k-fold fixes

    train_ds_base = None

    train_datasets = []

    # rem_dataset is remaining portion of dataset

    if isinstance(dataset, DiskDataset):

      rem_dataset = dataset

    else:

      rem_dataset = DiskDataset.from_numpy(dataset.X, dataset.y, dataset.w, dataset.ids)

    for fold in range(k):

      # Note starts as 1/k since fold starts at 0. Ends at 1 since fold goes up

      # to k-1.

        frac_train=frac_fold,

        frac_valid=1 - frac_fold,

        frac_test=0)

      cv_dataset = rem_dataset.select(fold_inds)

      cv_dataset = rem_dataset.select(fold_inds, select_dir=cv_dir)

      cv_datasets.append(cv_dataset)

      rem_dataset = rem_dataset.select(rem_inds)

      update_train_base_merge = filter(lambda x: x is not None,

                                       [train_ds_base, cv_dataset])

      train_ds_base = DiskDataset.merge(

          update_train_base_merge, merge_dir=cv_dir)

      train_ds_base = DiskDataset.merge(update_train_base_merge)

    return list(zip(train_datasets, cv_datasets))

  def train_valid_test_split(self,