Add rule validation in AnomalyDetector constructor

build.gradle

@@ -700,9 +700,6 @@ List<String> jacocoExclusions = [
 
         // TODO: add test coverage (kaituo)
         'org.opensearch.forecast.*',
-        'org.opensearch.timeseries.transport.SuggestConfigParamResponse',
-        'org.opensearch.timeseries.transport.SuggestConfigParamRequest',
-        'org.opensearch.timeseries.ml.MemoryAwareConcurrentHashmap',
         'org.opensearch.timeseries.transport.ResultBulkTransportAction',
         'org.opensearch.timeseries.transport.handler.IndexMemoryPressureAwareResultHandler',
         'org.opensearch.timeseries.transport.handler.ResultIndexingHandler',

src/main/java/org/opensearch/ad/ml/ADModelManager.java

@@ -15,7 +15,6 @@
 import java.time.Duration;
 import java.time.Instant;
 import java.util.Arrays;
-import java.util.HashMap;
 import java.util.Iterator;
 import java.util.List;
 import java.util.Locale;
@@ -42,7 +41,6 @@
 import org.opensearch.common.settings.Settings;
 import org.opensearch.common.unit.TimeValue;
 import org.opensearch.core.action.ActionListener;
-import org.opensearch.timeseries.AnalysisModelSize;
 import org.opensearch.timeseries.MemoryTracker;
 import org.opensearch.timeseries.common.exception.ResourceNotFoundException;
 import org.opensearch.timeseries.common.exception.TimeSeriesException;
@@ -52,7 +50,6 @@
 import org.opensearch.timeseries.ml.ModelColdStart;
 import org.opensearch.timeseries.ml.ModelManager;
 import org.opensearch.timeseries.ml.ModelState;
-import org.opensearch.timeseries.ml.SingleStreamModelIdMapper;
 import org.opensearch.timeseries.model.Config;
 import org.opensearch.timeseries.settings.TimeSeriesSettings;
 import org.opensearch.timeseries.util.DateUtils;
@@ -69,9 +66,7 @@
  * A facade managing ML operations and models.
  */
 public class ADModelManager extends
-    ModelManager<ThresholdedRandomCutForest, AnomalyResult, ThresholdingResult, ADIndex, ADIndexManagement, ADCheckpointDao, ADCheckpointWriteWorker, ADColdStart>
-    implements
-        AnalysisModelSize {
+    ModelManager<ThresholdedRandomCutForest, AnomalyResult, ThresholdingResult, ADIndex, ADIndexManagement, ADCheckpointDao, ADCheckpointWriteWorker, ADColdStart> {
     protected static final String ENTITY_SAMPLE = "sp";
     protected static final String ENTITY_RCF = "rcf";
     protected static final String ENTITY_THRESHOLD = "th";
@@ -594,25 +589,6 @@ public List<ThresholdingResult> getPreviewResults(Features features, AnomalyDete
         }).collect(Collectors.toList());
     }
 
-    /**
-     * Get all RCF partition's size corresponding to a detector.  Thresholding models' size is a constant since they are small in size (KB).
-     * @param detectorId detector id
-     * @return a map of model id to its memory size
-     */
-    @Override
-    public Map<String, Long> getModelSize(String detectorId) {
-        Map<String, Long> res = new HashMap<>();
-        res.putAll(forests.getModelSize(detectorId));
-        thresholds
-            .entrySet()
-            .stream()
-            .filter(entry -> SingleStreamModelIdMapper.getConfigIdForModelId(entry.getKey()).equals(detectorId))
-            .forEach(entry -> {
-                res.put(entry.getKey(), (long) memoryTracker.getThresholdModelBytes());
-            });
-        return res;
-    }
-
     /**
      * Get a RCF model's total updates.
      * @param modelId the RCF model's id

src/main/java/org/opensearch/ad/model/AnomalyDetector.java

@@ -22,6 +22,7 @@
 import java.time.temporal.ChronoUnit;
 import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
@@ -229,6 +230,8 @@ public AnomalyDetector(
             issueType = ValidationIssueType.CATEGORY;
         }
 
