From 0c5b4b94cb5b192160463fa8a5f58eac587fe95d Mon Sep 17 00:00:00 2001 From: Kaituo Li Date: Tue, 15 Aug 2023 13:09:17 -0700 Subject: [PATCH] Update RCF to v3.8 and Enable Auto AD with 'Alert Once' Option (#979) * Update RCF to v3.8 and Enable Auto AD with 'Alert Once' Option This PR added support for automatic Anomaly Detection (AD) and the 'Alert Once' option introduced in RCF 3.8. Testing done: 1. Deserialization Test: * Verified model deserialization from 3.0-rc3. * Ensured consistent scoring using the rc3 checkpoint and rc3 dependency on identical data. 2. Backward Compatibility Test: * Executed a mixed cluster with versions 2.10 and 3.0. * Validated that older detectors still produce results without throwing any exceptions in a blue-green simulation scenario. Signed-off-by: Kaituo Li * reduce recall since alertOnce reduced recall Signed-off-by: Kaituo Li * remove commented out code Signed-off-by: Kaituo Li --------- Signed-off-by: Kaituo Li --- build.gradle | 6 +- .../opensearch/ad/ml/EntityColdStarter.java | 8 +- .../org/opensearch/ad/ml/ModelManager.java | 9 ++ .../opensearch/ad/task/ADBatchTaskCache.java | 5 + .../org/opensearch/ad/MemoryTrackerTests.java | 31 ++++ .../ad/e2e/SingleStreamModelPerfIT.java | 4 +- .../opensearch/ad/ml/CheckpointDaoTests.java | 147 +++++++++++++++++- .../ad/ml/EntityColdStarterTests.java | 9 +- .../opensearch/ad/ml/ModelManagerTests.java | 15 +- .../test/org/opensearch/ad/util/MLUtil.java | 4 + .../org/opensearch/ad/ml/rcf_3_0_rc3_hc.json | 138 ++++++++++++++++ .../ad/ml/rcf_3_0_rc3_single_stream.json | 28 ++++ 12 files changed, 380 insertions(+), 24 deletions(-) create mode 100644 src/test/resources/org/opensearch/ad/ml/rcf_3_0_rc3_hc.json create mode 100644 src/test/resources/org/opensearch/ad/ml/rcf_3_0_rc3_single_stream.json diff --git a/build.gradle b/build.gradle index 53cd202ad..c0278088e 100644 --- a/build.gradle +++ b/build.gradle @@ -126,9 +126,9 @@ dependencies { implementation group: 'com.yahoo.datasketches', name: 'memory', version: '0.12.2' implementation group: 'commons-lang', name: 'commons-lang', version: '2.6' implementation group: 'org.apache.commons', name: 'commons-pool2', version: '2.10.0' - implementation 'software.amazon.randomcutforest:randomcutforest-serialization:3.0-rc3' - implementation 'software.amazon.randomcutforest:randomcutforest-parkservices:3.0-rc3' - implementation 'software.amazon.randomcutforest:randomcutforest-core:3.0-rc3' + implementation 'software.amazon.randomcutforest:randomcutforest-serialization:3.8.0' + implementation 'software.amazon.randomcutforest:randomcutforest-parkservices:3.8.0' + implementation 'software.amazon.randomcutforest:randomcutforest-core:3.8.0' // we inherit jackson-core from opensearch core implementation "com.fasterxml.jackson.core:jackson-databind:2.14.1" diff --git a/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java b/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java index 7013e7a97..29de17d02 100644 --- a/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java +++ b/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java @@ -61,6 +61,7 @@ import org.opensearch.timeseries.util.ExceptionUtil; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** @@ -375,17 +376,18 @@ private void trainModelFromDataSegments( // overlapping x3, x4, and only store x5, x6. .shingleSize(shingleSize) .internalShinglingEnabled(true) - .anomalyRate(1 - this.thresholdMinPvalue); + .anomalyRate(1 - this.thresholdMinPvalue) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true); if (rcfSeed > 0) { rcfBuilder.randomSeed(rcfSeed); } ThresholdedRandomCutForest trcf = new ThresholdedRandomCutForest(rcfBuilder); - while (!dataPoints.isEmpty()) { trcf.process(dataPoints.poll(), 0); } - EntityModel model = entityState.getModel(); if (model == null) { model = new EntityModel(entity, new ArrayDeque<>(), null); diff --git a/src/main/java/org/opensearch/ad/ml/ModelManager.java b/src/main/java/org/opensearch/ad/ml/ModelManager.java index 6bd52d0e9..f77eecb16 100644 --- a/src/main/java/org/opensearch/ad/ml/ModelManager.java +++ b/src/main/java/org/opensearch/ad/ml/ModelManager.java @@ -51,6 +51,7 @@ import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; @@ -532,6 +533,10 @@ private void trainModelForStep( .boundingBoxCacheFraction(TimeSeriesSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) .shingleSize(detector.getShingleSize()) .anomalyRate(1 - thresholdMinPvalue) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) + .internalShinglingEnabled(false) .build(); Arrays.stream(dataPoints).forEach(s -> trcf.process(s, 0)); @@ -622,6 +627,10 @@ public List getPreviewResults(double[][] dataPoints, int shi .boundingBoxCacheFraction(AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) .shingleSize(shingleSize) .anomalyRate(1 - this.thresholdMinPvalue) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) + .internalShinglingEnabled(false) .build(); return Arrays.stream(dataPoints).map(point -> { AnomalyDescriptor descriptor = trcf.process(point, 0); diff --git a/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java b/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java index 2bc002600..2071ad97f 100644 --- a/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java +++ b/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java @@ -30,6 +30,7 @@ import org.opensearch.timeseries.model.Entity; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** @@ -80,6 +81,10 @@ protected ADBatchTaskCache(ADTask adTask) { .boundingBoxCacheFraction(AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) .shingleSize(shingleSize) .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) + .internalShinglingEnabled(false) .build(); this.thresholdModelTrained = false; diff --git a/src/test/java/org/opensearch/ad/MemoryTrackerTests.java b/src/test/java/org/opensearch/ad/MemoryTrackerTests.java index 9e2fda3e1..00060a060 100644 --- a/src/test/java/org/opensearch/ad/MemoryTrackerTests.java +++ b/src/test/java/org/opensearch/ad/MemoryTrackerTests.java @@ -33,6 +33,7 @@ import org.opensearch.timeseries.settings.TimeSeriesSettings; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; public class MemoryTrackerTests extends OpenSearchTestCase { @@ -109,6 +110,9 @@ public void setUp() throws Exception { .boundingBoxCacheFraction(TimeSeriesSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) .shingleSize(shingleSize) .internalShinglingEnabled(true) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); detector = mock(AnomalyDetector.class); @@ -152,6 +156,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(shingleSize) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(603708, tracker.estimateTRCFModelSize(rcf2)); assertTrue(tracker.isHostingAllowed(detectorId, rcf2)); @@ -171,6 +178,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(false) // same with dimension for opportunistic memory saving .shingleSize(1) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(1685208, tracker.estimateTRCFModelSize(rcf3)); @@ -188,6 +198,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(1) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(521304, tracker.estimateTRCFModelSize(rcf4)); @@ -205,6 +218,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(2) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(467340, tracker.estimateTRCFModelSize(rcf5)); @@ -222,6 +238,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(4) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(603676, tracker.estimateTRCFModelSize(rcf6)); @@ -239,6 +258,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(16) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(401481, tracker.estimateTRCFModelSize(rcf7)); @@ -256,6 +278,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(32) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(1040432, tracker.estimateTRCFModelSize(rcf8)); @@ -273,6 +298,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(64) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); assertEquals(1040688, tracker.estimateTRCFModelSize(rcf9)); @@ -290,6 +318,9 @@ public void testEstimateModelSize() { .