// This example shows spike detection as above, but demonstrates how to train a model
// that can run predictions on streaming data, and how to persist the trained model and then re-load it.
public static void SsaSpikeDetectorPrediction()
{
// 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 recurring pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List <SsaSpikeData>();
for (int i = 0; i < TrainingSeasons; i++)
{
for (int j = 0; j < SeasonalitySize; j++)
{
data.Add(new SsaSpikeData(j));
}
}
// Convert data to IDataView.
var dataView = ml.Data.ReadFromEnumerable(data);
// Setup IidSpikeDetector arguments
var inputColumnName = nameof(SsaSpikeData.Value);
var outputColumnName = nameof(SsaSpikePrediction.Prediction);
var args = new SsaSpikeDetector.Arguments()
{
Source = inputColumnName,
Name = outputColumnName,
Confidence = 95, // The confidence for spike detection in the range [0, 100]
PvalueHistoryLength = 8, // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
};
// Train the change point detector.
ITransformer model = new SsaSpikeEstimator(ml, args).Fit(dataView);
// Create a prediction engine from the model for feeding new data.
var engine = model.CreateTimeSeriesPredictionFunction <SsaSpikeData, SsaSpikePrediction>(ml);
// Start streaming new data points with no change point to the prediction engine.
Console.WriteLine($"Output from spike predictions on new data:");
Console.WriteLine("Data\tAlert\tScore\tP-Value");
SsaSpikePrediction prediction = null;
for (int j = 0; j < 2; j++)
{
for (int i = 0; i < 5; i++)
{
var value = i;
prediction = engine.Predict(new SsaSpikeData(value));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
}
}
// Now send a data point that reflects a spike.
var newValue = 100;
prediction = engine.Predict(new SsaSpikeData(newValue));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", newValue, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
// Now we demonstrate saving and loading the model.
// Save the model that exists within the prediction engine.
// The engine has been updating this model with every new data point.
var modelPath = "model.zip";
engine.CheckPoint(ml, modelPath);
// Load the model.
using (var file = File.OpenRead(modelPath))
model = TransformerChain.LoadFrom(ml, file);
// We must create a new prediction engine from the persisted model.
engine = model.CreateTimeSeriesPredictionFunction <SsaSpikeData, SsaSpikePrediction>(ml);
// Run predictions on the loaded model.
for (int i = 0; i < 5; i++)
{
var value = i;
prediction = engine.Predict(new SsaSpikeData(value));
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
}
// Output from spike predictions on new data:
// Data Alert Score P-Value
// 0 0 - 1.01 0.50
// 1 0 - 0.24 0.22
// 2 0 - 0.31 0.30
// 3 0 0.44 0.01
// 4 0 2.16 0.00
// 0 0 - 0.78 0.27
// 1 0 - 0.80 0.30
// 2 0 - 0.84 0.31
// 3 0 0.33 0.31
// 4 0 2.21 0.07
// 100 1 86.17 0.00 <-- alert is on, predicted spike
// 0 0 - 2.74 0.40 <-- saved to disk, re-loaded, and running new predictions
// 1 0 - 1.47 0.42
// 2 0 - 17.50 0.24
// 3 0 - 30.82 0.16
// 4 0 - 23.24 0.28
}
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// SsaSpikeDetector is applied then to identify spiking points in the series.
