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_2_3_gen_shop_features.py
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_2_3_gen_shop_features.py
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# coding: utf-8
# In[1]:
import os
import pickle
import gc
import pandas as pd
import numpy as np
from tqdm import tqdm
from utils import load_pickle,dump_pickle, get_nominal_dfal, feats_root
# In[2]:
def gen_shop_total_da_click(update=False):
dfal = get_nominal_dfal()
feat_path = os.path.join(feats_root, 'shop_total_click_da.pkl')
if os.path.exists(feat_path) and update == False:
print('Found ' + feat_path)
else:
print('Generating ' + feat_path)
shop_all_click_da = dfal.groupby(['shop_id', 'da']) .size().reset_index() .rename(columns={0: 'agg_shop_total_click_da'})
dump_pickle(shop_all_click_da, feat_path)
print('gen_shop_total_da_click completed.')
# In[3]:
def gen_shop_da_feature_click(updata=False):
"""生成用户相关所有数据的每天点击统计量"""
dfal = get_nominal_dfal()
stats_feat = [
'item_category_list', 'item_brand_id', 'item_city_id',
'user_gender_id', 'user_occupation_id', 'item_price_level',
'item_sales_level', 'item_collected_level', 'item_pv_level',
'user_age_level', 'user_star_level', 'context_page_id', 'item_id',
'user_id'
]
tbar = tqdm(stats_feat)
for feat in tbar:
feat_path = os.path.join(feats_root, 'shop_' + feat + '_click_da.pkl')
if os.path.exists(feat_path) and updata == False:
tbar.set_description('Found {:>60}'.format(os.path.basename(feat_path)))
else:
tbar.set_description('Generating {:>60}'.format(
os.path.basename(feat_path)))
shop_feat_click_da = dfal.groupby(['shop_id', 'da', feat]) .size().reset_index() .rename(columns={0: 'agg_shop_%s_click_da' % feat})
dump_pickle(shop_feat_click_da, feat_path)
print('gen_shop_da_feature_click completed.')
# In[4]:
def gen_shop_ho_feature_click(updata=False):
"""生成用户相关所有数据的每天每小时点击统计量"""
dfal = get_nominal_dfal()
stats_feat = [
'item_category_list', 'item_brand_id', 'item_city_id',
'user_gender_id', 'user_occupation_id', 'item_price_level',
'item_sales_level', 'item_collected_level', 'item_pv_level',
'user_age_level', 'user_star_level', 'context_page_id', 'item_id',
'user_id'
]
tbar = tqdm(stats_feat)
for feat in tbar:
feat_path = os.path.join(feats_root, 'shop_' + feat + '_click_ho.pkl')
if os.path.exists(feat_path) and updata == False:
tbar.set_description('Found {:>60}'.format(os.path.basename(feat_path)))
else:
tbar.set_description('Generating {:>60}'.format(os.path.basename(feat_path)))
shop_feat_click_ho = dfal.groupby(['shop_id', 'da', 'ho', feat]) .size().reset_index() .rename(columns={0: 'agg_shop_%s_click_ho' % feat})
dump_pickle(shop_feat_click_ho, feat_path)
print('gen_shop_ho_feature_click completed.')
# In[5]:
def add_shop_total_da_click(data):
"""
添加用户当天的点击总数
拼接键['user_id', 'da']
"""
feat_path = feats_root + 'shop_total_click_da.pkl'
if not os.path.exists(feat_path):
gen_shop_total_da_click()
shop_total_click_da = load_pickle(feat_path)
data = pd.merge(data, shop_total_click_da, 'left', ['da','shop_id'])
print('add_shop_total_da_click completed.')
return data
# In[6]:
def add_shop_da_feature_click(data):
stats_feat = [
'item_category_list', 'item_brand_id', 'item_city_id',
'user_gender_id', 'user_occupation_id', 'item_price_level',
'item_sales_level', 'item_collected_level', 'item_pv_level',
'user_age_level', 'user_star_level', 'context_page_id', 'item_id',
'user_id'
]
tbar = tqdm(stats_feat)
for feat in tbar:
feat_path = os.path.join(feats_root, 'shop_' + feat + '_click_da.pkl')
feat_da_click = load_pickle(feat_path)
tbar.set_description('adding ' + os.path.basename(feat_path))
data = pd.merge(data, feat_da_click, 'left', [feat, 'da', 'shop_id'])
print('add_shop_da_feature_click completed.')
