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#!/srv/envs/nvwa/bin/python
# -*- coding: utf-8 -*-
import pickle
import xlearn as xl
import pandas as pd
import pymysql
from datetime import datetime
# utils 包必须要导，否则ffm转化时用到的pickle找不到utils，会报错
import utils
import warnings
from multiprocessing import Pool
from userProfile import get_active_users
from sklearn.preprocessing import MinMaxScaler
import time
from config import *
from utils import judge_online,con_sql
def get_video_id(cache_video_id):
if flag:
db = pymysql.connect(host=ONLINE_EAGLE_HOST, port=4000, user='root', passwd='3SYz54LS9#^9sBvC', db='eagle')
else:
# 本地数据库，没有密码，可能报错
db = pymysql.connect(host=LOCAL_EAGLE_HOST, port=4000, user='root', db='eagle')
cursor = db.cursor()
sql = "select diary_id from feed_diary_boost;"
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()
if df.empty:
return cache_video_id
else:
video_id = df[0].values.tolist()
print("videoid")
print(video_id[:2])
return video_id
# 将device_id、city_id拼接到对应的城市热门日记表。注意：下面预测集特征顺序要与训练集保持一致
def feature_en(x_list, device_id):
data = pd.DataFrame(x_list)
# 下面的列名一定要用cid，不能用diaryid，因为预测模型用到的ffm上是cid
data = data.rename(columns={0: "cid"})
data["device_id"] = device_id
now = datetime.now()
data["hour"] = now.hour
data["minute"] = now.minute
data.loc[data["hour"] == 0, ["hour"]] = 24
data.loc[data["minute"] == 0, ["minute"]] = 60
data["hour"] = data["hour"].astype("category")
data["minute"] = data["minute"].astype("category")
# 虽然预测y，但ffm转化需要y，并不影响预测结果
data["y"] = 0
print("done 特征工程")
return data
# 把ffm.pkl load进来，将上面的数据转化为ffm格式
def transform_ffm_format(df,queue_name,device_id):
with open(path + "ffm.pkl", "rb") as f:
ffm_format_pandas = pickle.load(f)
data = ffm_format_pandas.native_transform(df)
predict_file_name = path + "result/{0}_{1}.csv".format(device_id, queue_name)
data.to_csv(predict_file_name, index=False, header=None)
print("done ffm")
return predict_file_name
def predict(queue_name,queue_arg,device_id):
data = feature_en(queue_arg[0], device_id)
data_file_path = transform_ffm_format(data,queue_name,device_id)
ffm_model = xl.create_ffm()
ffm_model.setTest(data_file_path)
ffm_model.setSigmoid()
ffm_model.predict(path + "model.out",
path + "result/output{0}_{1}.csv".format(device_id, queue_name))
def save_result(queue_name,queue_arg,device_id):
score_df = pd.read_csv(path + "result/output{0}_{1}.csv".format(device_id, queue_name), header=None)
mm_scaler = MinMaxScaler()
mm_scaler.fit(score_df)
score_df = pd.DataFrame(mm_scaler.transform(score_df))
score_df = score_df.rename(columns={0: "score"})
score_df["cid"] = queue_arg[0]
# 去掉cid前面的"diary|"
score_df["cid"] = score_df["cid"].apply(lambda x:x[6:])
# print("score_df：")
# print(score_df.head(1))
# print(score_df.shape)
if queue_arg[1] != []:
df_temp = pd.DataFrame(queue_arg[1]).rename(columns={0: "cid"})
df_temp["score"] = 0
df_temp = df_temp.sort_index(axis=1,ascending=False)
df_temp["cid"] = df_temp["cid"].apply(lambda x: x[6:])
predict_score_df = score_df.append(df_temp)
return predict_score_df
else:
return score_df
def get_score(queue_arg):
if flag:
db = pymysql.connect(host=SCORE_DB_ONLINE["host"], port=SCORE_DB_ONLINE["port"],
