Updated doc comments and renamed public types

docs/api-reference/time-series-root-cause-localization.md

@@ -1,7 +1,7 @@
-At Mircosoft, we develop a decision tree based root cause localization method which helps to find out the root causes for an anomaly incident at a specific timestamp incrementally. 
+At Microsoft, we have developed a decision tree based root cause localization method which helps to find out the root causes for an anomaly incident at a specific timestamp incrementally. 
 
 ## Multi-Dimensional Root Cause Localization
-It's a common case that one measure is collected with many dimensions (*e.g.*, Province, ISP) whose values are categorical(*e.g.*, Beijing or Shanghai for dimension Province). When a measure's value deviates from its expected value, this measure encounters anomalies. In such case, operators would like to localize the root cause dimension combinations rapidly and accurately. Multi-dimensional root cause localization is critical to troubleshoot and mitigate such case.
+It's a common case that one measure is collected with many dimensions (*e.g.*, Province, ISP) whose values are categorical(*e.g.*, Beijing or Shanghai for dimension Province). When a measure's value deviates from its expected value, this measure encounters anomalies. In such case, users would like to localize the root cause dimension combinations rapidly and accurately. Multi-dimensional root cause localization is critical to troubleshoot and mitigate such case.
 
 ## Algorithm
 
@@ -13,7 +13,7 @@ The decision tree based root cause localization method is unsupervised, which me
 
 ### Decision Tree
 
-[Decision tree](https://en.wikipedia.org/wiki/Decision_tree) algorithm chooses the highest information gain to split or construct a decision tree.  We use it to choose the dimension which contributes the most to the anomaly. Following are some concepts used in decision tree.
+The [Decision tree](https://en.wikipedia.org/wiki/Decision_tree) algorithm chooses the highest information gain to split or construct a decision tree.  We use it to choose the dimension which contributes the most to the anomaly. Below are some concepts used in decision trees.
 
 #### Information Entropy
 
@@ -30,7 +30,7 @@ $$Gain(D, a) = Ent(D) - \sum_{v=1}^{|V|} \frac{|D^V|}{|D |} Ent(D^v) $$
 
 Where $Ent(D^v)$ is the entropy of set points in D for which dimension $a$ is equal to $v$, $|D|$ is the total number of points in dataset $D$.  $|D^V|$ is the total number of points in dataset $D$ for which dimension $a$ is equal to $v$.
 
-For all aggregated dimensions, we calculate the information for each dimension. The greater the reduction in this uncertainty, the more information is gained about D from dimension $a$.
+For all aggregated dimensions, we calculate the information for each dimension. The greater the reduction in this uncertainty, the more information is gained about $D$ from dimension $a$.
 
 #### Entropy Gain Ratio
 

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

@@ -7,6 +7,7 @@ namespace Samples.Dynamic
 {
     public static class LocalizeRootCause
     {
+        // In the root cause detection input, this string identifies an aggregation as opposed to a dimension value"
         private static string AGG_SYMBOL = "##SUM##";
         public static void Example()
         {
@@ -34,63 +35,63 @@ public static void Example()
             //Score: 0.26670448876705927, Path: DataCenter, Direction: Up, Dimension:[Country, UK] [DeviceType, ##SUM##] [DataCenter, DC1]
         }
 
-        private static List<Point> GetPoints()
+        private static List<TimeSeriesPoint> GetPoints()
         {
-            List<Point> points = new List<Point>();
+            List<TimeSeriesPoint> points = new List<TimeSeriesPoint>();
 
             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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(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));
+            points.Add(new TimeSeriesPoint(1200, 300, true, dic9));
 
