fixed yapf conventions (8751d999) · Commits · 钟慕尧 / deepchem

deepchem/models/torch_models/cgcnn.py

+8 −6

Original line number	Diff line number	Diff line
		@@ -253,12 +253,14 @@ class CGCNNModel(TorchModel):
		Here is a simple example of code that uses the CGCNNModel with
		materials dataset.

		>> import deepchem as dc
		>> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer, "transformers": []}
		>> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config)
		>> train, valid, test = datasets
		>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001)
		>> model.fit(train, nb_epoch=50)
		Examples
		--------
		>>> import deepchem as dc
		>>> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer(), "transformers": []}
		>>> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config)
		>>> train, valid, test = datasets
		>>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001)
		>>> avg_loss = model.fit(train, nb_epoch=50)

		This model takes arbitary crystal structures as an input, and predict material properties
		using the element information and connection of atoms in the crystal. If you want to get

+7 −6

Original line number	Diff line number	Diff line
		@@ -18,10 +18,11 @@ 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)