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving .shingleSize(65) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); expectThrows(IllegalArgumentException.class, () -> tracker.estimateTRCFModelSize(rcf10)); } diff --git a/src/test/java/org/opensearch/ad/e2e/SingleStreamModelPerfIT.java b/src/test/java/org/opensearch/ad/e2e/SingleStreamModelPerfIT.java index 04f959442..68056ef39 100644 --- a/src/test/java/org/opensearch/ad/e2e/SingleStreamModelPerfIT.java +++ b/src/test/java/org/opensearch/ad/e2e/SingleStreamModelPerfIT.java @@ -47,7 +47,7 @@ public void testDataset() throws Exception { // TODO: this test case will run for a much longer time and timeout with security enabled if (!isHttps()) { disableResourceNotFoundFaultTolerence(); - verifyAnomaly("synthetic", 1, 1500, 8, .4, .9, 10); + verifyAnomaly("synthetic", 1, 1500, 8, .4, .7, 10); } } @@ -96,7 +96,7 @@ private void verifyTestResults( // recall = windows containing predicted anomaly points / total anomaly windows double recall = anomalies.size() > 0 ? positiveAnomalies / anomalies.size() : 1; - assertTrue(recall >= minRecall); + assertTrue(String.format(Locale.ROOT, "recall should be %f but got %f", recall, minRecall), recall >= minRecall); assertTrue(errors <= maxError); LOG.info("Precision: {}, Window recall: {}", precision, recall); diff --git a/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java b/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java index e9ce094ed..9a36be401 100644 --- a/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java +++ b/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java @@ -111,6 +111,7 @@ import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper; @@ -256,6 +257,10 @@ private ThresholdedRandomCutForest createTRCF() { .precision(Precision.FLOAT_32) .randomSeed(seed) .boundingBoxCacheFraction(0) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) + .internalShinglingEnabled(false) .build(); for (double[] point : data) { forest.process(point, 0); @@ -910,8 +915,6 @@ public void testFromEntityModelCheckpointBWC() throws FileNotFoundException, IOE assertEquals(1, forest.getDimensions()); assertEquals(10, forest.getNumberOfTrees()); assertEquals(256, forest.getSampleSize()); - // there are at least 10 scores in the checkpoint - assertTrue(trcf.getThresholder().getCount() > 10); Random random = new Random(0); for (int i = 0; i < 100; i++) { @@ -979,8 +982,6 @@ public void testFromEntityModelCheckpointNoThreshold() throws FileNotFoundExcept assertEquals(1, forest.getDimensions()); assertEquals(10, forest.getNumberOfTrees()); assertEquals(256, forest.getSampleSize()); - // there are no scores in the checkpoint - assertEquals(0, trcf.getThresholder().getCount()); } public void testFromEntityModelCheckpointWithEntity() throws Exception { @@ -1082,11 +1083,12 @@ public void testDeserializeTRCFModel() throws Exception { grade.add(0.0); grade.add(0.0); grade.add(0.0); + // rcf 3.8 has a number of improvements on thresholder and predictor corrector. + // We don't expect the results have the same anomaly grade. for (int i = 0; i < coldStartData.size(); i++) { forest.process(coldStartData.get(i), 0); AnomalyDescriptor descriptor = forest.process(coldStartData.get(i), 0); assertEquals(descriptor.getRCFScore(), scores.get(i), 1e-9); - assertEquals(descriptor.getAnomalyGrade(), grade.get(i), 1e-9); } } @@ -1098,4 +1100,139 @@ public void testShouldSave() { // 1658863778000L + 6 hrs < Instant.now assertTrue(checkpointDao.shouldSave(Instant.ofEpochMilli(1658863778000L), false, Duration.ofHours(6), clock)); } + + // This test is intended to check if given a checkpoint created by RCF-3.0-rc3 ("rcf_3_0_rc3_single_stream.json") + // and given the same sample data will rc3 and current RCF version (this test originally created when 3.0-rc3 is in use) + // will produce the same anomaly scores. + // The scores in this method were produced from AD running with RCF3.0-rc3 dependency + // and this test runs