def main(trained_model,
test_file,
viterbi,
output_tags="output.tag",
output_predictions="output.pred"):
test_data, identifier = load_data(testing_file)
evaluate = True
# extract features
if not "crf" in trained_model:
if not isinstance(trained_model, list):
with open(trained_model, 'rb') as frb:
clf, previous_n, next_n, word_vocab, other_features = pickle.load(
frb)
else:
clf, previous_n, next_n, word_vocab, other_features = trained_model
tic = time.clock()
with open(output_tags, 'w') as fw:
confidences = []
for i in range(len(test_data) + len(identifier)):
if i % 2 == 1:
if "crf" in trained_model:
y, tmp_conf = crf.predict(test_data[i / 2][0],
trained_model)
fw.write(" ".join([
test_data[i / 2][0][j] + "_" + y[j]
for j in range(len(test_data[i / 2][0]))
]))
else:
y, tmp_conf = predict_tags_n(viterbi, previous_n, next_n,
clf, test_data[i / 2][0],
word_vocab, other_features)
fw.write(" ".join([
test_data[i / 2][0][j] + "_" + int2tags[int(y[j])]
for j in range(len(test_data[i / 2][0]))
]))
assert (len(y) == len(tmp_conf))
confidences.append(tmp_conf)
fw.write("\n")
else:
fw.write(identifier[i / 2])
fw.write("\n")
print(time.clock() - tic)
if evaluate:
eval_mode_batch(output_tags, confidences, helper.cities)
else:
predict_mode_batch(output_tags, output_predictions, helper.cityies)
def main(trained_model,testing_file,viterbi,output_tags="output.tag", output_predictions="output.pred"):
test_data, identifier = load_data(testing_file)
evaluate = True
## extract features
if not "crf" in trained_model:
if not isinstance(trained_model, list):
clf, previous_n, next_n, word_vocab,other_features = pickle.load( open( trained_model, "rb" ) )
else:
clf, previous_n, next_n, word_vocab,other_features = trained_model
tic = time.clock()
f = open(output_tags,'w')
confidences = []
for i in range(len(test_data)+len(identifier)):
if i%2 == 1:
if "crf" in trained_model:
y, tmp_conf = crf.predict(test_data[i/2][0], trained_model)
f.write(" ".join([test_data[i/2][0][j]+"_"+y[j] for j in range(len(test_data[i/2][0]))]))
else:
y, tmp_conf = predict_tags_n(viterbi, previous_n,next_n, clf, test_data[i/2][0], word_vocab,other_features)
f.write(" ".join([test_data[i/2][0][j]+"_"+int2tags[int(y[j])] for j in range(len(test_data[i/2][0]))]))
assert(len(y) == len(tmp_conf))
confidences.append(tmp_conf)
f.write("\n")
else:
f.write(identifier[i/2])
f.write("\n")
#print time.clock()-tic
f.close()
if evaluate:
eval_mode_batch(output_tags, confidences, helper.cities)
else:
predict_mode_batch(output_tags, output_predictions, helper.cities)
return
def predictCRF(trained_model, words, cities):
tags, confidences = crf.predict(words, trained_model)
pred, conf_scores, conf_cnts = predict_mode(words, tags, confidences,
cities, True)
return pred, conf_scores, conf_cnts
def predictCRF(trained_model, words, cities):
tags, confidences = crf.predict(words, trained_model)
pred, conf_scores, conf_cnts = predict_mode(words, tags, confidences, cities, True)
return pred, conf_scores, conf_cnts