Rename regression evaluation metrics to make the names consistent with loss functions (#58887) (#58927)

client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/evaluation/MlEvaluationNamedXContentProvider.java

@@ -23,7 +23,7 @@ import org.elasticsearch.client.ml.dataframe.evaluation.classification.Classific
 import org.elasticsearch.client.ml.dataframe.evaluation.classification.MulticlassConfusionMatrixMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredErrorMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicErrorMetric;
-import org.elasticsearch.client.ml.dataframe.evaluation.regression.PseudoHuberMetric;
+import org.elasticsearch.client.ml.dataframe.evaluation.regression.HuberMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -105,8 +105,8 @@ public class MlEvaluationNamedXContentProvider implements NamedXContentProvider
                 MeanSquaredLogarithmicErrorMetric::fromXContent),
             new NamedXContentRegistry.Entry(
                 EvaluationMetric.class,
-                new ParseField(registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME)),
+                new ParseField(registeredMetricName(Regression.NAME, HuberMetric.NAME)),
-                PseudoHuberMetric::fromXContent),
+                HuberMetric::fromXContent),
             new NamedXContentRegistry.Entry(
                 EvaluationMetric.class,
                 new ParseField(registeredMetricName(Regression.NAME, RSquaredMetric.NAME)),
@@ -156,8 +156,8 @@ public class MlEvaluationNamedXContentProvider implements NamedXContentProvider
                 MeanSquaredLogarithmicErrorMetric.Result::fromXContent),
             new NamedXContentRegistry.Entry(
                 EvaluationMetric.Result.class,
-                new ParseField(registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME)),
+                new ParseField(registeredMetricName(Regression.NAME, HuberMetric.NAME)),
-                PseudoHuberMetric.Result::fromXContent),
+                HuberMetric.Result::fromXContent),
             new NamedXContentRegistry.Entry(
                 EvaluationMetric.Result.class,
                 new ParseField(registeredMetricName(Regression.NAME, RSquaredMetric.NAME)),

client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/HuberMetric.java

@@ -18,6 +18,7 @@
  */
 package org.elasticsearch.client.ml.dataframe.evaluation.regression;
 
+import org.elasticsearch.client.ml.dataframe.Regression.LossFunction;
 import org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.common.Nullable;
 import org.elasticsearch.common.ParseField;
@@ -34,30 +35,30 @@ import static org.elasticsearch.common.xcontent.ConstructingObjectParser.optiona
 /**
  * Calculates the pseudo Huber loss function.
  *
- * equation: pseudohuber = 1/n * Σ(δ^2 * sqrt(1 + a^2 / δ^2) - 1)
+ * equation: huber = 1/n * Σ(δ^2 * sqrt(1 + a^2 / δ^2) - 1)
  * where: a = y - y´
  *        δ - parameter that controls the steepness
  */
-public class PseudoHuberMetric implements EvaluationMetric {
+public class HuberMetric implements EvaluationMetric {
 
-    public static final String NAME = "pseudo_huber";
+    public static final String NAME = LossFunction.HUBER.toString();
 
     public static final ParseField DELTA = new ParseField("delta");
 
-    private static final ConstructingObjectParser<PseudoHuberMetric, Void> PARSER =
+    private static final ConstructingObjectParser<HuberMetric, Void> PARSER =
-        new ConstructingObjectParser<>(NAME, true, args -> new PseudoHuberMetric((Double) args[0]));
+        new ConstructingObjectParser<>(NAME, true, args -> new HuberMetric((Double) args[0]));
 
     static {
         PARSER.declareDouble(optionalConstructorArg(), DELTA);
     }
 
-    public static PseudoHuberMetric fromXContent(XContentParser parser) {
+    public static HuberMetric fromXContent(XContentParser parser) {
         return PARSER.apply(parser, null);
     }
 
     private final Double delta;
 
-    public PseudoHuberMetric(@Nullable Double delta) {
+    public HuberMetric(@Nullable Double delta) {
         this.delta = delta;
     }
 
@@ -80,7 +81,7 @@ public class PseudoHuberMetric implements EvaluationMetric {
     public boolean equals(Object o) {
         if (this == o) return true;
         if (o == null || getClass() != o.getClass()) return false;
-        PseudoHuberMetric that = (PseudoHuberMetric) o;
+        HuberMetric that = (HuberMetric) o;
         return Objects.equals(this.delta, that.delta);
     }
 

client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/MeanSquaredErrorMetric.java

