Merge branch 'walid/pass_featurizer_kwargs' into walid/roberta-tokenizer (ceda8ccb) · Commits · 钟慕尧 / deepchem

deepchem/feat/base_classes.py

+12 −6

Original line number	Diff line number	Diff line
		@@ -23,10 +23,14 @@ class Featurizer(object):
		new datatype.
		"""

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

		`**kwargs` will get passed directly to `Featurizer._featurize`

		Parameters
		----------
		datapoints: Iterable[Any]
		@@ -47,7 +51,7 @@ class Featurizer(object):
		if i % log_every_n == 0:
		logger.info("Featurizing datapoint %i" % i)
		try:
		features.append(self._featurize(point))
		features.append(self._featurize(point, **kwargs))
		except:
		logger.warning(
		"Failed to featurize datapoint %d. Appending empty array")
		@@ -55,17 +59,19 @@ class Featurizer(object):

		return np.asarray(features)

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

		`**kwargs` will get passed directly to `Featurizer.featurize`

		Parameters
		----------
		datapoints: Iterable[Any]
		Any blob of data you like. Subclasss should instantiate this.
		"""
		return self.featurize(datapoints)
		return self.featurize(datapoints, **kwargs)

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

		Parameters