def generate_lookup_table():
db_driver = idb.ISSuedb()
output = db_driver.db_retrieve(
"select name from sqlite_master where type='table' order by name;")
table_dict = {i[0].replace("$", "_"): i[0] for i in output}
file_list = os.listdir(SRC_DIR)
file_list = [os.path.join(SRC_DIR, f) for f in file_list]
file_list_test = os.listdir(TEST_DIR)
file_list_test = [os.path.join(TEST_DIR, f) for f in file_list_test]
files = file_list + file_list_test
files_dict = {i: False for i in files}
reload = util.Reload(TSV_FILE)
for item in table_dict:
flag = False
for f in files:
if item in f:
flag = True
print("{}\t{}".format(table_dict[item], f))
files_dict[f] = True
break
if not flag:
print("{}\tNULL".format(table_dict[item]))
for f in files_dict:
if not files_dict[f]:
print("NULL\t{}".format(f))
db_driver.db_close()
def query_issue(scan_output, max_depth=4):
"""
根据 已经排序过的app相似度降序列表 scan_output 搜索所有可能 issue
~1分钟得出结果
:param scan_output: 格式参考 descript（）函数的输出
:param max_depth: 限制搜索深度，取最相似的前几个
:return: 所有查询
"""
# TODO 查询的 key 哪里出来的？
logger = logging.getLogger("StreamLogger")
rdb = issuedb.ISSuedb()
sql = """select issue_num, comments, state, title, body, commit_id, labels from {}
order by length(body) desc"""
overall_table = {}
# 所有相关app和item
for i in range(min(len(scan_output), max_depth)):
one_dict = {}
app = scan_output[i][0]
one_dict['sim'] = scan_output[i][1]
tab_name = table2tsv.file2table(app)
one_dict['data'] = []
one_dict['keys'] = []
score_list = scan_output[i][2]
keys_sea = _filter_search_keys(score_list, threshold=0.7)
logger.debug(f"{app}\t{tab_name}\tsimilar keys length: {len(keys_sea)}")
output = rdb.db_retrieve(sql.format(tab_name))
head = ["issue_num", "comments", "state", "title", "body", "commit_id", "labels"]
f_output = issuedb.retrieve_formatter(head, output)
title_list = util.get_col(output, head.index('title'))
body_list = util.get_col(output, head.index('body'))
label_list = util.get_col(output, head.index('labels'))
reply_list = util.get_col(output, head.index('issue_num'))
pre_calc_val = _pre_calc(title_list=title_list,
body_list=body_list,
label_list=label_list,
reply_list=reply_list,
keys_sea=keys_sea)
for k in keys_sea:
keys = []
for i in k:
keys.append(" ".join(i))
keys = " ".join(keys)
ess_keys = nlp_util.stem_sentence(keys)
tmp = search_rank.sort_candidate_seq(f_output, ess_keys, pre_calc_val)
leng = min(3, len(tmp))
one_dict['keys'].extend([ess_keys] * leng)
one_dict['data'].extend(tmp[:leng])
overall_table[tab_name] = one_dict
logger.debug(pp.pformat(overall_table))
logger.debug("#" * 50)
return overall_table
def rank_review(app_score_list: list, max_depth=4) -> list:
hot_keywords, two_keywords = get_keywords()
rdb = issuedb.ISSuedb()
all_review = []
# number = [5000, 10000, 15000, 20000]
# number = [1000, 2000, 3000, 4000]
for m in range(min(len(app_score_list), max_depth)):
score_list = app_score_list[m][2]
app_weight = app_score_list[m][1]
keys_sea = _filter_search_keys(score_list, threshold=0.7)
ess_keys = set()
for r in keys_sea:
for a_list in r:
ess_keys = ess_keys.union(a_list)
ess_keys = " ".join(list(ess_keys))
ess_keys = nlp_util.stem_sentence(ess_keys)
ess_keys = set(ess_keys)
app = app_score_list[m][0]
app_name = os.path.basename(app)[:-4]
score = {
'star_num': 0,
'hot_key_words': 0,
'helpful_num': 0,
'ui_key': 0,
'similar_app': 0, # app相似度
'two_gram_keywords': 0,
}
sql = """select review_id,content,star_num,helpful_num from {} order by length(content) desc"""
tab_name = table2tsv.file2table(app) # csv -> 数据库名字
output = rdb.db_retrieve(sql.format(tab_name)) # sql查询结果
# head = ["review_id", "content", "bold", "star_num", "helpful_num", "reply_content"]
head = ["review_id", "content", "star_num", "helpful_num"]
f_output = issuedb.retrieve_formatter(head, output)
