TimeSeriesCatalog.DetectIidSpike Method

Definition

Namespace:

Microsoft.ML

Assembly:

Microsoft.ML.TimeSeries.dll

Package:

Microsoft.ML.TimeSeries v3.0.1

Overloads

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)	Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.
DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)	Obsolete. Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

DetectIidSpike(TransformsCatalog, String, String, Double, Int32, AnomalySide)

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

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 IidSpikeEstimator

Parameters

catalog

TransformsCatalog

The transform's catalog.

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. The column data is a vector of Double. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.

inputColumnName

String

Name of column to transform. The column data must be Single. If set to null, the value of the outputColumnName will be used as source.

confidence

Double

The confidence for spike detection in the range [0, 100].

pvalueHistoryLength

Int32

The size of the sliding window for computing the p-value.

side

AnomalySide

The argument that determines whether to detect positive or negative anomalies, or both.

Returns

IidSpikeEstimator

Examples

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; }
        }
    }
}

DetectIidSpike(TransformsCatalog, String, String, Int32, Int32, AnomalySide)

Caution

This API method is deprecated, please use the overload with confidence parameter of type double.

Create IidSpikeEstimator, which predicts spikes in independent identically distributed (i.i.d.) time series based on adaptive kernel density estimations and martingale scores.

[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);

[<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.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 IidSpikeEstimator

Parameters

catalog

TransformsCatalog

The transform's catalog.

outputColumnName

String

Name of the column resulting from the transformation of inputColumnName. The column data is a vector of Double. The vector contains 3 elements: alert (non-zero value means a spike), raw score, and p-value.

inputColumnName

String

Name of column to transform. The column data must be Single. If set to null, the value of the outputColumnName will be used as source.

confidence

Int32

The confidence for spike detection in the range [0, 100].

pvalueHistoryLength

Int32

The size of the sliding window for computing the p-value.

side

AnomalySide

The argument that determines whether to detect positive or negative anomalies, or both.

Returns

IidSpikeEstimator

Attributes

ObsoleteAttribute

Examples

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; }
        }
    }
}