Sklearn：天池新人实战赛o2o优惠券使用预测 part2
2024-05-17 04:44:47
日萌社
人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）
阿里云官网：天池新人实战赛o2o优惠券使用预测
数据集下载链接：https://pan.baidu.com/s/13OtaUv6j4x8dD7cgD4sL5g
提取码：7tze
Sklearn：天池新人实战赛o2o优惠券使用预测 part1
Sklearn：天池新人实战赛o2o优惠券使用预测 part2
Sklearn：天池新人实战赛o2o优惠券使用预测 part3
特征处理
In [1]:
import pandas as pdimport numpy as npfrom datetime import dateimport datetime as dt获取数据
In [2]:
off_train = pd.read_csv('./data/ccf_offline_stage1_train.csv', keep_default_na=False, header=0)off_test = pd.read_csv('./data/ccf_offline_stage1_test_revised.csv',keep_default_na=False, header=0)on_train = pd.read_csv('./data/ccf_online_stage1_train.csv', keep_default_na=False, header=0)In [3]:
# 更换特征名称off_train.columns=['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']off_test.columns = ['user_id','merchant_id','coupon_id','discount_rate','distance','date_received']on_train.columns = ['user_id','merchant_id','action','coupon_id','discount_rate','date_received','date']
数据分批处理
#将数据分为3个数据集 利用滑窗法
#将2016年1月1日到4月13日的数据提取特征，利用4月14日的到5月14日的作为测试集
#将2月1日到5月14日的作为数据集提取特征，利用5月15日6月15日的作为测试集
#将3月15日到6月30日作为数据集提取特征，再测试7月1日到7月31日的数据
In [4]:
# 将2016年1月1日到4月13日的数据提取特征feature1 = off_train[(off_train.date >= '20160101') & (off_train.date <= '20160413') | ( (off_train.date == 'null') & (off_train.date_received >= '20160101') & (off_train.date_received <= '20160413'))]# 利用4月14日的到5月14日的作为测试集dataset1 = off_train[(off_train.date_received >= '201604014') & (off_train.date_received <= '20160514')]In [5]:
# 在2月1日到5月14日之间使用了券,只要领取时间在2月1日到5月14日之间,并包括没有数据中没有领取券的feature2 = off_train[(off_train.date >= '20160201') & (off_train.date <= '20160514') | ( (off_train.date == 'null') & (off_train.date_received >= '20160201') & (off_train.date_received <= '20160514'))]# 提取数据集2的测试集dataset2 = off_train[(off_train.date_received >= '20160515') & (off_train.date_received <= '20160615')]In [6]:
# 数据集3的特征为 取数据中领券和用券日期大于3月15日和小于6月30日的feature3 = off_train[((off_train.date >= '20160315') & (off_train.date <= '20160630')) | ( (off_train.date == 'null') & (off_train.date_received >= '20160315') & (off_train.date_received <= '20160630'))]# 使数据集3等于test集 没有label标签dataset3 = off_test特征工程
用户特征处理
In [7]:
def is_firstlastone(x): if x == 0: return 1 elif x > 0: return 0 else: return -1 # 表明这个优惠券只接受了一次
def get_day_gap_before(s): date_received, dates = s.split('-') dates = dates.split(':') gaps = [] for d in dates: # 将时间差转化为天数 this_gap = (dt.date(int(date_received[0:4]), int(date_received[4:6]), int( date_received[6:8]))-dt.date(int(d[0:4]), int(d[4:6]), int(d[6:8]))).days if this_gap > 0: gaps.append(this_gap) if len(gaps) == 0: return -1 else: return min(gaps)
def get_day_gap_after(s): date_received, dates = s.split('-') dates = dates.split(':') gaps = [] for d in dates: this_gap = (dt.datetime(int(d[0:4]), int(d[4:6]), int(d[6:8]))-dt.datetime( int(date_received[0:4]), int(date_received[4:6]), int(date_received[6:8]))).days if this_gap > 0: gaps.append(this_gap) if len(gaps) == 0: return -1 else: return min(gaps)In [8]:
def GetOtherFeature(dataset): # 对于测试集，提取用户的Id dataset3 = dataset t = dataset3[['user_id']] # 相当于给原有数据加上一列，这个月用户收取的所有优惠券数目，并初始化为1 t['this_month_user_receive_all_coupon_count'] = 1
# 将t按照用户id进行分组，然后统计所有用户收取的优惠券数目,并初始化一个索引值 t = t.groupby('user_id').agg('sum').reset_index() # 提取数据集的优惠券Id和用户Id t1 = dataset3[['user_id', 'coupon_id']] # 提取这个月用户收到的相同的优惠券的数量 t1['this_month_user_receive_same_coupn_count'] = 1 t1 = t1.groupby(['user_id', 'coupon_id']).agg('sum').reset_index()
# 提取数据集的用户id，优惠券id以及优惠券接收的时间 t2 = dataset3[['user_id', 'coupon_id', 'date_received']]
# 将数据转换为str类型 t2.date_received = t2.date_received.astype('str') # 如果出现相同的用户接收相同的优惠券在接收时间上用‘：’连接上第n次接受优惠券的时间 t2 = t2.groupby(['user_id', 'coupon_id'])['date_received'].agg( lambda x: ':'.join(x)).reset_index() # 将接收时间的一组按着':'分开，这样就可以计算接受了优惠券的数量,apply是合并 t2['receive_number'] = t2.date_received.apply(lambda s: len(s.split(':'))) t2 = t2[t2.receive_number > 1] # 最大接受的日期 t2['max_date_received'] = t2.date_received.apply( lambda s: max([int(d) for d in s.split(':')])) # 最小的接收日期 t2['min_date_received'] = t2.date_received.apply( lambda s: min([int(d) for d in s.split(':')])) t2 = t2[['user_id', 'coupon_id', 'max_date_received', 'min_date_received']]
t3 = dataset3[['user_id', 'coupon_id', 'date_received']] # 将两表融合只保留左表数据,这样得到的表，相当于保留了最近接收时间和最远接受时间 t3 = pd.merge(t3, t2, on=['user_id', 'coupon_id'], how='left') # 这个优惠券最近接受时间 t3['this_month_user_receive_same_coupon_lastone'] = t3.max_date_received - \ t3.date_received.astype(int) # 这个优惠券最远接受时间 t3['this_month_user_receive_same_coupon_firstone'] = t3.date_received.astype( int)-t3.min_date_received
t3.this_month_user_receive_same_coupon_lastone = t3.this_month_user_receive_same_coupon_lastone.apply( is_firstlastone) t3.this_month_user_receive_same_coupon_firstone = t3.this_month_user_receive_same_coupon_firstone.apply( is_firstlastone) t3 = t3[['user_id', 'coupon_id', 'date_received', 'this_month_user_receive_same_coupon_lastone', 'this_month_user_receive_same_coupon_firstone']] # 将表格中接收优惠券日期中为最近和最远的日期时置为1其余为0，若只接受了一次优惠券为-1
# 提取第四个特征,一个用户所接收到的所有优惠券的数量 t4 = dataset3[['user_id', 'date_received']] t4['this_day_receive_all_coupon_count'] = 1 t4 = t4.groupby(['user_id', 'date_received']).agg('sum').reset_index()
# 提取第五个特征,一个用户不同时间所接收到不同优惠券的数量 t5 = dataset3[['user_id', 'coupon_id', 'date_received']] t5['this_day_user_receive_same_coupon_count'] = 1 t5 = t5.groupby(['user_id', 'coupon_id', 'date_received'] ).agg('sum').reset_index() # 一个用户不同优惠券 的接受时间 t6 = dataset3[['user_id', 'coupon_id', 'date_received']] t6.date_received = t6.date_received.astype('str') t6 = t6.groupby(['user_id', 'coupon_id'])['date_received'].agg( lambda x: ':'.join(x)).reset_index() # 重命名inplace代表深拷贝 t6.rename(columns={'date_received': 'dates'}, inplace=True)
t7 = dataset3[['user_id', 'coupon_id', 'date_received']] # 将t6和t7融合 t7 = pd.merge(t7, t6, on=['user_id', 'coupon_id'], how='left') # 注意这里所有的时间格式都已经是'str'格式 t7['date_received_date'] = t7.date_received.astype('str')+'-'+t7.dates # print(t7) t7['day_gap_before'] = t7.date_received_date.apply(get_day_gap_before) t7['day_gap_after'] = t7.date_received_date.apply(get_day_gap_after) t7 = t7[['user_id', 'coupon_id', 'date_received', 'day_gap_before', 'day_gap_after']]
