[7.x] [ML] adding docs + hlrc for data frame analysis feature_processors (#61149) (#61493)

* [ML] adding docs + hlrc for data frame analysis feature_processors (#61149)

Adds HLRC and some docs for the new feature_processors field in Data frame analytics.

Co-authored-by: Przemysław Witek <przemyslaw.witek@elastic.co>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>

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

@@ -18,6 +18,8 @@
  */
 package org.elasticsearch.client.ml.dataframe;
 
+import org.elasticsearch.client.ml.inference.NamedXContentObjectHelper;
+import org.elasticsearch.client.ml.inference.preprocessing.PreProcessor;
 import org.elasticsearch.common.Nullable;
 import org.elasticsearch.common.ParseField;
 import org.elasticsearch.common.Strings;
@@ -26,6 +28,7 @@ import org.elasticsearch.common.xcontent.XContentBuilder;
 import org.elasticsearch.common.xcontent.XContentParser;
 
 import java.io.IOException;
+import java.util.List;
 import java.util.Locale;
 import java.util.Objects;
 
@@ -53,7 +56,9 @@ public class Classification implements DataFrameAnalysis {
     static final ParseField CLASS_ASSIGNMENT_OBJECTIVE = new ParseField("class_assignment_objective");
     static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
     static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
+    static final ParseField FEATURE_PROCESSORS = new ParseField("feature_processors");
 
+    @SuppressWarnings("unchecked")
     private static final ConstructingObjectParser<Classification, Void> PARSER =
         new ConstructingObjectParser<>(
             NAME.getPreferredName(),
@@ -70,7 +75,8 @@ public class Classification implements DataFrameAnalysis {
                 (Double) a[8],
                 (Integer) a[9],
                 (Long) a[10],
-                (ClassAssignmentObjective) a[11]));
+                (ClassAssignmentObjective) a[11],
+                (List<PreProcessor>) a[12]));
 
     static {
         PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE);
@@ -86,6 +92,10 @@ public class Classification implements DataFrameAnalysis {
         PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
         PARSER.declareString(
             ConstructingObjectParser.optionalConstructorArg(), ClassAssignmentObjective::fromString, CLASS_ASSIGNMENT_OBJECTIVE);
+        PARSER.declareNamedObjects(ConstructingObjectParser.optionalConstructorArg(),
+            (p, c, n) -> p.namedObject(PreProcessor.class, n, c),
+            (classification) -> {},
+            FEATURE_PROCESSORS);
     }
 
     private final String dependentVariable;
@@ -100,12 +110,13 @@ public class Classification implements DataFrameAnalysis {
     private final ClassAssignmentObjective classAssignmentObjective;
     private final Integer numTopClasses;
     private final Long randomizeSeed;
+    private final List<PreProcessor> featureProcessors;
 
     private Classification(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
                            @Nullable Integer maxTrees, @Nullable Double featureBagFraction,
                            @Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
                            @Nullable Double trainingPercent, @Nullable Integer numTopClasses, @Nullable Long randomizeSeed,
-                           @Nullable ClassAssignmentObjective classAssignmentObjective) {
+                           @Nullable ClassAssignmentObjective classAssignmentObjective, @Nullable List<PreProcessor> featureProcessors) {
         this.dependentVariable = Objects.requireNonNull(dependentVariable);
         this.lambda = lambda;
         this.gamma = gamma;
@@ -118,6 +129,7 @@ public class Classification implements DataFrameAnalysis {
         this.classAssignmentObjective = classAssignmentObjective;
         this.numTopClasses = numTopClasses;
         this.randomizeSeed = randomizeSeed;
+        this.featureProcessors = featureProcessors;
     }
 
     @Override
@@ -173,6 +185,10 @@ public class Classification implements DataFrameAnalysis {
         return numTopClasses;
     }
 
+    public List<PreProcessor> getFeatureProcessors() {
+        return featureProcessors;
+    }
+
     @Override
     public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
         builder.startObject();
@@ -210,6 +226,9 @@ public class Classification implements DataFrameAnalysis {
         if (numTopClasses != null) {
             builder.field(NUM_TOP_CLASSES.getPreferredName(), numTopClasses);
         }
+        if (featureProcessors != null) {
+            NamedXContentObjectHelper.writeNamedObjects(builder, params, true, FEATURE_PROCESSORS.getPreferredName(), featureProcessors);
+        }
         builder.endObject();
         return builder;
     }
@@ -217,7 +236,7 @@ public class Classification implements DataFrameAnalysis {
     @Override
     public int hashCode() {
         return Objects.hash(dependentVariable, lambda, gamma, eta, maxTrees, featureBagFraction, numTopFeatureImportanceValues,
-            predictionFieldName, trainingPercent, randomizeSeed, numTopClasses, classAssignmentObjective);
+            predictionFieldName, trainingPercent, randomizeSeed, numTopClasses, classAssignmentObjective, featureProcessors);
     }
 
