The FACTMAC Procedure

Note: Input data must be in a CAS table that is accessible in your CAS session. You must refer to this table by using a two-level name. The first level must be a CAS engine libref, and the second level must be the table name. For more information, see the sections Using CAS Sessions and CAS Engine Librefs and Loading a SAS Data Set onto a CAS Server in Chapter 2, Shared Concepts.

This example shows how to use the FACTMAC procedure to learn a factorization machine model from observations in a SAS data table. This example uses the cars data set in the Sashelp library and illustrates the prediction of gas mileage of cars based on make and model. The analysis uses four variables: car make, car model, car type, and a variable named mpg_city, which measures the car’s fuel usage (in miles per gallon) for city driving. The remaining variables in the data table are not used.

You can load the cars data set into your CAS session by naming your CAS engine libref in the first statement of the following DATA step:

data mycas.cars;
   set sashelp.cars;
run;

These statements assume that your CAS engine libref is named mycas, but you can substitute any appropriately defined CAS engine libref.

The following statements run PROC FACTMAC and output the results to ODS tables:

proc factmac data=mycas.cars outmodel=mycas.factors maxiter=50
             nfactors=5  learnstep=0.002;
   input make model type /level=nominal;
   target mpg_city  /level=interval;
   output out=mycas.score_out copyvars=(make model type mpg_city);
run;

proc print data=mycas.factors(obs=48);
run;

The NFACTORS= option requests that the model estimate five factors, the LEARNSTEP= option sets the optimization learning step to 0.002, the MAXITER= option requests that the optimization stop after 50 iterations, and the OUTMODEL= option requests that the model parameters be written to the mycas.factors data table. The INPUT statement specifies that the make, model, and type variables are to be used as nominal inputs. The TARGET statement specifies that mpg_city is the target variable to be predicted. The OUTPUT statement requests that the predictions be written to the data table mycas.score_out and that the make, model, type and mpg_city variables be copied from the mycas.cars data table to the mycas.score_out data table.

Figure 1 shows the global bias, the bias values for each level, and the list of the factors for the first 48 observations.