review comments - 4. Single Constructor (FastTreeTweedie)

Author: abgoswam

src/Microsoft.ML.FastTree/FastTreeTweedie.cs

@@ -48,39 +48,12 @@ public sealed partial class FastTreeTweedieTrainer
 
         private SchemaShape.Column[] _outputColumns;
 
-        /// <summary>
-        /// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/>
-        /// </summary>
-        /// <param name="env">The private instance of <see cref="IHostEnvironment"/>.</param>
-        /// <param name="labelColumn">The name of the label column.</param>
-        /// <param name="featureColumn">The name of the feature column.</param>
-        /// <param name="weightColumn">The name for the column containing the initial weight.</param>
-        /// <param name="learningRate">The learning rate.</param>
-        /// <param name="minDatapointsInLeaves">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
-        /// <param name="numLeaves">The max number of leaves in each regression tree.</param>
-        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
-        public FastTreeTweedieTrainer(IHostEnvironment env,
-            string labelColumn = DefaultColumnNames.Label,
-            string featureColumn = DefaultColumnNames.Features,
-            string weightColumn = null,
-            int numLeaves = Defaults.NumLeaves,
-            int numTrees = Defaults.NumTrees,
-            int minDatapointsInLeaves = Defaults.MinDocumentsInLeaves,
-            double learningRate = Defaults.LearningRates)
-            : base(env, TrainerUtils.MakeR4ScalarColumn(labelColumn), featureColumn, weightColumn, null, numLeaves, numTrees, minDatapointsInLeaves, learningRate)
-        {
-            Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
-            Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
-
-            Initialize();
-        }
-
         /// <summary>
         /// Initializes a new instance of <see cref="FastTreeTweedieTrainer"/> by using the <see cref="Options"/> class.
         /// </summary>
         /// <param name="env">The instance of <see cref="IHostEnvironment"/>.</param>
         /// <param name="options">Algorithm advanced settings.</param>
-        public FastTreeTweedieTrainer(IHostEnvironment env, Options options)
+        internal FastTreeTweedieTrainer(IHostEnvironment env, Options options)
             : base(env, options, TrainerUtils.MakeR4ScalarColumn(options.LabelColumn))
         {
             Initialize();

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -225,7 +225,19 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionContext.Regr
         {
             Contracts.CheckValue(ctx, nameof(ctx));
             var env = CatalogUtils.GetEnvironment(ctx);
-            return new FastTreeTweedieTrainer(env, labelColumn, featureColumn, weights, numLeaves, numTrees, minDatapointsInLeaves, learningRate);
+
+            var options = new FastTreeTweedieTrainer.Options()
+            {
+                LabelColumn = labelColumn,
+                FeatureColumn = featureColumn,
+                WeightColumn = weights,
+                NumLeaves = numLeaves,
+                NumTrees = numTrees,
+                MinDocumentsInLeafs = minDatapointsInLeaves,
+                LearningRates = learningRate,
+            };
+
+            return new FastTreeTweedieTrainer(env, options);
         }
 
         /// <summary>

test/Microsoft.ML.Tests/TrainerEstimators/TreeEstimators.cs

@@ -197,7 +197,7 @@ public void GAMRegressorEstimator()
         public void TweedieRegressorEstimator()
         {
             var dataView = GetRegressionPipeline();
-            var trainer = new FastTreeTweedieTrainer(Env, 
+            var trainer = ML.Regression.Trainers.FastTreeTweedie( 
                 new FastTreeTweedieTrainer.Options { 
                     EntropyCoefficient = 0.3,
                     OptimizationAlgorithm = BoostedTreeArgs.OptimizationAlgorithmType.AcceleratedGradientDescent,