recover changes

  model: string or user-defined model stucture

      choice of which model to use, deepchem provides implementation of

      logistic regression, random forest, multitask network, 

      bypass multitask network, irv, graph convolution, deep tensor NN(only for QM);

      bypass multitask network, irv, graph convolution;

      for user define model, it should include function: fit, evaluate

  split: string,  optional (default=None)

      choice of splitter function, None = using the default splitter

        n_features = 75

      elif model in [

          'tf', 'tf_robust', 'logreg', 'rf', 'irv', 'tf_regression',

          'rf_regression', 'DTNN'

          'rf_regression'

      ]:

        featurizer = 'ECFP'

        n_features = 1024

          return

      elif dataset in ['qm7', 'qm7b', 'qm9']:

        featurizer = None  # qm* datasets are already featurized

        if isinstance(model, str) and not model in ['tf_regression', 'DTNN']:

        if isinstance(model, str) and not model in ['tf_regression']:

          return

        elif model in ['tf_regression']:

          model = 'tf_regression_ft'

          return

        if split in ['scaffold', 'butina']:

          return

    if model == 'DTNN' and not dataset in ['qm7', 'qm7b', 'qm9']:

      return

    if not split in [

        None, 'index', 'random', 'scaffold', 'butina', 'stratified'

    ]: