Reformat test_gcn.py with yapf 0.22 (a94fa1d3) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_gcn.py

+43 −37

Original line number	Diff line number	Diff line
		@@ -22,12 +22,13 @@ except:
		def test_gcn_regression():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		tasks, dataset, transformers, metric = get_dataset('regression',
		featurizer=featurizer)
		tasks, dataset, transformers, metric = get_dataset(
		'regression', featurizer=featurizer)

		# initialize models
		n_tasks = len(tasks)
		model = GCNModel(mode='regression',
		model = GCNModel(
		mode='regression',
		n_tasks=n_tasks,
		number_atom_features=30,
		batch_size=10,
		@@ -43,7 +44,8 @@ def test_gcn_regression():

		tasks, all_dataset, transformers = load_delaney(featurizer=featurizer)
		train_set, _, _ = all_dataset
		model = dc.models.GCNModel(n_tasks=len(tasks),
		model = dc.models.GCNModel(
		n_tasks=len(tasks),
		graph_conv_layers=[2],
		residual=False,
		predictor_hidden_feats=2)
		@@ -55,12 +57,13 @@ def test_gcn_regression():
		def test_gcn_classification():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		tasks, dataset, transformers, metric = get_dataset('classification',
		featurizer=featurizer)
		tasks, dataset, transformers, metric = get_dataset(
		'classification', featurizer=featurizer)

		# initialize models
		n_tasks = len(tasks)
		model = GCNModel(mode='classification',
		model = GCNModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		batch_size=10,
		@@ -77,7 +80,8 @@ def test_gcn_classification():
		tasks, all_dataset, transformers = load_bace_classification(
		featurizer=featurizer)
		train_set, _, _ = all_dataset
		model = dc.models.GCNModel(mode='classification',
		model = dc.models.GCNModel(
		mode='classification',
		n_tasks=len(tasks),
		graph_conv_layers=[2],
		residual=False,
		@@ -90,13 +94,14 @@ def test_gcn_classification():
		def test_gcn_reload():
		# load datasets
		featurizer = MolGraphConvFeaturizer()
		tasks, dataset, transformers, metric = get_dataset('classification',
		featurizer=featurizer)
		tasks, dataset, transformers, metric = get_dataset(
		'classification', featurizer=featurizer)

		# initialize models
		n_tasks = len(tasks)
		model_dir = tempfile.mkdtemp()
		model = GCNModel(mode='classification',
		model = GCNModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		model_dir=model_dir,
		@@ -107,7 +112,8 @@ def test_gcn_reload():
		scores = model.evaluate(dataset, [metric], transformers)
		assert scores['mean-roc_auc_score'] >= 0.85

		reloaded_model = GCNModel(mode='classification',
		reloaded_model = GCNModel(
		mode='classification',
		n_tasks=n_tasks,
		number_atom_features=30,
		model_dir=model_dir,

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