TimeSeriesPredictionEngine<TSrc,TDst>.CheckPoint Yöntem
Tanım
Önemli
Bazı bilgiler ürünün ön sürümüyle ilgilidir ve sürüm öncesinde önemli değişiklikler yapılmış olabilir. Burada verilen bilgilerle ilgili olarak Microsoft açık veya zımni hiçbir garanti vermez.
Aşırı Yüklemeler
| CheckPoint(IHostEnvironment, Stream) |
Güncelleştirilmiş duruma sahip denetim Stream noktalarıTimeSeriesPredictionEngine<TSrc,TDst>. |
| CheckPoint(IHostEnvironment, String) |
Güncelleştirilmiş duruma sahip diske denetim noktaları TimeSeriesPredictionEngine<TSrc,TDst> . |
CheckPoint(IHostEnvironment, Stream)
Güncelleştirilmiş duruma sahip denetim Stream noktalarıTimeSeriesPredictionEngine<TSrc,TDst>.
public void CheckPoint (Microsoft.ML.Runtime.IHostEnvironment env, System.IO.Stream stream);member this.CheckPoint : Microsoft.ML.Runtime.IHostEnvironment * System.IO.Stream -> unitPublic Sub CheckPoint (env As IHostEnvironment, stream As Stream)Parametreler
- env
- IHostEnvironment
Genellikle MLContext.
- stream
- Stream
Güncelleştirilmiş modelin kaydedilmesi gereken akış.
Örnekler
Bu, Tekil Spektrum Analizi (SSA) modelini kullanarak değişiklik noktasını algılayan denetim noktası zaman serisine bir örnektir.
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;
}
}
}
}
Şunlara uygulanır
CheckPoint(IHostEnvironment, String)
Güncelleştirilmiş duruma sahip diske denetim noktaları TimeSeriesPredictionEngine<TSrc,TDst> .
public void CheckPoint (Microsoft.ML.Runtime.IHostEnvironment env, string modelPath);member this.CheckPoint : Microsoft.ML.Runtime.IHostEnvironment * string -> unitPublic Sub CheckPoint (env As IHostEnvironment, modelPath As String)Parametreler
- env
- IHostEnvironment
Genellikle MLContext.
- modelPath
- String
Güncelleştirilmiş modelin kaydedilmesi gereken disk üzerindeki dosyanın yolu.
Örnekler
Bu, Tekil Spektrum Analizi (SSA) modelini kullanarak değişiklik noktasını algılayan denetim noktası zaman serisine bir örnektir.
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;
}
}
}
}