PredictionFunctionExtensions.CreateTimeSeriesEngine Methode

Definition

Namespace:: Microsoft.ML.Transforms.TimeSeries

Assembly:: Microsoft.ML.TimeSeries.dll

Paket:: Microsoft.ML.TimeSeries v4.0.1

Paket:: Microsoft.ML.TimeSeries v1.7.0

Paket:: Microsoft.ML.TimeSeries v2.0.1

Paket:: Microsoft.ML.TimeSeries v3.0.1

Paket:: Microsoft.ML.TimeSeries v5.0.0-preview.1.25125.4

Paket:: Microsoft.ML.TimeSeries v1.2.0

Paket:: Microsoft.ML.TimeSeries v1.3.1

Paket:: Microsoft.ML.TimeSeries v1.4.0

Paket:: Microsoft.ML.TimeSeries v1.5.5

Paket:: Microsoft.ML.TimeSeries v1.6.0

Wichtig

Einige Informationen beziehen sich auf Vorabversionen, die vor dem Release ggf. grundlegend überarbeitet werden. Microsoft übernimmt hinsichtlich der hier bereitgestellten Informationen keine Gewährleistungen, seien sie ausdrücklich oder konkludent.

Überlädt

Name	Beschreibung
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions)	TimeSeriesPredictionEngine<TSrc,TDst> erstellt ein Vorhersagemodul für eine Zeitreihenpipeline. Es aktualisiert den Zustand des Zeitreihenmodells mit Beobachtungen, die in der Vorhersagephase zu sehen sind, und ermöglicht die Kontrolle des Modells.
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)	TimeSeriesPredictionEngine<TSrc,TDst> erstellt ein Vorhersagemodul für eine Zeitreihenpipeline. Es aktualisiert den Zustand des Zeitreihenmodells mit Beobachtungen, die in der Vorhersagephase zu sehen sind, und ermöglicht die Kontrolle des Modells.

CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions)

Quelle:: PredictionEngine.cs

Quelle:: PredictionEngine.cs

Quelle:: PredictionEngine.cs

TimeSeriesPredictionEngine<TSrc,TDst> erstellt ein Vorhersagemodul für eine Zeitreihenpipeline. Es aktualisiert den Zustand des Zeitreihenmodells mit Beobachtungen, die in der Vorhersagephase zu sehen sind, und ermöglicht die Kontrolle des Modells.

public static Microsoft.ML.Transforms.TimeSeries.TimeSeriesPredictionEngine<TSrc,TDst> CreateTimeSeriesEngine<TSrc,TDst>(this Microsoft.ML.ITransformer transformer, Microsoft.ML.Runtime.IHostEnvironment env, Microsoft.ML.PredictionEngineOptions options) where TSrc : class where TDst : class, new();

static member CreateTimeSeriesEngine : Microsoft.ML.ITransformer * Microsoft.ML.Runtime.IHostEnvironment * Microsoft.ML.PredictionEngineOptions -> Microsoft.ML.Transforms.TimeSeries.TimeSeriesPredictionEngine<'Src, 'Dst (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))> (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))

<Extension()>
Public Function CreateTimeSeriesEngine(Of TSrc As Class, TDst As Class) (transformer As ITransformer, env As IHostEnvironment, options As PredictionEngineOptions) As TimeSeriesPredictionEngine(Of TSrc, TDst)

Typparameter

TSrc

Klasse, die das Eingabeschema für das Modell beschreibt.

TDst

Klasse, die das Ausgabeschema der Vorhersage beschreibt.

Parameter

transformer: ITransformer

Die Zeitreihenpipeline in Form eines ITransformer.

env: IHostEnvironment

Normalerweise MLContext

options: PredictionEngineOptions

Erweiterte Konfigurationsoptionen.

Gibt zurück

TimeSeriesPredictionEngine<TSrc,TDst>

Beispiele

Dies ist ein Beispiel zum Erkennen des Änderungspunkts mithilfe des SSA-Modells (Singular Spectrum Analysis).

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 DetectChangePointBySsa
    {
        // This example creates a time series (list of Data with the i-th element
        // corresponding to the i-th time slot). It demonstrates stateful prediction
        // engine that updates the state of the model and allows for
        // saving/reloading. The estimator is applied then to identify points where
        // data distribution changed. This estimator can account for temporal
        // seasonality in the data.
        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 a recurring pattern
            const int SeasonalitySize = 5;
            const int TrainingSeasons = 3;
            const int TrainingSize = SeasonalitySize * TrainingSeasons;
            var data = new List<TimeSeriesData>()
            {
                new TimeSeriesData(0),
                new TimeSeriesData(1),
                new TimeSeriesData(2),
                new TimeSeriesData(3),
                new TimeSeriesData(4),

                new TimeSeriesData(0),
                new TimeSeriesData(1),
                new TimeSeriesData(2),
                new TimeSeriesData(3),
                new TimeSeriesData(4),

                new TimeSeriesData(0),
                new TimeSeriesData(1),
                new TimeSeriesData(2),
                new TimeSeriesData(3),
                new TimeSeriesData(4),
            };

