Update (97d7f88d) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_gat.py

+56 −51

Original line number	Diff line number	Diff line
		@@ -9,15 +9,16 @@ from deepchem.models import GATModel
		from deepchem.models.tests.test_graph_models import get_dataset

		try:
		import torch # noqa
		import torch_geometric # noqa
		has_pytorch_and_pyg = True
		import dgl
		import dgllife
		import torch
		has_torch_and_dgl = True
		except:
		has_pytorch_and_pyg = False
		has_torch_and_dgl = False


		@unittest.skipIf(not has_pytorch_and_pyg,
		'PyTorch and PyTorch Geometric are not installed')
		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gat_regression():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		@@ -26,17 +27,20 @@ def test_gat_regression():

		# initialize models
		n_tasks = len(tasks)
		model = GATModel(mode='regression', n_tasks=n_tasks, batch_size=10)
		model = GATModel(
		mode='regression',
		n_tasks=n_tasks,
		number_atom_features=30,
		batch_size=10)

		# overfit test
		# GAT's convergence is a little slow
		model.fit(dataset, nb_epoch=300)
		model.fit(dataset, nb_epoch=100)
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean_absolute_error'] < 0.75
		assert scores['mean_absolute_error'] < 0.5


		@unittest.skipIf(not has_pytorch_and_pyg,
		'PyTorch and PyTorch Geometric are not installed')
		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gat_classification():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		@@ -48,18 +52,17 @@ def test_gat_classification():
		model = GATModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		batch_size=10,
		learning_rate=0.001)

		# overfit test
		# GAT's convergence is a little slow
		model.fit(dataset, nb_epoch=150)
		model.fit(dataset, nb_epoch=50)
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean-roc_auc_score'] >= 0.70

		assert scores['mean-roc_auc_score'] >= 0.85

		@unittest.skipIf(not has_pytorch_and_pyg,
		'PyTorch and PyTorch Geometric are not installed')
		@unittest.skipIf(not has_torch_and_dgl,
		'PyTorch, DGL, or DGL-LifeSci are not installed')
		def test_gat_reload():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		@@ -72,17 +75,19 @@ def test_gat_reload():
		model = GATModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		model_dir=model_dir,
		batch_size=10,
		learning_rate=0.001)

		model.fit(dataset, nb_epoch=150)
		model.fit(dataset, nb_epoch=50)
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean-roc_auc_score'] >= 0.70
		assert scores['mean-roc_auc_score'] >= 0.85

		reloaded_model = GATModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		model_dir=model_dir,
		batch_size=10,
		learning_rate=0.001)

deepchem/models/torch_models/gat.py

+324 −209

File changed.

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deepchem/models/torch_models/gcn.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -302,6 +302,7 @@ class GCNModel(TorchModel):
		This can include any keyword argument of TorchModel.
		"""
		model = GCN(
		n_tasks=n_tasks,
		graph_conv_layers=graph_conv_layers,
		activation=activation,
		residual=residual,
		@@ -309,7 +310,6 @@ class GCNModel(TorchModel):
		dropout=dropout,
		predictor_hidden_feats=predictor_hidden_feats,
		predictor_dropout=predictor_dropout,
		n_tasks=n_tasks,
		mode=mode,
		number_atom_features=number_atom_features,
		n_classes=n_classes,

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