@@ -18,6 +18,7 @@
  */
 package org.elasticsearch.client.ml.dataframe.evaluation.regression;
 
+import org.elasticsearch.client.ml.dataframe.Regression.LossFunction;
 import org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.common.ParseField;
 import org.elasticsearch.common.xcontent.ConstructingObjectParser;
@@ -37,7 +38,7 @@ import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constru
  */
 public class MeanSquaredErrorMetric implements EvaluationMetric {
 
-    public static final String NAME = "mean_squared_error";
+    public static final String NAME = LossFunction.MSE.toString();
 
     private static final ObjectParser<MeanSquaredErrorMetric, Void> PARSER = new ObjectParser<>(NAME, true, MeanSquaredErrorMetric::new);
 

client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/MeanSquaredLogarithmicErrorMetric.java

@@ -18,6 +18,7 @@
  */
 package org.elasticsearch.client.ml.dataframe.evaluation.regression;
 
+import org.elasticsearch.client.ml.dataframe.Regression.LossFunction;
 import org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.common.Nullable;
 import org.elasticsearch.common.ParseField;
@@ -39,7 +40,7 @@ import static org.elasticsearch.common.xcontent.ConstructingObjectParser.optiona
  */
 public class MeanSquaredLogarithmicErrorMetric implements EvaluationMetric {
 
-    public static final String NAME = "mean_squared_logarithmic_error";
+    public static final String NAME = LossFunction.MSLE.toString();
 
     public static final ParseField OFFSET = new ParseField("offset");
 

client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java

@@ -143,7 +143,7 @@ import org.elasticsearch.client.ml.dataframe.evaluation.classification.Classific
 import org.elasticsearch.client.ml.dataframe.evaluation.classification.MulticlassConfusionMatrixMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredErrorMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicErrorMetric;
-import org.elasticsearch.client.ml.dataframe.evaluation.regression.PseudoHuberMetric;
+import org.elasticsearch.client.ml.dataframe.evaluation.regression.HuberMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -1889,7 +1889,7 @@ public class MachineLearningIT extends ESRestHighLevelClientTestCase {
                     predictedRegression,
                     new MeanSquaredErrorMetric(),
                     new MeanSquaredLogarithmicErrorMetric(1.0),
-                    new PseudoHuberMetric(1.0),
+                    new HuberMetric(1.0),
                     new RSquaredMetric()));
 
         EvaluateDataFrameResponse evaluateDataFrameResponse =
@@ -1906,9 +1906,9 @@ public class MachineLearningIT extends ESRestHighLevelClientTestCase {
         assertThat(msleResult.getMetricName(), equalTo(MeanSquaredLogarithmicErrorMetric.NAME));
         assertThat(msleResult.getValue(), closeTo(0.02759231770210426, 1e-9));
 
-        PseudoHuberMetric.Result pseudoHuberResult = evaluateDataFrameResponse.getMetricByName(PseudoHuberMetric.NAME);
+        HuberMetric.Result huberResult = evaluateDataFrameResponse.getMetricByName(HuberMetric.NAME);
-        assertThat(pseudoHuberResult.getMetricName(), equalTo(PseudoHuberMetric.NAME));
+        assertThat(huberResult.getMetricName(), equalTo(HuberMetric.NAME));
-        assertThat(pseudoHuberResult.getValue(), closeTo(0.029669771640929276, 1e-9));
+        assertThat(huberResult.getValue(), closeTo(0.029669771640929276, 1e-9));
 
         RSquaredMetric.Result rSquaredResult = evaluateDataFrameResponse.getMetricByName(RSquaredMetric.NAME);
         assertThat(rSquaredResult.getMetricName(), equalTo(RSquaredMetric.NAME));

client/rest-high-level/src/test/java/org/elasticsearch/client/RestHighLevelClientTests.java

