[GitHub] [incubator-pinot] jihaozh commented on a change in pull request #4466: Adding week over week std rule detector

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

jihaozh commented on a change in pull request #4466: Adding week over week std rule detector
URL: https://github.com/apache/incubator-pinot/pull/4466#discussion_r306938805
 
 

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 File path: thirdeye/thirdeye-pinot/src/main/java/org/apache/pinot/thirdeye/detection/components/WoWStdRuleDetector.java
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.pinot.thirdeye.detection.components;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
+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.DetectionUtils;
+import org.apache.pinot.thirdeye.detection.InputDataFetcher;
+import org.apache.pinot.thirdeye.detection.Pattern;
+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.spec.WoWStdRuleDetectorSpec;
+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.model.DetectionResult;
+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.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import static org.apache.pinot.thirdeye.dataframe.DoubleSeries.*;
+import static org.apache.pinot.thirdeye.dataframe.util.DataFrameUtils.*;
+
+@Components(title = "WoW mean and std-dev forecasting and detection",
+    type = "WOW_STD_RULE",
+    tags = {DetectionTag.RULE_DETECTION},
+    description = "Forecast using history WoW mean and std-dev",
+    params = {
+        @Param(name = "offset", defaultValue = "wo1w"),
+        @Param(name = "pattern", allowableValues = {"up", "down"}),
+        @Param(name = "lookback"),
+        @Param(name = "sensitivity")})
+public class WoWStdRuleDetector implements AnomalyDetector<WoWStdRuleDetectorSpec>,
+                                           BaselineProvider<WoWStdRuleDetectorSpec> {
+  private static final Logger LOG = LoggerFactory.getLogger(WoWStdRuleDetector.class);
+  private InputDataFetcher dataFetcher;
+  private Pattern pattern;
+  private String monitoringGranularity;
+  private TimeGranularity timeGranularity;
+  private double sensitivity;
+  private int lookback;
+
+  private static final String COL_CURR = "current";
+  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 COL_CHANGE = "change";
+
+  @Override
+  public void init(WoWStdRuleDetectorSpec spec, InputDataFetcher dataFetcher) {
+    this.dataFetcher = dataFetcher;
+    this.pattern = spec.getPattern();
+    this.lookback = spec.getLookback();
+    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;
+
+    if (isMultiDayGranularity()) {
+      trainStart = window.getStart().minusDays(timeGranularity.getSize() * lookback);
+    } else if (this.monitoringGranularity.equals("1_MONTHS")) {
+      trainStart = window.getStart().minusMonths(lookback);
+    } else {
+      trainStart = window.getStart().minusWeeks(lookback);
+    }
+
+    DatasetConfigDTO datasetConfig = this.dataFetcher.fetchData(new InputDataSpec()
+        .withMetricIdsForDataset(Collections.singleton(metricEntity.getId()))).getDatasetForMetricId()
+        .get(metricEntity.getId());
+    DataFrame inputDf = fetchData(metricEntity, trainStart.getMillis(), window.getEndMillis());
+    DataFrame resultDF = computePredictionInterval(inputDf, window.getStartMillis(), datasetConfig.getTimezone());
+    resultDF = resultDF.joinLeft(inputDf.renameSeries(COL_VALUE, COL_CURR), COL_TIME);
+
+    // Exclude the end because baseline calculation should not contain the end
+    if (resultDF.size() > 1) {
+      resultDF = resultDF.head(resultDF.size() - 1);
+    }
+
+    return TimeSeries.fromDataFrame(resultDF);
+  }
+
+  @Override
+  public DetectionResult runDetection(Interval window, String metricUrn) {
+    MetricEntity me = MetricEntity.fromURN(metricUrn);
+    DateTime fetchStart;
+    //get historical data
+    if (isMultiDayGranularity()) {
+      fetchStart = window.getStart().minusDays(timeGranularity.getSize() * lookback);
+    } else if (this.monitoringGranularity.equals("1_MONTHS")) {
+      fetchStart = window.getStart().minusMonths(lookback);
+    } else {
+      fetchStart = window.getStart().minusWeeks(lookback);
+    }
+
+    MetricSlice slice = MetricSlice.from(me.getId(), fetchStart.getMillis(), window.getEndMillis(), me.getFilters(), timeGranularity);
+    DatasetConfigDTO datasetConfig = this.dataFetcher.fetchData(new InputDataSpec()
+        .withMetricIdsForDataset(Collections.singleton(me.getId()))).getDatasetForMetricId()
+        .get(me.getId());
+    // getting data (window + earliest lookback) all at once.
+    LOG.info("Getting data for" + slice.toString());
+    DataFrame dfInput = fetchData(me, fetchStart.getMillis(), window.getEndMillis());
+    DataFrame dfCurr = new DataFrame(dfInput).renameSeries(COL_VALUE, COL_CURR);
+    DataFrame dfBase = computePredictionInterval(dfInput, window.getStartMillis(), datasetConfig.getTimezone());
+    DataFrame df = new DataFrame(dfCurr).addSeries(dfBase, COL_VALUE, COL_ERROR);
+    df.addSeries(COL_DIFF, df.getDoubles(COL_CURR).subtract(df.get(COL_VALUE)));
+    df.addSeries(COL_ANOMALY, BooleanSeries.fillValues(df.size(), false));
+
