Add rule validation in AnomalyDetector constructor

This commit introduces rule validation within the AnomalyDetector constructor. Any validation errors are now propagated and displayed on the frontend to ensure immediate feedback.

Testing:
* Verified that validation errors are properly propagated and shown on the frontend.
* Added UTs to cover the new validation logic.

Signed-off-by: Kaituo Li <kaituo@amazon.com>

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);
         }
     }