# f_output[0].review_id
for i in f_output:
if len(i.content) < 100:
break
processed_content = nlp_process(i.content) # 没有移除数字
score_sum = 0
score['star_num'] = star_score[i.star_num]
score['hot_key_words'] = keywords_in_content(hot_keywords, processed_content, False) * app_weight # 关键词计分
score['ui_key_words'] = ui_key_word(ess_keys, processed_content) * app_weight
# score['two_gram_keywords'] = two_gram_key_word(two_keywords, processed_content)
score['helpful_num'] = int(i.helpful_num) * 0.25 # bug TypeError: can't multiply sequence by non-int of type 'float'
if score['helpful_num'] > 25:
score['helpful_num'] = 25
for k in score:
score_sum += score[k]
if score_sum > 3: # 3 2 1
all_review.append([app_name, score_sum, i])
# if len(all_review) > number[m]:
# break
# 然后对all_review进行排序
result = sorted(all_review, key=itemgetter(1), reverse=True)
return result[:400]
def query_issue(scan_output, max_depth=4):
"""
根据 已经排序过的app相似度降序列表 scan_output 搜索所有可能 issue
~1分钟得出结果
:param scan_output: 格式参考 descript（）函数的输出
:param max_depth: 限制搜索深度，取最相似的前几个
:return: 所有查询
"""
# TODO 查询的 key 哪里出来的？
logger = logging.getLogger("StreamLogger")
rdb = issuedb.ISSuedb()
# sql = """select review_id,content,bold,star_num,helpful_num,reply_content from {}
# order by length(content) desc"""
sql = """select review_id,content,star_num from {}
order by length(content) desc"""
overall_table = {}
# 所有相关app和item
for i in range(min(len(scan_output), max_depth)):
one_dict = {}
app = scan_output[i][0]
one_dict['sim'] = scan_output[i][1] # similarity_score
# print(app)
tab_name = table2tsv.file2table(app) # suppose running
print("@@@@@@@@@@@@")
print(tab_name)
one_dict['data'] = []
one_dict['keys'] = []
score_list = scan_output[i][2]
keys_sea = _filter_search_keys(score_list, threshold=0.7)
logger.debug(f"{app}\t{tab_name}\tsimilar keys length: {len(keys_sea)}")
output = rdb.db_retrieve(sql.format(tab_name)) # output is list of tuple
# head = ["review_id", "content", "bold", "star_num", "helpful_num", "reply_content"]
head = ["review_id", "content", "star_num"]
f_output = issuedb.retrieve_formatter(head, output)
# title_list = util.get_col(output, head.index('title'))
body_list = util.get_col(output, head.index('content'))
star_list = util.get_col(output, head.index('star_num'))
# reply_list = util.get_col(output, head.index('reply_content'))
# bold_list = util.get_col(output, head.index('bold'))
# label_list = util.get_col(output, head.index('labels'))
# reply_list = util.get_col(output, head.index('issue_num'))
pre_calc_val = _pre_calc(body_list=body_list, keys_sea=keys_sea) # suppose running
for k in keys_sea:
keys = []
for i in k:
keys.append(" ".join(i))
keys = " ".join(keys)
ess_keys = nlp_util.stem_sentence(keys)
tmp = search_rank.sort_candidate_seq(f_output, ess_keys, pre_calc_val)
leng = min(3, len(tmp))
one_dict['keys'].extend([ess_keys] * leng)
one_dict['data'].extend(tmp[:leng])
overall_table[tab_name] = one_dict
logger.debug(pp.pformat(overall_table))
logger.debug("#" * 50)
return overall_table
"coef"] * the_score_detail[i]["val"]
the_score_detail["total"] = sum(
[the_score_detail[i]["z_term"] for i in the_score_detail])
score.append(the_score_detail)
tmp = zip(corpus, score)
return list(tmp)
if __name__ == '__main__':
from model import nlp_util
from model import issuedb
reload = util.Reload()
rdb = issuedb.ISSuedb()
sql = """select issue_num, comments, state, title, body, commit_id from {}
where labels like '%bug%' or commit_id is not null order by length(body) desc"""
# all_cor = []
# std_tbs = url_repo.get_std_name_list(github=True)
# for tb in std_tbs:
# output = rdb.db_retrieve(sql.format(tb))
# for i in range(len(output)):
# tmp = list(output[i])
# tmp.insert(0, tb)
# tmp = tuple(tmp)
# all_cor.append(tmp)
#
# # pp.pprint(all_cor)
#