# 将所有特征融合在一张表中 other_feature3 = pd.merge(t1, t, on='user_id') other_feature3 = pd.merge(other_feature3, t3, on=['user_id', 'coupon_id']) other_feature3 = pd.merge(other_feature3, t4, on=['user_id', 'date_received']) other_feature3 = pd.merge(other_feature3, t5, on=[ 'user_id', 'coupon_id', 'date_received']) other_feature3 = pd.merge(other_feature3, t7, on=[ 'user_id', 'coupon_id', 'date_received']) return other_feature3提取优惠券的相关特征
In [9]:
def calc_discount_rate(s): s = str(s) s = s.split(':') if len(s) == 1: return float(s[0]) else: return 1.0-float(s[1])/float(s[0])def get_discount_man(s): s = str(s) s = s.split(':') if len(s)==1: return 'null' else: return int(s[0])def get_discount_jian(s): s = str(s) s = s.split(':') if len(s) == 1: return 'null' else: return int(s[1])
def is_man_jian(s): s = str(s) s = s.split(':') if len(s)==1: return 0 else: return 1 In [10]:
def GetCouponFeature(dataset, feature): # 对于数据集 # 将时间转化为第几周 # 显示时间是第几周 # tt是获取到的特征中消费的最大时间 dataset3 = dataset tt = feature[feature.date != 'null'].date.unique().max() dataset3['day_of_week'] = dataset3.date_received.astype('str').apply( lambda x: date(int(x[0:4]), int(x[4:6]), int(x[6:8])).weekday()+1) # 显示时间是几月 dataset3['day_of_month'] = dataset3.date_received.astype( 'str').apply(lambda x: int(x[6:8])) # 显示时期和截止日之间的天数 dataset3['days_distance'] = dataset3.date_received.astype('str').apply( lambda x: (date(int(x[0:4]), int(x[4:6]), int(x[6:8]))-date(int(tt[0:4]), int(tt[4:6]), int(tt[6:8]))).days) # 显示满了多少钱后开始减 dataset3['discount_man'] = dataset3.discount_rate.apply(get_discount_man) # 显示满减的减少的钱 dataset3['discount_jian'] = dataset3.discount_rate.apply(get_discount_jian) # 返回优惠券是否是满减券 dataset3['is_man_jian'] = dataset3.discount_rate.apply(is_man_jian) # 显示打折力度 dataset3['discount_rate'] = dataset3.discount_rate.apply( calc_discount_rate) d = dataset3[['coupon_id']] d['coupon_count'] = 1 # 显示每一种优惠券的数量 d = d.groupby('coupon_id').agg('sum').reset_index()
dataset3 = pd.merge(dataset3, d, on='coupon_id', how='left') return dataset3提取商品的特征
In [11]:
def GetMerchantFeature(feature): #提取商品的特征 #对于数据集 feature3 = feature merchant3 = feature3[['merchant_id','coupon_id','distance','date_received','date']]
t = merchant3[['merchant_id']] #删除重复行数据 t.drop_duplicates(inplace=True)
#显示卖出的商品 t1 = merchant3[merchant3.date!='null'][['merchant_id']] t1['total_sales'] = 1 #显示每个商品的销售数量 t1 = t1.groupby('merchant_id').agg('sum').reset_index()
#显示使用了优惠券消费的商品，正样本 t2 = merchant3[(merchant3.date!='null')&(merchant3.coupon_id!='null')][['merchant_id']] t2['sales_use_coupon'] = 1 t2 = t2.groupby('merchant_id').agg('sum').reset_index()
#显示了商品的优惠券的总数量 t3 = merchant3[merchant3.coupon_id != 'null'][['merchant_id']] t3 ['total_coupon'] = 1 t3 = t3.groupby('merchant_id').agg('sum').reset_index()
#显示商品销量和距离的关系 t4 = merchant3[(merchant3.date != 'null')&(merchant3.coupon_id != 'null')][['merchant_id','distance']] #把数据中的null值全部替换为-1 t4.replace('null',-1,inplace=True) t4.distance = t4.distance.astype('int') #再把数据中的-1全部替换为NaN t4.replace(-1,np.nan,inplace=True)
#返回用户离商品的距离最小值 t5 = t4.groupby('merchant_id').agg('min').reset_index() t5.rename(columns={'distance':'merchant_min_distance'},inplace = True)
#返回用户离商品的距离最大值 t6 = t4.groupby('merchant_id').agg('max').reset_index() t6.rename(columns={'distance':'merchant_max_distance'},inplace = True) #print(t6)
#返回距离的平均值 t7 = t4.groupby('merchant_id').agg('mean').reset_index() t7.rename(columns = {'distance':'merchant_mean_distance'},inplace= True)
#返回距离的中位值 t8 = t4.groupby('merchant_id').agg('median').reset_index() t8.rename(columns={'distance':'merchant_median_distance'},inplace = True)
merchant3_feature = pd.merge(t,t1,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t2,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t3,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t5,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t6,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t7,on='merchant_id',how='left') merchant3_feature = pd.merge(merchant3_feature,t8,on='merchant_id',how='left')
#将数据中的NaN用0来替换 merchant3_feature.sales_use_coupon = merchant3_feature.sales_use_coupon.replace(np.nan,0) #即优惠券的使用率 merchant3_feature['merchant_coupon_transfer_rate'] = merchant3_feature.sales_use_coupon.astype('float')/merchant3_feature.total_coupon #即卖出商品中使用优惠券的占比 merchant3_feature['coupon_rate'] = merchant3_feature.sales_use_coupon.astype('float') / merchant3_feature.total_sales #将数据中的NaN用0来替换 merchant3_feature.total_coupon = merchant3_feature.total_coupon.replace(np.nan,0) return merchant3_feature用户的相关信息
In [12]:
def get_user_date_datereceived_gap(s): s = s.split(':') return (date(int(s[0][0:4]), int(s[0][4:6]), int(s[0][6:8])) - date(int(s[1][0:4]), int(s[1][4:6]), int(s[1][6:8]))).daysIn [13]:
def GetUserRelateInfo(feature):
feature3 = feature user3 = feature3[['user_id','merchant_id','coupon_id','discount_rate','distance','date_received','date']]
t = user3[['user_id']] #去掉数据中重复的用户Id t.drop_duplicates(inplace=True)
#用户购买商品的种类数 t1 = user3[user3.date!='null'][['user_id','merchant_id']] #同样去掉重复用的用户id和商品id t1.drop_duplicates(inplace=True) t1.merchant_id = 1 t1 = t1.groupby('user_id').agg('sum').reset_index() t1.rename(columns={'merchant_id':'count_merchant'},inplace=True)
#使用了优惠券购买商品的用户id和距离 t2 = user3[(user3.date!='null')&(user3.coupon_id!='null')][['user_id','distance']] #将null值替换为-1 t2.replace('null',-1,inplace=True) t2.distance = t2.distance.astype('int')#转换数据类型为int t2.replace(-1,np.nan,inplace=True)
#得到使用优惠券购买商品的用户离店铺的最短距离 t3 = t2.groupby('user_id').agg('min').reset_index() t3.rename(columns={'distance':'user_min_distance'},inplace=True)
#得到最大距离 t4 = t2.groupby('user_id').agg('max').reset_index() t4.rename(columns={'distance':'user_max_distance'},inplace=True)
#得到平均距离 t5 = t2.groupby('user_id').agg('mean').reset_index() t5.rename(columns={'distance':'user_mean_distance'},inplace=True)
#得到中间距离 t6 = t2.groupby('user_id').agg('median').reset_index() t6.rename(columns={'distance':'user_median_distance'},inplace=True)
#每个用户使用优惠券购买的物品数量 t7 = user3[(user3.date != 'null')&(user3.coupon_id != 'null')][['user_id']] t7['buy_use_coupon'] = 1 t7 = t7.groupby('user_id').agg('sum').reset_index()
