Implement pseudo Huber loss (PseudoHuber) evaluation metric for regression analysis

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

@@ -23,6 +23,7 @@
 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.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -102,6 +103,10 @@ Evaluation.class, new ParseField(BinarySoftClassification.NAME), BinarySoftClass
                 EvaluationMetric.class,
                 new ParseField(registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME)),
                 MeanSquaredLogarithmicErrorMetric::fromXContent),
+            new NamedXContentRegistry.Entry(
+                EvaluationMetric.class,
+                new ParseField(registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME)),
+                PseudoHuberMetric::fromXContent),
             new NamedXContentRegistry.Entry(
                 EvaluationMetric.class,
                 new ParseField(registeredMetricName(Regression.NAME, RSquaredMetric.NAME)),
@@ -149,6 +154,10 @@ Evaluation.class, new ParseField(BinarySoftClassification.NAME), BinarySoftClass
                 EvaluationMetric.Result.class,
                 new ParseField(registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME)),
                 MeanSquaredLogarithmicErrorMetric.Result::fromXContent),
+            new NamedXContentRegistry.Entry(
+                EvaluationMetric.Result.class,
+                new ParseField(registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME)),
+                PseudoHuberMetric.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/PseudoHuberMetric.java

@@ -0,0 +1,142 @@
+/*
+ * Licensed to Elasticsearch under one or more contributor
+ * license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright
+ * ownership. Elasticsearch licenses this file to you under
+ * the Apache License, Version 2.0 (the "License"); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.elasticsearch.client.ml.dataframe.evaluation.regression;
+
+import org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric;
+import org.elasticsearch.common.Nullable;
+import org.elasticsearch.common.ParseField;
+import org.elasticsearch.common.xcontent.ConstructingObjectParser;
+import org.elasticsearch.common.xcontent.XContentBuilder;
+import org.elasticsearch.common.xcontent.XContentParser;
+
+import java.io.IOException;
+import java.util.Objects;
+
+import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constructorArg;
+import static org.elasticsearch.common.xcontent.ConstructingObjectParser.optionalConstructorArg;
+
+/**
+ * Calculates the pseudo Huber loss function.
+ *
+ * equation: pseudohuber = 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 static final String NAME = "pseudo_huber";
+
+    public static final ParseField DELTA = new ParseField("delta");
+
+    private static final ConstructingObjectParser<PseudoHuberMetric, Void> PARSER =
+        new ConstructingObjectParser<>(NAME, true, args -> new PseudoHuberMetric((Double) args[0]));
+
+    static {
+        PARSER.declareDouble(optionalConstructorArg(), DELTA);
+    }
+
+    public static PseudoHuberMetric fromXContent(XContentParser parser) {
+        return PARSER.apply(parser, null);
+    }
+
+    private final Double delta;
+
+    public PseudoHuberMetric(@Nullable Double delta) {
+        this.delta = delta;
+    }
+
+    @Override
+    public String getName() {
+        return NAME;
+    }
+
+    @Override
+    public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
+        builder.startObject();
+        if (delta != null) {
+            builder.field(DELTA.getPreferredName(), delta);
+        }
+        builder.endObject();
+        return builder;
+    }
+
+    @Override
+    public boolean equals(Object o) {
+        if (this == o) return true;
+        if (o == null || getClass() != o.getClass()) return false;
+        PseudoHuberMetric that = (PseudoHuberMetric) o;
+        return Objects.equals(this.delta, that.delta);
+    }
+
+    @Override
+    public int hashCode() {
+        return Objects.hash(delta);
+    }
+
+    public static class Result implements EvaluationMetric.Result  {
+
+        public static final ParseField VALUE = new ParseField("value");
+        private final double value;
+
+        public static Result fromXContent(XContentParser parser) {
+            return PARSER.apply(parser, null);
+        }
+
+        private static final ConstructingObjectParser<Result, Void> PARSER =
+            new ConstructingObjectParser<>("pseudo_huber_result", true, args -> new Result((double) args[0]));
+
+        static {
+            PARSER.declareDouble(constructorArg(), VALUE);
+        }
+
+        public Result(double value) {
+            this.value = value;
+        }
+
+        @Override
+        public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
+            builder.startObject();
+            builder.field(VALUE.getPreferredName(), value);
+            builder.endObject();
+            return builder;
+        }
+
+        public double getValue() {
+            return value;
+        }
+
+        @Override
+        public String getMetricName() {
+            return NAME;
+        }
+
+        @Override
+        public boolean equals(Object o) {
+            if (this == o) return true;
+            if (o == null || getClass() != o.getClass()) return false;
+            Result that = (Result) o;
+            return Objects.equals(that.value, this.value);
+        }
+
+        @Override
+        public int hashCode() {
+            return Double.hashCode(value);
+        }
+    }
+}

