Added missing calculate FAR for the EvaluationBinary

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationBinary.java

@@ -56,6 +56,9 @@
 @EqualsAndHashCode(callSuper = true)
 @Data
 public class EvaluationBinary extends BaseEvaluation<EvaluationBinary> {
+
+    public enum Metric {ACCURACY, F1, PRECISION, RECALL, GMEASURE, MCC, FAR}
+
     public static final int DEFAULT_PRECISION = 4;
     public static final double DEFAULT_EDGE_VALUE = 0.0;
 
@@ -218,6 +221,12 @@ public void eval(INDArray labels, INDArray networkPredictions, INDArray maskArra
         }
     }
 
+    /**
+     * Merge the other evaluation object into this one. The result is that this {@link #EvaluationBinary}  instance contains the counts
+     * etc from both
+     *
+     * @param other EvaluationBinary object to merge into this one.
+     */
     @Override
     public void merge(EvaluationBinary other) {
         if (other.countTruePositive == null) {
@@ -507,6 +516,34 @@ private void assertIndex(int outputNum) {
         }
     }
 
+    /**
+     * Average False Alarm Rate (FAR) (see {@link #falseAlarmRate(int)}) for all labels.
+     *
+     * @return The FAR for all labels.
+     */
+    public double averageFalseAlarmRate() {
+        double ret = 0.0;
+        for (int i = 0; i < numLabels(); i++) {
+            ret += falseAlarmRate(i);
+        }
+
+        ret /= (double) numLabels();
+        return ret;
+    }
+
+    /**
+     * False Alarm Rate (FAR) reflects rate of misclassified to classified records
+     * <a href="http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw">http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1058&context=isw</a><br>
+     *
+     * @param outputNum Class index to calculate False Alarm Rate (FAR)
+     * @return The FAR for the outcomes
+     */
+    public double falseAlarmRate(int outputNum) {
+        assertIndex(outputNum);
+
+        return (falsePositiveRate(outputNum) + falseNegativeRate(outputNum)) / 2.0;
+    }
+
     /**
      * Get a String representation of the EvaluationBinary class, using the default precision
      */
@@ -591,6 +628,35 @@ public String stats(int printPrecision) {
         return sb.toString();
     }
 
+    /**
+     * Calculate specific metric (see {@link Metric}) for a given label.
+     *
+     * @param metric The Metric to calculate.
+     * @param outputNum Class index to calculate.
+     *
+     * @return Calculated metric.
+     */
+    public double scoreForMetric(Metric metric, int outputNum){
+        switch (metric){
+            case ACCURACY:
+                return accuracy(outputNum);
+            case F1:
+                return f1(outputNum);
+            case PRECISION:
+                return precision(outputNum);
+            case RECALL:
+                return recall(outputNum);
+            case GMEASURE:
+                return gMeasure(outputNum);
+            case MCC:
+                return matthewsCorrelation(outputNum);
+            case FAR:
+                return falseAlarmRate(outputNum);
+            default:
+                throw new IllegalStateException("Unknown metric: " + metric);
+        }
+    }
+
     public static EvaluationBinary fromJson(String json) {
         return fromJson(json, EvaluationBinary.class);
     }

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/evaluation/EvaluationBinaryTest.java

@@ -27,7 +27,7 @@
 import org.nd4j.linalg.indexing.NDArrayIndex;
 
 import static org.junit.Assert.assertEquals;
-
+import static org.nd4j.evaluation.classification.EvaluationBinary.Metric.*;
 /**
  * Created by Alex on 20/03/2017.
  */
@@ -87,12 +87,14 @@ public void testEvaluationBinary() {
             e.eval(lCol, pCol);
 
             assertEquals(acc, eb.accuracy(i), eps);
-            assertEquals(e.accuracy(), eb.accuracy(i), eps);
-            assertEquals(e.precision(1), eb.precision(i), eps);
-            assertEquals(e.recall(1), eb.recall(i), eps);
-            assertEquals(e.f1(1), eb.f1(i), eps);
+            assertEquals(e.accuracy(), eb.scoreForMetric(ACCURACY, i), eps);
+            assertEquals(e.precision(1), eb.scoreForMetric(PRECISION, i), eps);
+            assertEquals(e.recall(1), eb.scoreForMetric(RECALL, i), eps);
+            assertEquals(e.f1(1), eb.scoreForMetric(F1, i), eps);
+            assertEquals(e.falseAlarmRate(), eb.scoreForMetric(FAR, i), eps);
             assertEquals(e.falsePositiveRate(1), eb.falsePositiveRate(i), eps);
 
+
             assertEquals(tpCount, eb.truePositives(i));
             assertEquals(tnCount, eb.trueNegatives(i));