TimeSeriesCatalog.DetectEntireAnomalyBySrCnn Método
Definição
Importante
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Sobrecargas
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions) |
Criar Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, que detecta anomalias de timeseries para entrada inteira usando o algoritmo SRCNN. |
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode) |
Criar Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, que detecta anomalias de timeseries para entrada inteira usando o algoritmo SRCNN. |
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions)
Criar Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, que detecta anomalias de timeseries para entrada inteira usando o algoritmo 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 IDataViewParâmetros
- catalog
- AnomalyDetectionCatalog
O AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Nome da coluna resultante do processamento de dados de inputColumnName.
Os dados da coluna são um vetor de Double. O comprimento desse vetor varia dependendo options.DetectMode.DetectModede .
Define as configurações da operação de carga.
Retornos
Exemplos
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; }
}
}
}
Aplica-se a
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode)
Criar Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, que detecta anomalias de timeseries para entrada inteira usando o algoritmo 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 IDataViewParâmetros
- catalog
- AnomalyDetectionCatalog
O AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Nome da coluna resultante do processamento de dados de inputColumnName.
Os dados da coluna são um vetor de Double. O comprimento desse vetor varia dependendo detectModede .
- threshold
- Double
O limite para determinar uma anomalia. Uma anomalia é detectada quando a pontuação bruta de SR calculada para um determinado ponto é maior que o limite definido. Esse limite deve ficar entre [0,1] e seu valor padrão é 0,3.
- batchSize
- Int32
Divida os dados de entrada em lotes para ajustar o modelo srcnn. Quando definido como -1, use toda a entrada para ajustar o modelo em vez de lote por lote, quando definido como um inteiro positivo, use esse número como tamanho de lote. Deve ser -1 ou um inteiro positivo não menor que 12. O valor padrão é 1024.
- sensitivity
- Double
Confidencialidade de limites, útil somente quando srCnnDetectMode é AnomalyAndMargin. Deve estar em [0.100]. O valor padrão é 99.
- detectMode
- SrCnnDetectMode
Um tipo de enumeração de SrCnnDetectMode. Quando definido como AnomalyOnly, o vetor de saída seria um vetor duplo de 3 elementos (IsAnomaly, RawScore, Mag). Quando definido como AnomalyAndExpectedValue, o vetor de saída seria um vetor duplo de 4 elementos (IsAnomaly, RawScore, Mag, ExpectedValue). Quando definido como AnomalyAndMargin, o vetor de saída seria um vetor duplo de 7 elementos (IsAnomaly, AnomalyScore, Mag, ExpectedValue, BoundaryUnit, UpperBoundary, LowerBoundary). O RawScore é gerado pela SR para determinar se um ponto é uma anomalia ou não, no modo AnomalyAndMargin, quando um ponto é uma anomalia, um AnomalyScore será calculado de acordo com a configuração de confidencialidade. O valor padrão é AnomalyOnly.
Retornos
Exemplos
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; }
}
}
}
Aplica-se a
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