            // Convert data to IDataView.
            var dataView = ml.Data.LoadFromEnumerable(data);

            // Setup SsaChangePointDetector arguments
            var inputColumnName = nameof(TimeSeriesData.Value);
            var outputColumnName = nameof(ChangePointPrediction.Prediction);
            double confidence = 95;
            int changeHistoryLength = 8;

            // Train the change point detector.
            ITransformer model = ml.Transforms.DetectChangePointBySsa(
                outputColumnName, inputColumnName, confidence, changeHistoryLength,
                TrainingSize, SeasonalitySize + 1).Fit(dataView);

            // Create a prediction engine from the model for feeding new data.
            var engine = model.CreateTimeSeriesEngine<TimeSeriesData,
                ChangePointPrediction>(ml);

            // Start streaming new data points with no change point to the
            // prediction engine.
            Console.WriteLine($"Output from ChangePoint predictions on new data:");
            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");

            // Output from ChangePoint predictions on new data:
            // Data    Alert   Score   P-Value Martingale value

            for (int i = 0; i < 5; i++)
                PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));

            // 0       0      -1.01    0.50    0.00
            // 1       0      -0.24    0.22    0.00
            // 2       0      -0.31    0.30    0.00
            // 3       0       0.44    0.01    0.00
            // 4       0       2.16    0.00    0.24

            // Now stream data points that reflect a change in trend.
            for (int i = 0; i < 5; i++)
            {
                int value = (i + 1) * 100;
                PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
            }
            // 100     0      86.23    0.00    2076098.24
            // 200     0     171.38    0.00    809668524.21
            // 300     1     256.83    0.01    22130423541.93    <-- alert is on, note that delay is expected
            // 400     0     326.55    0.04    241162710263.29
            // 500     0     364.82    0.08    597660527041.45   <-- saved to disk

            // Now we demonstrate saving and loading the model.

            // Save the model that exists within the prediction engine.
            // The engine has been updating this model with every new data point.
            var modelPath = "model.zip";
            engine.CheckPoint(ml, modelPath);

            // Load the model.
            using (var file = File.OpenRead(modelPath))
                model = ml.Model.Load(file, out DataViewSchema schema);

            // We must create a new prediction engine from the persisted model.
            engine = model.CreateTimeSeriesEngine<TimeSeriesData,
                ChangePointPrediction>(ml);

            // Run predictions on the loaded model.
            for (int i = 0; i < 5; i++)
            {
                int value = (i + 1) * 100;
                PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
            }

            // 100     0     -58.58    0.15    1096021098844.34  <-- loaded from disk and running new predictions
            // 200     0     -41.24    0.20    97579154688.98
            // 300     0     -30.61    0.24    95319753.87
            // 400     0      58.87    0.38    14.24
            // 500     0     219.28    0.36    0.05

        }

        private static void PrintPrediction(float value, ChangePointPrediction
            prediction) =>
            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value,
            prediction.Prediction[0], prediction.Prediction[1],
            prediction.Prediction[2], prediction.Prediction[3]);

        class ChangePointPrediction
        {
            [VectorType(4)]
            public double[] Prediction { get; set; }
        }

        class TimeSeriesData
        {
            public float Value;

            public TimeSeriesData(float value)
            {
                Value = value;
            }
        }
    }
}

Gilt für:

CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)

Quelle:: PredictionEngine.cs

Quelle:: PredictionEngine.cs

Quelle:: PredictionEngine.cs

public static Microsoft.ML.Transforms.TimeSeries.TimeSeriesPredictionEngine<TSrc,TDst> CreateTimeSeriesEngine<TSrc,TDst>(this Microsoft.ML.ITransformer transformer, Microsoft.ML.Runtime.IHostEnvironment env, bool ignoreMissingColumns = false, Microsoft.ML.Data.SchemaDefinition inputSchemaDefinition = default, Microsoft.ML.Data.SchemaDefinition outputSchemaDefinition = default) where TSrc : class where TDst : class, new();

static member CreateTimeSeriesEngine : Microsoft.ML.ITransformer * Microsoft.ML.Runtime.IHostEnvironment * bool * Microsoft.ML.Data.SchemaDefinition * Microsoft.ML.Data.SchemaDefinition -> Microsoft.ML.Transforms.TimeSeries.TimeSeriesPredictionEngine<'Src, 'Dst (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))> (requires 'Src : null and 'Dst : null and 'Dst : (new : unit -> 'Dst))

<Extension()>
Public Function CreateTimeSeriesEngine(Of TSrc As Class, TDst As Class) (transformer As ITransformer, env As IHostEnvironment, Optional ignoreMissingColumns As Boolean = false, Optional inputSchemaDefinition As SchemaDefinition = Nothing, Optional outputSchemaDefinition As SchemaDefinition = Nothing) As TimeSeriesPredictionEngine(Of TSrc, TDst)