#law = train_SVC(vec, law_label)
print('time SVC')
sys.stdout.flush()
#time = train_SVC(vec, time_label)
#test
print('predict')
sys.stdout.flush()
predictor = PredictorLocal(tfidf, accu, law, time)
test_label, test_predict = predictor.predict_file(test_filename)
#metrics
judge = Judger("../baseline/accu.txt", "../baseline/law.txt")
result = judge.test2(test_label, test_predict)
print(result)
rst = judge.get_score(result)
print(rst)
rstr = "ACCU:(%.4f, %.4f, %.4f); LAW:(%.4f, %.4f, %.4f) TIME: %.4f"% \
(rst[0][0], rst[0][1], rst[0][2], rst[1][0], rst[1][1], rst[1][2], rst[2])
sinfo = 'Prog:%s TrainFile:%s Seg:%s DIM:%s NGRAM:%d RESULT: %s' % (
sys.argv[0], train_fname, seg_method, dim, ngram, rstr)
logger.info(sinfo)
print('begin test model:')
print('saving model')
joblib.dump(tfidf, 'predictor/model/tfidf.model')
joblib.dump(accu, 'predictor/model/accu.model')
joblib.dump(law, 'predictor/model/law.model')
joblib.dump(time, 'predictor/model/time.model')
class Evaluator(object):
def __init__(self, predictor, input_path='./input', output='./out'):
self.predictor = predictor
self.input_path = input_path
self.output_path = output
self.judger = Judger('./data/accu.txt', './data/law.txt')
self.cnt = 0
def format_result(self, result):
rex = {"accusation": [], "articles": [], "imprisonment": -3}
res_acc = []
for x in result["accusation"]:
if not (x is None):
res_acc.append(int(x))
rex["accusation"] = res_acc
if not (result["imprisonment"] is None):
rex["imprisonment"] = int(result["imprisonment"])
else:
rex["imprisonment"] = -3
res_art = []
for x in result["articles"]:
if not (x is None):
res_art.append(int(x))
rex["articles"] = res_art
return rex
def get_batch(self):
v = self.predictor.batch_size
if not (type(v) is int) or v <= 0:
raise NotImplementedError
return v
def solve(self, fact):
result = self.predictor.predict(fact)
for a in range(0, len(result)):
result[a] = self.format_result(result[a])
return result
def output_result(self, file_name):
inf = open(os.path.join(self.input_path, file_name), "r")
ouf = open(os.path.join(self.output_path, file_name), "w")
fact = []
for line in inf:
fact.append(json.loads(line)["fact"])
if len(fact) == self.get_batch():
result = self.solve(fact)
self.cnt += len(result)
for x in result:
print(json.dumps(x), file=ouf)
fact = []
if len(fact) != 0:
result = self.solve(fact)
self.cnt += len(result)
for x in result:
print(json.dumps(x), file=ouf)
fact = []
ouf.close()
def scoring(self, file_name):
result = self.judger.test(self.input_path, self.output_path, file_name)
return self.judger.get_score(result)
