PredictionFunctionExtensions.CreateTimeSeriesEngine Metoda
Definice
Důležité
Některé informace platí pro předběžně vydaný produkt, který se může zásadně změnit, než ho výrobce nebo autor vydá. Microsoft neposkytuje žádné záruky, výslovné ani předpokládané, týkající se zde uváděných informací.
Přetížení
| CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions) |
TimeSeriesPredictionEngine<TSrc,TDst> vytvoří prediktivní modul pro kanál časových řad. Aktualizuje stav modelu časových řad s pozorováními, které jsou vidět ve fázi předpovědi, a umožňuje kontrolní body modelu. |
| CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition) |
TimeSeriesPredictionEngine<TSrc,TDst> vytvoří prediktivní modul pro kanál časových řad. Aktualizuje stav modelu časových řad s pozorováními, které jsou vidět ve fázi předpovědi, a umožňuje kontrolní body modelu. |
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions)
TimeSeriesPredictionEngine<TSrc,TDst> vytvoří prediktivní modul pro kanál časových řad. Aktualizuje stav modelu časových řad s pozorováními, které jsou vidět ve fázi předpovědi, a umožňuje kontrolní body modelu.
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)Parametry typu
- TSrc
Třída popisující vstupní schéma modelu
- TDst
Třída popisující výstupní schéma predikce
Parametry
- transformer
- ITransformer
Kanál časových ITransformerřad ve formě .
- env
- IHostEnvironment
Obvykle MLContext
- options
- PredictionEngineOptions
Pokročilé možnosti konfigurace
Návraty
Příklady
Toto je příklad pro detekci bodu změn pomocí modelu SSA (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;
}
}
}
}
Platí pro
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)
TimeSeriesPredictionEngine<TSrc,TDst> vytvoří prediktivní modul pro kanál časových řad. Aktualizuje stav modelu časových řad s pozorováními, které jsou vidět ve fázi předpovědi, a umožňuje kontrolní body modelu.
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)Parametry typu
- TSrc
Třída popisující vstupní schéma modelu
- TDst
Třída popisující výstupní schéma predikce
Parametry
- transformer
- ITransformer
Kanál časových ITransformerřad ve formě .
- env
- IHostEnvironment
Obvykle MLContext
- ignoreMissingColumns
- Boolean
Pokud chcete ignorovat chybějící sloupce. Výchozí hodnota je false.
- inputSchemaDefinition
- SchemaDefinition
Definice vstupního schématu Výchozí hodnota je null.
- outputSchemaDefinition
- SchemaDefinition
Definice výstupního schématu Výchozí hodnota je null.
Návraty
Příklady
Toto je příklad pro detekci bodu změn pomocí modelu SSA (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;
}
}
}
}