def test_rep(self):
print("test rep")
path2 = "./orig_0.8_rep.txt"
s = similarity.Similarity(self.path1, path2).similar()
print('%.2f' % s)
# 对比判断测试是否正确
self.assertGreaterEqual(s, 0)
self.assertLessEqual(s, 1)
def district_similarity():
"""Compute district similarity matrix using census, NCES, and census district data.
OUTPUT: Similarity object
"""
census = get_census.all_states()
columns = [
"STNAME", "LATCOD", "LONCOD", "TOTALREV", "TFEDREV", "TSTREV",
"TLOCREV", "TOTALEXP", "TCURSSVC", "TCAPOUT", "HR1", "HE1", "HE2"
]
nces = get_nces.districts(columns=columns, nonneg=True)
ddf = pd.concat([census, nces.loc[census.index]], axis=1)
sim = similarity.Similarity(
ddf,
ref_columns=["District Name", "State", "STNAME", "LATCOD", "LONCOD"])
return sim
mid.load_mention_2_id()
print 'finish loading mention2id'
# training phase
# Step3: load questions
print 'begin to load questions'
query_list = query.QueryList()
query_list.read_query_file(gl.testing_data_split_file_name)
fh = codecs.open(gl.testing_data_result_file_name, 'w', encoding='utf-8')
fh_nomatch = codecs.open(gl.testing_data_not_match_result_file_name,
'w',
encoding='utf-8')
sim = similarity.Similarity()
for qid, qry in enumerate(query_list.query_list):
if qid % 100 == 0:
print 'Processed', qid, 'questions'
# print '||||'.join(qry.tokens)
# print 'entity:' + entity + 'len(entity):' + str(len(entity))
# print 'rest_token:', '----'.join(rest_token)
# input('Press a digit to continue\n')
qry_possible_id_dict = {}
for entity in qry.tokens:
possible_ids = mid.find_id_set(entity)
for pid in possible_ids:
# print 'possible id:(' + pid + ')' + str(len(pid))
if not qry.valid_pid(pid):
continue
try:
def __init__(self, sim_arr):
self.simdf = pd.read_csv("../input/compute_similarity.csv")
self.sim_arr = sim_arr
self.data_from_sim = similarity.Similarity(self.simdf)
def entity_recog_thread(threadName, threadNo):
for fidx in xrange(20):
if fidx % 20 != threadNo:
continue
folder_idx = 's_' + str("%04d" % fidx) + '/'
for x in ascii_lowercase:
question_file_name = gl.processed_data_split_file_folder + folder_idx + 'zhidao_xa' + x + '.process-data'
if not os.path.isfile(question_file_name):
continue
if not os.path.exists(gl.res_data_file_folder + folder_idx):
os.makedirs(gl.res_data_file_folder + folder_idx)
result_file_name = gl.res_data_file_folder + folder_idx + 'zhidao_xa' + x + '.res-data'
result_not_match_file_name = gl.res_data_file_folder + folder_idx + 'not_match.zhidao_xa' + x + '.res-data'
if os.path.isfile(result_file_name):
continue
query_list = query.QueryList()
query_list.read_query_file(question_file_name)
fh = codecs.open(result_file_name, 'w', encoding='utf-8')
fh_nomatch = codecs.open(result_not_match_file_name, 'w', encoding='utf-8')
sim = similarity.Similarity()
for qid, qry in enumerate(query_list.query_list):
if qid % 100 == 0:
print threadName, 'Processed', qid, 'questions'
# print '||||'.join(qry.tokens)
# print 'entity:' + entity + 'len(entity):' + str(len(entity))
# print 'rest_token:', '----'.join(rest_token)
# input('Press a digit to continue\n')
qry_possible_id_dict = {}
for entity in qry.tokens:
possible_ids = mid.find_id_set(entity)
for pid in possible_ids:
# print 'possible id:(' + pid + ')' + str(len(pid))
if not qry.valid_pid(pid):
continue
try:
qry_possible_id_dict[qid+1].append(pid)
except KeyError:
