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2_2_gen_time_features.py
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2_2_gen_time_features.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Mar 13 10:08:54 2018
@author: Liaowei
"""
import pandas as pd
import numpy as np
import time
import datetime
import os
from utils import raw_data_path,feature_data_path,result_path,cache_pkl_path,dump_pickle,load_pickle
from smooth import BayesianSmoothing
from tqdm import tqdm
'''
生成时间聚合特征和时间差特征
'''
# In[ ]: 用户搜索次数的特征，包括：
#当日搜索特征、目前小时搜索次数
def gen_user_search_count(file_name):
data = load_pickle(path=raw_data_path + file_name + '.pkl')
data = data.loc[:,['user_id', 'item_id', 'shop_id','day', 'hour', 'second_cate']]
data_select = pd.DataFrame()
#聚类一下
user_day_search = data.groupby(['user_id', 'day']).count().iloc[:,0]
#获取每个样本的,user_id,day组成的索引，以索引聚类后的数据
x = data.loc[:, ('user_id', 'day')].values
k = user_day_search.loc[[tuple(i) for i in x]]
data_select['user_day_search'] = k.values
user_hour_search = data.groupby(['user_id', 'day', 'hour']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'hour')].values
k = user_hour_search.loc[[tuple(i) for i in x]]
data_select['user_hour_search'] = k.values
user_day_item_search = data.groupby(['user_id', 'day', 'item_id']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'item_id')].values
k = user_day_item_search.loc[[tuple(i) for i in x]]
data_select['user_day_item_search'] = k.values
user_hour_item_search = data.groupby(['user_id', 'day', 'hour', 'item_id']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'hour', 'item_id')].values
k = user_hour_item_search.loc[[tuple(i) for i in x]]
data_select['user_hour_item_search'] = k.values
user_day_shop_search = data.groupby(['user_id', 'day', 'shop_id']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'shop_id')].values
k = user_day_shop_search.loc[[tuple(i) for i in x]]
data_select['user_day_shop_search'] = k.values
user_hour_shop_search = data.groupby(['user_id', 'day', 'hour', 'shop_id']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'hour', 'shop_id')].values
k = user_hour_shop_search.loc[[tuple(i) for i in x]]
data_select['user_hour_shop_search'] = k.values
user_day_catesearch = data.groupby(['user_id', 'day', 'second_cate']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'second_cate')].values
k = user_day_catesearch.loc[[tuple(i) for i in x]]
data_select['user_day_cate_search'] = k.values
user_hour_cate_search = data.groupby(['user_id', 'day', 'hour', 'second_cate']).count().iloc[:,0]
x = data.loc[:, ('user_id', 'day', 'hour', 'second_cate')].values
k = user_hour_cate_search.loc[[tuple(i) for i in x]]
data_select['user_hour_cate_search'] = k.values
dump_pickle(data_select, feature_data_path +file_name + '_user_search_count')
# In[]:生成用户的时间差特征：
def gen_user_search_time(file_name):
'''
#用当次搜索距离当天第一次搜索该商品时间差
#用当次搜索距离当天第最后一次搜索该商品时间差
#用当次搜索距离当天第一次搜索该店铺时间差
#用当次搜索距离当天第最后一次搜索该店铺时间差
#用当次搜索距离当天第一次搜索该品牌时间差
#用当次搜索距离当天第最后一次搜索该品牌时间差
#用当次搜索距离当天第一次搜索该类目时间差
#用当次搜索距离当天第最后一次搜索该类目时间差
'''
data_select = pd.DataFrame()
data = load_pickle(path=raw_data_path + file_name + '.pkl')
cols = ['item_id','shop_id', 'item_brand_id','second_cate']
for col in cols:
data_filter = data[['user_id', col,'day','context_timestamp']].groupby(['user_id', col,'day'])
max_time = data_filter.agg(max)
min_time = data_filter.agg(min)
x = data.loc[:, ('user_id', col, 'day')].values
m = max_time.loc[[tuple(i) for i in x]]
n = min_time.loc[[tuple(i) for i in x]]
data_select['sub_maxtime_'+col] = data['context_timestamp'].values - np.squeeze(m.values)
data_select['sub_mintime_'+col] = data['context_timestamp'].values - np.squeeze(n.values)
data_select['sub_maxtime_'+col] = data_select['sub_maxtime_'+col].apply(lambda x: x.total_seconds())
data_select['sub_mintime_'+col] = data_select['sub_mintime_'+col].apply(lambda x: x.total_seconds())
dump_pickle(data_select, feature_data_path +file_name + '_user_search_time')
# In[]
if __name__ == '__main__':
gen_user_search_count('train')
gen_user_search_time('train')
gen_user_search_count('test')
gen_user_search_time('test')