public void ChangePointDetectionWithSeasonality()
{
using (var env = new ConsoleEnvironment(conc: 1))
{
const int ChangeHistorySize = 10;
const int SeasonalitySize = 10;
const int NumberOfSeasonsInTraining = 5;
const int MaxTrainingSize = NumberOfSeasonsInTraining * SeasonalitySize;
List <Data> data = new List <Data>();
var dataView = env.CreateStreamingDataView(data);
var args = new SsaChangePointDetector.Arguments()
{
Confidence = 95,
Source = "Value",
Name = "Change",
ChangeHistoryLength = ChangeHistorySize,
TrainingWindowSize = MaxTrainingSize,
SeasonalWindowSize = SeasonalitySize
};
for (int j = 0; j < NumberOfSeasonsInTraining; j++)
{
for (int i = 0; i < SeasonalitySize; i++)
{
data.Add(new Data(i));
}
}
for (int i = 0; i < ChangeHistorySize; i++)
{
data.Add(new Data(i * 100));
}
// Train
var detector = new SsaChangePointEstimator(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, -3.31410598754883, 0.5, 5.12000000000001E-08, 0, 1.5700820684432983, 5.2001145245395008E-07,
0.012414560443710681, 0, 1.2854313254356384, 0.28810801662678009, 0.02038940454467935, 0, -1.0950627326965332, 0.36663890634019225, 0.026956459625565483
};
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.Equal(expectedValues[index++], row.Change[0], precision: 7); // Alert
Assert.Equal(expectedValues[index++], row.Change[1], precision: 7); // Raw score
Assert.Equal(expectedValues[index++], row.Change[2], precision: 7); // P-Value score
Assert.Equal(expectedValues[index++], row.Change[3], precision: 7); // Martingale score
}
}
}
public void ChangePointDetectionWithSeasonality()
{
var env = new MLContext(1);
const int changeHistorySize = 10;
const int seasonalitySize = 10;
const int numberOfSeasonsInTraining = 5;
const int maxTrainingSize = numberOfSeasonsInTraining * seasonalitySize;
List <Data> data = new List <Data>();
var dataView = env.Data.LoadFromEnumerable(data);
var args = new SsaChangePointDetector.Options()
{
Confidence = 95,
Source = "Value",
Name = "Change",
ChangeHistoryLength = changeHistorySize,
TrainingWindowSize = maxTrainingSize,
SeasonalWindowSize = seasonalitySize
};
for (int j = 0; j < numberOfSeasonsInTraining; j++)
{
for (int i = 0; i < seasonalitySize; i++)
{
data.Add(new Data(i));
}
}
for (int i = 0; i < changeHistorySize; i++)
{
data.Add(new Data(i * 100));
}
// Train
var detector = new SsaChangePointEstimator(env, args).Fit(dataView);
// Transform
var output = detector.Transform(dataView);
// Get predictions
var enumerator = env.Data.CreateEnumerable <Prediction>(output, true).GetEnumerator();
Prediction row = null;
// [TEST_STABILITY]: dotnet core 3.1 generates slightly different result
#if NETCOREAPP3_1
List <double> expectedValues = new List <double>()
{
0, -3.31410551071167, 0.5, 5.12000000000001E-08, 0, 1.570083498954773, 5.2001145245395008E-07,
0.012414560443710681, 0, 1.2854313850402832, 0.2881081472302483, 0.020389485008225454, 0, -1.0950632095336914, 0.3666388047550645, 0.02695657272695535
};
#else
List <double> expectedValues = new List <double>()
{
0, -3.31410598754883, 0.5, 5.12000000000001E-08, 0, 1.5700820684432983, 5.2001145245395008E-07,
0.012414560443710681, 0, 1.2854313254356384, 0.28810801662678009, 0.02038940454467935, 0, -1.0950627326965332, 0.36663890634019225, 0.026956459625565483
};
#endif
int index = 0;
while (enumerator.MoveNext() && index < expectedValues.Count)
{
row = enumerator.Current;
Assert.Equal(expectedValues[index++], row.Change[0], precision: 7); // Alert
Assert.Equal(expectedValues[index++], row.Change[1], precision: 7); // Raw score
Assert.Equal(expectedValues[index++], row.Change[2], precision: 7); // P-Value score
Assert.Equal(expectedValues[index++], row.Change[3], precision: 7); // Martingale score
}
}
