TimeSeriesCatalog.DetectAnomalyBySrCnn Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.TimeSeries.dll

Paket:

Microsoft.ML.TimeSeries v3.0.1

Buat SrCnnAnomalyEstimator, yang mendeteksi anomali timeseries menggunakan algoritma 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);

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

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Data kolom adalah vektor dari Double. Vektor berisi 3 elemen: pemberitahuan (1 berarti anomali sementara 0 berarti normal), skor mentah, dan besarnya residu spektual.

inputColumnName

String

Nama kolom yang akan diubah. Data kolom harus Single.

windowSize

Int32

Ukuran jendela geser untuk menghitung sisa spektral.

backAddWindowSize

Int32

Jumlah titik untuk menambahkan kembali jendela pelatihan. Tidak lebih dari windowSize, biasanya menyimpan nilai default.

lookaheadWindowSize

Int32

Jumlah titik jahat yang digunakan dalam prediksi. Tidak lebih dari windowSize, biasanya menyimpan nilai default.

averagingWindowSize

Int32

Ukuran jendela geser untuk menghasilkan peta saliency untuk seri. Tidak lebih dari windowSize, biasanya menyimpan nilai default.

judgementWindowSize

Int32

Ukuran jendela geser untuk menghitung skor anomali untuk setiap titik data. Tidak lebih dari windowSize.

threshold

Double

Ambang batas untuk menentukan anomali, skor yang lebih besar dari ambang dianggap sebagai anomali. Harus dalam (0,1)

Mengembalikan

SrCnnAnomalyEstimator

Contoh

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; }
        }
    }
}