Remove Model and Transformer debug code

    batch_size = self.model_params["batch_size"]

    for (X_batch, y_batch, w_batch, ids_batch) in dataset.iterbatches(batch_size):

      # HACK(JG): This is a hack to perform 10-fold averaging of y_pred on

      # HACK(JG): This is a hack to perform n-fold averaging of y_pred on

      # a given X_batch.  If fit_transformers exist, we will apply them to

      # X_batch 10 times and average the resulting y_pred before we undo 

      # transforms on y_pred and y

      # X_batch 1 times and average the resulting y_pred before we undo 

      # transforms on y_pred and y.  In the future the averaging will be

      # performed n_sample times, where n_sample can be user-specified.

      if self.fit_transformers:

          y_pred = np.reshape(y_pred, np.shape(y_b))

          y_preds.append(y_pred)

        #print(y_batch)

        #print(y_b)

        #print(y_preds)

        y_pred = np.array(y_preds).mean(axis=0)

        #print(y_pred)

        #break

      else:

        #print(y_batch)

        y_pred = self.predict_on_batch(X_batch)

        y_pred = np.reshape(y_pred, np.shape(y_batch))

        #print(y_pred)

      # Now undo transformations on y, y_pred

    for _, row in df.iterrows(): 

      X = load_from_disk(row['X-transformed'])

      #self.feature_max = np.maximum(self.feature_max,X.max(axis=0))

      self.feature_max = np.maximum(self.feature_max,X.max(axis=0))

  def transform(self, dataset, parallel=False):

    X_bin = []

    if self.update_state: 

      #self.set_max(df)

      self.set_max(df)

      self.update_state = False

    if self.transform_X:

      for i in range(X.shape[1]):