Rename to num_top_feature_important_values

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

@@ -46,7 +46,7 @@ public static Builder builder(String dependentVariable) {
     static final ParseField ETA = new ParseField("eta");
     static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees");
     static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
-    static final ParseField TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("top_feature_importance_values");
+    static final ParseField NUM_TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("num_top_feature_importance_values");
     static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
     static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
     static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
@@ -76,7 +76,7 @@ public static Builder builder(String dependentVariable) {
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA);
         PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES);
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
-        PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), TOP_FEATURE_IMPORTANCE_VALUES);
+        PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_FEATURE_IMPORTANCE_VALUES);
         PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
         PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_CLASSES);
@@ -89,23 +89,23 @@ public static Builder builder(String dependentVariable) {
     private final Double eta;
     private final Integer maximumNumberTrees;
     private final Double featureBagFraction;
-    private final Integer topFeatureImportanceValues;
+    private final Integer numTopFeatureImportanceValues;
     private final String predictionFieldName;
     private final Double trainingPercent;
     private final Integer numTopClasses;
     private final Long randomizeSeed;
 
     private Classification(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
                            @Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction,
-                           @Nullable Integer topFeatureImportanceValues, @Nullable String predictionFieldName,
+                           @Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
                            @Nullable Double trainingPercent, @Nullable Integer numTopClasses, @Nullable Long randomizeSeed) {
         this.dependentVariable = Objects.requireNonNull(dependentVariable);
         this.lambda = lambda;
         this.gamma = gamma;
         this.eta = eta;
         this.maximumNumberTrees = maximumNumberTrees;
         this.featureBagFraction = featureBagFraction;
-        this.topFeatureImportanceValues = topFeatureImportanceValues;
+        this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
         this.predictionFieldName = predictionFieldName;
         this.trainingPercent = trainingPercent;
         this.numTopClasses = numTopClasses;
@@ -141,8 +141,8 @@ public Double getFeatureBagFraction() {
         return featureBagFraction;
     }
 
-    public Integer getTopFeatureImportanceValues() {
-        return topFeatureImportanceValues;
+    public Integer getNumTopFeatureImportanceValues() {
+        return numTopFeatureImportanceValues;
     }
 
     public String getPredictionFieldName() {
@@ -180,8 +180,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
         if (featureBagFraction != null) {
             builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction);
         }
-        if (topFeatureImportanceValues != null) {
-            builder.field(TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), topFeatureImportanceValues);
+        if (numTopFeatureImportanceValues != null) {
+            builder.field(NUM_TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), numTopFeatureImportanceValues);
         }
         if (predictionFieldName != null) {
             builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName);
@@ -201,7 +201,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
 
     @Override
     public int hashCode() {
-        return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
+        return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, numTopFeatureImportanceValues,
             predictionFieldName, trainingPercent, randomizeSeed, numTopClasses);
     }
 
@@ -216,7 +216,7 @@ public boolean equals(Object o) {
             && Objects.equals(eta, that.eta)
             && Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
             && Objects.equals(featureBagFraction, that.featureBagFraction)
-            && Objects.equals(topFeatureImportanceValues, that.topFeatureImportanceValues)
+            && Objects.equals(numTopFeatureImportanceValues, that.numTopFeatureImportanceValues)
             && Objects.equals(predictionFieldName, that.predictionFieldName)
             && Objects.equals(trainingPercent, that.trainingPercent)
             && Objects.equals(randomizeSeed, that.randomizeSeed)
@@ -235,7 +235,7 @@ public static class Builder {
         private Double eta;
         private Integer maximumNumberTrees;
         private Double featureBagFraction;
-        private Integer topFeatureImportanceValues;
+        private Integer numTopFeatureImportanceValues;
         private String predictionFieldName;
         private Double trainingPercent;
         private Integer numTopClasses;
@@ -270,8 +270,8 @@ public Builder setFeatureBagFraction(Double featureBagFraction) {
             return this;
         }
 
-        public Builder setTopFeatureImportanceValues(Integer topFeatureImportanceValues) {
-            this.topFeatureImportanceValues = topFeatureImportanceValues;
+        public Builder setNumTopFeatureImportanceValues(Integer numTopFeatureImportanceValues) {
+            this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
             return this;
         }
 
@@ -297,7 +297,7 @@ public Builder setNumTopClasses(Integer numTopClasses) {
 
         public Classification build() {
             return new Classification(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction,
-                topFeatureImportanceValues, predictionFieldName, trainingPercent, numTopClasses, randomizeSeed);
+                numTopFeatureImportanceValues, predictionFieldName, trainingPercent, numTopClasses, randomizeSeed);
         }
     }
 }

