remove kwargs param (91945985) · Commits · 钟慕尧 / deepchem

deepchem/feat/base_classes.py

+14 −18

Original line number	Diff line number	Diff line
		@@ -25,8 +25,7 @@ class Featurizer(object):

		def featurize(self,
		datapoints: Iterable[Any],
		log_every_n: int = 1000,
		**kwargs) -> np.ndarray:
		log_every_n: int = 1000) -> np.ndarray:
		"""Calculate features for datapoints.

		`**kwargs` will get passed directly to `Featurizer._featurize`
		@@ -51,7 +50,7 @@ class Featurizer(object):
		if i % log_every_n == 0:
		logger.info("Featurizing datapoint %i" % i)
		try:
		features.append(self._featurize(point, **kwargs))
		features.append(self._featurize(point))
		except:
		logger.warning(
		"Failed to featurize datapoint %d. Appending empty array")
		@@ -59,7 +58,7 @@ class Featurizer(object):

		return np.asarray(features)

		def __call__(self, datapoints: Iterable[Any], **kwargs):
		def __call__(self, datapoints: Iterable[Any]):
		"""Calculate features for datapoints.

		`**kwargs` will get passed directly to `Featurizer.featurize`
		@@ -69,9 +68,9 @@ class Featurizer(object):
		datapoints: Iterable[Any]
		Any blob of data you like. Subclasss should instantiate this.
		"""
		return self.featurize(datapoints, **kwargs)
		return self.featurize(datapoints)

		def _featurize(self, datapoint: Any, **kwargs):
		def _featurize(self, datapoint: Any):
		"""Calculate features for a single datapoint.

		Parameters
		@@ -161,8 +160,7 @@ class ComplexFeaturizer(Featurizer):

		def featurize(self,
		complexes: Iterable[Tuple[str, str]],
		log_every_n: int = 100,
		**kwargs) -> np.ndarray:
		log_every_n: int = 100) -> np.ndarray:
		"""
		Calculate features for mol/protein complexes.

		@@ -186,7 +184,7 @@ class ComplexFeaturizer(Featurizer):
		if idx % log_every_n == 0:
		logger.info("Featurizing datapoint %i" % idx)
		try:
		features.append(self._featurize(point, **kwargs))
		features.append(self._featurize(point))
		successes.append(idx)
		except:
		logger.warning(
		@@ -209,7 +207,7 @@ class ComplexFeaturizer(Featurizer):

		return np.asarray(features)

		def _featurize(self, complex: Tuple[str, str], **kwargs):
		def _featurize(self, complex: Tuple[str, str]):
		"""
		Calculate features for single mol/protein complex.

		@@ -239,7 +237,7 @@ class MolecularFeaturizer(Featurizer):
		The subclasses of this class require RDKit to be installed.
		"""

		def featurize(self, molecules, log_every_n=1000, **kwargs) -> np.ndarray:
		def featurize(self, molecules, log_every_n=1000) -> np.ndarray:
		"""Calculate features for molecules.

		Parameters
		@@ -283,7 +281,7 @@ class MolecularFeaturizer(Featurizer):
		new_order = rdmolfiles.CanonicalRankAtoms(mol)
		mol = rdmolops.RenumberAtoms(mol, new_order)

		features.append(self._featurize(mol, **kwargs))
		features.append(self._featurize(mol))
		except Exception as e:
		if isinstance(mol, Chem.rdchem.Mol):
		mol = Chem.MolToSmiles(mol)
		@@ -321,8 +319,7 @@ class MaterialStructureFeaturizer(Featurizer):

		def featurize(self,
		structures: Iterable[Union[Dict[str, Any], PymatgenStructure]],
		log_every_n: int = 1000,
		**kwargs) -> np.ndarray:
		log_every_n: int = 1000) -> np.ndarray:
		"""Calculate features for crystal structures.

		Parameters
		@@ -353,7 +350,7 @@ class MaterialStructureFeaturizer(Featurizer):
		try:
		if isinstance(structure, Dict):
		structure = Structure.from_dict(structure)
		features.append(self._featurize(structure, **kwargs))
		features.append(self._featurize(structure))
		except:
		logger.warning(
		"Failed to featurize datapoint %i. Appending empty array" % idx)
		@@ -387,8 +384,7 @@ class MaterialCompositionFeaturizer(Featurizer):

		def featurize(self,
		compositions: Iterable[str],
		log_every_n: int = 1000,
		**kwargs) -> np.ndarray:
		log_every_n: int = 1000) -> np.ndarray:
		"""Calculate features for crystal compositions.

		Parameters
		@@ -416,7 +412,7 @@ class MaterialCompositionFeaturizer(Featurizer):
		logger.info("Featurizing datapoint %i" % idx)
		try:
		c = Composition(composition)
		features.append(self._featurize(c, **kwargs))
		features.append(self._featurize(c))
		except:
		logger.warning(
		"Failed to featurize datapoint %i. Appending empty array" % idx)

deepchem/feat/molecule_featurizers/roberta_tokenizer.py

+6 −2

Original line number	Diff line number	Diff line
		@@ -36,10 +36,12 @@ class RobertaFeaturizer(RobertaTokenizerFast, MolecularFeaturizer):
		"""


		def __init__(self, **kwargs):
		#def __init__(self, **kwargs):
		def __init__(self, input_ids, attention_mask):
		super().__init__(**kwargs)
		return


		def _featurize(self, mol: RDKitMol, **kwargs) -> List[List[int]]:
		"""Calculate encoding using HuggingFace's RobertaTokenizerFast

		@@ -61,7 +63,9 @@ class RobertaFeaturizer(RobertaTokenizerFast, MolecularFeaturizer):
		smiles_string = Chem.MolToSmiles(mol)
		# the encoding is natively a dictionary with keys 'input_ids' and 'attention_mask'
		# -> make this a list of two lists to allow np to handle it
		encoding = list(self(smiles_string, **kwargs).values())
		# encoding = list(self(smiles_string, **kwargs).values())
		encoding = list(self(smiles_string, input_ids, attention_mask).values())

		return encoding

		def __call__(self, args, *kwargs) -> Dict[str, List[int]]:

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