@@ -62,7 +62,7 @@ import org.elasticsearch.client.ml.dataframe.evaluation.classification.Classific
 import org.elasticsearch.client.ml.dataframe.evaluation.classification.MulticlassConfusionMatrixMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredErrorMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicErrorMetric;
-import org.elasticsearch.client.ml.dataframe.evaluation.regression.PseudoHuberMetric;
+import org.elasticsearch.client.ml.dataframe.evaluation.regression.HuberMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -765,7 +765,7 @@ public class RestHighLevelClientTests extends ESTestCase {
                 registeredMetricName(Classification.NAME, MulticlassConfusionMatrixMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredErrorMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME),
-                registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME),
+                registeredMetricName(Regression.NAME, HuberMetric.NAME),
                 registeredMetricName(Regression.NAME, RSquaredMetric.NAME)));
         assertEquals(Integer.valueOf(12), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric.Result.class));
         assertThat(names,
@@ -782,7 +782,7 @@ public class RestHighLevelClientTests extends ESTestCase {
                 registeredMetricName(Classification.NAME, MulticlassConfusionMatrixMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredErrorMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME),
-                registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME),
+                registeredMetricName(Regression.NAME, HuberMetric.NAME),
                 registeredMetricName(Regression.NAME, RSquaredMetric.NAME)));
         assertEquals(Integer.valueOf(4), categories.get(org.elasticsearch.client.ml.inference.preprocessing.PreProcessor.class));
         assertThat(names, hasItems(FrequencyEncoding.NAME, OneHotEncoding.NAME, TargetMeanEncoding.NAME, CustomWordEmbedding.NAME));

client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java

@@ -162,7 +162,7 @@ import org.elasticsearch.client.ml.dataframe.evaluation.classification.Multiclas
 import org.elasticsearch.client.ml.dataframe.evaluation.classification.MulticlassConfusionMatrixMetric.PredictedClass;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredErrorMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicErrorMetric;
-import org.elasticsearch.client.ml.dataframe.evaluation.regression.PseudoHuberMetric;
+import org.elasticsearch.client.ml.dataframe.evaluation.regression.HuberMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.BinarySoftClassification;
@@ -3573,7 +3573,7 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
                     // Evaluation metrics // <4>
                     new MeanSquaredErrorMetric(), // <5>
                     new MeanSquaredLogarithmicErrorMetric(1.0), // <6>
-                    new PseudoHuberMetric(1.0), // <7>
+                    new HuberMetric(1.0), // <7>
                     new RSquaredMetric()); // <8>
             // end::evaluate-data-frame-evaluation-regression
 
@@ -3588,8 +3588,8 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
                 response.getMetricByName(MeanSquaredLogarithmicErrorMetric.NAME); // <3>
             double meanSquaredLogarithmicError = meanSquaredLogarithmicErrorResult.getValue(); // <4>
 
-            PseudoHuberMetric.Result pseudoHuberResult = response.getMetricByName(PseudoHuberMetric.NAME); // <5>
+            HuberMetric.Result huberResult = response.getMetricByName(HuberMetric.NAME); // <5>
-            double pseudoHuber = pseudoHuberResult.getValue(); // <6>
+            double huber = huberResult.getValue(); // <6>
 
             RSquaredMetric.Result rSquaredResult = response.getMetricByName(RSquaredMetric.NAME); // <7>
             double rSquared = rSquaredResult.getValue(); // <8>
@@ -3597,7 +3597,7 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
 
             assertThat(meanSquaredError, closeTo(0.021, 1e-3));
             assertThat(meanSquaredLogarithmicError, closeTo(0.003, 1e-3));
-            assertThat(pseudoHuber, closeTo(0.01, 1e-3));
+            assertThat(huber, closeTo(0.01, 1e-3));
             assertThat(rSquared, closeTo(0.941, 1e-3));
         }
     }

client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/HuberMetricResultTests.java

@@ -25,20 +25,20 @@ import org.elasticsearch.test.AbstractXContentTestCase;
 
 import java.io.IOException;
 
-public class PseudoHuberMetricResultTests extends AbstractXContentTestCase<PseudoHuberMetric.Result> {
+public class HuberMetricResultTests extends AbstractXContentTestCase<HuberMetric.Result> {
 
-    public static PseudoHuberMetric.Result randomResult() {
+    public static HuberMetric.Result randomResult() {
-        return new PseudoHuberMetric.Result(randomDouble());
+        return new HuberMetric.Result(randomDouble());
     }
 
     @Override
-    protected PseudoHuberMetric.Result createTestInstance() {
+    protected HuberMetric.Result createTestInstance() {
         return randomResult();
     }
 
