Commons math removal pvo review

Clustering/Core/src/main/java/org/tribuo/clustering/evaluation/ClusteringMetrics.java

@@ -20,6 +20,7 @@
 import org.tribuo.evaluation.metrics.MetricTarget;
 import org.tribuo.util.infotheory.InformationTheory;
 
+import java.util.List;
 import java.util.function.BiFunction;
 
 /**
@@ -65,14 +66,26 @@ public ClusteringMetric forTarget(MetricTarget<ClusterID> tgt) {
      * @return The adjusted normalized mutual information.
      */
     public static double adjustedMI(ClusteringMetric.Context context) {
-        double mi = InformationTheory.mi(context.getPredictedIDs(), context.getTrueIDs());
-        double predEntropy = InformationTheory.entropy(context.getPredictedIDs());
-        double trueEntropy = InformationTheory.entropy(context.getTrueIDs());
-        double expectedMI = InformationTheory.expectedMI(context.getPredictedIDs(), context.getTrueIDs());
+        return adjustedMI(context.getPredictedIDs(), context.getTrueIDs());
+    }
+
+    public static double adjustedMI(List<Integer> predictedIDs, List<Integer> trueIDs) {
+        double mi = InformationTheory.mi(predictedIDs, trueIDs);
+        double predEntropy = InformationTheory.entropy(predictedIDs);
+        double trueEntropy = InformationTheory.entropy(trueIDs);
+        double expectedMI = InformationTheory.expectedMI(trueIDs, predictedIDs);
 
         double minEntropy = Math.min(predEntropy, trueEntropy);
+        double denominator = minEntropy - expectedMI;
+
+        if(denominator < 0) {
+            denominator = Math.min(denominator, -2.220446049250313e-16);
+        }else {
+            denominator = Math.max(denominator, 2.220446049250313e-16);
+        }
+
 
-        return (mi - expectedMI) / (minEntropy - expectedMI);
+        return (mi - expectedMI) / (denominator);
     }
 
     /**

Clustering/Core/src/test/java/org/tribuo/clustering/evaluation/ClusteringMetricsTest.java

@@ -0,0 +1,60 @@
+package org.tribuo.clustering.evaluation;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.tribuo.clustering.evaluation.ClusteringMetrics.adjustedMI;
+
+import java.util.Arrays;
+import java.util.List;
+
+import org.junit.jupiter.api.Test;
+import org.tribuo.util.infotheory.InformationTheory;
+
+public class ClusteringMetricsTest {
+
+    /*
+     * import numpy as np
+     * from sklearn.metrics import adjusted_mutual_info_score
+     * score = adjusted_mutual_info_score([0,0,1,1], [1,0,1,1])
+     *
+     * a = np.random.randint(0,2,500)
+     * #see printArrayAsJavaDoubles in /tribuo-math/src/test/resources/eigendecomposition-test.py
+     * print(printArrayAsJavaDoubles(a))
+     * b = np.random.randint(0,2,500)
+     * print(printArrayAsJavaDoubles(b))
+     * score = adjusted_mutual_info_score(a, b)
+     */
+    @Test
+    void testAdjustedMI() throws Exception {
+        double logBase = InformationTheory.LOG_BASE; 
+        InformationTheory.LOG_BASE = InformationTheory.LOG_E; 
+        List<Integer> a = Arrays.asList(0, 3, 2, 3, 4, 4, 4, 1, 3, 3, 4, 3, 2, 3, 2, 4, 2, 2, 1, 4, 1, 2, 0, 4, 4, 4, 3, 3, 2, 2, 0, 4, 0, 1, 3, 0, 4, 0, 0, 4, 0, 0, 2, 2, 2, 2, 0, 3, 0, 2, 2, 3, 1, 0, 1, 0, 3, 4, 4, 4, 0, 1, 1, 3, 3, 1, 3, 4, 0, 3, 4, 1, 0, 3, 2, 2, 2, 1, 1, 2, 3, 2, 1, 3, 0, 4, 4, 0, 4, 0, 2, 1, 4, 0, 3, 0, 1, 1, 1, 0);
+        List<Integer> b = Arrays.asList(4, 2, 4, 0, 4, 4, 3, 3, 3, 2, 2, 0, 1, 3, 2, 1, 2, 0, 0, 4, 3, 3, 0, 1, 1, 1, 1, 4, 4, 4, 3, 1, 0, 0, 0, 1, 4, 1, 1, 1, 3, 3, 1, 2, 3, 0, 4, 0, 2, 3, 4, 2, 3, 2, 1, 0, 2, 4, 2, 2, 4, 1, 2, 4, 3, 1, 1, 1, 3, 0, 2, 3, 2, 0, 1, 0, 0, 4, 0, 3, 0, 0, 0, 1, 3, 2, 3, 4, 2, 4, 1, 0, 3, 3, 0, 2, 1, 0, 4, 1);
+        assertEquals(0.01454420034676734, adjustedMI(a,b), 1e-14);
+
+        a = Arrays.asList(1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1);
+        b = Arrays.asList(1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0);
+        assertEquals(-0.0014006748276587267, adjustedMI(a,b), 1e-14);
+
+        a = Arrays.asList(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2);
+        b = Arrays.asList(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 2, 0, 2, 0, 2, 0, 0, 2, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 2, 2, 2, 0);
+        assertEquals(0.31766625364399165, adjustedMI(a,b), 1e-14);
+
+        assertEquals(1.0, adjustedMI(Arrays.asList(0,0,1,1), Arrays.asList(0,0,1,1)));
+        assertEquals(1.0, adjustedMI(Arrays.asList(0,0,1,1), Arrays.asList(1,1,0,0)));
+        assertEquals(0.0, adjustedMI(Arrays.asList(0,0,0,0), Arrays.asList(1,2,3,4)));
+        assertEquals(0.0, adjustedMI(Arrays.asList(0,0,1,1), Arrays.asList(1,1,1,1)));
+        assertEquals(0.0834628172282441, adjustedMI(Arrays.asList(0,0,0,1,0,1,1,1), Arrays.asList(0,0,0,0,1,1,1,1)), 1e-15);
+        assertEquals(0, adjustedMI(Arrays.asList(1,0,1,1), Arrays.asList(0,0,1,1)), 1e-14);
+
+        InformationTheory.LOG_BASE = logBase;
+    }
+}
+
+//used to create third example
+//Random rng = new Random();
+//a = new ArrayList<>();
+//for(int i=0; i<100; i++) {
+//  int v = rng.nextDouble()*i < 20 ? 0 : i < 50 ? 1 : 2;
+//  a.add(v);
+//}
+//System.out.println(a);