text: import os
  import numpy as np
  import pandas as pd
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.model_selection import cross_val_score, KFold, StratifiedKFold
  from automatminer.utils.ml import regression_or_classification
  from automatminer.utils.ml import AMM_CLF_NAME, AMM_REG_NAME
  from benchdev.config import BENCHMARK_FULL_SET
  
  
  benchmark_dir = os.environ["AMM_DATASET_DIR"]
  
  for p in BENCHMARK_FULL_SET:
      pname = p["name"]
      print("Loading {}".format(pname))
      df = pd.read_pickle(os.path.join(benchmark_dir, p["data_pickle"]))
      target = p["target"]
      ltype = p["problem_type"]
      if ltype == AMM_REG_NAME:
          kf = KFold(n_splits=5, random_state=18012019, shuffle=True)
          estimator = DummyRegressor(strategy="mean")
          scoring = "neg_mean_absolute_error"
          multiplier = -1
      elif ltype == AMM_CLF_NAME:
          kf = StratifiedKFold(n_splits=5, random_state=18012019, shuffle=True)
          estimator = DummyClassifier(strategy="stratified")
          multiplier = 1
          scoring = "roc_auc"
      else:
          raise ValueError("problem type {} is not known.".format(ltype))
  
      cvs = cross_val_score(estimator,
                            df.drop(columns=[target]),
                            y=df[target],
                            scoring=scoring,
                            cv=kf)
  
      cvs = multiplier * cvs
      mean_cvs = np.mean(cvs)
      print(pname, mean_cvs)