My code can't give you "this same type of information within Enterprise Miner".
But my code can give you the better WOE by Genetic Algorithm under SAS/IML .
I don't know if you need my code . Browse it if you would like to better WOE.
My code take the famous German Credit Card data as an example.
/*
proc import datafile='c:\temp\1--German Credit.xlsx' dbms=xlsx out=have replace;
run;
*/
%let var=history;
%let group=6 ;
%let n_iter=100;
data temp;
set have;
keep &var good_bad ;
run;
proc sql noprint;
select sum(good_bad='bad'),sum(good_bad='good'),
floor(min(&var)),ceil(max(&var)) into : n_bad,: n_good,: min,: max
from temp;
quit;
%put &n_bad &n_good &min &max;
proc sort data=temp;by &var ;run;
proc iml;
use temp(where=(&var is not missing));
read all var {&var good_bad};
close;
start function(x) global(bin,&var ,good_bad,group,woe);
if countunique(x)=group-1 then do;
col_x=t(x);
call sort(col_x,1);
cutpoints= .M//col_x//.I ;
b=bin(&var ,cutpoints,'right');
if countunique(b)=group then do;
do i=1 to group;
idx=loc(b=i);
temp=good_bad[idx];
n_bad=sum(temp='bad');
n_good=sum(temp='good');
bad_dist=n_bad/&n_bad ;
good_dist=n_good/&n_good ;
if Bad_Dist>0.05 & Good_Dist>0.05 then woe[i]=log(Bad_Dist/Good_Dist);
else woe[i]=.;
end;
if countmiss(woe)=0 then do;
/*
xx=j(group,1,1)||woe||woe##2;
*/
xx=j(group,1,1)||woe;
beta=solve(xx`*xx,xx`*bin);
yhat=xx*beta;
sse=ssq(bin-yhat);
end;
else sse=999999;
end;
else sse=999999;
end;
else sse=999999;
return (sse);
finish;
group=&group ;
bin=t(1:group);
woe=j(group,1,.);
encoding=j(2,group-1,&min );
encoding[2,]=&max ;
id=gasetup(2,group-1,123456789);
call gasetobj(id,0,"function");
call gasetsel(id,10,1,1);
call gainit(id,1000,encoding);
niter = &n_iter ;
do i = 1 to niter;
call garegen(id);
call gagetval(value, id);
end;
call gagetmem(mem, value, id, 1);
col_mem=t(mem);
call sort(col_mem,1);
cutpoints= .M//col_mem//.I ;
b=bin(&var ,cutpoints,'right');
create cutpoints var {cutpoints};
append;
close;
create group var {b};
append;
close;
print value[l = "Min Value:"] ;
call gaend(id);
quit;
data all_group;
set temp(keep=&var rename=(&var=b) where=(b is missing)) group;
run;
data all;
merge all_group temp;
rename b=group;
run;
title "变量: &var" ;
proc sql;
create table woe_&var as
select group label=' ',
min(&var) as min label='最小值',max(&var) as max label='最大值',count(*) as n label='频数',
calculated n/(select count(*) from all) as per format=percent7.2 label='占比',
sum(good_bad='bad') as n_bad label='bad的个数',sum(good_bad='good') as n_good label='good的个数',
sum(good_bad='bad')/(select sum(good_bad='bad') from all ) as bad_dist label='bad的占比',
sum(good_bad='good')/(select sum(good_bad='good') from all ) as good_dist label='good的占比',
log(calculated Bad_Dist/calculated Good_Dist) as woe
from all
group by group
order by woe;
create index group on woe_&var;
select *,sum( (Bad_Dist-Good_Dist)*woe ) as iv
from woe_&var ;
quit;
title ' ';
data fmt_&var ;
set cutpoints;
start=lag(cutpoints);
end=cutpoints;
if start=.M then hlo='IL';
if end=.I then hlo='IH';
if _n_ ne 1 then do;group+1;output;end;
run;
data fmt_&var(index=(group));
merge fmt_&var woe_&var(keep=group woe);
by group;
retain fmtname "&var" type 'I';
keep group fmtname type start end woe hlo;
rename woe=label;
label group=' ';
run;
proc format cntlin=fmt_&var library=z;
run;
/*
proc print data=woe_&var noobs label;run;
proc sgplot data=woe_&var;
reg y=group x=woe/degree=2 cli clm jitter;
run;
*/
proc sgplot data=woe_&var noautolegend;
vbar group/response=woe nostatlabel missing;
vline group/response=woe nostatlabel missing markers MARKERATTRS=(symbol=circlefilled
size=12) MARKERFILLATTRS=(color=white) MARKEROUTLINEATTRS=graphdata1
FILLEDOUTLINEDMARKERS;
run;
ods select fitplot;
proc reg data=woe_&var;
model group=woe/ cli clm ;
quit;
proc copy in=work out=z;
select woe_: fmt_: ;
run;
