TimeSeriesCatalog.DetectIidChangePoint 메서드

참조

정의

네임스페이스:: Microsoft.ML

어셈블리:: Microsoft.ML.TimeSeries.dll

패키지:: Microsoft.ML.TimeSeries v3.0.1

패키지:: Microsoft.ML.TimeSeries v1.5.5

패키지:: Microsoft.ML.TimeSeries v1.6.0

패키지:: Microsoft.ML.TimeSeries v1.7.0

패키지:: Microsoft.ML.TimeSeries v2.0.0

패키지:: Microsoft.ML.TimeSeries v1.2.0

패키지:: Microsoft.ML.TimeSeries v1.3.1

패키지:: Microsoft.ML.TimeSeries v1.4.0

중요

일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.

오버로드

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)	적응형 커널 밀도 예측 및 martingale 점수를 기반으로 독립적으로 동일하게 분산된(즉, d.d.) 시계열에서 변경 지점을 예측하는 CreateIidChangePointEstimator.
DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)	사용되지 않음. 적응형 커널 밀도 예측 및 martingale 점수를 기반으로 독립적으로 동일하게 분산된(즉, d.d.) 시계열에서 변경 지점을 예측하는 CreateIidChangePointEstimator.

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

적응형 커널 밀도 예측 및 martingale 점수를 기반으로 독립적으로 동일하게 분산된(즉, d.d.) 시계열에서 변경 지점을 예측하는 CreateIidChangePointEstimator.

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

매개 변수

catalog: TransformsCatalog

변환의 카탈로그입니다.

outputColumnName: String

의 변환에서 생성된 열의 inputColumnName이름입니다. 열 데이터는 벡터입니다 Double. 벡터에는 경고(0이 아닌 값은 변경 지점을 의미함), 원시 점수, p-Value 및 martingale 점수의 4가지 요소가 포함됩니다.

inputColumnName: String

변환할 열의 이름입니다. 열 데이터는 이어야 Single합니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.

confidence: Double

[0, 100] 범위의 변경 지점 검색에 대한 신뢰도입니다.

changeHistoryLength: Int32

martingale 점수를 계산하기 위한 p-값의 슬라이딩 윈도우 길이입니다.

martingale: MartingaleType

득점에 사용되는 마팅게일입니다.

eps: Double

Power martingale에 대한 epsilon 매개 변수입니다.

반환

IidChangePointEstimator

예제

// 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;
            }
        }
    }
}

적용 대상

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

주의

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

적응형 커널 밀도 예측 및 martingale 점수를 기반으로 독립적으로 동일하게 분산된(즉, d.d.) 시계열에서 변경 지점을 예측하는 CreateIidChangePointEstimator.

[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

매개 변수

catalog: TransformsCatalog

변환의 카탈로그입니다.

outputColumnName: String

inputColumnName: String

변환할 열의 이름입니다. 열 데이터는 이어야 Single합니다. 이 값으로 null설정하면 해당 값이 outputColumnName 원본으로 사용됩니다.

confidence: Int32

[0, 100] 범위의 변경 지점 검색에 대한 신뢰도입니다.

changeHistoryLength: Int32

martingale 점수를 계산하기 위한 p-값의 슬라이딩 윈도우 길이입니다.

martingale: MartingaleType

득점에 사용되는 마팅게일입니다.

eps: Double

Power martingale에 대한 epsilon 매개 변수입니다.

반환

IidChangePointEstimator

특성: ObsoleteAttribute

예제

// 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;
            }
        }
    }
}

적용 대상

다음을 통해 공유

TimeSeriesCatalog.DetectIidChangePoint 메서드

정의

오버로드

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

매개 변수

반환

예제

적용 대상

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

매개 변수

반환

예제

적용 대상

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