class_docstr: Test ResidualPlot visualizer
class_docstr_tok: ["Test","ResidualPlot","visualizer"]
docstr: Test that visualizer can handle sklearn pipeline as model input
  within a quickmethod
docstr_tok: ["Test","that","visualizer","can","handle","sklearn","pipeline","as","model","input","within","a","quickmethod"]
code: def test_pipeline_as_model_input_quickmethod(self):
      """
      Test that visualizer can handle sklearn pipeline as model input
      within a quickmethod
      """
      X, y = load_energy(return_dataset=True).to_numpy()
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, random_state=42
      )
  
      model = Pipeline(
          [
              ("minmax", MinMaxScaler()),
              ("ridge", Ridge(random_state=8893)),
          ]
      )
  
      oz = residuals_plot(
          model, X_train, y_train, X_test, y_test, show=False
      )
      self.assert_images_similar(oz, tol=3.0)
  
imports: import sys
  import pytest
  import matplotlib
  import matplotlib.pyplot
  from yellowbrick.datasets import load_energy
  from yellowbrick.exceptions import YellowbrickValueError
  from yellowbrick.regressor.residuals import ResidualsPlot, residuals_plot
  from unittest import mock
  from tests.fixtures import Dataset, Split
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from sklearn.datasets import make_regression
  from sklearn.linear_model import Ridge, Lasso
  from sklearn.neural_network import MLPRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 19 for calling the TestResidualsPlot obj's underlying member method code with example usage: obj.test_pipeline_as_model_input_quickmethod() without return types