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

Example 31 with MergedAnomalyResultDTO

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

the class AnomaliesResource method getAnomaliesForMetricIdInRange.

// ----------- HELPER FUNCTIONS
/**
   * Get anomalies for metric id in a time range
   * @param metricId
   * @param startTime
   * @param endTime
   * @return
   */
private List<MergedAnomalyResultDTO> getAnomaliesForMetricIdInRange(Long metricId, Long startTime, Long endTime) {
    MetricConfigDTO metricConfig = metricConfigDAO.findById(metricId);
    String dataset = metricConfig.getDataset();
    String metric = metricConfig.getName();
    List<MergedAnomalyResultDTO> mergedAnomalies = mergedAnomalyResultDAO.findByCollectionMetricTime(dataset, metric, startTime, endTime, false);
    try {
        mergedAnomalies = AlertFilterHelper.applyFiltrationRule(mergedAnomalies, alertFilterFactory);
    } catch (Exception e) {
        LOG.warn("Failed to apply alert filters on anomalies for metricid:{}, start:{}, end:{}, exception:{}", metricId, new DateTime(startTime), new DateTime(endTime), e);
    }
    return mergedAnomalies;
}

Example 32 with MergedAnomalyResultDTO

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

the class AnomaliesResource method getAnomalyCountForMetricInRange.

/**
   * Get count of anomalies for metric in time range
   * @param metricId
   * @param startTime
   * @param endTime
   * @return
   */
@GET
@Path("getAnomalyCount/{metricId}/{startTime}/{endTime}")
public AnomaliesSummary getAnomalyCountForMetricInRange(@PathParam("metricId") Long metricId, @PathParam("startTime") Long startTime, @PathParam("endTime") Long endTime) {
    AnomaliesSummary anomaliesSummary = new AnomaliesSummary();
    List<MergedAnomalyResultDTO> mergedAnomalies = getAnomaliesForMetricIdInRange(metricId, startTime, endTime);
    int resolvedAnomalies = 0;
    int unresolvedAnomalies = 0;
    for (MergedAnomalyResultDTO mergedAnomaly : mergedAnomalies) {
        AnomalyFeedbackDTO anomalyFeedback = mergedAnomaly.getFeedback();
        if (anomalyFeedback == null || anomalyFeedback.getFeedbackType() == null) {
            unresolvedAnomalies++;
        } else if (anomalyFeedback != null && anomalyFeedback.getFeedbackType() != null && anomalyFeedback.getFeedbackType().equals(AnomalyFeedbackType.ANOMALY)) {
            unresolvedAnomalies++;
        } else {
            resolvedAnomalies++;
        }
    }
    anomaliesSummary.setMetricId(metricId);
    anomaliesSummary.setStartTime(startTime);
    anomaliesSummary.setEndTime(endTime);
    anomaliesSummary.setNumAnomalies(mergedAnomalies.size());
    anomaliesSummary.setNumAnomaliesResolved(resolvedAnomalies);
    anomaliesSummary.setNumAnomaliesUnresolved(unresolvedAnomalies);
    return anomaliesSummary;
}

Example 33 with MergedAnomalyResultDTO

use of com.linkedin.thirdeye.datalayer.dto.MergedAnomalyResultDTO 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);
}

Example 34 with MergedAnomalyResultDTO

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

the class AlertFilterEvaluationUtil method updatePrecisionAndRecall.

/**
   * Evaluate alert filter given merged anomalies and output precision and recall
   * @param mergedAnomalyResultDTOS
   * @throws Exception
   */
public void updatePrecisionAndRecall(List<MergedAnomalyResultDTO> mergedAnomalyResultDTOS) throws Exception {
    int TP = 0;
    int FP = 0;
    int FN = 0;
    for (MergedAnomalyResultDTO anomaly : mergedAnomalyResultDTOS) {
        boolean predLabel = alertFilter.isQualified(anomaly);
        AnomalyFeedbackDTO feedback = anomaly.getFeedback();
        boolean label = !(feedback == null || feedback.getFeedbackType() == AnomalyFeedbackType.NOT_ANOMALY);
        //predicted true
        if (predLabel) {
            if (label) {
                TP++;
            } else {
                FP++;
            }
        } else if (label) {
            // else if predicted false but label is true
            FN++;
        }
    }
    if (TP + FN == 0) {
        throw new Exception("No true labels in dataset. Check data");
    }
    if (TP + FP == 0) {
        throw new Exception("No predicted true labels. Check model input");
    }
    this.precision = 1.000 * TP / (TP + FP);
    this.recall = 1.000 * TP / (TP + FN);
}

Example 35 with MergedAnomalyResultDTO

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

the class AlertFilterEvaluationUtil method updateFeedbackSummary.

/**
   * Provide feedback summary give a list of merged anomalies
   * @param anomalies
   */
public void updateFeedbackSummary(List<MergedAnomalyResultDTO> anomalies) {
    int totalAnomalies = 0;
    int totalResponses = 0;
    int trueAnomalies = 0;
    int falseAlarm = 0;
    int nonActionable = 0;
    for (MergedAnomalyResultDTO anomaly : anomalies) {
        totalAnomalies++;
        // evaluate feedbacks
        AnomalyFeedbackDTO feedback = anomaly.getFeedback();
        if (feedback != null) {
            totalResponses++;
            AnomalyFeedbackType feedbackType = feedback.getFeedbackType();
            switch(feedbackType) {
                case ANOMALY:
                    trueAnomalies++;
                    break;
                case ANOMALY_NO_ACTION:
                    nonActionable++;
                    break;
                case NOT_ANOMALY:
                    falseAlarm++;
                    break;
            }
        }
    }
    this.totalAnomalies = totalAnomalies;
    this.totalResponses = totalResponses;
    this.trueAnomalies = trueAnomalies;
    this.falseAlarm = falseAlarm;
    this.nonActionable = nonActionable;
}