[7.x][ML] Extend default evaluation metrics to all available (#63939) (…

…#63965)

This commit extends the set of default metrics for the
data frame analytics evaluation API to all available metrics.
The motivation is that if the user skips setting an explicit
set of metrics, they get most of the evaluation offering.

Backport of #63939

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/classification/Classification.java

@@ -93,7 +93,7 @@ public Classification(String actualField,
     }
 
     private static List<EvaluationMetric> defaultMetrics() {
-        return Arrays.asList(new MulticlassConfusionMatrix());
+        return Arrays.asList(new Accuracy(), new MulticlassConfusionMatrix(), new Precision(), new Recall());
     }
 
     public Classification(StreamInput in) throws IOException {

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/regression/Huber.java

@@ -80,6 +80,10 @@ public Huber(StreamInput in) throws IOException {
         this.delta = in.readDouble();
     }
 
+    public Huber() {
+        this(DEFAULT_DELTA);
+    }
+
     public Huber(@Nullable Double delta) {
         this.delta = delta != null ? delta : DEFAULT_DELTA;
     }

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/regression/Regression.java

@@ -76,7 +76,7 @@ public Regression(String actualField, String predictedField, @Nullable List<Eval
     }
 
     private static List<EvaluationMetric> defaultMetrics() {
-        return Arrays.asList(new MeanSquaredError(), new RSquared());
+        return Arrays.asList(new MeanSquaredError(), new RSquared(), new Huber());
     }
 
     public Regression(StreamInput in) throws IOException {

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/classification/ClassificationTests.java

@@ -40,6 +40,7 @@
 
 import static org.elasticsearch.test.hamcrest.OptionalMatchers.isEmpty;
 import static org.elasticsearch.test.hamcrest.OptionalMatchers.isPresent;
+import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.equalTo;
 import static org.hamcrest.Matchers.greaterThan;
 import static org.hamcrest.Matchers.is;
@@ -110,6 +111,14 @@ public void testConstructor_GivenEmptyMetrics() {
         assertThat(e.getMessage(), equalTo("[classification] must have one or more metrics"));
     }
 
+    public void testConstructor_GivenDefaultMetrics() {
+        Classification classification = new Classification("actual", "predicted", null, null);
+
+        List<EvaluationMetric> metrics = classification.getMetrics();
+
+        assertThat(metrics, containsInAnyOrder(new Accuracy(), new MulticlassConfusionMatrix(), new Precision(), new Recall()));
+    }
+
     public void testGetFields() {
         Classification evaluation = new Classification("foo", "bar", "results", null);
         EvaluationFields fields = evaluation.getFields();

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/outlierdetection/OutlierDetectionTests.java

@@ -25,6 +25,7 @@
 import java.util.Collections;
 import java.util.List;
 
+import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.equalTo;
 import static org.hamcrest.Matchers.greaterThan;
 import static org.hamcrest.Matchers.is;
@@ -89,6 +90,17 @@ public void testConstructor_GivenEmptyMetrics() {
         assertThat(e.getMessage(), equalTo("[outlier_detection] must have one or more metrics"));
     }
 
+    public void testConstructor_GivenDefaultMetrics() {
+        OutlierDetection outlierDetection = new OutlierDetection("actual", "predicted", null);
+
+        List<EvaluationMetric> metrics = outlierDetection.getMetrics();
+
+        assertThat(metrics, containsInAnyOrder(new AucRoc(false),
+            new Precision(Arrays.asList(0.25, 0.5, 0.75)),
+            new Recall(Arrays.asList(0.25, 0.5, 0.75)),
+            new ConfusionMatrix(Arrays.asList(0.25, 0.5, 0.75))));
+    }
+
     public void testGetFields() {
         OutlierDetection evaluation = new OutlierDetection("foo", "bar", null);
         EvaluationFields fields = evaluation.getFields();

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/dataframe/evaluation/regression/RegressionTests.java

