code: def main():
      boston = load_boston()
      X, y = shuffle(boston.data, boston.target, random_state=13)
      X = X.astype(np.float32)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.25, shuffle=False
      )
  
      p = Pipeline(
          [
              NumpyShapePrinter(),
              AddFeatures(
                  [
                      PCA(n_components=2),
                      FastICA(n_components=2),
                  ]
              ),
              NumpyShapePrinter(),
              RidgeModelStacking(
                  [
                      GradientBoostingRegressor(),
                      GradientBoostingRegressor(n_estimators=500),
                      GradientBoostingRegressor(max_depth=5),
                      KMeans(),
                  ]
              ),
              NumpyShapePrinter(),
          ]
      )
  
      print("Fitting on train:")
      p = p.fit(X_train, y_train)
      print("")
      print("Transforming train and test:")
      y_train_predicted = p.predict(X_train)
      y_test_predicted = p.predict(X_test)
      print("")
      print("Evaluating transformed train:")
      score_train = r2_score(y_train_predicted, y_train)
      print("R2 regression score:", score_train)
      print("")
      print("Evaluating transformed test:")
      score_test = r2_score(y_test_predicted, y_test)
      print("R2 regression score:", score_test)
  
      assert y_train_predicted.shape == (379,)
      assert y_test_predicted.shape == (127,)
      assert isinstance(score_train, float)
      assert isinstance(score_test, float)
  
      return (
          y_train_predicted,
          y_test_predicted,
          score_train,
          score_test,
      )
  
file_path: Neuraxle/examples/sklearn/plot_boston_housing_regression_with_model_stacking.py
imports: import numpy
  from sklearn.cluster import KMeans
  from sklearn.datasets import load_boston
  from sklearn.decomposition import PCA, FastICA
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.metrics import r2_score
  from sklearn.model_selection import train_test_split
  from sklearn.utils import shuffle
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.numpy import NumpyShapePrinter
  from neuraxle.steps.sklearn import RidgeModelStacking
  from neuraxle.union import AddFeatures
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: y_train_predicted,  y_test_predicted,  score_train,  score_test