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

Example 16 with RawAnomalyResultDTO

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

the class AnomalyResource method viewRawAnomaliesInRange.

//View raw anomalies for collection
@GET
@Path("/raw-anomalies/view")
@Produces(MediaType.APPLICATION_JSON)
public String viewRawAnomaliesInRange(@QueryParam("functionId") String functionId, @QueryParam("dataset") String dataset, @QueryParam("startTimeIso") String startTimeIso, @QueryParam("endTimeIso") String endTimeIso, @QueryParam("metric") String metric) throws JsonProcessingException {
    if (StringUtils.isBlank(functionId) && StringUtils.isBlank(dataset)) {
        throw new IllegalArgumentException("must provide dataset or functionId");
    }
    DateTime endTime = DateTime.now();
    if (StringUtils.isNotEmpty(endTimeIso)) {
        endTime = ISODateTimeFormat.dateTimeParser().parseDateTime(endTimeIso);
    }
    DateTime startTime = endTime.minusDays(7);
    if (StringUtils.isNotEmpty(startTimeIso)) {
        startTime = ISODateTimeFormat.dateTimeParser().parseDateTime(startTimeIso);
    }
    List<RawAnomalyResultDTO> rawAnomalyResults = new ArrayList<>();
    if (StringUtils.isNotBlank(functionId)) {
        rawAnomalyResults = rawAnomalyResultDAO.findAllByTimeAndFunctionId(startTime.getMillis(), endTime.getMillis(), Long.valueOf(functionId));
    } else if (StringUtils.isNotBlank(dataset)) {
        List<AnomalyFunctionDTO> anomalyFunctions = anomalyFunctionDAO.findAllByCollection(dataset);
        List<Long> functionIds = new ArrayList<>();
        for (AnomalyFunctionDTO anomalyFunction : anomalyFunctions) {
            if (StringUtils.isNotBlank(metric) && !anomalyFunction.getTopicMetric().equals(metric)) {
                continue;
            }
            functionIds.add(anomalyFunction.getId());
        }
        for (Long id : functionIds) {
            rawAnomalyResults.addAll(rawAnomalyResultDAO.findAllByTimeAndFunctionId(startTime.getMillis(), endTime.getMillis(), id));
        }
    }
    String response = new ObjectMapper().writerWithDefaultPrettyPrinter().writeValueAsString(rawAnomalyResults);
    return response;
}

Example 17 with RawAnomalyResultDTO

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

the class OnboardResource method deleteRawResults.

// Delete raw anomaly results from rawResultDAO
private int deleteRawResults(List<RawAnomalyResultDTO> rawResults) {
    LOG.info("Deleting raw anomaly results...");
    int rawAnomaliesDeleted = 0;
    for (RawAnomalyResultDTO rawResult : rawResults) {
        LOG.info("...Deleting raw anomaly result id {} for functionId {}", rawResult.getId(), rawResult.getFunctionId());
        rawAnomalyResultDAO.delete(rawResult);
        rawAnomaliesDeleted++;
    }
    return rawAnomaliesDeleted;
}

Example 18 with RawAnomalyResultDTO

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

the class MergedAnomalyResultManagerImpl method convertMergeAnomalyDTO2Bean.

protected MergedAnomalyResultBean convertMergeAnomalyDTO2Bean(MergedAnomalyResultDTO entity) {
    MergedAnomalyResultBean bean = convertDTO2Bean(entity, MergedAnomalyResultBean.class);
    if (entity.getFeedback() != null && entity.getFeedback().getId() != null) {
        bean.setAnomalyFeedbackId(entity.getFeedback().getId());
    }
    if (entity.getFunction() != null) {
        bean.setFunctionId(entity.getFunction().getId());
    }
    if (entity.getAnomalyResults() != null && !entity.getAnomalyResults().isEmpty()) {
        List<Long> rawAnomalyIds = new ArrayList<>();
        for (RawAnomalyResultDTO rawAnomalyDTO : entity.getAnomalyResults()) {
            rawAnomalyIds.add(rawAnomalyDTO.getId());
        }
        bean.setRawAnomalyIdList(rawAnomalyIds);
    }
    return bean;
}

Example 19 with RawAnomalyResultDTO

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

the class TestWeekOverWeekRuleFunction method analyzeWoW.

