def retrain(self, n, keep_top_n, smiles_and_scores):
print("writing dataset...")
name = 'molexit-%d' % n
dataset = '../models/molexit/%s.txt' % name
dataset_scores = []
with open(dataset, 'w') as f:
for smi, score in list(
reversed(sorted(smiles_and_scores,
key=lambda p: p[1])))[:keep_top_n]:
dsmi = self.converter.encode(
pybel.readstring("smi", smi.strip()).write("can").strip())
tok = DeepSMILESTokenizer(dsmi)
tokens = tok.get_tokens()
f.write(' '.join([t.value for t in tokens]))
f.write("\n")
dataset_scores.append(score)
print('dataset: size: %s, mean score: %s, max score: %s' %
(len(dataset_scores), np.mean(dataset_scores),
np.max(dataset_scores)))
print('training new LM...')
self.lm_trainer.train(10, dataset, '../models/molexit', name)
vocab = get_arpa_vocab('../models/molexit/%s.arpa' % name)
self.lm = KenLMDeepSMILESLanguageModel(
'../models/molexit/%s.klm' % name, vocab)
def eval_function(text):
generated = ''.join(text)
try:
decoded = DeepSMILESLanguageModelUtils.decode(generated,
start='<s>',
end='</s>')
DeepSMILESLanguageModelUtils.sanitize(decoded)
except Exception:
return 0
extracted = DeepSMILESLanguageModelUtils.extract(generated,
start='<s>',
end='</s>')
tokenized = DeepSMILESTokenizer(extracted)
score = len(tokenized.get_tokens()) / (
text_length - 1) # provide more reward for longer text sequences
logger.info("%s, %s" % (generated, str(score)))
return score
best = mcts.get_best_sequence()
generated_text = ''.join(best[0])
logger.info("best generated text: %s" % generated_text)
decoded = DeepSMILESLanguageModelUtils.decode(generated_text,
start='<s>',
end='</s>')
smiles = DeepSMILESLanguageModelUtils.sanitize(decoded)
logger.info("best SMILES: %s, J: %s (%s seconds)" %
(smiles, scorer.score(smiles), str((end - start))))
log_top_best(all_smiles, 5, logger)
logger.info("writing dataset...")
name = 'molexit-%d' % n
dataset = '../models/molexit/%s.txt' % name
with open(dataset, 'w') as f:
for smi in list(
reversed(sorted(all_smiles.items(),
key=lambda kv: kv[1][0])))[:keep_top_n]:
dsmi = smiles_to_deepsmiles(smi[0].strip())
tok = DeepSMILESTokenizer(dsmi)
tokens = tok.get_tokens()
f.write(' '.join([t.value for t in tokens]))
f.write("\n")
logger.info('training new LM...')
lm_trainer.train(10, dataset, '../models/molexit', name)
vocab = get_arpa_vocab('../models/molexit/%s.arpa' % name)
lm = KenLMDeepSMILESLanguageModel('../models/molexit/%s.klm' % name, vocab)