PredictionFunctionExtensions.CreateTimeSeriesEngine Méthode
Définition
Important
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Surcharges
| CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions) |
TimeSeriesPredictionEngine<TSrc,TDst> crée un moteur de prédiction pour un pipeline de série chronologique. Il met à jour l’état du modèle de série chronologique avec des observations observées lors de la phase de prédiction et permet de contrôler le modèle. |
| CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition) |
TimeSeriesPredictionEngine<TSrc,TDst> crée un moteur de prédiction pour un pipeline de série chronologique. Il met à jour l’état du modèle de série chronologique avec des observations observées lors de la phase de prédiction et permet de contrôler le modèle. |
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions)
TimeSeriesPredictionEngine<TSrc,TDst> crée un moteur de prédiction pour un pipeline de série chronologique. Il met à jour l’état du modèle de série chronologique avec des observations observées lors de la phase de prédiction et permet de contrôler le modèle.
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)Paramètres de type
- TSrc
Classe décrivant le schéma d’entrée au modèle.
- TDst
Classe décrivant le schéma de sortie de la prédiction.
Paramètres
- transformer
- ITransformer
Pipeline de série chronologique sous la forme d’un ITransformer.
- env
- IHostEnvironment
Généralement MLContext
- options
- PredictionEngineOptions
Options de configuration avancées.
Retours
Exemples
Il s’agit d’un exemple de détection du point de modification à l’aide du modèle 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;
}
}
}
}
S’applique à
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)
TimeSeriesPredictionEngine<TSrc,TDst> crée un moteur de prédiction pour un pipeline de série chronologique. Il met à jour l’état du modèle de série chronologique avec des observations observées lors de la phase de prédiction et permet de contrôler le modèle.
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)Paramètres de type
- TSrc
Classe décrivant le schéma d’entrée au modèle.
- TDst
Classe décrivant le schéma de sortie de la prédiction.
Paramètres
- transformer
- ITransformer
Pipeline de série chronologique sous la forme d’un ITransformer.
- env
- IHostEnvironment
Généralement MLContext
- ignoreMissingColumns
- Boolean
Pour ignorer les colonnes manquantes. La valeur par défaut est false.
- inputSchemaDefinition
- SchemaDefinition
Définition du schéma d’entrée. La valeur par défaut est Null.
- outputSchemaDefinition
- SchemaDefinition
Définition du schéma de sortie. La valeur par défaut est Null.
Retours
Exemples
Il s’agit d’un exemple de détection du point de modification à l’aide du modèle 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;
}
}
}
}