Merge pull request #2621 from arunppsg/mat_feat (bd910d32) · 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

+5 −8

Original line number	Diff line number	Diff line
		@@ -30,7 +30,7 @@ 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 +93,10 @@ 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,