Merge pull request #2628 from Suzukazole/loaderupdate

class _USPTOLoader(_MolnetLoader):

  def __init__(self, *args, subset: str, sep_reagent: bool, **kwargs):

  def __init__(self, *args, subset: str, sep_reagent: bool,

               skip_transform: bool, **kwargs):

    super(_USPTOLoader, self).__init__(*args, **kwargs)

    self.subset = subset

    self.sep_reagent = sep_reagent

    self.name = 'USPTO_' + subset

    self.skip_transform = skip_transform

  def create_dataset(self) -> Dataset:

    if self.subset not in ['MIT', 'STEREO', '50K', 'FULL']:

    data_dir: Optional[str] = None,

    save_dir: Optional[str] = None,

    subset: str = "MIT",

    sep_reagent: bool = True,  # functionality to be added!

    sep_reagent: bool = True,

    skip_transform: bool = True,

    **kwargs

) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:

  """Load USPTO Datasets.

  USPTO is a dataset of over 1.8 Million organic chemical reactions extracted

  from US patents and patent applications. The dataset contains the reactions

  in the form of reaction SMILES, which have the general format:

  The USPTO dataset consists of over 1.8 Million organic chemical reactions

  extracted from US patents and patent applications. The dataset contains the

  reactions in the form of reaction SMILES, which have the general format:

  reactant>reagent>product.

  Molnet provides ability to load subsets of the USPTO dataset namely MIT,

  The 50K dataset contatins 50,000 reactions and is the benchmark for

  retrosynthesis predictions. The reactions are additionally classified into 10

  reaction classes. The canonicalized version of the dataset used by the loader

  is the same as that used by somnath et. al.

  is the same as that used by Somnath et. al.

  The loader uses the SpecifiedSplitter to use the same splits as specified

  by Schwaller and Coley. Custom splitters could also be used. There is also a

  toggle to load the dataset with the reagents separated or mixed. This alters

  the entries in src by replacing the '>' with '.', effectively loading them as

  a unified SMILES string.

  by Schwaller et. al and Dai et. al. Custom splitters could also be used. There

  is a toggle in the loader to skip the source/target transformation needed for

  seq2seq tasks. There is an additional toggle to load the dataset with the

  reagents and reactants separated or mixed. This alters the entries in source

  by replacing the '>' with '.' , effectively loading them as an unified

  SMILES string.

  Parameters

  ----------

  subset: str (default 'MIT')

    Subset of dataset to download. 'FULL', 'MIT', 'STEREO', and '50K' are supported.

  sep_reagent: bool (default True)

    Toggle to load dataset with reactants and reado I call it

    Toggle to load dataset with reactants and reagents either separated or mixed.

  skip_transform: bool (default True)

    Toggle to skip the source/target transformation.

  Returns

  -------

  tasks, datasets, transformers: tuple

    transformers : list

      ``deepchem.trans.transformers.Transformer`` instances applied

      to dataset.

  References

  ----------

  .. [1] Lowe, D.. (2017). Chemical reactions from US patents (1976-Sep2016)

        (Version 1). figshare. https://doi.org/10.6084/m9.figshare.5104873.v1

  .. [2] Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas,

         C., & Lee, A. A. (2019). Molecular transformer: a model for

         uncertainty-calibrated chemical reaction prediction.

         ACS central science, 5(9), 1572-1583.

  .. [3] Somnath, V. R., Bunne, C., Coley, C. W., Krause, A., & Barzilay, R.

         (2020). Learning Graph Models for Retrosynthesis Prediction.

         arXiv preprint arXiv:2006.07038.

  .. [4] Dai, H., Li, C., Coley, C. W., Dai, B., & Song, L. (2020).

         Retrosynthesis prediction with conditional graph logic network.

         arXiv preprint arXiv:2001.01408.

  .. [1] Lowe, D. Chemical reactions from US patents (1976-Sep2016)

        (Version 1). figshare (2017). https://doi.org/10.6084/m9.figshare.5104873.v1

  .. [2] Somnath, Vignesh Ram, et al. "Learning graph models for retrosynthesis

         prediction." arXiv preprint arXiv:2006.07038 (2020).

  .. [3] Schwaller, Philippe, et al. "Molecular transformer: a model for

         uncertainty-calibrated chemical reaction prediction."

         ACS central science 5.9 (2019): 1572-1583.

  .. [4] Dai, Hanjun, et al. "Retrosynthesis prediction with conditional

         graph logic network." arXiv preprint arXiv:2001.01408 (2020).

  """

  if skip_transform:

    if not sep_reagent:

      raise ValueError(

          "To enable mixed training you must not skip the transformation.")

    transformers = []

  else:

    if sep_reagent:

      transformers = [

          TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=True)

      ]

    else:

      transformers = [

          TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=False)

      ]

  loader = _USPTOLoader(

      featurizer,

      splitter,

      save_dir,

      subset=subset,

      sep_reagent=sep_reagent,

      skip_transform=skip_transform,

      **kwargs)

  return loader.load_dataset(loader.name, reload)