changes default featurizer and fixed examples (bdebd383) · Commits · 钟慕尧 / deepchem

deepchem/molnet/load_function/material_datasets/load_perovskite.py

+11 −15

Original line number	Diff line number	Diff line
		@@ -18,11 +18,10 @@ class _PerovskiteLoader(_MolnetLoader):
		targz_file = os.path.join(self.data_dir, 'perovskite.tar.gz')
		if not os.path.exists(dataset_file):
		if not os.path.exists(targz_file):
		dc.utils.data_utils.download_url(
		url=PEROVSKITE_URL, dest_dir=self.data_dir)
		dc.utils.data_utils.download_url(url=PEROVSKITE_URL,
		dest_dir=self.data_dir)
		dc.utils.data_utils.untargz_file(targz_file, self.data_dir)
		loader = dc.data.JsonLoader(
		tasks=self.tasks,
		loader = dc.data.JsonLoader(tasks=self.tasks,
		feature_field="structure",
		label_field="formation_energy",
		featurizer=self.featurizer)
		@@ -30,7 +29,7 @@ class _PerovskiteLoader(_MolnetLoader):


		def load_perovskite(
		featurizer: Union[dc.feat.Featurizer, str] = dc.feat.SineCoulombMatrix(),
		featurizer: Union[dc.feat.Featurizer, str] = dc.feat.CGCNNFeaturizer(),
		splitter: Union[dc.splits.Splitter, str, None] = 'random',
		transformers: List[Union[TransformerGenerator, str]] = ['normalization'],
		reload: bool = True,
		@@ -93,13 +92,10 @@ def load_perovskite(

		Examples
		--------
		>>>
		>> import deepchem as dc
		>> tasks, datasets, transformers = dc.molnet.load_perovskite()
		>> train_dataset, val_dataset, test_dataset = datasets
		>> n_tasks = len(tasks)
		>> n_features = train_dataset.get_data_shape()[0]
		>> model = dc.models.MultitaskRegressor(n_tasks, n_features)
		>>> import deepchem as dc
		>>> tasks, datasets, transformers = dc.molnet.load_perovskite()
		>>> train_dataset, val_dataset, test_dataset = datasets
		>>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001)

		"""
		loader = _PerovskiteLoader(featurizer, splitter, transformers,

Admin message