             return points;
         }

src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs

@@ -134,7 +134,7 @@ public static SsaSpikeEstimator DetectSpikeBySsa(this TransformsCatalog catalog,
         /// <param name="windowSize">The size of the sliding window for computing spectral residual.</param>
         /// <param name="backAddWindowSize">The number of points to add back of training window. No more than <paramref name="windowSize"/>, usually keep default value.</param>
         /// <param name="lookaheadWindowSize">The number of pervious points used in prediction. No more than <paramref name="windowSize"/>, usually keep default value.</param>
-        /// <param name="averageingWindowSize">The size of sliding window to generate a saliency map for the series. No more than <paramref name="windowSize"/>, usually keep default value.</param>
+        /// <param name="averagingWindowSize">The size of sliding window to generate a saliency map for the series. No more than <paramref name="windowSize"/>, usually keep default value.</param>
         /// <param name="judgementWindowSize">The size of sliding window to calculate the anomaly score for each data point. No more than <paramref name="windowSize"/>.</param>
         /// <param name="threshold">The threshold to determine anomaly, score larger than the threshold is considered as anomaly. Should be in (0,1)</param>
         /// <example>
@@ -145,8 +145,8 @@ public static SsaSpikeEstimator DetectSpikeBySsa(this TransformsCatalog catalog,
         /// </format>
         /// </example>
         public static SrCnnAnomalyEstimator DetectAnomalyBySrCnn(this TransformsCatalog catalog, string outputColumnName, string inputColumnName,
-            int windowSize = 64, int backAddWindowSize = 5, int lookaheadWindowSize = 5, int averageingWindowSize = 3, int judgementWindowSize = 21, double threshold = 0.3)
-            => new SrCnnAnomalyEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, windowSize, backAddWindowSize, lookaheadWindowSize, averageingWindowSize, judgementWindowSize, threshold, inputColumnName);
+            int windowSize = 64, int backAddWindowSize = 5, int lookaheadWindowSize = 5, int averagingWindowSize = 3, int judgementWindowSize = 21, double threshold = 0.3)
+            => new SrCnnAnomalyEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, windowSize, backAddWindowSize, lookaheadWindowSize, averagingWindowSize, judgementWindowSize, threshold, inputColumnName);
 
         /// <summary>
         /// Create <see cref="SrCnnEntireAnomalyDetector"/>, which detects timeseries anomalies for entire input using SRCNN algorithm.
@@ -156,7 +156,7 @@ public static SrCnnAnomalyEstimator DetectAnomalyBySrCnn(this TransformsCatalog
         /// <param name="outputColumnName">Name of the column resulting from data processing of <paramref name="inputColumnName"/>.
         /// The column data is a vector of <see cref="System.Double"/>. The length of this vector varies depending on <paramref name="detectMode"/>.</param>
         /// <param name="inputColumnName">Name of column to process. The column data must be <see cref="System.Double"/>.</param>
-        /// <param name="threshold">The threshold to determine anomaly, score larger than the threshold is considered as anomaly. Must be in [0,1]. Default value is 0.3.</param>
+        /// <param name="threshold">The threshold to determine an anomaly. An anomaly is detected when the calculated SR raw score for a given point is more than the set threshold. This threshold must  fall between [0,1], and its default value is 0.3.</param>
         /// <param name="batchSize">Divide the input data into batches to fit srcnn model.
         /// When set to -1, use the whole input to fit model instead of batch by batch, when set to a positive integer, use this number as batch size.
         /// Must be -1 or a positive integer no less than 12. Default value is 1024.</param>
@@ -165,6 +165,7 @@ public static SrCnnAnomalyEstimator DetectAnomalyBySrCnn(this TransformsCatalog
         /// When set to AnomalyOnly, the output vector would be a 3-element Double vector of (IsAnomaly, RawScore, Mag).
         /// When set to AnomalyAndExpectedValue, the output vector would be a 4-element Double vector of (IsAnomaly, RawScore, Mag, ExpectedValue).
         /// When set to AnomalyAndMargin, the output vector would be a 7-element Double vector of (IsAnomaly, AnomalyScore, Mag, ExpectedValue, BoundaryUnit, UpperBoundary, LowerBoundary).
+        /// The RawScore is output by SR to determine whether a point is an anomaly or not, under AnomalyAndMargin mode, when a point is an anomaly, an AnomalyScore will be calculated according to sensitivity setting.
         /// Default value is AnomalyOnly.</param>
         /// <example>
         /// <format type="text/markdown">
@@ -182,7 +183,10 @@ public static IDataView DetectEntireAnomalyBySrCnn(this AnomalyDetectionCatalog
         /// </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>
+        /// <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 items which have a large difference between value and expected value will get a high score.
+        /// For a small beta, root cause items which have a high relative change will get a low score.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[