with the most recent RCF dependency that is being pulled by this project. + public void testDeserialize_rcf3_rc3_single_stream_model() throws Exception { + // Model in file rcf_3_0_rc3_single_stream.json is a checkpoint creatd by RCF-3.0-rc3 + // I generate the json response file using Postman's Send and Download + URI uri = ClassLoader.getSystemResource("org/opensearch/ad/ml/rcf_3_0_rc3_single_stream.json").toURI(); + String filePath = Paths.get(uri).toString(); + String json = Files.readString(Paths.get(filePath), Charset.defaultCharset()); + // For the parsing of .toTrcf to work I had to manually change "\u003d" in code back to =. + // In the byte array it doesn't seem like this is an issue but whenever reading the byte array response into a file it + // converts "=" to "\u003d" https://groups.google.com/g/google-gson/c/JDHUo9DWyyM?pli=1 + // I also needed to bypass the trcf as it wasn't being read as a key value but instead part of the string + Map map = gson.fromJson(json, Map.class); + String model = (String) ((Map) ((Map) ((ArrayList) ((Map) map.get("hits")).get("hits")).get(0)).get("_source")).get("modelV2"); + ThresholdedRandomCutForest forest = checkpointDao.toTrcf(model); + + // single-stream model uses external shingling + List coldStartData = new ArrayList<>(); + double[] sample1 = new double[] { 64, 58, 59, 60, 61, 62, 63, 57.0 }; + double[] sample2 = new double[] { 58, 59, 60, 61, 62, 63, 57.0, 1.0 }; + double[] sample3 = new double[] { 59, 60, 61, 62, 63, 57.0, 1.0, -19.0 }; + double[] sample4 = new double[] { 60, 61, 62, 63, 57.0, 1.0, -19.0, 13.0 }; + double[] sample5 = new double[] { 61, 62, 63, 57.0, 1.0, -19.0, 13.0, 41.0 }; + + coldStartData.add(sample1); + coldStartData.add(sample2); + coldStartData.add(sample3); + coldStartData.add(sample4); + coldStartData.add(sample5); + + // This scores were generated with the sample data but on RCF3.0-rc1 and we are comparing them + // to the scores generated by the imported RCF3.0-rc2.1 + List scores = new ArrayList<>(); + scores.add(3.3830441158587066); + scores.add(2.825961659490065); + scores.add(2.4685871670647384); + scores.add(2.3123460886413647); + scores.add(2.1401987653477135); + + // rcf 3.8 has a number of improvements on thresholder and predictor corrector. + // We don't expect the results have the same anomaly grade. + for (int i = 0; i < coldStartData.size(); i++) { + forest.process(coldStartData.get(i), 0); + AnomalyDescriptor descriptor = forest.process(coldStartData.get(i), 0); + assertEquals(descriptor.getRCFScore(), scores.get(i), 1e-9); + } + } + + // This test is intended to check if given a checkpoint created by RCF-3.0-rc3 ("rcf_3_0_rc3_hc.json") + // and given the same sample data will rc3 and current RCF version (this test originally created when 3.0-rc3 is in use) + // will produce the same anomaly scores. + // The scores in this method were produced from AD running with RCF3.0-rc3 dependency + // and this test runs with the most recent RCF dependency that is being pulled by this project. + public void testDeserialize_rcf3_rc3_hc_model() throws Exception { + // Model in rcf_3_0_rc3_hc.json is a checkpoint creatd by RCF-3.0-rc3 + // I generate the json response file using Postman's Send and Download + URI uri = ClassLoader.getSystemResource("org/opensearch/ad/ml/rcf_3_0_rc3_hc.json").toURI(); + String filePath = Paths.get(uri).toString(); + String json = Files.readString(Paths.get(filePath), Charset.defaultCharset()); + // For the parsing of .toTrcf to work I had to manually change "\u003d" in code back to =. + // In the byte array it doesn't seem like this is an issue but whenever reading the byte array response into a file it + // converts "=" to "\u003d" https://groups.google.com/g/google-gson/c/JDHUo9DWyyM?pli=1 + // I also needed to bypass the trcf as it wasn't being read as a key value but instead part of the string + Map map = gson.fromJson(json, Map.class); + String model = (String) ((Map) ((Map) ((ArrayList) ((Map) map.get("hits")).get("hits")).get(0)).get("_source")).get("modelV2"); + model = model.split(":")[1]; + model = model.substring(1, model.length() - 2); + // Simulate JSON parsing by replacing Unicode escape sequence with the actual character + // Without escaping Java string, we experience model corruption exception. + model = unescapeJavaString(model); + + ThresholdedRandomCutForest forest = checkpointDao.toTrcf(model); + + // hc model uses internal shingling + List coldStartData = new ArrayList<>(); + double[] sample1 = new double[] { 53, 54, 55, 56, 57.0 }; + double[] sample2 = new double[] { 54, 55, 56, 57.0, 1.0 }; + double[] sample3 = new double[] { 55, 56, 57.0, 1.0, -19.0 }; + double[] sample4 = new double[] { 56, 57.0, 1.0, -19.0, 13.0 }; + double[] sample5 = new double[] { 57.0, 1.0, -19.0, 13.0, 41.0 }; + + coldStartData.add(sample1); + coldStartData.add(sample2); + coldStartData.add(sample3); + coldStartData.add(sample4); + coldStartData.add(sample5); + + // This scores were generated with the sample data but on RCF3.0-rc1 and we are comparing them + // to the scores generated by the imported RCF3.0-rc2.1 + List scores = new ArrayList<>(); + scores.add(1.86645896573027); + scores.add(1.8760247712797833); + scores.add(1.6809181763279901); + scores.add(1.7126716645678555); + scores.add(1.323776514074674); + + // rcf 3.8 has a number of improvements on thresholder and predictor corrector. + // We don't expect the results have the same anomaly grade. + for (int i = 0; i < coldStartData.size(); i++) { + forest.process(coldStartData.get(i), 0); + AnomalyDescriptor descriptor = forest.process(coldStartData.get(i), 0); + assertEquals(descriptor.getRCFScore(), scores.get(i), 1e-9); + } + } + + public static String unescapeJavaString(String st) { + StringBuilder sb = new StringBuilder(st.length()); + + for (int i = 0; i < st.length(); i++) { + char ch = st.charAt(i); + if (ch == '\\') { + char nextChar = (i == st.length() - 1) ? '\\' : st.charAt(i + 1); + switch (nextChar) { + case 'u': + sb.append((char) Integer.parseInt(st.substring(i + 2, i + 6), 16)); + i += 5; + break; + case '\\': + sb.append('\\'); + i++; + break; + default: + sb.append(ch); + break; + } + } else { + sb.append(ch); + } + } + return sb.toString(); + } } diff --git a/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java b/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java index fa9a2290d..67ef7f7ab 100644 --- a/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java +++ b/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java @@ -67,6 +67,7 @@ import test.org.opensearch.ad.util.MultiDimDataWithTime; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.google.common.collect.ImmutableList; @@ -222,6 +223,9 @@ private void diffTesting(ModelState modelState, List cold .sampleSize(AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE) .internalShinglingEnabled(true) .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) .build(); for (int i = 0; i < coldStartData.size(); i++) { @@ -516,7 +520,10 @@ public void testTrainModelFromExistingSamplesEnoughSamples() { .parallelExecutionEnabled(false) .sampleSize(AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE) .internalShinglingEnabled(true) - .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE); + .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true); Tuple, ThresholdedRandomCutForest> models = MLUtil.prepareModel(inputDimension, rcfConfig); Queue samples = models.v1(); ThresholdedRandomCutForest rcf = models.v2(); diff --git a/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java b/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java index 62fe01158..837cb2d7f 100644 --- a/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java +++ b/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java @@ -64,7 +64,6 @@ import org.opensearch.ad.settings.AnomalyDetectorSettings; import org.opensearch.cluster.node.DiscoveryNode; import org.opensearch.cluster.service.ClusterService; -import org.opensearch.common.settings.ClusterSettings; import org.opensearch.common.settings.Settings; import