def test_attentivefp_classification():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset(
'classification', featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(
mode='classification',
n_tasks=n_tasks,
batch_size=10,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
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)
def test_attentivefp_regression():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset(
'regression', featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=100)
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)
def test_attentivefp_regression():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='regression', n_tasks=n_tasks, batch_size=10)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.5
def test_attentivefp_classification():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = AttentiveFPModel(mode='classification',
n_tasks=n_tasks,
batch_size=10,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
def test_attentivefp_reload():
# load datasets
featurizer = MolGraphConvFeaturizer(use_edges=True)
tasks, dataset, transformers, metric = get_dataset('classification',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model_dir = tempfile.mkdtemp()
model = AttentiveFPModel(mode='classification',
n_tasks=n_tasks,
model_dir=model_dir,
batch_size=10,
learning_rate=0.001)
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.85
reloaded_model = AttentiveFPModel(mode='classification',
n_tasks=n_tasks,
model_dir=model_dir,
batch_size=10,
learning_rate=0.001)
reloaded_model.restore()
pred_mols = ["CCCC", "CCCCCO", "CCCCC"]
X_pred = featurizer(pred_mols)
random_dataset = dc.data.NumpyDataset(X_pred)
original_pred = model.predict(random_dataset)
reload_pred = reloaded_model.predict(random_dataset)
assert np.all(original_pred == reload_pred)