Rule Engine with Machine Learning, Deep Learning, Neural Network

The following figure shows the deep learning process of this example model;

The following is a coding of this model;

/*
 * This model is an example of deep learning. Three neural models  are 
 * evaluated. Then maximum, minimum and average of three neural network 
 * outputs are computed. Then, these six values are fed to Model4 and 
 * Model5 models to produce final scores. Then final average of final scores
 * is computed. Finally, final average as well as max/min/avg values are used to 
 * classify into verbal class names.
 *
 */

// declare runtime options;
DECLARE OPTIONS EXPLICIT,ERRORNULL,MAXFIRE(0);
// DECLARE OPTIONS USEBETWEEN('2017-1-1', '2017-12-31');

// define input data fields and values in appearing order;
DECLARE Gender AS STRING INPUT VALUES IN GENDER OF Model1;
DECLARE Race AS STRING INPUT VALUES IN RACE OF Model1;
DECLARE Jobpost AS STRING INPUT VALUES IN JOBPOST OF Model1;
DECLARE CLASSIFICATION1 AS STRING INPUT VALUES IN 
			CLASSIFICATION1 OF Model1;
DECLARE EDUCLEVEL AS STRING INPUT 
			VALUES IN EDUCLEVEL OFModel1;
DECLARE AGEGROUP AS STRING INPUT 
			VALUES IN AGEGROUP OF Model1;
DECLARE Salary AS INTEGER INPUT;

// define output fields in appearing order;
DECLARE "Model1 score", "Model2 score", "Model3 score" 
			AS REAL OUTPUT;
DECLARE "" AS STRING OUTPUT INITIAL VALUE '';
DECLARE "Maximum score", "Minimum score", "Average score" 
			AS REAL OUTPUT;
DECLARE " " AS STRING OUTPUT INITIAL VALUE '';
DECLARE "Final score1", "Final score2", "Final average" 
			AS REAL OUTPUT;
DECLARE "  " AS STRING OUTPUT INITIAL VALUE '';
DECLARE "Risk level", "Risk level (MMA)" AS STRING OUTPUT;

/*
 * DATA MODEL EVALUATION
 */

RULE 1: // evaluate model 1;
IF TRUE THEN
	SET "Model1 score" AS PREDICT(Model1) USING(
		GENDER AS Gender,
		RACE AS Race,
		JOBPOST AS Jobpost,
		CLASSIFICATION1 AS CLASSIFICATION1,
		EDUCLEVEL  AS  EDUCLEVEL,
		AGEGROUP AS  AGEGROUP,
		SALARY AS Salary
	) 
END;

RULE 2: // evaluate model 2;
IF TRUE THEN
	SET "Model2 score" AS PREDICT(Model2) USING(
		GENDER AS Gender,
		RACE AS Race,
		JOBPOST AS Jobpost,
		CLASSIFICATION1 AS CLASSIFICATION1,
		EDUCLEVEL  AS  EDUCLEVEL,
		AGEGROUP AS  AGEGROUP,
		SALARY AS Salary
	) 
END;

RULE 3: // evaluate model 3;
IF TRUE THEN
	SET "Model3 score" AS PREDICT(Model3) USING(
		GENDER AS Gender,
		RACE AS Race,
		JOBPOST AS Jobpost,
		CLASSIFICATION1 AS CLASSIFICATION1,
		EDUCLEVEL  AS  EDUCLEVEL,
		AGEGROUP AS  AGEGROUP,
		SALARY AS Salary
	) 
END;


/*
 * COMPUTE MAXIMUM / MINIMUM / AVERAGE
 */

RULE 4: // compute max/min/avg;
IF TRUE THEN 
{
	SET "Maximum score" 
		AS MAX("Model1 score", "Model2 score", "Model3 score");
	SET "Minimum score" 
		AS MIN("Model1 score", "Model2 score", "Model3 score");
	SET "Average score" 
		AS AVG("Model1 score", "Model2 score", "Model3 score");
}
END;


/*
 * EVALUATE INTEGRATION NETWORKS
 */

RULE 5: // evaluate model 4;
IF TRUE THEN
	SET "Final score1" AS PREDICT(Model4) USING(
		Model1Score AS "Model1 score",
		Model2Score AS "Model2 score",
		Model3Score AS "Model3 score",
		MaximumScore AS "Maximum score",
		MinimumScore  AS  "Minimum score",
		AverageScore AS  "Average score"
	) 
END;

RULE 6: // evaluate model 5;
IF TRUE THEN
	SET "Final score2" AS PREDICT(Model5) USING(
		Model1Score AS "Model1 score",
		Model2Score AS "Model2 score",
		Model3Score AS "Model3 score",
		MaximumScore AS "Maximum score",
		MinimumScore  AS  "Minimum score",
		AverageScore AS  "Average score"
	) 
END;


/*
 * COMPUTE INTEGRATION MODEL AVERAGE
 */

RULE 7:  // compute average of final scores
IF TRUE THEN 
	SET "Final average" AS AVG("Final score1", "Final score2")
END;


/*
 * FINAL CLASSIFICATIONS
 */

RULE  8:   // classify risk levels with final average;
CASE  
WHEN "Final average" >= 0.7 THEN 
	SET "Risk level" AS 'Very high risk'    
WHEN "Final average" < 0.01 THEN 
	SET "Risk level" AS 'Low risk'    
WHEN "Final average" < 0.1 THEN 
	SET "Risk level" AS 'Medium risk'

WHEN "Maximum score" >= 0.6 OR "Minimum score" >= 0.2 
		OR "Average score" >= 0.5 THEN 
	SET "Risk level" AS 'Very high risk'    
WHEN "Maximum score" >= 0.3 OR "Minimum score" >= 0.05 
		OR "Average score" >= 0.2 THEN 
	SET "Risk level" AS 'High risk'    
WHEN "Maximum score" >= 0.2 OR "Minimum score" >= 0.0 
		OR "Average score" >= 0.1 THEN 
	SET "Risk level" AS 'Medium risk'    
ELSE 
	SET "Risk level" AS 'Low risk'   
END;

RULE  9:   // classify risk levels without final average;
CASE  
WHEN "Maximum score" >= 0.6 OR "Minimum score" >= 0.2 
		OR "Average score" >= 0.5 THEN 
	SET "Risk level (MMA)" AS 'Very high risk'    
WHEN "Maximum score" >= 0.3 OR "Minimum score" >= 0.05 
		OR "Average score" >= 0.2 THEN 
	SET "Risk level (MMA)" AS 'High risk'    
WHEN "Maximum score" >= 0.2 OR "Minimum score" >= 0.0 
		OR "Average score" >= 0.1 THEN 
	SET "Risk level (MMA)" AS 'Medium risk'    
ELSE 
	SET "Risk level (MMA)" AS 'Low risk'   
END;