TimeSeriesPredictionEngine<TSrc,TDst>.CheckPoint Metoda

Definicja

Przestrzeń nazw:

Microsoft.ML.Transforms.TimeSeries

Zestaw:

Microsoft.ML.TimeSeries.dll

Pakiet:

Microsoft.ML.TimeSeries v3.0.1

Przeciążenia

CheckPoint(IHostEnvironment, Stream)	Punkty kontrolne TimeSeriesPredictionEngine<TSrc,TDst> do stanu Stream ze zaktualizowanym stanem.
CheckPoint(IHostEnvironment, String)	Punkty kontrolne TimeSeriesPredictionEngine<TSrc,TDst> na dysku ze zaktualizowanym stanem.

CheckPoint(IHostEnvironment, Stream)

Punkty kontrolne TimeSeriesPredictionEngine<TSrc,TDst> do stanu Stream ze zaktualizowanym stanem.

public void CheckPoint (Microsoft.ML.Runtime.IHostEnvironment env, System.IO.Stream stream);

member this.CheckPoint : Microsoft.ML.Runtime.IHostEnvironment * System.IO.Stream -> unit

Public Sub CheckPoint (env As IHostEnvironment, stream As Stream)

Parametry

env

IHostEnvironment

Zwykle MLContext.

stream

Stream

Przesyłanie strumieniowe, w którym należy zapisać zaktualizowany model.

Przykłady

Jest to przykład tworzenia punktów kontrolnych szeregów czasowych, które wykrywają punkt zmian przy użyciu modelu analizy pojedynczego spektrum (SSA).

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 DetectChangePointBySsaStream
    {
        // 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.
            byte[] modelBytes;
            using (var stream = new MemoryStream())
            {
                engine.CheckPoint(ml, stream);
                modelBytes = stream.ToArray();
            }

            // Load the model.
            using (var stream = new MemoryStream(modelBytes))
                model = ml.Model.Load(stream, 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;
            }
        }
    }
}

CheckPoint(IHostEnvironment, String)

Punkty kontrolne TimeSeriesPredictionEngine<TSrc,TDst> na dysku ze zaktualizowanym stanem.

public void CheckPoint (Microsoft.ML.Runtime.IHostEnvironment env, string modelPath);

member this.CheckPoint : Microsoft.ML.Runtime.IHostEnvironment * string -> unit

Public Sub CheckPoint (env As IHostEnvironment, modelPath As String)

Parametry

env

IHostEnvironment

Zwykle MLContext.

modelPath

String

Ścieżka do pliku na dysku, na którym należy zapisać zaktualizowany model.

Przykłady

Jest to przykład tworzenia punktów kontrolnych szeregów czasowych, które wykrywają punkt zmian przy użyciu modelu analizy pojedynczego spektrum (SSA).

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;
            }
        }
    }
}