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util.py
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util.py
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#encoding=utf8
import lightgbm as lgb
import pandas as pd
import numpy as np
import gc,time,datetime
from sklearn.model_selection import KFold, cross_val_score, train_test_split, StratifiedKFold
from scipy import sparse
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from xgboost import XGBClassifier
import catboost as cb
def deal_make(data):
if 'VIVO' in data:
return 'OPPO'
elif 'IPHONE' in data:
return 'APPLE'
elif 'OPPO' in data:
return 'OPPO'
elif 'PAC' in data:
return 'OPPO'
elif 'MI' in data:
return 'XiaoMi'
elif 'REDMI' in data:
return 'XiaoMi'
elif 'HUAWEI' in data:
return 'HUAWEI'
elif 'MHA' in data:
return 'HUAWEI'
elif 'PRA' in data:
return 'HUAWEI'
elif 'FRD' in data:
return 'HUAWEI'
elif 'ALP' in data:
return 'HUAWEI'
elif 'Apple' in data:
return 'APPLE'
elif 'Lenovo' in data:
return 'Lenovo'
elif 'iPad' in data:
return 'APPLE'
elif 'Meizu' in data:
return 'Meizu'
elif 'SAMSUNG' in data:
return 'SAMSUNG'
elif 'samsung' in data:
return 'SAMSUNG'
elif 'Philips' in data:
return 'Philips'
elif 'Hisense' in data:
return 'Hisense'
else:
return data
op_delete_col = ['device_code3','mac1','ip2','ip2_sub']
tr_delete_col = ['code1','code2','acc_id2','acc_id3','market_code','market_type']
def delete_col(data,delete_cols):
new_data = data.copy()
for col in delete_cols:
new_data = new_data.drop([col],axis = 1)
gc.collect()
return new_data
'''
train_op = delete_col(train_op,op_delete_col)
train_tr = delete_col(train_tr,tr_delete_col)
test_op = delete_col(test_op,op_delete_col)
test_tr = delete_col(test_tr,tr_delete_col)
'''
def hourgetSeg(x):
if x >=0 and x<4:
return 1
elif x>=4 and x<8:
return 2
elif x>=8 and x<12:
return 3
elif x>=12 and x<16:
return 4
elif x>=16 and x<20:
return 5
elif x>=20 and x<24:
return 6
def max_list(data):
lt = []
for item in data:
lt.append(item)
temp = 0
for i in lt:
if lt.count(i) > temp:
max_str = i
temp = lt.count(i)
return max_str
def get_change_count(data):
new_list = []
count = 0
for item in data:
new_list.append(item)
for i in range(len(new_list)-1):
if(new_list[i]!=new_list[i+1]):
count +=1
return count
def get_time_mean(data):
time_list = []
for item in data:
time_list.append(item)
time_delta = []
if(len(time_list)>1):
for i in range(len(time_list)-1):
time_delta.append((time_list[i+1]-time_list[i]))
time_delta = np.array(time_delta)
return np.mean(time_delta)
else:
return 0
def get_time_var(data):
time_list = []
for item in data:
time_list.append(item)
time_delta = []
if(len(time_list)>1):
for i in range(len(time_list)-1):
time_delta.append((time_list[i+1]-time_list[i]))
time_delta = np.array(time_delta)
return np.var(time_delta)
else:
return 0
def get_10min_count(data):
time_list = []
if(len(data)>=1):
for item in data:
if(item + 600*1000 > max(data)):
time_list.append(item)
return len(time_list)
else:
return 0
def get_5min_count(data):
time_list = []
if(len(data)>=1):
for item in data:
if(item + 300*1000 > max(data)):
time_list.append(item)
return len(time_list)
else:
return 0
'''
def get_feature(op,trans,label):
op = op.groupby(['UID']).size().reset_index().rename(columns = {0:'UID_op_size'})
tr = trans.groupby(['UID']).size().reset_index().rename(columns = {0:'UID_tr_size'})
label = label.merge(op,on = ['UID'],how = 'left').fillna(0)
label = label.merge(tr,on = ['UID'],how = 'left').fillna(0)
for feature in op.columns[2:]:
label =feat_count(label,op,['UID'],feature,'op')
label =feat_nunique(label,op,['UID'],feature,'op')
for feature in trans.columns[2:]:
if trans[feature].dtype == 'object':