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

@@ -46,7 +46,7 @@ public static Builder builder(String dependentVariable) {
     static final ParseField ETA = new ParseField("eta");
     static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees");
     static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
-    static final ParseField TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("top_feature_importance_values");
+    static final ParseField NUM_TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("num_top_feature_importance_values");
     static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
     static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
     static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
@@ -74,7 +74,7 @@ public static Builder builder(String dependentVariable) {
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA);
         PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES);
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
-        PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), TOP_FEATURE_IMPORTANCE_VALUES);
+        PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_FEATURE_IMPORTANCE_VALUES);
         PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
         PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
@@ -86,22 +86,22 @@ public static Builder builder(String dependentVariable) {
     private final Double eta;
     private final Integer maximumNumberTrees;
     private final Double featureBagFraction;
-    private final Integer topFeatureImportanceValues;
+    private final Integer numTopFeatureImportanceValues;
     private final String predictionFieldName;
     private final Double trainingPercent;
     private final Long randomizeSeed;
 
-    private Regression(String dependentVariable,  @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
+    private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
                        @Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction,
-                       @Nullable Integer topFeatureImportanceValues, @Nullable String predictionFieldName,
+                       @Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
                        @Nullable Double trainingPercent, @Nullable Long randomizeSeed) {
         this.dependentVariable = Objects.requireNonNull(dependentVariable);
         this.lambda = lambda;
         this.gamma = gamma;
         this.eta = eta;
         this.maximumNumberTrees = maximumNumberTrees;
         this.featureBagFraction = featureBagFraction;
-        this.topFeatureImportanceValues = topFeatureImportanceValues;
+        this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
         this.predictionFieldName = predictionFieldName;
         this.trainingPercent = trainingPercent;
         this.randomizeSeed = randomizeSeed;
@@ -136,8 +136,8 @@ public Double getFeatureBagFraction() {
         return featureBagFraction;
     }
 
-    public Integer getTopFeatureImportanceValues() {
-        return topFeatureImportanceValues;
+    public Integer getNumTopFeatureImportanceValues() {
+        return numTopFeatureImportanceValues;
     }
 
     public String getPredictionFieldName() {
@@ -171,8 +171,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
         if (featureBagFraction != null) {
             builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction);
         }
-        if (topFeatureImportanceValues != null) {
-            builder.field(TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), topFeatureImportanceValues);
+        if (numTopFeatureImportanceValues != null) {
+            builder.field(NUM_TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), numTopFeatureImportanceValues);
         }
         if (predictionFieldName != null) {
             builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName);
@@ -189,7 +189,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
 
     @Override
     public int hashCode() {
-        return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
+        return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, numTopFeatureImportanceValues,
             predictionFieldName, trainingPercent, randomizeSeed);
     }
 
@@ -204,7 +204,7 @@ public boolean equals(Object o) {
             && Objects.equals(eta, that.eta)
             && Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
             && Objects.equals(featureBagFraction, that.featureBagFraction)
-            && Objects.equals(topFeatureImportanceValues, that.topFeatureImportanceValues)
+            && Objects.equals(numTopFeatureImportanceValues, that.numTopFeatureImportanceValues)
             && Objects.equals(predictionFieldName, that.predictionFieldName)
             && Objects.equals(trainingPercent, that.trainingPercent)
             && Objects.equals(randomizeSeed, that.randomizeSeed);
@@ -222,7 +222,7 @@ public static class Builder {
         private Double eta;
         private Integer maximumNumberTrees;
         private Double featureBagFraction;
-        private Integer topFeatureImportanceValues;
+        private Integer numTopFeatureImportanceValues;
         private String predictionFieldName;
         private Double trainingPercent;
         private Long randomizeSeed;
@@ -256,8 +256,8 @@ public Builder setFeatureBagFraction(Double featureBagFraction) {
             return this;
         }
 
-        public Builder setTopFeatureImportanceValues(Integer topFeatureImportanceValues) {
-            this.topFeatureImportanceValues = topFeatureImportanceValues;
+        public Builder setNumTopFeatureImportanceValues(Integer numTopFeatureImportanceValues) {
+            this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
             return this;
         }
 
@@ -277,8 +277,8 @@ public Builder setRandomizeSeed(Long randomizeSeed) {
         }
 
         public Regression build() {
-            return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
-                predictionFieldName, trainingPercent, randomizeSeed);
+            return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction,
+                numTopFeatureImportanceValues, predictionFieldName, trainingPercent, randomizeSeed);
         }
     }
 }

