add root cause localization transformer

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/LocalizeRootCauseByDT.cs

@@ -0,0 +1,76 @@
+using System;
+using System.Collections.Generic;
+using System.Drawing;
+using System.IO;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.TimeSeries;
+
+namespace Samples.Dynamic
+{
+    public static class LocalizeRootCause
+    {
+        public static void Example()
+        {
+            // 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 mlContext = new MLContext();
+
+            // Create an empty list as the dataset. The 'NormalizeText' API does not
+            // require training data as the estimator ('TextNormalizingEstimator')
+            // created by 'NormalizeText' API is not a trainable estimator. The
+            // empty list is only needed to pass input schema to the pipeline.
+            var emptySamples = new List<RootCauseLocalizationData>();
+
+            // Convert sample list to an empty IDataView.
+            var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);
+
+            // A pipeline for localizeing root cause.
+            var localizePipeline = mlContext.Transforms.LocalizeRootCauseByDT(nameof(RootCauseLocalizationTransformedData.RootCause), nameof(RootCauseLocalizationData.Input));
+
+            // Fit to data.
+            var localizeTransformer = localizePipeline.Fit(emptyDataView);
+
+            // Create the prediction engine to get the root cause result from the
+            // input data.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<RootCauseLocalizationData,
+                RootCauseLocalizationTransformedData>(localizeTransformer);
+
+            // Call the prediction API.
+            var data = new RootCauseLocalizationData(new DateTime(), new Dictionary<String, String>(), new List<MetricSlice>() { new MetricSlice(new DateTime(), new List<Microsoft.ML.Transforms.TimeSeries.Point>()) }, "SUM", "SUM");
+
+            var prediction = predictionEngine.Predict(data);
+
+            // Print the localization result.
+            Console.WriteLine($"Localized result: {prediction.RootCause}");
+        }
+
+
+        private class RootCauseLocalizationData
+        {
+            [RootCauseLocalizationInputType]
+            public RootCauseLocalizationInput Input { get; set; }
+
+            public RootCauseLocalizationData()
+            {
+                Input = null;
+            }
+
+            public RootCauseLocalizationData(DateTime anomalyTimestamp, Dictionary<string, string> anomalyDimensions, List<MetricSlice> slices,String aggregateType, string aggregateSymbol)
+            {
+                Input = new RootCauseLocalizationInput(anomalyTimestamp, anomalyDimensions, slices, DTRootCauseLocalizationEstimator.AggregateType.Sum, aggregateSymbol);
+            }
+        }
+
+        private class RootCauseLocalizationTransformedData
+        {
+            [RootCauseType()]
+            public RootCause RootCause { get; set; }
+
+            public RootCauseLocalizationTransformedData()
+            {
+                RootCause = null;
+            }
+        }
+    }
+}