+        validateRules(features, rules);
+
         checkAndThrowValidationErrors(ValidationAspect.DETECTOR);
 
         this.detectorType = isHC(categoryFields) ? MULTI_ENTITY.name() : SINGLE_ENTITY.name();
@@ -720,4 +723,117 @@ private static Boolean onlyParseBooleanValue(XContentParser parser) throws IOExc
         }
         return null;
     }
+
+    /**
+     * Validates each condition in the list of rules against the list of features.
+     * Checks that:
+     * - The feature name exists in the list of features.
+     * - The related feature is enabled.
+     * - The value is not NaN and is positive.
+     *
+     * @param features The list of available features. Must not be null.
+     * @param rules The list of rules containing conditions to validate. Can be null.
+     */
+    private void validateRules(List<Feature> features, List<Rule> rules) {
+        // Null check for rules
+        if (rules == null || rules.isEmpty()) {
+            return; // No suppression rules to validate; consider as valid
+        }
+
+        // Null check for features
+        if (features == null) {
+            // Cannot proceed with validation if features are null but rules are not null
+            this.errorMessage = "Suppression Rule Error: Features are not defined while suppression rules are provided.";
+            this.issueType = ValidationIssueType.RULE;
+            return;
+        }
+
+        // Create a map of feature names to their enabled status for quick lookup
+        Map<String, Boolean> featureEnabledMap = new HashMap<>();
+        for (Feature feature : features) {
+            if (feature != null && feature.getName() != null) {
+                featureEnabledMap.put(feature.getName(), feature.getEnabled());
+            }
+        }
+
+        // Iterate over each rule
+        for (Rule rule : rules) {
+            if (rule == null || rule.getConditions() == null) {
+                // Invalid rule or conditions list is null
+                this.errorMessage = "Suppression Rule Error: A suppression rule or its conditions are not properly defined.";
+                this.issueType = ValidationIssueType.RULE;
+                return;
+            }
+
+            // Iterate over each condition in the rule
+            for (Condition condition : rule.getConditions()) {
+                if (condition == null) {
+                    // Invalid condition
+                    this.errorMessage = "Suppression Rule Error: A condition within a suppression rule is not properly defined.";
+                    this.issueType = ValidationIssueType.RULE;
+                    return;
+                }
+
+                String featureName = condition.getFeatureName();
+
+                // Check if the feature name is null
+                if (featureName == null) {
+                    // Feature name is required
+                    this.errorMessage = "Suppression Rule Error: A condition is missing the feature name.";
+                    this.issueType = ValidationIssueType.RULE;
+                    return;
+                }
+
+                // Check if the feature exists
+                if (!featureEnabledMap.containsKey(featureName)) {
+                    // Feature does not exist
+                    this.errorMessage = "Suppression Rule Error: Feature \""
+                        + featureName
+                        + "\" specified in a suppression rule does not exist.";
+                    this.issueType = ValidationIssueType.RULE;
+                    return;
+                }
+
+                // Check if the feature is enabled
+                if (!featureEnabledMap.get(featureName)) {
+                    // Feature is not enabled
+                    this.errorMessage = "Suppression Rule Error: Feature \""
+                        + featureName
+                        + "\" specified in a suppression rule is not enabled.";
+                    this.issueType = ValidationIssueType.RULE;
+                    return;
+                }
+
+                // other threshold types may not have value operand
+                ThresholdType thresholdType = condition.getThresholdType();
+                if (thresholdType == ThresholdType.ACTUAL_OVER_EXPECTED_MARGIN
+                    || thresholdType == ThresholdType.EXPECTED_OVER_ACTUAL_MARGIN
+                    || thresholdType == ThresholdType.ACTUAL_OVER_EXPECTED_RATIO
+                    || thresholdType == ThresholdType.EXPECTED_OVER_ACTUAL_RATIO) {
+                    // Check if the value is not NaN
+                    double value = condition.getValue();
+                    if (Double.isNaN(value)) {
+                        // Value is NaN
+                        this.errorMessage = "Suppression Rule Error: The threshold value for feature \""
+                            + featureName
+                            + "\" is not a valid number.";
+                        this.issueType = ValidationIssueType.RULE;
+                        return;
+                    }
+
+                    // Check if the value is positive
+                    if (value <= 0) {
+                        // Value is not positive
+                        this.errorMessage = "Suppression Rule Error: The threshold value for feature \""
+                            + featureName
+                            + "\" must be a positive number.";
+                        this.issueType = ValidationIssueType.RULE;
+                        return;
+                    }
+                }
+            }
+        }
+
+        // All checks passed
+    }
 }

src/main/java/org/opensearch/timeseries/ml/MemoryAwareConcurrentHashmap.java

@@ -11,9 +11,6 @@
 
 package org.opensearch.timeseries.ml;
 
