Some docs

Author: wschin

src/Microsoft.ML.FastTree/FastTree.cs

@@ -2813,7 +2813,7 @@ public abstract class TreeEnsembleModelParameters :
     {
         // The below two properties are necessary for tree Visualizer
         [BestFriend]
-        internal InternalTreeEnsemble TrainedEnsemble;
+        internal InternalTreeEnsemble TrainedEnsemble { get; }
 
         int ITreeEnsemble.NumTrees => TrainedEnsemble.NumTrees;
 
@@ -2919,6 +2919,12 @@ protected TreeEnsembleModelParameters(IHostEnvironment env, string name, ModelLo
             OutputType = NumberType.Float;
         }
 
+        /// <summary>
+        /// This function should be implemented in derived classs to create strongly-typed TreeEnsemble
+        /// from <see cref="TrainedEnsemble"/> and possibly other internal attributes in
+        /// <see cref="TreeEnsembleModelParameters"/>. This also implies we always call this function
+        /// after initializing <see cref="TrainedEnsemble"/>.
+        /// </summary>
         protected abstract void CreateTreeEnsembleFromInternalDataStructure();
 
         [BestFriend]
@@ -3382,6 +3388,16 @@ public TreeNode(Dictionary<string, object> keyValues)
         }
     }
 
+    /// <summary>
+    /// <see cref="TreeEnsembleModelParametersBasedOnRegressionTree"/> is derived from
+    /// <see cref="TreeEnsembleModelParameters"/> plus a strongly-typed public attribute,
+    /// <see cref="TrainedTreeEnsemble"/>, for exposing trained model's details to users.
+    /// Its function, <see cref="CreateTreeEnsembleFromInternalDataStructure"/>, is
+    /// called to create <see cref="TrainedTreeEnsemble"/> inside <see cref="TreeEnsembleModelParameters"/>.
+    /// Note that the major difference between <see cref="TreeEnsembleModelParametersBasedOnQuantileRegressionTree"/>
+    /// and <see cref="TreeEnsembleModelParametersBasedOnRegressionTree"/> is the type of
+    /// <see cref="TrainedTreeEnsemble"/>.
+    /// </summary>
     public abstract class TreeEnsembleModelParametersBasedOnRegressionTree : TreeEnsembleModelParameters
     {
         private TreeEnsemble<RegressionTree> _trainedTreeEnsemble;
@@ -3403,6 +3419,9 @@ protected TreeEnsembleModelParametersBasedOnRegressionTree(IHostEnvironment env,
         {
         }
 
+        /// <summary>
+        /// See <see cref="TreeEnsembleModelParameters.CreateTreeEnsembleFromInternalDataStructure"/>.
+        /// </summary>
         protected override void CreateTreeEnsembleFromInternalDataStructure()
         {
             var trees = TrainedEnsemble.Trees.Select(tree => new RegressionTree(tree));
@@ -3411,6 +3430,16 @@ protected override void CreateTreeEnsembleFromInternalDataStructure()
         }
     }
 
+    /// <summary>
+    /// <see cref="TreeEnsembleModelParametersBasedOnQuantileRegressionTree"/> is derived from
+    /// <see cref="TreeEnsembleModelParameters"/> plus a strongly-typed public attribute,
+    /// <see cref="TrainedTreeEnsemble"/>, for exposing trained model's details to users.
+    /// Its function, <see cref="CreateTreeEnsembleFromInternalDataStructure"/>, is
+    /// called to create <see cref="TrainedTreeEnsemble"/> inside <see cref="TreeEnsembleModelParameters"/>.
+    /// Note that the major difference between <see cref="TreeEnsembleModelParametersBasedOnQuantileRegressionTree"/>
+    /// and <see cref="TreeEnsembleModelParametersBasedOnRegressionTree"/> is the type of
+    /// <see cref="TrainedTreeEnsemble"/>.
+    /// </summary>
     public abstract class TreeEnsembleModelParametersBasedOnQuantileRegressionTree : TreeEnsembleModelParameters
     {
         private TreeEnsemble<QuantileRegressionTree> _trainedTreeEnsemble;
@@ -3432,6 +3461,9 @@ protected TreeEnsembleModelParametersBasedOnQuantileRegressionTree(IHostEnvironm
         {
         }
 
+        /// <summary>
+        /// See <see cref="TreeEnsembleModelParameters.CreateTreeEnsembleFromInternalDataStructure"/>.
+        /// </summary>
         protected override void CreateTreeEnsembleFromInternalDataStructure()
         {
             var trees = TrainedEnsemble.Trees.Select(tree => new QuantileRegressionTree((InternalQuantileRegressionTree)tree));