#购买物品的总数 t8 = user3[user3.date != 'null'][['user_id']] t8['buy_total'] = 1 t8 = t8.groupby('user_id').agg('sum').reset_index()
#接受的优惠券的总数 t9 = user3[user3.coupon_id != 'null'][['user_id']] t9['coupon_received'] = 1 t9 = t9.groupby('user_id').agg('sum').reset_index()
#接受到优惠券的日期和使用之间的间隔 t10 = user3[(user3.date_received != 'null')&(user3.date != 'null')][['user_id','date_received','date']] t10['user_date_datereceived_gap'] = t10.date + ':'+ t10.date_received t10.user_date_datereceived_gap = t10.user_date_datereceived_gap.apply(get_user_date_datereceived_gap) t10 = t10[['user_id','user_date_datereceived_gap']]
#将用户优惠券使用时间的间隔取平均数 t11 = t10.groupby('user_id').agg('mean').reset_index() t11.rename(columns={'user_date_datereceived_gap':'avg_user_date_datereceived_gap'},inplace=True)
#间隔天数的最小值 t12 = t10.groupby('user_id').agg('min').reset_index() t12.rename(columns={'user_date_datereceived_gap':'min_user_date_datereceived_gap'},inplace=True)
#间隔天数的最大值 t13 = t10.groupby('user_id').agg('max').reset_index() t13.rename(columns={'user_date_datareceived_gap':'max_user_date_datereceived_gap'},inplace=True)
#将提取的特征合并 user3_feature = pd.merge(t,t1,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t3,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t4,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t5,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t6,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t7,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t8,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t9,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t11,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t12,on='user_id',how='left') user3_feature = pd.merge(user3_feature,t13,on='user_id',how='left')
user3_feature.count_merchant = user3_feature.count_merchant.replace(np.nan,0) user3_feature.buy_user_coupon = user3_feature.buy_use_coupon.replace(np.nan,0) user3_feature['buy_use_coupon_rate'] = user3_feature.buy_use_coupon.astype('float') / user3_feature.buy_total.astype('float')#使用优惠券购买的商品占总数的多少 user3_feature['user_coupon_transfer_rate'] = user3_feature.buy_use_coupon.astype('float') / user3_feature.coupon_received.astype('float') user3_feature.buy_total = user3_feature.buy_total.replace(np.nan,0)#将数据中的NaN值转为0 user3_feature.coupon_received = user3_feature.coupon_received.replace(np.nan,0) return user3_feature用户和商家之间的特征关系
In [14]:
def GetUserMerchantRelateInfo(feature): #4.user_merchant: #times_user_buy_merchant_before. feature3 = feature all_user_merchant = feature3[['user_id','merchant_id']] all_user_merchant.drop_duplicates(inplace=True)
#只保留销售了商品的商户id t = feature3[['user_id','merchant_id','date']] t = t[t.date!='null'][['user_id','merchant_id']]
#用户一共买了这家商户的多少商品 t['user_merchant_buy_total'] = 1 t = t.groupby(['user_id','merchant_id']).agg('sum').reset_index() t.drop_duplicates(inplace=True)
t1 = feature3[['user_id','merchant_id','coupon_id']] t1 = t1[t1.coupon_id!='null'][['user_id','merchant_id']]
#用户一共收到一个商户的多少优惠券 t1['user_merchant_received'] = 1 t1 = t1.groupby(['user_id','merchant_id']).agg('sum').reset_index() t1.drop_duplicates(inplace = True)
t2 = feature3[['user_id','merchant_id','date','date_received']] t2 = t2[(t2.date!='null')&(t2.date_received!='null')][['user_id','merchant_id']]
#用户在一家商户中使用优惠券购买的商品的数目 t2['user_merchant_buy_use_coupon'] = 1 t2 = t2.groupby(['user_id','merchant_id']).agg('sum').reset_index() t2.drop_duplicates(inplace = True)
#用户在一家商家的所有记录总数 t3 = feature3[['user_id','merchant_id']] t3['user_merchant_any'] = 1 t3 = t3.groupby(['user_id','merchant_id']).agg('sum').reset_index() t3.drop_duplicates(inplace = True)
t4 = feature3[['user_id','merchant_id','date','coupon_id']] t4 = t4[(t4.date!='null')&(t4.coupon_id=='null')][['user_id','merchant_id']]
#用户没有使用优惠券购买的商品的数目 t4['user_merchant_buy_common'] = 1 t4 = t4.groupby(['user_id','merchant_id']).agg('sum').reset_index() t4.drop_duplicates(inplace = True)
user_merchant3 = pd.merge(all_user_merchant,t,on=['user_id','merchant_id'],how='left') user_merchant3 = pd.merge(user_merchant3,t1,on=['user_id','merchant_id'],how='left') user_merchant3 = pd.merge(user_merchant3,t2,on=['user_id','merchant_id'],how='left') user_merchant3 = pd.merge(user_merchant3,t3,on=['user_id','merchant_id'],how='left') user_merchant3 = pd.merge(user_merchant3,t4,on=['user_id','merchant_id'],how='left')
#都是针对一家商户和一个用户 user_merchant3.user_merchant_buy_use_coupon = user_merchant3.user_merchant_buy_use_coupon.replace(np.nan,0) user_merchant3.user_merchant_buy_common = user_merchant3.user_merchant_buy_common.replace(np.nan,0) #y优惠券的转换率，用户使用了的优惠券/一共收到的优惠券 user_merchant3['user_merchant_coupon_transfer_rate'] = user_merchant3.user_merchant_buy_use_coupon.astype('float') / user_merchant3.user_merchant_received.astype('float') #用户使用优惠券的概率，在一家商户使用优惠券购买的商品/在一家商户购买商品的总数 user_merchant3['user_merchant_coupon_buy_rate'] = user_merchant3.user_merchant_buy_use_coupon.astype('float') / user_merchant3.user_merchant_buy_total.astype('float') #用户在商户消费的概率 用户在商户购买的总数/在一家商户浏览的总次数 user_merchant3['user_merchant_rate'] = user_merchant3.user_merchant_buy_total.astype('float') / user_merchant3.user_merchant_any.astype('float') #用户在一家商户不适用优惠券购买的概率 普通购买的商品数/购买商品的总数 user_merchant3['user_merchant_common_buy_rate'] = user_merchant3.user_merchant_buy_common.astype('float') / user_merchant3.user_merchant_buy_total.astype('float') return user_merchant3构建训练集和测试集
In [15]:
def get_label(s): s = s.split(':') if s[0]=='null': return 0 elif (date(int(s[0][0:4]),int(s[0][4:6]),int(s[0][6:8]))-date(int(s[1][0:4]),int(s[1][4:6]),int(s[1][6:8]))).days<=15: return 1 else: return -1 In [16]:
def GenerateData(dataset, feature, label=True): # 获取各个特征处理后的结果 coupon_feature = GetCouponFeature(dataset, feature) merchant_feature = GetMerchantFeature(feature) user_feature = GetUserRelateInfo(feature) user_merchant = GetUserMerchantRelateInfo(feature) other_feature = GetOtherFeature(dataset)
dataset = pd.merge(coupon_feature, merchant_feature, on='merchant_id', how='left') dataset = pd.merge(dataset, user_feature, on='user_id', how='left') dataset = pd.merge(dataset, user_merchant, on=[ 'user_id', 'merchant_id'], how='left') dataset = pd.merge(dataset, other_feature, on=[ 'user_id', 'coupon_id', 'date_received'], how='left') dataset.drop_duplicates(inplace=True)
