Granular Monotonic Binning in SAS

In the post (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), it is shown how to do a finer monotonic binning with isotonic regression in R.

Below is a SAS macro implementing the monotonic binning with the same idea of isotonic regression. This macro is more efficient than the one shown in (https://statcompute.wordpress.com/2012/06/10/a-sas-macro-implementing-monotonic-woe-transformation-in-scorecard-development) without iterative binning and is also able to significantly increase the binning granularity.

%macro monobin(data = , y = , x = );

options mprint mlogic;

data _data_ (keep = _x _y);

set &data;

where &y in (0, 1) and &x ~= .;

_y = &y;

_x = &x;

run;

proc transreg data = _last_ noprint;

model identity(_y) = monotone(_x);

output out = _tmp1 tip = _t;

run;

proc summary data = _last_ nway;

class _t_x;

output out = _data_ (drop = _freq_ _type_) mean(_y) = _rate;

run;

proc sort data = _last_;

by _rate;

run;

data _tmp2;

set _last_;

by _rate;

_idx = _n_;

if _rate = 0 then _idx = _idx + 1;

if _rate = 1 then _idx = _idx - 1;

run;

proc sql noprint;

create table

_tmp3 as

select

a.*,

b._idx

from

_tmp1 as a inner join _tmp2 as b

on

a._t_x = b._t_x;

create table

_tmp4 as

select

a._idx,

min(a._x) as _min_x,

max(a._x) as _max_x,

sum(a._y) as _bads,

count(a._y) as _freq,

mean(a._y) as _rate,

sum(a._y) / b.bads as _bpct,

sum(1 - a._y) / (b.freq - b.bads) as _gpct,

log(calculated _bpct / calculated _gpct) as _woe,

(calculated _bpct - calculated _gpct) * calculated _woe as _iv

from

_tmp3 as a, (select count(*) as freq, sum(_y) as bads from _tmp3) as b

group by

a._idx;

quit;

title "Monotonic WoE Binning for %upcase(%trim(&x))";

proc print data = _last_ label noobs;

var _min_x _max_x _bads _freq _rate _woe _iv;

label

_min_x = "Lower"

_max_x = "Upper"

_bads = "#Bads"

_freq = "#Freq"

_rate = "BadRate"

_woe = "WoE"

_iv = "IV";

sum _bads _freq _iv;

run;

title;

%mend monobin;

Below is the sample output for LTV, showing an identical binning scheme to the one generated by the R isobin() function.

更多相关文章请看 https://statcompute.wordpress.com/category/scorecard/

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