TimeSeriesCatalog.DetectIidChangePoint 方法

参考

定义

命名空间:: Microsoft.ML

程序集:: Microsoft.ML.TimeSeries.dll

包:: Microsoft.ML.TimeSeries v3.0.1

包:: Microsoft.ML.TimeSeries v1.5.5

包:: Microsoft.ML.TimeSeries v1.6.0

包:: Microsoft.ML.TimeSeries v1.7.0

包:: Microsoft.ML.TimeSeries v2.0.0

包:: Microsoft.ML.TimeSeries v1.2.0

包:: Microsoft.ML.TimeSeries v1.3.1

包:: Microsoft.ML.TimeSeries v1.4.0

重要

一些信息与预发行产品相关，相应产品在发行之前可能会进行重大修改。对于此处提供的信息，Microsoft 不作任何明示或暗示的担保。

重载

DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)	创建 IidChangePointEstimator，它基于自适应内核密度估计和马丁加尔分数预测独立相同分布 (i.i.d.) 时序中的更改点。
DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)	已过时. 创建 IidChangePointEstimator，它基于自适应内核密度估计和马丁加尔分数预测独立相同分布 (i.i.d.) 时序中的更改点。

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

创建 IidChangePointEstimator，它基于自适应内核密度估计和马丁加尔分数预测独立相同分布 (i.i.d.) 时序中的更改点。

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

参数

catalog: TransformsCatalog

转换的目录。

outputColumnName: String

由转换 inputColumnName生成的列的名称。列数据是一个向量 Double。矢量包含 4 个元素：警报 (非零值表示更改点) 、原始分数、p 值和马丁加尔分数。

inputColumnName: String

要转换的列的名称。列数据必须是 Single。 If set to null, the value of the outputColumnName will be used as source.

confidence: Double

[0， 100] 范围内更改点检测的置信度。

changeHistoryLength: Int32

用于计算马丁加尔分数的 p 值上的滑动窗口的长度。

martingale: MartingaleType

用于评分的马丁格尔。

eps: Double

Power martingale 的 epsilon 参数。

IidChangePointEstimator

示例

// 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;
            }
        }
    }
}

适用于

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

注意

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