dataset.user_merchant_buy_total = dataset.user_merchant_buy_total.replace( np.nan, 0) dataset.user_merchant_any = dataset.user_merchant_any.replace(np.nan, 0) dataset.user_merchant_received = dataset.user_merchant_received.replace( np.nan, 0) dataset['is_weekend'] = dataset.day_of_week.apply( lambda x: 1 if x in (6, 7) else 0) weekday_dummies = pd.get_dummies(dataset.day_of_week) weekday_dummies.columns = [ 'weekday'+str(i+1) for i in range(weekday_dummies.shape[1])] dataset = pd.concat([dataset, weekday_dummies], axis=1)
# 如果是训练集要记得处理label标签值 但是在测试集中不用处理label标签 注意off_train和off_test字段 if label: dataset['label'] = dataset.date.astype( 'str') + ':' + dataset.date_received.astype('str') dataset.label = dataset.label.apply(get_label) dataset.drop(['merchant_id', 'day_of_week', 'date', 'date_received', 'coupon_count'], axis=1, inplace=True)
else: dataset.drop(['merchant_id', 'day_of_week', 'coupon_count'], axis=1, inplace=True)
# 所有的表都要一起处理null dataset = dataset.replace('null', np.nan)
return datasetIn [17]:
GenerateData1 = GenerateData(dataset1, feature1)GenerateData2 = GenerateData(dataset2, feature2)GenerateData3 = GenerateData(dataset3, feature3, False)
保存处理好的特征值, 以便后续使用
In [18]:
GenerateData1.to_csv('./GenerateData1.csv', index=None)GenerateData2.to_csv('./GenerateData2.csv', index=None)GenerateData3.to_csv('./GenerateData3.csv', index=None)模型融合
In [1]:
import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt
import datetime as dtfrom datetime import datefrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import log_loss, roc_auc_score, auc, roc_curvefrom sklearn.linear_model import SGDClassifier, LogisticRegressionfrom xgboost import XGBClassifierIn [2]
def get_processed_data(): dataset1 = pd.read_csv('./GenerateData1.csv') dataset2 = pd.read_csv('./GenerateData2.csv') dataset3 = pd.read_csv('./GenerateData3.csv')
dataset1.label.replace(-1, 0, inplace=True) dataset2.label.replace(-1, 0, inplace=True)
dataset1.drop_duplicates(inplace=True) dataset2.drop_duplicates(inplace=True) dataset3.drop_duplicates(inplace=True) # 按照行或列进行合并,axis=0为列索引，axis=1为行索引 因为特征处理都一样, 所以按照列索引 dataset12 = pd.concat([dataset1, dataset2], axis=0)
dataset12.fillna(-1, inplace=True)# dataset3.fillna(0, inplace=True)
return dataset12, dataset3In [3]:
dataset12, dataset3 = get_processed_data()In [4]:
predict_dataset = dataset3[['user_id', 'coupon_id', 'date_received']].copy()dataset12_label = dataset12.label# 降低维度, 把没有必要的字段删除dataset12_x = dataset12.drop(['user_id','label','coupon_id','day_gap_before','day_gap_after'],axis=1)dataset3.fillna(-1, inplace=True)dataset3_x = dataset3.drop(['user_id','coupon_id','date_received','day_gap_before','day_gap_after'],axis=1)In [5]:
x_train, x_test, y_train, y_test = train_test_split(dataset12_x, dataset12_label, test_size=0.25, random_state=88)X_train, X_train_lr, y_train, y_train_lr = train_test_split(x_train, y_train, test_size=0.5)GBDT
In [6]:
gbdt_model =GradientBoostingClassifier(learning_rate=0.1, n_estimators=190, min_samples_split=5, min_samples_leaf=5, max_depth=15, random_state=0, max_features=24,)
XGB
In [7]:
XGB_model = XGBClassifier(max_depth=15, learning_rate=0.01,eta=1, gamma=0, n_jobs=-1)# XGB_model.fit(x_train, y_train)In [8]:
from sklearn.preprocessing import OneHotEncoderIn [9]:
grd_enc = OneHotEncoder()In [10]:
gbdt_model.fit(X_train, y_train)Out[10]:
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,learning_rate=0.1, loss='deviance', max_depth=15,max_features=24, max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=5, min_samples_split=5,min_weight_fraction_leaf=0.0, n_estimators=190,n_iter_no_change=None, presort='deprecated',random_state=0, subsample=1.0, tol=0.0001,validation_fraction=0.1, verbose=0,warm_start=False)In [11]:
# 训练one-hot编码器grd_enc.fit(gbdt_model.apply(X_train)[:, :, 0])Out[11]:
OneHotEncoder(categories='auto', drop=None, dtype=<class 'numpy.float64'>,handle_unknown='error', sparse=True)In [27]:
# gbdt_model.apply(X_train)In [24]:
gbdt_model.apply(X_train)[:, :, 0] # 将三维的以二维的形式表示Out[24]:
array([[1407., 1448., 1478., ..., 1232., 1428., 2841.],[1495., 1500., 1807., ..., 1232., 1428., 2841.],[ 284., 377., 614., ..., 1232., 756., 647.],...,[ 275., 362., 599., ..., 1232., 772., 1505.],[1409., 1456., 1690., ..., 1251., 1450., 2885.],[ 139., 160., 371., ..., 1126., 253., 2742.]])In [12]:
XGB_model.fit(grd_enc.transform(gbdt_model.apply(X_train_lr)[:, :, 0]), y_train_lr)Out[12]:
XGBClassifier(base_score=0.5, booster=None, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, eta=1, gamma=0, gpu_id=-1,importance_type='gain', interaction_constraints=None,learning_rate=0.01, max_delta_step=0, max_depth=15,min_child_weight=1, missing=nan, monotone_constraints=None,n_estimators=100, n_jobs=-1, num_parallel_tree=1,objective='binary:logistic', random_state=0, reg_alpha=0,reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=None,validate_parameters=False, verbosity=None)In [13]:
# 用训练好的XGB模型多X_test做预测y_pred_grd_lm = XGB_model.predict_proba(grd_enc.transform(gbdt_model.apply(x_test)[:, :, 0]))[:, 1]In [14]:
# 根据预测结果输出fpr_grd_lm, tpr_grd_lm, _ = roc_curve(y_test, y_pred_grd_lm)In [15]:
fpr_grd_lmOut[15]:
array([0.00000000e+00, 1.97818067e-05, 3.95636133e-05, ...,7.50620654e-01, 7.51045963e-01, 1.00000000e+00])In [16]:
tpr_grd_lmOut[16]:
array([0. , 0.0058958 , 0.01106967, ..., 0.98856937, 0.98856937,1. ])In [17]:
_ Out[17]:
array([1.7995733 , 0.7995733 , 0.7982012 , ..., 0.18624917, 0.1855642 ,0.18549208], dtype=float32)In [18]:
print("AUC准确率:", roc_auc_score(y_test,y_pred_grd_lm))AUC准确率: 0.867398106049461
模型融合 0.7441
In [19]:
dataset_preds = dataset3[['user_id','coupon_id','date_received']]y_pred_grd_lm = XGB_model.predict_proba(grd_enc.transform(gbdt_model.apply(dataset3_x)[:, :, 0]))[:, 1]dataset_preds['label'] = y_pred_grd_lmdataset_preds.sort_values(by=['coupon_id','label'],inplace=True)dataset_preds.to_csv("GBDT_XGB_preds1.csv",index=None,header=None)线上线下特征合并
In [ ]:
import datetimeimport osfrom concurrent.futures import ProcessPoolExecutorfrom math import ceilimport pandas as pd读入源数据