@Test(dataProvider = "timeSeriesDataProvider")
public void analyzeWoW(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
    properties.put(WeekOverWeekRuleFunction.BASELINE, "w/w");
    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);
    List<TimeSeries> singleBaseline = new ArrayList<>();
    singleBaseline.add(baselines.get(0));
    anomalyDetectionContext.setBaselines(mainMetric, singleBaseline);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    List<RawAnomalyResultDTO> rawAnomalyResults = function.analyze(anomalyDetectionContext);
    compareWoWRawAnomalies(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));
    List<Interval> actualDataRanges = function.getDataModel().getAllDataIntervals(observedStartTime, observedStartTime + bucketMillis * 5);
    Assert.assertEquals(actualDataRanges, expectedDataRanges);
}

Example 20 with RawAnomalyResultDTO

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

the class TestWeekOverWeekRuleFunction method recomputeMergedAnomalyWeight.

@Test(dataProvider = "timeSeriesDataProvider")
public void recomputeMergedAnomalyWeight(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);
    MergedAnomalyResultDTO mergedAnomaly = new MergedAnomalyResultDTO();
    mergedAnomaly.setStartTime(expectedRawAnomalies.get(0).getStartTime());
    mergedAnomaly.setEndTime(expectedRawAnomalies.get(1).getEndTime());
    mergedAnomaly.setAnomalyResults(expectedRawAnomalies);
    function.updateMergedAnomalyInfo(anomalyDetectionContext, mergedAnomaly);
    // Test weight; weight is the percentage change between the sums of observed values and
    // expected values, respectively. Note that expected values are generated by the trained model,
    // which takes as input one or many baseline time series.
    final long oneWeekInMillis = TimeUnit.DAYS.toMillis(7);
    double observedTotal = 0d;
    double baselineTotal = 0d;
    int bucketCount = 0;
    Interval interval = new Interval(mergedAnomaly.getStartTime(), mergedAnomaly.getEndTime());
    TimeSeries observedTS = anomalyDetectionContext.getTransformedCurrent(mainMetric);
    List<TimeSeries> baselineTSs = anomalyDetectionContext.getTransformedBaselines(mainMetric);
    for (long timestamp : observedTS.timestampSet()) {
        if (interval.contains(timestamp)) {
            ++bucketCount;
            observedTotal += observedTS.get(timestamp);
            for (int i = 0; i < baselineTSs.size(); ++i) {
                TimeSeries baselineTS = baselineTSs.get(i);
                long baseTimeStamp = timestamp - oneWeekInMillis * (i + 1);
                baselineTotal += baselineTS.get(baseTimeStamp);
            }
        }
    }
    baselineTotal /= baselineTSs.size();
    // Compare anomaly weight, avg. current, avg. baseline, score, etc
    double expectedWeight = (observedTotal - baselineTotal) / baselineTotal;
    Assert.assertEquals(mergedAnomaly.getWeight(), expectedWeight, EPSILON);
    double avgCurrent = observedTotal / bucketCount;
    Assert.assertEquals(mergedAnomaly.getAvgCurrentVal(), avgCurrent, EPSILON);
    double avgBaseline = baselineTotal / bucketCount;
    Assert.assertEquals(mergedAnomaly.getAvgBaselineVal(), avgBaseline, EPSILON);
    // Test Score; score is the average of all raw anomalies' score
    double expectedScore = 0d;
    for (RawAnomalyResultDTO rawAnomaly : expectedRawAnomalies) {
        expectedScore += rawAnomaly.getScore();
    }
    expectedScore /= expectedRawAnomalies.size();
    Assert.assertEquals(mergedAnomaly.getScore(), expectedScore, EPSILON);
}