TimeSeriesCatalog.DetectEntireAnomalyBySrCnn Method
Definition
Important
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Overloads
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions) |
Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm. |
| DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode) |
Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm. |
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, SrCnnEntireAnomalyDetectorOptions)
Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm.
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 IDataViewParameters
- catalog
- AnomalyDetectionCatalog
The AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Name of the column resulting from data processing of inputColumnName.
The column data is a vector of Double. The length of this vector varies depending on options.DetectMode.DetectMode.
Defines the settings of the load operation.
Returns
Examples
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; }
}
}
}
Applies to
DetectEntireAnomalyBySrCnn(AnomalyDetectionCatalog, IDataView, String, String, Double, Int32, Double, SrCnnDetectMode)
Create Microsoft.ML.TimeSeries.SrCnnEntireAnomalyDetector, which detects timeseries anomalies for entire input using SRCNN algorithm.
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 IDataViewParameters
- catalog
- AnomalyDetectionCatalog
The AnomalyDetectionCatalog.
- input
- IDataView
Input DataView.
- outputColumnName
- String
Name of the column resulting from data processing of inputColumnName.
The column data is a vector of Double. The length of this vector varies depending on detectMode.
- threshold
- Double
The threshold to determine an anomaly. An anomaly is detected when the calculated SR raw score for a given point is more than the set threshold. This threshold must fall between [0,1], and its default value is 0.3.
- batchSize
- Int32
Divide the input data into batches to fit srcnn model. When set to -1, use the whole input to fit model instead of batch by batch, when set to a positive integer, use this number as batch size. Must be -1 or a positive integer no less than 12. Default value is 1024.
- sensitivity
- Double
Sensitivity of boundaries, only useful when srCnnDetectMode is AnomalyAndMargin. Must be in [0,100]. Default value is 99.
- detectMode
- SrCnnDetectMode
An enum type of SrCnnDetectMode. When set to AnomalyOnly, the output vector would be a 3-element Double vector of (IsAnomaly, RawScore, Mag). When set to AnomalyAndExpectedValue, the output vector would be a 4-element Double vector of (IsAnomaly, RawScore, Mag, ExpectedValue). When set to AnomalyAndMargin, the output vector would be a 7-element Double vector of (IsAnomaly, AnomalyScore, Mag, ExpectedValue, BoundaryUnit, UpperBoundary, LowerBoundary). The RawScore is output by SR to determine whether a point is an anomaly or not, under AnomalyAndMargin mode, when a point is an anomaly, an AnomalyScore will be calculated according to sensitivity setting. Default value is AnomalyOnly.
Returns
Examples
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; }
}
}
}