+    // Filter 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.addSeries(COL_DIFF_VIOLATION, map((Series.BooleanFunction) values -> values[0] || values[1],
+//        df.getDoubles(COL_DIFF).gte(df.getDoubles(COL_UP_ERROR)),df.getDoubles(COL_DIFF).lte(df.getDoubles(COL_DOWN_ERROR))));
+    df.mapInPlace(BooleanSeries.ALL_TRUE, COL_ANOMALY, COL_PATTERN, COL_DIFF_VIOLATION);
+
+    // Anomalies
+    List<MergedAnomalyResultDTO> anomalyResults = DetectionUtils.makeAnomalies(slice, df, COL_ANOMALY,
+        window.getEndMillis(),
+        DetectionUtils.getMonitoringGranularityPeriod(timeGranularity.toAggregationGranularityString(),
+            datasetConfig), datasetConfig);
+    dfBase = dfBase.joinRight(df.retainSeries(COL_TIME, COL_CURR), COL_TIME);
+    DetectionResult dr1 = DetectionResult.from(anomalyResults, TimeSeries.fromDataFrame(dfBase));
+    return DetectionResult.from(anomalyResults, TimeSeries.fromDataFrame(dfBase));
+  }
+
+  private DataFrame computePredictionInterval(DataFrame inputDF, long windowStartTime, String timezone) {
+
+    DataFrame resultDF = new DataFrame();
+    //filter the data inside window for current values.
+    DataFrame forecastDF = inputDF.filter(new Series.LongConditional() {
+      @Override
+      public boolean apply(long... values) {
+        return values[0] >= windowStartTime;
+      }
+    }, COL_TIME).dropNull();
+
+    int size = forecastDF.size();
+    double[] baselineArray = new double[size];
+    double[] upperBoundArray = new double[size];
+    double[] lowerBoundArray = new double[size];
+    long[] resultTimeArray = new long[size];
+    double[] errorArray = new double[size];
+    double[] std = new double[size];
+    double[] mean = new double[size];
+
+    //get the trainingDF for each week, which is the number of lookback to 1 week before the each week predict start time
+    for (int k = 0; k < size; k++) {
+      DataFrame trainingDF;
+      trainingDF = getLookbackDF(inputDF, forecastDF.getLong(COL_TIME, k));
+      //the get historical WoW mean and std.
+      std[k]= trainingDF.getDoubles(COL_CHANGE).std().value();
+      mean[k] = trainingDF.getDoubles(COL_CHANGE).mean().value();
+
+      // We need at least 4 weeks of data
+      if (trainingDF.size() < 4) {
+        continue;
+      }
+
+      //calculate baseline, error , upper and lower bound for prediction window.
+      resultTimeArray[k] = forecastDF.getLong(COL_TIME, k);
+      baselineArray[k] = trainingDF.getDouble(COL_VALUE,trainingDF.size()-1) * (1 + mean[k]);
+      errorArray[k] = trainingDF.getDouble(COL_VALUE,trainingDF.size()-1) * sensitivity * std[k];
+      upperBoundArray[k] = baselineArray[k] + errorArray[k];
+      lowerBoundArray[k] = baselineArray[k] - errorArray[k];
+    }
+    //Construct the dataframe.
+    resultDF.addSeries(COL_TIME, LongSeries.buildFrom(resultTimeArray)).setIndex(COL_TIME);
+    resultDF.addSeries(COL_VALUE, DoubleSeries.buildFrom(baselineArray));
+    resultDF.addSeries(COL_UPPER_BOUND, DoubleSeries.buildFrom(upperBoundArray));
+    resultDF.addSeries(COL_LOWER_BOUND, DoubleSeries.buildFrom(lowerBoundArray));
+    resultDF.addSeries(COL_ERROR, DoubleSeries.buildFrom(errorArray));
+    return resultDF;
+  }
+
+  /**
+   * Fetch data from metric
+   *
+   * @param metricEntity metric entity
+   * @param start start timestamp
+   * @param end end timestamp
+   * @return Data Frame that has data from start to end
+   */
+  private DataFrame fetchData(MetricEntity metricEntity, long start, long end) {
+    List<MetricSlice> slices = new ArrayList<>();
+    MetricSlice sliceData = MetricSlice.from(metricEntity.getId(), start, end,
+        metricEntity.getFilters(), timeGranularity);
+    slices.add(sliceData);
+    LOG.info("Getting data for" + sliceData.toString());
+    InputData data = this.dataFetcher.fetchData(new InputDataSpec().withTimeseriesSlices(slices)
+        .withMetricIdsForDataset(Collections.singletonList(metricEntity.getId())));
+    return data.getTimeseries().get(sliceData);
+  }
+
+  /**
+   * Returns a data frame containing lookback number of data before prediction time
+   * @param originalDF the original dataframe
+   * @param time the prediction time, in unix timestamp
+   * @return DataFrame containing lookback number of data
+   */
+
+  private DataFrame getLookbackDF(DataFrame originalDF, Long time) {
+    LongSeries longSeries = (LongSeries) originalDF.get(COL_TIME);
+    int indexFinish = longSeries.find(time);
+    DataFrame df = DataFrame.builder(COL_TIME, COL_VALUE).build();
+
+    if (indexFinish != -1) {
+      int indexStart = Math.max(0, indexFinish - lookback);
+      df = df.append(originalDF.slice(indexStart, indexFinish));
+    }
+    // calculate percentage change
+    df.addSeries(COL_CURR,df.getDoubles(COL_VALUE).shift(-1));
 
 Review comment:
   The `originalDF` might not be weekly granularity. Looks like it's set by the `monitoringGranularity`. Is this calculating WoW percentage change? Shifting by 1 might not get the correct result.
   
   If we are always expecting the `originalDF` to be `7_DAYS` granularity, maybe we shouldn't let it be configurable in the specs.

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