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rule.py
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import pandas as pd
import numpy as np
from features_generator import *
import pickle
'''文件路径定义'''
ACTION_FILE = './datasets/JData_Action.csv'
COMMENT_FILE = './datasets/JData_Comment.csv'
PRODUCT_FILE = './datasets/JData_Product.csv'
USER_FILE = './datasets/JData_User.csv'
'''
def load_rule_prdict_uid(start_date = '2016-02-01 00:00:00', end_date = '2016-04-16 00:00:00', \
sub_start_date = '2016-04-09 00:00:00', sub_end_date = '2016-04-16 00:00:00'):
# 根据规则生成预测uid
# rule： Action_cnt_ratio_buy==1 Action_cnt_ratio_browse==1 user_lv_cd>2
user_act = load_action_ratio_with_timeWindow(start_date = start_date, end_date = end_date, \
sub_end_date = sub_end_date, sub_start_date = sub_start_date)
JUser = pd.read_csv(USER_FILE, encoding='gbk')
user_act = pd.merge(user_act, JUser, on='user_id', how='left')
predict_uid = get_action_data(start_date = sub_start_date, end_date = sub_end_date)
addCart_uid = predict_uid[(predict_uid.cate==8)&(predict_uid.type==2)].user_id.drop_duplicates()
buy_uid = predict_uid[(predict_uid.cate==8)&(predict_uid.type==4)].user_id.drop_duplicates()
explore_user = predict_uid[predict_uid.cate==8].user_id.drop_duplicates()
addCart_uid = addCart_uid[~addCart_uid.isin(buy_uid)]
user_act = user_act[user_act.user_id.isin(explore_user)]
user_act = user_act[user_act.user_id.isin(addCart_uid)]
##不含sid
uid = user_act[(user_act.Action_cnt_ratio_buy==1)&(user_act.Action_cnt_ratio_browse==1)&(user_act.user_lv_cd>2)].user_id.to_frame()
return uid
'''
def load_rule_prdict_uid(start_date = '2016-02-01 00:00:00', end_date = '2016-04-16 00:00:00', \
sub_start_date = '2016-04-09 00:00:00', sub_end_date = '2016-04-16 00:00:00'):
'''
根据规则生成预测uid
rule： UCPair_action_ratio_browse==1 UCPair_action_ratio_buy==1 lv>2 date_cnt<=2(cate=8) 对商品操作数，加购？购买数
'''
user_act = load_UCPair_action_ratio_with_timeWindow(start_date = start_date, end_date = end_date, \
sub_end_date = sub_end_date, sub_start_date = sub_start_date)
user_act.columns = ['user_id', 'cate', 'uc_act_ratio_browse', 'uc_act_ratio_cart', 'uc_act_ratio_delcart', \
'uc_act_ratio_buy', 'uc_act_ratio_favor', 'uc_act_ratio_click']
JUser = pd.read_csv(USER_FILE, encoding='gbk')
user_act = pd.merge(user_act, JUser, on='user_id', how='left')
predict_uid = get_action_data(start_date = sub_start_date, end_date = sub_end_date)
addCart_uid = predict_uid[(predict_uid.cate==8)&(predict_uid.type==2)].user_id.drop_duplicates()
buy_uid = predict_uid[(predict_uid.cate==8)&(predict_uid.type==4)].user_id.drop_duplicates()
explore_user = predict_uid[predict_uid.cate==8].user_id.drop_duplicates()
addCart_uid = addCart_uid[~addCart_uid.isin(buy_uid)]
user_act = user_act[user_act.user_id.isin(explore_user)]
user_act = user_act[user_act.user_id.isin(addCart_uid)]
##不含sid
user_date_cnt = load_UCPair_action_date_cnt(start_date = sub_start_date, end_date = sub_end_date)
user_date_cnt.columns = ['user_id', 'cate', 'date_cnt']
user_act = pd.merge(user_act, user_date_cnt, on=['user_id'], how='left')
uid = user_act[(user_act.uc_act_ratio_buy==1)&(user_act.uc_act_ratio_browse==1)&(user_act.user_lv_cd>2)&(user_act.date_cnt<=2)].user_id.to_frame()
return uid