GH-15809: add AIC and Loglikelihood to ModelMetricsBinomial

h2o-algos/src/main/java/hex/psvm/MetricBuilderPSVM.java

@@ -76,7 +76,7 @@ public ModelMetrics makeModelMetrics(Model m, Frame f, Frame frameWithWeights, F
     } else {
       auc = AUC2.emptyAUC();
     }
-    ModelMetricsBinomial mm = new ModelMetricsBinomial(m, f, _count, mse, _domain, sigma, auc, Double.NaN, null, _customMetric);
+    ModelMetricsBinomial mm = new ModelMetricsBinomial(m, f, _count, mse, _domain, sigma, auc, Double.NaN, Double.NaN, Double.NaN, null, _customMetric);
     if (m != null) m.addModelMetrics(mm);
     return mm;
   }

h2o-algos/src/main/java/hex/tree/isofor/MetricBuilderAnomalySupervised.java

@@ -35,7 +35,7 @@ public MetricBuilderAnomalySupervised(String[] domain) {
       auc = AUC2.emptyAUC();
     }
     ModelMetricsBinomial mm = new ModelMetricsBinomial(m, f, _count, mse, _domain,
-            sigma, auc, logloss, null, _customMetric);
+            sigma, auc, logloss, 0, 0, null, _customMetric);
     if (m != null) {
       m.addModelMetrics(mm);
     }

h2o-core/src/main/java/hex/ModelMetricsBinomial.java

@@ -19,15 +19,19 @@
 public class ModelMetricsBinomial extends ModelMetricsSupervised {
   public final AUC2 _auc;
   public final double _logloss;
+  public final double _loglikelihood;
+  public final double _aic;
   public double _mean_per_class_error;
   public final GainsLift _gainsLift;
 
   public ModelMetricsBinomial(Model model, Frame frame, long nobs, double mse, String[] domain,
-                              double sigma, AUC2 auc, double logloss, GainsLift gainsLift,
+                              double sigma, AUC2 auc, double logloss, double loglikelihood, double aic, GainsLift gainsLift,
                               CustomMetric customMetric) {
     super(model, frame,  nobs, mse, domain, sigma, customMetric);
     _auc = auc;
     _logloss = logloss;
+    _loglikelihood = loglikelihood;
+    _aic = aic;
     _gainsLift = gainsLift;
     _mean_per_class_error = cm() == null ? Double.NaN : cm().mean_per_class_error();
   }
@@ -49,6 +53,8 @@ public String toString() {
       sb.append(" pr_auc: " + (float)_auc.pr_auc() + "\n");
     }
     sb.append(" logloss: " + (float)_logloss + "\n");
+    sb.append(" loglikelihood: " + (float)_loglikelihood + "\n");
+    sb.append(" AIC: " + (float)_aic + "\n");
     sb.append(" mean_per_class_error: " + (float)_mean_per_class_error + "\n");
     sb.append(" default threshold: " + (_auc == null ? 0.5 : (float)_auc.defaultThreshold()) + "\n");
     if (cm() != null) sb.append(" CM: " + cm().toASCII());
@@ -57,6 +63,8 @@ public String toString() {
   }
 
   public double logloss() { return _logloss; }
+  public double loglikelihood() { return _loglikelihood; }
+  public double aic() { return _aic; }
   public double mean_per_class_error() { return _mean_per_class_error; }
   @Override public AUC2 auc_obj() { return _auc; }
   @Override public ConfusionMatrix cm() {
@@ -161,6 +169,7 @@ private static class BinomialMetrics extends MRTask<BinomialMetrics> {
 
   public static class MetricBuilderBinomial<T extends MetricBuilderBinomial<T>> extends MetricBuilderSupervised<T> {
     protected double _logloss;
+    protected double _loglikelihood;
     protected AUC2.AUCBuilder _auc;
 
     public MetricBuilderBinomial( String[] domain ) { super(2,domain); _auc = new AUC2.AUCBuilder(AUC2.NBINS); }
@@ -256,6 +265,8 @@ private ModelMetrics makeModelMetrics(final Model m, final Frame f, final Frame
 
