Loading deepchem/models/tests/test_attentivefp.py +27 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,19 @@ def test_attentivefp_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = AttentiveFPModel( mode='regression', n_tasks=len(tasks), num_layers=1, num_timesteps=1, graph_feat_size=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -56,6 +69,20 @@ def test_attentivefp_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = AttentiveFPModel( mode='classification', n_tasks=len(tasks), num_layers=1, num_timesteps=1, graph_feat_size=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading deepchem/models/tests/test_gat.py +29 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,20 @@ def test_gat_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GATModel( mode='regression', n_tasks=len(tasks), graph_attention_layers=[2], n_attention_heads=1, residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -62,6 +76,21 @@ def test_gat_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GATModel( mode='classification', n_tasks=len(tasks), graph_attention_layers=[2], n_attention_heads=1, residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading deepchem/models/tests/test_gcn.py +26 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,18 @@ def test_gcn_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GCNModel( n_tasks=len(tasks), graph_conv_layers=[2], residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -62,6 +74,20 @@ def test_gcn_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GCNModel( mode='classification', n_tasks=len(tasks), graph_conv_layers=[2], residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading deepchem/models/tests/test_mpnn.py +31 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,21 @@ def test_mpnn_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = MPNNModel( mode='regression', n_tasks=len(tasks), node_out_feats=2, edge_hidden_feats=2, num_step_message_passing=1, num_step_set2set=1, num_layer_set2set=1) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -56,6 +71,22 @@ def test_mpnn_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = MPNNModel( mode='classification', n_tasks=len(tasks), node_out_feats=2, edge_hidden_feats=2, num_step_message_passing=1, num_step_set2set=1, num_layer_set2set=1) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading Loading
deepchem/models/tests/test_attentivefp.py +27 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,19 @@ def test_attentivefp_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = AttentiveFPModel( mode='regression', n_tasks=len(tasks), num_layers=1, num_timesteps=1, graph_feat_size=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -56,6 +69,20 @@ def test_attentivefp_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = AttentiveFPModel( mode='classification', n_tasks=len(tasks), num_layers=1, num_timesteps=1, graph_feat_size=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading
deepchem/models/tests/test_gat.py +29 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,20 @@ def test_gat_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GATModel( mode='regression', n_tasks=len(tasks), graph_attention_layers=[2], n_attention_heads=1, residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -62,6 +76,21 @@ def test_gat_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GATModel( mode='classification', n_tasks=len(tasks), graph_attention_layers=[2], n_attention_heads=1, residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading
deepchem/models/tests/test_gcn.py +26 −0 Original line number Diff line number Diff line Loading @@ -39,6 +39,18 @@ def test_gcn_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GCNModel( n_tasks=len(tasks), graph_conv_layers=[2], residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -62,6 +74,20 @@ def test_gcn_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = dc.models.GCNModel( mode='classification', n_tasks=len(tasks), graph_conv_layers=[2], residual=False, predictor_hidden_feats=2) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading
deepchem/models/tests/test_mpnn.py +31 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,21 @@ def test_mpnn_regression(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean_absolute_error'] < 0.5 # test on a small MoleculeNet dataset from deepchem.molnet import load_delaney tasks, all_dataset, transformers = load_delaney(featurizer=featurizer) train_set, _, _ = all_dataset model = MPNNModel( mode='regression', n_tasks=len(tasks), node_out_feats=2, edge_hidden_feats=2, num_step_message_passing=1, num_step_set2set=1, num_layer_set2set=1) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading @@ -56,6 +71,22 @@ def test_mpnn_classification(): scores = model.evaluate(dataset, [metric], transformers) assert scores['mean-roc_auc_score'] >= 0.85 # test on a small MoleculeNet dataset from deepchem.molnet import load_bace_classification tasks, all_dataset, transformers = load_bace_classification( featurizer=featurizer) train_set, _, _ = all_dataset model = MPNNModel( mode='classification', n_tasks=len(tasks), node_out_feats=2, edge_hidden_feats=2, num_step_message_passing=1, num_step_set2set=1, num_layer_set2set=1) model.fit(train_set, nb_epoch=1) @unittest.skipIf(not has_torch_and_dgl, 'PyTorch, DGL, or DGL-LifeSci are not installed') Loading