@@ -25,6 +25,7 @@
 import java.util.Collections;
 import java.util.List;
 
+import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.equalTo;
 import static org.hamcrest.Matchers.greaterThan;
 import static org.hamcrest.Matchers.is;
@@ -76,6 +77,14 @@ public void testConstructor_GivenEmptyMetrics() {
         assertThat(e.getMessage(), equalTo("[regression] must have one or more metrics"));
     }
 
+    public void testConstructor_GivenDefaultMetrics() {
+        Regression regression = new Regression("actual", "predicted", null);
+
+        List<EvaluationMetric> metrics = regression.getMetrics();
+
+        assertThat(metrics, containsInAnyOrder(new Huber(), new MeanSquaredError(), new RSquared()));
+    }
+
     public void testGetFields() {
         Regression evaluation = new Regression("foo", "bar", null);
         EvaluationFields fields = evaluation.getFields();

x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/ClassificationEvaluationIT.java

@@ -33,6 +33,7 @@
 import static java.util.stream.Collectors.toList;
 import static org.hamcrest.Matchers.closeTo;
 import static org.hamcrest.Matchers.contains;
+import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.containsString;
 import static org.hamcrest.Matchers.empty;
 import static org.hamcrest.Matchers.equalTo;
@@ -82,7 +83,13 @@ public void testEvaluate_DefaultMetrics() {
         assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Classification.NAME.getPreferredName()));
         assertThat(
             evaluateDataFrameResponse.getMetrics().stream().map(EvaluationMetricResult::getMetricName).collect(toList()),
-            contains(MulticlassConfusionMatrix.NAME.getPreferredName()));
+            containsInAnyOrder(
+                MulticlassConfusionMatrix.NAME.getPreferredName(),
+                Accuracy.NAME.getPreferredName(),
+                Precision.NAME.getPreferredName(),
+                Recall.NAME.getPreferredName()
+            )
+        );
     }
 
     public void testEvaluate_AllMetrics() {

x-pack/plugin/ml/qa/native-multi-node-tests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/RegressionEvaluationIT.java

@@ -25,6 +25,7 @@
 import static java.util.stream.Collectors.toList;
 import static org.hamcrest.Matchers.closeTo;
 import static org.hamcrest.Matchers.contains;
+import static org.hamcrest.Matchers.containsInAnyOrder;
 import static org.hamcrest.Matchers.equalTo;
 import static org.hamcrest.Matchers.hasSize;
 
@@ -53,7 +54,12 @@ public void testEvaluate_DefaultMetrics() {
         assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Regression.NAME.getPreferredName()));
         assertThat(
             evaluateDataFrameResponse.getMetrics().stream().map(EvaluationMetricResult::getMetricName).collect(toList()),
-            contains(MeanSquaredError.NAME.getPreferredName(), RSquared.NAME.getPreferredName()));
+            containsInAnyOrder(
+                MeanSquaredError.NAME.getPreferredName(),
+                RSquared.NAME.getPreferredName(),
+                Huber.NAME.getPreferredName()
+            )
+        );
     }
 
     public void testEvaluate_AllMetrics() {

x-pack/plugin/src/test/resources/rest-api-spec/test/ml/evaluate_data_frame.yml

@@ -938,6 +938,10 @@ setup:
           }
 
   - is_true: classification.multiclass_confusion_matrix
+  - is_true: classification.accuracy
+  - is_true: classification.precision
+  - is_true: classification.recall
+  - is_false: classification.auc_roc
 ---
 "Test classification given missing actual_field":
   - do:
@@ -1104,8 +1108,8 @@ setup:
 
   - match: { regression.mse.value: 28.67749840974834 }
   - match: { regression.r_squared.value: 0.8551031778603486 }
+  - match: { regression.huber.value: 1.9205280586939963 }
   - is_false: regression.msle.value
-  - is_false: regression.huber.value
 ---
 "Test regression given missing actual_field":
   - do: