TimeSeriesCatalog.DetectIidSpike 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
| DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide) |
Cree IidSpikeEstimator, que predice picos en series temporales distribuidas de forma independiente (i.i.d.) basadas en estimaciones de densidad de kernel adaptable y puntuaciones de martingalas. |
| DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide) |
Obsoletos.
Cree IidSpikeEstimator, que predice picos en series temporales distribuidas de forma independiente (i.i.d.) basadas en estimaciones de densidad de kernel adaptable y puntuaciones de martingalas. |
DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)
Cree IidSpikeEstimator, que predice picos en series temporales distribuidas de forma independiente (i.i.d.) basadas en estimaciones de densidad de kernel adaptable y puntuaciones de martingalas.
public static Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator DetectIidSpike (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, double confidence, int pvalueHistoryLength, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided);static member DetectIidSpike : Microsoft.ML.TransformsCatalog * string * string * double * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide -> Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator<Extension()>
Public Function DetectIidSpike (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Double, pvalueHistoryLength As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided) As IidSpikeEstimatorParámetros
- 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 3 elementos: alert (un valor distinto de cero significa un pico), raw score y p-value.
- 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 picos en el intervalo [0, 100].
- pvalueHistoryLength
- Int32
Tamaño de la ventana deslizante para calcular el valor p.
- side
- AnomalySide
Argumento que determina si se deben detectar anomalías positivas o negativas, o ambas.
Devoluciones
Ejemplos
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectIidSpikeBatchPrediction
{
// 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 spiking points in the series.
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 spike
const int Size = 10;
var data = new List<TimeSeriesData>(Size + 1)
{
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
// This is a spike.
new TimeSeriesData(10),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup the estimator arguments
string outputColumnName = nameof(IidSpikePrediction.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed data.
var transformedData = ml.Transforms.DetectIidSpike(outputColumnName,
inputColumnName, 95.0d, Size / 4).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// IidSpikePrediction.
var predictionColumn = ml.Data.CreateEnumerable<IidSpikePrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 10 1 10.00 0.00 <-- alert is on, predicted spike
// 5 0 5.00 0.26
// 5 0 5.00 0.26
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
}
private static void PrintPrediction(float value, IidSpikePrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2]);
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
class IidSpikePrediction
{
[VectorType(3)]
public double[] Prediction { get; set; }
}
}
}
Se aplica a
DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)
Precaución
This API method is deprecated, please use the overload with confidence parameter of type double.
Cree IidSpikeEstimator, que predice picos en series temporales distribuidas de forma independiente (i.i.d.) basadas en estimaciones de densidad de kernel adaptable y 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.IidSpikeEstimator DetectIidSpike (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided);public static Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator DetectIidSpike (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, int confidence, int pvalueHistoryLength, Microsoft.ML.Transforms.TimeSeries.AnomalySide side = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided);[<System.Obsolete("This API method is deprecated, please use the overload with confidence parameter of type double.")>]
static member DetectIidSpike : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide -> Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimatorstatic member DetectIidSpike : Microsoft.ML.TransformsCatalog * string * string * int * int * Microsoft.ML.Transforms.TimeSeries.AnomalySide -> Microsoft.ML.Transforms.TimeSeries.IidSpikeEstimator<Extension()>
Public Function DetectIidSpike (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, confidence As Integer, pvalueHistoryLength As Integer, Optional side As AnomalySide = Microsoft.ML.Transforms.TimeSeries.AnomalySide.TwoSided) As IidSpikeEstimatorParámetros
- 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 3 elementos: alert (un valor distinto de cero significa un pico), raw score y p-value.
- 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 picos en el intervalo [0, 100].
- pvalueHistoryLength
- Int32
Tamaño de la ventana deslizante para calcular el valor p.
- side
- AnomalySide
Argumento que determina si se deben detectar anomalías positivas o negativas, o ambas.
Devoluciones
- Atributos
Ejemplos
using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DetectIidSpikeBatchPrediction
{
// 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 spiking points in the series.
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 spike
const int Size = 10;
var data = new List<TimeSeriesData>(Size + 1)
{
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
// This is a spike.
new TimeSeriesData(10),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
new TimeSeriesData(5),
};
// Convert data to IDataView.
var dataView = ml.Data.LoadFromEnumerable(data);
// Setup the estimator arguments
string outputColumnName = nameof(IidSpikePrediction.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed data.
var transformedData = ml.Transforms.DetectIidSpike(outputColumnName,
inputColumnName, 95.0d, Size / 4).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of
// IidSpikePrediction.
var predictionColumn = ml.Data.CreateEnumerable<IidSpikePrediction>(
transformedData, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
int k = 0;
foreach (var prediction in predictionColumn)
PrintPrediction(data[k++].Value, prediction);
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 10 1 10.00 0.00 <-- alert is on, predicted spike
// 5 0 5.00 0.26
// 5 0 5.00 0.26
// 5 0 5.00 0.50
// 5 0 5.00 0.50
// 5 0 5.00 0.50
}
private static void PrintPrediction(float value, IidSpikePrediction
prediction) =>
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value,
prediction.Prediction[0], prediction.Prediction[1],
prediction.Prediction[2]);
class TimeSeriesData
{
public float Value;
public TimeSeriesData(float value)
{
Value = value;
}
}
class IidSpikePrediction
{
[VectorType(3)]
public double[] Prediction { get; set; }
}
}
}
Se aplica a
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