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import pandas as pd
import pymysql
import datetime
def con_sql(db,sql):
cursor = db.cursor()
try:
cursor.execute(sql)
result = cursor.fetchall()
df = pd.DataFrame(list(result))
except Exception:
print("发生异常", Exception)
df = pd.DataFrame()
finally:
db.close()
return df
def get_data():
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select max(stat_date) from esmm_train_data"
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=60)).strftime("%Y-%m-%d")
print(start)
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select e.y,e.z,e.stat_date,e.ucity_id,e.clevel1_id,e.ccity_name," \
"u.device_type,u.manufacturer,u.channel,c.top,cl.l1,cl.l2,e.device_id,cut.time " \
"from esmm_train_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_level2 cl on e.cid_id = cl.cid " \
"left join cid_time_cut cut on e.cid_id = cut.cid " \
"where e.stat_date >= '{}'".format(start)
df = con_sql(db, sql)
# print(df.shape)
df = df.rename(columns={0: "y", 1: "z", 2: "stat_date", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name",
6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "l1",11: "l2",
12: "device_id", 13: "time"})
print("esmm data ok")
# print(df.head(2)
print("before")
print(df.shape)
print("after")
df = df.drop_duplicates()
df = df.drop_duplicates(["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "l1","l2", "time", "stat_date"])
print(df.shape)
unique_values = []
features = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date"]
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()))
for i in ["l1","l2"]:
df[i] = df[i].astype("str")
df[i] = df[i].fillna("lost")
# l1和l2中的值与top类别是一个类别
df[i] = df[i]+"top"
unique_values.extend(list(df[i].unique()))
print("features:")
print(len(unique_values))
print(df.head(2))
temp = list(range(1,len(unique_values)+1))
value_map = dict(zip(unique_values,temp))
df = df.drop("device_id", axis=1)
train = df
test = df[df["stat_date"] == validate_date+"stat_date"]
for i in ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "l1", "time", "stat_date","l2"]:
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
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):
db = pymysql.connect(host='10.66.157.22', port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='jerry_test')
sql = "select e.y,e.z,e.label,e.ucity_id,e.clevel1_id,e.ccity_name," \
"u.device_type,u.manufacturer,u.channel,c.top,cl.l1,cl.l2,e.device_id,e.cid_id,cut.time " \
"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_level2 cl on e.cid_id = cl.cid " \
"left join cid_time_cut cut on e.cid_id = cut.cid"
df = con_sql(db, sql)
df = df.rename(columns={0: "y", 1: "z", 2: "label", 3: "ucity_id", 4: "clevel1_id", 5: "ccity_name",
6: "device_type", 7: "manufacturer", 8: "channel", 9: "top", 10: "l1",11:"l2",
12: "device_id", 13: "cid_id", 14: "time"})
df["stat_date"] = date
print("predict shape")
print(df.shape)
df["uid"] = df["device_id"]
df["city"] = df["ucity_id"]
features = ["ucity_id", "clevel1_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "time", "stat_date"]
for i in features:
df[i] = df[i].astype("str")
df[i] = df[i].fillna("lost")
df[i] = df[i] + i
for i in ["l1","l2"]:
df[i] = df[i].astype("str")
df[i] = df[i].fillna("lost")
# l1和l2中的值与top类别是一个类别
df[i] = df[i]+"top"
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", "clevel1_id", "ccity_name", "device_type", "manufacturer",
"channel", "top", "l1", "time", "stat_date","l2"]:
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)
print(native_pre.head())
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)
print(nearby_pre.head())
nearby_pre[["uid","city","cid_id"]].to_csv(path+"nearby.csv",index=False)
write_csv(nearby_pre, "nearby", 160000)
if __name__ == '__main__':
# path = "/home/gmuser/esmm_data/"
path = "/data/esmm/"
date,value = get_data()
get_predict(date, value)