     @Override
@@ -236,7 +255,8 @@ public class Classification implements DataFrameAnalysis {
             && Objects.equals(trainingPercent, that.trainingPercent)
             && Objects.equals(randomizeSeed, that.randomizeSeed)
             && Objects.equals(numTopClasses, that.numTopClasses)
-            && Objects.equals(classAssignmentObjective, that.classAssignmentObjective);
+            && Objects.equals(classAssignmentObjective, that.classAssignmentObjective)
+            && Objects.equals(featureProcessors, that.featureProcessors);
     }
 
     @Override
@@ -270,6 +290,7 @@ public class Classification implements DataFrameAnalysis {
         private Integer numTopClasses;
         private Long randomizeSeed;
         private ClassAssignmentObjective classAssignmentObjective;
+        private List<PreProcessor> featureProcessors;
 
         private Builder(String dependentVariable) {
             this.dependentVariable = Objects.requireNonNull(dependentVariable);
@@ -330,10 +351,15 @@ public class Classification implements DataFrameAnalysis {
             return this;
         }
 
+        public Builder setFeatureProcessors(List<PreProcessor> featureProcessors) {
+            this.featureProcessors = featureProcessors;
+            return this;
+        }
+
         public Classification build() {
             return new Classification(dependentVariable, lambda, gamma, eta, maxTrees, featureBagFraction,
                 numTopFeatureImportanceValues, predictionFieldName, trainingPercent, numTopClasses, randomizeSeed,
-                classAssignmentObjective);
+                classAssignmentObjective, featureProcessors);
         }
     }
 }

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

@@ -18,6 +18,8 @@
  */
 package org.elasticsearch.client.ml.dataframe;
 
+import org.elasticsearch.client.ml.inference.NamedXContentObjectHelper;
+import org.elasticsearch.client.ml.inference.preprocessing.PreProcessor;
 import org.elasticsearch.common.Nullable;
 import org.elasticsearch.common.ParseField;
 import org.elasticsearch.common.Strings;
@@ -26,6 +28,7 @@ import org.elasticsearch.common.xcontent.XContentBuilder;
 import org.elasticsearch.common.xcontent.XContentParser;
 
 import java.io.IOException;
+import java.util.List;
 import java.util.Locale;
 import java.util.Objects;
 
@@ -55,7 +58,9 @@ public class Regression implements DataFrameAnalysis {
     static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
     static final ParseField LOSS_FUNCTION = new ParseField("loss_function");
     static final ParseField LOSS_FUNCTION_PARAMETER = new ParseField("loss_function_parameter");
+    static final ParseField FEATURE_PROCESSORS = new ParseField("feature_processors");
 
+    @SuppressWarnings("unchecked")
     private static final ConstructingObjectParser<Regression, Void> PARSER =
         new ConstructingObjectParser<>(
             NAME.getPreferredName(),
@@ -72,7 +77,8 @@ public class Regression implements DataFrameAnalysis {
                 (Double) a[8],
                 (Long) a[9],
                 (LossFunction) a[10],
-                (Double) a[11]
+                (Double) a[11],
+                (List<PreProcessor>) a[12]
             ));
 
     static {
@@ -88,6 +94,10 @@ public class Regression implements DataFrameAnalysis {
         PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
         PARSER.declareString(optionalConstructorArg(), LossFunction::fromString, LOSS_FUNCTION);
         PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), LOSS_FUNCTION_PARAMETER);
+        PARSER.declareNamedObjects(ConstructingObjectParser.optionalConstructorArg(),
+            (p, c, n) -> p.namedObject(PreProcessor.class, n, c),
+            (regression) -> {},
+            FEATURE_PROCESSORS);
     }
 
     private final String dependentVariable;
@@ -102,12 +112,13 @@ public class Regression implements DataFrameAnalysis {
     private final Long randomizeSeed;
     private final LossFunction lossFunction;
     private final Double lossFunctionParameter;
+    private final List<PreProcessor> featureProcessors;
 
     private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
                        @Nullable Integer maxTrees, @Nullable Double featureBagFraction,
                        @Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
                        @Nullable Double trainingPercent, @Nullable Long randomizeSeed, @Nullable LossFunction lossFunction,
-                       @Nullable Double lossFunctionParameter) {
+                       @Nullable Double lossFunctionParameter, @Nullable List<PreProcessor> featureProcessors) {
         this.dependentVariable = Objects.requireNonNull(dependentVariable);
         this.lambda = lambda;
         this.gamma = gamma;
@@ -120,6 +131,7 @@ public class Regression implements DataFrameAnalysis {
         this.randomizeSeed = randomizeSeed;
         this.lossFunction = lossFunction;
         this.lossFunctionParameter = lossFunctionParameter;
+        this.featureProcessors = featureProcessors;
     }
 