     @Override
-    protected PseudoHuberMetric.Result doParseInstance(XContentParser parser) throws IOException {
+    protected HuberMetric.Result doParseInstance(XContentParser parser) throws IOException {
-        return PseudoHuberMetric.Result.fromXContent(parser);
+        return HuberMetric.Result.fromXContent(parser);
     }
 
     @Override

client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/HuberMetricTests.java

@@ -25,7 +25,7 @@ import org.elasticsearch.test.AbstractXContentTestCase;
 
 import java.io.IOException;
 
-public class PseudoHuberMetricTests extends AbstractXContentTestCase<PseudoHuberMetric> {
+public class HuberMetricTests extends AbstractXContentTestCase<HuberMetric> {
 
     @Override
     protected NamedXContentRegistry xContentRegistry() {
@@ -33,13 +33,13 @@ public class PseudoHuberMetricTests extends AbstractXContentTestCase<PseudoHuber
     }
 
     @Override
-    protected PseudoHuberMetric createTestInstance() {
+    protected HuberMetric createTestInstance() {
-        return new PseudoHuberMetric(randomBoolean() ? randomDouble() : null);
+        return new HuberMetric(randomBoolean() ? randomDouble() : null);
     }
 
     @Override
-    protected PseudoHuberMetric doParseInstance(XContentParser parser) throws IOException {
+    protected HuberMetric doParseInstance(XContentParser parser) throws IOException {
-        return PseudoHuberMetric.fromXContent(parser);
+        return HuberMetric.fromXContent(parser);
     }
 
     @Override

client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/evaluation/regression/RegressionTests.java

@@ -45,7 +45,7 @@ public class RegressionTests extends AbstractXContentTestCase<Regression> {
             metrics.add(new MeanSquaredLogarithmicErrorMetricTests().createTestInstance());
         }
         if (randomBoolean()) {
-            metrics.add(new PseudoHuberMetricTests().createTestInstance());
+            metrics.add(new HuberMetricTests().createTestInstance());
         }
         if (randomBoolean()) {
             metrics.add(new RSquaredMetric());

docs/reference/ml/df-analytics/apis/evaluate-dfanalytics.asciidoc

@@ -125,15 +125,15 @@ which outputs a prediction of values.
   (Optional, object) Specifies the metrics that are used for the evaluation.
   Available metrics:
 
-  `mean_squared_error`:::
+  `mse`:::
     (Optional, object) Average squared difference between the predicted values and the actual (`ground truth`) value.
     For more information, read https://en.wikipedia.org/wiki/Mean_squared_error[this wiki article].
 
-  `mean_squared_logarithmic_error`:::
+  `msle`:::
     (Optional, object) Average squared difference between the logarithm of the predicted values and the logarithm of the actual
     (`ground truth`) value.
 
-  `pseudo_huber`:::
+  `huber`:::
     (Optional, object) Pseudo Huber loss function.
     For more information, read https://en.wikipedia.org/wiki/Huber_loss#Pseudo-Huber_loss_function[this wiki article].
 
@@ -280,7 +280,7 @@ POST _ml/data_frame/_evaluate
       "predicted_field": "ml.price_prediction", <4>
       "metrics": {  
         "r_squared": {},
-        "mean_squared_error": {}                             
+        "mse": {}
       }
     }
   }
@@ -317,7 +317,7 @@ POST _ml/data_frame/_evaluate
       "predicted_field": "ml.G3_prediction", <3>
       "metrics": {  
         "r_squared": {},
-        "mean_squared_error": {}                             
+        "mse": {}
       }
     }
   }
@@ -356,7 +356,7 @@ POST _ml/data_frame/_evaluate
       "predicted_field": "ml.G3_prediction", <3>
       "metrics": {  
         "r_squared": {},
-        "mean_squared_error": {}                             
+        "mse": {}
       }
     }
   }

