PredictionFunctionExtensions.CreateTimeSeriesEngine Метод

Ссылка

Определение

Пространство имен:: Microsoft.ML.Transforms.TimeSeries

Сборка:: Microsoft.ML.TimeSeries.dll

Пакет:: Microsoft.ML.TimeSeries v3.0.1

Пакет:: Microsoft.ML.TimeSeries v1.7.0

Пакет:: Microsoft.ML.TimeSeries v2.0.0

Пакет:: Microsoft.ML.TimeSeries v1.2.0

Пакет:: Microsoft.ML.TimeSeries v1.3.1

Пакет:: Microsoft.ML.TimeSeries v1.4.0

Пакет:: Microsoft.ML.TimeSeries v1.5.5

Пакет:: Microsoft.ML.TimeSeries v1.6.0

Важно!

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Перегрузки

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

TimeSeriesPredictionEngine<TSrc,TDst> создает подсистему прогнозирования для конвейера временных рядов. Он обновляет состояние модели временных рядов с наблюдениями, наблюдаемыми на этапе прогнозирования, и позволяет выполнять контрольные точки модели.

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

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

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)

Параметры типа

TSrc

Класс, описывающий входную схему для модели.

TDst

Класс, описывающий схему вывода прогноза.

Параметры

transformer: ITransformer

Конвейер временных рядов в виде .ITransformer

env: IHostEnvironment

Обычно MLContext

options: PredictionEngineOptions

Дополнительные параметры конфигурации.

Возвращаемое значение

TimeSeriesPredictionEngine<TSrc,TDst>

Примеры

Это пример обнаружения точки изменения с помощью модели "Анализ сингулярного спектра" (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;
            }
        }
    }
}

Применяется к

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

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)

Параметры типа

TSrc

Класс, описывающий входную схему для модели.

TDst

Класс, описывающий схему вывода прогноза.

Параметры

transformer: ITransformer

Конвейер временных рядов в виде .ITransformer

env: IHostEnvironment

Обычно MLContext

ignoreMissingColumns: Boolean

Пропуск отсутствующих столбцов. Значение по умолчанию — false.

inputSchemaDefinition: SchemaDefinition

Определение схемы ввода. Значением по умолчанию является NULL.

outputSchemaDefinition: SchemaDefinition

Определение схемы вывода. Значением по умолчанию является NULL.

Возвращаемое значение

TimeSeriesPredictionEngine<TSrc,TDst>

Примеры

Это пример обнаружения точки изменения с помощью модели "Анализ сингулярного спектра" (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;
            }
        }
    }
}

Применяется к

Поделиться через

PredictionFunctionExtensions.CreateTimeSeriesEngine Метод

Определение

Перегрузки

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

Параметры типа

Параметры

Возвращаемое значение

Примеры

Применяется к

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

Параметры типа

Параметры

Возвращаемое значение

Примеры

Применяется к

Дополнительные ресурсы