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

Example 46 with MergedAnomalyResultDTO

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

the class TestAlertFilterUtil method getMockMergedAnomalies.

private List<MergedAnomalyResultDTO> getMockMergedAnomalies(int posIdx, int negIdx) {
    List<MergedAnomalyResultDTO> anomalyResultDTOS = new ArrayList<>();
    int[] ws = { 1, 1, 2, 3, 4, 4, 5, 6, 7 };
    double[] severity = { 2.0, 4.0, 2.0, 3.0, 1.0, 3.0, 2.0, 1.0, 3.0 };
    AnomalyFeedbackDTO positiveFeedback = new AnomalyFeedbackDTO();
    AnomalyFeedbackDTO negativeFeedback = new AnomalyFeedbackDTO();
    positiveFeedback.setFeedbackType(ANOMALY);
    negativeFeedback.setFeedbackType(NOT_ANOMALY);
    for (int i = 0; i < ws.length; i++) {
        MergedAnomalyResultDTO anomaly = new MergedAnomalyResultDTO();
        anomaly.setStartTime(0l);
        anomaly.setEndTime(ws[i] * 3600 * 1000l);
        anomaly.setWeight(severity[i]);
        if (i == posIdx) {
            anomaly.setFeedback(positiveFeedback);
        } else if (i == negIdx) {
            anomaly.setFeedback(negativeFeedback);
        } else {
            anomaly.setFeedback(null);
        }
        anomalyResultDTOS.add(anomaly);
    }
    return anomalyResultDTOS;
}

Example 47 with MergedAnomalyResultDTO

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

the class RunAdhocDatabaseQueriesTool method updateNotified.

private void updateNotified() {
    List<MergedAnomalyResultDTO> mergedResults = mergedResultDAO.findAll();
    for (MergedAnomalyResultDTO mergedResult : mergedResults) {
        mergedResult.setNotified(true);
        mergedResultDAO.update(mergedResult);
    }
}

Example 48 with MergedAnomalyResultDTO

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

the class TestMinMaxThresholdFunction method recomputeMergedAnomalyWeight.

@Test(dataProvider = "timeSeriesDataProvider")
public void recomputeMergedAnomalyWeight(Properties properties, TimeSeriesKey timeSeriesKey, long bucketSizeInMs, TimeSeries observedTimeSeries) throws Exception {
    AnomalyDetectionContext anomalyDetectionContext = new AnomalyDetectionContext();
    anomalyDetectionContext.setBucketSizeInMS(bucketSizeInMs);
    properties.put(MinMaxThresholdDetectionModel.MAX_VAL, "20");
    properties.put(MinMaxThresholdDetectionModel.MIN_VAL, "12");
    // Create anomaly function spec
    AnomalyFunctionDTO functionSpec = new AnomalyFunctionDTO();
    functionSpec.setMetric(mainMetric);
    functionSpec.setProperties(TestWeekOverWeekRuleFunction.toString(properties));
    AnomalyDetectionFunction function = new MinMaxThresholdFunction();
    function.init(functionSpec);
    anomalyDetectionContext.setAnomalyDetectionFunction(function);
    anomalyDetectionContext.setCurrent(mainMetric, observedTimeSeries);
    anomalyDetectionContext.setTimeSeriesKey(timeSeriesKey);
    List<RawAnomalyResultDTO> expectedRawAnomalies = new ArrayList<>();
    RawAnomalyResultDTO rawAnomaly1 = new RawAnomalyResultDTO();
    rawAnomaly1.setStartTime(observedStartTime + bucketMillis * 3);
    rawAnomaly1.setEndTime(observedStartTime + bucketMillis * 4);
    rawAnomaly1.setWeight(0.1d);
    rawAnomaly1.setScore(13.6d);
    expectedRawAnomalies.add(rawAnomaly1);
    RawAnomalyResultDTO rawAnomaly2 = new RawAnomalyResultDTO();
    rawAnomaly2.setStartTime(observedStartTime + bucketMillis * 4);
    rawAnomaly2.setEndTime(observedStartTime + bucketMillis * 5);
    rawAnomaly2.setWeight(-0.33333d);
    rawAnomaly2.setScore(13.6d);
    expectedRawAnomalies.add(rawAnomaly2);
    MergedAnomalyResultDTO mergedAnomaly = new MergedAnomalyResultDTO();
    mergedAnomaly.setStartTime(expectedRawAnomalies.get(0).getStartTime());
    mergedAnomaly.setEndTime(expectedRawAnomalies.get(1).getEndTime());
    mergedAnomaly.setAnomalyResults(expectedRawAnomalies);
    function.updateMergedAnomalyInfo(anomalyDetectionContext, mergedAnomaly);
    double currentTotal = 0d;
    double deviationFromThreshold = 0d;
    Interval interval = new Interval(mergedAnomaly.getStartTime(), mergedAnomaly.getEndTime());
    TimeSeries currentTS = anomalyDetectionContext.getTransformedCurrent(mainMetric);
    for (long timestamp : currentTS.timestampSet()) {
        if (interval.contains(timestamp)) {
            double value = currentTS.get(timestamp);
            currentTotal += value;
            deviationFromThreshold += computeDeviationFromMinMax(value, 12d, 20d);
        }
    }
    double score = currentTotal / 2d;
    double weight = deviationFromThreshold / 2d;
    Assert.assertEquals(mergedAnomaly.getScore(), score, EPSILON);
    Assert.assertEquals(mergedAnomaly.getAvgCurrentVal(), score, EPSILON);
    Assert.assertEquals(mergedAnomaly.getWeight(), weight, EPSILON);
}

Example 49 with MergedAnomalyResultDTO

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

the class AnomalyResource method deleteAnomalyFunctions.

