def fusion():
logging.warning(u"运行日志：在线模块数据融合")
print("run fusion...")
# differAllData = pd.read_csv('dislikeData.csv',names=['uid', 'cid', 'score'])
# changedData = pd.read_csv('changedBigraph.csv',names=['uid', 'cid', 'score'])
differAllData = pd.read_csv('../file_saved/dislikeData.csv',
names=['uid', 'cid', 'score'])
changedData = pd.read_csv('../file_saved/changedBigraph.csv',
names=['uid', 'cid', 'score'])
dbHandle = DatabaseIo()
if not dbHandle:
return None
#找出两个df中uid和cid对相同的行，存到mergeData中
mergeData = pd.merge(differAllData,
changedData,
on=['uid', 'cid'],
how='inner')
print mergeData
if not mergeData.empty:
muid = mergeData['uid'].values.tolist()
mcid = mergeData['cid'].values.tolist()
#ct_list储存mergeData中对应用户-课程的点击数
ct_list = []
for i in range(len(muid)):
# sql_select_click_times = '''select click_times
# FROM account_course5000
# WHERE user_id = '{0}'
# AND course_id = '{1}'
# '''
sql_select_click_times = DataBaseQuery["online_select_click_times"]
result = dbHandle.doSql(execType=DataBaseOperateType.SearchMany,
sql=sql_select_click_times.format(
muid[i], mcid[i]))
if not result:
ct_list.append(0)
else:
ct_list.append(float(result[0][0]))
cmax = max(ct_list)
#norm_ctlist为ct_list进行规范化后的结果，作为惩罚因子加到对应的推荐值上
norm_ctlist = []
for i in ct_list:
norm_ctlist.append(1 - (i / (cmax + 1)))
#dvalue融合了课程类别统计的推荐值，cvalue是增量数据进行二部图运算得到的推荐值
# fvalue为dvalue、cvalue和惩罚因子norm_ctlist融合后的推荐值
dvalue = mergeData['score_x'].values.tolist()
cvalue = mergeData['score_y'].values.tolist()
fvalue = map(lambda (a, b, c): a * 0.3 + b + c,
zip(cvalue, dvalue, norm_ctlist))
#mergeData增量数据变为进行融合后的结果集
mergeData.drop(['score_x', 'score_y'], axis=1, inplace=True)
mergeData['score'] = fvalue
#fusionData为增量数据和全量数据的融合数据
#先在融合了类别因素的全量数据中添加mergeData，就会出现重复的用户-课程对，利用drop_duplicates去重并保留添加的mergeData数据
fusionData = differAllData.append(mergeData)
fusionData.drop_duplicates(subset=['uid', 'cid'],
keep='last',
inplace=True)
fusionData.sort_values(by=['uid'], ascending=True, inplace=True)
fusionData.reset_index(inplace=True)
fusionData.drop(['index'], axis=1, inplace=True)
print("fusion success")
else:
#增量数据与离线全量数据中没有相同的用户-课程对，不进行融合，只根据类别加分
fusionData = differAllData
print("no changed data..")
# baseData = pd.read_csv('../resultToRoc.csv')
baseData = pd.read_csv('../file_saved/resultToRoc.csv')
updatedBaseData = baseData.append(fusionData)
updatedBaseData.drop_duplicates(subset=['uid', 'cid'],
keep='last',
inplace=True)
# updatedBaseData.to_csv('../resultToRoc.csv',index=None)
updatedBaseData.to_csv('../file_saved/resultToRoc.csv', index=None)
# updatedBaseData.to_csv('online.csv', header=None, index=None)
updatedBaseData.to_csv('../file_saved/online.csv', header=None, index=None)
return fusionData
def differFusion(d):
logging.warning(u"运行日志：在线模块类别特征融合")
print("run differFusion...")
# sql_select_differ = '''select course_differ
# FROM course_info
# WHERE id = '{0}'
# '''
# sql_select_differ = '''select classify_id
# FROM course_classify5000
# WHERE id = '{0}'
# '''
sql_select_differ = DataBaseQuery["online_select_differ"]
dbHandle = DatabaseIo()
if not dbHandle:
return None
changedData = getChangedData(d)
group = changedData.groupby(['uid'])
grouped_cData = pd.DataFrame({
'cid': group['cid'].apply(list)
}).reset_index()
gclist = grouped_cData['cid'].values.tolist()
ulist = grouped_cData['uid'].values.tolist()
#增量数据中每个用户所选的课程的类别列表
uclist = []
for i in range(len(ulist)):
temp = []
temp.append(ulist[i])
for j in range(len(gclist[i])):
result = dbHandle.doSql(execType=DataBaseOperateType.SearchMany,
sql=sql_select_differ.format(gclist[i][j]))
# gclist[i][j] = int(transformCourseType(result[0][0]))
gclist[i][j] = int(result[0][0])
temp.append(list(set(gclist[i])))
uclist.append(temp)
# print uclist
# allData = pd.read_csv('../resultToRoc.csv')
allData = pd.read_csv('../file_saved/resultToRoc.csv')
auid = allData['uid'].values.tolist()
acid = allData['cid'].values.tolist()
avalue = allData['score'].values.tolist()
print uclist
# 从全量数据和增量数据中匹配交互过相同类别课程的目标用户，并对该目标用户在全量数据中与增量数据中类别相匹配的课程的推荐值增加0.2
# 如果对一个目标用户，增量数据里面有全量数据中没有交互过的类别，那不算它，只给交互过的类别增加0.2
for i in range(len(uclist)):
for j in range(len(auid)):
if auid[j] == long(uclist[i][0]):
result = dbHandle.doSql(
execType=DataBaseOperateType.SearchMany,
sql=sql_select_differ.format(acid[j]))
# checkcid = int(transformCourseType(result[0][0]))
checkcid = int(result[0][0])
if checkcid in uclist[i][1]:
avalue[j] += 0.2
# print checkcid,acid[j],avalue[j]
differData = pd.DataFrame({0: auid, 1: acid, 2: avalue})
# differData.to_csv('differData.csv',index=None,header=None)
differData.to_csv('../file_saved/differData.csv', index=None, header=None)
print("differFusion success")
return differData