     private ModelMetrics makeModelMetrics(Model m, Frame f, GainsLift gl) {
       double mse = Double.NaN;
+      double loglikelihood = Double.NaN;
+      double aic = Double.NaN;
       double logloss = Double.NaN;
       double sigma = Double.NaN;
       final AUC2 auc;
@@ -267,7 +278,7 @@ private ModelMetrics makeModelMetrics(Model m, Frame f, GainsLift gl) {
       } else {
         auc = new AUC2();
       }
-      ModelMetricsBinomial mm = new ModelMetricsBinomial(m, f, _count, mse, _domain, sigma, auc,  logloss, gl, _customMetric);
+      ModelMetricsBinomial mm = new ModelMetricsBinomial(m, f, _count, mse, _domain, sigma, auc,  logloss, loglikelihood, aic, gl, _customMetric);
       if (m!=null) m.addModelMetrics(mm);
       return mm;
     }

h2o-core/src/main/java/hex/ModelMetricsBinomialGLM.java

@@ -14,7 +14,7 @@ public ModelMetricsBinomialGLM(Model model, Frame frame, long nobs, double mse,
                                  double sigma, AUC2 auc, double logloss, double resDev, double nullDev,
                                  double aic, long nDof, long rDof, GainsLift gainsLift,
                                  CustomMetric customMetric, double loglikelihood) {
-    super(model, frame, nobs, mse, domain, sigma, auc, logloss, gainsLift, customMetric);
+    super(model, frame, nobs, mse, domain, sigma, auc, logloss, loglikelihood, aic, gainsLift, customMetric);
     _resDev = resDev;
     _nullDev = nullDev;
     _AIC = aic;

h2o-core/src/main/java/hex/ModelMetricsBinomialGeneric.java

@@ -16,7 +16,7 @@ public ModelMetricsBinomialGeneric(Model model, Frame frame, long nobs, double m
                                      CustomMetric customMetric, double mean_per_class_error, TwoDimTable thresholds_and_metric_scores,
                                      TwoDimTable max_criteria_and_metric_scores, TwoDimTable confusion_matrix, double r2,
                                      final String description) {
-    super(model, frame, nobs, mse, domain, sigma, auc, logloss, null, customMetric);
+    super(model, frame, nobs, mse, domain, sigma, auc, logloss, 0, 0, null, customMetric);
     _gainsLiftTable = gainsLiftTable;
     _thresholds_and_metric_scores = thresholds_and_metric_scores;
     _max_criteria_and_metric_scores = max_criteria_and_metric_scores;

h2o-core/src/main/java/water/api/schemas3/ModelMetricsBinomialGenericV3.java

@@ -10,6 +10,8 @@ public S fillFromImpl(ModelMetricsBinomialGeneric modelMetrics) {
     super.fillFromImpl(modelMetrics);
     r2 = modelMetrics.r2();
     logloss = modelMetrics._logloss;
+    loglikelihood = modelMetrics._loglikelihood;
+    AIC = modelMetrics._aic;
 
     if (modelMetrics != null && modelMetrics._confusion_matrix != null) {
       final ConfusionMatrixV3 convertedConfusionMatrix = new ConfusionMatrixV3();

h2o-core/src/main/java/water/api/schemas3/ModelMetricsBinomialV3.java

@@ -18,6 +18,12 @@ public class ModelMetricsBinomialV3<I extends ModelMetricsBinomial, S extends Mo
   @API(help="The logarithmic loss for this scoring run.", direction=API.Direction.OUTPUT)
   public double logloss;
 
+  @API(help="The negative logarithmic likelihood for this scoring run.", direction=API.Direction.OUTPUT)
+  public double loglikelihood;
+
+  @API(help="The AIC for this scoring run.", direction=API.Direction.OUTPUT)
+  public double AIC;
+
   @API(help="The AUC for this scoring run.", direction=API.Direction.OUTPUT)
   public double AUC;