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

@@ -12,9 +12,9 @@ import org.elasticsearch.plugins.spi.NamedXContentProvider;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.Accuracy;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.Classification;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.MulticlassConfusionMatrix;
+import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.Huber;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredError;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicError;
-import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.PseudoHuber;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.RSquared;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.softclassification.AucRoc;
@@ -101,8 +101,8 @@ public class MlEvaluationNamedXContentProvider implements NamedXContentProvider
                 new ParseField(registeredMetricName(Regression.NAME, MeanSquaredLogarithmicError.NAME)),
                 MeanSquaredLogarithmicError::fromXContent),
             new NamedXContentRegistry.Entry(EvaluationMetric.class,
-                new ParseField(registeredMetricName(Regression.NAME, PseudoHuber.NAME)),
+                new ParseField(registeredMetricName(Regression.NAME, Huber.NAME)),
-                PseudoHuber::fromXContent),
+                Huber::fromXContent),
             new NamedXContentRegistry.Entry(EvaluationMetric.class,
                 new ParseField(registeredMetricName(Regression.NAME, RSquared.NAME)),
                 RSquared::fromXContent)
@@ -156,8 +156,8 @@ public class MlEvaluationNamedXContentProvider implements NamedXContentProvider
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicError.NAME),
                 MeanSquaredLogarithmicError::new),
             new NamedWriteableRegistry.Entry(EvaluationMetric.class,
-                registeredMetricName(Regression.NAME, PseudoHuber.NAME),
+                registeredMetricName(Regression.NAME, Huber.NAME),
-                PseudoHuber::new),
+                Huber::new),
             new NamedWriteableRegistry.Entry(EvaluationMetric.class,
                 registeredMetricName(Regression.NAME, RSquared.NAME),
                 RSquared::new),
@@ -193,8 +193,8 @@ public class MlEvaluationNamedXContentProvider implements NamedXContentProvider
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicError.NAME),
                 MeanSquaredLogarithmicError.Result::new),
             new NamedWriteableRegistry.Entry(EvaluationMetricResult.class,
-                registeredMetricName(Regression.NAME, PseudoHuber.NAME),
+                registeredMetricName(Regression.NAME, Huber.NAME),
-                PseudoHuber.Result::new),
+                Huber.Result::new),
             new NamedWriteableRegistry.Entry(EvaluationMetricResult.class,
                 registeredMetricName(Regression.NAME, RSquared.NAME),
                 RSquared.Result::new)

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

@@ -19,6 +19,7 @@ import org.elasticsearch.search.aggregations.AggregationBuilders;
 import org.elasticsearch.search.aggregations.Aggregations;
 import org.elasticsearch.search.aggregations.PipelineAggregationBuilder;
 import org.elasticsearch.search.aggregations.metrics.NumericMetricsAggregation;
+import org.elasticsearch.xpack.core.ml.dataframe.analyses.Regression.LossFunction;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetricResult;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationParameters;
@@ -37,13 +38,13 @@ import static org.elasticsearch.xpack.core.ml.dataframe.evaluation.MlEvaluationN
 /**
  * Calculates the pseudo Huber loss function.
  *
- * equation: pseudohuber = 1/n * Σ(δ^2 * sqrt(1 + a^2 / δ^2) - 1)
+ * equation: huber = 1/n * Σ(δ^2 * sqrt(1 + a^2 / δ^2) - 1)
  * where: a = y - y´
  *        δ - parameter that controls the steepness
  */
-public class PseudoHuber implements EvaluationMetric {
+public class Huber implements EvaluationMetric {
 
-    public static final ParseField NAME = new ParseField("pseudo_huber");
+    public static final ParseField NAME = new ParseField(LossFunction.HUBER.toString());
 
     public static final ParseField DELTA = new ParseField("delta");
     private static final double DEFAULT_DELTA = 1.0;
@@ -58,25 +59,25 @@ public class PseudoHuber implements EvaluationMetric {
         return new MessageFormat(PAINLESS_TEMPLATE, Locale.ROOT).format(args);
     }
 
-    private static final ConstructingObjectParser<PseudoHuber, Void> PARSER =
+    private static final ConstructingObjectParser<Huber, Void> PARSER =
-        new ConstructingObjectParser<>(NAME.getPreferredName(), true, args -> new PseudoHuber((Double) args[0]));
+        new ConstructingObjectParser<>(NAME.getPreferredName(), true, args -> new Huber((Double) args[0]));
 
     static {
         PARSER.declareDouble(optionalConstructorArg(), DELTA);
     }
 
-    public static PseudoHuber fromXContent(XContentParser parser) {
+    public static Huber fromXContent(XContentParser parser) {
         return PARSER.apply(parser, null);
     }
 
     private final double delta;
     private EvaluationMetricResult result;
 