label =feat_count(label,trans,['UID'],feature)
label =feat_nunique(label,trans,['UID'],feature)
else:
label =feat_count(label,trans,['UID'],feature)
label =feat_nunique(label,trans,['UID'],feature)
label =feat_max(label,trans,['UID'],feature)
label =feat_min(label,trans,['UID'],feature)
label =feat_sum(label,trans,['UID'],feature)
label =feat_mean(label,trans,['UID'],feature)
label =feat_std(label,trans,['UID'],feature)
label = label.drop(['UID'],axis = 1)
gc.collect()
return label
'''
def get_cross(op,trans,label):
op_cross_1 = ['mode','version','os','device1','device_code1']
op_cross_2 = ['ip1','mac1','device_code3','device2','mac2','wifi','geo_code','ip1_sub']
op_cross_feature = []
for feat_1 in op_cross_1:
for feat_2 in op_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
op_cross_feature.append(col_name)
op[col_name] = op[feat_1].astype(str).values + '_' + op[feat_2].astype(str).values
for col in op_cross_feature:
temp = op[['UID',col]].merge(op.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
gc.collect()
del temp,temp1
tr_cross_1 = ['channel','amt_src1','merchant','trans_type1','device1','device_code1']
tr_cross_2 = ['acc_id1','trans_type2','device_code3','device2','mac1','ip1','market_code','market_type','ip1_sub','amt_src2']
tr_cross_feature = []
for feat_1 in tr_cross_1:
for feat_2 in tr_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
tr_cross_feature.append(col_name)
trans[col_name] = trans[feat_1].astype(str).values + '_' + trans[feat_2].astype(str).values
for col in tr_cross_feature:
temp = trans[['UID',col]].merge(trans.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
gc.collect()
del temp,temp1
print('cross done!')
label = label.drop(['UID'],axis = 1)
return label
def get_time_cross_nunique(op,trans,label):
op_cross_1 = ['mode','version','os','device1','device_code1']
op_cross_2 = ['ip1','mac1','device_code3','device2','mac2','wifi','geo_code','ip1_sub']
op_cross_feature = []
for feat_1 in op_cross_1:
for feat_2 in op_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
op_cross_feature.append(col_name)
op[col_name] = op[feat_1].astype(str).values + '_' + op[feat_2].astype(str).values
for col in op_cross_feature:
temp = op[['UID',col]].merge(op.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
label = label.merge(op.groupby(['UID'])[col].count().reset_index(),on = 'UID',how = 'left')
label = label.merge(op.groupby(['UID'])[col].nunique().reset_index(),on = 'UID',how = 'left')
for fea in ['day','hour','hourSeg']:
temp = op[['UID',col]].merge(op.groupby([col,fea]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
gc.collect()
del temp,temp1
tr_cross_1 = ['channel','amt_src1','merchant','trans_type1','device1','device_code1']
tr_cross_2 = ['acc_id1','trans_type2','device_code3','device2','mac1','ip1','market_code','market_type','ip1_sub','amt_src2']
tr_cross_feature = []
for feat_1 in tr_cross_1:
for feat_2 in tr_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
tr_cross_feature.append(col_name)
trans[col_name] = trans[feat_1].astype(str).values + '_' + trans[feat_2].astype(str).values
for col in tr_cross_feature:
temp = trans[['UID',col]].merge(trans.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
label = label.merge(trans.groupby(['UID'])[col].count().reset_index(),on = 'UID',how = 'left')
label = label.merge(trans.groupby(['UID'])[col].nunique().reset_index(),on = 'UID',how = 'left')
for fea in ['day','hour','hourSeg']:
temp = trans[['UID',col]].merge(trans.groupby([col,fea]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
gc.collect()
del temp,temp1
label = label.drop(['UID'],axis = 1)
print('time_cross_nunique done!')
