TimeSeriesCatalog.DetectIidChangePoint Método
Definição
Importante
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Sobrecargas
| DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double) |
Criar IidChangePointEstimator, que prevê pontos de alteração em uma série temporal distribuída de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale. |
| DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double) |
Obsoleto.
Criar IidChangePointEstimator, que prevê pontos de alteração em uma série temporal distribuída de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale. |
DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)
Criar IidChangePointEstimator, que prevê pontos de alteração em uma série temporal distribuída de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale.
public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);static member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * double * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator<Extension()>
Public Function DetectIidChangePoint (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, changeHistoryLength As Integer, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As IidChangePointEstimatorParâmetros
- catalog
- TransformsCatalog
O catálogo da transformação.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
Os dados da coluna são um vetor de Double. O vetor contém 4 elementos: alerta (valor não zero significa um ponto de alteração), pontuação bruta, pontuação p-Value e martingale.
- inputColumnName
- String
Nome da coluna a ser transformada. Os dados da coluna devem ser Single. Se definido como null, o valor do outputColumnName será usado como origem.
- confidence
- Double
A confiança para a detecção de ponto de alteração no intervalo [0, 100].
- changeHistoryLength
- Int32
O comprimento da janela deslizante em p-values para calcular a pontuação martingale.
- martingale
- MartingaleType
O martingale usado para marcar.
- eps
- Double
O parâmetro epsilon para o martingale do Power.
Retornos
Exemplos
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectIidChangePointBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify points where data distribution changed.
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 change
const int Size = 16;
var data = new List<TimeSeriesData>(Size)
{
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
//Change point data.
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
string outputColumnName = nameof(ChangePointPrediction.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed data.
var transformedData = ml.Transforms.DetectIidChangePoint(
outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView)
.Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 7 1 7.00 0.00 10298.67 <-- alert is on, predicted changepoint
// 7 0 7.00 0.13 33950.16
// 7 0 7.00 0.26 60866.34
// 7 0 7.00 0.38 78362.04
// 7 0 7.00 0.50 0.01
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
}
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;
}
}
}
}
Aplica-se a
DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)
Cuidado
This API method is deprecated, please use the overload with confidence parameter of type double.
Criar IidChangePointEstimator, que prevê pontos de alteração em uma série temporal distribuída de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale.
[System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")]
public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);public static Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator DetectIidChangePoint (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int changeHistoryLength, Microsoft.ML.Transforms.TimeSeries.MartingaleType martingale = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, double eps = 0.1);[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimatorstatic member DetectIidChangePoint : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.MartingaleType * double -> Microsoft.ML.Transforms.TimeSeries.IidChangePointEstimator<Extension()>
Public Function DetectIidChangePoint (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, changeHistoryLength As Integer, Optional martingale As MartingaleType = Microsoft.ML.Transforms.TimeSeries.MartingaleType.Power, Optional eps As Double = 0.1) As IidChangePointEstimatorParâmetros
- catalog
- TransformsCatalog
O catálogo da transformação.
- outputColumnName
- String
Nome da coluna resultante da transformação de inputColumnName.
Os dados da coluna são um vetor de Double. O vetor contém 4 elementos: alerta (valor não zero significa um ponto de alteração), pontuação bruta, pontuação p-Value e martingale.
- inputColumnName
- String
Nome da coluna a ser transformada. Os dados da coluna devem ser Single. Se definido como null, o valor do outputColumnName será usado como origem.
- confidence
- Int32
A confiança para a detecção de ponto de alteração no intervalo [0, 100].
- changeHistoryLength
- Int32
O comprimento da janela deslizante em p-values para calcular a pontuação martingale.
- martingale
- MartingaleType
O martingale usado para marcar.
- eps
- Double
O parâmetro epsilon para o martingale do Power.
Retornos
- Atributos
Exemplos
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectIidChangePointBatchPrediction
{
// This example creates a time series (list of Data with the i-th element
// corresponding to the i-th time slot). The estimator is applied then to
// identify points where data distribution changed.
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 change
const int Size = 16;
var data = new List<TimeSeriesData>(Size)
{
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
//Change point data.
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
new TimeSeriesData(7),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup estimator arguments
string outputColumnName = nameof(ChangePointPrediction.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed data.
var transformedData = ml.Transforms.DetectIidChangePoint(
outputColumnName, inputColumnName, 95.0d, Size / 4).Fit(dataView)
.Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// ChangePointPrediction.
var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value Martingale value
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 5 0 5.00 0.50 0.00
// 7 1 7.00 0.00 10298.67 <-- alert is on, predicted changepoint
// 7 0 7.00 0.13 33950.16
// 7 0 7.00 0.26 60866.34
// 7 0 7.00 0.38 78362.04
// 7 0 7.00 0.50 0.01
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
// 7 0 7.00 0.50 0.00
}
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;
}
}
}
}