Loading deepchem/models/graph_models.py +3 −2 Original line number Diff line number Diff line Loading @@ -657,8 +657,9 @@ class GraphConvModel(KerasModel): """Graph Convolutional Models. This class implements the graph convolutional model from the following paper [1]_. following paper [1]_. These graph convolutions start with a per-atom set of descriptors for each atom in a molecule, then combine and recombine these descriptors over convolutional layers. References ---------- Loading deepchem/utils/test/test_evaluate.py +18 −2 Original line number Diff line number Diff line Loading @@ -318,15 +318,31 @@ def test_gc_binary_classification(): assert multitask_scores["metric-1"] >= 0 def test_gc_binary_kappa_classification(): """Test multiclass classification evaluation.""" smiles = ["C", "CC"] featurizer = dc.feat.ConvMolFeaturizer() X = featurizer.featurize(smiles) y = np.random.randint(2, size=(len(smiles),)) dataset = dc.data.NumpyDataset(X, y) model = dc.models.GraphConvModel(1, mode="classification") # TODO: Fix this case with correct thresholding evaluator = Evaluator(model, dataset, []) multitask_scores = evaluator.compute_model_performance( dc.metrics.kappa_score, n_classes=2, threshold=True) assert len(multitask_scores) == 1 assert multitask_scores["metric-1"] >= 0 def test_gc_multiclass_classification(): """Test multiclass classification evaluation.""" np.random.seed(123) np.random.seed(1234) smiles = ["C", "CC"] featurizer = dc.feat.ConvMolFeaturizer() X = featurizer.featurize(smiles) y = np.random.randint(5, size=(len(smiles),)) dataset = dc.data.NumpyDataset(X, y) model = dc.models.GraphConvModel(1, mode="classification") model = dc.models.GraphConvModel(1, mode="classification", n_classes=5) # TODO: Fix this case with correct thresholding evaluator = Evaluator(model, dataset, []) multitask_scores = evaluator.compute_model_performance( Loading Loading
deepchem/models/graph_models.py +3 −2 Original line number Diff line number Diff line Loading @@ -657,8 +657,9 @@ class GraphConvModel(KerasModel): """Graph Convolutional Models. This class implements the graph convolutional model from the following paper [1]_. following paper [1]_. These graph convolutions start with a per-atom set of descriptors for each atom in a molecule, then combine and recombine these descriptors over convolutional layers. References ---------- Loading
deepchem/utils/test/test_evaluate.py +18 −2 Original line number Diff line number Diff line Loading @@ -318,15 +318,31 @@ def test_gc_binary_classification(): assert multitask_scores["metric-1"] >= 0 def test_gc_binary_kappa_classification(): """Test multiclass classification evaluation.""" smiles = ["C", "CC"] featurizer = dc.feat.ConvMolFeaturizer() X = featurizer.featurize(smiles) y = np.random.randint(2, size=(len(smiles),)) dataset = dc.data.NumpyDataset(X, y) model = dc.models.GraphConvModel(1, mode="classification") # TODO: Fix this case with correct thresholding evaluator = Evaluator(model, dataset, []) multitask_scores = evaluator.compute_model_performance( dc.metrics.kappa_score, n_classes=2, threshold=True) assert len(multitask_scores) == 1 assert multitask_scores["metric-1"] >= 0 def test_gc_multiclass_classification(): """Test multiclass classification evaluation.""" np.random.seed(123) np.random.seed(1234) smiles = ["C", "CC"] featurizer = dc.feat.ConvMolFeaturizer() X = featurizer.featurize(smiles) y = np.random.randint(5, size=(len(smiles),)) dataset = dc.data.NumpyDataset(X, y) model = dc.models.GraphConvModel(1, mode="classification") model = dc.models.GraphConvModel(1, mode="classification", n_classes=5) # TODO: Fix this case with correct thresholding evaluator = Evaluator(model, dataset, []) multitask_scores = evaluator.compute_model_performance( Loading
