[ML] Adds feature importance to option to inference processor (elasti…

…c#52218) (elastic#52666)

This adds machine learning model feature importance calculations to the inference processor.

The new flag in the configuration matches the analytics parameter name: `num_top_feature_importance_values`
Example:
```
"inference": {
   "field_mappings": {},
   "model_id": "my_model",
   "inference_config": {
      "regression": {
         "num_top_feature_importance_values": 3
      }
   }
}
```

This will write to the document as follows:
```
"inference" : {
   "feature_importance" : {
      "FlightTimeMin" : -76.90955548511226,
      "FlightDelayType" : 114.13514762158526,
      "DistanceMiles" : 13.731580450792187
   },
   "predicted_value" : 108.33165831875137,
   "model_id" : "my_model"
}
```

This is done through calculating the [SHAP values](https://arxiv.org/abs/1802.03888).

It requires that models have populated `number_samples` for each tree node. This is not available to models that were created before 7.7.

Additionally, if the inference config is requesting feature_importance, and not all nodes have been upgraded yet, it will not allow the pipeline to be created. This is to safe-guard in a mixed-version environment where only some ingest nodes have been upgraded.

NOTE: the algorithm is a Java port of the one laid out in ml-cpp: https://github.com/elastic/ml-cpp/blob/master/lib/maths/CTreeShapFeatureImportance.cc

usability blocked by: elastic/ml-cpp#991

docs/reference/ingest/processors/inference.asciidoc

@@ -44,6 +44,12 @@ include::common-options.asciidoc[]
 Specifies the field to which the inference prediction is written. Defaults to 
 `predicted_value`.
 
+`num_top_feature_importance_values`::::
+(Optional, integer)
+Specifies the maximum number of
+{ml-docs}/dfa-regression.html#dfa-regression-feature-importance[feature
+importance] values per document. By default, it is zero and no feature importance
+calculation occurs.
 
 [discrete]
 [[inference-processor-classification-opt]]
@@ -63,6 +69,12 @@ Specifies the number of top class predictions to return. Defaults to 0.
 Specifies the field to which the top classes are written. Defaults to 
 `top_classes`.
 
+`num_top_feature_importance_values`::::
+(Optional, integer)
+Specifies the maximum number of
+{ml-docs}/dfa-classification.html#dfa-classification-feature-importance[feature
+importance] values per document. By default, it is zero and no feature importance
+calculation occurs.
 
 [discrete]
 [[inference-processor-config-example]]

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/TrainedModelDefinition.java

@@ -32,6 +32,7 @@
 import java.util.ArrayList;
 import java.util.Collection;
 import java.util.Collections;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Objects;
@@ -73,6 +74,7 @@ public static TrainedModelDefinition.Builder fromXContent(XContentParser parser,
 
     private final TrainedModel trainedModel;
     private final List<PreProcessor> preProcessors;
+    private Map<String, String> decoderMap;
 
     private TrainedModelDefinition(TrainedModel trainedModel, List<PreProcessor> preProcessors) {
         this.trainedModel = ExceptionsHelper.requireNonNull(trainedModel, TRAINED_MODEL);
@@ -115,13 +117,35 @@ public List<PreProcessor> getPreProcessors() {
         return preProcessors;
     }
 
-    private void preProcess(Map<String, Object> fields) {
+    void preProcess(Map<String, Object> fields) {
         preProcessors.forEach(preProcessor -> preProcessor.process(fields));
     }
 
     public InferenceResults infer(Map<String, Object> fields, InferenceConfig config) {
         preProcess(fields);
-        return trainedModel.infer(fields, config);
+        if (config.requestingImportance() && trainedModel.supportsFeatureImportance() == false) {
+            throw ExceptionsHelper.badRequestException(
+                "Feature importance is not supported for the configured model of type [{}]",
+                trainedModel.getName());
+        }
+        return trainedModel.infer(fields,
+            config,
+            config.requestingImportance() ? getDecoderMap() : Collections.emptyMap());
+    }
+
+    private Map<String, String> getDecoderMap() {
+        if (decoderMap != null) {
+            return decoderMap;
+        }
+        synchronized (this) {
+            if (decoderMap != null) {
+                return decoderMap;
+            }
+            this.decoderMap = preProcessors.stream()
+                .map(PreProcessor::reverseLookup)
+                .collect(HashMap::new, Map::putAll, Map::putAll);
+            return decoderMap;
+        }
     }
 