user=SCORE_DB_ONLINE["user"],passwd=SCORE_DB_ONLINE["passwd"],
db=SCORE_DB_ONLINE["db"])
else:
db = pymysql.connect(host=SCORE_DB_LOCAL["host"], port=SCORE_DB_LOCAL["port"],
user=SCORE_DB_LOCAL["user"], passwd=SCORE_DB_LOCAL["passwd"],
db=SCORE_DB_LOCAL["db"])
# 去除diary_id 前面的"diary|"
diary_list = tuple(list(map(lambda x:x[6:],queue_arg[2])))
sql = "select score,diary_id from biz_feed_diary_score where diary_id in {};".format(diary_list)
score_df = con_sql(db,sql)
print("get score")
return score_df
def update_dairy_queue(score_df,predict_score_df,total_video_id):
diary_id = score_df["cid"].values.tolist()
if total_video_id != []:
video_id = list(set(diary_id)&set(total_video_id))
if len(video_id)>0:
not_video = list(set(diary_id) - set(video_id))
# 为了相加时cid能够匹配，先把cid变成索引
not_video_df = score_df.loc[score_df["cid"].isin(not_video)].set_index(["cid"])
not_video_predict_df = predict_score_df.loc[predict_score_df["cid"].isin(not_video)].set_index(["cid"])
not_video_df["score"] = not_video_df["score"] + not_video_predict_df["score"]
not_video_df = not_video_df.sort_values(by="score", ascending=False)
video_df = score_df.loc[score_df["cid"].isin(video_id)].set_index(["cid"])
video_predict_df = predict_score_df.loc[predict_score_df["cid"].isin(video_id)].set_index(["cid"])
video_df["score"] = video_df["score"] + video_predict_df["score"]
video_df = video_df.sort_values(by="score", ascending=False)
not_video_id = not_video_df.index.tolist()
video_id = video_df.index.tolist()
new_queue = not_video_id
i = 1
for j in video_id:
new_queue.insert(i, j)
i += 5
print("分数合并成功")
return new_queue
# 如果取交集后没有视频日记
else:
score_df = score_df.set_index(["cid"])
predict_score_df = predict_score_df.set_index(["cid"])
score_df["score"]=score_df["score"]+predict_score_df["score"]
score_df = score_df.sort_values(by="score", ascending=False)
print("分数合并成功1")
return score_df.index.tolist()
# 如果total_video_id是空列表
else:
score_df = score_df.set_index(["cid"])
predict_score_df = predict_score_df.set_index(["cid"])
score_df["score"] = score_df["score"] + predict_score_df["score"]
score_df = score_df.sort_values(by="score", ascending=False)
# print("分数合并成功1")
return score_df.index.tolist()
def update_sql_dairy_queue(queue_name, diary_id,device_id, city_id):
if flag:
db = pymysql.connect(host=QUEUE_ONLINE_HOST, port=3306, user='doris', passwd='o5gbA27hXHHm',
db='doris_prod')
else:
db = pymysql.connect(host=LOCAL_HOST, port=3306, user='work',passwd='workwork', db='doris_test')
cursor = db.cursor()
id_str = str(diary_id[0])
for i in range(1, len(diary_id)):
id_str = id_str + "," + str(diary_id[i])
sql = "update device_diary_queue set {}='{}' where device_id = '{}' and city_id = '{}'".format\
(queue_name,id_str,device_id, city_id)
cursor.execute(sql)
db.commit()
db.close()
print("成功写入diary_id")
def queue_compare(old_list, new_list):
# 去掉前面的"diary|"
old_list = list(map(lambda x: int(x[6:]),old_list))
# print("旧表前十个")
# print(old_list[:10])
# print("新表前十个")
# print(new_list[:10])
temp = list(range(len(old_list)))
x_dict = dict(zip(old_list, temp))
temp = list(range(len(new_list)))
y_dict = dict(zip(new_list, temp))
i = 0
for key in x_dict.keys():
if x_dict[key] != y_dict[key]:
i += 1
if i >0:
print("日记队列更新前日记总个数{}，位置发生变化个数{}，发生变化率{}%".format(len(old_list), i,
round(i / len(old_list) * 100), 2))
return True
else:
return False
def get_queue(device_id, city_id,queue_name):
if flag:
db = pymysql.connect(host=QUEUE_ONLINE_HOST, port=3306, user='doris',passwd='o5gbA27hXHHm',
db='doris_prod')
else:
db = pymysql.connect(host=LOCAL_HOST, port=3306, user='work',
passwd='workwork', db='doris_test')
cursor = db.cursor()
sql = "select {} from device_diary_queue " \
"where device_id = '{}' and city_id = '{}';".format(queue_name,device_id, city_id)
cursor.execute(sql)
result = cursor.fetchall()
df = pd.DataFrame(list(result))
if df.empty:
print("该用户对应的日记为空")
return False
else:
queue_list = df.loc[0, 0].split(",")
queue_list = list(map(lambda x: "diary|" + str(x), queue_list))
db.close()
print("成功获取queue")
return queue_list
def pipe_line(queue_name, queue_arg, device_id,total_video_id):
predict(queue_name, queue_arg, device_id)
predict_score_df = save_result(queue_name, queue_arg, device_id)
score_df = get_score(queue_arg)
if score_df.empty:
print("获取的日记列表是空")
return False
else:
score_df = score_df.rename(columns={0: "score", 1: "cid"})
diary_queue = update_dairy_queue(score_df, predict_score_df,total_video_id)
return diary_queue
def user_update(device_id, city_id, queue_name,data_set_cid,total_video_id):
queue_list = get_queue(device_id, city_id, queue_name)
if queue_list:
queue_predict = list(set(queue_list) & set(data_set_cid))
queue_not_predict = list(set(queue_list) - set(data_set_cid))
queue_arg = [queue_predict, queue_not_predict, queue_list]
if queue_predict != []:
diary_queue = pipe_line(queue_name, queue_arg, device_id,total_video_id)
if diary_queue and queue_compare(queue_list, diary_queue):
update_sql_dairy_queue(queue_name, diary_queue, device_id, city_id)
print("更新结束")
else:
print("获取的日记列表是空或者日记队列顺序没有变化，所以不更新日记队列")
else:
print("预测集是空，不需要预测")
else:
print("日记队列为空")
def multi_proecess_update(device_id, city_id, data_set_cid,total_video_id):
queue_name_list = ["native_queue","nearby_queue","nation_queue","megacity_queue"]
pool = Pool(4)
for queue_name in queue_name_list:
pool.apply_async(user_update, (device_id, city_id, queue_name,data_set_cid,total_video_id,))
pool.close()
pool.join()
if __name__ == "__main__":
warnings.filterwarnings("ignore")
flag,path = judge_online()
# 增加缓存日记视频列表
cache_video_id = []
cache_device_city_list = []
differ = 0
while True:
start = time.time()
device_city_list = get_active_users(flag, path, differ)
time1 = time.time()
print("获取用户活跃表耗时：{}秒".format(time1-start))
# 过滤掉5分钟内预测过的用户
device_city_list = list(set(tuple(device_city_list))-set(tuple(cache_device_city_list)))
print("device_city_list")
print(device_city_list)
if datetime.now().minute % 5 == 0:
cache_device_city_list = []
if device_city_list != []:
data_set_cid = pd.read_csv(path + "data_set_cid.csv")["cid"].values.tolist()
total_video_id = get_video_id(cache_video_id)
cache_video_id = total_video_id
cache_device_city_list.extend(device_city_list)
for device_city in device_city_list:
multi_proecess_update(device_city[0], device_city[1], data_set_cid, total_video_id)
differ = time.time()-start
print("differ:{}秒".format(differ))
# # TODO 上线后把预测用户改成多进程预测