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using System;
using System.Collections.Generic;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.TimeSeries;
namespace Samples.Dynamic
{
public static class DetectIidSpike
{
// 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 IidSpikeDetector arguments
string outputColumnName = nameof(IidSpikePrediction.Prediction);
string inputColumnName = nameof(TimeSeriesData.Value);
// The transformed model.
ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName,
inputColumnName, 95.0d, Size).Fit(dataView);
// Create a time series prediction engine from the model.
var engine = model.CreateTimeSeriesEngine<TimeSeriesData,
IidSpikePrediction>(ml);
Console.WriteLine($"{outputColumnName} column obtained " +
$"post-transformation.");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// Create non-anomalous data and check for anomaly.
for (int index = 0; index < 5; index++)
{
// Anomaly spike detection.
PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
}
// 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
// Spike.
PrintPrediction(10, engine.Predict(new TimeSeriesData(10)));
// 10 1 10.00 0.00 <-- alert is on, predicted spike (check-point model)
// Checkpoint the model.
var modelPath = "temp.zip";
engine.CheckPoint(ml, modelPath);
// Load the model.
using (var file = File.OpenRead(modelPath))
model = ml.Model.Load(file, out DataViewSchema schema);
for (int index = 0; index < 5; index++)
{
// Anomaly spike detection.
PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
}
// 5 0 5.00 0.26 <-- load model from disk.
// 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; }
}
}
}
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