Add MAE metric to evaluator

import warnings

from deepchem.utils.save import log

from sklearn.metrics import mean_squared_error

from sklearn.metrics import mean_absolute_error

from sklearn.metrics import roc_auc_score

from sklearn.metrics import r2_score

from sklearn.metrics import matthews_corrcoef

      colnames = ["task_name", "roc_auc_score", "matthews_corrcoef",

                  "recall_score", "accuracy_score"]

    elif task_type == "regression":

      colnames = ["task_name", "r2_score", "rms_error"]

      colnames = ["task_name", "r2_score", "rms_error", "mae"]

    else:

      raise ValueError("Unrecognized task type: %s" % task_type)

        try:

          r2s = r2_score(y, y_pred)

          rms = np.sqrt(mean_squared_error(y, y_pred))

          mae = mean_absolute_error(y, y_pred)

        except ValueError:

          r2s = np.nan

          rms = np.nan

        performance_df.loc[i] = [task_name, r2s, rms]

          mae = np.nan

        performance_df.loc[i] = [task_name, r2s, rms, mae]

    log("Saving predictions to %s" % csv_out, self.verbose)

    pred_y_df.to_csv(csv_out)