org.opensearch.common.unit.TimeValue; import org.opensearch.core.action.ActionListener; @@ -79,8 +78,6 @@ import org.opensearch.timeseries.ml.SingleStreamModelIdMapper; import org.opensearch.timeseries.model.Entity; import org.opensearch.timeseries.util.DiscoveryNodeFilterer; -import org.powermock.modules.junit4.PowerMockRunner; -import org.powermock.modules.junit4.PowerMockRunnerDelegate; import test.org.opensearch.ad.util.MLUtil; import test.org.opensearch.ad.util.RandomModelStateConfig; @@ -90,8 +87,7 @@ import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.amazon.randomcutforest.returntypes.DiVector; -@RunWith(PowerMockRunner.class) -@PowerMockRunnerDelegate(JUnitParamsRunner.class) +@RunWith(JUnitParamsRunner.class) @SuppressWarnings("unchecked") public class ModelManagerTests { @@ -140,7 +136,6 @@ public class ModelManagerTests { private double thresholdMinPvalue; private int minPreviewSize; private Duration modelTtl; - private Duration checkpointInterval; private ThresholdedRandomCutForest rcf; @Mock @@ -158,7 +153,6 @@ public class ModelManagerTests { private double[] attribution; private double[] point; private DiVector attributionVec; - private ClusterSettings clusterSettings; @Mock private ActionListener rcfResultListener; @@ -183,11 +177,10 @@ public void setup() { numSamples = 10; numFeatures = 1; rcfTimeDecay = 1.0 / 1024; - numMinSamples = 1; + numMinSamples = 32; thresholdMinPvalue = 0.95; minPreviewSize = 500; modelTtl = Duration.ofHours(1); - checkpointInterval = Duration.ofHours(1); shingleSize = 1; attribution = new double[] { 1, 1 }; attributionVec = new DiVector(attribution.length); @@ -197,7 +190,7 @@ public void setup() { } point = new double[] { 2 }; - rcf = spy(ThresholdedRandomCutForest.builder().dimensions(numFeatures).sampleSize(numSamples).numberOfTrees(numTrees).build()); + rcf = mock(ThresholdedRandomCutForest.class); double score = 11.; double confidence = 0.091353632; @@ -406,6 +399,8 @@ public void getRcfResult_throwToListener_whenNoCheckpoint() { @Test public void getRcfResult_throwToListener_whenHeapLimitExceed() { + rcf = ThresholdedRandomCutForest.builder().dimensions(numFeatures).sampleSize(numSamples).numberOfTrees(numTrees).build(); + doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(rcf)); diff --git a/src/test/java/test/org/opensearch/ad/util/MLUtil.java b/src/test/java/test/org/opensearch/ad/util/MLUtil.java index babae59ef..53e620d90 100644 --- a/src/test/java/test/org/opensearch/ad/util/MLUtil.java +++ b/src/test/java/test/org/opensearch/ad/util/MLUtil.java @@ -28,6 +28,7 @@ import org.opensearch.common.collect.Tuple; import org.opensearch.timeseries.model.Entity; +import com.amazon.randomcutforest.config.TransformMethod; import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** @@ -108,6 +109,9 @@ public static EntityModel createNonEmptyModel(String detectorId, int sampleSize, .parallelExecutionEnabled(false) .internalShinglingEnabled(true) .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) + .transformMethod(TransformMethod.NORMALIZE) + .alertOnce(true) + .autoAdjust(true) ); for (int i = 0; i < numDataPoints; i++) { trcf.process(new double[] { random.nextDouble() }, i); diff --git a/src/test/resources/org/opensearch/ad/ml/rcf_3_0_rc3_hc.json b/src/test/resources/org/opensearch/ad/ml/rcf_3_0_rc3_hc.json new file mode 100644 index 000000000..ce00bfcc1 --- /dev/null +++ b/src/test/resources/org/opensearch/ad/ml/rcf_3_0_rc3_hc.json @@ -0,0 +1,138 @@ +{ + "took" : 3, + "timed_out" : false, + "_shards" : { + "total" : 1, + "successful" : 1, + "skipped" : 0, + "failed" : 0 + }, + "hits" : { + "total" : { + "value" : 7, + "relation" : "eq" + }, + "max_score" : 1.0, + "hits" : [ + { + "_index" : ".opendistro-anomaly-checkpoints", + "_id" : "IB2714kBGKQTJF7HPFVf_entity_wQ8Tgkt3JlsUVcG0TqRUYw", + "_score" : 1.0, + "_source" : { + "schema_version" : 3, + "modelV2" : 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+ "timestamp" : "2023-08-08T19:22:06.423106457Z" + } + } + ] + } +}