-    public PseudoHuber(StreamInput in) throws IOException {
+    public Huber(StreamInput in) throws IOException {
         this.delta = in.readDouble();
     }
 
-    public PseudoHuber(@Nullable Double delta) {
+    public Huber(@Nullable Double delta) {
         this.delta = delta != null ? delta : DEFAULT_DELTA;
     }
 
@@ -130,7 +131,7 @@ public class PseudoHuber implements EvaluationMetric {
     public boolean equals(Object o) {
         if (this == o) return true;
         if (o == null || getClass() != o.getClass()) return false;
-        PseudoHuber that = (PseudoHuber) o;
+        Huber that = (Huber) o;
         return this.delta == that.delta;
     }
 

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

@@ -18,6 +18,7 @@ import org.elasticsearch.search.aggregations.AggregationBuilders;
 import org.elasticsearch.search.aggregations.Aggregations;
 import org.elasticsearch.search.aggregations.PipelineAggregationBuilder;
 import org.elasticsearch.search.aggregations.metrics.NumericMetricsAggregation;
+import org.elasticsearch.xpack.core.ml.dataframe.analyses.Regression.LossFunction;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetricResult;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationParameters;
@@ -40,7 +41,7 @@ import static org.elasticsearch.xpack.core.ml.dataframe.evaluation.MlEvaluationN
  */
 public class MeanSquaredError implements EvaluationMetric {
 
-    public static final ParseField NAME = new ParseField("mean_squared_error");
+    public static final ParseField NAME = new ParseField(LossFunction.MSE.toString());
 
     private static final String PAINLESS_TEMPLATE =
         "def diff = doc[''{0}''].value - doc[''{1}''].value;" +

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

@@ -19,6 +19,7 @@ import org.elasticsearch.search.aggregations.AggregationBuilders;
 import org.elasticsearch.search.aggregations.Aggregations;
 import org.elasticsearch.search.aggregations.PipelineAggregationBuilder;
 import org.elasticsearch.search.aggregations.metrics.NumericMetricsAggregation;
+import org.elasticsearch.xpack.core.ml.dataframe.analyses.Regression.LossFunction;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetric;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetricResult;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationParameters;
@@ -42,7 +43,7 @@ import static org.elasticsearch.xpack.core.ml.dataframe.evaluation.MlEvaluationN
  */
 public class MeanSquaredLogarithmicError implements EvaluationMetric {
 
-    public static final ParseField NAME = new ParseField("mean_squared_logarithmic_error");
+    public static final ParseField NAME = new ParseField(LossFunction.MSLE.toString());
 
     public static final ParseField OFFSET = new ParseField("offset");
     private static final double DEFAULT_OFFSET = 1.0;

x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/action/EvaluateDataFrameActionResponseTests.java

@@ -17,7 +17,7 @@ import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.Preci
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.classification.RecallResultTests;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredError;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicError;
-import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.PseudoHuber;
+import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.Huber;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.RSquared;
 
 import java.util.Arrays;
@@ -41,7 +41,7 @@ public class EvaluateDataFrameActionResponseTests extends AbstractWireSerializin
                 MulticlassConfusionMatrixResultTests.createRandom(),
                 new MeanSquaredError.Result(randomDouble()),
                 new MeanSquaredLogarithmicError.Result(randomDouble()),
-                new PseudoHuber.Result(randomDouble()),
+                new Huber.Result(randomDouble()),
                 new RSquared.Result(randomDouble()));
         return new Response(evaluationName, randomSubsetOf(metrics));
     }

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

@@ -19,37 +19,37 @@ import java.util.Collections;
 import static org.elasticsearch.xpack.core.ml.dataframe.evaluation.MockAggregations.mockSingleValue;
 import static org.hamcrest.Matchers.equalTo;
 
-public class PseudoHuberTests extends AbstractSerializingTestCase<PseudoHuber> {
+public class HuberTests extends AbstractSerializingTestCase<Huber> {
 
     @Override
-    protected PseudoHuber doParseInstance(XContentParser parser) throws IOException {
+    protected Huber doParseInstance(XContentParser parser) throws IOException {
-        return PseudoHuber.fromXContent(parser);
+        return Huber.fromXContent(parser);
     }
 
