public void SpikeDetection()
{
var env = new MLContext();
const int Size = 10;
const int PvalHistoryLength = Size / 4;
// Generate sample series data with a spike
List <Data> data = new List <Data>(Size);
var dataView = env.Data.LoadFromEnumerable(data);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new Data(5));
}
data.Add(new Data(10)); // This is the spike
for (int i = 0; i < Size / 2 - 1; i++)
{
data.Add(new Data(5));
}
// Convert to statically-typed data view.
var staticData = dataView.AssertStatic(env, c => new { Value = c.R4.Scalar });
// Build the pipeline
var staticLearningPipeline = staticData.MakeNewEstimator()
.Append(r => r.Value.IidSpikeDetect(80, PvalHistoryLength));
// Train
var detector = staticLearningPipeline.Fit(staticData);
// Transform
var output = detector.Transform(staticData);
// Get predictions
var enumerator = env.Data.CreateEnumerable <SpikePrediction>(output.AsDynamic, true).GetEnumerator();
var expectedValues = new List <double[]>()
{
// Alert Score P-Value
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 1, 10, 0.0 }, // alert is on, predicted spike
new double[] { 0, 5, 0.261375 },
new double[] { 0, 5, 0.261375 },
new double[] { 0, 5, 0.50 },
new double[] { 0, 5, 0.50 }
};
SpikePrediction row = null;
for (var i = 0; enumerator.MoveNext() && i < expectedValues.Count; i++)
{
row = enumerator.Current;
CompareNumbersWithTolerance(expectedValues[i][0], row.Data[0], digitsOfPrecision: 7);
CompareNumbersWithTolerance(expectedValues[i][1], row.Data[1], digitsOfPrecision: 7);
CompareNumbersWithTolerance(expectedValues[i][2], row.Data[2], digitsOfPrecision: 7);
}
}
public void SpikeDetection()
{
var env = new MLContext(1);
const int Size = 10;
const int PvalHistoryLength = Size / 4;
// Generate sample series data with a spike
List <Data> data = new List <Data>(Size);
var dataView = env.Data.LoadFromEnumerable(data);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new Data(5));
}
data.Add(new Data(10)); // This is the spike
for (int i = 0; i < Size / 2 - 1; i++)
{
data.Add(new Data(5));
}
// Build the pipeline
var learningPipeline = ML.Transforms.DetectIidSpike("Data", "Value", 80, PvalHistoryLength);
// Train
var detector = learningPipeline.Fit(dataView);
// Transform
var output = detector.Transform(dataView);
// Get predictions
var enumerator = env.Data.CreateEnumerable <SpikePrediction>(output, true).GetEnumerator();
var expectedValues = new List <double[]>()
{
// Alert Score P-Value
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 0, 5, 0.5 },
new double[] { 1, 10, 0.0 }, // alert is on, predicted spike
new double[] { 0, 5, 0.261375 },
new double[] { 0, 5, 0.261375 },
new double[] { 0, 5, 0.50 },
new double[] { 0, 5, 0.50 }
};
SpikePrediction row = null;
for (var i = 0; enumerator.MoveNext() && i < expectedValues.Count; i++)
{
row = enumerator.Current;
CompareNumbersWithTolerance(expectedValues[i][0], row.Data[0], digitsOfPrecision: 7);
CompareNumbersWithTolerance(expectedValues[i][1], row.Data[1], digitsOfPrecision: 7);
CompareNumbersWithTolerance(expectedValues[i][2], row.Data[2], digitsOfPrecision: 7);
}
}
public void SsaSpikeDetection()
{
var env = new MLContext(1);
const int size = 16;
const int changeHistoryLength = size / 4;
const int trainingWindowSize = size / 2;
const int seasonalityWindowSize = size / 8;
// Generate sample series data with a spike