return label
def get_cross_nunique(op,trans,label):
op_cross_1 = ['mode','version','os','device1','device_code1']
op_cross_2 = ['ip1','mac1','device_code3','device2','mac2','wifi','geo_code','ip1_sub']
op_cross_feature = []
for feat_1 in op_cross_1:
for feat_2 in op_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
op_cross_feature.append(col_name)
op[col_name] = op[feat_1].astype(str).values + '_' + op[feat_2].astype(str).values
for col in op_cross_feature:
temp = op[['UID',col]].merge(op.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
label = label.merge(op.groupby(['UID'])[col].count().reset_index(),on = 'UID',how = 'left')
label = label.merge(op.groupby(['UID'])[col].nunique().reset_index(),on = 'UID',how = 'left')
gc.collect()
del temp,temp1
tr_cross_1 = ['channel','amt_src1','merchant','trans_type1','device1','device_code1']
tr_cross_2 = ['acc_id1','trans_type2','device_code3','device2','mac1','ip1','market_code','market_type','ip1_sub','amt_src2']
tr_cross_feature = []
for feat_1 in tr_cross_1:
for feat_2 in tr_cross_2:
col_name = "cross_" + feat_1 + "_and_" + feat_2
tr_cross_feature.append(col_name)
trans[col_name] = trans[feat_1].astype(str).values + '_' + trans[feat_2].astype(str).values
for col in tr_cross_feature:
temp = trans[['UID',col]].merge(trans.groupby([col]).size().reset_index().rename(columns = {0:'size_%s'%col}),on =col, how = 'left')[['UID','size_%s'%col]]
temp1 = temp.groupby(['UID'])['size_%s'%col].mean().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].sum().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].max().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
temp1 = temp.groupby(['UID'])['size_%s'%col].min().reset_index()
label = label.merge(temp1, on = 'UID',how = 'left')
label = label.merge(trans.groupby(['UID'])[col].count().reset_index(),on = 'UID',how = 'left')
label = label.merge(trans.groupby(['UID'])[col].nunique().reset_index(),on = 'UID',how = 'left')
gc.collect()
del temp,temp1
label = label.drop(['UID'],axis = 1)
print('cross_nunique done!')
return label
def get_ratio(op,trans,label):
op_feature = ['mode','version','os','device1','device_code1','ip1','mac1',
'device_code3','device2','mac2','wifi','geo_code','ip1_sub',]
for i in range(len(op_feature)-1):
for j in range(i+1,len(op_feature)):
temp = op.groupby(['UID'])[op_feature[i],op_feature[j]].size().reset_index().rename(columns = {0:'count_i_j'})
temp1 = op.groupby(['UID'])[op_feature[i]].size().reset_index()
temp2 = op.groupby(['UID'])[op_feature[j]].size().reset_index()
temp['ratio_' + op_feature[i] + '_of_' + op_feature[j]] = temp['count_i_j'] / temp2[op_feature[j]]
temp['ratio_' + op_feature[j] + '_of_' + op_feature[i]] = temp['count_i_j'] / temp1[op_feature[i]]
label = label.merge(temp,on = 'UID',how = 'left')
del temp,temp1,temp2
gc.collect()
tr_feature = ['channel','amt_src1','merchant','trans_type1','device1','device_code1',
'acc_id1','trans_type2','device_code3','device2','mac1','ip1','market_code',
'market_type','ip1_sub','amt_src2','acc_id2','acc_id3']
for i in range(len(tr_feature)-1):
for j in range(i+1,len(tr_feature)):
temp = trans.groupby(['UID'])[tr_feature[i],tr_feature[j]].size().reset_index().rename(columns = {0:'count_i_j'})
temp1 = trans.groupby(['UID'])[tr_feature[i]].size().reset_index()
temp2 = trans.groupby(['UID'])[tr_feature[j]].size().reset_index()
temp['ratio_' + tr_feature[i] + '_of_' + tr_feature[j]] = temp['count_i_j'] / temp2[tr_feature[j]]
temp['ratio_' + tr_feature[j] + '_of_' + tr_feature[i]] = temp['count_i_j'] / temp1[tr_feature[i]]
label = label.merge(temp,on = 'UID',how = 'left')
del temp,temp1,temp2
gc.collect()
label = label.drop(['UID'],axis = 1)
print('ratio done!')
