[GitHub] [incubator-pinot] tangdian commented on a change in pull request #4067: [TE] Holt Winters detector

[GitBox <gi...@apache.org>]

tangdian commented on a change in pull request #4067: [TE] Holt Winters detector
URL: https://github.com/apache/incubator-pinot/pull/4067#discussion_r273631601
 
 

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 File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/HoltWintersDetector.java
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+package org.apache.pinot.thirdeye.detection.components;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.concurrent.TimeUnit;
+import org.apache.commons.math3.analysis.MultivariateFunction;
+import org.apache.commons.math3.optim.PointValuePair;
+import org.apache.pinot.thirdeye.common.time.TimeGranularity;
+import org.apache.pinot.thirdeye.dataframe.BooleanSeries;
+import org.apache.pinot.thirdeye.dataframe.DataFrame;
+import org.apache.pinot.thirdeye.dataframe.DoubleSeries;
+import org.apache.pinot.thirdeye.dataframe.LongSeries;
+import org.apache.pinot.thirdeye.dataframe.Series;
+import org.apache.pinot.thirdeye.dataframe.util.MetricSlice;
+import org.apache.pinot.thirdeye.datalayer.dto.DatasetConfigDTO;
+import org.apache.pinot.thirdeye.datalayer.dto.MergedAnomalyResultDTO;
+import org.apache.pinot.thirdeye.detection.ConfigUtils;
+import org.apache.pinot.thirdeye.detection.DetectionUtils;
+import org.apache.pinot.thirdeye.detection.InputDataFetcher;
+import org.apache.pinot.thirdeye.detection.Pattern;
+import org.apache.pinot.thirdeye.detection.algorithm.AlgorithmUtils;
+import org.apache.pinot.thirdeye.detection.annotation.Components;
+import org.apache.pinot.thirdeye.detection.annotation.DetectionTag;
+import org.apache.pinot.thirdeye.detection.annotation.Param;
+import org.apache.pinot.thirdeye.detection.annotation.Tune;
+import org.apache.pinot.thirdeye.detection.spec.HoltWintersDetectorSpec;
+import org.apache.pinot.thirdeye.detection.spi.components.AnomalyDetector;
+import org.apache.pinot.thirdeye.detection.spi.components.BaselineProvider;
+import org.apache.pinot.thirdeye.detection.spi.components.Tunable;
+import org.apache.pinot.thirdeye.detection.spi.model.InputData;
+import org.apache.pinot.thirdeye.detection.spi.model.InputDataSpec;
+import org.apache.pinot.thirdeye.detection.spi.model.TimeSeries;
+import org.apache.pinot.thirdeye.rootcause.impl.MetricEntity;
+import org.joda.time.DateTime;
+import org.joda.time.Interval;
+import org.joda.time.Period;
+import org.apache.commons.math3.optim.MaxIter;
+import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction;
+import org.apache.commons.math3.optim.MaxEval;
+import org.apache.commons.math3.optim.SimpleBounds;
+import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.BOBYQAOptimizer;
+import org.apache.commons.math3.optim.InitialGuess;
+import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import static org.apache.pinot.thirdeye.dataframe.util.DataFrameUtils.*;
+
+
+@Components(title = "Holt Winters triple exponential smoothing forecasting and detection",
+    type = "HOLT_WINTERS_RULE",
+    tags = {DetectionTag.RULE_DETECTION},
+    description = "Forecast with holt winters triple exponential smoothing and generate anomalies",
+    params = {
+        @Param(name = "alpha"),
+        @Param(name = "beta"),
+        @Param(name = "gamma"),
+        @Param(name = "period"),
+        @Param(name = "pattern"),
+        @Param(name = "sensitivity"),
+        @Param(name = "kernelSmoothing")
+    })
+public class HoltWintersDetector implements BaselineProvider<HoltWintersDetectorSpec>,
+                                            AnomalyDetector<HoltWintersDetectorSpec> {
+  private static final Logger LOG = LoggerFactory.getLogger(HoltWintersDetector.class);
+  private InputDataFetcher dataFetcher;
+  private static final String COL_CURR = "current";
+  private static final String COL_BASE = "baseline";
