TimeSeriesCatalog.DetectEntireAnomalyBySrCnn Metode
Definisi
Penting
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Overload
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions) |
Buat Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, yang mendeteksi anomali timeseries untuk seluruh input menggunakan algoritma SRCNN. |
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode) |
Buat Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, yang mendeteksi anomali timeseries untuk seluruh input menggunakan algoritma SRCNN. |
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions)
Buat Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, yang mendeteksi anomali timeseries untuk seluruh input menggunakan algoritma SRCNN.
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 IDataViewParameter
- catalog
- AnomalyDetectionCatalog
The AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Nama kolom yang dihasilkan dari pemrosesan inputColumnNamedata .
Data kolom adalah vektor dari Double. Panjang vektor ini bervariasi tergantung pada options.DetectMode.DetectMode.
Menentukan pengaturan operasi pemuatan.
Mengembalikan
Contoh
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; }
}
}
}
Berlaku untuk
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode)
Buat Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, yang mendeteksi anomali timeseries untuk seluruh input menggunakan algoritma SRCNN.
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 IDataViewParameter
- catalog
- AnomalyDetectionCatalog
The AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Nama kolom yang dihasilkan dari pemrosesan inputColumnNamedata .
Data kolom adalah vektor dari Double. Panjang vektor ini bervariasi tergantung pada detectMode.
- threshold
- Double
Ambang batas untuk menentukan anomali. Anomali terdeteksi ketika skor mentah SR yang dihitung untuk titik tertentu lebih dari ambang yang ditetapkan. Ambang batas ini harus berada di antara [0,1], dan nilai defaultnya adalah 0,3.
- batchSize
- Int32
Bagi data input menjadi batch agar sesuai dengan model srcnn. Saat diatur ke -1, gunakan seluruh input untuk menyesuaikan model alih-alih batch menurut batch, saat diatur ke bilangan bulat positif, gunakan angka ini sebagai ukuran batch. Harus -1 atau bilangan bulat positif tidak kurang dari 12. Nilai defaultnya adalah 1024.
- sensitivity
- Double
Sensitivitas batasan, hanya berguna ketika srCnnDetectMode adalah AnomalyAndMargin. Harus dalam [0,100]. Nilai defaultnya adalah 99.
- detectMode
- SrCnnDetectMode
Jenis enum .SrCnnDetectMode Ketika diatur ke AnomalyOnly, vektor output akan menjadi vektor Double 3 elemen (IsAnomaly, RawScore, Mag). Ketika diatur ke AnomalyAndExpectedValue, vektor output akan menjadi vektor Ganda 4 elemen (IsAnomaly, RawScore, Mag, ExpectedValue). Ketika diatur ke AnomalyAndMargin, vektor output akan menjadi vektor Double 7 elemen (IsAnomaly, AnomalyScore, Mag, ExpectedValue, BoundaryUnit, UpperBoundary, LowerBoundary). RawScore adalah output oleh SR untuk menentukan apakah titik adalah anomali atau tidak, di bawah mode AnomalyAndMargin, ketika titik adalah anomali, AnomalyScore akan dihitung sesuai dengan pengaturan sensitivitas. Nilai defaultnya adalah AnomalyOnly.
Mengembalikan
Contoh
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; }
}
}
}