Move _add_user_specified_features into _standardize_df. Cleaned up some prints

        0, nb_sample, np.ceil(float(nb_sample)/shard_size)+1, dtype=int)

    shard_files = []

    for j in range(len(interval_points)-1):

      #log("Sharding and standardizing into shard-%s / %s shards" % (str(j+1), len(interval_points)-1), self.verbose)

      log("Sharding and standardizing into shard-%s / %s shards" % (str(j+1), len(interval_points)-1), self.verbose)

      raw_df_shard = raw_df.iloc[range(interval_points[j], interval_points[j+1])]

      df = self._standardize_df(raw_df_shard)

      #log("Aggregating User-Specified Features", self.verbose)

      self._add_user_specified_features(df)

      for compound_featurizer in self.compound_featurizers:

        log("Currently featurizing feature_type: %s"

      df["ligand_pdb"] = ori_df[[self.ligand_pdb_field]]

    if self.ligand_mol2_field is not None:

      df["ligand_mol2"] = ori_df[[self.ligand_mol2_field]]

    if self.user_specified_features is not None:

      log("Aggregating User-Specified Features", self.verbose)

      self._add_user_specified_features(df)

    return df

  def _featurize_complexes(self, df, featurizer):

    """Returns size of internal dataset."""

    return self.num_samples

    irint("feature types %s" % self.feature_types)

  def itersamples(self):

    """Iterates over samples in this object."""

    compound_ids = set(list(self.compounds_df["mol_id"]))

    """

    Factory method that initializes model of requested type.

    """

    print(model_instance.__class__)

    print(task_types)

    print(model_params)

    if model_instance.__class__ in Model.non_sklearn_models:

      model = model_instance(task_types, model_params, initialize_raw_model)

    else:

                  "decay": 1e-4, "batch_size": 5,

                  "nb_epoch": 10}

  model_name = "singletask_deep_regressor"

  #model_name = "SingleTaskDNN"

  nb_hidden_vals = [10, 100]

  learning_rate_vals = [.01, .001]

  init_vals = ["glorot_uniform"]