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TimeSeriesCatalog.DetectAnomalyBySrCnn Méthode

Définition

Créez SrCnnAnomalyEstimator, qui détecte les anomalies de série chronologique à l’aide de l’algorithme SRCNN.

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.SrCnnAnomalyEstimator
static 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

Paramètres

catalog
TransformsCatalog

Catalogue de la transformation.

outputColumnName
String

Nom de la colonne résultant de la transformation de inputColumnName. Les données de colonne sont un vecteur de Double. Le vecteur contient 3 éléments : alerte (1 signifie anomalie alors que 0 signifie normal), score brut et magnitude de résidus spectreaux.

inputColumnName
String

Nom de la colonne à transformer. Les données de colonne doivent être Single.

windowSize
Int32

Taille de la fenêtre glissante pour calculer les résidus spectreaux.

backAddWindowSize
Int32

Nombre de points à ajouter à la fenêtre d’entraînement. Plus que windowSize, conservez généralement la valeur par défaut.

lookaheadWindowSize
Int32

Nombre de points pervieux utilisés dans la prédiction. Plus que windowSize, conservez généralement la valeur par défaut.

averagingWindowSizeaverageingWindowSize
Int32

Taille de la fenêtre glissante pour générer une carte de saliency pour la série. Plus que windowSize, conservez généralement la valeur par défaut.

judgementWindowSize
Int32

Taille de la fenêtre glissante pour calculer le score d’anomalie pour chaque point de données. Pas plus que windowSize.

threshold
Double

Seuil permettant de déterminer l’anomalie, le score supérieur au seuil est considéré comme une anomalie. Doit être dans (0,1)

Retours

Exemples

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; }
        }
    }
}

S’applique à