/**
* Given a list of anomaly scores return a list of averaged records.
* anomalyScores is assumed to be a list of records of the form:
* <pre>
* Sample:
* dt = Tuple(2013, 8, 10, 23, 0) --> Date Fields
* sample = (double) 6.0
* metric(avg) = (double) 1.0
* </pre>
*
* @param anomalyScores List of {@link Sample} objects (described contents above)
* @param windowSize Count of historical items over which to compute the average
*
* @return Each record in the returned list contains [datetime field, value, averaged score]
*/
public AveragedAnomalyRecordList AnomalyScoreMovingAverage(List <Sample> anomalyScores, int windowSize)
{
List <double> historicalValues = new List <double>();
double total = 0.0;
List <Sample> averagedRecordList = new List <Sample>();
foreach (Sample record in anomalyScores)
{
////////////////////////////////////////////////////////////////////////////////////////////
// Python version has check for malformed records here, but can't happen in java version. //
////////////////////////////////////////////////////////////////////////////////////////////
MovingAverage.Calculation calc = MovingAverage.Compute(historicalValues, total, record.score, windowSize);
Sample avgRecord = new Sample(
record.date,
record.value,
calc.GetAverage());
averagedRecordList.Add(avgRecord);
total = calc.GetTotal();
if (LOG.IsDebugEnabled)
{
LOG.Debug(string.Format("Aggregating input record: {0}, Result: {1}", record, averagedRecordList[averagedRecordList.Count - 1]));
}
}
return(new AveragedAnomalyRecordList(averagedRecordList, historicalValues, total));
}
/**
* Compute updated probabilities for anomalyScores using the given params.
*
* @param anomalyScores a list of records. Each record is a list with a {@link Sample} containing the
* following three elements: [timestamp, value, score]
* @param params Associative <see cref="NamedTuple"/> returned by the {@link AnomalyLikelihoodMetrics} from
* {@link #estimateAnomalyLikelihoods(List, int, int)}
* @return
*/
public AnomalyLikelihoodMetrics UpdateAnomalyLikelihoods(List <Sample> anomalyScores, AnomalyParams @params)
{
int anomalySize = anomalyScores.Count;
if (LOG.IsDebugEnabled)
{
LOG.Debug("in updateAnomalyLikelihoods");
LOG.Debug(string.Format("Number of anomaly scores: {0}", anomalySize));
LOG.Debug(string.Format("First 20: {0}", anomalyScores.SubList(0, Math.Min(20, anomalySize))));
LOG.Debug(string.Format("Params: {0}", @params));
}
if (anomalyScores.Count == 0)
{
throw new ArgumentException("Must have at least one anomaly score.");
}
if (!IsValidEstimatorParams(@params))
{
throw new ArgumentException("\"params\" is not a valid parameter structure");
}
double[] histLikelihoods;
if ((histLikelihoods = @params.HistoricalLikelihoods()) == null || histLikelihoods.Length == 0)
{
Parameters anomalyParameters = Parameters.Empty();
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_DIST, @params.Distribution());
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MVG_AVG, @params.MovingAverage());
anomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_HIST_LIKE, histLikelihoods = new double[] { 1 });
@params = new AnomalyParams(anomalyParameters);
//@params = new NamedTuple(
// new string[] { "distribution", "movingAverage", "historicalLikelihoods" },
// @params.Distribution(),
// @params.MovingAverage(),
// histLikelihoods = new double[] { 1 });
}
// Compute moving averages of these new scores using the previous values
// as well as likelihood for these scores using the old estimator
MovingAverage mvgAvg = (MovingAverage)@params.MovingAverage();
List <double> historicalValues = mvgAvg.GetSlidingWindow();
double total = mvgAvg.GetTotal();
int windowSize = mvgAvg.GetWindowSize();
List <Sample> aggRecordList = new List <Sample>(anomalySize);
double[] likelihoods = new double[anomalySize];
int i = 0;
foreach (Sample sample in anomalyScores)
{
MovingAverage.Calculation calc = MovingAverage.Compute(historicalValues, total, sample.score, windowSize);
aggRecordList.Add(
new Sample(
sample.date,
sample.value,
calc.GetAverage()));
total = calc.GetTotal();
likelihoods[i++] = NormalProbability(calc.GetAverage(), (Statistic)@params.Distribution());
}
// Filter the likelihood values. First we prepend the historical likelihoods
// to the current set. Then we filter the values. We peel off the likelihoods
// to return and the last windowSize values to store for later.
double[] likelihoods2 = ArrayUtils.Concat(histLikelihoods, likelihoods);
double[] filteredLikelihoods = FilterLikelihoods(likelihoods2);
likelihoods = Arrays.CopyOfRange(filteredLikelihoods, filteredLikelihoods.Length - likelihoods.Length, filteredLikelihoods.Length);
double[] historicalLikelihoods = Arrays.CopyOf(likelihoods2, likelihoods2.Length - Math.Min(windowSize, likelihoods2.Length));
// Update the estimator
Parameters newAnomalyParameters = Parameters.Empty();
newAnomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_DIST, @params.Distribution());
newAnomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_MVG_AVG, new MovingAverage(historicalValues, total, windowSize));
newAnomalyParameters.SetParameterByKey(Parameters.KEY.ANOMALY_KEY_HIST_LIKE, historicalLikelihoods);
AnomalyParams newParams = new AnomalyParams(newAnomalyParameters);
//AnomalyParams newParams = new AnomalyParams(
// new string[] { "distribution", "movingAverage", "historicalLikelihoods" },
// @params.Distribution(),
// new MovingAverage(historicalValues, total, windowSize),
// historicalLikelihoods);
return(new AnomalyLikelihoodMetrics(
likelihoods,
new AveragedAnomalyRecordList(aggRecordList, historicalValues, total),
newParams));
}