In [ ]:
# In[] 读入源数据def get_source_data(): # 源数据路径 DataPath = 'dataset'
# 读入源数据 off_train = pd.read_csv(os.path.join(DataPath, 'ccf_offline_stage1_train.csv'), parse_dates=['Date_received', 'Date']) off_train.columns = ['User_id', 'Merchant_id', 'Coupon_id', 'Discount_rate', 'Distance', 'Date_received', 'Date']
on_train = pd.read_csv(os.path.join(DataPath, 'ccf_online_stage1_train.csv'), parse_dates=['Date_received', 'Date']) on_train.columns = ['User_id', 'Merchant_id', 'Action', 'Coupon_id', 'Discount_rate', 'Date_received', 'Date']
off_test = pd.read_csv(os.path.join(DataPath, 'ccf_offline_stage1_test_revised.csv'), parse_dates=['Date_received']) off_test.columns = ['User_id', 'Merchant_id', 'Coupon_id', 'Discount_rate', 'Distance', 'Date_received']
print(off_train.info()) print(off_train.head(5)) return off_train, on_train, off_test
数据基本处理
null,na 特殊处理
In [ ]:
# In[] null,na 特殊处理def null_process_offline(dataset, predict=False): dataset.Distance.fillna(11, inplace=True) dataset.Distance = dataset.Distance.astype(int) dataset.Coupon_id.fillna(0, inplace=True) dataset.Coupon_id = dataset.Coupon_id.astype(int) dataset.Date_received.fillna(date_null, inplace=True)
dataset[['discount_rate_x', 'discount_rate_y']] = dataset[dataset.Discount_rate.str.contains(':') == True][ 'Discount_rate'].str.split(':', expand=True).astype(int) dataset['discount_rate'] = 1 - dataset.discount_rate_y / dataset.discount_rate_x dataset.discount_rate = dataset.discount_rate.fillna(dataset.Discount_rate).astype(float) if predict: return dataset else: dataset.Date.fillna(date_null, inplace=True) return datasetdef null_process_online(dataset): dataset.Coupon_id.fillna(0, inplace=True) # online.Coupon_id = online.Coupon_id.astype(int) dataset.Date_received.fillna(date_null, inplace=True) dataset.Date.fillna(date_null, inplace=True) return dataset生成交叉训练集
In [ ]:
def data_process(off_train, on_train, off_test): # train feature split # 交叉训练集一：收到券的日期大于4月14日和小于5月14日 time_range = ['2016-04-16', '2016-05-15'] dataset1 = off_train[(off_train.Date_received >= time_range[0]) & (off_train.Date_received <= time_range[1])].copy() dataset1['label'] = 0 dataset1.loc[ (dataset1.Date != date_null) & (dataset1.Date - dataset1.Date_received <= datetime.timedelta(15)), 'label'] = 1 # 交叉训练集一特征offline：线下数据中领券和用券日期大于1月1日和小于4月13日 time_range_date_received = ['2016-01-01', '2016-03-31'] time_range_date = ['2016-01-01', '2016-04-15'] feature1_off = off_train[(off_train.Date >= time_range_date[0]) & (off_train.Date <= time_range_date[1]) | ( (off_train.Coupon_id == 0) & (off_train.Date_received >= time_range_date_received[0]) & ( off_train.Date_received <= time_range_date_received[1]))] # 交叉训练集一特征online：线上数据中领券和用券日期大于1月1日和小于4月13日[on_train.date == 'null' to on_train.coupon_id == 0] feature1_on = on_train[(on_train.Date >= time_range_date[0]) & (on_train.Date <= time_range_date[1]) | ( (on_train.Coupon_id == 0) & (on_train.Date_received >= time_range_date_received[0]) & ( on_train.Date_received <= time_range_date_received[1]))]
# 交叉训练集二：收到券的日期大于5月15日和小于6月15日 time_range = ['2016-05-16', '2016-06-15'] dataset2 = off_train[(off_train.Date_received >= time_range[0]) & (off_train.Date_received <= time_range[1])] dataset2['label'] = 0 dataset2.loc[ (dataset2.Date != date_null) & (dataset2.Date - dataset2.Date_received <= datetime.timedelta(15)), 'label'] = 1 # 交叉训练集二特征offline：线下数据中领券和用券日期大于2月1日和小于5月14日 time_range_date_received = ['2016-02-01', '2016-04-30'] time_range_date = ['2016-02-01', '2016-05-15'] feature2_off = off_train[(off_train.Date >= time_range_date[0]) & (off_train.Date <= time_range_date[1]) | ( (off_train.Coupon_id == 0) & (off_train.Date_received >= time_range_date_received[0]) & ( off_train.Date_received <= time_range_date_received[1]))] # 交叉训练集二特征online：线上数据中领券和用券日期大于2月1日和小于5月14日 feature2_on = on_train[(on_train.Date >= time_range_date[0]) & (on_train.Date <= time_range_date[1]) | ( (on_train.Coupon_id == 0) & (on_train.Date_received >= time_range_date_received[0]) & ( on_train.Date_received <= time_range_date_received[1]))]
# 测试集 dataset3 = off_test # 测试集特征offline :线下数据中领券和用券日期大于3月15日和小于6月30日的 time_range = ['2016-03-16', '2016-06-30'] feature3_off = off_train[((off_train.Date >= time_range[0]) & (off_train.Date <= time_range[1])) | ( (off_train.Coupon_id == 0) & (off_train.Date_received >= time_range[0]) & ( off_train.Date_received <= time_range[1]))] # 测试集特征online :线上数据中领券和用券日期大于3月15日和小于6月30日的 feature3_on = on_train[((on_train.Date >= time_range[0]) & (on_train.Date <= time_range[1])) | ( (on_train.Coupon_id == 0) & (on_train.Date_received >= time_range[0]) & ( on_train.Date_received <= time_range[1]))]
# get train feature ProcessDataSet1 = get_features(dataset1, feature1_off, feature1_on) ProcessDataSet2 = get_features(dataset2, feature2_off, feature2_on) ProcessDataSet3 = get_features(dataset3, feature3_off, feature3_on)
return ProcessDataSet1, ProcessDataSet2, ProcessDataSet3
def get_features(dataset, feature_off, feature_on): dataset = get_offline_features(dataset, feature_off) return get_online_features(feature_on, dataset)特征工程
对线下数据处理
In [ ]:
def get_offline_features(X, offline): # X = X[:1000]
print(len(X), len(X.columns))
temp = offline[offline.Coupon_id != 0] coupon_consume = temp[temp.Date != date_null] coupon_no_consume = temp[temp.Date == date_null]
user_coupon_consume = coupon_consume.groupby('User_id')
X['weekday'] = X.Date_received.dt.weekday X['day'] = X.Date_received.dt.day
'''user features'''