return label
def get_most_list(op,tr,label):
for item in op.columns :
if item!='UID':
result = op.groupby(['UID'])[item].apply(max_list).reset_index().rename(columns = {item:'op_%s'%item})
label = label.merge(result, on = ['UID'], how = 'left')
for item in tr.columns :
if item!='UID':
result = tr.groupby(['UID'])[item].apply(max_list).reset_index().rename(columns = {item:'tr_%s'%item})
label = label.merge(result, on = ['UID'], how = 'left')
label = label.drop(['UID'],axis = 1)
gc.collect()
print('Done!')
return label
def get_time_features(train_op,train_tr,label):
a = train_tr.groupby(['UID','day'])['timestamp'].apply(get_time_var).reset_index()
a.columns = ['UID','day'] + ['time_var']
b = train_tr.groupby(['UID','day'])['timestamp'].apply(get_time_mean).reset_index()
b.columns = ['UID','day'] + ['time_var']
c = train_tr.groupby(['UID','day'])['timestamp'].apply(get_10min_count).reset_index()
c.columns = ['UID','day'] + ['10min_count']
d = train_tr.groupby(['UID','day'])['timestamp'].apply(get_5min_count).reset_index()
d.columns = ['UID','day'] + ['5min_count']
for item in [a,b,c,d]:
result = item.groupby(['UID'])[item.columns[2]].mean().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].min().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].max().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].sum().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
a = train_op.groupby(['UID','day'])['timestamp'].apply(get_time_var).reset_index()
a.columns = ['UID','day'] + ['time_var']
b = train_op.groupby(['UID','day'])['timestamp'].apply(get_time_mean).reset_index()
b.columns = ['UID','day'] + ['time_var']
c = train_op.groupby(['UID','day'])['timestamp'].apply(get_10min_count).reset_index()
c.columns = ['UID','day'] + ['10min_count']
d = train_op.groupby(['UID','day'])['timestamp'].apply(get_5min_count).reset_index()
d.columns = ['UID','day'] + ['5min_count']
for item in [a,b,c,d]:
result = item.groupby(['UID'])[item.columns[2]].mean().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].min().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].max().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
result = item.groupby(['UID'])[item.columns[2]].sum().reset_index()
label = label.merge(result,on = ['UID'],how = 'left').fillna(0)
label = label.drop(['UID'],axis = 1)
gc.collect()
print('Done!')
return label
def get_all_time_fea(op,trans,label):
op_cross_1 = ['mode','version','os','device1','device_code1']
op_cross_2 = ['ip1','mac1','device_code3','device2','mac2','wifi','geo_code','ip1_sub']
time_fea = ['day','hour','hourSeg']
for fea1 in time_fea:
for fea2 in op_cross_1+op_cross_2:
temp1 = op.groupby(['UID',fea1])[fea2].count().reset_index()
label = label.merge(temp1.groupby('UID')[fea2].sum().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].mean().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].max().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].min().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].count().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].nunique().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].std().reset_index(), on = 'UID',how = 'left')
temp2 = op.groupby(['UID',fea1])[fea2].nunique().reset_index()
label = label.merge(temp2.groupby('UID')[fea2].sum().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].mean().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].max().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].min().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].count().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].nunique().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].std().reset_index(), on = 'UID',how = 'left')
gc.collect()
del temp1,temp2
tr_cross_1 = ['channel','amt_src1','merchant','trans_type1','device1','device_code1']
tr_cross_2 = ['acc_id1','trans_type2','device_code3','device2','mac1','ip1','market_code','market_type','ip1_sub','amt_src2']
for fea1 in time_fea:
for fea2 in tr_cross_1+tr_cross_2:
temp1 = trans.groupby(['UID',fea1])[fea2].count().reset_index()
label = label.merge(temp1.groupby('UID')[fea2].sum().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].mean().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].max().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].min().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].count().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].nunique().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp1.groupby('UID')[fea2].std().reset_index(), on = 'UID',how = 'left')
temp2 = trans.groupby(['UID',fea1])[fea2].nunique().reset_index()
label = label.merge(temp2.groupby('UID')[fea2].sum().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].mean().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].max().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].min().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].count().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].nunique().reset_index(), on = 'UID',how = 'left')