     @Override
@@ -175,6 +187,10 @@ public class Regression implements DataFrameAnalysis {
         return lossFunctionParameter;
     }
 
+    public List<PreProcessor> getFeatureProcessors() {
+        return featureProcessors;
+    }
+
     @Override
     public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
         builder.startObject();
@@ -212,6 +228,9 @@ public class Regression implements DataFrameAnalysis {
         if (lossFunctionParameter != null) {
             builder.field(LOSS_FUNCTION_PARAMETER.getPreferredName(), lossFunctionParameter);
         }
+        if (featureProcessors != null) {
+            NamedXContentObjectHelper.writeNamedObjects(builder, params, true, FEATURE_PROCESSORS.getPreferredName(), featureProcessors);
+        }
         builder.endObject();
         return builder;
     }
@@ -219,7 +238,7 @@ public class Regression implements DataFrameAnalysis {
     @Override
     public int hashCode() {
         return Objects.hash(dependentVariable, lambda, gamma, eta, maxTrees, featureBagFraction, numTopFeatureImportanceValues,
-            predictionFieldName, trainingPercent, randomizeSeed, lossFunction, lossFunctionParameter);
+            predictionFieldName, trainingPercent, randomizeSeed, lossFunction, lossFunctionParameter, featureProcessors);
     }
 
     @Override
@@ -238,7 +257,8 @@ public class Regression implements DataFrameAnalysis {
             && Objects.equals(trainingPercent, that.trainingPercent)
             && Objects.equals(randomizeSeed, that.randomizeSeed)
             && Objects.equals(lossFunction, that.lossFunction)
-            && Objects.equals(lossFunctionParameter, that.lossFunctionParameter);
+            && Objects.equals(lossFunctionParameter, that.lossFunctionParameter)
+            && Objects.equals(featureProcessors, that.featureProcessors);
     }
 
     @Override
@@ -259,6 +279,7 @@ public class Regression implements DataFrameAnalysis {
         private Long randomizeSeed;
         private LossFunction lossFunction;
         private Double lossFunctionParameter;
+        private List<PreProcessor> featureProcessors;
 
         private Builder(String dependentVariable) {
             this.dependentVariable = Objects.requireNonNull(dependentVariable);
@@ -319,9 +340,15 @@ public class Regression implements DataFrameAnalysis {
             return this;
         }
 
+        public Builder setFeatureProcessors(List<PreProcessor> featureProcessors) {
+            this.featureProcessors = featureProcessors;
+            return this;
+        }
+
         public Regression build() {
             return new Regression(dependentVariable, lambda, gamma, eta, maxTrees, featureBagFraction,
-                numTopFeatureImportanceValues, predictionFieldName, trainingPercent, randomizeSeed, lossFunction, lossFunctionParameter);
+                numTopFeatureImportanceValues, predictionFieldName, trainingPercent, randomizeSeed, lossFunction, lossFunctionParameter,
+                featureProcessors);
         }
     }
 

client/rest-high-level/src/main/java/org/elasticsearch/client/ml/inference/preprocessing/OneHotEncoding.java

@@ -114,7 +114,7 @@ public class OneHotEncoding implements PreProcessor {
         return Objects.hash(field, hotMap, custom);
     }
 
-    public Builder builder(String field) {
+    public static Builder builder(String field) {
         return new Builder(field);
     }
 

