def test_dag_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'GraphConv')
max_atoms = max([mol.get_num_atoms() for mol in dataset.X])
transformer = dc.trans.DAGTransformer(max_atoms=max_atoms)
dataset = transformer.transform(dataset)
model = DAGModel(
len(tasks), max_atoms=max_atoms, mode='classification', use_queue=False)
model.fit(dataset, nb_epoch=10)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean-roc_auc_score'],
scores2['mean-roc_auc_score'])
def test_dag_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'GraphConv')
max_atoms = max([mol.get_num_atoms() for mol in dataset.X])
transformer = dc.trans.DAGTransformer(max_atoms=max_atoms)
dataset = transformer.transform(dataset)
model = DAGModel(
len(tasks), max_atoms=max_atoms, mode='classification', use_queue=False)
model.fit(dataset, nb_epoch=10)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean-roc_auc_score'],
scores2['mean-roc_auc_score'])
def test_dag_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'GraphConv')
max_atoms = max([mol.get_num_atoms() for mol in dataset.X])
transformer = dc.trans.DAGTransformer(max_atoms=max_atoms)
dataset = transformer.transform(dataset)
model = DAGModel(len(tasks),
max_atoms=max_atoms,
mode='regression',
learning_rate=0.003,
use_queue=False)
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert all(s < 0.15 for s in scores['mean_absolute_error'])
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean_absolute_error'],
scores2['mean_absolute_error'])
def test_dag_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'GraphConv')
max_atoms = max([mol.get_num_atoms() for mol in dataset.X])
transformer = dc.trans.DAGTransformer(max_atoms=max_atoms)
dataset = transformer.transform(dataset)
model = DAGModel(
len(tasks),
max_atoms=max_atoms,
mode='regression',
learning_rate=0.003,
use_queue=False)
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert all(s < 0.15 for s in scores['mean_absolute_error'])
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean_absolute_error'],
scores2['mean_absolute_error'])
model = DAGModel(
n_tasks=n_tasks,
max_atoms=max_atoms,
n_atom_feat=n_atom_feat,
n_graph_feat=n_graph_feat,
mode="regression",
batch_size=batch_size,
learning_rate=1e-3,
use_queue=False,
model_dir="/home/rod/Dropbox/Quimica/Analysis/ANalisis/Borradores/DAGModel/"
) #To prevent overfitting
# Fit trained model
print("Fitting model")
model.fit(train_dataset, nb_epoch=nb_epoch)
model.save()
print("Evaluating model")
train_scores = model.evaluate(train_dataset, [metric])
valid_scores = model.evaluate(valid_dataset, [metric])
print("Train scores")
print(train_scores)
print("Validation scores")
print(valid_scores)
"""
#Check model predictions
from rdkit import Chem
smiles = ['COC(C)(C)CCCC(C)CC=CC(C)=CC(=O)OC(C)C',
'CCOC(=O)CC',
'CSc1nc(NC(C)C)nc(NC(C)C)n1',