CausalAnalysis

Introduction

Algorithmic implementation of indicators contribution analysis, causal inference using Java. Helps to quickly find the root cause of indicators

Feature

Its main feature are as follows:

Supported:

• Rapid localisation of the root cause dimension by JS scatter calculation.
• Supports quadratic contribution disaggregation of metrics to quickly locate key factors.

Plan:

• relevance analysis.
• causal inference.

Demos

• CausalAnalysis-Demos

For Developers

Using ad4j

• add to maven pom:

<dependency>
    <groupId>org.algorithmtools</groupId>
    <artifactId>ca4j</artifactId>
    <version>${version}</version>
    <scope>system</scope>
    <systemPath>${project.basedir}/lib/ca4j-${version}.jar</systemPath>
</dependency>

• business use: Example of the test module under the example package

public class ContributionAnalysisExample {

  public static void main(String[] args) {
    // 1. Transfer biz data to indicator series info
    long currentTime = System.currentTimeMillis();
    List<IndicatorSeries> indicatorSeriesX0 = Arrays.asList(new IndicatorSeries(currentTime - 86400000 + 1, 1, "A")
            , new IndicatorSeries(currentTime - 86400000 + 2, 2, "B")
            , new IndicatorSeries(currentTime - 86400000 + 3, 3, "C")
            , new IndicatorSeries(currentTime - 86400000 + 4, 6, "D")
            , new IndicatorSeries(currentTime - 86400000 + 5, 5, "E")
    );
    List<IndicatorSeries> indicatorSeriesX1 = Arrays.asList(new IndicatorSeries(currentTime + 1, 1, "A")
            , new IndicatorSeries(currentTime + 2, 1.5, "B")
            , new IndicatorSeries(currentTime + 3, 3, "C")
            , new IndicatorSeries(currentTime + 4, 8, "D")
            , new IndicatorSeries(currentTime + 5, 3, "E")
    );
    IndicatorPairSeries series = new IndicatorPairSeries("i-1", "i-1-name", IndicatorStatType.Unique_Continuity, indicatorSeriesX1, indicatorSeriesX0);

    // 2. Get a PlusContributionAnalysisEngin object
    // PlusContributionAnalysisEngin the calculation of contributions to indicators of the additive/subtractive type.
    // MultiplyContributionAnalysisEngin the calculation of the contribution of indicators to the multiplication type.
    // DivisionContributionAnalysisEngin the calculation of the contribution to the indicators of division type
    PlusContributionAnalysisEngin engin = new PlusContributionAnalysisEngin(CausalAnalysisContext.createDefault());

    // 3. analyse
    ContributionResult result = engin.analyse(series);

    // 4. Business process analysis result. Like Records,Alarms,Print,Deep analysis...
    System.out.println(result);
  }

}

Print result:

Overview:17.0-->16.5	ChangeValue(-0.5)	ChangeRate(-0.029411764705882353)
FactorTermContribution:
FactorTerm:A	1.0-->1.0	ChangeValue(ChangeRate):0.0(0.0)	ContributionValue(ContributionRate):0.0(0.0)	 ContributionProportion:0.0
FactorTerm:B	2.0-->1.5	ChangeValue(ChangeRate):-0.5(-0.25)	ContributionValue(ContributionRate):-0.5(-0.029411764705882353)	 ContributionProportion:0.1111111111111111
FactorTerm:C	3.0-->3.0	ChangeValue(ChangeRate):0.0(0.0)	ContributionValue(ContributionRate):0.0(0.0)	 ContributionProportion:0.0
FactorTerm:D	6.0-->8.0	ChangeValue(ChangeRate):2.0(0.3333333333333333)	ContributionValue(ContributionRate):2.0(0.11764705882352941)	 ContributionProportion:0.4444444444444444
FactorTerm:E	5.0-->3.0	ChangeValue(ChangeRate):-2.0(-0.4)	ContributionValue(ContributionRate):-2.0(-0.11764705882352941)	 ContributionProportion:0.4444444444444444
Contribution Sum:-0.5(-0.02941176470588236)

Participate in Contributions

Welcome to join the community, build a win-win situation, please refer to the contribution process： How to contribute.

Thank you to all the people who already contributed to CausalAnalysis!

Get Help

• Create an issue and describe it clearly.