TimeSeriesCatalog.DetectIidSpike 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

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)	建立 IidSpikeEstimator ，其會根據自適性核心密度估計和 martingale 分數，預測獨立分佈 (i.d.) 時間序列的尖峰。
DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)	已淘汰. 建立 IidSpikeEstimator ，其會根據自適性核心密度估計和 martingale 分數，預測獨立分佈 (i.d.) 時間序列的尖峰。

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

建立 IidSpikeEstimator ，其會根據自適性核心密度估計和 martingale 分數，預測獨立分佈 (i.d.) 時間序列的尖峰。

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

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 3 個元素：警示 (非零值表示尖峰) 、原始分數和 p 值。

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] 中尖峰偵測的信賴度。

pvalueHistoryLength: Int32

計算 p 值之滑動視窗的大小。

side: AnomalySide

判斷是否要偵測正面或負面異常的引數，或兩者。

傳回

IidSpikeEstimator

範例

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; }
        }
    }
}

適用於

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

警告

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