# 优惠券消费次数 temp = user_coupon_consume.size().reset_index(name='u2') X = pd.merge(X, temp, how='left', on='User_id') # X.u2.fillna(0, inplace=True) # X.u2 = X.u2.astype(int)
# 优惠券不消费次数 temp = coupon_no_consume.groupby('User_id').size().reset_index(name='u3') X = pd.merge(X, temp, how='left', on='User_id')
# 使用优惠券次数与没使用优惠券次数比值 X['u19'] = X.u2 / X.u3
# 领取优惠券次数 X['u1'] = X.u2.fillna(0) + X.u3.fillna(0)
# 优惠券核销率 X['u4'] = X.u2 / X.u1
# 普通消费次数 temp = offline[(offline.Coupon_id == 0) & (offline.Date != date_null)] temp1 = temp.groupby('User_id').size().reset_index(name='u5') X = pd.merge(X, temp1, how='left', on='User_id')
# 一共消费多少次 X['u25'] = X.u2 + X.u5
# 用户使用优惠券消费占比 X['u20'] = X.u2 / X.u25
# 正常消费平均间隔 temp = pd.merge(temp, temp.groupby('User_id').Date.max().reset_index(name='max')) temp = pd.merge(temp, temp.groupby('User_id').Date.min().reset_index(name='min')) temp = pd.merge(temp, temp.groupby('User_id').size().reset_index(name='len')) temp['u6'] = ((temp['max'] - temp['min']).dt.days / (temp['len'] - 1)) temp = temp.drop_duplicates('User_id') X = pd.merge(X, temp[['User_id', 'u6']], how='left', on='User_id')
# 优惠券消费平均间隔 temp = pd.merge(coupon_consume, user_coupon_consume.Date.max().reset_index(name='max')) temp = pd.merge(temp, temp.groupby('User_id').Date.min().reset_index(name='min')) temp = pd.merge(temp, temp.groupby('User_id').size().reset_index(name='len')) temp['u7'] = ((temp['max'] - temp['min']).dt.days / (temp['len'] - 1)) temp = temp.drop_duplicates('User_id') X = pd.merge(X, temp[['User_id', 'u7']], how='left', on='User_id')
# 15天内平均会普通消费几次 X['u8'] = X.u6 / 15
# 15天内平均会优惠券消费几次 X['u9'] = X.u7 / 15
# 领取优惠券到使用优惠券的平均间隔时间 temp = coupon_consume.copy() temp['days'] = (temp.Date - temp.Date_received).dt.days temp = (temp.groupby('User_id').days.sum() / temp.groupby('User_id').size()).reset_index(name='u10') X = pd.merge(X, temp, how='left', on='User_id')
# 在15天内使用掉优惠券的值大小 X['u11'] = X.u10 / 15
# 领取优惠券到使用优惠券间隔小于15天的次数 temp = coupon_consume.copy() temp['days'] = (temp.Date - temp.Date_received).dt.days temp = temp[temp.days <= 15] temp = temp.groupby('User_id').size().reset_index(name='u21') X = pd.merge(X, temp, how='left', on='User_id')
# 用户15天使用掉优惠券的次数除以使用优惠券的次数 X['u22'] = X.u21 / X.u2
# 用户15天使用掉优惠券的次数除以领取优惠券未消费的次数 X['u23'] = X.u21 / X.u3
# 用户15天使用掉优惠券的次数除以领取优惠券的总次数 X['u24'] = X.u21 / X.u1
# 消费优惠券的平均折率 temp = user_coupon_consume.discount_rate.mean().reset_index(name='u45') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销优惠券的最低消费折率 temp = user_coupon_consume.discount_rate.min().reset_index(name='u27') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销优惠券的最高消费折率 temp = user_coupon_consume.discount_rate.max().reset_index(name='u28') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销过的不同优惠券数量 temp = coupon_consume.groupby(['User_id', 'Coupon_id']).size() temp = temp.groupby('User_id').size().reset_index(name='u32') X = pd.merge(X, temp, how='left', on='User_id')
# 用户领取所有不同优惠券数量 temp = offline[offline.Date_received != date_null] temp = temp.groupby(['User_id', 'Coupon_id']).size().reset_index(name='u47') X = pd.merge(X, temp, how='left', on=['User_id', 'Coupon_id'])
# 用户核销过的不同优惠券数量占所有不同优惠券的比重 X['u33'] = X.u32 / X.u47
# 用户平均每种优惠券核销多少张 X['u34'] = X.u2 / X.u47
# 核销优惠券用户-商家平均距离 temp = offline[(offline.Coupon_id != 0) & (offline.Date != date_null) & (offline.Distance != 11)] temp = temp.groupby('User_id').Distance temp = pd.merge(temp.count().reset_index(name='x'), temp.sum().reset_index(name='y'), on='User_id') temp['u35'] = temp.y / temp.x temp = temp[['User_id', 'u35']] X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销优惠券中的最小用户-商家距离 temp = coupon_consume[coupon_consume.Distance != 11] temp = temp.groupby('User_id').Distance.min().reset_index(name='u36') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销优惠券中的最大用户-商家距离 temp = coupon_consume[coupon_consume.Distance != 11] temp = temp.groupby('User_id').Distance.max().reset_index(name='u37') X = pd.merge(X, temp, how='left', on='User_id')
# 优惠券类型 discount_types = [ '0.2', '0.5', '0.6', '0.7', '0.75', '0.8', '0.85', '0.9', '0.95', '30:20', '50:30', '10:5', '20:10', '100:50', '200:100', '50:20', '30:10', '150:50', '100:30', '20:5', '200:50', '5:1', '50:10', '100:20', '150:30', '30:5', '300:50', '200:30', '150:20', '10:1', '50:5', '100:10', '200:20', '300:30', '150:10', '300:20', '500:30', '20:1', '100:5', '200:10', '30:1', '150:5', '300:10', '200:5', '50:1', '100:1', ] X['discount_type'] = -1 for k, v in enumerate(discount_types): X.loc[X.Discount_rate == v, 'discount_type'] = k
# 不同优惠券领取次数 temp = offline.groupby(['User_id', 'Discount_rate']).size().reset_index(name='u41') X = pd.merge(X, temp, how='left', on=['User_id', 'Discount_rate'])
# 不同优惠券使用次数 temp = coupon_consume.groupby(['User_id', 'Discount_rate']).size().reset_index(name='u42') X = pd.merge(X, temp, how='left', on=['User_id', 'Discount_rate'])
# 不同优惠券不使用次数 temp = coupon_no_consume.groupby(['User_id', 'Discount_rate']).size().reset_index(name='u43') X = pd.merge(X, temp, how='left', on=['User_id', 'Discount_rate'])
# 不同打折优惠券使用率 X['u44'] = X.u42 / X.u41
# 满减类型优惠券领取次数 temp = offline[offline.Discount_rate.str.contains(':') == True] temp = temp.groupby('User_id').size().reset_index(name='u48') X = pd.merge(X, temp, how='left', on='User_id')
# 打折类型优惠券领取次数 temp = offline[offline.Discount_rate.str.contains('\.') == True] temp = temp.groupby('User_id').size().reset_index(name='u49') X = pd.merge(X, temp, how='left', on='User_id')
'''offline merchant features'''
# 商户消费次数 temp = offline[offline.Date != date_null].groupby('Merchant_id').size().reset_index(name='m0') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券被领取后核销次数 temp = coupon_consume.groupby('Merchant_id').size().reset_index(name='m1') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商户正常消费笔数 X['m2'] = X.m0.fillna(0) - X.m1.fillna(0)
# 商家优惠券被领取次数 temp = offline[offline.Date_received != date_null].groupby('Merchant_id').size().reset_index(name='m3') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券被领取后核销率 X['m4'] = X.m1 / X.m3
# 商家优惠券被领取后不核销次数 temp = coupon_no_consume.groupby('Merchant_id').size().reset_index(name='m7') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商户当天优惠券领取次数 temp = X[X.Date_received != date_null] temp = temp.groupby(['Merchant_id', 'Date_received']).size().reset_index(name='m5') X = pd.merge(X, temp, how='left', on=['Merchant_id', 'Date_received'])
# 商户当天优惠券领取人数 temp = X[X.Date_received != date_null] temp = temp.groupby(['User_id', 'Merchant_id', 'Date_received']).size().reset_index() temp = temp.groupby(['Merchant_id', 'Date_received']).size().reset_index(name='m6') X = pd.merge(X, temp, how='left', on=['Merchant_id', 'Date_received'])
# 商家优惠券核销的平均消费折率 temp = coupon_consume.groupby('Merchant_id').discount_rate.mean().reset_index(name='m8') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券核销的最小消费折率 