xiaohui-sun commented on a change in pull request #4190: [TE] pass predicted
time series thought out the detection pipeline
URL: https://github.com/apache/incubator-pinot/pull/4190#discussion_r282211262
##########
File path:
thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/wrapper/AnomalyDetectorWrapper.java
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@@ -201,8 +206,58 @@ public DetectionPipelineResult run() throws Exception {
anomaly.getProperties().put(PROP_DETECTOR_COMPONENT_NAME,
this.detectorName);
}
long lastTimeStamp = this.getLastTimeStamp();
- return new DetectionPipelineResult(anomalies.stream().filter(anomaly ->
anomaly.getEndTime() <= lastTimeStamp).collect(
- Collectors.toList()), lastTimeStamp);
+ List<MergedAnomalyResultDTO> anomalyResults =
anomalies.stream().filter(anomaly -> anomaly.getEndTime() <=
lastTimeStamp).collect(
+ Collectors.toList());
+ PredictionResult predictedTimeSeries = new
PredictionResult(this.detectorName, this.metricUrn,
predictedResult.getDataFrame());
+ return new DetectionPipelineResult(anomalyResults, lastTimeStamp,
Collections.singletonList(predictedTimeSeries));
+ }
+
+ /**
+ * Join two time series, including current, baseline, lower bound and upper
bound. If two time series have overlapped region, take the average
+ * @param ts1 timeseries 1
+ * @param ts2 timeseries 2
+ * @return the consolidated time series
+ */
+ static TimeSeries consolidateTimeSeries(TimeSeries ts1, TimeSeries ts2) {
+ DataFrame df1 = ts1.getDataFrame();
+ DataFrame df2 = ts2.getDataFrame();
+ DataFrame joinedDf = df1.joinOuter(df2, COL_TIME);
+ consolidateJoinedDf(joinedDf, COL_VALUE);
+ consolidateJoinedDf(joinedDf, COL_CURRENT);
+ consolidateJoinedDf(joinedDf, COL_LOWER_BOUND);
+ consolidateJoinedDf(joinedDf, COL_UPPER_BOUND);
+ return TimeSeries.fromDataFrame(joinedDf);
+ }
+
+ private static void consolidateJoinedDf(DataFrame joinedDf, String
columnName) {
+ String columnNameLeft = columnName + DataFrame.COLUMN_JOIN_LEFT;
+ String columnNameRight = columnName + DataFrame.COLUMN_JOIN_RIGHT;
+ if (joinedDf.contains(columnNameLeft) &&
joinedDf.contains(columnNameRight)) {
+ joinedDf.addSeries(columnName,
robustAverage(joinedDf.getDoubles(columnNameLeft),
joinedDf.getDoubles(columnNameRight)));
+ }
+ }
+
+ /**
+ * Calculate the average of two double time series. If the value in either
one is missing, take the available value as the result
+ * @param s1 series 1
+ * @param s2 series 2
+ * @return the averaged time series
+ */
+ private static DoubleSeries robustAverage(DoubleSeries s1, DoubleSeries s2) {
Review comment:
ok let's just take the latest values. Technically they should be the same.
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