code: def create_regression_dataset(metafeatures, experiments):
      X = []
      X_indices = []
      Y = []
      for dataset_name in experiments:
          experiment = experiments[dataset_name]
          mf = metafeatures.loc[dataset_name]
          for i, run in enumerate(experiment):
              x1 = pd.Series(
                  data=[run.params[param] for param in run.params],
                  index=run.params.keys(),
              )
              x2 = mf
              X.append(x1.append(x2))
              X_indices.append("%s_%d" % (dataset_name, i))
              Y.append(run.result)
      X = pd.DataFrame(X, index=X_indices)
      Y = pd.DataFrame(Y, index=X_indices)
      logging.info("X.shape %s", X.shape)
      logging.info("Y.shape %s", Y.shape)
      return X, Y
  
file_path: auto-sklearn/autosklearn/metalearning/metalearning/create_datasets.py
imports: import itertools
  import logging
  import numpy
  import pandas
  import scipy.stats
label_desc: Use image node_id 1 for calling a global function with example usage: create_regression_dataset(metafeatures, experiments) and returns: X,  Y