label = label.merge(temp2.groupby('UID')[fea2].std().reset_index(), on = 'UID',how = 'left')
gc.collect()
del temp1,temp2
label = label.drop(['UID'],axis = 1)
return label
def get_feature(op,trans,label):
for feature in op.columns[:]:
if feature not in ['day']:
if feature != 'UID':
label = label.merge(op.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
for deliver in ['ip1','mac1','mac2','geo_code']:
if feature not in deliver:
if feature != 'UID':
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
else:
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
else:
print(feature)
label =label.merge(op.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].max().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].min().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].sum().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].mean().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].std().reset_index(),on='UID',how='left')
for deliver in ['ip1','mac1','mac2']:
if feature not in deliver:
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].max().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].min().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].sum().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].mean().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].std().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
for feature in trans.columns[1:]:
if feature not in ['trans_amt','bal','day']:
if feature != 'UID':
label =label.merge(trans.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
for deliver in ['merchant','ip1','mac1','geo_code',]:
if feature not in deliver:
if feature != 'UID':
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
else:
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
#if feature in ['merchant','code2','acc_id1','market_code','market_code']:
# label[feature+'_z'] = 0
# label[feature+'_z'] = label[feature+'_y']/label[feature+'_x']
else:
print(feature)
label =label.merge(trans.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].max().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].min().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].sum().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].mean().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].std().reset_index(),on='UID',how='left')
for deliver in ['merchant','ip1','mac1']:
if feature not in deliver:
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].max().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].min().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].sum().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].mean().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].std().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
label = label.drop(['UID'],axis = 1)
print("Done")
return label
def get_new_feature(op,trans,label):
for feature in op.columns[:]:
if feature not in ['day']:
if feature != 'UID':
label = label.merge(op.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
for deliver in ['mode','ip1','ip1_sub','mac1','mac2','geo_code','device1','device_code1','device_code3']:
if feature not in deliver:
if feature != 'UID':
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
else:
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
gc.collect()
else:
print(feature)
label =label.merge(op.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].max().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].min().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].sum().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].mean().reset_index(),on='UID',how='left')
label =label.merge(op.groupby(['UID'])[feature].std().reset_index(),on='UID',how='left')
for deliver in ['ip1','ip1_sub','mac1','mac2','geo_code','device1','device_code1','device_code3','geo_code']:
if feature not in deliver:
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].max().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].min().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].sum().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].mean().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = op[['UID',deliver]].merge(op.groupby([deliver])[feature].std().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
gc.collect()
for feature in trans.columns[1:]:
if feature not in ['trans_amt','bal','day']:
if feature != 'UID':
label =label.merge(trans.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
for deliver in ['merchant','ip1','ip1_sub','mac1','geo_code','amt_src1','acc_id1','market_code','device_code1','device_code3']:
if feature not in deliver:
if feature != 'UID':
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
else:
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID_x','UID_y']]
temp = temp.groupby('UID_x')['UID_y'].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
gc.collect()
#if feature in ['merchant','code2','acc_id1','market_code','market_code']:
