class_docstr: Abstract base class for chemically aware splits..
class_docstr_tok: ["Abstract","base","class","for","chemically","aware","splits",".."]
docstr: Splits self into train/test sets.
  Returns Dataset objects.
docstr_tok: ["Splits","self","into","train\/test","sets",".","Returns","Dataset","objects","."]
code: def train_test_indices(
      self,
      dataset,
      train_dir=None,
      test_dir=None,
      seed=None,
      frac_train=0.8,
      verbose=True,
  ):
      """
      Splits self into train/test sets.
      Returns Dataset objects.
      """
      valid_dir = None
      train_inds, _, test_inds = self.train_valid_test_indices(
          dataset,
          train_dir,
          valid_dir,
          test_dir,
          frac_train=frac_train,
          frac_test=1 - frac_train,
          frac_valid=0.0,
          verbose=verbose,
      )
      return train_inds, test_inds
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import tempfile
  import numpy
  import itertools
  from rdkit import Chem
  from rdkit import DataStructs
  from rdkit.Chem import AllChem
  from rdkit.ML.Cluster import Butina
  import deepchem
  from deepchem.data import DiskDataset
  from deepchem.utils import ScaffoldGenerator
  from deepchem.utils.save import log
  from deepchem.data import NumpyDataset
  from deepchem.utils.save import load_data
label_desc: Use image node_id 7 for calling the Splitter obj's underlying member method code with example usage: obj.train_test_indices(dataset, train_dir, test_dir, seed, frac_train, verbose) and returns: train_inds,  test_inds