Returning multiple dimensions in RCA for anomaly detection

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

@@ -0,0 +1,114 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML;
+using Microsoft.ML.TimeSeries;
+
+namespace Samples.Dynamic
+{
+    public static class LocalizeRootCauseMultipleDimensions
+    {
+        private static string AGG_SYMBOL = "##SUM##";
+
+        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 root cause localization input instance.
+            DateTime timestamp = GetTimestamp();
+            var data = new RootCauseLocalizationInput(timestamp, GetAnomalyDimension(), new List<MetricSlice>() { new MetricSlice(timestamp, GetPoints()) }, AggregateType.Sum, AGG_SYMBOL);
+
+            // Get the root cause localization result.
+            RootCauses prediction = mlContext.AnomalyDetection.LocalizeRootCauses(data);
+
+            // Print the localization result.
+            int count = 0;
+            foreach (RootCauseItem item in prediction.Causes[0].Items)
+            {
+                count++;
+                Console.WriteLine($"Root cause item #{count} ...");
+                Console.WriteLine($"Score: {item.Score}, Path: {String.Join(" ",item.Path)}, Direction: {item.Direction}, Dimension:{String.Join(" ", item.Dimension)}");
+            }
+
+            //Item #1 ...
+            //Score: 0.26670448876705927, Path: DataCenter, Direction: Up, Dimension:[Country, UK] [DeviceType, ##SUM##] [DataCenter, DC1]
+        }
+
+        private static List<Point> GetPoints()
+        {
+            List<Point> points = new List<Point>();
+
+            Dictionary<string, Object> dic1 = new Dictionary<string, Object>();
+            dic1.Add("Country", "UK");
+            dic1.Add("DeviceType", "Laptop");
+            dic1.Add("DataCenter", "DC1");
+            points.Add(new Point(200, 100, true, dic1));
+
+            Dictionary<string, Object> dic2 = new Dictionary<string, Object>();
+            dic2.Add("Country", "UK");
+            dic2.Add("DeviceType", "Mobile");
+            dic2.Add("DataCenter", "DC1");
+            points.Add(new Point(1000, 100, true, dic2));
+
+            Dictionary<string, Object> dic3 = new Dictionary<string, Object>();
+            dic3.Add("Country", "UK");
+            dic3.Add("DeviceType", AGG_SYMBOL);
+            dic3.Add("DataCenter", "DC1");
+            points.Add(new Point(1200, 200, true, dic3));
+
+            Dictionary<string, Object> dic4 = new Dictionary<string, Object>();
+            dic4.Add("Country", "UK");
+            dic4.Add("DeviceType", "Laptop");
+            dic4.Add("DataCenter", "DC2");
+            points.Add(new Point(100, 100, false, dic4));
+
+            Dictionary<string, Object> dic5 = new Dictionary<string, Object>();
+            dic5.Add("Country", "UK");
+            dic5.Add("DeviceType", "Mobile");
+            dic5.Add("DataCenter", "DC2");
+            points.Add(new Point(200, 200, false, dic5));
+
+            Dictionary<string, Object> dic6 = new Dictionary<string, Object>();
+            dic6.Add("Country", "UK");
+            dic6.Add("DeviceType", AGG_SYMBOL);
+            dic6.Add("DataCenter", "DC2");
+            points.Add(new Point(300, 300, false, dic6));
+
+            Dictionary<string, Object> dic7 = new Dictionary<string, Object>();
+            dic7.Add("Country", "UK");
+            dic7.Add("DeviceType", AGG_SYMBOL);
+            dic7.Add("DataCenter", AGG_SYMBOL);
+            points.Add(new Point(1500, 500, true, dic7));
+
+            Dictionary<string, Object> dic8 = new Dictionary<string, Object>();
+            dic8.Add("Country", "UK");
+            dic8.Add("DeviceType", "Laptop");
+            dic8.Add("DataCenter", AGG_SYMBOL);
+            points.Add(new Point(300, 200, true, dic8));
+
+            Dictionary<string, Object> dic9 = new Dictionary<string, Object>();
+            dic9.Add("Country", "UK");
+            dic9.Add("DeviceType", "Mobile");
+            dic9.Add("DataCenter", AGG_SYMBOL);
+            points.Add(new Point(1200, 300, true, dic9));
+
+            return points;
+        }
+
+        private static Dictionary<string, Object> GetAnomalyDimension()
+        {
+            Dictionary<string, Object> dim = new Dictionary<string, Object>();
+            dim.Add("Country", "UK");
+            dim.Add("DeviceType", AGG_SYMBOL);
+            dim.Add("DataCenter", AGG_SYMBOL);
+
+            return dim;
+        }
+
+        private static DateTime GetTimestamp()
+        {
+            return new DateTime(2020, 3, 23, 0, 0, 0);
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Program.cs