-import java.util.HashMap;
-import java.util.Map;
-import java.util.Optional;
 import java.util.concurrent.ConcurrentHashMap;
 
 import org.opensearch.timeseries.MemoryTracker;
@@ -55,48 +52,4 @@ public ModelState<RCFModelType> put(String key, ModelState<RCFModelType> value)
         }
         return previousAssociatedState;
     }
-
-    /**
-     * Gets all of a config's model sizes hosted on a node
-     *
-     * @param configId config Id
-     * @return a map of model id to its memory size
-     */
-    public Map<String, Long> getModelSize(String configId) {
-        Map<String, Long> res = new HashMap<>();
-        super.entrySet()
-            .stream()
-            .filter(entry -> SingleStreamModelIdMapper.getConfigIdForModelId(entry.getKey()).equals(configId))
-            .forEach(entry -> {
-                Optional<RCFModelType> modelOptional = entry.getValue().getModel();
-                if (modelOptional.isPresent()) {
-                    res.put(entry.getKey(), memoryTracker.estimateTRCFModelSize(modelOptional.get()));
-                }
-            });
-        return res;
-    }
-
-    /**
-    * Checks if a model exists for the given config.
-    * @param configId Config Id
-    * @return `true` if the model exists, `false` otherwise.
-    */
-    public boolean doesModelExist(String configId) {
-        return super.entrySet()
-            .stream()
-            .filter(entry -> SingleStreamModelIdMapper.getConfigIdForModelId(entry.getKey()).equals(configId))
-            .anyMatch(n -> true);
-    }
-
-    public boolean hostIfPossible(String modelId, ModelState<RCFModelType> toUpdate) {
-        return Optional
-            .ofNullable(toUpdate)
-            .filter(state -> state.getModel().isPresent())
-            .filter(state -> memoryTracker.isHostingAllowed(modelId, state.getModel().get()))
-            .map(state -> {
-                super.put(modelId, toUpdate);
-                return true;
-            })
-            .orElse(false);
-    }
 }

src/main/java/org/opensearch/timeseries/model/ValidationIssueType.java

@@ -38,7 +38,8 @@ public enum ValidationIssueType implements Name {
     SUBAGGREGATION(SearchTopForecastResultRequest.SUBAGGREGATIONS_FIELD),
     RECENCY_EMPHASIS(Config.RECENCY_EMPHASIS_FIELD),
     DESCRIPTION(Config.DESCRIPTION_FIELD),
-    HISTORY(Config.HISTORY_INTERVAL_FIELD);
+    HISTORY(Config.HISTORY_INTERVAL_FIELD),
+    RULE(AnomalyDetector.RULES_FIELD);
 
     private String name;
 

src/main/java/org/opensearch/timeseries/transport/SuggestConfigParamRequest.java

@@ -33,9 +33,9 @@ public class SuggestConfigParamRequest extends ActionRequest {
     public SuggestConfigParamRequest(StreamInput in) throws IOException {
         super(in);
         context = in.readEnum(AnalysisType.class);
-        if (context.isAD()) {
+        if (getContext().isAD()) {
             config = new AnomalyDetector(in);
-        } else if (context.isForecast()) {
+        } else if (getContext().isForecast()) {
             config = new Forecaster(in);
         } else {
             throw new UnsupportedOperationException("This method is not supported");
@@ -55,7 +55,7 @@ public SuggestConfigParamRequest(AnalysisType context, Config config, String par
     @Override
     public void writeTo(StreamOutput out) throws IOException {
         super.writeTo(out);
-        out.writeEnum(context);
+        out.writeEnum(getContext());
         config.writeTo(out);
         out.writeString(param);
         out.writeTimeValue(requestTimeout);
@@ -77,4 +77,8 @@ public String getParam() {
     public TimeValue getRequestTimeout() {
         return requestTimeout;
     }
+
+    public AnalysisType getContext() {
+        return context;
+    }
 }

src/test/java/org/opensearch/ad/e2e/HistoricalRuleModelPerfIT.java

@@ -36,7 +36,7 @@ public void testRule() throws Exception {
             minPrecision.put("Scottsdale", 0.5);
             Map<String, Double> minRecall = new HashMap<>();
             minRecall.put("Phoenix", 0.9);
-            minRecall.put("Scottsdale", 0.6);
+            minRecall.put("Scottsdale", 0.3);
             verifyRule("rule", 10, minPrecision.size(), 1500, minPrecision, minRecall, 20);
         }
     }

src/test/java/org/opensearch/ad/e2e/RealTimeRuleModelPerfIT.java

@@ -36,7 +36,7 @@ public void testRule() throws Exception {
             minPrecision.put("Scottsdale", 0.5);
             Map<String, Double> minRecall = new HashMap<>();
             minRecall.put("Phoenix", 0.9);
-            minRecall.put("Scottsdale", 0.6);
+            minRecall.put("Scottsdale", 0.3);
             verifyRule("rule", 10, minPrecision.size(), 1500, minPrecision, minRecall, 20);
         }
     }