Create a Function and Prepare Test Data in R

1applyFeatureTransformations <- function(dt,use_kept_column = keep_columns,use_median_impute_model=median_impute_model,  use_dummy_model=dummy_model,use_trans_model=trans_model){
2    #consolidate loan status
3    dt$loan_status = ifelse(str_detect(dt$loan_status,"Paid"),dt$loan_status,"Default")
4    #parse int_rate
5    dt$int_rate = (as.numeric(gsub(pattern = "%",replacement = "",x = dt$int_rate)))
6    #impute median
7    dt = predict(median_impute_model,dt)
8    #parse revol_util
9    dt$revol_util = (as.numeric(gsub(pattern = "%",replacement = "",x = dt$int_rate)))
10        dt$earliest_cr_line = parse_date_time(str_c("01",dt$issue_d),"dmy" ) - parse_date_time(str_c("01",dt$earliest_cr_line),"dmy" )
11        dt$earliest_cr_line = as.numeric(dt$earliest_cr_line,units = "days")
12    #binary variables for addr_state
13    dt$is_ny = ifelse(dt$addr_state=="NY",1,0)
14    dt$is_pa = ifelse(dt$addr_state=="PA",1,0)
15    dt$is_nj = ifelse(dt$addr_state=="NJ",1,0)
16    dt$is_oh = ifelse(dt$addr_state=="OH",1,0)
17    dt$is_fl = ifelse(dt$addr_state=="FL",1,0)
18    dt$is_co = ifelse(dt$addr_state=="CO",1,0)
19    dt$is_ga = ifelse(dt$addr_state=="GA",1,0)
20    dt$is_va = ifelse(dt$addr_state=="VA",1,0)
21    dt$is_az = ifelse(dt$addr_state=="AZ",1,0)
22    dt$is_ca = ifelse(dt$addr_state=="CA",1,0)
23    #transform transactions
24    dt$annual_inc = dt$annual_inc/dt$funded_amnt
25    dt$revol_bal = dt$revol_bal/dt$funded_amnt
26    dt$avg_cur_bal = dt$avg_cur_bal/dt$funded_amnt
27    dt$bc_open_to_buy = dt$bc_open_to_buy/dt$funded_amnt
28    #if purpose falling outside of recognized values
29    all_purpose = c('debt_consolidation','small_business','other','credit_card','major_purchase','moving','home_improvement','house','car','medical','renewable_energy','vacation','wedding')
30    dt$purpose = ifelse(dt$purpose %in% all_purpose,dt$purpose,"other")
31    #create dummy variables
32    loan_status = dt$loan_status
33    dt = as.data.frame(predict(use_dummy_model,dt))
34    dt$loan_status = loan_status
35    #center,scale data
36    trans_model_test = preProcess(dt,method=c("center","scale"))
37    dt = predict(trans_model_test, dt)    
38    #remove all unused features
39    colnames(dt) = str_replace_all(colnames(dt)," ","_")
40    colnames(dt) = str_replace_all(colnames(dt),"<","_")
41    colnames(dt) = str_replace_all(colnames(dt),"/","_")
42    dt = dt[use_kept_column]
43    #set loan with status 'Fully Paid' as a positive sample
44    dt$loan_status = ifelse(dt$loan_status == "Fully Paid","Fully.Paid",dt$loan_status)
45    dt$loan_status = factor(dt$loan_status,levels = c("Default","Fully.Paid"))
46    return(dt)
47}
48

1#later used for evaluate investment
2data_test_grade = data_test$grade
3data_test_funded_amnt = data_test$funded_amnt
4data_test_total_pymnt = data_test$total_pymnt
5