TimeSeriesCatalog.DetectIidChangePoint 方法

參考

定義

命名空間:: Microsoft.ML

組件:: Microsoft.ML.TimeSeries.dll

套件:: Microsoft.ML.TimeSeries v3.0.1

套件:: Microsoft.ML.TimeSeries v1.5.5

套件:: Microsoft.ML.TimeSeries v1.6.0

套件:: Microsoft.ML.TimeSeries v1.7.0

套件:: Microsoft.ML.TimeSeries v2.0.0

套件:: Microsoft.ML.TimeSeries v1.2.0

套件:: Microsoft.ML.TimeSeries v1.3.1

套件:: Microsoft.ML.TimeSeries v1.4.0

重要

部分資訊涉及發行前產品，在發行之前可能會有大幅修改。 Microsoft 對此處提供的資訊，不做任何明確或隱含的瑕疵擔保。

多載

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)	建立 IidChangePointEstimator ，其會根據調適性核心密度估計和 martingale 分數，預測獨立相同分佈 (i.d.) 時間序列中的變更點。
DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)	已淘汰. 建立 IidChangePointEstimator ，其會根據調適性核心密度估計和 martingale 分數，預測獨立相同分佈 (i.d.) 時間序列中的變更點。

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

建立 IidChangePointEstimator ，其會根據調適性核心密度估計和 martingale 分數，預測獨立相同分佈 (i.d.) 時間序列中的變更點。

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

參數

catalog: TransformsCatalog

轉換的目錄。

outputColumnName: String

轉換所產生的 inputColumnName 資料行名稱。資料行資料是的 Double 向量。向量包含 4 個元素：警示 (非零值表示變更點) 、原始分數、p 值和 martingale 分數。

inputColumnName: String

要轉換的資料行名稱。資料行資料必須是 Single 。如果設定為 null ，則會將的值 outputColumnName 當做來源使用。

confidence: Double

範圍 [0， 100] 中變更點偵測的信賴度。

changeHistoryLength: Int32

計算 martingale 分數之 p 值上的滑動視窗長度。

martingale: MartingaleType

用於評分的 martingale。

eps: Double

Power martingale 的 epsilon 參數。

傳回

IidChangePointEstimator

範例

// 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;
            }
        }
    }
}

適用於

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

警告

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