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

@@ -179,6 +179,7 @@ import org.elasticsearch.client.ml.inference.TrainedModelDefinition;
 import org.elasticsearch.client.ml.inference.TrainedModelDefinitionTests;
 import org.elasticsearch.client.ml.inference.TrainedModelInput;
 import org.elasticsearch.client.ml.inference.TrainedModelStats;
+import org.elasticsearch.client.ml.inference.preprocessing.OneHotEncoding;
 import org.elasticsearch.client.ml.inference.trainedmodel.RegressionConfig;
 import org.elasticsearch.client.ml.inference.trainedmodel.TargetType;
 import org.elasticsearch.client.ml.job.config.AnalysisConfig;
@@ -3003,6 +3004,9 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
                 .setRandomizeSeed(1234L) // <10>
                 .setClassAssignmentObjective(Classification.ClassAssignmentObjective.MAXIMIZE_ACCURACY) // <11>
                 .setNumTopClasses(1) // <12>
+                .setFeatureProcessors(Arrays.asList(OneHotEncoding.builder("categorical_feature") // <13>
+                    .addOneHot("cat", "cat_column")
+                    .build()))
                 .build();
             // end::put-data-frame-analytics-classification
 
@@ -3019,6 +3023,9 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase {
                 .setRandomizeSeed(1234L) // <10>
                 .setLossFunction(Regression.LossFunction.MSE) // <11>
                 .setLossFunctionParameter(1.0) // <12>
+                .setFeatureProcessors(Arrays.asList(OneHotEncoding.builder("categorical_feature") // <13>
+                    .addOneHot("cat", "cat_column")
+                    .build()))
                 .build();
             // end::put-data-frame-analytics-regression
 

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

@@ -18,10 +18,20 @@
  */
 package org.elasticsearch.client.ml.dataframe;
 
+import org.elasticsearch.client.ml.inference.MlInferenceNamedXContentProvider;
+import org.elasticsearch.client.ml.inference.preprocessing.FrequencyEncodingTests;
+import org.elasticsearch.client.ml.inference.preprocessing.OneHotEncodingTests;
+import org.elasticsearch.client.ml.inference.preprocessing.TargetMeanEncodingTests;
+import org.elasticsearch.common.xcontent.NamedXContentRegistry;
 import org.elasticsearch.common.xcontent.XContentParser;
 import org.elasticsearch.test.AbstractXContentTestCase;
 
 import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.function.Predicate;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
 
 public class ClassificationTests extends AbstractXContentTestCase<Classification> {
 
@@ -38,9 +48,20 @@ public class ClassificationTests extends AbstractXContentTestCase<Classification
             .setRandomizeSeed(randomBoolean() ? null : randomLong())
             .setClassAssignmentObjective(randomBoolean() ? null : randomFrom(Classification.ClassAssignmentObjective.values()))
             .setNumTopClasses(randomBoolean() ? null : randomIntBetween(0, 10))
+            .setFeatureProcessors(randomBoolean() ? null :
+                Stream.generate(() -> randomFrom(FrequencyEncodingTests.createRandom(),
+                    OneHotEncodingTests.createRandom(),
+                    TargetMeanEncodingTests.createRandom()))
+                    .limit(randomIntBetween(1, 10))
+                    .collect(Collectors.toList()))
             .build();
     }
 
+    @Override
+    protected Predicate<String> getRandomFieldsExcludeFilter() {
+        return field -> field.startsWith("feature_processors");
+    }
+
     @Override
     protected Classification createTestInstance() {
         return randomClassification();
@@ -55,4 +76,11 @@ public class ClassificationTests extends AbstractXContentTestCase<Classification
     protected boolean supportsUnknownFields() {
         return true;
     }
+
+    @Override
+    protected NamedXContentRegistry xContentRegistry() {
+        List<NamedXContentRegistry.Entry> namedXContent = new ArrayList<>();
+        namedXContent.addAll(new MlInferenceNamedXContentProvider().getNamedXContentParsers());
+        return new NamedXContentRegistry(namedXContent);
+    }
 }

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

@@ -20,6 +20,7 @@
 package org.elasticsearch.client.ml.dataframe;
 
 import org.elasticsearch.Version;
+import org.elasticsearch.client.ml.inference.MlInferenceNamedXContentProvider;
 import org.elasticsearch.common.settings.Settings;
 import org.elasticsearch.common.unit.ByteSizeUnit;
 import org.elasticsearch.common.unit.ByteSizeValue;
@@ -101,6 +102,7 @@ public class DataFrameAnalyticsConfigTests extends AbstractXContentTestCase<Data
         List<NamedXContentRegistry.Entry> namedXContent = new ArrayList<>();
         namedXContent.addAll(new SearchModule(Settings.EMPTY, false, Collections.emptyList()).getNamedXContents());
         namedXContent.addAll(new MlDataFrameAnalysisNamedXContentProvider().getNamedXContentParsers());
+        namedXContent.addAll(new MlInferenceNamedXContentProvider().getNamedXContentParsers());
         return new NamedXContentRegistry(namedXContent);
     }
 }