+  private static final String COL_ANOMALY = "anomaly";
+  private static final String COL_PATTERN = "pattern";
+  private static final String COL_DIFF = "diff";
+  private static final String COL_DIFF_VIOLATION = "diff_violation";
+  private static final String COL_ERROR = "error";
+  private static final String TRUE = "true";
+  private static final long PARTITION_PERIOD = 604800000L;
+  private static final long KERNEL_PERIOD = 3600000L;
+  private static final int LOOKBACK = 60;
+  private static final Period LOOKBACK_PERIOD = ConfigUtils.parsePeriod("60DAYS");
+
+  private int period;
+  private double alpha;
+  private double beta;
+  private double gamma;
+  private Pattern pattern;
+  private TimeGranularity timeGranularity;
+  private double sensitivity;
+  private String monitoringGranularity;
+  private boolean kernelSmoothing;
+
+  @Override
+  public void init(HoltWintersDetectorSpec spec, InputDataFetcher dataFetcher) {
+    this.period = spec.getPeriod();
+    this.alpha = spec.getAlpha();
+    this.beta = spec.getBeta();
+    this.gamma = spec.getGamma();
+    this.dataFetcher = dataFetcher;
+    this.pattern = Pattern.valueOf(spec.getPattern().toUpperCase());
+    this.kernelSmoothing = spec.getKernelSmoothing().equalsIgnoreCase(TRUE);
+
+    this.sensitivity = spec.getSensitivity();
+    this.monitoringGranularity = spec.getMonitoringGranularity();
+    if (this.monitoringGranularity.equals("1_MONTHS")) {
+      this.timeGranularity = MetricSlice.NATIVE_GRANULARITY;
+    } else {
+      this.timeGranularity = TimeGranularity.fromString(spec.getMonitoringGranularity());
+    }
+  }
+
+  @Override
+  public TimeSeries computePredictedTimeSeries(MetricSlice slice) {
+    MetricEntity metricEntity = MetricEntity.fromSlice(slice, 0);
+    Interval window = new Interval(slice.getStart(), slice.getEnd());
+    DateTime trainStart = new DateTime(window.getStart()).minus(LOOKBACK_PERIOD);
+    DataFrame inputDf = fetchDataByPart(metricEntity, trainStart.getMillis(), window.getEndMillis());
+    DataFrame resultDF = computePredictionInterval(inputDf,
+        window.getStartMillis());
+
+    // Exclude the end
+    if (resultDF.size() > 1) {
+      resultDF = resultDF.head(resultDF.size() - 1);
+    }
+
+    return TimeSeries.fromDataFrame(resultDF);
+  }
+
+  @Override
+  public List<MergedAnomalyResultDTO> runDetection(Interval window, String metricUrn) {
+    MetricEntity metricEntity = MetricEntity.fromURN(metricUrn);
+    DateTime trainStart = new DateTime(window.getStart()).minus(LOOKBACK_PERIOD);
+    DatasetConfigDTO datasetConfig = this.dataFetcher.fetchData(new InputDataSpec()
+        .withMetricIdsForDataset(Collections.singleton(metricEntity.getId()))).getDatasetForMetricId()
+        .get(metricEntity.getId());
+
+    MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), trainStart.getMillis(), window.getEndMillis(),
+        metricEntity.getFilters(), timeGranularity);
+
+    DataFrame dfInput = fetchDataByPart(metricEntity, trainStart.getMillis(), window.getEndMillis());
+
+    // Kernel smoothing
+    if (kernelSmoothing && !TimeUnit.DAYS.equals(datasetConfig.bucketTimeGranularity().getUnit())) {
+      int kernelSize = (int) (KERNEL_PERIOD / datasetConfig.bucketTimeGranularity().toMillis());
+      // kernel smoothing
+      if (kernelSize > 1) {
+        final int kernelOffset = kernelSize / 2;
+        double[] values = dfInput.getDoubles(COL_VALUE).values();
+        for (int i = 0; i < values.length - kernelSize + 1; i++) {
+          values[i + kernelOffset] = AlgorithmUtils.robustMean(dfInput.getDoubles(COL_VALUE)
+              .slice(i, i + kernelSize), kernelSize).getDouble(kernelSize - 1);
+        }
+        dfInput.addSeries(COL_VALUE, values);
+      }
+    }
+
+    DataFrame dfCurr = new DataFrame(dfInput).renameSeries(COL_VALUE, COL_CURR);