qry_possible_id_dict[qid+1] = [pid]
if (qid+1) not in qry_possible_id_dict:
# print 'Unfortunately,' + qry.query_origin + ' does not match any kb entity'
fh_nomatch.write('<question id=' + str(qid + 1) + '>\t')
fh_nomatch.write(qry.query_origin + '\n')
continue
# remove duplicate possible_ids because different entities might have the same possible_id
possible_ids = list(set(qry_possible_id_dict[qid+1]))
tokens = ''.join(qry.tokens)
scores = [(len(set(pid)), pid) for pid in possible_ids]
scores = sorted(scores, key=lambda s: -s[0])
# for item in scores:
# print 'Score for ' + item[1] + ':', item[0]
# raw_input('*****************\n')
if len(scores) == 0:
scores = [(0, tokens[0])]
total_answer_scores = []
for rank in xrange(min(len(scores), 30)):
pid = scores[rank][1]
# pid = scores[0][1]
try:
info = kb.knowledge_about[pid]
except KeyError:
# print 'Unfortunately,' + pid + 'not fount in the knowledge base'
# return something
fh.write('<question id=' + str(qid + 1) + '>\t')
fh.write(qry.query_origin + '\n')
fh.write('<answer id=' + str(qid + 1) + '>\t')
fh.write('[THIS-IS-AN-ANSWER.]\n')
print qid+1, '[THIS-IS-AN-ANSWER.]'
fh.write('==================================================\n')
continue
if len(info) == 0:
fh.write('---------------------------------------------\n')
fh.write('<subject id=' + str(qid + 1) + '-' + str(rank) + '>\t')
fh.write(pid + '\n')
# fh.write('---------------------------------------------\n')
possile_answers = []
for idx, obj in enumerate(info):
attr, entity2 = obj
if 1:
# if attr == 'BaiduCARD': # only extract BaiduCard relation
possile_answers.append({'pid': pid, 'answer': entity2, 'relation': attr})
if idx > 30:
break;
answer_scores = [(sim.similarity_customize_overlap(item['answer'], qry.answer), item) for item in possile_answers]
# print 'pid', pid
# for item in answer_scores:
# print 'Score for ' + item[1]['answer'] + ':', item[0]
answer_scores = sorted(answer_scores, key=lambda s: -s[0])
total_answer_scores = total_answer_scores + answer_scores
best_match, best_match_score = '[THIS-IS-AN-ANSWER.]', 0.0
if len(total_answer_scores) != 0:
best_match = total_answer_scores[0][1]['answer']
best_match_score = total_answer_scores[0][0]
fh.write('<question id=' + str(qid + 1) + '>\t')
fh.write(qry.query_origin + '\n')
if len(total_answer_scores) != 0 and best_match_score !=0:
for idx, candidate in enumerate(total_answer_scores):
fh.write('---------------------------------------------\n')
fh.write('<subject id=' + str(qid + 1) + '-' + str(idx) + '>\t')
fh.write(candidate[1]['pid'] + '\n')
fh.write('<relation id=' + str(qid + 1) + '-' + str(idx) + '>\t')
fh.write(candidate[1]['relation'] + '\n')
fh.write('<object id=' + str(qid + 1) + '-' + str(idx) + '>\t')
fh.write(candidate[1]['answer'] + '\n')
# fh.write('<best match subject id=' + str(qid + 1) + '>\t')
# fh.write(total_answer_scores[0][1]['pid'] + '\n')
# fh.write('<best match answer id=' + str(qid + 1) + '>\t')
# fh.write(best_match + '\n')
# fh.write('<best match score id=' + str(qid + 1) + '>\t')
# fh.write(str(best_match_score) + '\n')
else:
fh.write('---------------------------------------------\n')
fh.write('[NO-SUBJECT.]' + '\n')
# print qid+1, best_match[1]
fh.write('==================================================\n')
print 'closing file', result_file_name
fh.close()
fh_nomatch.close()