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