// Delete anomaly function
@DELETE
@Path("/anomaly-function/delete")
public Response deleteAnomalyFunctions(@NotNull @QueryParam("id") Long id, @QueryParam("functionName") String functionName) throws IOException {
    if (id == null) {
        throw new IllegalArgumentException("id is a required query param");
    }
    // call endpoint to stop if active
    AnomalyFunctionDTO anomalyFunctionSpec = anomalyFunctionDAO.findById(id);
    if (anomalyFunctionSpec == null) {
        throw new IllegalStateException("No anomalyFunctionSpec with id " + id);
    }
    // delete dependent entities
    // email config mapping
    List<EmailConfigurationDTO> emailConfigurations = emailConfigurationDAO.findByFunctionId(id);
    for (EmailConfigurationDTO emailConfiguration : emailConfigurations) {
        emailConfiguration.getFunctions().remove(anomalyFunctionSpec);
        emailConfigurationDAO.update(emailConfiguration);
    }
    // raw result mapping
    List<RawAnomalyResultDTO> rawResults = rawAnomalyResultDAO.findAllByTimeAndFunctionId(0, System.currentTimeMillis(), id);
    for (RawAnomalyResultDTO result : rawResults) {
        rawAnomalyResultDAO.delete(result);
    }
    // merged anomaly mapping
    List<MergedAnomalyResultDTO> mergedResults = anomalyMergedResultDAO.findByFunctionId(id);
    for (MergedAnomalyResultDTO result : mergedResults) {
        anomalyMergedResultDAO.delete(result);
    }
    // delete from db
    anomalyFunctionDAO.deleteById(id);
    return Response.noContent().build();
}

Example 50 with MergedAnomalyResultDTO

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

the class AnomalyResource method viewMergedAnomaliesInRange.

// View merged anomalies for collection
@GET
@Path("/anomalies/view")
public List<MergedAnomalyResultDTO> viewMergedAnomaliesInRange(@NotNull @QueryParam("dataset") String dataset, @QueryParam("startTimeIso") String startTimeIso, @QueryParam("endTimeIso") String endTimeIso, @QueryParam("metric") String metric, @QueryParam("dimensions") String exploredDimensions, @DefaultValue("true") @QueryParam("applyAlertFilter") boolean applyAlertFiler) {
    if (StringUtils.isBlank(dataset)) {
        throw new IllegalArgumentException("dataset is a required query param");
    }
    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<MergedAnomalyResultDTO> anomalyResults = new ArrayList<>();
    try {
        if (StringUtils.isNotBlank(exploredDimensions)) {
            // Decode dimensions map from request, which may contain encode symbols such as "%20D", etc.
            exploredDimensions = URLDecoder.decode(exploredDimensions, UTF8);
            try {
                // Ensure the dimension names are sorted in order to match the string in backend database
                DimensionMap sortedDimensions = OBJECT_MAPPER.readValue(exploredDimensions, DimensionMap.class);
                exploredDimensions = OBJECT_MAPPER.writeValueAsString(sortedDimensions);
            } catch (IOException e) {
                LOG.warn("exploreDimensions may not be sorted because failed to read it as a json string: {}", e.toString());
            }
        }
        boolean loadRawAnomalies = false;
        if (StringUtils.isNotBlank(metric)) {
            if (StringUtils.isNotBlank(exploredDimensions)) {
                anomalyResults = anomalyMergedResultDAO.findByCollectionMetricDimensionsTime(dataset, metric, exploredDimensions, startTime.getMillis(), endTime.getMillis(), loadRawAnomalies);
            } else {
                anomalyResults = anomalyMergedResultDAO.findByCollectionMetricTime(dataset, metric, startTime.getMillis(), endTime.getMillis(), loadRawAnomalies);
            }
        } else {
            anomalyResults = anomalyMergedResultDAO.findByCollectionTime(dataset, startTime.getMillis(), endTime.getMillis(), loadRawAnomalies);
        }
    } catch (Exception e) {
        LOG.error("Exception in fetching anomalies", e);
    }
    if (applyAlertFiler) {
        // TODO: why need try catch?
        try {
            anomalyResults = AlertFilterHelper.applyFiltrationRule(anomalyResults, alertFilterFactory);
        } catch (Exception e) {
            LOG.warn("Failed to apply alert filters on anomalies for dataset:{}, metric:{}, start:{}, end:{}, exception:{}", dataset, metric, startTimeIso, endTimeIso, e);
        }
    }
    return anomalyResults;
}