In my project I am combining three unique input sources to generate one score. Imagine this formula
Integrated score = weight_1 * Score_1 + weight_2 * Score_2 + weight_3 * Score_3
So, to do this, I have utilised the following code
DATA w_matrix_t;
/*Create a row count to identify the model weight combination*/
RETAIN model_combination;
model_combination = 0;
DO n_1 = 0 TO 100 BY 1;
DO n_2 = 0 TO 100 BY 1;
IF (100 - n_1 - n_2) ge 0 AND (100 - n_1 - n_2) le 100 THEN DO;
n_3 = 100 - n_1 - n_2;
model_combination+1;
output;
END;
END;
END;
RUN;
DATA w_matrix;
SET w_matrix_t;
w_1 = n_1/100;
w_2 = n_2/100;
w_3 = n_3/100;
/*Drop the old variables*/
DROP n_1 n_2 n_3;
RUN;
PROC SQL;
CREATE TABLE weights_added AS
SELECT
w.model_combination
, w.w_1
, w.w_2
, w.w_3
, fit.name
, fit.logsalary
, (
w.w_1*fit.crhits +
w.w_2*fit.natbat +
w.w_3*fit.nbb
) AS y_hat_int
FROM
work.w_matrix AS w
CROSS JOIN
sashelp.baseball AS fit
ORDER BY
model_combination;
QUIT;
My question is, is there a more efficient way of making this join? The purpose is to create a large table that contains the entire sashelp.baseball dataset duplicated for all combinations of weights.
In my live data, I have three input sources of 46,000 observations each and that cross join takes 1 hour. I also have three input sources of 465,000 each, I imagine this will take a very long time.
The reason I do it this way is because I calculate my Somers' D using Proc freq and by group processing (by model combination)
5000 copies of a 500,000 row table will be a pretty big table with 2.5B rows
Here is an example of data step stacking; one copy of have data set per row of weights. The example features SET weights to process each weight (via implicit loop) and SET have POINT= / OUTPUT inside an explicit loop (the inner loop). The inner loop copies the data while it computes the weighted sum.
data have;
set sashelp.baseball (obs=200); * keep it small for demonstration;
run;
data weights (keep=comboId w1 w2 w3);
do i = 0 to 100; do j = 0 to 100; if (i+j) <= 100 then do;
comboId + 1;
w1 = i / 100;
w2 = j / 100;
w3 = (100 - i - j) / 100;
output;
end; end; end;
run;
data want (keep=comboid w1-w3 name logsalary y_hat_int);
do while (not endOfWeights);
set weights end = endOfWeights;
do row = 1 to RowsInHave;
set have (keep=name logsalary crhits natbat nbb) nobs = RowsInHave point = row;
y_hat_int = w1 * crhits + w2 * natbat + w3 * nbb;
output;
end;
end;
stop;
run;
proc freq data=want noprint;
by comboId;
table y_hat_int / out=freqout ;
format y_hat_int 4.;
run;
proc contents data=want;
run;
Off the cuff, a single table containing 5,151 copies of a 200 row extract from baseball is nominally 72.7MB, so expect 5,151 copies of a 465K row table to have ~ 2.4G rows and be ~ 170 GB disk. On a disk spinning @7200 achieving max performance throughout your looking at best 20 minutes just writing, and probably much more.