add test evaluating anomaly results #13
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| private void verifyAnomaly(String datasetName, int intervalMinutes, int trainTestSplit, int shingleSize, | ||
| double minPrecision, double minRecall, double maxError) throws Exception { | ||
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| RestClient client = client(); |
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How many nodes do we have for this cluster? We want both single node and multi-node cluster to test.
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Default setting is a single node. I will add a multi-node test case in a separate pr.
| public class DetectionResultEvalutationIT extends ESRestTestCase { | ||
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| public void testDataset() throws Exception { | ||
| verifyAnomaly("synthetic", 1, 1500, 8, .9, .9, 10); |
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How long does the test take to run?
| Map<String, Object> response = entityAsMap(client.performRequest(request)); | ||
| double anomalyGrade = (double)response.get("anomalyGrade"); | ||
| if (anomalyGrade > 0) { | ||
| System.out.println("LLL," + begin + "," + anomalyGrade); |
| double precision = positives > 0 ? truePositives / positives : 1; | ||
| assertTrue(precision >= minPrecision); | ||
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| double recall = anomalies.size() > 0 ? positiveAnomalies / anomalies.size() : 1; |
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Could you add a comment to explain how you compute precision and recall? It's not easy to understand as precision is computed based on points and recall is based on windows.
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added comments showing the definitions in the update
| + " \"Feature1\": { \"type\": \"double\" }, \"Feature2\": { \"type\": \"double\" } } } }"; | ||
| request.setJsonEntity(requestBody); | ||
| client.performRequest(request); | ||
| Thread.sleep(1_000); |
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Is it possible that sometimes sleeping 1 second is not enough? Have you tried to run the test multiple times (say 100 times)? If yes, would it always pass? I am afraid concurrency and inter-process communication(if you are running the test in a simulated multi-node cluster) would sometimes cause the test to fail.
Also, would https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-refresh.html help?
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I have run it for many times (certainly more than 30 times and less than 100 times) without failure. And it passes github checks multiple times. From my experience, it has been more reliable than some randomized unit tests.
| for (int i = trainTestSplit; i < data.size(); i++) { | ||
| Instant begin = Instant.from(DateTimeFormatter.ISO_INSTANT.parse(data.get(i).get("timestamp").getAsString())); | ||
| Instant end = begin.plus(intervalMinutes, ChronoUnit.MINUTES); | ||
| String requestBody = String.format("{ \"period_start\": %d, \"period_end\": %d }", begin.toEpochMilli(), end.toEpochMilli()); |
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To remove duplicated code, it may be worth adding a function that runs a detector given a detectorID, period_start, and period end and returns the response.
| e.printStackTrace(); | ||
| } | ||
| } | ||
| return new double[] {positives, truePositives, positiveAnomalies.size(), errors}; |
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Will truePositives and positiveAnomalies.size() always be the same?
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no, because truePositives is a count of correct data points. positiveAnomalies is a count of correctly found anomaly windows. The former is no less than the latter.
| } catch (Exception e ) { | ||
| throw new RuntimeException(e); | ||
| } }); | ||
| Thread.sleep(1000); |
This change adds an anomaly detection test running on rest api and a synthetic dataset with known anomalies to verify the machine learning algorithms in the anomaly detection core along with some basic functionality such as creating detectors.