TimeSeriesCatalog.DetectIidSpike 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

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)	Create IidSpikeEstimator，它根据自适应内核密度估计和马丁加尔分数预测独立分布 (i.i.d.) 时序中的峰值。
DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)	已过时. Create IidSpikeEstimator，它根据自适应内核密度估计和马丁加尔分数预测独立分布 (i.i.d.) 时序中的峰值。

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)

Create IidSpikeEstimator，它根据自适应内核密度估计和马丁加尔分数预测独立分布 (i.i.d.) 时序中的峰值。

public static Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator DetectIidSpike (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int pvalueHistoryLength, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided);

static member DetectIidSpike : Microsoft.ML.TransformsCatalog * string * string * double * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide -> Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator

<Extension()>
Public Function DetectIidSpike (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, pvalueHistoryLength As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided) As IidSpikeEstimator

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 3 个元素：警报 (非零值表示峰值) 、原始分数和 p 值。

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] 范围内的峰值检测置信度。

pvalueHistoryLength: Int32

用于计算 p 值的滑动窗口的大小。

side: AnomalySide

确定是检测正异常还是负异常还是同时检测两者的参数。

IidSpikeEstimator

示例

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

namespace Samples.Dynamic
{
    public static class DetectIidSpikeBatchPrediction
    {
        // 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 spiking points in the series.
        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 spike
            const int Size = 10;
            var data = new List<TimeSeriesData>(Size + 1)
            {
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),

                // This is a spike.
                new TimeSeriesData(10),

                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
                new TimeSeriesData(5),
            };

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

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

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

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

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

            Console.WriteLine("Data\tAlert\tScore\tP-Value");

            int k = 0;
            foreach (var prediction in predictionColumn)
                PrintPrediction(data[k++].Value, prediction);

            // Prediction column obtained post-transformation.
            // Data    Alert   Score P-Value
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
            // 10      1       10.00   0.00   <-- alert is on, predicted spike
            // 5       0       5.00    0.26
            // 5       0       5.00    0.26
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
            // 5       0       5.00    0.50
        }

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

        class TimeSeriesData
        {
            public float Value;

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

        class IidSpikePrediction
        {
            [VectorType(3)]
            public double[] Prediction { get; set; }
        }
    }
}

适用于

DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)

注意

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