List <Data> data = new List <Data>(size);
var dataView = env.Data.LoadFromEnumerable(data);
for (int i = 0; i < size / 2; i++)
{
data.Add(new Data(5));
}
data.Add(new Data(10)); // This is the spike
for (int i = 0; i < size / 2 - 1; i++)
{
data.Add(new Data(5));
}
// Build the pipeline
var learningPipeline = ML.Transforms.DetectSpikeBySsa("Data", "Value", 80.0d, changeHistoryLength, trainingWindowSize, seasonalityWindowSize);
// Train
var detector = learningPipeline.Fit(dataView);
// Transform
var output = detector.Transform(dataView);
// Get predictions
var enumerator = env.Data.CreateEnumerable <SpikePrediction>(output, true).GetEnumerator();
var expectedValues = new List <double[]>()
{
// Alert Score P-Value
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 1, 5.0, 0.0 }, // alert is on, predicted spike
new double[] { 1, -2.5, 0.093146 },
new double[] { 0, -2.5, 0.215437 },
new double[] { 0, 0.0, 0.465745 },
new double[] { 0, 0.0, 0.465745 },
new double[] { 0, 0.0, 0.261375 },
new double[] { 0, 0.0, 0.377615 },
new double[] { 0, 0.0, 0.50 }
};
SpikePrediction row = null;
for (var i = 0; enumerator.MoveNext() && i < expectedValues.Count; i++)
{
row = enumerator.Current;
CompareNumbersWithTolerance(expectedValues[i][0], row.Data[0], digitsOfPrecision: 6);
CompareNumbersWithTolerance(expectedValues[i][1], row.Data[1], digitsOfPrecision: 6);
CompareNumbersWithTolerance(expectedValues[i][2], row.Data[2], digitsOfPrecision: 6);
}
}
public void SsaSpikeDetection()
{
var env = new MLContext(conc: 1);
const int Size = 16;
const int ChangeHistoryLength = Size / 4;
const int TrainingWindowSize = Size / 2;
const int SeasonalityWindowSize = Size / 8;
// Generate sample series data with a spike
List <Data> data = new List <Data>(Size);
var dataView = env.CreateStreamingDataView(data);
for (int i = 0; i < Size / 2; i++)
{
data.Add(new Data(5));
}
data.Add(new Data(10)); // This is the spike
for (int i = 0; i < Size / 2 - 1; i++)
{
data.Add(new Data(5));
}
// Convert to statically-typed data view.
var staticData = dataView.AssertStatic(env, c => new { Value = c.R4.Scalar });
// Build the pipeline
var staticLearningPipeline = staticData.MakeNewEstimator()
.Append(r => r.Value.SsaSpikeDetect(80, ChangeHistoryLength, TrainingWindowSize, SeasonalityWindowSize));
// Train
var detector = staticLearningPipeline.Fit(staticData);
// Transform
var output = detector.Transform(staticData);
// Get predictions
var enumerator = output.AsDynamic.AsEnumerable <SpikePrediction>(env, true).GetEnumerator();
var expectedValues = new List <double[]>()
{
// Alert Score P-Value
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 0, 0.0, 0.5 },
new double[] { 1, 5.0, 0.0 }, // alert is on, predicted spike
new double[] { 1, -2.5, 0.093146 },
new double[] { 0, -2.5, 0.215437 },
new double[] { 0, 0.0, 0.465745 },
new double[] { 0, 0.0, 0.465745 },
new double[] { 0, 0.0, 0.261375 },
new double[] { 0, 0.0, 0.377615 },
new double[] { 0, 0.0, 0.50 }
};
SpikePrediction row = null;
for (var i = 0; enumerator.MoveNext() && i < expectedValues.Count; i++)
{
row = enumerator.Current;
CompareNumbersWithTolerance(expectedValues[i][0], row.Data[0], digitsOfPrecision: 6);
CompareNumbersWithTolerance(expectedValues[i][1], row.Data[1], digitsOfPrecision: 6);
CompareNumbersWithTolerance(expectedValues[i][2], row.Data[2], digitsOfPrecision: 6);
}
}