temp = coupon_consume.groupby('Merchant_id').discount_rate.max().reset_index(name='m9') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券核销的最大消费折率 temp = coupon_consume.groupby('Merchant_id').discount_rate.min().reset_index(name='m10') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券核销不同的用户数量 temp = coupon_consume.groupby(['Merchant_id', 'User_id']).size() temp = temp.groupby('Merchant_id').size().reset_index(name='m11') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家优惠券领取不同的用户数量 temp = offline[offline.Date_received != date_null].groupby(['Merchant_id', 'User_id']).size() temp = temp.groupby('Merchant_id').size().reset_index(name='m12') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 核销商家优惠券的不同用户数量其占领取不同的用户比重 X['m13'] = X.m11 / X.m12
# 商家优惠券平均每个用户核销多少张 X['m14'] = X.m1 / X.m12
# 商家被核销过的不同优惠券数量 temp = coupon_consume.groupby(['Merchant_id', 'Coupon_id']).size() temp = temp.groupby('Merchant_id').size().reset_index(name='m15') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家领取过的不同优惠券数量的比重 temp = offline[offline.Date_received != date_null].groupby(['Merchant_id', 'Coupon_id']).size() temp = temp.groupby('Merchant_id').count().reset_index(name='m18') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家被核销过的不同优惠券数量占所有领取过的不同优惠券数量的比重 X['m19'] = X.m15 / X.m18
# 商家被核销优惠券的平均时间 temp = pd.merge(coupon_consume, coupon_consume.groupby('Merchant_id').Date.max().reset_index(name='max')) temp = pd.merge(temp, temp.groupby('Merchant_id').Date.min().reset_index(name='min')) temp = pd.merge(temp, temp.groupby('Merchant_id').size().reset_index(name='len')) temp['m20'] = ((temp['max'] - temp['min']).dt.days / (temp['len'] - 1)) temp = temp.drop_duplicates('Merchant_id') X = pd.merge(X, temp[['Merchant_id', 'm20']], how='left', on='Merchant_id')
# 商家被核销优惠券中的用户-商家平均距离 temp = coupon_consume[coupon_consume.Distance != 11].groupby('Merchant_id').Distance temp = pd.merge(temp.count().reset_index(name='x'), temp.sum().reset_index(name='y'), on='Merchant_id') temp['m21'] = temp.y / temp.x temp = temp[['Merchant_id', 'm21']] X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家被核销优惠券中的用户-商家最小距离 temp = coupon_consume[coupon_consume.Distance != 11] temp = temp.groupby('Merchant_id').Distance.min().reset_index(name='m22') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家被核销优惠券中的用户-商家最大距离 temp = coupon_consume[coupon_consume.Distance != 11] temp = temp.groupby('Merchant_id').Distance.max().reset_index(name='m23') X = pd.merge(X, temp, how='left', on='Merchant_id')
"""offline coupon features"""
# 此优惠券一共发行多少张 temp = offline[offline.Coupon_id != 0].groupby('Coupon_id').size().reset_index(name='c1') X = pd.merge(X, temp, how='left', on='Coupon_id')
# 此优惠券一共被使用多少张 temp = coupon_consume.groupby('Coupon_id').size().reset_index(name='c2') X = pd.merge(X, temp, how='left', on='Coupon_id')
# 优惠券使用率 X['c3'] = X.c2 / X.c1
# 没有使用的数目 X['c4'] = X.c1 - X.c2
# 此优惠券在当天发行了多少张 temp = X.groupby(['Coupon_id', 'Date_received']).size().reset_index(name='c5') X = pd.merge(X, temp, how='left', on=['Coupon_id', 'Date_received'])
# 优惠券类型(直接优惠为0, 满减为1) X['c6'] = 0 X.loc[X.Discount_rate.str.contains(':') == True, 'c6'] = 1
# 不同打折优惠券领取次数 temp = offline.groupby('Discount_rate').size().reset_index(name='c8') X = pd.merge(X, temp, how='left', on='Discount_rate')
# 不同打折优惠券使用次数 temp = coupon_consume.groupby('Discount_rate').size().reset_index(name='c9') X = pd.merge(X, temp, how='left', on='Discount_rate')
# 不同打折优惠券不使用次数 temp = coupon_no_consume.groupby('Discount_rate').size().reset_index(name='c10') X = pd.merge(X, temp, how='left', on='Discount_rate')
# 不同打折优惠券使用率 X['c11'] = X.c9 / X.c8
# 优惠券核销平均时间 temp = pd.merge(coupon_consume, coupon_consume.groupby('Coupon_id').Date.max().reset_index(name='max')) temp = pd.merge(temp, temp.groupby('Coupon_id').Date.min().reset_index(name='min')) temp = pd.merge(temp, temp.groupby('Coupon_id').size().reset_index(name='count')) temp['c12'] = ((temp['max'] - temp['min']).dt.days / (temp['count'] - 1)) temp = temp.drop_duplicates('Coupon_id') X = pd.merge(X, temp[['Coupon_id', 'c12']], how='left', on='Coupon_id')
'''user merchant feature'''
# 用户领取商家的优惠券次数 temp = offline[offline.Coupon_id != 0] temp = temp.groupby(['User_id', 'Merchant_id']).size().reset_index(name='um1') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户领取商家的优惠券后不核销次数 temp = coupon_no_consume.groupby(['User_id', 'Merchant_id']).size().reset_index(name='um2') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户领取商家的优惠券后核销次数 temp = coupon_consume.groupby(['User_id', 'Merchant_id']).size().reset_index(name='um3') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户领取商家的优惠券后核销率 X['um4'] = X.um3 / X.um1
# 用户对每个商家的不核销次数占用户总的不核销次数的比重 temp = coupon_no_consume.groupby('User_id').size().reset_index(name='temp') X = pd.merge(X, temp, how='left', on='User_id') X['um5'] = X.um2 / X.temp X.drop(columns='temp', inplace=True)
# 用户在商店总共消费过几次 temp = offline[offline.Date != date_null].groupby(['User_id', 'Merchant_id']).size().reset_index(name='um6') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户在商店普通消费次数 temp = offline[(offline.Coupon_id == 0) & (offline.Date != date_null)] temp = temp.groupby(['User_id', 'Merchant_id']).size().reset_index(name='um7') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户当天在此商店领取的优惠券数目 temp = offline[offline.Date_received != date_null] temp = temp.groupby(['User_id', 'Merchant_id', 'Date_received']).size().reset_index(name='um8') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id', 'Date_received'])
# 用户领取优惠券不同商家数量 temp = offline[offline.Coupon_id == offline.Coupon_id] temp = temp.groupby(['User_id', 'Merchant_id']).size().reset_index() temp = temp.groupby('User_id').size().reset_index(name='um9') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销优惠券不同商家数量 temp = coupon_consume.groupby(['User_id', 'Merchant_id']).size() temp = temp.groupby('User_id').size().reset_index(name='um10') X = pd.merge(X, temp, how='left', on='User_id')
# 用户核销过优惠券的不同商家数量占所有不同商家的比重 X['um11'] = X.um10 / X.um9
# 用户平均核销每个商家多少张优惠券 X['um12'] = X.u2 / X.um9
'''other feature'''
