCurve visualizer
class_docstr_tok: ["Test","the","PrecisionRecallCurve","visualizer"]
docstr: Test that visualizer quickmethod can be accessed within a
  sklearn pipeline
docstr_tok: ["Test","that","visualizer","quickmethod","can","be","accessed","within","a","sklearn","pipeline"]
code: def test_within_pipeline_quickmethod(self):
      """
      Test that visualizer quickmethod can be accessed within a
      sklearn pipeline
      """
      X, y = load_occupancy(return_dataset=True).to_pandas()
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=42
      )
  
      model = Pipeline(
          [
              ("minmax", MinMaxScaler()),
              (
                  "prc",
                  precision_recall_curve(
                      SVC(random_state=42),
                      X_train,
                      y_train,
                      X_test,
                      y_test,
                      per_class=True,
                      micro=False,
                      fill_area=False,
                      iso_f1_curves=True,
                      ap_score=False,
                      classes=["unoccupied", "occupied"],
                      show=False,
                  ),
              ),
          ]
      )
      self.assert_images_similar(model["prc"], tol=5.5)
  
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 17 for calling the TestPrecisionRecallCurve obj's underlying member method code with example usage: obj.test_within_pipeline_quickmethod() without return types