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

@@ -18,10 +18,20 @@
  */
 package org.elasticsearch.client.ml.dataframe;
 
+import org.elasticsearch.client.ml.inference.MlInferenceNamedXContentProvider;
+import org.elasticsearch.client.ml.inference.preprocessing.FrequencyEncodingTests;
+import org.elasticsearch.client.ml.inference.preprocessing.OneHotEncodingTests;
+import org.elasticsearch.client.ml.inference.preprocessing.TargetMeanEncodingTests;
+import org.elasticsearch.common.xcontent.NamedXContentRegistry;
 import org.elasticsearch.common.xcontent.XContentParser;
 import org.elasticsearch.test.AbstractXContentTestCase;
 
 import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.function.Predicate;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
 
 public class RegressionTests extends AbstractXContentTestCase<Regression> {
 
@@ -37,9 +47,20 @@ public class RegressionTests extends AbstractXContentTestCase<Regression> {
             .setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
             .setLossFunction(randomBoolean() ? null : randomFrom(Regression.LossFunction.values()))
             .setLossFunctionParameter(randomBoolean() ? null : randomDoubleBetween(1.0, Double.MAX_VALUE, true))
+            .setFeatureProcessors(randomBoolean() ? null :
+                Stream.generate(() -> randomFrom(FrequencyEncodingTests.createRandom(),
+                    OneHotEncodingTests.createRandom(),
+                    TargetMeanEncodingTests.createRandom()))
+                    .limit(randomIntBetween(1, 10))
+                    .collect(Collectors.toList()))
             .build();
     }
 
+    @Override
+    protected Predicate<String> getRandomFieldsExcludeFilter() {
+        return field -> field.startsWith("feature_processors");
+    }
+
     @Override
     protected Regression createTestInstance() {
         return randomRegression();
@@ -54,4 +75,11 @@ public class RegressionTests extends AbstractXContentTestCase<Regression> {
     protected boolean supportsUnknownFields() {
         return true;
     }
+
+    @Override
+    protected NamedXContentRegistry xContentRegistry() {
+        List<NamedXContentRegistry.Entry> namedXContent = new ArrayList<>();
+        namedXContent.addAll(new MlInferenceNamedXContentProvider().getNamedXContentParsers());
+        return new NamedXContentRegistry(namedXContent);
+    }
 }

docs/java-rest/high-level/ml/put-data-frame-analytics.asciidoc

@@ -124,6 +124,8 @@ include-tagged::{doc-tests-file}[{api}-classification]
 <10> The seed to be used by the random generator that picks which rows are used in training.
 <11> The optimization objective to target when assigning class labels. Defaults to maximize_minimum_recall.
 <12> The number of top classes to be reported in the results. Defaults to 2.
+<13> Custom feature processors that will create new features for analysis from the included document
+     fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
 
 ===== Regression
 
@@ -146,6 +148,8 @@ include-tagged::{doc-tests-file}[{api}-regression]
 <10> The seed to be used by the random generator that picks which rows are used in training.
 <11> The loss function used for regression. Defaults to `mse`.
 <12> An optional parameter to the loss function.
+<13> Custom feature processors that will create new features for analysis from the included document
+fields. Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs for all features.
 
 ==== Analyzed fields
 

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

@@ -115,6 +115,10 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=eta]
 (Optional, double)
 include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]
 
+`feature_processors`::::
+(Optional, list)
+include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dfas-feature-processors]
+
 `gamma`::::
 (Optional, double)
 include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=gamma]
@@ -215,6 +219,10 @@ include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=eta]
 (Optional, double)
 include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]
 
+`feature_processors`::::
+(Optional, list)
+include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dfas-feature-processors]
+
 `gamma`::::
 (Optional, double)
 include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=gamma]

docs/reference/ml/ml-shared.asciidoc

@@ -517,6 +517,14 @@ Specifies the rate at which the `eta` increases for each new tree that is added
 forest. For example, a rate of `1.05` increases `eta` by 5%.
 end::dfas-eta-growth[]
 
+tag::dfas-feature-processors[]
+A collection of feature preprocessors that modify one or more included fields.
+The analysis uses the resulting one or more features instead of the
+original document field. Multiple `feature_processors` entries can refer to the
+same document fields.
+Note, automatic categorical {ml-docs}/ml-feature-encoding.html[feature encoding] still occurs.
+end::dfas-feature-processors[]
+
 tag::dfas-iteration[]
 The number of iterations on the analysis.
 end::dfas-iteration[]