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

@@ -143,6 +143,7 @@
 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.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -1856,12 +1857,15 @@ public void testEvaluateDataFrame_Regression() throws IOException {
                 new Regression(
                     actualRegression,
                     predictedRegression,
-                    new MeanSquaredErrorMetric(), new MeanSquaredLogarithmicErrorMetric(1.0), new RSquaredMetric()));
+                    new MeanSquaredErrorMetric(),
+                    new MeanSquaredLogarithmicErrorMetric(1.0),
+                    new PseudoHuberMetric(1.0),
+                    new RSquaredMetric()));
 
         EvaluateDataFrameResponse evaluateDataFrameResponse =
             execute(evaluateDataFrameRequest, machineLearningClient::evaluateDataFrame, machineLearningClient::evaluateDataFrameAsync);
         assertThat(evaluateDataFrameResponse.getEvaluationName(), equalTo(Regression.NAME));
-        assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(3));
+        assertThat(evaluateDataFrameResponse.getMetrics().size(), equalTo(4));
 
         MeanSquaredErrorMetric.Result mseResult = evaluateDataFrameResponse.getMetricByName(MeanSquaredErrorMetric.NAME);
         assertThat(mseResult.getMetricName(), equalTo(MeanSquaredErrorMetric.NAME));
@@ -1872,6 +1876,10 @@ public void testEvaluateDataFrame_Regression() throws IOException {
         assertThat(msleResult.getMetricName(), equalTo(MeanSquaredLogarithmicErrorMetric.NAME));
         assertThat(msleResult.getError(), closeTo(0.02759231770210426, 1e-9));
 
+        PseudoHuberMetric.Result pseudoHuberResult = evaluateDataFrameResponse.getMetricByName(PseudoHuberMetric.NAME);
+        assertThat(pseudoHuberResult.getMetricName(), equalTo(PseudoHuberMetric.NAME));
+        assertThat(pseudoHuberResult.getValue(), closeTo(0.029669771640929276, 1e-9));
+
         RSquaredMetric.Result rSquaredResult = evaluateDataFrameResponse.getMetricByName(RSquaredMetric.NAME);
         assertThat(rSquaredResult.getMetricName(), equalTo(RSquaredMetric.NAME));
         assertThat(rSquaredResult.getValue(), closeTo(-5.1000000000000005, 1e-9));

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

@@ -62,6 +62,7 @@
 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.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.regression.Regression;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
@@ -702,7 +703,7 @@ public void testDefaultNamedXContents() {
 
     public void testProvidedNamedXContents() {
         List<NamedXContentRegistry.Entry> namedXContents = RestHighLevelClient.getProvidedNamedXContents();
-        assertEquals(66, namedXContents.size());
+        assertEquals(68, namedXContents.size());
         Map<Class<?>, Integer> categories = new HashMap<>();
         List<String> names = new ArrayList<>();
         for (NamedXContentRegistry.Entry namedXContent : namedXContents) {
@@ -749,7 +750,7 @@ public void testProvidedNamedXContents() {
         assertTrue(names.contains(TimeSyncConfig.NAME));
         assertEquals(Integer.valueOf(3), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.Evaluation.class));
         assertThat(names, hasItems(BinarySoftClassification.NAME, Classification.NAME, Regression.NAME));
-        assertEquals(Integer.valueOf(11), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric.class));
+        assertEquals(Integer.valueOf(12), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric.class));
         assertThat(names,
             hasItems(
                 registeredMetricName(BinarySoftClassification.NAME, AucRocMetric.NAME),
@@ -764,8 +765,9 @@ public void testProvidedNamedXContents() {
                 registeredMetricName(Classification.NAME, MulticlassConfusionMatrixMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredErrorMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME),
+                registeredMetricName(Regression.NAME, PseudoHuberMetric.NAME),
                 registeredMetricName(Regression.NAME, RSquaredMetric.NAME)));
-        assertEquals(Integer.valueOf(11), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric.Result.class));
+        assertEquals(Integer.valueOf(12), categories.get(org.elasticsearch.client.ml.dataframe.evaluation.EvaluationMetric.Result.class));
         assertThat(names,
             hasItems(
                 registeredMetricName(BinarySoftClassification.NAME, AucRocMetric.NAME),
@@ -780,6 +782,7 @@ public void testProvidedNamedXContents() {
                 registeredMetricName(Classification.NAME, MulticlassConfusionMatrixMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredErrorMetric.NAME),
                 registeredMetricName(Regression.NAME, MeanSquaredLogarithmicErrorMetric.NAME),
+                registeredMetricName(Regression.NAME, PseudoHuberMetric.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

