update docs (ae746a07) · Commits · 钟慕尧 / deepchem

deepchem/molnet/load_function/uspto_datasets.py

+19 −10

Original line number	Diff line number	Diff line
		@@ -25,7 +25,8 @@ USPTO_TASK: List[str] = []

		class _USPTOLoader(_MolnetLoader):

		def __init__(self, args, subset: str, sep_reagent: bool, skip_transform: 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
		@@ -94,10 +95,11 @@ def load_uspto(
		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 and Coley. 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
		----------
		@@ -138,6 +140,8 @@ def load_uspto(
		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
		@@ -155,13 +159,18 @@ def load_uspto(

		if skip_transform:
		if not sep_reagent:
		raise ValueError("To enable mixed training you must not skip the transformation.")
		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)]
		transformers = [
		TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=True)
		]
		else:
		transformers = [TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=False)]
		transformers = [
		TransformerGenerator(dc.trans.RxnSplitTransformer, sep_reagent=False)
		]

		loader = _USPTOLoader(
		featurizer,

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