TimeSeriesCatalog.DetectAnomalyBySrCnn 方法
定義
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。
建立 SrCnnAnomalyEstimator ，以使用 SRCNN 演算法偵測時間異常狀況。
public static Microsoft.ML.Transforms.TimeSeries.SrCnnAnomalyEstimator DetectAnomalyBySrCnn (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int windowSize = 64, int backAddWindowSize = 5, int lookaheadWindowSize = 5, int averagingWindowSize = 3, int judgementWindowSize = 21, double threshold = 0.3);public static Microsoft.ML.Transforms.TimeSeries.SrCnnAnomalyEstimator DetectAnomalyBySrCnn (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int windowSize = 64, int backAddWindowSize = 5, int lookaheadWindowSize = 5, int averageingWindowSize = 3, int judgementWindowSize = 21, double threshold = 0.3);static member DetectAnomalyBySrCnn : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * int * double -> Microsoft.ML.Transforms.TimeSeries.SrCnnAnomalyEstimatorstatic member DetectAnomalyBySrCnn : Microsoft.ML.TransformsCatalog * string * string * int * int * int * int * int * double -> Microsoft.ML.Transforms.TimeSeries.SrCnnAnomalyEstimator<Extension()>
Public Function DetectAnomalyBySrCnn (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, Optional windowSize As Integer = 64, Optional backAddWindowSize As Integer = 5, Optional lookaheadWindowSize As Integer = 5, Optional averagingWindowSize As Integer = 3, Optional judgementWindowSize As Integer = 21, Optional threshold As Double = 0.3) As SrCnnAnomalyEstimator<Extension()>
Public Function DetectAnomalyBySrCnn (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, Optional windowSize As Integer = 64, Optional backAddWindowSize As Integer = 5, Optional lookaheadWindowSize As Integer = 5, Optional averageingWindowSize As Integer = 3, Optional judgementWindowSize As Integer = 21, Optional threshold As Double = 0.3) As SrCnnAnomalyEstimator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 (1 表示異常，0 表示標準) 、原始分數，以及反射殘差的大小。
- windowSize
- Int32
計算剩餘剩餘的滑動視窗大小。
- backAddWindowSize
- Int32
要加回定型視窗的點數。
windowSize不超過 ，通常會保留預設值。
- lookaheadWindowSize
- Int32
預測中使用的效能點數目。
windowSize不超過 ，通常會保留預設值。
- averagingWindowSizeaverageingWindowSize
- Int32
要為數列產生 Saliency 地圖的滑動視窗大小。
windowSize不超過 ，通常會保留預設值。
- judgementWindowSize
- Int32
滑動視窗的大小，可計算每個資料點的異常分數。
windowSize不超過 。
- threshold
- Double
判斷異常的臨界值，大於閾值的分數會被視為異常。 應位於 (0，1)
傳回
範例
using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;
namespace Samples.Dynamic
{
public static class DetectAnomalyBySrCnn
{
// 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 an anomaly
var data = new List<TimeSeriesData>();
for (int index = 0; index < 20; index++)
{
data.Add(new TimeSeriesData(5));
}
data.Add(new TimeSeriesData(10));
for (int index = 0; index < 5; index++)
{
data.Add(new TimeSeriesData(5));
}
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup the estimator arguments
string outputColumnName = nameof(SrCnnAnomalyDetection.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed model.
ITransformer model = ml.Transforms.DetectAnomalyBySrCnn(
outputColumnName, inputColumnName, 16, 5, 5, 3, 8, 0.35).Fit(
dataView);
// Create a time series prediction engine from the model.
var engine = model.CreateTimeSeriesEngine<TimeSeriesData,
SrCnnAnomalyDetection>(ml);
Console.WriteLine($"{outputColumnName} column obtained post-" +
$"transformation.");
Console.WriteLine("Data\tAlert\tScore\tMag");
// Prediction column obtained post-transformation.
// Data Alert Score Mag
// Create non-anomalous data and check for anomaly.
for (int index = 0; index < 20; index++)
{
// Anomaly detection.
PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
}
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.00 0.00
//5 0 0.03 0.18
//5 0 0.03 0.18
//5 0 0.03 0.18
//5 0 0.03 0.18
//5 0 0.03 0.18
// Anomaly.
PrintPrediction(10, engine.Predict(new TimeSeriesData(10)));
//10 1 0.47 0.93 <-- alert is on, predicted anomaly
// Checkpoint the model.
var modelPath = "temp.zip";
engine.CheckPoint(ml, modelPath);
// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);
for (int index = 0; index < 5; index++)
{
// Anomaly detection.
PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
}
//5 0 0.31 0.50
//5 0 0.05 0.30
//5 0 0.01 0.23
//5 0 0.00 0.21
//5 0 0.01 0.25
}
private static void PrintPrediction(float value, SrCnnAnomalyDetection
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction
.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
private class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
private class SrCnnAnomalyDetection
{
[VectorType(3)]
public double[] Prediction { get; set; }
}
}
}