+    DataFrame dfBase = computePredictionInterval(dfInput, window.getStartMillis()).renameSeries(COL_VALUE, COL_BASE);
+    DataFrame df = new DataFrame(dfCurr).addSeries(dfBase);
+    df.addSeries(COL_DIFF, df.getDoubles(COL_CURR).subtract(df.get(COL_BASE)));
+    df.addSeries(COL_ANOMALY, BooleanSeries.fillValues(df.size(), false));
+
+    // consistent with pattern
+    if (pattern.equals(Pattern.UP_OR_DOWN) ) {
+      df.addSeries(COL_PATTERN, BooleanSeries.fillValues(df.size(), true));
+    } else {
+      df.addSeries(COL_PATTERN, pattern.equals(Pattern.UP) ? df.getDoubles(COL_DIFF).gt(0) :
+          df.getDoubles(COL_DIFF).lt(0));
+    }
+    df.addSeries(COL_DIFF_VIOLATION, df.getDoubles(COL_DIFF).abs().gte(df.getDoubles(COL_ERROR)));
+    df.mapInPlace(BooleanSeries.ALL_TRUE, COL_ANOMALY, COL_PATTERN, COL_DIFF_VIOLATION);
+
+    // anomalies
+    List<MergedAnomalyResultDTO> result = DetectionUtils.makeAnomalies(sliceData, df, COL_ANOMALY,
+        window.getEndMillis(),
+        DetectionUtils.getMonitoringGranularityPeriod(monitoringGranularity, datasetConfig), datasetConfig);
+
+    return result;
+  }
+
+  /**
+   * Break a time period into parts and fetch it one by one, and put it together into a DataFrame
+   *
+   * @param metricEntity metric entity
+   * @param start start timestamp
+   * @param end end timestamp
+   * @return Data Frame that has data from start to end
+   */
+  private DataFrame fetchDataByPart(MetricEntity metricEntity, long start, long end) {
+    long duration = end - start;
+    List<MetricSlice> slices = new ArrayList<>();
+    long temp = start;
+    int parts = (int) (duration / PARTITION_PERIOD);
+    for (int i = 0; i < parts; i++) {
+      MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), temp, temp + PARTITION_PERIOD,
+          metricEntity.getFilters(), timeGranularity);
+      temp += PARTITION_PERIOD;
+      slices.add(sliceData);
+    }
+    MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), temp, end,
+        metricEntity.getFilters(), timeGranularity);
+    slices.add(sliceData);
+
+    InputData data = this.dataFetcher.fetchData(new InputDataSpec().withTimeseriesSlices(slices)
+        .withMetricIdsForDataset(Collections.singletonList(metricEntity.getId())));
+
+    LongSeries longSeries = LongSeries.buildFrom();
+    DoubleSeries doubleSeries = DoubleSeries.buildFrom();
+
+    DataFrame df = new DataFrame();
+    df.addSeries(COL_TIME, longSeries);
+    df.addSeries(COL_VALUE, doubleSeries);
+    df.setIndex(COL_TIME);
+
+    Set<Map.Entry<MetricSlice, DataFrame>> set = data.getTimeseries().entrySet();
+    for (Map.Entry<MetricSlice, DataFrame> entry: set) {
+      df = df.append(entry.getValue());
+    }
+    df = df.sortedBy(COL_TIME);
+
+    // Remove duplicates
+    long prev = -1;
+    for (int i = 0; i < df.size(); i++) {
+      long curr = df.getLongs(COL_TIME).get(i);
+      if (curr == prev) {
+        DataFrame df1 = df.slice(0, i);
+        DataFrame df2 = df.slice(i+1, df.size());
+        df = df1.append(df2);
+      }
+      prev = curr;
+    }
+    return df;
+  }
+
+  /**
+   * Returns a data frame containing the same time daily data
+   * @param originalDF the original dataframe
+   * @param time the time of day
+   * @return dataframe containing same time of daily data for LOOKBACK number of days
+   */
+  private DataFrame getDailyDF(DataFrame originalDF, Long time) {
+    LongSeries longSeries = (LongSeries) originalDF.get(COL_TIME);
+    Long start = longSeries.getLong(0);
+    DateTime dt = new DateTime(time);
+    DataFrame df = DataFrame.builder(COL_TIME, COL_VALUE).build();
+
+    for (int i = 0; i < LOOKBACK; i++) {
+      DateTime subDt = new DateTime(dt);
+      subDt = subDt.minusDays(1);
+      long t = subDt.getMillis();
 
 Review comment:
   I had to do subDt = subDt.minusDays(1), because subDt.minusDays(1) does not mutate subDt

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