def update(weights, predicts, sentence, edge_feats):
for i, word in enumerate(sentence):
h_predict = predicts[i]
h_gold = word.head
if h_predict != h_gold:
for fidx in edge_feats.get((h_predict, i), []):
weights[fidx] = weights[fidx] - 1
for fidx in edge_feats.get((h_gold, i), []):
weights[fidx] = weights[fidx] + 1
return weights
def main(args):
if len(args) < 2:
Log.w("Arguments Error")
return
path = args[0]
iteration = int(args[1])
Log.i("Training file: %s" % path)
Log.i("----------------")
sentences = c.readconllfile(path)
featdict, weights = train(sentences, iteration)
c.save(featdict, weights)
if __name__ == "__main__":
Log.setConfig(loglevel=Log.DEBUG, verbose=True)
main(c.readargs())
Log.finalize()
total = total + 1
result.append(line)
results.append(result)
return results, float(correct / total)
def main(args):
if len(args) < 2:
Log.w("Arguments Error")
return
path = args[0]
mfile = args[1]
Log.i("Training file: %s" % path)
Log.i("----------------")
sentences = c.readconllfile(path)
Log.i("Model file: %s" % mfile)
Log.i("----------------")
featdict, weights = c.load(mfile)
output, score = parse(sentences, weights)
for each in output:
for token in each:
print(token)
print()
Log.i("[DONE] accuracy: {:.2%}".format(score))
if __name__ == "__main__":
Log.setConfig(loglevel=Log.DEBUG, verbose=True)
main(c.readargs())
Log.finalize()
