TimeSeriesCatalog.DetectEntireAnomalyBySrCnn 方法
定義
重要
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多載
| 名稱 | Description |
|---|---|
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions) |
創建 Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector，利用 SRCNN 演算法偵測整個輸入的時間序列異常。 |
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode) |
創建 Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector，利用 SRCNN 演算法偵測整個輸入的時間序列異常。 |
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions)
創建 Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector，利用 SRCNN 演算法偵測整個輸入的時間序列異常。
public static Microsoft.ML.IDataView DetectEntireAnomalyBySrCnn(this Microsoft.ML.AnomalyDetectionCatalog catalog, Microsoft.ML.IDataView input, string outputColumnName, string inputColumnName, Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetectorOptions options);static member DetectEntireAnomalyBySrCnn : Microsoft.ML.AnomalyDetectionCatalog * Microsoft.ML.IDataView * string * string * Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetectorOptions -> Microsoft.ML.IDataView<Extension()>
Public Function DetectEntireAnomalyBySrCnn (catalog As AnomalyDetectionCatalog, input As IDataView, outputColumnName As String, inputColumnName As String, options As SrCnnEntireAnomalyDetectorOptions) As IDataView參數
- catalog
- AnomalyDetectionCatalog
異常偵測目錄。
- input
- IDataView
輸入 DataView。
- outputColumnName
- String
由資料 inputColumnName處理所得欄位名稱。
欄位資料是 的 Double向量。 此向量的長度會根據 options.DetectMode.DetectMode而變化。
定義負載操作的設定。
傳回
範例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.TimeSeries;
namespace Samples.Dynamic
{
public static class DetectEntireAnomalyBySrCnn
{
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 an anomaly
var data = new List<TimeSeriesData>();
for (int index = 0; index < 20; index++)
{
data.Add(new TimeSeriesData { Value = 5 });
}
data.Add(new TimeSeriesData { Value = 10 });
for (int index = 0; index < 5; index++)
{
data.Add(new TimeSeriesData { Value = 5 });
}
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup the detection arguments
string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// Do batch anomaly detection
var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName,
threshold: 0.35, batchSize: 512, sensitivity: 90.0, detectMode: SrCnnDetectMode.AnomalyAndMargin);
// Getting the data of the newly created column as an IEnumerable of
// SrCnnAnomalyDetection.
var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
outputDataView, reuseRowObject: false);
Console.WriteLine("Index\tData\tAnomaly\tAnomalyScore\tMag\tExpectedValue\tBoundaryUnit\tUpperBoundary\tLowerBoundary");
int k = 0;
foreach (var prediction in predictionColumn)
{
PrintPrediction(k, data[k].Value, prediction);
k++;
}
//Index Data Anomaly AnomalyScore Mag ExpectedValue BoundaryUnit UpperBoundary LowerBoundary
//0 5.00 0 0.00 0.21 5.00 5.00 5.01 4.99
//1 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//2 5.00 0 0.00 0.03 5.00 5.00 5.01 4.99
//3 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//4 5.00 0 0.00 0.03 5.00 5.00 5.01 4.99
//5 5.00 0 0.00 0.06 5.00 5.00 5.01 4.99
//6 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//7 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//8 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//9 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//10 5.00 0 0.00 0.00 5.00 5.00 5.01 4.99
//11 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//12 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//13 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//14 5.00 0 0.00 0.07 5.00 5.00 5.01 4.99
//15 5.00 0 0.00 0.08 5.00 5.00 5.01 4.99
//16 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//17 5.00 0 0.00 0.05 5.00 5.00 5.01 4.99
//18 5.00 0 0.00 0.12 5.00 5.00 5.01 4.99
//19 5.00 0 0.00 0.17 5.00 5.00 5.01 4.99
//20 10.00 1 0.50 0.80 5.00 5.00 5.01 4.99
//21 5.00 0 0.00 0.16 5.00 5.00 5.01 4.99
//22 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//23 5.00 0 0.00 0.05 5.00 5.00 5.01 4.99
//24 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//25 5.00 0 0.00 0.19 5.00 5.00 5.01 4.99
}
private static void PrintPrediction(int idx, double value, SrCnnAnomalyDetection prediction) =>
Console.WriteLine("{0}\t{1:0.00}\t{2}\t\t{3:0.00}\t{4:0.00}\t\t{5:0.00}\t\t{6:0.00}\t\t{7:0.00}\t\t{8:0.00}",
idx, value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2],
prediction.Prediction[3], prediction.Prediction[4], prediction.Prediction[5], prediction.Prediction[6]);
private class TimeSeriesData
{
public double Value { get; set; }
}
private class SrCnnAnomalyDetection
{
[VectorType]
public double[] Prediction { get; set; }
}
}
}
適用於
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode)
創建 Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector，利用 SRCNN 演算法偵測整個輸入的時間序列異常。
