def test_graph_conv_error_bars(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'GraphConv')
batch_size = 50
model = GraphConvTensorGraph(
len(tasks), batch_size=batch_size, mode='regression')
model.fit(dataset, nb_epoch=1)
mu, sigma = model.bayesian_predict(
dataset, transformers, untransform=True, n_passes=24)
assert mu.shape == (len(dataset), len(tasks))
assert sigma.shape == (len(dataset), len(tasks))
def test_graph_conv_atom_features(self):
tasks, dataset, transformers, metric = self.get_dataset('regression',
'Raw',
num_tasks=1)
atom_feature_name = 'feature'
y = []
for mol in dataset.X:
atom_features = []
for atom in mol.GetAtoms():
val = np.random.normal()
mol.SetProp(
"atom %08d %s" % (atom.GetIdx(), atom_feature_name),
str(val))
atom_features.append(np.random.normal())
y.append(np.sum(atom_features))
featurizer = ConvMolFeaturizer(atom_properties=[atom_feature_name])
X = featurizer.featurize(dataset.X)
dataset = deepchem.data.NumpyDataset(X, np.array(y))
batch_size = 50
model = GraphConvTensorGraph(
len(tasks),
number_atom_features=featurizer.feature_length(),
batch_size=batch_size,
mode='regression')
model.fit(dataset, nb_epoch=1)
y_pred1 = model.predict(dataset)
model.save()
model2 = TensorGraph.load_from_dir(model.model_dir)
y_pred2 = model2.predict(dataset)
self.assertTrue(np.all(y_pred1 == y_pred2))
def test_graph_conv_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'GraphConv')
batch_size = 50
model = GraphConvTensorGraph(
len(tasks), batch_size=batch_size, mode='regression')
model.fit(dataset, nb_epoch=1)
scores = model.evaluate(dataset, [metric], transformers)
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores = model.evaluate(dataset, [metric], transformers)
def test_change_loss_function(self):
tasks, dataset, transformers, metric = self.get_dataset('regression',
'GraphConv',
num_tasks=1)
batch_size = 50
model = GraphConvTensorGraph(len(tasks),
batch_size=batch_size,
mode='regression')
model.fit(dataset, nb_epoch=1)
model.save()
model2 = TensorGraph.load_from_dir(model.model_dir, restore=False)
dummy_label = model2.labels[-1]
dummy_ouput = model2.outputs[-1]
loss = ReduceSum(L2Loss(in_layers=[dummy_label, dummy_ouput]))
module = model2.create_submodel(loss=loss)
model2.restore()
model2.fit(dataset, nb_epoch=1, submodel=module)