TimeSeriesCatalog.DetectIidChangePoint Método
Definición
Importante
Parte de la información hace referencia a la versión preliminar del producto, que puede haberse modificado sustancialmente antes de lanzar la versión definitiva. Microsoft no otorga ninguna garantía, explícita o implícita, con respecto a la información proporcionada aquí.
Sobrecargas
| DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double) |
Cree IidChangePointEstimator, que predice puntos de cambio en una serie temporal distribuida de forma independiente (i.i.d.) basada en las estimaciones de densidad del kernel adaptable y las puntuaciones de martingalas. |
| DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double) |
Obsoletos.
Cree IidChangePointEstimator, que predice puntos de cambio en una serie temporal distribuida de forma independiente (i.i.d.) basada en las estimaciones de densidad del kernel adaptable y las puntuaciones de martingalas. |
DetectIidChangePoint(TransformsCatalog, String, String, Double, Int32, MartingaleType, Double)
Cree IidChangePointEstimator, que predice puntos de cambio en una serie temporal distribuida de forma independiente (i.i.d.) basada en las estimaciones de densidad del kernel adaptable y las puntuaciones de martingalas.
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 IidChangePointEstimatorParámetros
- catalog
- TransformsCatalog
Catálogo de la transformación.
- outputColumnName
- String
Nombre de la columna resultante de la transformación de inputColumnName.
Los datos de columna son un vector de Double. El vector contiene 4 elementos: alert (un valor distinto de cero significa un punto de cambio), raw score, p-Value y martingale score.
- inputColumnName
- String
Nombre de la columna que se va a transformar. Los datos de columna deben ser Single. Si se establece nullen , el valor de outputColumnName se usará como origen.
- confidence
- Double
Confianza para la detección de puntos de cambio en el intervalo [0, 100].
- changeHistoryLength
- Int32
Longitud de la ventana deslizante en valores p para calcular la puntuación de martingala.
- martingale
- MartingaleType
La martingala utilizada para puntuar.
- eps
- Double
Parámetro epsilon para la martingala de Power.
Devoluciones
Ejemplos
// 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;
}
}
}
}
Se aplica a
DetectIidChangePoint(TransformsCatalog, String, String, Int32, Int32, MartingaleType, Double)
Precaución
This API method is deprecated, please use the overload with confidence parameter of type double.
Cree IidChangePointEstimator, que predice puntos de cambio en una serie temporal distribuida de forma independiente (i.i.d.) basada en las estimaciones de densidad del kernel adaptable y las puntuaciones de martingalas.
[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 IidChangePointEstimatorParámetros
- catalog
- TransformsCatalog
Catálogo de la transformación.
- outputColumnName
- String
Nombre de la columna resultante de la transformación de inputColumnName.
Los datos de columna son un vector de Double. El vector contiene 4 elementos: alert (un valor distinto de cero significa un punto de cambio), raw score, p-Value y martingale score.
- inputColumnName
- String
Nombre de la columna que se va a transformar. Los datos de columna deben ser Single. Si se establece nullen , el valor de outputColumnName se usará como origen.
- confidence
- Int32
Confianza para la detección de puntos de cambio en el intervalo [0, 100].
- changeHistoryLength
- Int32
Longitud de la ventana deslizante en valores p para calcular la puntuación de martingala.
- martingale
- MartingaleType
La martingala utilizada para puntuar.
- eps
- Double
Parámetro epsilon para la martingala de Power.
Devoluciones
- Atributos
Ejemplos
// 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;
}
}
}
}
Se aplica a
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