Examples with RawAnomalyResultDTO com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO used on opensource projects

Example 21 with RawAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method compareActualAndExpectedRawAnomalies.

private void compareActualAndExpectedRawAnomalies(List<RawAnomalyResultDTO> actualAnomalyResults, List<RawAnomalyResultDTO> expectedRawAnomalies) {
    Assert.assertEquals(actualAnomalyResults.size(), expectedRawAnomalies.size());
    for (int i = 0; i < actualAnomalyResults.size(); ++i) {
        RawAnomalyResultDTO actualAnomaly = actualAnomalyResults.get(i);
        RawAnomalyResultDTO expectedAnomaly = actualAnomalyResults.get(i);
        Assert.assertEquals(actualAnomaly.getWeight(), expectedAnomaly.getWeight(), EPSILON);
        Assert.assertEquals(actualAnomaly.getScore(), expectedAnomaly.getScore(), EPSILON);
    }
}

Example 22 with RawAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method analyzeWo2WAvg.

@Test(dataProvider = "timeSeriesDataProvider")
public void analyzeWo2WAvg(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries, List<TimeSeries> baselines) throws Exception {
    // Expected RawAnomalies without smoothing
    List<RawAnomalyResultDTO> expectedRawAnomalies = new ArrayList<>();
    RawAnomalyResultDTO rawAnomaly1 = new RawAnomalyResultDTO();
    rawAnomaly1.setStartTime(observedStartTime + bucketMillis * 2);
    rawAnomaly1.setEndTime(observedStartTime + bucketMillis * 3);
    rawAnomaly1.setWeight(0.3d);
    rawAnomaly1.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly1);
    RawAnomalyResultDTO rawAnomaly2 = new RawAnomalyResultDTO();
    rawAnomaly2.setStartTime(observedStartTime + bucketMillis * 3);
    rawAnomaly2.setEndTime(observedStartTime + bucketMillis * 4);
    rawAnomaly2.setWeight(0.22727272727272727);
    rawAnomaly2.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly2);
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    // Append properties for anomaly function specific setting
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/2wAvg");
    properties.put(SimpleThresholdDetectionModel.CHANGE_THRESHOLD, "0.2");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    WeekOverWeekRuleFunction function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    List<RawAnomalyResultDTO> rawAnomalyResults = function.analyze(anomalyDetectionContext);
    compareWo2WAvgRawAnomalies(rawAnomalyResults);
    // Test data model
    List<Interval> expectedDataRanges = new ArrayList<>();
    expectedDataRanges.add(new Interval(observedStartTime, observedStartTime + bucketMillis * 5));
    expectedDataRanges.add(new Interval(observedStartTime - oneWeekInMillis, observedStartTime + bucketMillis * 5 - oneWeekInMillis));
    expectedDataRanges.add(new Interval(observedStartTime - oneWeekInMillis * 2, observedStartTime + bucketMillis * 5 - oneWeekInMillis * 2));
    List<Interval> actualDataRanges = function.getDataModel().getAllDataIntervals(observedStartTime, observedStartTime + bucketMillis * 5);
    Assert.assertEquals(actualDataRanges, expectedDataRanges);
}

Example 23 with RawAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method compareWoWRawAnomalies.

private void compareWoWRawAnomalies(List<RawAnomalyResultDTO> actualAnomalyResults) {
    // Expected RawAnomalies of WoW without smoothing
    List<RawAnomalyResultDTO> expectedRawAnomalies = new ArrayList<>();
    RawAnomalyResultDTO rawAnomaly1 = new RawAnomalyResultDTO();
    rawAnomaly1.setStartTime(observedStartTime + bucketMillis);
    rawAnomaly1.setEndTime(observedStartTime + bucketMillis * 2);
    rawAnomaly1.setWeight(-0.25d);
    rawAnomaly1.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly1);
    RawAnomalyResultDTO rawAnomaly2 = new RawAnomalyResultDTO();
    rawAnomaly2.setStartTime(observedStartTime + bucketMillis * 4);
    rawAnomaly2.setEndTime(observedStartTime + bucketMillis * 5);
    rawAnomaly2.setWeight(-0.2857142857d);
    rawAnomaly2.setScore(15d);
    expectedRawAnomalies.add(rawAnomaly2);
    compareActualAndExpectedRawAnomalies(actualAnomalyResults, expectedRawAnomalies);
}

Example 24 with RawAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO in project pinot by linkedin.

the class TestWeekOverWeekRuleFunction method testTotalCountThresholdFunction.

