Beispiel #1
0
        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
        // SsaChangePointDetector is applied then to identify points where data distribution changed.
        // SsaChangePointDetector differs from IidChangePointDetector in that it can account for temporal seasonality
        // in the data.
        public static void SsaChangePointDetectorTransform()
        {
            // 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 then a change in trend
            const int SeasonalitySize = 5;
            const int TrainingSeasons = 3;
            const int TrainingSize    = SeasonalitySize * TrainingSeasons;
            var       data            = new List <SsaChangePointData>();

            for (int i = 0; i < TrainingSeasons; i++)
            {
                for (int j = 0; j < SeasonalitySize; j++)
                {
                    data.Add(new SsaChangePointData(j));
                }
            }
            // This is a change point
            for (int i = 0; i < SeasonalitySize; i++)
            {
                data.Add(new SsaChangePointData(i * 100));
            }

            // Convert data to IDataView.
            var dataView = ml.CreateStreamingDataView(data);

            // Setup SsaChangePointDetector arguments
            var inputColumnName  = nameof(SsaChangePointData.Value);
            var outputColumnName = nameof(ChangePointPrediction.Prediction);
            var args             = new SsaChangePointDetector.Arguments()
            {
                Source              = inputColumnName,
                Name                = outputColumnName,
                Confidence          = 95,                 // The confidence for spike detection in the range [0, 100]
                ChangeHistoryLength = 8,                  // The length of the window for detecting a change in trend; 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 SsaChangePointEstimator(ml, args).Fit(dataView).Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
            var predictionColumn = transformedData.AsEnumerable <ChangePointPrediction>(ml, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
            int k = 0;

            foreach (var prediction in predictionColumn)
            {
                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
            }
            Console.WriteLine("");

            // Prediction column obtained post-transformation.
            // Data    Alert   Score   P-Value Martingale value
            // 0       0     - 2.53    0.50    0.00
            // 1       0     - 0.01    0.01    0.00
            // 2       0       0.76    0.14    0.00
            // 3       0       0.69    0.28    0.00
            // 4       0       1.44    0.18    0.00
            // 0       0     - 1.84    0.17    0.00
            // 1       0       0.22    0.44    0.00
            // 2       0       0.20    0.45    0.00
            // 3       0       0.16    0.47    0.00
            // 4       0       1.33    0.18    0.00
            // 0       0     - 1.79    0.07    0.00
            // 1       0       0.16    0.50    0.00
            // 2       0       0.09    0.50    0.00
            // 3       0       0.08    0.45    0.00
            // 4       0       1.31    0.12    0.00
            // 0       0     - 1.79    0.07    0.00
            // 100     1      99.16    0.00    4031.94     <-- alert is on, predicted changepoint
            // 200     0     185.23    0.00    731260.87
            // 300     0     270.40    0.01    3578470.47
            // 400     0     357.11    0.03    45298370.86
        }
Beispiel #2
0
        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot).
        // SsaChangePointDetector is applied then to identify points where data distribution changed.
        public static void SsaChangePointDetectorTransform()
        {
            // 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 change
            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 change point
            for (int i = 0; i < size / 2; i++)
            {
                data.Add(new Data(7));
            }

            // Convert data to IDataView.
            var dataView = ml.CreateStreamingDataView(data);

            // Setup IidSpikeDetector arguments
            string outputColumnName = "Prediction";
            string inputColumnName  = "Value";
            var    args             = new SsaChangePointDetector.Arguments()
            {
                Source              = inputColumnName,
                Name                = outputColumnName,
                Confidence          = 95,       // The confidence for spike detection in the range [0, 100]
                ChangeHistoryLength = size / 4, // The length of the sliding window on p-values for computing the martingale score.
                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 SsaChangePointEstimator(ml, args).Fit(dataView).Transform(dataView);

            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
            var predictionColumn = transformedData.AsEnumerable <ChangePointPrediction>(ml, reuseRowObject: false);

            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
            int k = 0;

            foreach (var prediction in predictionColumn)
            {
                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
            }
            Console.WriteLine("");

            // Prediction column obtained post-transformation.
            // Data Alert      Score   P-Value Martingale value
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 5       0       0.00    0.50    0.00
            // 7       1       2.00    0.00    10298.67   <-- alert is on, predicted changepoint
            // 7       0       1.00    0.31    15741.58
            // 7       0       0.00    0.28    26487.48
            // 7       0       0.00    0.28    44569.02
            // 7       0       0.00    0.28    0.01
            // 7       0       0.00    0.38    0.01
            // 7       0       0.00    0.50    0.00
            // 7       0       0.00    0.50    0.00
        }