@@ -1,25 +1,29 @@
 using System;
 using System.Reflection;
+using System.Text.RegularExpressions;
 using Samples.Dynamic;
 
 namespace Microsoft.ML.Samples
 {
     public static class Program
     {
-        public static void Main(string[] args) => RunAll();
+        public static void Main(string[] args) => RunAll("LocalizeRootCauseMultipleDimensions");
 
-        internal static void RunAll()
+        internal static void RunAll(string pattern = null)
         {
             int samples = 0;
             foreach (var type in Assembly.GetExecutingAssembly().GetTypes())
             {
-                var sample = type.GetMethod("Example", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy);
-
-                if (sample != null)
+                if (pattern?.Equals(type.Name) ?? false)
                 {
-                    Console.WriteLine(type.Name);
-                    sample.Invoke(null, null);
-                    samples++;
+                    var sample = type.GetMethod("Example", BindingFlags.Public | BindingFlags.Static | BindingFlags.FlattenHierarchy);
+
+                    if (sample != null)
+                    {
+                        Console.WriteLine(type.Name);
+                        sample.Invoke(null, null);
+                        samples++;
+                    }
                 }
             }
 

src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using System.Linq;
 using System.Reflection;
 using Microsoft.ML.Data;
 using Microsoft.ML.Runtime;
@@ -164,6 +165,24 @@ public static SrCnnAnomalyEstimator DetectAnomalyBySrCnn(this TransformsCatalog
         /// </format>
         /// </example>
         public static RootCause LocalizeRootCause(this AnomalyDetectionCatalog catalog, RootCauseLocalizationInput src, double beta = 0.5)
+        {
+            return LocalizeRootCauses(catalog, src, beta).Causes.FirstOrDefault();
+        }
+
+        /// <summary>
+        /// Create <see cref="RootCause"/>, which localizes root causes using decision tree algorithm.
+        /// </summary>
+        /// <param name="catalog">The anomaly detection catalog.</param>
+        /// <param name="src">Root cause's input. The data is an instance of <see cref="Microsoft.ML.TimeSeries.RootCauseLocalizationInput"/>.</param>
+        /// <param name="beta">Beta is a weight parameter for user to choose. It is used when score is calculated for each root cause item. The range of beta should be in [0,1]. For a larger beta, root cause point which has a large difference between value and expected value will get a high score. On the contrary, for a small beta, root cause items which has a high relative change will get a high score.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[LocalizeRootCause](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/LocalizeRootCauseMultipleDimensions.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static RootCauses LocalizeRootCauses(this AnomalyDetectionCatalog catalog, RootCauseLocalizationInput src, double beta = 0.5)
         {
             IHostEnvironment host = CatalogUtils.GetEnvironment(catalog);
 
@@ -175,8 +194,7 @@ public static RootCause LocalizeRootCause(this AnomalyDetectionCatalog catalog,
 
             //find out the root cause
             RootCauseAnalyzer analyzer = new RootCauseAnalyzer(src, beta);
-            RootCause dst = analyzer.Analyze();
-            return dst;
+            return analyzer.AnalyzeAll();
         }
 
         private static void CheckRootCauseInput(IHostEnvironment host, RootCauseLocalizationInput src)