def test_dag_regression_uncertainty():
import tensorflow as tf
np.random.seed(1234)
tf.random.set_seed(1234)
tasks, dataset, transformers, metric = get_dataset('regression',
'GraphConv')
batch_size = 10
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,
batch_size=batch_size,
use_queue=False,
dropout=0.05,
uncertainty=True)
model.fit(dataset, nb_epoch=750)
# Predict the output and uncertainty.
pred, std = model.predict_uncertainty(dataset)
mean_error = np.mean(np.abs(dataset.y - pred))
mean_value = np.mean(np.abs(dataset.y))
mean_std = np.mean(std)
# The DAG models have high error with dropout
# Despite a lot of effort tweaking it , there appears to be
# a limit to how low the error can go with dropout.
#assert mean_error < 0.5 * mean_value
assert mean_error < .7 * mean_value
assert mean_std > 0.5 * mean_error
assert mean_std < mean_value
def test_dag_regression_uncertainty(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=0.002,
use_queue=False,
dropout=0.1,
uncertainty=True)
model.fit(dataset, nb_epoch=100)
# Predict the output and uncertainty.
pred, std = model.predict_uncertainty(dataset)
mean_error = np.mean(np.abs(dataset.y - pred))
mean_value = np.mean(np.abs(dataset.y))
mean_std = np.mean(std)
assert mean_error < 0.5 * mean_value
assert mean_std > 0.5 * mean_error
assert mean_std < mean_value
def test_dag_regression_uncertainty(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=0.002,
use_queue=False,
dropout=0.1,
uncertainty=True)
model.fit(dataset, nb_epoch=100)
# Predict the output and uncertainty.
pred, std = model.predict_uncertainty(dataset)
mean_error = np.mean(np.abs(dataset.y - pred))
mean_value = np.mean(np.abs(dataset.y))
mean_std = np.mean(std)
assert mean_error < 0.5 * mean_value
assert mean_std > 0.5 * mean_error
assert mean_std < mean_value
def test_dag_model():
tasks, dataset, transformers, metric = 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',
learning_rate=0.001)
model.fit(dataset, nb_epoch=30)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
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'])
def test_dag_regression_model():
import tensorflow as tf
np.random.seed(1234)
tf.random.set_seed(1234)
tasks, dataset, transformers, metric = 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)
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.15
def test_dag_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'GraphConv')
batch_size = 10
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',
learning_rate=0.03,
batch_size=batch_size,
use_queue=False)
model.fit(dataset, nb_epoch=40)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
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'])
# Fit
metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)
# Do setup required for tf/keras models
n_tasks = 1 #Only solubility to predict
n_atom_feat = 75
batch_size = 64
n_graph_feat = 30
nb_epoch = 10
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")
