public void ChangeDetection()
{
using (var env = new ConsoleEnvironment(conc: 1))
{
const int size = 10;
List <Data> data = new List <Data>(size);
var dataView = env.CreateStreamingDataView(data);
List <Data> tempData = new List <Data>();
for (int i = 0; i < size / 2; i++)
{
tempData.Add(new Data(5));
}
for (int i = 0; i < size / 2; i++)
{
tempData.Add(new Data((float)(5 + i * 1.1)));
}
foreach (var d in tempData)
{
data.Add(new Data(d.Value));
}
var args = new IidChangePointDetector.Arguments()
{
Confidence = 80,
Source = "Value",
Name = "Change",
ChangeHistoryLength = size
};
// Train
var detector = new IidChangePointEstimator(env, args).Fit(dataView);
// Transform
var output = detector.Transform(dataView);
// Get predictions
var enumerator = output.AsEnumerable <Prediction>(env, true).GetEnumerator();
Prediction row = null;
List <double> expectedValues = new List <double>()
{
0, 5, 0.5, 5.1200000000000114E-08, 0, 5, 0.4999999995, 5.1200000046080209E-08, 0, 5, 0.4999999995, 5.1200000092160303E-08,
0, 5, 0.4999999995, 5.12000001382404E-08
};
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.Equal(expectedValues[index++], row.Change[0]);
Assert.Equal(expectedValues[index++], row.Change[1]);
Assert.Equal(expectedValues[index++], row.Change[2]);
Assert.Equal(expectedValues[index++], row.Change[3]);
}
}
}
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// IidChangePointDetector is applied then to identify points where data distribution changed.
public static void IidChangePointDetectorTransform()
{
// 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 <IidChangePointData>(Size);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(5));
}
// This is a change point
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(7));
}
// Convert data to IDataView.
var dataView = ml.CreateStreamingDataView(data);
// Setup IidSpikeDetector arguments
string outputColumnName = nameof(ChangePointPrediction.Prediction);
string inputColumnName = nameof(IidChangePointData.Value);
var args = new IidChangePointDetector.Arguments()
{
Source = inputColumnName,
Name = outputColumnName,
Confidence = 95, // The confidence for spike detection in the range [0, 100]
ChangeHistoryLength = Size / 4, // The length of the sliding window on p-values for computing the martingale score.
};
// The transformed data.
var transformedData = new IidChangePointEstimator(ml, args).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
var predictionColumn = transformedData.AsEnumerable <ChangePointPrediction>(ml, 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)
{
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
Console.WriteLine("");
// 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
}
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// IidChangePointDetector is applied then to identify points where data distribution changed using time series
// prediction engine. The engine is checkpointed and then loaded back from disk into memory and used for prediction.
public static void IidChangePointDetectorPrediction()
{
// 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 <IidChangePointData>(Size);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(5));
}
// This is a change point
for (int i = 0; i < Size / 2; i++)
{
data.Add(new IidChangePointData(7));
}
// Convert data to IDataView.
var dataView = ml.CreateStreamingDataView(data);
// Setup IidSpikeDetector arguments
string outputColumnName = nameof(ChangePointPrediction.Prediction);
string inputColumnName = nameof(IidChangePointData.Value);
var args = new IidChangePointDetector.Arguments()
{
Source = inputColumnName,
Name = outputColumnName,
Confidence = 95, // The confidence for spike detection in the range [0, 100]
ChangeHistoryLength = Size / 4, // The length of the sliding window on p-values for computing the martingale score.
};
// Time Series model.
ITransformer model = new IidChangePointEstimator(ml, args).Fit(dataView);
// Create a time series prediction engine from the model.
var engine = model.CreateTimeSeriesPredictionFunction <IidChangePointData, ChangePointPrediction>(ml);
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(5));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 5, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Change point
var changePointPrediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, changePointPrediction.Prediction[0],
changePointPrediction.Prediction[1], changePointPrediction.Prediction[2], changePointPrediction.Prediction[3]);
// Checkpoint the model.
var modelPath = "temp.zip";
engine.CheckPoint(ml, modelPath);
// Reference to current time series engine because in the next step "engine" will point to the
// checkpointed model being loaded from disk.
var timeseries1 = engine;
// Load the model.
using (var file = File.OpenRead(modelPath))
model = TransformerChain.LoadFrom(ml, file);
// Create a time series prediction engine from the checkpointed model.
engine = model.CreateTimeSeriesPredictionFunction <IidChangePointData, ChangePointPrediction>(ml);
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Prediction from the original time series engine should match the prediction from
// check pointed model.
engine = timeseries1;
for (int index = 0; index < 8; index++)
{
// Anomaly change point detection.
var prediction = engine.Predict(new IidChangePointData(7));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
}
// Data Alert Score P-Value Martingale value
// 5 0 5.00 0.50 0.00 <-- Time Series 1.
// 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 (and model is checkpointed).
// 7 0 7.00 0.13 33950.16 <-- Time Series 2 : Model loaded back from disk and prediction is made.
// 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
// 7 0 7.00 0.13 33950.16 <-- Time Series 1 and prediction is made.
// 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
}