return data
# In[7]:
def add_shop_ho_feature_click(data):
stats_feat = [
'item_category_list', 'item_brand_id', 'item_city_id',
'user_gender_id', 'user_occupation_id', 'item_price_level',
'item_sales_level', 'item_collected_level', 'item_pv_level',
'user_age_level', 'user_star_level', 'context_page_id', 'item_id',
'user_id'
]
tbar = tqdm(stats_feat)
for feat in tbar:
feat_path = os.path.join(feats_root, 'shop_' + feat + '_click_ho.pkl')
feat_da_click = load_pickle(feat_path)
tbar.set_description('adding ' + os.path.basename(feat_path))
data = pd.merge(data, feat_da_click, 'left', [feat, 'ho', 'da', 'shop_id'])
print('add_shop_ho_feature_click completed.')
return data
# In[8]:
def gen_shop_click_stats(data, col):
clicks_shop = pd.DataFrame(data.groupby(['shop_id', col])['dt'].count(), )
clicks_shop.rename(columns={'dt': col+'_m'}, inplace=True)
clicks_shop.reset_index(inplace=True)
clicks_shop_avg = pd.DataFrame(clicks_shop.groupby(['shop_id'])[col+'_m'].mean()).rename(columns={col+'_m': col+'_avg'}).reset_index()
clicks_shop_max = pd.DataFrame(clicks_shop.groupby(['shop_id'])[col+'_m'].max()).rename(columns={col+'_m': col+'_max'}).reset_index()
clicks_shop_min = pd.DataFrame(clicks_shop.groupby(['shop_id'])[col+'_m'].min()).rename(columns={col+'_m': col+'_min'}).reset_index()
data = pd.merge(data, clicks_shop_avg, how='left', on='shop_id')
data = pd.merge(data, clicks_shop_max, how='left', on='shop_id')
data = pd.merge(data, clicks_shop_min, how='left', on='shop_id')
print('gen_shop_click_stats {} completed.'.format(col))
return data
# In[9]:
def add_shop_click_stats(data):
feat_path = os.path.join(feats_root, 'shop_click_stats.pkl')
if not os.path.exists(feat_path):
gen_shop_stats_feature()
shop_click_stats = load_pickle(feat_path)
data = pd.merge(data, shop_click_stats, how='left', on='shop_id')
print('add_shop_click_stats completed.')
return data
# In[10]:
def gen_shop_stats_feature(updata=False):
feat_path = os.path.join(feats_root, 'shop_click_stats.pkl')
if os.path.exists(feat_path) and updata == False:
print('Found ' + feat_path)
else:
dfal = get_nominal_dfal()
dfal = add_shop_total_da_click(dfal)
dfal = add_shop_da_feature_click(dfal)
print('generating ' + feat_path)
columns_da = list(filter(lambda x: x.endswith('_click_da'), dfal.columns.values))
columns_ho = list(filter(lambda x: x.endswith('_click_ho'), dfal.columns.values))
tbar = tqdm(columns_da)
for col in tbar:
tbar.set_description('add_shop_click_stats ' + col)
dfal = gen_shop_click_stats(dfal, col)
print('gen_shop_click_stats completed.')
feat_names = list(filter(lambda x: '_click_da_' in x, dfal.columns.values))
dfal = dfal[feat_names + ['shop_id']].drop_duplicates(['shop_id'])
print('gen_shop_stats_feature shape:', dfal.shape)
dump_pickle(dfal, feat_path)
print('gen_shop_stats_feature completed.')
# In[11]:
if __name__ == '__main__':
gen_shop_total_da_click(False)
gen_shop_da_feature_click(False)
gen_shop_ho_feature_click(False)
gen_shop_stats_feature(False)