     @Override

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/preprocessing/CustomWordEmbedding.java

@@ -25,6 +25,7 @@
 import java.io.IOException;
 import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.Collections;
 import java.util.List;
 import java.util.Map;
 import java.util.Objects;
@@ -235,6 +236,11 @@ public void process(Map<String, Object> fields) {
         fields.put(destField, concatEmbeddings(processedFeatures));
     }
 
+    @Override
+    public Map<String, String> reverseLookup() {
+        return Collections.singletonMap(destField, fieldName);
+    }
+
     @Override
     public long ramBytesUsed() {
         long size = SHALLOW_SIZE;

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/preprocessing/FrequencyEncoding.java

@@ -97,6 +97,11 @@ public String getFeatureName() {
         return featureName;
     }
 
+    @Override
+    public Map<String, String> reverseLookup() {
+        return Collections.singletonMap(featureName, field);
+    }
+
     @Override
     public String getName() {
         return NAME.getPreferredName();

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/preprocessing/OneHotEncoding.java

@@ -18,8 +18,10 @@
 
 import java.io.IOException;
 import java.util.Collections;
+import java.util.HashMap;
 import java.util.Map;
 import java.util.Objects;
+import java.util.stream.Collectors;
 
 /**
  * PreProcessor for one hot encoding a set of categorical values for a given field.
@@ -80,6 +82,11 @@ public Map<String, String> getHotMap() {
         return hotMap;
     }
 
+    @Override
+    public Map<String, String> reverseLookup() {
+        return hotMap.entrySet().stream().collect(Collectors.toMap(HashMap.Entry::getValue, (entry) -> field));
+    }
+
     @Override
     public String getName() {
         return NAME.getPreferredName();

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/preprocessing/PreProcessor.java

@@ -24,4 +24,9 @@ public interface PreProcessor extends NamedXContentObject, NamedWriteable, Accou
      * @param fields The fields and their values to process
      */
     void process(Map<String, Object> fields);
+
+    /**
+     * @return Reverse lookup map to match resulting features to their original feature name
+     */
+    Map<String, String> reverseLookup();
 }

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/preprocessing/TargetMeanEncoding.java

@@ -108,6 +108,11 @@ public String getFeatureName() {
         return featureName;
     }
 
+    @Override
+    public Map<String, String> reverseLookup() {
+        return Collections.singletonMap(featureName, field);
+    }
+
     @Override
     public String getName() {
         return NAME.getPreferredName();

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/results/ClassificationInferenceResults.java

