[ML] Make Ensemble feature names optional (#51996)

The featureNames field is requisite in individual models but is not required by the Ensemble.
elastic · Feb 7, 2020 · d43bcbf · d43bcbf
1 parent 3ccea31
commit d43bcbf
Show file tree

Hide file tree

Showing 5 changed files with 11 additions and 8 deletions.
diff --git a/...l/src/main/java/org/elasticsearch/client/ml/inference/trainedmodel/ensemble/Ensemble.java b/...l/src/main/java/org/elasticsearch/client/ml/inference/trainedmodel/ensemble/Ensemble.java
@@ -103,7 +103,7 @@ public String getName() {
     @Override
     public XContentBuilder toXContent(XContentBuilder builder, ToXContent.Params params) throws IOException {
         builder.startObject();
-        if (featureNames != null) {
+        if (featureNames != null && featureNames.isEmpty() == false) {
             builder.field(FEATURE_NAMES.getPreferredName(), featureNames);
         }
         if (models != null) {
@@ -157,7 +157,7 @@ public static Builder builder() {
     }
 
     public static class Builder {
-        private List<String> featureNames;
+        private List<String> featureNames = Collections.emptyList();
         private List<TrainedModel> trainedModels;
         private OutputAggregator outputAggregator;
         private TargetType targetType;

diff --git a/.../test/java/org/elasticsearch/client/ml/inference/trainedmodel/ensemble/EnsembleTests.java b/.../test/java/org/elasticsearch/client/ml/inference/trainedmodel/ensemble/EnsembleTests.java
@@ -86,7 +86,7 @@ public static Ensemble createRandom(TargetType targetType) {
                 .toArray() :
             null;
 
-        return new Ensemble(featureNames,
+        return new Ensemble(randomBoolean() ? featureNames : Collections.emptyList(),
             models,
             outputAggregator,
             targetType,

diff --git a/docs/reference/ml/df-analytics/apis/put-inference.asciidoc b/docs/reference/ml/df-analytics/apis/put-inference.asciidoc
@@ -150,7 +150,7 @@ See <<ml-put-inference-preprocessor-example>> for more details.
 The definition for a binary decision tree.
 
 `tree`.`feature_names`:::
-(Required, string) 
+(Required, string)
 Features expected by the tree, in their expected order.
 
 `tree`.`tree_structure`:::
@@ -221,7 +221,7 @@ children).
 The definition for an ensemble model.
 
 `ensemble`.`feature_names`:::
-(Required, string) 
+(Optional, string)
 Features expected by the ensemble, in their expected order.
 
 `ensemble`.`trained_models`:::

diff --git a/...c/main/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/Ensemble.java b/...c/main/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/Ensemble.java
@@ -207,7 +207,9 @@ public String getName() {
     @Override
     public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
         builder.startObject();
-        builder.field(FEATURE_NAMES.getPreferredName(), featureNames);
+        if (featureNames.isEmpty() == false) {
+            builder.field(FEATURE_NAMES.getPreferredName(), featureNames);
+        }
         NamedXContentObjectHelper.writeNamedObjects(builder, params, true, TRAINED_MODELS.getPreferredName(), models);
         NamedXContentObjectHelper.writeNamedObjects(builder,
             params,
@@ -327,8 +329,9 @@ public static class Builder {
         private double[] classificationWeights;
         private boolean modelsAreOrdered;
 
-        private Builder (boolean modelsAreOrdered) {
+        private Builder(boolean modelsAreOrdered) {
             this.modelsAreOrdered = modelsAreOrdered;
+            this.featureNames = Collections.emptyList();
         }
 
         private static Builder builderForParser() {

diff --git a/...t/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/EnsembleTests.java b/...t/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/ensemble/EnsembleTests.java
@@ -92,7 +92,7 @@ public static Ensemble createRandom() {
                 .toArray() :
             null;
 
-        return new Ensemble(featureNames,
+        return new Ensemble(randomBoolean() ? featureNames : Collections.emptyList(),
             models,
             outputAggregator,
             targetType,