@Test(dataProvider = "timeSeriesDataProvider")
public void testTotalCountThresholdFunction(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries, List<TimeSeries> baselines) throws Exception {
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    // Append properties for anomaly function specific setting
    String totalCountTimeSeriesName = "totalCount";
    TimeSeries totalCountTimeSeries = new TimeSeries();
    {
        totalCountTimeSeries.set(observedStartTime, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 2, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 3, 10d);
        totalCountTimeSeries.set(observedStartTime + bucketMillis * 4, 10d);
        Interval totalCountTimeSeriesInterval = new Interval(observedStartTime, observedStartTime + bucketMillis * 5);
        totalCountTimeSeries.setTimeSeriesInterval(totalCountTimeSeriesInterval);
    }
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_METRIC_NAME, totalCountTimeSeriesName);
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_THRESHOLD, "51");
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/2wAvg");
    properties.put(SimpleThresholdDetectionModel.CHANGE_THRESHOLD, "0.2");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    // Create anomalyDetectionContext using anomaly function spec
    WeekOverWeekRuleFunction function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    anomalyDetectionContext.setCurrent(totalCountTimeSeriesName, totalCountTimeSeries);
    List<RawAnomalyResultDTO> rawAnomalyResults = function.analyze(anomalyDetectionContext);
    // No anomalies after smoothing the time series
    Assert.assertEquals(rawAnomalyResults.size(), 0);
    // Test disabled total count by lowering the threshold
    anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    properties.put(TotalCountThresholdRemovalFunction.TOTAL_COUNT_THRESHOLD, "0");
    // Create anomaly function spec
    functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(toString(properties));
    function = new WeekOverWeekRuleFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setBaselines(mainMetric, baselines);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    anomalyDetectionContext.setCurrent(totalCountTimeSeriesName, totalCountTimeSeries);
    rawAnomalyResults = function.analyze(anomalyDetectionContext);
    compareWo2WAvgRawAnomalies(rawAnomalyResults);
}

Example 25 with RawAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.RawAnomalyResultDTO in project pinot by linkedin.

the class FetchMetricDataAndExistingAnomaliesTool method fetchRawAnomaliesInRangeByFunctionId.

public List<ResultNode> fetchRawAnomaliesInRangeByFunctionId(long functionId, DateTime startTime, DateTime endTime) {
    AnomalyFunctionDTO anomalyFunction = anomalyFunctionDAO.findById(functionId);
    LOG.info(String.format("Loading raw anaomaly results of functionId {} from db...", Long.toString(functionId)));
    List<ResultNode> resultNodes = new ArrayList<>();
    if (anomalyFunction == null) {
        // no such function
        return resultNodes;
    }
    List<RawAnomalyResultDTO> rawResults = rawAnomalyResultDAO.findAllByTimeAndFunctionId(startTime.getMillis(), endTime.getMillis(), functionId);
    for (RawAnomalyResultDTO rawResult : rawResults) {
        ResultNode res = new ResultNode();
        res.functionId = functionId;
        res.functionName = anomalyFunction.getFunctionName();
        res.startTime = new DateTime(rawResult.getStartTime());
        res.endTime = new DateTime(rawResult.getEndTime());
        res.dimensions = rawResult.getDimensions();
        res.setFilters(anomalyFunction.getFilters());
        res.severity = rawResult.getWeight();
        AnomalyFeedbackDTO feedback = rawResult.getFeedback();
        res.feedbackType = (feedback == null) ? null : feedback.getFeedbackType();
        resultNodes.add(res);
    }
    return resultNodes;
}