@@ -35,9 +35,25 @@ public ClassificationInferenceResults(double value,
                                           String classificationLabel,
                                           List<TopClassEntry> topClasses,
                                           InferenceConfig config) {
-        super(value);
-        assert config instanceof ClassificationConfig;
-        ClassificationConfig classificationConfig = (ClassificationConfig)config;
+        this(value, classificationLabel, topClasses, Collections.emptyMap(), (ClassificationConfig)config);
+    }
+
+    public ClassificationInferenceResults(double value,
+                                          String classificationLabel,
+                                          List<TopClassEntry> topClasses,
+                                          Map<String, Double> featureImportance,
+                                          InferenceConfig config) {
+        this(value, classificationLabel, topClasses, featureImportance, (ClassificationConfig)config);
+    }
+
+    private ClassificationInferenceResults(double value,
+                                           String classificationLabel,
+                                           List<TopClassEntry> topClasses,
+                                           Map<String, Double> featureImportance,
+                                           ClassificationConfig classificationConfig) {
+        super(value,
+            SingleValueInferenceResults.takeTopFeatureImportances(featureImportance,
+                classificationConfig.getNumTopFeatureImportanceValues()));
         this.classificationLabel = classificationLabel;
         this.topClasses = topClasses == null ? Collections.emptyList() : Collections.unmodifiableList(topClasses);
         this.topNumClassesField = classificationConfig.getTopClassesResultsField();
@@ -74,16 +90,17 @@ public boolean equals(Object object) {
         if (object == this) { return true; }
         if (object == null || getClass() != object.getClass()) { return false; }
         ClassificationInferenceResults that = (ClassificationInferenceResults) object;
-        return Objects.equals(value(), that.value()) &&
-            Objects.equals(classificationLabel, that.classificationLabel) &&
-            Objects.equals(resultsField, that.resultsField) &&
-            Objects.equals(topNumClassesField, that.topNumClassesField) &&
-            Objects.equals(topClasses, that.topClasses);
+        return Objects.equals(value(), that.value())
+            && Objects.equals(classificationLabel, that.classificationLabel)
+            && Objects.equals(resultsField, that.resultsField)
+            && Objects.equals(topNumClassesField, that.topNumClassesField)
+            && Objects.equals(topClasses, that.topClasses)
+            && Objects.equals(getFeatureImportance(), that.getFeatureImportance());
     }
 
     @Override
     public int hashCode() {
-        return Objects.hash(value(), classificationLabel, topClasses, resultsField, topNumClassesField);
+        return Objects.hash(value(), classificationLabel, topClasses, resultsField, topNumClassesField, getFeatureImportance());
     }
 
     @Override
@@ -100,6 +117,9 @@ public void writeResult(IngestDocument document, String parentResultField) {
             document.setFieldValue(parentResultField + "." + topNumClassesField,
                 topClasses.stream().map(TopClassEntry::asValueMap).collect(Collectors.toList()));
         }
+        if (getFeatureImportance().size() > 0) {
+            document.setFieldValue(parentResultField + ".feature_importance", getFeatureImportance());
+        }
     }
 
     @Override

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/results/RawInferenceResults.java

@@ -10,18 +10,19 @@
 import org.elasticsearch.ingest.IngestDocument;
 
 import java.io.IOException;
+import java.util.Map;
 import java.util.Objects;
 
 public class RawInferenceResults extends SingleValueInferenceResults {
 
     public static final String NAME = "raw";
 
-    public RawInferenceResults(double value) {
-        super(value);
+    public RawInferenceResults(double value, Map<String, Double> featureImportance) {
+        super(value, featureImportance);
     }
 
     public RawInferenceResults(StreamInput in) throws IOException {
-        super(in.readDouble());
+        super(in);
     }
 
     @Override
@@ -34,12 +35,13 @@ public boolean equals(Object object) {
         if (object == this) { return true; }
         if (object == null || getClass() != object.getClass()) { return false; }
         RawInferenceResults that = (RawInferenceResults) object;
-        return Objects.equals(value(), that.value());
+        return Objects.equals(value(), that.value())
+            && Objects.equals(getFeatureImportance(), that.getFeatureImportance());
     }
 
     @Override
     public int hashCode() {
-        return Objects.hash(value());
+        return Objects.hash(value(), getFeatureImportance());
     }
 
     @Override

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/results/RegressionInferenceResults.java

@@ -13,6 +13,8 @@
 import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
 
 import java.io.IOException;
+import java.util.Collections;
+import java.util.Map;
 import java.util.Objects;
 
 public class RegressionInferenceResults extends SingleValueInferenceResults {
@@ -22,14 +24,22 @@ public class RegressionInferenceResults extends SingleValueInferenceResults {
     private final String resultsField;
 
     public RegressionInferenceResults(double value, InferenceConfig config) {
-        super(value);
-        assert config instanceof RegressionConfig;
-        RegressionConfig regressionConfig = (RegressionConfig)config;
+        this(value, (RegressionConfig) config, Collections.emptyMap());
+    }
+
+    public RegressionInferenceResults(double value, InferenceConfig config, Map<String, Double> featureImportance) {
+        this(value, (RegressionConfig)config, featureImportance);
+    }
+
+    private RegressionInferenceResults(double value, RegressionConfig regressionConfig, Map<String, Double> featureImportance) {
+        super(value,
+            SingleValueInferenceResults.takeTopFeatureImportances(featureImportance,
+                regressionConfig.getNumTopFeatureImportanceValues()));
         this.resultsField = regressionConfig.getResultsField();
     }
 
     public RegressionInferenceResults(StreamInput in) throws IOException {
-        super(in.readDouble());
+        super(in);
         this.resultsField = in.readString();
     }
 