@@ -162,6 +162,7 @@
 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.RSquaredMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.AucRocMetric;
 import org.elasticsearch.client.ml.dataframe.evaluation.softclassification.BinarySoftClassification;
@@ -3572,7 +3573,8 @@ public void testEvaluateDataFrame_Regression() throws Exception {
                     // Evaluation metrics // <4>
                     new MeanSquaredErrorMetric(), // <5>
                     new MeanSquaredLogarithmicErrorMetric(1.0), // <6>
-                    new RSquaredMetric()); // <7>
+                    new PseudoHuberMetric(1.0), // <7>
+                    new RSquaredMetric()); // <8>
             // end::evaluate-data-frame-evaluation-regression
 
             EvaluateDataFrameRequest request = new EvaluateDataFrameRequest(indexName, null, evaluation);
@@ -3586,12 +3588,16 @@ public void testEvaluateDataFrame_Regression() throws Exception {
                 response.getMetricByName(MeanSquaredLogarithmicErrorMetric.NAME); // <3>
             double meanSquaredLogarithmicError = meanSquaredLogarithmicErrorResult.getError(); // <4>
 
-            RSquaredMetric.Result rSquaredResult = response.getMetricByName(RSquaredMetric.NAME); // <5>
-            double rSquared = rSquaredResult.getValue(); // <6>
+            PseudoHuberMetric.Result pseudoHuberResult = response.getMetricByName(PseudoHuberMetric.NAME); // <5>
+            double pseudoHuber = pseudoHuberResult.getValue(); // <6>
+
+            RSquaredMetric.Result rSquaredResult = response.getMetricByName(RSquaredMetric.NAME); // <7>
+            double rSquared = rSquaredResult.getValue(); // <8>
             // end::evaluate-data-frame-results-regression
 
             assertThat(meanSquaredError, closeTo(0.021, 1e-3));
             assertThat(meanSquaredLogarithmicError, closeTo(0.003, 1e-3));
+            assertThat(pseudoHuber, 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/PseudoHuberMetricResultTests.java

@@ -0,0 +1,53 @@
+/*
+ * Licensed to Elasticsearch under one or more contributor
+ * license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright
+ * ownership. Elasticsearch licenses this file to you under
+ * the Apache License, Version 2.0 (the "License"); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.elasticsearch.client.ml.dataframe.evaluation.regression;
+
+import org.elasticsearch.client.ml.dataframe.evaluation.MlEvaluationNamedXContentProvider;
+import org.elasticsearch.common.xcontent.NamedXContentRegistry;
+import org.elasticsearch.common.xcontent.XContentParser;
+import org.elasticsearch.test.AbstractXContentTestCase;
+
+import java.io.IOException;
+
+public class PseudoHuberMetricResultTests extends AbstractXContentTestCase<PseudoHuberMetric.Result> {
+
+    public static PseudoHuberMetric.Result randomResult() {
+        return new PseudoHuberMetric.Result(randomDouble());
+    }
+
+    @Override
+    protected PseudoHuberMetric.Result createTestInstance() {
+        return randomResult();
+    }
+
+    @Override
+    protected PseudoHuberMetric.Result doParseInstance(XContentParser parser) throws IOException {
+        return PseudoHuberMetric.Result.fromXContent(parser);
+    }
+
+    @Override
+    protected boolean supportsUnknownFields() {
+        return true;
+    }
+
+    @Override
+    protected NamedXContentRegistry xContentRegistry() {
+        return new NamedXContentRegistry(new MlEvaluationNamedXContentProvider().getNamedXContentParsers());
+    }
+}

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

@@ -0,0 +1,49 @@
+/*
+ * Licensed to Elasticsearch under one or more contributor
+ * license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright
+ * ownership. Elasticsearch licenses this file to you under
+ * the Apache License, Version 2.0 (the "License"); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.elasticsearch.client.ml.dataframe.evaluation.regression;
+
+import org.elasticsearch.client.ml.dataframe.evaluation.MlEvaluationNamedXContentProvider;
+import org.elasticsearch.common.xcontent.NamedXContentRegistry;
+import org.elasticsearch.common.xcontent.XContentParser;
+import org.elasticsearch.test.AbstractXContentTestCase;
+
+import java.io.IOException;
+
+public class PseudoHuberMetricTests extends AbstractXContentTestCase<PseudoHuberMetric> {
+
+    @Override
+    protected NamedXContentRegistry xContentRegistry() {
+        return new NamedXContentRegistry(new MlEvaluationNamedXContentProvider().getNamedXContentParsers());
+    }
+
+    @Override
+    protected PseudoHuberMetric createTestInstance() {
+        return new PseudoHuberMetric(randomBoolean() ? randomDouble() : null);
+    }
+
+    @Override
+    protected PseudoHuberMetric doParseInstance(XContentParser parser) throws IOException {
+        return PseudoHuberMetric.fromXContent(parser);
+    }
+
+    @Override
+    protected boolean supportsUnknownFields() {
+        return true;
+    }
+}