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

@@ -1299,7 +1299,7 @@ public void testPutDataFrameAnalyticsConfig_GivenRegression() throws Exception {
                 .setEta(1.0)
                 .setMaximumNumberTrees(10)
                 .setFeatureBagFraction(0.5)
-                .setTopFeatureImportanceValues(3)
+                .setNumTopFeatureImportanceValues(3)
                 .build())
             .setDescription("this is a regression")
             .build();
@@ -1342,7 +1342,7 @@ public void testPutDataFrameAnalyticsConfig_GivenClassification() throws Excepti
                 .setEta(1.0)
                 .setMaximumNumberTrees(10)
                 .setFeatureBagFraction(0.5)
-                .setTopFeatureImportanceValues(3)
+                .setNumTopFeatureImportanceValues(3)
                 .build())
             .setDescription("this is a classification")
             .build();

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

@@ -2975,7 +2975,7 @@ public void testPutDataFrameAnalytics() throws Exception {
                 .setEta(5.5) // <4>
                 .setMaximumNumberTrees(50) // <5>
                 .setFeatureBagFraction(0.4) // <6>
-                .setTopFeatureImportanceValues(3) // <7>
+                .setNumTopFeatureImportanceValues(3) // <7>
                 .setPredictionFieldName("my_prediction_field_name") // <8>
                 .setTrainingPercent(50.0) // <9>
                 .setRandomizeSeed(1234L) // <10>
@@ -2990,7 +2990,7 @@ public void testPutDataFrameAnalytics() throws Exception {
                 .setEta(5.5) // <4>
                 .setMaximumNumberTrees(50) // <5>
                 .setFeatureBagFraction(0.4) // <6>
-                .setTopFeatureImportanceValues(3) // <7>
+                .setNumTopFeatureImportanceValues(3) // <7>
                 .setPredictionFieldName("my_prediction_field_name") // <8>
                 .setTrainingPercent(50.0) // <9>
                 .setRandomizeSeed(1234L) // <10>
@@ -3672,7 +3672,7 @@ public void testPutTrainedModel() throws Exception {
         }
         {
             PutTrainedModelRequest request = new PutTrainedModelRequest(trainedModelConfig);
-            
+
             // tag::put-trained-model-execute-listener
             ActionListener<PutTrainedModelResponse> listener = new ActionListener<>() {
                 @Override

client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/ClassificationTests.java

@@ -32,7 +32,7 @@ public static Classification randomClassification() {
             .setEta(randomBoolean() ? null : randomDoubleBetween(0.001, 1.0, true))
             .setMaximumNumberTrees(randomBoolean() ? null : randomIntBetween(1, 2000))
             .setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
-            .setTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
+            .setNumTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
             .setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
             .setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
             .setRandomizeSeed(randomBoolean() ? null : randomLong())

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

@@ -32,7 +32,7 @@ public static Regression randomRegression() {
             .setEta(randomBoolean() ? null : randomDoubleBetween(0.001, 1.0, true))
             .setMaximumNumberTrees(randomBoolean() ? null : randomIntBetween(1, 2000))
             .setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
-            .setTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
+            .setNumTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
             .setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
             .setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
             .build();

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

@@ -148,9 +148,9 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
 (Optional, long)
 include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
 
-`analysis`.`classification`.`top_feature_importance_values`::::
+`analysis`.`classification`.`num_top_feature_importance_values`::::
 (Optional, integer)
-include::{docdir}/ml/ml-shared.asciidoc[tag=top-feature-importance-values]
+include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
 
 `analysis`.`classification`.`training_percent`::::
 (Optional, integer)
@@ -231,9 +231,9 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
 (Optional, string)
 include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
 
-`analysis`.`regression`.`top_feature_importance_values`::::
+`analysis`.`regression`.`num_top_feature_importance_values`::::
 (Optional, integer)
-include::{docdir}/ml/ml-shared.asciidoc[tag=top-feature-importance-values]
+include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
 
 `analysis`.`regression`.`training_percent`::::
 (Optional, integer)

docs/reference/ml/ml-shared.asciidoc

@@ -640,12 +640,12 @@ tag::indices[]
 An array of index names. Wildcards are supported. For example:
 `["it_ops_metrics", "server*"]`.
 
-tag::top-feature-importance-values[]
+tag::num-top-feature-importance-values[]
 Advanced configuration option. If set, feature importance for the top
 most important features will be computed. Importance is calculated
 using the SHAP (SHapley Additive exPlanations) method as described in
 https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf[Lundberg, S. M., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017.].
-end::top-feature-importance-values[]
+end::num-top-feature-importance-values[]
 
 +
 --