TimeSeriesCatalog.LocalizeRootCause Método

Definição

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.TimeSeries.dll

Pacote:

Microsoft.ML.TimeSeries v3.0.1

Criar RootCause, que localiza causas raiz usando o algoritmo de árvore de decisão.

public static Microsoft.ML.TimeSeries.RootCause LocalizeRootCause (this Microsoft.ML.AnomalyDetectionCatalog catalog, Microsoft.ML.TimeSeries.RootCauseLocalizationInput src, double beta = 0.3, double rootCauseThreshold = 0.95);

static member LocalizeRootCause : Microsoft.ML.AnomalyDetectionCatalog * Microsoft.ML.TimeSeries.RootCauseLocalizationInput * double * double -> Microsoft.ML.TimeSeries.RootCause

<Extension()>
Public Function LocalizeRootCause (catalog As AnomalyDetectionCatalog, src As RootCauseLocalizationInput, Optional beta As Double = 0.3, Optional rootCauseThreshold As Double = 0.95) As RootCause

Parâmetros

catalog

AnomalyDetectionCatalog

O catálogo de detecção de anomalias.

src

RootCauseLocalizationInput

Entrada da causa raiz. Os dados são uma instância de RootCauseLocalizationInput.

beta

Double

Beta é um parâmetro de peso para o usuário escolher. Ela é usada quando a pontuação é calculada para cada item de causa raiz. O intervalo de beta deve estar em [0,1]. Para um beta maior, os itens de causa raiz que têm uma grande diferença entre o valor e o valor esperado obterão uma pontuação alta. Para um beta pequeno, os itens de causa raiz que têm uma alteração relativa alta obterão uma pontuação baixa.

rootCauseThreshold

Double

Um limite para determinar se o ponto deve ser a causa raiz. O intervalo desse limite deve estar em [0,1]. Se o delta do ponto for igual ou maior que rootCauseThreshold multiplicado pelo delta do ponto de dimensão de anomalias, esse ponto será tratado como uma causa raiz. Um limite diferente resultará em resultados diferentes. Os usuários podem escolher o delta de acordo com seus dados e exigências.

Retornos

RootCause

Exemplos

using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.TimeSeries;

namespace Samples.Dynamic
{
    public static class LocalizeRootCause
    {
        // In the root cause detection input, this string identifies an aggregation as opposed to a dimension value"
        private static string AGG_SYMBOL = "##SUM##";
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Create an root cause localization input instance.
            DateTime timestamp = GetTimestamp();
            var data = new RootCauseLocalizationInput(timestamp, GetAnomalyDimension(), new List<MetricSlice>() { new MetricSlice(timestamp, GetPoints()) }, AggregateType.Sum, AGG_SYMBOL);

            // Get the root cause localization result.
            RootCause prediction = mlContext.AnomalyDetection.LocalizeRootCause(data);

            // Print the localization result.
            int count = 0;
            foreach (RootCauseItem item in prediction.Items)
            {
                count++;
                Console.WriteLine($"Root cause item #{count} ...");
                Console.WriteLine($"Score: {item.Score}, Path: {String.Join(" ", item.Path)}, Direction: {item.Direction}, Dimension:{String.Join(" ", item.Dimension)}");
            }

            //Item #1 ...
            //Score: 0.26670448876705927, Path: DataCenter, Direction: Up, Dimension:[Country, UK] [DeviceType, ##SUM##] [DataCenter, DC1]
        }

        private static List<TimeSeriesPoint> GetPoints()
        {
            List<TimeSeriesPoint> points = new List<TimeSeriesPoint>();

            Dictionary<string, Object> dic1 = new Dictionary<string, Object>();
            dic1.Add("Country", "UK");
            dic1.Add("DeviceType", "Laptop");
            dic1.Add("DataCenter", "DC1");
            points.Add(new TimeSeriesPoint(200, 100, true, dic1));

            Dictionary<string, Object> dic2 = new Dictionary<string, Object>();
            dic2.Add("Country", "UK");
            dic2.Add("DeviceType", "Mobile");
            dic2.Add("DataCenter", "DC1");
            points.Add(new TimeSeriesPoint(1000, 100, true, dic2));

            Dictionary<string, Object> dic3 = new Dictionary<string, Object>();
            dic3.Add("Country", "UK");
            dic3.Add("DeviceType", AGG_SYMBOL);
            dic3.Add("DataCenter", "DC1");
            points.Add(new TimeSeriesPoint(1200, 200, true, dic3));

            Dictionary<string, Object> dic4 = new Dictionary<string, Object>();
            dic4.Add("Country", "UK");
            dic4.Add("DeviceType", "Laptop");
            dic4.Add("DataCenter", "DC2");
            points.Add(new TimeSeriesPoint(100, 100, false, dic4));

            Dictionary<string, Object> dic5 = new Dictionary<string, Object>();
            dic5.Add("Country", "UK");
            dic5.Add("DeviceType", "Mobile");
            dic5.Add("DataCenter", "DC2");
            points.Add(new TimeSeriesPoint(200, 200, false, dic5));

            Dictionary<string, Object> dic6 = new Dictionary<string, Object>();
            dic6.Add("Country", "UK");
            dic6.Add("DeviceType", AGG_SYMBOL);
            dic6.Add("DataCenter", "DC2");
            points.Add(new TimeSeriesPoint(300, 300, false, dic6));

            Dictionary<string, Object> dic7 = new Dictionary<string, Object>();
            dic7.Add("Country", "UK");
            dic7.Add("DeviceType", AGG_SYMBOL);
            dic7.Add("DataCenter", AGG_SYMBOL);
            points.Add(new TimeSeriesPoint(1500, 500, true, dic7));

            Dictionary<string, Object> dic8 = new Dictionary<string, Object>();
            dic8.Add("Country", "UK");
            dic8.Add("DeviceType", "Laptop");
            dic8.Add("DataCenter", AGG_SYMBOL);
            points.Add(new TimeSeriesPoint(300, 200, true, dic8));

            Dictionary<string, Object> dic9 = new Dictionary<string, Object>();
            dic9.Add("Country", "UK");
            dic9.Add("DeviceType", "Mobile");
            dic9.Add("DataCenter", AGG_SYMBOL);
            points.Add(new TimeSeriesPoint(1200, 300, true, dic9));

            return points;
        }

        private static Dictionary<string, Object> GetAnomalyDimension()
        {
            Dictionary<string, Object> dim = new Dictionary<string, Object>();
            dim.Add("Country", "UK");
            dim.Add("DeviceType", AGG_SYMBOL);
            dim.Add("DataCenter", AGG_SYMBOL);

            return dim;
        }

        private static DateTime GetTimestamp()
        {
            return new DateTime(2020, 3, 23, 0, 0, 0);
        }
    }
}