pred = PredictorLocal(tfidf, law, accu, time)
all_test_predicts = []
all_test_metas = []
test_f = open(test_filename)
for line in test_f:
js = json.loads(line)
text = js["fact"]
meta = js["meta"]
ans = pred.predict(text)
all_test_predicts.append(ans)
all_test_metas.append(meta)
#metrics
judge = Judger("../baseline/accu.txt", "../baseline/law.txt")
result = judge.test2(all_test_metas, all_test_predicts)
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 = 'Seg:%s DIM:%s NGRAM:%d RESULT: %s' % (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')
print('law SVC')
sys.stdout.flush()
#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')