     @Override
-    protected PseudoHuber createTestInstance() {
+    protected Huber createTestInstance() {
         return createRandom();
     }
 
     @Override
-    protected Writeable.Reader<PseudoHuber> instanceReader() {
+    protected Writeable.Reader<Huber> instanceReader() {
-        return PseudoHuber::new;
+        return Huber::new;
     }
 
-    public static PseudoHuber createRandom() {
+    public static Huber createRandom() {
-        return new PseudoHuber(randomBoolean() ? randomDoubleBetween(0.0, 1000.0, false) : null);
+        return new Huber(randomBoolean() ? randomDoubleBetween(0.0, 1000.0, false) : null);
     }
 
     public void testEvaluate() {
         Aggregations aggs = new Aggregations(Arrays.asList(
-            mockSingleValue("regression_pseudo_huber", 0.8123),
+            mockSingleValue("regression_huber", 0.8123),
             mockSingleValue("some_other_single_metric_agg", 0.2377)
         ));
 
-        PseudoHuber pseudoHuber = new PseudoHuber((Double) null);
+        Huber huber = new Huber((Double) null);
-        pseudoHuber.process(aggs);
+        huber.process(aggs);
 
-        EvaluationMetricResult result = pseudoHuber.getResult().get();
+        EvaluationMetricResult result = huber.getResult().get();
         String expected = "{\"value\":0.8123}";
         assertThat(Strings.toString(result), equalTo(expected));
     }
@@ -59,10 +59,10 @@ public class PseudoHuberTests extends AbstractSerializingTestCase<PseudoHuber> {
             mockSingleValue("some_other_single_metric_agg", 0.2377)
         ));
 
-        PseudoHuber pseudoHuber = new PseudoHuber((Double) null);
+        Huber huber = new Huber((Double) null);
-        pseudoHuber.process(aggs);
+        huber.process(aggs);
 
-        EvaluationMetricResult result = pseudoHuber.getResult().get();
+        EvaluationMetricResult result = huber.getResult().get();
-        assertThat(result, equalTo(new PseudoHuber.Result(0.0)));
+        assertThat(result, equalTo(new Huber.Result(0.0)));
     }
 }

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

@@ -42,7 +42,7 @@ public class MeanSquaredErrorTests extends AbstractSerializingTestCase<MeanSquar
 
     public void testEvaluate() {
         Aggregations aggs = new Aggregations(Arrays.asList(
-            mockSingleValue("regression_mean_squared_error", 0.8123),
+            mockSingleValue("regression_mse", 0.8123),
             mockSingleValue("some_other_single_metric_agg", 0.2377)
         ));
 

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

@@ -42,7 +42,7 @@ public class MeanSquaredLogarithmicErrorTests extends AbstractSerializingTestCas
 
     public void testEvaluate() {
         Aggregations aggs = new Aggregations(Arrays.asList(
-            mockSingleValue("regression_mean_squared_logarithmic_error", 0.8123),
+            mockSingleValue("regression_msle", 0.8123),
             mockSingleValue("some_other_single_metric_agg", 0.2377)
         ));
 

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

@@ -11,9 +11,9 @@ import org.elasticsearch.action.index.IndexRequest;
 import org.elasticsearch.action.support.WriteRequest;
 import org.elasticsearch.xpack.core.ml.action.EvaluateDataFrameAction;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.EvaluationMetricResult;
+import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.Huber;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredError;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.MeanSquaredLogarithmicError;
-import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.PseudoHuber;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.RSquared;
 import org.elasticsearch.xpack.core.ml.dataframe.evaluation.regression.Regression;
 import org.junit.After;
@@ -105,18 +105,18 @@ public class RegressionEvaluationIT extends MlNativeDataFrameAnalyticsIntegTestC
         assertThat(msleResult.getValue(), closeTo(Math.pow(Math.log(1000 + 1), 2), 10E-6));
     }
 
-    public void testEvaluate_PseudoHuber() {
+    public void testEvaluate_Huber() {
         EvaluateDataFrameAction.Response evaluateDataFrameResponse =
             evaluateDataFrame(
                 HOUSES_DATA_INDEX,
-                new Regression(PRICE_FIELD, PRICE_PREDICTION_FIELD, Collections.singletonList(new PseudoHuber((Double) null))));
+                new Regression(PRICE_FIELD, PRICE_PREDICTION_FIELD, Collections.singletonList(new Huber((Double) null))));
 
         assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Regression.NAME.getPreferredName()));
         assertThat(evaluateDataFrameResponse.getMetrics(), hasSize(1));
 
