TimeSeriesCatalog.DetectIidSpike Método
Definição
Importante
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Sobrecargas
| DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide) |
Create IidSpikeEstimator, que prevê picos em séries temporais distribuídas de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale. |
| DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide) |
Obsoleto.
Create IidSpikeEstimator, que prevê picos em séries temporais distribuídas de forma idêntica independente (i.i.d.) com base em estimativas de densidade de kernel adaptável e pontuações de martingale. |
DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)
Create IidSpikeEstimator, que prevê picos em séries temporais distribuídas 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.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 IidSpikeEstimatorParâmetros
- 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 três elementos: alerta (valor não zero significa um pico), pontuação bruta e p-value.
- 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 detecção de pico no intervalo [0, 100].
- pvalueHistoryLength
- Int32
O tamanho da janela deslizante para calcular o valor p.
- side
- AnomalySide
O argumento que determina se as anomalias positivas ou negativas devem ser detectadas ou ambas.
Retornos
Exemplos
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; }
}
}
}
Aplica-se a
DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)
Cuidado
This API method is deprecated, please use the overload with confidence parameter of type double.
Create IidSpikeEstimator, que prevê picos em séries temporais distribuídas 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.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 IidSpikeEstimatorParâmetros
- 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 três elementos: alerta (valor não zero significa um pico), pontuação bruta e p-value.
- 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 detecção de pico no intervalo [0, 100].
- pvalueHistoryLength
- Int32
O tamanho da janela deslizante para calcular o valor p.
- side
- AnomalySide
O argumento que determina se as anomalias positivas ou negativas devem ser detectadas ou ambas.
Retornos
- Atributos
Exemplos
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; }
}
}
}
Aplica-se a
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