# label[feature+'_z'] = 0
# label[feature+'_z'] = label[feature+'_y']/label[feature+'_x']
else:
print(feature)
label =label.merge(trans.groupby(['UID'])[feature].count().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].nunique().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].max().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].min().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].sum().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].mean().reset_index(),on='UID',how='left')
label =label.merge(trans.groupby(['UID'])[feature].std().reset_index(),on='UID',how='left')
for deliver in ['merchant','ip1','mac1','ip1_sub','geo_code','amt_src1','acc_id1','market_code','device_code1','device_code3']:
if feature not in deliver:
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].count().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].nunique().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].sum().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].max().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].min().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].sum().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].mean().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
temp = trans[['UID',deliver]].merge(trans.groupby([deliver])[feature].std().reset_index(),on=deliver,how='left')[['UID',feature]]
temp = temp.groupby('UID')[feature].mean().reset_index()
temp.columns = ['UID',feature+deliver]
label =label.merge(temp,on='UID',how='left')
del temp
gc.collect()
label = label.drop(['UID'],axis = 1)
print("Done")
return label
def get_merchant_feature(trans,label):
tr = trans.groupby(['UID','merchant']).size().reset_index().rename(columns = {0:'UID_merchant_tr_size'})
for feature in trans.columns[2:]:
if feature!= 'merchant' and trans[feature].dtype == 'object':
tr =feat_count(tr,trans,['UID','merchant'],feature)
tr =feat_nunique(tr,trans,['UID','merchant'],feature)
elif feature!= 'merchant':
tr =feat_count(tr,trans,['UID','merchant'],feature)
tr =feat_nunique(tr,trans,['UID','merchant'],feature)
tr =feat_max(tr,trans,['UID','merchant'],feature)
tr =feat_min(tr,trans,['UID','merchant'],feature)
tr =feat_sum(tr,trans,['UID','merchant'],feature)
tr =feat_mean(tr,trans,['UID','merchant'],feature)
tr =feat_std(tr,trans,['UID','merchant'],feature)
for feature in tr.columns[2:]:
label =feat_count(label,tr,['UID'],feature)
label =feat_nunique(label,tr,['UID'],feature)
label =feat_max(label,tr,['UID'],feature)
label =feat_min(label,tr,['UID'],feature)
label =feat_sum(label,tr,['UID'],feature)
label =feat_mean(label,tr,['UID'],feature)
label =feat_std(label,tr,['UID'],feature)
label = label.drop(['UID'],axis = 1)
gc.collect()
print('Done!')
return label
##get_bool_hour_features:::
def get_hour_features(train,test,train_UID,test_UID):
train1 = train[['UID','hour']]
test1 = test[['UID','hour']]
data = pd.concat([train,test],axis = 0,sort=False)
base_train_csr = sparse.csr_matrix((len(train_UID), 0))
base_predict_csr = sparse.csr_matrix((len(test_UID), 0))
for feature in ['hour']:
data['hour_isin_03'] = data['hour'].apply(lambda x: 1 if (x<=3) &(x>=0) else 0)
data['hour_isin_46'] = data['hour'].apply(lambda x: 1 if (x<=6) &(x>=4) else 0)
data['hour_isin_712'] = data['hour'].apply(lambda x: 1 if (x<=12) &(x>=7) else 0)
data['hour_isin_1219'] = data['hour'].apply(lambda x: 1 if (x<=19) &(x>=12) else 0)
data['hour_isin_2023'] = data['hour'].apply(lambda x: 1 if (x<=23) &(x>=20) else 0)
train_process = train_UID.merge(data_process,on = ['UID'], how = 'left')
test_process = test_UID.merge(data_process,on = ['UID'], how = 'left')
cv.fit(data_process[feature])
base_train_csr = sparse.hstack((base_train_csr, cv.transform(train_process[feature].astype(str))), 'csr', 'int')
base_predict_csr = sparse.hstack((base_predict_csr, cv.transform(test_process[feature].astype(str))), 'csr', 'int')
print(feature + ' finished!')
gc.collect()
print('Done!')
return base_train_csr,base_predict_csr
def get_onehot_features(train,test,train_UID,test_UID):
cv = CountVectorizer(min_df=5)
data = pd.concat([train,test],axis = 0,sort = False)
base_train_csr = sparse.csr_matrix((len(train_UID), 0))
base_predict_csr = sparse.csr_matrix((len(test_UID), 0))
features = list(train.select_dtypes(include=[np.object]).columns)
for feature in features:
data[feature] = data[feature].astype(str).fillna(str(-1))
data_process = data.groupby(['UID'])[feature].apply(lambda x:','.join(x)).reset_index()
train_process = train_UID.merge(data_process,on = ['UID'], how = 'left').fillna(str(-1))
test_process = test_UID.merge(data_process,on = ['UID'], how = 'left').fillna(str(-1))
cv.fit(data_process[feature])
base_train_csr = sparse.hstack((base_train_csr, cv.transform(train_process[feature].astype(str))), 'csr', 'int')
base_predict_csr = sparse.hstack((base_predict_csr, cv.transform(test_process[feature].astype(str))), 'csr', 'int')
print(feature + ' finished!')
gc.collect()
print('Done!')
