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TimeSeriesCatalog.DetectIidChangePoint Méthode

Définition

Surcharges

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)

Créez IidChangePointEstimator, qui prédit les points de modification dans une série chronologique distribuée de manière indépendante (i.i.d.) en fonction des estimations de densité de noyau adaptative et des scores martingale.

DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)
Obsolète.

Créez IidChangePointEstimator, qui prédit les points de modification dans une série chronologique distribuée de manière indépendante (i.i.d.) en fonction des estimations de densité de noyau adaptative et des scores martingale.

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)

Créez IidChangePointEstimator, qui prédit les points de modification dans une série chronologique distribuée de manière indépendante (i.i.d.) en fonction des estimations de densité de noyau adaptative et des scores martingale.

public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);
static member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * double * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator
<Extension()>
Public Function DetectIidChangePoint (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, changeHistoryLength As Integer, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As IidChangePointEstimator

Paramètres

catalog
TransformsCatalog

Catalogue de la transformation.

outputColumnName
String

Nom de la colonne résultant de la transformation de inputColumnName. Les données de colonne sont un vecteur de Double. Le vecteur contient 4 éléments : alerte (valeur non nulle signifie un point de modification), score brut, valeur p et score martingale.

inputColumnName
String

Nom de la colonne à transformer. Les données de colonne doivent être Single. Si elle est définie sur null, la valeur du outputColumnName fichier sera utilisée comme source.

confidence
Double

Confiance pour la détection des points de modification dans la plage [0, 100].

changeHistoryLength
Int32

Longueur de la fenêtre glissante sur les valeurs p pour calculer le score martingale.

martingale
MartingaleType

Martingale utilisé pour le scoring.

eps
Double

Paramètre epsilon pour Power martingale.

Retours

Exemples

// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

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

namespace Samples.Dynamic
{
    public static class DetectIidChangePointBatchPrediction
    {
        // This example creates a time series (list of Data with the i-th element
        // corresponding to the i-th time slot). The estimator is applied then to
        // identify points where data distribution changed.
        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 ml = new MLContext();

            // Generate sample series data with a change
            const int Size = 16;
            var data = new List<TimeSeriesData>(Size)
            {
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),

                //Change point data.
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
            };

            // Convert data to IDataView.
            var dataView = ml.Data.LoadFromEnumerable(data);

            // Setup estimator arguments
            string outputColumnName = nameof(ChangePointPrediction.Prediction);
            string inputColumnName = nameof(TimeSeriesData.Value);

            // The transformed data.
            var transformedData = ml.Transforms.DetectIidChangePoint(
                outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView)
                .Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of
            // ChangePointPrediction.
            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
                transformedData, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained " +
                $"post-transformation.");

            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
            int k = 0;
            foreach (var prediction in predictionColumn)
                PrintPrediction(data[k++].Value, prediction);

            // Prediction column obtained post-transformation.
            // Data Alert      Score   P-Value Martingale value
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint
            // 7       0       7.00    0.13    33950.16
            // 7       0       7.00    0.26    60866.34
            // 7       0       7.00    0.38    78362.04
            // 7       0       7.00    0.50    0.01
            // 7       0       7.00    0.50    0.00
            // 7       0       7.00    0.50    0.00
            // 7       0       7.00    0.50    0.00
        }

        private static void PrintPrediction(float value, ChangePointPrediction
            prediction) =>
            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value,
            prediction.Prediction[0], prediction.Prediction[1],
            prediction.Prediction[2], prediction.Prediction[3]);

        class ChangePointPrediction
        {
            [VectorType(4)]
            public double[] Prediction { get; set; }
        }

        class TimeSeriesData
        {
            public float Value;

            public TimeSeriesData(float value)
            {
                Value = value;
            }
        }
    }
}

S’applique à

DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)

Attention

This API method is deprecated, please use the overload with confidence parameter of type double.

Créez IidChangePointEstimator, qui prédit les points de modification dans une série chronologique distribuée de manière indépendante (i.i.d.) en fonction des estimations de densité de noyau adaptative et des scores martingale.

[System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);
public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);
[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator
static member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator
<Extension()>
Public Function DetectIidChangePoint (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, changeHistoryLength As Integer, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As IidChangePointEstimator

Paramètres

catalog
TransformsCatalog

Catalogue de la transformation.

outputColumnName
String

Nom de la colonne résultant de la transformation de inputColumnName. Les données de colonne sont un vecteur de Double. Le vecteur contient 4 éléments : alerte (valeur non nulle signifie un point de modification), score brut, valeur p et score martingale.

inputColumnName
String

Nom de la colonne à transformer. Les données de colonne doivent être Single. Si elle est définie sur null, la valeur du outputColumnName fichier sera utilisée comme source.

confidence
Int32

Confiance pour la détection des points de modification dans la plage [0, 100].

changeHistoryLength
Int32

Longueur de la fenêtre glissante sur les valeurs p pour calculer le score martingale.

martingale
MartingaleType

Martingale utilisé pour le scoring.

eps
Double

Paramètre epsilon pour Power martingale.

Retours

Attributs

Exemples

// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.

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

namespace Samples.Dynamic
{
    public static class DetectIidChangePointBatchPrediction
    {
        // This example creates a time series (list of Data with the i-th element
        // corresponding to the i-th time slot). The estimator is applied then to
        // identify points where data distribution changed.
        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 ml = new MLContext();

            // Generate sample series data with a change
            const int Size = 16;
            var data = new List<TimeSeriesData>(Size)
            {
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),

                //Change point data.
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
                new TimeSeriesData(7),
            };

            // Convert data to IDataView.
            var dataView = ml.Data.LoadFromEnumerable(data);

            // Setup estimator arguments
            string outputColumnName = nameof(ChangePointPrediction.Prediction);
            string inputColumnName = nameof(TimeSeriesData.Value);

            // The transformed data.
            var transformedData = ml.Transforms.DetectIidChangePoint(
                outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView)
                .Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of
            // ChangePointPrediction.
            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
                transformedData, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained " +
                $"post-transformation.");

            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
            int k = 0;
            foreach (var prediction in predictionColumn)
                PrintPrediction(data[k++].Value, prediction);

            // Prediction column obtained post-transformation.
            // Data Alert      Score   P-Value Martingale value
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 5       0       5.00    0.50    0.00
            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint
            // 7       0       7.00    0.13    33950.16
            // 7       0       7.00    0.26    60866.34
            // 7       0       7.00    0.38    78362.04
            // 7       0       7.00    0.50    0.01
            // 7       0       7.00    0.50    0.00
            // 7       0       7.00    0.50    0.00
            // 7       0       7.00    0.50    0.00
        }

        private static void PrintPrediction(float value, ChangePointPrediction
            prediction) =>
            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value,
            prediction.Prediction[0], prediction.Prediction[1],
            prediction.Prediction[2], prediction.Prediction[3]);

        class ChangePointPrediction
        {
            [VectorType(4)]
            public double[] Prediction { get; set; }
        }

        class TimeSeriesData
        {
            public float Value;

            public TimeSeriesData(float value)
            {
                Value = value;
            }
        }
    }
}

S’applique à