TimeSeriesPredictionEngine<TSrc,TDst>.CheckPoint 메서드
정의
중요
일부 정보는 릴리스되기 전에 상당 부분 수정될 수 있는 시험판 제품과 관련이 있습니다. Microsoft는 여기에 제공된 정보에 대해 어떠한 명시적이거나 묵시적인 보증도 하지 않습니다.
오버로드
| CheckPoint(IHostEnvironment, Stream) |
업데이트된 TimeSeriesPredictionEngine<TSrc,TDst> 상태의 Stream 검사점입니다. |
| CheckPoint(IHostEnvironment, String) |
업데이트된 상태의 TimeSeriesPredictionEngine<TSrc,TDst> 디스크에 대한 검사점입니다. |
CheckPoint(IHostEnvironment, Stream)
업데이트된 TimeSeriesPredictionEngine<TSrc,TDst> 상태의 Stream 검사점입니다.
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)매개 변수
- env
- IHostEnvironment
일반적으로 MLContext입니다.
- stream
- Stream
업데이트된 모델을 저장해야 하는 스트림입니다.
예제
SSA(단수 스펙트럼 분석) 모델을 사용하여 변경 지점을 검색하는 검사점 시계열의 예입니다.
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;
}
}
}
}
적용 대상
CheckPoint(IHostEnvironment, String)
업데이트된 상태의 TimeSeriesPredictionEngine<TSrc,TDst> 디스크에 대한 검사점입니다.
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)매개 변수
- env
- IHostEnvironment
일반적으로 MLContext입니다.
- modelPath
- String
업데이트된 모델을 저장해야 하는 디스크의 파일 경로입니다.
예제
SSA(단수 스펙트럼 분석) 모델을 사용하여 변경 지점을 검색하는 검사점 시계열의 예입니다.
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;
}
}
}
}