added to description of convmolfeaturizer

import os

import tempfile

import deepchem as dc

def test_singleton_csv_fragment_load_with_per_atom_fragmentation():

  """Test a case where special form of  dataaset is created from csv:

   dataset of fragments of molecules  for subsequent model interpretation """

  with tempfile.NamedTemporaryFile(mode='w', delete=False) as fin:

    fin.write("smiles,endpoint\nc1ccccc1,1")

  featurizer = dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True)

  tasks = ["endpoint"]

  loader = dc.data.CSVFragmentLoader(

      tasks=tasks, feature_field="smiles", featurizer=featurizer)

  X = loader.create_dataset(fin.name)

  assert len(X) == 6

  os.remove(fin.name)

  Examples

  ---------

  Using ConvMolFeaturizer to create featurized fragments derived from molecules of interest

  Using ConvMolFeaturizer to create featurized fragments derived from molecules of interest.

  This is used only in the context of performing interpretation of models using atomic

  contributions (atom-based model interpretation)

  >>> import deepchem as dc

  >>> smiles = ["C", "CCC"]

  >>> featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=True)

  >>> f = featurizer.featurize(smiles)

  >>> len(f) # contains 2 lists with  featurized fragments from 2 mols

  See Also

  --------

  Detailed examples of `GraphConvModel` interpretation are provided in Tutorial #28

  References

  ---------

import tempfile

import os

import numpy as np

import deepchem as dc