return base_train_csr,base_predict_csr
##处理时间戳函数
def timestamp(data):
arr = data['day'].astype(str) + ',' + data['time']
brr = arr.apply(lambda x:'2018-04-' + x.split(',')[0] + ' ' + x.split(',')[1])
data['timestamp'] = brr.apply(lambda x:int(time.mktime(time.strptime(x,"%Y-%m-%d %H:%M:%S"))))
print('Done!')
return brr
def timestamp2(data):
arr = data['day'].astype(str) + ',' + data['time']
brr = arr.apply(lambda x:'2018-06-' + x.split(',')[0] + ' ' + x.split(',')[1])
data['timestamp'] = brr.apply(lambda x:int(time.mktime(time.strptime(x,"%Y-%m-%d %H:%M:%S"))))
print('Done!')
return brr
def tpr_weight_funtion(y_true,y_predict):
d = pd.DataFrame()
d['prob'] = list(y_predict)
d['y'] = list(y_true)
d = d.sort_values(['prob'], ascending=[0])
y = d.y
PosAll = pd.Series(y).value_counts()[1]
NegAll = pd.Series(y).value_counts()[0]
pCumsum = d['y'].cumsum()
nCumsum = np.arange(len(y)) - pCumsum + 1
pCumsumPer = pCumsum / PosAll
nCumsumPer = nCumsum / NegAll
TR1 = pCumsumPer[abs(nCumsumPer-0.001).idxmin()]
TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()]
TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()]
return 0.4 * TR1 + 0.3 * TR2 + 0.3 * TR3
def tpr_weight(y_predict,d_train):
y_true = d_train.get_label().copy()
d = pd.DataFrame()
d['prob'] = list(y_predict)
d['y'] = list(y_true)
d = d.sort_values(['prob'], ascending=[0])
y = d.y
PosAll = pd.Series(y).value_counts()[1]
NegAll = pd.Series(y).value_counts()[0]
pCumsum = d['y'].cumsum()
nCumsum = np.arange(len(y)) - pCumsum + 1
pCumsumPer = pCumsum / PosAll
nCumsumPer = nCumsum / NegAll
TR1 = pCumsumPer[abs(nCumsumPer-0.001).idxmin()]
TR2 = pCumsumPer[abs(nCumsumPer-0.005).idxmin()]
TR3 = pCumsumPer[abs(nCumsumPer-0.01).idxmin()]
return ('TPR_weight',0.4 * TR1 + 0.3 * TR2 + 0.3 * TR3,True)
def lgb_crossvalidation(train,label,test,test_id,round = 5000):
lgb_model = lgb.LGBMClassifier(boosting_type='gbdt', num_leaves=100, reg_alpha=3, reg_lambda=5, max_depth=-1,
n_estimators=round, objective='binary', subsample=0.9, colsample_bytree=0.77, subsample_freq=1, learning_rate=0.05,
random_state=1000, n_jobs=16, min_child_weight=4, min_child_samples=5, min_split_gain=0)
skf = StratifiedKFold(n_splits=5, random_state=2018, shuffle=True)
best_score = []
oof_preds = np.zeros(train.shape[0])
sub_preds = np.zeros(test_id.shape[0])
for index, (train_index, test_index) in enumerate(skf.split(train, label)):
lgb_model.fit(train[train_index], label[train_index], verbose=50,
eval_set=[(train[train_index], label[train_index]),
(train[test_index], label[test_index])], early_stopping_rounds=100)
best_score.append(lgb_model.best_score_['valid_1']['binary_logloss'])
print(best_score)
oof_preds[test_index] = lgb_model.predict_proba(train[test_index], num_iteration=lgb_model.best_iteration_)[:,1]
test_pred = lgb_model.predict_proba(test, num_iteration=lgb_model.best_iteration_)[:, 1]
sub_preds += test_pred / 5
#print('test mean:', test_pred.mean())
#predict_result['predicted_score'] = predict_result['predicted_score'] + test_pred
m = tpr_weight_funtion(y_predict=oof_preds,y_true=label)
print('final_tpr: ',m)
sub = pd.read_csv('./data/sub.csv')
sub['Tag'] = sub_preds
print('mean: ',sub_preds.mean())
sub.to_csv('./submit/lgb_baseline_%s.csv'%str(m),index=False)