Merge pull request #2036 from ncfrey/material_molnet_loaders (e61c7df7) · Commits · 钟慕尧 / deepchem

deepchem/data/data_loader.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -629,7 +629,7 @@ class JsonLoader(DataLoader):
		if self.id_field:
		ids = shard[self.id_field].values
		else:
		ids = np.ones(len(X))
		ids = np.ones(len(valid_inds))
		ids = ids[valid_inds]

		if len(self.tasks) > 0:

deepchem/feat/materials_featurizers.py

+6 −2

Original line number	Diff line number	Diff line
		@@ -37,6 +37,10 @@ class ElementPropertyFingerprint(MaterialCompositionFeaturizer):

		.. [4] Pymatgen: Ong, S.P. et al. Comput. Mater. Sci. 68, 314-319 (2013).

		Notes
		-----
		`NaN` feature values are automatically converted to 0 by this featurizer.

		"""

		def __init__(self, data_source='matminer'):
		@@ -78,7 +82,7 @@ class ElementPropertyFingerprint(MaterialCompositionFeaturizer):
		except:
		feats = []

		return np.array(feats)
		return np.nan_to_num(np.array(feats))


		class SineCoulombMatrix(MaterialStructureFeaturizer):

deepchem/molnet/init.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -31,6 +31,8 @@ from deepchem.molnet.load_function.kinase_datasets import load_kinase
		from deepchem.molnet.load_function.thermosol_datasets import load_thermosol
		from deepchem.molnet.load_function.hppb_datasets import load_hppb
		from deepchem.molnet.load_function.chembl25_datasets import load_chembl25
		from deepchem.molnet.load_function.material_datasets.load_bandgap import load_bandgap
		from deepchem.molnet.load_function.material_datasets.load_perovskite import load_perovskite

		from deepchem.molnet.dnasim import simulate_motif_density_localization
		from deepchem.molnet.dnasim import simulate_motif_counting

deepchem/molnet/load_function/load_dataset_template.py

+11 −18

Original line number	Diff line number	Diff line
		@@ -29,7 +29,7 @@ DEFAULT_FEATURIZERS = {k: DEFAULT_FEATURIZERS[k] for k in mydataset_featurizers}
		DEFAULT_TRANSFORMERS = get_defaults("trans")

		# dict of accepted splitters
		DEFAULT_SPLITTERS = get_defaults("split")
		DEFAULT_SPLITTERS = get_defaults("splits")

		# names of supported splitters
		mydataset_splitters = ['Splitter1', 'Splitter2', 'Splitter3']
		@@ -38,15 +38,16 @@ DEFAULT_SPLITTERS = {k: DEFAULT_SPLITTERS[k] for k in mydataset_splitters}

		def load_mydataset(
		featurizer: Featurizer = DEFAULT_FEATURIZERS['RawFeaturizer'],
		transformers: Tuple[Transformer] = (
		DEFAULT_TRANSFORMERS['PowerTransformer']),
		transformers: List[Transformer] = [
		DEFAULT_TRANSFORMERS['PowerTransformer']
		],
		splitter: Splitter = DEFAULT_SPLITTERS['RandomSplitter'],
		reload: bool = True,
		data_dir: Optional[str] = None,
		save_dir: Optional[str] = None,
		featurizer_kwargs: Optional[Dict[str, object]] = None,
		splitter_kwargs: Optional[Dict[str, object]] = None,
		transformer_kwargs: Optional[Dict[str, Dict[str, object]]] = None,
		featurizer_kwargs: Dict[str, object] = {},
		splitter_kwargs: Dict[str, object] = {},
		transformer_kwargs: Dict[str, Dict[str, object]] = {},
		**kwargs) -> Tuple[List, Tuple, List]:
		"""Load mydataset.

		@@ -76,7 +77,7 @@ def load_mydataset(
		----------
		featurizer : {List of allowed featurizers for this dataset}
		A featurizer that inherits from deepchem.feat.Featurizer.
		transformers : Tuple{List of allowed transformers for this dataset}
		transformers : List{List of allowed transformers for this dataset}
		A transformer that inherits from deepchem.trans.Transformer.
		splitter : {List of allowed splitters for this dataset}
		A splitter that inherits from deepchem.splits.splitters.Splitter.
		@@ -153,9 +154,9 @@ def load_mydataset(
		featurizer = featurizer(**featurizer_kwargs)

		if isinstance(splitter, str):
		splitter = DEFAULT_SPLITTERS[splitter](**splitter_kwargs)
		splitter = DEFAULT_SPLITTERS[splitter]()
		elif issubclass(splitter, Splitter):
		splitter = splitter(**splitter_kwargs)
		splitter = splitter()

		# Reload from disk
		if reload:
		@@ -198,16 +199,8 @@ def load_mydataset(
		# Featurize dataset
		dataset = loader.create_dataset(dataset_file)

		# 80/10/10 train/val/test split is default
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)
		dataset, **splitter_kwargs)

		# Initialize transformers
		transformers = [

deepchem/molnet/load_function/material_datasets/init.py

0 → 100644

+0 −0

Empty file added.

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