-        PseudoHuber.Result pseudoHuberResult = (PseudoHuber.Result) evaluateDataFrameResponse.getMetrics().get(0);
+        Huber.Result huberResult = (Huber.Result) evaluateDataFrameResponse.getMetrics().get(0);
-        assertThat(pseudoHuberResult.getMetricName(), equalTo(PseudoHuber.NAME.getPreferredName()));
+        assertThat(huberResult.getMetricName(), equalTo(Huber.NAME.getPreferredName()));
-        assertThat(pseudoHuberResult.getValue(), closeTo(Math.sqrt(1000000 + 1) - 1, 10E-6));
+        assertThat(huberResult.getValue(), closeTo(Math.sqrt(1000000 + 1) - 1, 10E-6));
     }
 
     public void testEvaluate_RSquared() {

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

@@ -841,15 +841,15 @@ setup:
               "regression": {
                 "actual_field": "regression_field_act",
                 "predicted_field": "regression_field_pred",
-                "metrics": { "mean_squared_error": {} }
+                "metrics": { "mse": {} }
               }
             }
           }
 
-  - match: { regression.mean_squared_error.value: 28.67749840974834 }
+  - match: { regression.mse.value: 28.67749840974834 }
-  - is_false: regression.mean_squared_logarithmic_error.value
+  - is_false: regression.msle.value
   - is_false: regression.r_squared.value
-  - is_false: regression.pseudo_huber.value
+  - is_false: regression.huber.value
 ---
 "Test regression mean_squared_logarithmic_error":
   - do:
@@ -861,17 +861,17 @@ setup:
               "regression": {
                 "actual_field": "regression_field_act",
                 "predicted_field": "regression_field_pred",
-                "metrics": { "mean_squared_logarithmic_error": { "offset": 6.0 } }
+                "metrics": { "msle": { "offset": 6.0 } }
               }
             }
           }
 
-  - match: { regression.mean_squared_logarithmic_error.value: 0.08680568028334916 }
+  - match: { regression.msle.value: 0.08680568028334916 }
-  - is_false: regression.mean_squared_error.value
+  - is_false: regression.mse.value
   - is_false: regression.r_squared.value
-  - is_false: regression.pseudo_huber.value
+  - is_false: regression.huber.value
 ---
-"Test regression pseudo_huber":
+"Test regression huber":
   - do:
       ml.evaluate_data_frame:
         body: >
@@ -881,14 +881,14 @@ setup:
               "regression": {
                 "actual_field": "regression_field_act",
                 "predicted_field": "regression_field_pred",
-                "metrics": { "pseudo_huber": { "delta": 2.0 } }
+                "metrics": { "huber": { "delta": 2.0 } }
               }
             }
           }
 
-  - match: { regression.pseudo_huber.value: 3.5088110471730145 }
+  - match: { regression.huber.value: 3.5088110471730145 }
-  - is_false: regression.mean_squared_logarithmic_error.value
+  - is_false: regression.msle.value
-  - is_false: regression.mean_squared_error.value
+  - is_false: regression.mse.value
   - is_false: regression.r_squared.value
 ---
 "Test regression r_squared":
@@ -906,9 +906,9 @@ setup:
             }
           }
   - match: { regression.r_squared.value: 0.8551031778603486 }
-  - is_false: regression.mean_squared_error
+  - is_false: regression.mse
-  - is_false: regression.mean_squared_logarithmic_error.value
+  - is_false: regression.msle.value
-  - is_false: regression.pseudo_huber.value
+  - is_false: regression.huber.value
 
 ---
 "Test regression with null metrics":
@@ -925,9 +925,10 @@ setup:
             }
           }
 
-  - match: { regression.mean_squared_error.value: 28.67749840974834 }
+  - match: { regression.mse.value: 28.67749840974834 }
   - match: { regression.r_squared.value: 0.8551031778603486 }
-  - is_false: regression.mean_squared_logarithmic_error.value
+  - is_false: regression.msle.value
+  - is_false: regression.huber.value
 ---
 "Test regression given missing actual_field":
   - do: