Scrub Fast Tree Family (#2753)

Author: wschin

docs/samples/Microsoft.ML.Samples/Dynamic/FastTreeRegression.cs

@@ -30,22 +30,12 @@ public static void Example()
             // We will train a FastTreeRegression model with 1 tree on these two columns to predict Age.
             string outputColumnName = "Features";
             var pipeline = ml.Transforms.Concatenate(outputColumnName, new[] { "Parity", "Induced" })
-                .Append(ml.Regression.Trainers.FastTree(labelColumnName: "Age", featureColumnName: outputColumnName, numTrees: 1, numLeaves: 2, minDatapointsInLeaves: 1));
+                .Append(ml.Regression.Trainers.FastTree(labelColumnName: "Age", featureColumnName: outputColumnName, numberOfTrees: 1, numberOfLeaves: 2, minimumExampleCountPerLeaf: 1));
 
             var model = pipeline.Fit(trainData);
 
             // Get the trained model parameters.
             var modelParams = model.LastTransformer.Model;
-
-            // Let's see where an example with Parity = 1 and Induced = 1 would end up in the single trained tree.
-            var testRow = new VBuffer<float>(2, new[] { 1.0f, 1.0f });
-            // Use the path object to pass to GetLeaf, which will populate path with the IDs of th nodes from root to leaf.
-            List<int> path = default;
-            // Get the ID of the leaf this example ends up in tree 0.
-            var leafID = modelParams.GetLeaf(0, in testRow, ref path);
-            // Get the leaf value for this leaf ID in tree 0.
-            var leafValue = modelParams.GetLeafValue(0, leafID);
-            Console.WriteLine("The leaf value in tree 0 is: " + leafValue);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/GeneralizedAdditiveModels.cs

@@ -28,7 +28,7 @@ public static void Example()
                 .ToArray();
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
                     .Append(mlContext.Regression.Trainers.GeneralizedAdditiveModels(
-                        labelColumnName: labelName, featureColumnName: "Features", maxBins: 16));
+                        labelColumnName: labelName, featureColumnName: "Features", maxBinCountPerFeature: 16));
             var fitPipeline = pipeline.Fit(data);
 
             // Extract the model from the pipeline
@@ -37,7 +37,7 @@ public static void Example()
             // Now investigate the properties of the Generalized Additive Model: The intercept and shape functions.
 
             // The intercept for the GAM models represent the average prediction for the training data
-            var intercept = gamModel.Intercept;
+            var intercept = gamModel.Bias;
             // Expected output: Average predicted cost: 22.53
             Console.WriteLine($"Average predicted cost: {intercept:0.00}");
 
@@ -93,7 +93,7 @@ public static void Example()
             // Distillation." <a href='https://arxiv.org/abs/1710.06169'>arXiv:1710.06169</a>."
             Console.WriteLine();
             Console.WriteLine("Student-Teacher Ratio");
-            for (int i = 0; i < teacherRatioBinUpperBounds.Length; i++)
+            for (int i = 0; i < teacherRatioBinUpperBounds.Count; i++)
             {
                 Console.WriteLine($"x < {teacherRatioBinUpperBounds[i]:0.00} => {teacherRatioBinEffects[i]:0.000}");
             }

docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs

@@ -78,9 +78,9 @@ public static void FastTreeBinaryClassification()
                         Score: mlContext.BinaryClassification.Trainers.FastTree(
                             row.Label,
                             row.Features,
-                            numTrees: 100, // try: (int) 20-2000
-                            numLeaves: 20, // try: (int) 2-128
-                            minDatapointsInLeaves: 10, // try: (int) 1-100
+                            numberOfTrees: 100, // try: (int) 20-2000
+                            numberOfLeaves: 20, // try: (int) 2-128
+                            minimumExampleCountPerLeaf: 10, // try: (int) 1-100
                             learningRate: 0.2))) // try: (float) 0.025-0.4
                 .Append(row => (
                     Label: row.Label,

docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs

@@ -38,9 +38,9 @@ public static void FastTreeRegression()
                 .Append(r => (r.label, score: mlContext.Regression.Trainers.FastTree(
                                             r.label,
                                             r.features,
-                                            numTrees: 100, // try: (int) 20-2000
-                                            numLeaves: 20, // try: (int) 2-128
-                                            minDatapointsInLeaves: 10, // try: (int) 1-100
+                                            numberOfTrees: 100, // try: (int) 20-2000
+                                            numberOfLeaves: 20, // try: (int) 2-128
+                                            minimumExampleCountPerLeaf: 10, // try: (int) 1-100
                                             learningRate: 0.2, // try: (float) 0.025-0.4    
                                         onFit: p => pred = p)
                                 )

src/Microsoft.ML.FastTree/BoostingFastTree.cs

@@ -18,16 +18,16 @@ private protected BoostingFastTreeTrainerBase(IHostEnvironment env, TOptions opt
 
         private protected BoostingFastTreeTrainerBase(IHostEnvironment env,
             SchemaShape.Column label,
-            string featureColumn,
-            string weightColumn,
-            string groupIdColumn,
-            int numLeaves,
-            int numTrees,
-            int minDatapointsInLeaves,
+            string featureColumnName,
+            string exampleWeightColumnName,
+            string rowGroupColumnName,
+            int numberOfLeaves,
+            int numberOfTrees,
+            int minimumExampleCountPerLeaf,
             double learningRate)
-            : base(env, label, featureColumn, weightColumn, groupIdColumn, numLeaves, numTrees, minDatapointsInLeaves)
+            : base(env, label, featureColumnName, exampleWeightColumnName, rowGroupColumnName, numberOfLeaves, numberOfTrees, minimumExampleCountPerLeaf)
         {
-            FastTreeTrainerOptions.LearningRates = learningRate;
+            FastTreeTrainerOptions.LearningRate = learningRate;
         }
 
         private protected override void CheckOptions(IChannel ch)
@@ -40,10 +40,10 @@ private protected override void CheckOptions(IChannel ch)
             if (FastTreeTrainerOptions.CompressEnsemble && FastTreeTrainerOptions.WriteLastEnsemble)
                 throw ch.Except("Ensemble compression cannot be done when forcing to write last ensemble (hl)");
 
-            if (FastTreeTrainerOptions.NumLeaves > 2 && FastTreeTrainerOptions.HistogramPoolSize > FastTreeTrainerOptions.NumLeaves - 1)
+            if (FastTreeTrainerOptions.NumberOfLeaves > 2 && FastTreeTrainerOptions.HistogramPoolSize > FastTreeTrainerOptions.NumberOfLeaves - 1)
                 throw ch.Except("Histogram pool size (ps) must be at least 2.");
 
-            if (FastTreeTrainerOptions.NumLeaves > 2 && FastTreeTrainerOptions.HistogramPoolSize > FastTreeTrainerOptions.NumLeaves - 1)
+            if (FastTreeTrainerOptions.NumberOfLeaves > 2 && FastTreeTrainerOptions.HistogramPoolSize > FastTreeTrainerOptions.NumberOfLeaves - 1)
                 throw ch.Except("Histogram pool size (ps) must be at most numLeaves - 1.");
 
             if (FastTreeTrainerOptions.EnablePruning && !HasValidSet)
@@ -61,12 +61,12 @@ private protected override void CheckOptions(IChannel ch)
         private protected override TreeLearner ConstructTreeLearner(IChannel ch)
         {
             return new LeastSquaresRegressionTreeLearner(
-                TrainSet, FastTreeTrainerOptions.NumLeaves, FastTreeTrainerOptions.MinDocumentsInLeafs, FastTreeTrainerOptions.EntropyCoefficient,
+                TrainSet, FastTreeTrainerOptions.NumberOfLeaves, FastTreeTrainerOptions.MinimumExampleCountPerLeaf, FastTreeTrainerOptions.EntropyCoefficient,
                 FastTreeTrainerOptions.FeatureFirstUsePenalty, FastTreeTrainerOptions.FeatureReusePenalty, FastTreeTrainerOptions.SoftmaxTemperature,
-                FastTreeTrainerOptions.HistogramPoolSize, FastTreeTrainerOptions.RngSeed, FastTreeTrainerOptions.SplitFraction, FastTreeTrainerOptions.FilterZeroLambdas,
-                FastTreeTrainerOptions.AllowEmptyTrees, FastTreeTrainerOptions.GainConfidenceLevel, FastTreeTrainerOptions.MaxCategoricalGroupsPerNode,
-                FastTreeTrainerOptions.MaxCategoricalSplitPoints, BsrMaxTreeOutput(), ParallelTraining,
-                FastTreeTrainerOptions.MinDocsPercentageForCategoricalSplit, FastTreeTrainerOptions.Bundling, FastTreeTrainerOptions.MinDocsForCategoricalSplit, FastTreeTrainerOptions.Bias);
+                FastTreeTrainerOptions.HistogramPoolSize, FastTreeTrainerOptions.Seed, FastTreeTrainerOptions.FeatureFractionPerSplit, FastTreeTrainerOptions.FilterZeroLambdas,
+                FastTreeTrainerOptions.AllowEmptyTrees, FastTreeTrainerOptions.GainConfidenceLevel, FastTreeTrainerOptions.MaximumCategoricalGroupCountPerNode,
+                FastTreeTrainerOptions.MaximumCategoricalSplitPointCount, BsrMaxTreeOutput(), ParallelTraining,
+                FastTreeTrainerOptions.MinimumExampleFractionForCategoricalSplit, FastTreeTrainerOptions.Bundling, FastTreeTrainerOptions.MinimumExamplesForCategoricalSplit, FastTreeTrainerOptions.Bias);
         }
 
         private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(IChannel ch)
@@ -94,7 +94,7 @@ private protected override OptimizationAlgorithm ConstructOptimizationAlgorithm(
             optimizationAlgorithm.ObjectiveFunction = ConstructObjFunc(ch);
             optimizationAlgorithm.Smoothing = FastTreeTrainerOptions.Smoothing;
             optimizationAlgorithm.DropoutRate = FastTreeTrainerOptions.DropoutRate;
-            optimizationAlgorithm.DropoutRng = new Random(FastTreeTrainerOptions.RngSeed);
+            optimizationAlgorithm.DropoutRng = new Random(FastTreeTrainerOptions.Seed);
             optimizationAlgorithm.PreScoreUpdateEvent += PrintTestGraph;
 
             return optimizationAlgorithm;
@@ -162,7 +162,7 @@ private protected override int GetBestIteration(IChannel ch)
         private protected double BsrMaxTreeOutput()
         {
             if (FastTreeTrainerOptions.BestStepRankingRegressionTrees)
-                return FastTreeTrainerOptions.MaxTreeOutput;
+                return FastTreeTrainerOptions.MaximumTreeOutput;
             else
                 return -1;
         }