new_contents: import numpy
  from sklearn.linear_model import Ridge
  from sklearn.kernel_ridge import KernelRidge
  from sklearn.metrics import mean_absolute_error as MAE
  
  from molml.features import EncodedBond, Connectivity
  
  from utils import load_qm7
  
  
  if __name__ == "__main__":
      # This is just boiler plate code to load the data
      Xin_train, Xin_test, y_train, y_test = load_qm7()
  
      feats = [
          EncodedBond(n_jobs=-1, max_depth=3),
          Connectivity(depth=1, n_jobs=-1),
          Connectivity(depth=3, use_coordination=True, n_jobs=-1),
      ]
      train_feats = []
      test_feats = []
      for tf in feats:
          X_train = tf.fit_transform(Xin_train)
          X_test = tf.transform(Xin_test)
          train_feats.append(X_train)
          test_feats.append(X_test)
  
      X_train = numpy.hstack(train_feats)
      X_test = numpy.hstack(test_feats)
  
      clfs = [
          Ridge(alpha=0.01),
          KernelRidge(alpha=1e-9, gamma=1e-5, kernel="rbf"),
      ]
      for clf in clfs:
          print clf
          clf.fit(X_train, y_train)
          train_error = MAE(clf.predict(X_train), y_train)
          test_error = MAE(clf.predict(X_test), y_test)
          print "Train MAE: %.4f Test MAE: %.4f" % (train_error, test_error)
          print
  
subject: Add another example script