Added boolean parameter for disabling apriori precision control (control_apriori_precision)

Author: agudys

adaa.analytics.rules/build.gradle

@@ -27,7 +27,7 @@ codeQuality {
 }
 
 sourceCompatibility = 1.8
-version = '1.5.1'
+version = '1.5.2'
 
 
 jar {

adaa.analytics.rules/src/main/java/adaa/analytics/rules/logic/induction/ClassificationFinder.java

@@ -469,7 +469,10 @@ protected ElementaryCondition induceCondition(
 		Set<Integer> positives = rule.getCoveredPositives();
 		double P = rule.getWeighted_P();
 		double N = rule.getWeighted_N();
-		double apriori_prec = P / (P + N);
+
+		double apriori_prec = params.isControlAprioriPrecision()
+				? P / (P + N)
+				: Double.MIN_VALUE;
 
 		List<Future<ConditionEvaluation>> futures = new ArrayList<Future<ConditionEvaluation>>();
 

adaa.analytics.rules/src/main/java/adaa/analytics/rules/logic/induction/ClassificationFinderPrecalculated.java

@@ -146,7 +146,9 @@ protected ElementaryCondition induceCondition(
 
         double classId = ((SingletonSet)rule.getConsequence().getValueSet()).getValue();
         Set<Integer> positives = rule.getCoveredPositives();
-        double apriori_prec = rule.getWeighted_P() / (rule.getWeighted_P() + rule.getWeighted_N());
+        double apriori_prec = params.isControlAprioriPrecision()
+                ? rule.getWeighted_P() / (rule.getWeighted_P() + rule.getWeighted_N())
+                : Double.MIN_VALUE;
 
         List<Future<ConditionEvaluation>> futures = new ArrayList<Future<ConditionEvaluation>>();
 

adaa.analytics.rules/src/main/java/adaa/analytics/rules/logic/induction/InductionParameters.java

@@ -60,6 +60,7 @@ public class InductionParameters implements Serializable {
 
 	private int maxPassesCount = 1;
 	private boolean meanBasedRegression = false;
+	private boolean controlAprioriPrecision = true;
 
 	public IQualityMeasure getInductionMeasure() {return inductionMeasure;}
 	public void setInductionMeasure(IQualityMeasure inductionMeasure) {this.inductionMeasure = inductionMeasure;}
@@ -114,6 +115,9 @@ public class InductionParameters implements Serializable {
 	public boolean isMeanBasedRegression() { return meanBasedRegression; }
 	public void setMeanBasedRegression(boolean value) { this.meanBasedRegression = value; }
 
+	public boolean isControlAprioriPrecision() { return controlAprioriPrecision; }
+	public void setControlAprioriPrecision(boolean v) { controlAprioriPrecision = v; }
+
 	public List<Double> getMinimumCoveredAll_list() { return minimumCoveredAll_list; }
 	public void setMinimumCoveredAll_list(List<Double> minimumCovered) {this.minimumCoveredAll_list.addAll(minimumCovered);}
 

adaa.analytics.rules/src/main/java/adaa/analytics/rules/operator/RuleGenerator.java

@@ -167,6 +167,8 @@ protected enum MeasureDestination {
 
 	public static final String PARAMETER_MEAN_BASED_REGRESSION = "mean_based_regression";
 
+	public static final String PARAMETER_CONTROL_APRORI_PRECISION = "control_apriori_precision";
+
 	/**
 	 * Invokes base class constructor.
 	 * @param description Operator description.
@@ -216,6 +218,7 @@ public Model learn(ExampleSet exampleSet) throws OperatorException {
 			params.setMaxPassesCount(getParameterAsInt(PARAMETER_MAX_PASSES_COUNT));
 			params.setBinaryContrastIncluded(getParameterAsBoolean(PARAMETER_INCLUDE_BINARY_CONTRAST));
 			params.setMeanBasedRegression(getParameterAsBoolean(PARAMETER_MEAN_BASED_REGRESSION));
+			params.setControlAprioriPrecision(getParameterAsBoolean(PARAMETER_CONTROL_APRORI_PRECISION));
 
 			String tmp = getParameterAsString(PARAMETER_MINCOV_ALL);
 			if (tmp.length() > 0) {
@@ -406,6 +409,9 @@ PARAMETER_PENALTY_SATURATION, getParameterDescription(PARAMETER_PENALTY_SATURATI
 		types.add(new ParameterTypeBoolean(PARAMETER_MEAN_BASED_REGRESSION, getParameterDescription(PARAMETER_MEAN_BASED_REGRESSION),
 				defaultParams.isMeanBasedRegression()));
 
+		types.add(new ParameterTypeBoolean(PARAMETER_CONTROL_APRORI_PRECISION, getParameterDescription(PARAMETER_CONTROL_APRORI_PRECISION),
+				defaultParams.isControlAprioriPrecision()));
+
 		return types;
     }