public static Microsoft.ML.IDataView DetectEntireAnomalyBySrCnn(this Microsoft.ML.AnomalyDetectionCatalog catalog, Microsoft.ML.IDataView input, string outputColumnName, string inputColumnName, double threshold = 0.3, int batchSize = 1024, double sensitivity = 99, Microsoft.ML.TimeSeries.SrCnnDetectMode detectMode = Microsoft.ML.TimeSeries.SrCnnDetectMode.AnomalyOnly);static member DetectEntireAnomalyBySrCnn : Microsoft.ML.AnomalyDetectionCatalog * Microsoft.ML.IDataView * string * string * double * int * double * Microsoft.ML.TimeSeries.SrCnnDetectMode -> Microsoft.ML.IDataView<Extension()>
Public Function DetectEntireAnomalyBySrCnn (catalog As AnomalyDetectionCatalog, input As IDataView, outputColumnName As String, inputColumnName As String, Optional threshold As Double = 0.3, Optional batchSize As Integer = 1024, Optional sensitivity As Double = 99, Optional detectMode As SrCnnDetectMode = Microsoft.ML.TimeSeries.SrCnnDetectMode.AnomalyOnly) As IDataView參數
- catalog
- AnomalyDetectionCatalog
異常偵測目錄。
- input
- IDataView
輸入 DataView。
- threshold
- Double
判斷異常的門檻。 當某點的SR原始分數超過設定閾值時，即偵測異常。 此閾值必須介於 [0,1] 之間，其預設值為 0.3。
- batchSize
- Int32
將輸入資料分成批次以符合 SRNN 模型。 設為 -1 時，使用整個輸入擬合模型，而非批次擬合;設為正整數時，使用此數值作為批次大小。 必須是 -1 或不少於12的正整數。 預設值是1024。
- sensitivity
- Double
邊界敏感度，僅在 srCnnDetectMode 是 AnomalyAndMargin 時才有用。 必須在 [0,100] 裡。 預設值是99。
- detectMode
- SrCnnDetectMode
一種列舉型的 SrCnnDetectMode。 當設定為 AnomalyOnly，輸出向量將是 （IsAnomaly、RawScore、Mag） 的三元素雙向量。 當設定為 AnomalyAndExpectedValue 時，輸出向量會是 （IsAnomaly， RawScore， Mag， ExpectedValue 的 4 元素雙向量）。 當設定為 AnomalyAndMargin 時，輸出向量將是 （IsAnomaly、AnomalyScore、Mag、ExpectedValue、BoundaryUnit、UpperBoundary、LowerBoundary 的 7 個元素雙向量）。 RawScore 由 SR 輸出，用以判斷某點是否為異常，在 AnomalyAndMargin 模式下，當點為異常時，會依敏感度設定計算 AnomalyScore。 預設值為 AnomalyOnly。
傳回
範例
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.TimeSeries;
namespace Samples.Dynamic
{
public static class DetectEntireAnomalyBySrCnn
{
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 an anomaly
var data = new List<TimeSeriesData>();
for (int index = 0; index < 20; index++)
{
data.Add(new TimeSeriesData { Value = 5 });
}
data.Add(new TimeSeriesData { Value = 10 });
for (int index = 0; index < 5; index++)
{
data.Add(new TimeSeriesData { Value = 5 });
}
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup the detection arguments
string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// Do batch anomaly detection
var outputDataView = ml.AnomalyDetection.DetectEntireAnomalyBySrCnn(dataView, outputColumnName, inputColumnName,
threshold: 0.35, batchSize: 512, sensitivity: 90.0, detectMode: SrCnnDetectMode.AnomalyAndMargin);
// Getting the data of the newly created column as an IEnumerable of
// SrCnnAnomalyDetection.
var predictionColumn = ml.Data.CreateEnumerable<SrCnnAnomalyDetection>(
outputDataView, reuseRowObject: false);
Console.WriteLine("Index\tData\tAnomaly\tAnomalyScore\tMag\tExpectedValue\tBoundaryUnit\tUpperBoundary\tLowerBoundary");
int k = 0;
foreach (var prediction in predictionColumn)
{
PrintPrediction(k, data[k].Value, prediction);
k++;
}
//Index Data Anomaly AnomalyScore Mag ExpectedValue BoundaryUnit UpperBoundary LowerBoundary
//0 5.00 0 0.00 0.21 5.00 5.00 5.01 4.99
//1 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//2 5.00 0 0.00 0.03 5.00 5.00 5.01 4.99
//3 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//4 5.00 0 0.00 0.03 5.00 5.00 5.01 4.99
//5 5.00 0 0.00 0.06 5.00 5.00 5.01 4.99
//6 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//7 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//8 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//9 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//10 5.00 0 0.00 0.00 5.00 5.00 5.01 4.99
//11 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//12 5.00 0 0.00 0.01 5.00 5.00 5.01 4.99
//13 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//14 5.00 0 0.00 0.07 5.00 5.00 5.01 4.99
//15 5.00 0 0.00 0.08 5.00 5.00 5.01 4.99
//16 5.00 0 0.00 0.02 5.00 5.00 5.01 4.99
//17 5.00 0 0.00 0.05 5.00 5.00 5.01 4.99
//18 5.00 0 0.00 0.12 5.00 5.00 5.01 4.99
//19 5.00 0 0.00 0.17 5.00 5.00 5.01 4.99
//20 10.00 1 0.50 0.80 5.00 5.00 5.01 4.99
//21 5.00 0 0.00 0.16 5.00 5.00 5.01 4.99
//22 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//23 5.00 0 0.00 0.05 5.00 5.00 5.01 4.99
//24 5.00 0 0.00 0.11 5.00 5.00 5.01 4.99
//25 5.00 0 0.00 0.19 5.00 5.00 5.01 4.99
}
private static void PrintPrediction(int idx, double value, SrCnnAnomalyDetection prediction) =>
Console.WriteLine("{0}\t{1:0.00}\t{2}\t\t{3:0.00}\t{4:0.00}\t\t{5:0.00}\t\t{6:0.00}\t\t{7:0.00}\t\t{8:0.00}",
idx, value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2],
prediction.Prediction[3], prediction.Prediction[4], prediction.Prediction[5], prediction.Prediction[6]);
private class TimeSeriesData
{
public double Value { get; set; }
}
private class SrCnnAnomalyDetection
{
[VectorType]
public double[] Prediction { get; set; }
}
}
}