@@ -44,19 +54,24 @@ public boolean equals(Object object) {
         if (object == this) { return true; }
         if (object == null || getClass() != object.getClass()) { return false; }
         RegressionInferenceResults that = (RegressionInferenceResults) object;
-        return Objects.equals(value(), that.value()) && Objects.equals(this.resultsField, that.resultsField);
+        return Objects.equals(value(), that.value())
+            && Objects.equals(this.resultsField, that.resultsField)
+            && Objects.equals(this.getFeatureImportance(), that.getFeatureImportance());
     }
 
     @Override
     public int hashCode() {
-        return Objects.hash(value(), resultsField);
+        return Objects.hash(value(), resultsField, getFeatureImportance());
     }
 
     @Override
     public void writeResult(IngestDocument document, String parentResultField) {
         ExceptionsHelper.requireNonNull(document, "document");
         ExceptionsHelper.requireNonNull(parentResultField, "parentResultField");
         document.setFieldValue(parentResultField + "." + this.resultsField, value());
+        if (getFeatureImportance().size() > 0) {
+            document.setFieldValue(parentResultField + ".feature_importance", getFeatureImportance());
+        }
     }
 
     @Override

x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/inference/results/SingleValueInferenceResults.java

@@ -5,34 +5,61 @@
  */
 package org.elasticsearch.xpack.core.ml.inference.results;
 
+import org.elasticsearch.Version;
 import org.elasticsearch.common.io.stream.StreamInput;
 import org.elasticsearch.common.io.stream.StreamOutput;
+import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
 
 import java.io.IOException;
+import java.util.Collections;
+import java.util.LinkedHashMap;
+import java.util.Map;
 
 public abstract class SingleValueInferenceResults implements InferenceResults {
 
     private final double value;
+    private final Map<String, Double> featureImportance;
+
+    static Map<String, Double> takeTopFeatureImportances(Map<String, Double> unsortedFeatureImportances, int numTopFeatures) {
+        return unsortedFeatureImportances.entrySet()
+            .stream()
+            .sorted((l, r)-> Double.compare(Math.abs(r.getValue()), Math.abs(l.getValue())))
+            .limit(numTopFeatures)
+            .collect(LinkedHashMap::new, (h, e) -> h.put(e.getKey(), e.getValue()) , LinkedHashMap::putAll);
+    }
 
     SingleValueInferenceResults(StreamInput in) throws IOException {
         value = in.readDouble();
+        if (in.getVersion().onOrAfter(Version.V_7_7_0)) {
+            this.featureImportance = in.readMap(StreamInput::readString, StreamInput::readDouble);
+        } else {
+            this.featureImportance = Collections.emptyMap();
+        }
     }
 
-    SingleValueInferenceResults(double value) {
+    SingleValueInferenceResults(double value, Map<String, Double> featureImportance) {
         this.value = value;
+        this.featureImportance = ExceptionsHelper.requireNonNull(featureImportance, "featureImportance");
     }
 
     public Double value() {
         return value;
     }
 
+    public Map<String, Double> getFeatureImportance() {
+        return featureImportance;
+    }
+
     public String valueAsString() {
         return String.valueOf(value);
     }
 
     @Override
     public void writeTo(StreamOutput out) throws IOException {
         out.writeDouble(value);
+        if (out.getVersion().onOrAfter(Version.V_7_7_0)) {
+            out.writeMap(this.featureImportance, StreamOutput::writeString, StreamOutput::writeDouble);
+        }
     }
 
 }