# 用户领取的所有优惠券数目 temp = X.groupby('User_id').size().reset_index(name='o1') X = pd.merge(X, temp, how='left', on='User_id')
# 用户领取的特定优惠券数目 temp = X.groupby(['User_id', 'Coupon_id']).size().reset_index(name='o2') X = pd.merge(X, temp, how='left', on=['User_id', 'Coupon_id'])
# multiple threads # data split stop = len(X) step = int(ceil(stop / cpu_jobs))
X_chunks = [X[i:i + step] for i in range(0, stop, step)] X_list = [X] * cpu_jobs counters = [i for i in range(cpu_jobs)]
start = datetime.datetime.now() with ProcessPoolExecutor() as e: X = pd.concat(e.map(task, X_chunks, X_list, counters)) print('time:', str(datetime.datetime.now() - start).split('.')[0]) # multiple threads
# 用户领取优惠券平均时间间隔 temp = pd.merge(X, X.groupby('User_id').Date_received.max().reset_index(name='max')) temp = pd.merge(temp, temp.groupby('User_id').Date_received.min().reset_index(name='min')) temp = pd.merge(temp, temp.groupby('User_id').size().reset_index(name='len')) temp['o7'] = ((temp['max'] - temp['min']).dt.days / (temp['len'] - 1)) temp = temp.drop_duplicates('User_id') X = pd.merge(X, temp[['User_id', 'o7']], how='left', on='User_id')
# 用户领取特定商家的优惠券数目 temp = X.groupby(['User_id', 'Merchant_id']).size().reset_index(name='o8') X = pd.merge(X, temp, how='left', on=['User_id', 'Merchant_id'])
# 用户领取的不同商家数目 temp = X.groupby(['User_id', 'Merchant_id']).size() temp = temp.groupby('User_id').size().reset_index(name='o9') X = pd.merge(X, temp, how='left', on='User_id')
# 用户当天领取的优惠券数目 temp = X.groupby(['User_id', 'Date_received']).size().reset_index(name='o10') X = pd.merge(X, temp, how='left', on=['User_id', 'Date_received'])
# 用户当天领取的特定优惠券数目 temp = X.groupby(['User_id', 'Coupon_id', 'Date_received']).size().reset_index(name='o11') X = pd.merge(X, temp, how='left', on=['User_id', 'Coupon_id', 'Date_received'])
# 用户领取的所有优惠券种类数目 temp = X.groupby(['User_id', 'Coupon_id']).size() temp = temp.groupby('User_id').size().reset_index(name='o12') X = pd.merge(X, temp, how='left', on='User_id')
# 商家被领取的优惠券数目 temp = X.groupby('Merchant_id').size().reset_index(name='o13') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家被领取的特定优惠券数目 temp = X.groupby(['Merchant_id', 'Coupon_id']).size().reset_index(name='o14') X = pd.merge(X, temp, how='left', on=['Merchant_id', 'Coupon_id'])
# 商家被多少不同用户领取的数目 temp = X.groupby(['Merchant_id', 'User_id']).size() temp = temp.groupby('Merchant_id').size().reset_index(name='o15') X = pd.merge(X, temp, how='left', on='Merchant_id')
# 商家发行的所有优惠券种类数目 temp = X.groupby(['Merchant_id', 'Coupon_id']).size() temp = temp.groupby('Merchant_id').size().reset_index(name='o16') X = pd.merge(X, temp, how='left', on='Merchant_id')
print(len(X), len(X.columns))
return X
对线上数据处理
In [ ]:
def get_online_features(online, X): # temp = online[online.Coupon_id == online.Coupon_id] # coupon_consume = temp[temp.Date == temp.Date] # coupon_no_consume = temp[temp.Date != temp.Date]
# 用户线上操作次数 temp = online.groupby('User_id').size().reset_index(name='on_u1') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上点击次数 temp = online[online.Action == 0].groupby('User_id').size().reset_index(name='on_u2') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上点击率 X['on_u3'] = X.on_u2 / X.on_u1
# 用户线上购买次数 temp = online[online.Action == 1].groupby('User_id').size().reset_index(name='on_u4') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上购买率 X['on_u5'] = X.on_u4 / X.on_u1
# 用户线上领取次数 temp = online[online.Coupon_id != 0].groupby('User_id').size().reset_index(name='on_u6') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上领取率 X['on_u7'] = X.on_u6 / X.on_u1
# 用户线上不消费次数 temp = online[(online.Date == date_null) & (online.Coupon_id != 0)] temp = temp.groupby('User_id').size().reset_index(name='on_u8') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上优惠券核销次数 temp = online[(online.Date != date_null) & (online.Coupon_id != 0)] temp = temp.groupby('User_id').size().reset_index(name='on_u9') X = pd.merge(X, temp, how='left', on='User_id')
# 用户线上优惠券核销率 X['on_u10'] = X.on_u9 / X.on_u6
# 用户线下不消费次数占线上线下总的不消费次数的比重 X['on_u11'] = X.u3 / (X.on_u8 + X.u3)
# 用户线下的优惠券核销次数占线上线下总的优惠券核销次数的比重 X['on_u12'] = X.u2 / (X.on_u9 + X.u2)
# 用户线下领取的记录数量占总的记录数量的比重 X['on_u13'] = X.u1 / (X.on_u6 + X.u1)
print(len(X), len(X.columns)) print('----------')
return XIn [ ]:
def task(X_chunk, X, counter): print(counter, end=',', flush=True) X_chunk = X_chunk.copy()
X_chunk['o17'] = -1 X_chunk['o18'] = -1
for i, user in X_chunk.iterrows(): temp = X[X.User_id == user.User_id]
temp1 = temp[temp.Date_received < user.Date_received] temp2 = temp[temp.Date_received > user.Date_received]
# 用户此次之后/前领取的所有优惠券数目 X_chunk.loc[i, 'o3'] = len(temp1) X_chunk.loc[i, 'o4'] = len(temp2)
# 用户此次之后/前领取的特定优惠券数目 X_chunk.loc[i, 'o5'] = len(temp1[temp1.Coupon_id == user.Coupon_id]) X_chunk.loc[i, 'o6'] = len(temp2[temp2.Coupon_id == user.Coupon_id])
# 用户上/下一次领取的时间间隔 temp1 = temp1.sort_values(by='Date_received', ascending=False) if len(temp1): X_chunk.loc[i, 'o17'] = (user.Date_received - temp1.iloc[0].Date_received).days
temp2 = temp2.sort_values(by='Date_received') if len(temp2): X_chunk.loc[i, 'o18'] = (temp2.iloc[0].Date_received - user.Date_received).days
return X_chunk数据处理保存
In [ ]:
# 程序开始时间打印 start = datetime.datetime.now() print(start.strftime('%Y-%m-%d %H:%M:%S')) cpu_jobs = os.cpu_count() - 1 date_null = pd.to_datetime('1970-01-01', format='%Y-%m-%d')
pd.set_option('expand_frame_repr', False) pd.set_option('display.max_rows', 200) pd.set_option('display.max_columns', 200)
# 预处理后数据存放路径 FeaturePath = 'data_preprocessed_2'
# 读入源数据 off_train, on_train, off_test = get_source_data()
# 源数据null处理 off_train = null_process_offline(off_train, predict=False) on_train = null_process_online(on_train) off_test = null_process_offline(off_test, predict=True)
# 获取训练特征集，测试特征集 ProcessDataSet1, ProcessDataSet2, ProcessDataSet3 = data_process(off_train, on_train, off_test)
# 源数据处理后的数据保存为文件 ProcessDataSet1.to_csv(os.path.join(FeaturePath, 'ProcessDataSet1.csv'), index=None) ProcessDataSet2.to_csv(os.path.join(FeaturePath, 'ProcessDataSet2.csv'), index=None) ProcessDataSet3.to_csv(os.path.join(FeaturePath, 'ProcessDataSet3.csv'), index=None)
# 花费时间打印 print((datetime.datetime.now() - start).seconds)Sklearn：天池新人实战赛o2o优惠券使用预测 part2相关推荐
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