/// <summary>
/// Create a new instance of <see cref="SsaChangePointEstimator"/>
/// </summary>
/// <param name="env">Host Environment.</param>
/// <param name="inputColumn">Name of the input column.</param>
/// <param name="outputColumn">Name of the output column.</param>
/// <param name="confidence">The confidence for change point detection in the range [0, 100].</param>
/// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
/// <param name="changeHistoryLength">The size of the sliding window for computing the p-value.</param>
/// <param name="seasonalityWindowSize">An upper bound on the largest relevant seasonality in the input time-series.</param>
/// <param name="errorFunction">The function used to compute the error between the expected and the observed value.</param>
/// <param name="martingale">The martingale used for scoring.</param>
/// <param name="eps">The epsilon parameter for the Power martingale.</param>
/// <p>Example code can be found by searching for <i>SsaChangePointDetector</i> in <a href='https://github.com/dotnet/machinelearning'>ML.NET.</a></p>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public SsaChangePointEstimator(IHostEnvironment env, string inputColumn, string outputColumn,
int confidence, int changeHistoryLength, int trainingWindowSize, int seasonalityWindowSize,
ErrorFunctionUtils.ErrorFunction errorFunction = ErrorFunctionUtils.ErrorFunction.SignedDifference,
MartingaleType martingale = MartingaleType.Power, double eps = 0.1)
: this(env, new SsaChangePointDetector.Arguments
{
Name = outputColumn,
Source = inputColumn,
Confidence = confidence,
ChangeHistoryLength = changeHistoryLength,
TrainingWindowSize = trainingWindowSize,
SeasonalWindowSize = seasonalityWindowSize,
Martingale = martingale,
PowerMartingaleEpsilon = eps,
ErrorFunction = errorFunction
})
{
}
public SsaAnomalyDetectionBase(SsaArguments args, string name, IHostEnvironment env)
: base(args.WindowSize, 0, args.Source, args.Name, name, env, args.Side, args.Martingale, args.AlertOn, args.PowerMartingaleEpsilon, args.AlertThreshold)
{
Host.CheckUserArg(2 <= args.SeasonalWindowSize, nameof(args.SeasonalWindowSize), "Must be at least 2.");
Host.CheckUserArg(0 <= args.DiscountFactor && args.DiscountFactor <= 1, nameof(args.DiscountFactor), "Must be in the range [0, 1].");
Host.CheckUserArg(Enum.IsDefined(typeof(ErrorFunctionUtils.ErrorFunction), args.ErrorFunction), nameof(args.ErrorFunction), ErrorFunctionUtils.ErrorFunctionHelpText);
SeasonalWindowSize = args.SeasonalWindowSize;
DiscountFactor = args.DiscountFactor;
ErrorFunction = args.ErrorFunction;
ErrorFunc = ErrorFunctionUtils.GetErrorFunction(ErrorFunction);
IsAdaptive = args.IsAdaptive;
// Creating the master SSA model
Model = new AdaptiveSingularSpectrumSequenceModeler(Host, args.InitialWindowSize, SeasonalWindowSize + 1, SeasonalWindowSize,
DiscountFactor, AdaptiveSingularSpectrumSequenceModeler.RankSelectionMethod.Exact, null, SeasonalWindowSize / 2, false, false);
StateRef = new State();
StateRef.InitState(WindowSize, InitialWindowSize, this, Host);
}
/// <summary>
/// Create a new instance of <see cref="SsaSpikeEstimator"/>
/// </summary>
/// <param name="env">Host Environment.</param>
/// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
/// <param name="confidence">The confidence for spike detection in the range [0, 100].</param>
/// <param name="pvalueHistoryLength">The size of the sliding window for computing the p-value.</param>
/// <param name="trainingWindowSize">The number of points from the beginning of the sequence used for training.</param>
/// <param name="seasonalityWindowSize">An upper bound on the largest relevant seasonality in the input time-series.</param>
/// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
/// The vector contains Alert, Raw Score, P-Value as first three values.</param>
/// <param name="side">The argument that determines whether to detect positive or negative anomalies, or both.</param>
/// <param name="errorFunction">The function used to compute the error between the expected and the observed value.</param>
public SsaSpikeEstimator(IHostEnvironment env,
string outputColumnName,
int confidence,
int pvalueHistoryLength,
int trainingWindowSize,
int seasonalityWindowSize,
string inputColumnName = null,
AnomalySide side = AnomalySide.TwoSided,
ErrorFunctionUtils.ErrorFunction errorFunction = ErrorFunctionUtils.ErrorFunction.SignedDifference)
: this(env, new SsaSpikeDetector.Arguments
{
Source = inputColumnName ?? outputColumnName,
Name = outputColumnName,
Confidence = confidence,
PvalueHistoryLength = pvalueHistoryLength,
TrainingWindowSize = trainingWindowSize,
SeasonalWindowSize = seasonalityWindowSize,
Side = side,
ErrorFunction = errorFunction
})
{
}
