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import pandas as pd
import pymysql
import datetime
def con_sql(db,sql):
cursor = db.cursor()
cursor.execute(sql)
result = cursor.fetchall()
df = pd.DataFrame(list(result))
db.close()
return df
def multi_hot(df,column,n):
df[column] = df[column].fillna("lost_na")
app_list_value = [i.split(",") for i in df[column].unique()]
app_list_unique = []
for i in app_list_value:
app_list_unique.extend(i)
app_list_unique = list(set(app_list_unique))
number = len(app_list_unique)
app_list_map = dict(zip(app_list_unique, list(range(n, number + n))))
df[column] = df[column].apply(app_list_func, args=(app_list_map,))
return number,app_list_map
def get_data():
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select max(stat_date) from {}".format(train_data_set)
validate_date = con_sql(db, sql)[0].values.tolist()[0]
print("validate_date:" + validate_date)
temp = datetime.datetime.strptime(validate_date, "%Y-%m-%d")
start = (temp - datetime.timedelta(days=100)).strftime("%Y-%m-%d")
print(start)
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select e.y,e.z,e.stat_date,e.ucity_id,feat.level2_ids,e.ccity_name,u.device_type,u.manufacturer," \
"u.channel,c.top,e.device_id,cut.time,dl.app_list,e.diary_service_id,feat.level3_ids,feat.level2," \
"wiki.tag,question.tag,search.tag,budan.tag,order_tag.tag,sixin.tag,cart.tag " \
"from {} e left join user_feature u on e.device_id = u.device_id " \
"left join cid_type_top c on e.device_id = c.device_id " \
"left join cid_time_cut cut on e.cid_id = cut.cid " \
"left join device_app_list dl on e.device_id = dl.device_id " \
"left join diary_feat feat on e.cid_id = feat.diary_id " \
"left join wiki_tag wiki on e.device_id = wiki.device_id " \
"left join question_tag question on e.device_id = question.device_id " \
"left join search_tag search on e.device_id = search.device_id " \
"left join budan_tag budan on e.device_id = budan.device_id " \
"left join order_tag on e.device_id = order_tag.device_id " \
"left join sixin_tag sixin on e.device_id = sixin.device_id " \
"left join cart_tag cart on e.device_id = cart.device_id " \
"where e.stat_date >= '{}'".format(train_data_set, start)
# 上面order_tag 表不要简称为order，因为order是mysql的保留字，例如order by
df = con_sql(db, sql)
# print(df.shape)
df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",
11: "time", 12: "app_list", 13: "service_id", 14: "level3_ids", 15: "level2",
16:"tag1",17:"tag2",18:"tag3",19:"tag4",20:"tag5",21:"tag6",22:"tag7"})
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
"from train_Knowledge_network_data"
knowledge = con_sql(db, sql)
knowledge = knowledge.rename(columns={0: "level2", 1: "method", 2: "min", 3: "max",
4: "treatment_time", 5: "maintain_time", 6: "recover_time"})
knowledge["level2"] = knowledge["level2"].astype("str")
df = pd.merge(df, knowledge, on='level2', how='left')
df = df.drop("level2", axis=1)
service_id = tuple(df["service_id"].unique())
db = pymysql.connect(host='172.16.30.143', port=3306, user='work',
passwd='BJQaT9VzDcuPBqkd', db='zhengxing')
sql = "select s.id,d.hospital_id from api_service s left join api_doctor d on s.doctor_id = d.id " \
"where s.id in {}".format(service_id)
hospital = con_sql(db, sql)
hospital = hospital.rename(columns={0: "service_id", 1: "hospital_id"})
# print(hospital.head())
# print("hospital")
# print(hospital.count())
hospital["service_id"] = hospital["service_id"].astype("str")
df = pd.merge(df, hospital, on='service_id', how='left')
df = df.drop("service_id", axis=1)
print(df.count())
print("before")
print(df.shape)
df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "app_list", "hospital_id", "level3_ids"])
print("after")
print(df.shape)
app_list_number, app_list_map = multi_hot(df, "app_list", 2)
level2_number, level2_map = multi_hot(df, "clevel2_id", 2 + app_list_number)
level3_number, level3_map = multi_hot(df, "level3_ids", 2 + app_list_number + level2_number)
for i in ["tag1","tag2","tag3","tag4","tag5","tag6","tag7"]:
df[i] = df[i].fillna("lost_na")
df[i] = df[i].apply(app_list_func, args=(level2_map,))
unique_values = []
features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "hospital_id",
"method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
for i in features:
df[i] = df[i].astype("str")
df[i] = df[i].fillna("lost")
# 下面这行代码是为了区分不同的列中有相同的值
df[i] = df[i] + i
unique_values.extend(list(df[i].unique()))
temp = list(range(2 + app_list_number + level2_number + level3_number,
2 + app_list_number + level2_number + level3_number + len(unique_values)))
value_map = dict(zip(unique_values, temp))
df = df.drop("device_id", axis=1)
# TODO 上线后把最近一天的数据集放进训练集，这样用户的正、负反馈能及时获取
train = df
test = df[df["stat_date"] == validate_date + "stat_date"]
for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "hospital_id",
"method", "min", "max", "treatment_time", "maintain_time", "recover_time"]:
train[i] = train[i].map(value_map)
test[i] = test[i].map(value_map)
# print("train shape")
# print(train.shape)
print("test shape")
print(test.shape)
write_csv(train, "tr", 100000)
write_csv(test, "va", 80000)
return validate_date, value_map, app_list_map, level2_map, level3_map
def app_list_func(x,l):
b = x.split(",")
e = []
for i in b:
if i in l.keys():
e.append(l[i])
else:
e.append(0)
return ",".join([str(j) for j in e])
def write_csv(df,name,n):
for i in range(0, df.shape[0], n):
if i == 0:
temp = df.iloc[0:n]
elif i + n > df.shape[0]:
temp = df.iloc[i:]
else:
temp = df.iloc[i:i + n]
temp.to_csv(path + name+ "/{}_{}.csv".format(name,i), index=False)
def get_predict(date,value_map,app_list_map,level2_map,level3_map):
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select e.y,e.z,e.label,e.ucity_id,feat.level2_ids,e.ccity_name," \
"u.device_type,u.manufacturer,u.channel,c.top,e.device_id,e.cid_id,cut.time," \
"dl.app_list,e.hospital_id,feat.level3_ids,feat.level2," \
"wiki.tag,question.tag,search.tag,budan.tag,order_tag.tag,sixin.tag,cart.tag " \
"from esmm_pre_data e left join user_feature u on e.device_id = u.device_id " \
"left join cid_type_top c on e.device_id = c.device_id " \
"left join cid_time_cut cut on e.cid_id = cut.cid " \
"left join device_app_list dl on e.device_id = dl.device_id " \
"left join diary_feat feat on e.cid_id = feat.diary_id " \
"left join wiki_tag wiki on e.device_id = wiki.device_id " \
"left join question_tag question on e.device_id = question.device_id " \
"left join search_tag search on e.device_id = search.device_id " \
"left join budan_tag budan on e.device_id = budan.device_id " \
"left join order_tag on e.device_id = order_tag.device_id " \
"left join sixin_tag sixin on e.device_id = sixin.device_id " \
"left join cart_tag cart on e.device_id = cart.device_id"
df = con_sql(db, sql)
df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel2_id", 5: "ccity_name",
6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "device_id",
11: "cid_id", 12: "time", 13: "app_list", 14: "hospital_id", 15: "level3_ids",
16: "level2",17:"tag1",18:"tag2",19:"tag3",20:"tag4",21:"tag5",22:"tag6",23:"tag7"})
db = pymysql.connect(host='172.16.40.158', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select level2_id,treatment_method,price_min,price_max,treatment_time,maintain_time,recover_time " \
"from train_Knowledge_network_data"
knowledge = con_sql(db, sql)
knowledge = knowledge.rename(columns={0: "level2", 1: "method", 2: "min", 3: "max",
4: "treatment_time", 5: "maintain_time", 6: "recover_time"})
knowledge["level2"] = knowledge["level2"].astype("str")
df = pd.merge(df, knowledge, on='level2', how='left')
df = df.drop("level2", axis=1)
df = df.drop_duplicates(["ucity_id", "clevel2_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "app_list", "hospital_id", "level3_ids"])
df["stat_date"] = date
print(df.head(6))
df["app_list"] = df["app_list"].fillna("lost_na")
df["app_list"] = df["app_list"].apply(app_list_func, args=(app_list_map,))
df["clevel2_id"] = df["clevel2_id"].fillna("lost_na")
df["clevel2_id"] = df["clevel2_id"].apply(app_list_func, args=(level2_map,))
df["level3_ids"] = df["level3_ids"].fillna("lost_na")
df["level3_ids"] = df["level3_ids"].apply(app_list_func, args=(level3_map,))
for i in ["tag1", "tag2", "tag3", "tag4", "tag5", "tag6", "tag7"]:
df[i] = df[i].fillna("lost_na")
df[i] = df[i].apply(app_list_func, args=(level2_map,))
# print("predict shape")
# print(df.shape)
df["uid"] = df["device_id"]
df["city"] = df["ucity_id"]
features = ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "hospital_id",
"method", "min", "max", "treatment_time", "maintain_time", "recover_time"]
for i in features:
df[i] = df[i].astype("str")
df[i] = df[i].fillna("lost")
df[i] = df[i] + i
native_pre = df[df["label"] == 0]
native_pre = native_pre.drop("label", axis=1)
nearby_pre = df[df["label"] == 1]
nearby_pre = nearby_pre.drop("label", axis=1)
for i in ["ucity_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date", "hospital_id",
"method", "min", "max", "treatment_time", "maintain_time", "recover_time"]:
native_pre[i] = native_pre[i].map(value_map)
# TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
native_pre[i] = native_pre[i].fillna(0)
nearby_pre[i] = nearby_pre[i].map(value_map)
# TODO 没有覆盖到的类别会处理成na，暂时用0填充，后续完善一下
nearby_pre[i] = nearby_pre[i].fillna(0)
print("native")
print(native_pre.shape)
native_pre[["uid", "city", "cid_id"]].to_csv(path + "native.csv", index=False)
write_csv(native_pre, "native", 200000)
print("nearby")
print(nearby_pre.shape)
nearby_pre[["uid", "city", "cid_id"]].to_csv(path + "nearby.csv", index=False)
write_csv(nearby_pre, "nearby", 160000)
if __name__ == '__main__':
train_data_set = "esmm_train_data_dur"
path = "/home/gmuser/esmm/"
date, value, app_list, level2, level3 = get_data()
get_predict(date, value, app_list, level2, level3)