// SsaSpikeDetector differs from IidSpikeDetector in that it can account for temporal seasonality
// in the data.
public static void SsaSpikeDetectorTransform()
{
// 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 recurring pattern and a spike within the pattern
const int SeasonalitySize = 5;
const int TrainingSeasons = 3;
const int TrainingSize = SeasonalitySize * TrainingSeasons;
var data = new List <SsaSpikeData>();
for (int i = 0; i < TrainingSeasons; i++)
{
for (int j = 0; j < SeasonalitySize; j++)
{
data.Add(new SsaSpikeData(j));
}
}
// This is a spike
data.Add(new SsaSpikeData(100));
for (int i = 0; i < SeasonalitySize; i++)
{
data.Add(new SsaSpikeData(i));
}
// Convert data to IDataView.
var dataView = ml.Data.ReadFromEnumerable(data);
// Setup IidSpikeDetector arguments
var inputColumnName = nameof(SsaSpikeData.Value);
var outputColumnName = nameof(SsaSpikePrediction.Prediction);
var args = new SsaSpikeDetector.Arguments()
{
Source = inputColumnName,
Name = outputColumnName,
Confidence = 95, // The confidence for spike detection in the range [0, 100]
PvalueHistoryLength = 8, // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
};
// The transformed data.
var transformedData = new SsaSpikeEstimator(ml, args).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of SsaSpikePrediction.
var predictionColumn = ml.CreateEnumerable <SsaSpikePrediction>(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)
{
Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
}
Console.WriteLine("");
// Prediction column obtained post-transformation.
// Data Alert Score P-Value
// 0 0 - 2.53 0.50
// 1 0 - 0.01 0.01
// 2 0 0.76 0.14
// 3 0 0.69 0.28
// 4 0 1.44 0.18
// 0 0 - 1.84 0.17
// 1 0 0.22 0.44
// 2 0 0.20 0.45
// 3 0 0.16 0.47
// 4 0 1.33 0.18
// 0 0 - 1.79 0.07
// 1 0 0.16 0.50
// 2 0 0.09 0.50
// 3 0 0.08 0.45
// 4 0 1.31 0.12
// 100 1 98.21 0.00 <-- alert is on, predicted spike
// 0 0 - 13.83 0.29
// 1 0 - 1.74 0.44
// 2 0 - 0.47 0.46
// 3 0 - 16.50 0.29
// 4 0 - 29.82 0.21
}
// This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
// SsaSpikeDetector is applied then to identify spiking points in the series.
public static void SsaSpikeDetectorTransform()
{
// 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 = 16;
var data = new List <Data>(size);
for (int i = 0; i < size / 2; i++)
{
data.Add(new Data(5));
}
// This is a spike
data.Add(new Data(10));
for (int i = 0; i < size / 2; i++)
{
data.Add(new Data(5));
}
// Convert data to IDataView.
var dataView = ml.CreateStreamingDataView(data);
// Setup IidSpikeDetector arguments
string outputColumnName = "Prediction";
string inputColumnName = "Value";
var args = new SsaSpikeDetector.Arguments()
{
Source = inputColumnName,
Name = outputColumnName,
Confidence = 95, // The confidence for spike detection in the range [0, 100]
PvalueHistoryLength = size / 4, // The size of the sliding window for computing the p-value
TrainingWindowSize = size / 2, // The number of points from the beginning of the sequence used for training.
SeasonalWindowSize = size / 8, // An upper bound on the largest relevant seasonality in the input time - series."
};
// The transformed data.
var transformedData = new SsaSpikeEstimator(ml, args).Fit(dataView).Transform(dataView);
// Getting the data of the newly created column as an IEnumerable of SpikePrediction.
var predictionColumn = transformedData.AsEnumerable <SpikePrediction>(ml, reuseRowObject: false);
Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
Console.WriteLine("Alert\tScore\tP-Value");
foreach (var prediction in predictionColumn)
{
Console.WriteLine("{0}\t{1:0.00}\t{2:0.00}", prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
}
Console.WriteLine("");
// Prediction column obtained post-transformation.
// Alert Score P-Value
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 0 0.00 0.50
// 1 5.00 0.00 <-- alert is on, predicted spike
// 0 -2.50 0.09
// 0 -2.50 0.22
// 0 0.00 0.47
// 0 0.00 0.47
// 0 0.00 0.26
// 0 0.00 0.38
// 0 0.00 0.50
// 0 0.00 0.50
}