def test_smiles_to_vec_regression(self):
dataset, metric = self.get_dataset(mode="regression",
featurizer="smiles2seq")
model = Smiles2Vec(char_to_idx=self.char_to_idx,
max_seq_len=self.max_seq_len,
use_conv=True,
n_tasks=self.n_tasks,
model_dir=None,
mode="regression")
model.fit(dataset, nb_epoch=500)
scores = model.evaluate(dataset, [metric], [])
assert all(s < 0.1 for s in scores['mean_absolute_error'])
def test_smiles_to_vec_classification(self):
dataset, metric = self.get_dataset(mode="classification",
featurizer="smiles2seq")
model = Smiles2Vec(char_to_idx=self.char_to_idx,
max_seq_len=self.max_seq_len,
use_conv=True,
n_tasks=self.n_tasks,
model_dir=None,
mode="classification")
model.fit(dataset, nb_epoch=500)
scores = model.evaluate(dataset, [metric], [])
assert scores['mean-roc_auc_score'] >= 0.9
def test_smiles_to_vec_regression():
n_tasks = 5
max_seq_len = 20
dataset, metric, char_to_idx = get_dataset(mode="regression",
featurizer="smiles2seq",
n_tasks=n_tasks,
max_seq_len=max_seq_len)
model = Smiles2Vec(char_to_idx=char_to_idx,
max_seq_len=max_seq_len,
use_conv=True,
n_tasks=n_tasks,
model_dir=None,
mode="regression")
model.fit(dataset, nb_epoch=500)
scores = model.evaluate(dataset, [metric], [])
assert scores['mean_absolute_error'] < 0.1