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Copy pathProcessTransactionsTest.py
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204 lines (106 loc) · 6.85 KB
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# -*- coding: utf-8 -*-
"""
Created on Sun Nov 19 14:29:09 2017
@author: TomoPC
"""
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
import time
from collections import Counter
import pandas as pd
import numpy as np
import seaborn as sns
import datetime as dt
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import os
train=pd.read_csv('test_with_members.csv')
train=train.drop(train.columns[0], axis=1)
train_orig=pd.read_csv('test_orig_with_members.csv')
train_orig=train_orig.drop(train_orig.columns[0], axis=1)
date=dt.date(2017, 3, 1)
path=os.getcwd()
transactions=pd.read_csv(path+'\\Data\\transactions.csv')
transactionsv2=pd.read_csv(path+'\\Data\\transactions_v2.csv')
transactions=pd.concat([transactions, transactionsv2])
del transactionsv2
transactions['payment_method_id'] = transactions['payment_method_id'].astype('int8')
transactions['payment_plan_days'] = transactions['payment_plan_days'].astype('int16')
transactions['plan_list_price'] = transactions['plan_list_price'].astype('int16')
transactions['actual_amount_paid'] = transactions['actual_amount_paid'].astype('int16')
transactions['is_auto_renew'] = transactions['is_auto_renew'].astype('int8') # chainging the type to boolean
transactions['is_cancel'] = transactions['is_cancel'].astype('int8')#changing the type to boolean
transactions['membership_expire_date'] = pd.to_datetime(transactions['membership_expire_date'].astype(str))
transactions['transaction_date'] = pd.to_datetime(transactions['transaction_date'].astype(str))
transactions=transactions[(transactions['membership_expire_date'] < date)]
transactions=transactions[(transactions['membership_expire_date'] > transactions['transaction_date'])]
transactions['membership_expire_date'] = transactions['membership_expire_date'].apply(dt.datetime.toordinal)
transactions['transaction_date'] = transactions['transaction_date'].apply(dt.datetime.toordinal)
#Get Average Time between transactions and Number of Transactions
transac_sort=pd.concat([transactions['msno'], transactions['transaction_date']], axis=1, keys=['msno', 'transaction_date'])
transac_sort=transac_sort.sort_values(['transaction_date'],ascending=True)
results=transac_sort.groupby('msno')['transaction_date'].agg(['max','min', 'count']).reset_index()
results['days_btw_transacs']=((results['max']-results['min'])/results['count']).astype(int)
results=results.rename(columns={'count': 'transaction_count'})
results=results.rename(columns={'min': 'registration_init_time_v2'})
train = pd.merge(left = train,right = results[['msno', 'days_btw_transacs', 'transaction_count', 'registration_init_time_v2']],how = 'left',on=['msno'])
train_orig = pd.merge(left = train_orig,right = results[['msno', 'days_btw_transacs', 'transaction_count', 'registration_init_time_v2']],how = 'left',on=['msno'])
#test = pd.merge(left = test,right = results[['msno', 'days_btw_transacs', 'transaction_count', 'registration_init_time_v2']],how = 'left',on=['msno'])
del transac_sort, results
#Get percent of transactions that were auto_renewed
count_auto_renew=transactions.groupby('msno')['is_auto_renew'].sum().reset_index()
count_auto_renew=count_auto_renew.rename(columns={'is_auto_renew': 'percent_auto_renew'})
train = pd.merge(left = train,right = count_auto_renew,how = 'left',on=['msno'])
train_orig = pd.merge(left = train_orig,right = count_auto_renew,how = 'left',on=['msno'])
#test = pd.merge(left = test,right = count_auto_renew,how = 'left',on=['msno'])
train['percent_auto_renew']=train['percent_auto_renew'].divide(train['transaction_count'])
train_orig['percent_auto_renew']=train_orig['percent_auto_renew'].divide(train_orig['transaction_count'])
#test['percent_auto_renew']=test['percent_auto_renew'].divide(test['transaction_count'])
del count_auto_renew
#Get Last Expiration date
results=transactions.groupby('msno')['membership_expire_date'].agg(['max']).reset_index()
results=results.rename(columns={'max': 'expiration_date'})
train = pd.merge(left = train,right = results, how = 'left',on=['msno'])
train_orig = pd.merge(left = train_orig,right = results, how = 'left',on=['msno'])
#test = pd.merge(left = test,right = results, how = 'left',on=['msno'])
del results
#Get Correct Registration Init Time
#Cumulative difference between list and paid
#Normalized Mean Paid
#Normalized Variance of Actual Paid
#Aggregate grouped by list and paid
transactions['total_membership_time']=transactions['membership_expire_date']-transactions['transaction_date']
transactions['amount_paid_per_day']=(transactions['actual_amount_paid'])/(transactions['payment_plan_days']+1)
transactions['list_price_per_day']=(transactions['plan_list_price'])/(transactions['payment_plan_days']+1)
paidperday=transactions.groupby('msno')['amount_paid_per_day'].agg(['max', 'min', 'mean', 'var'])
listperday=transactions.groupby('msno')['list_price_per_day'].agg(['max', 'min', 'mean'])
totaltime=transactions.groupby('msno')['total_membership_time'].agg(['sum'])
results=totaltime
results=results.drop(['sum'], axis=1)
results['mean_diff_pricevspaid']=(listperday['mean']-paidperday['mean'])
results['mean_amount_paid']=(paidperday['mean'])
#results['var_amount_paid']=(paidperday['var'])
results['cumulative_price_change']=paidperday['max']-paidperday['min']
results['total_paid_membership_time']=totaltime['sum']
results=results.reset_index()
train = pd.merge(left = train,right = results,how = 'left',on=['msno'])
train_orig = pd.merge(left = train_orig,right = results,how = 'left',on=['msno'])
#test = pd.merge(left = test,right = results,how = 'left',on=['msno'])
del results, paidperday, listperday, totaltime
#Get most common payment method
results=transactions.groupby('msno')['payment_method_id'].agg(lambda x: x.value_counts().index[0]).reset_index()
train = pd.merge(left = train,right = results,how = 'left',on=['msno'])
train_orig = pd.merge(left = train_orig,right = results,how = 'left',on=['msno'])
#test = pd.merge(left = test,right = results,how = 'left',on=['msno'])
train.to_csv('test_tr.csv')
train_orig.to_csv('test_orig_tr.csv')
#test.to_csv('test_tr.csv')
#Time since last price change
d = dt.strptime('2017-04-01', '%Y-%m-%d').date()
d=d.toordinal()
#transac_sort=pd.concat([transactions['msno'], transactions['transaction_date'], transactions['list_price_per_day']], axis=1, keys=['msno', 'transaction_date', 'list_price_per_day'])
#transac_sort=transac_sort.sort_values(['transaction_date'],ascending=True)
#results=transac_sort.groupby('msno')['list_price_per_day'].agg(['max','min', 'count']).reset_index()