More trainer extensions, bug fixes and consistency across trainer extensions

docs/code/MlNetCookBook.md

@@ -960,7 +960,7 @@ var learningPipeline = reader.MakeNewEstimator()
 IEstimator<ITransformer> dynamicPipe = learningPipeline.AsDynamic;
 
 // Create a binary classification trainer.
-var binaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron();
+var binaryTrainer = mlContext.BinaryClassification.Trainers.AveragedPerceptron("Label", "Features");
 
 // Append the OVA learner to the pipeline.
 dynamicPipe = dynamicPipe.Append(new Ova(mlContext, binaryTrainer));

src/Microsoft.ML.Data/Transforms/NormalizerCatalog.cs

@@ -56,7 +56,7 @@ public static NormalizingEstimator Normalize(this TransformsCatalog catalog,
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[ConcatWith] (](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/MinMaxNormalizer.cs?range =6-11,16-86)]
+        /// [!code-csharp[ConcatWith] (](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/MinMaxNormalizer.cs?range=6-11,16-86)]
         /// ]]>
         /// </format>
         /// </example>

src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs

@@ -59,7 +59,7 @@ public Arguments()
                 BasePredictors = new[]
                 {
                     ComponentFactoryUtils.CreateFromFunction(
-                        env => new MulticlassLogisticRegression(env, FeatureColumn, LabelColumn))
+                        env => new MulticlassLogisticRegression(env, LabelColumn, FeatureColumn))
                 };
             }
         }

src/Microsoft.ML.FastTree/FastTree.cs

@@ -121,10 +121,10 @@ private protected FastTreeTrainerBase(IHostEnvironment env,
             Args.FeatureColumn = featureColumn;
 
             if (weightColumn != null)
-                Args.WeightColumn = Optional<string>.Explicit(weightColumn); ;
+                Args.WeightColumn = Optional<string>.Explicit(weightColumn);
 
             if (groupIdColumn != null)
-                Args.GroupIdColumn = Optional<string>.Explicit(groupIdColumn); ;
+                Args.GroupIdColumn = Optional<string>.Explicit(groupIdColumn);
 
             // The discretization step renders this trainer non-parametric, and therefore it does not need normalization.
             // Also since it builds its own internal discretized columnar structures, it cannot benefit from caching.

src/Microsoft.ML.FastTree/RandomForestRegression.cs

@@ -160,11 +160,11 @@ public sealed class Arguments : FastForestArgumentsBase
         /// <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="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
+        /// <param name="weightColumn">The optional name for the column containing the initial weight.</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>
+        /// <param name="minDocumentsInLeafs">The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data.</param>
+        /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
         public FastForestRegression(IHostEnvironment env,
             string labelColumn,

src/Microsoft.ML.FastTree/TreeTrainersCatalog.cs

@@ -10,7 +10,7 @@
 namespace Microsoft.ML
 {
     /// <summary>
-    /// FastTree <see cref="TrainContextBase"/> extension methods.
+    /// Tree <see cref="TrainContextBase"/> extension methods.
     /// </summary>
     public static class TreeExtensions
     {
@@ -27,8 +27,8 @@ public static class TreeExtensions
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static FastTreeRegressionTrainer FastTree(this RegressionContext.RegressionTrainers ctx,
-            string label = DefaultColumnNames.Label,
-            string features = DefaultColumnNames.Features,
+            string label,
+            string features,
             string weights = null,
             int numLeaves = Defaults.NumLeaves,
             int numTrees = Defaults.NumTrees,
@@ -54,8 +54,8 @@ public static FastTreeRegressionTrainer FastTree(this RegressionContext.Regressi
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
-            string label = DefaultColumnNames.Label,
-            string features = DefaultColumnNames.Features,
+            string label,
+            string features,
             string weights = null,
             int numLeaves = Defaults.NumLeaves,
             int numTrees = Defaults.NumTrees,
@@ -71,7 +71,7 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
         /// <summary>
         /// Ranks a series of inputs based on their relevance, training a decision tree ranking model through the <see cref="FastTreeRankingTrainer"/>.
         /// </summary>
-        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
+        /// <param name="ctx">The <see cref="RankingContext"/>.</param>
         /// <param name="label">The label column.</param>
         /// <param name="features">The features column.</param>
         /// <param name="groupId">The groupId column.</param>
@@ -82,9 +82,9 @@ public static FastTreeBinaryClassificationTrainer FastTree(this BinaryClassifica
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainers ctx,
-            string label = DefaultColumnNames.Label,
-            string groupId = DefaultColumnNames.GroupId,
-            string features = DefaultColumnNames.Features,
+            string label,
+            string features,
+            string groupId ,
             string weights = null,
             int numLeaves = Defaults.NumLeaves,
             int numTrees = Defaults.NumTrees,
@@ -100,21 +100,17 @@ public static FastTreeRankingTrainer FastTree(this RankingContext.RankingTrainer
         /// <summary>
         /// Predict a target using a decision tree regression model trained with the <see cref="FastTreeRegressionTrainer"/>.
         /// </summary>
-        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
+        /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
         /// <param name="label">The label column.</param>
         /// <param name="features">The features column.</param>
         /// <param name="weights">The optional weights column.</param>
-        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
-        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
-        public static BinaryClassificationGamTrainer GeneralizedAdditiveMethods(this RegressionContext.RegressionTrainers ctx,
+        public static BinaryClassificationGamTrainer GeneralizedAdditiveMethods(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
             string label = DefaultColumnNames.Label,
             string features = DefaultColumnNames.Features,
             string weights = null,
-            int numLeaves = Defaults.NumLeaves,
-            int numTrees = Defaults.NumTrees,
             int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
             double learningRate = Defaults.LearningRates,
             Action<BinaryClassificationGamTrainer.Arguments> advancedSettings = null)
@@ -125,23 +121,19 @@ public static BinaryClassificationGamTrainer GeneralizedAdditiveMethods(this Reg
         }
 
         /// <summary>
-        /// Predict a target using a decision tree binary classification model trained with the <see cref="FastTreeBinaryClassificationTrainer"/>.
+        /// Predict a target using a decision tree binary classification model trained with the <see cref="RegressionGamTrainer"/>.
         /// </summary>
-        /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
+        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
         /// <param name="label">The label column.</param>
         /// <param name="features">The features column.</param>
         /// <param name="weights">The optional weights column.</param>
-        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
-        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
         /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
-        public static RegressionGamTrainer GeneralizedAdditiveMethods(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
-            string label = DefaultColumnNames.Label,
-            string features = DefaultColumnNames.Features,
+        public static RegressionGamTrainer GeneralizedAdditiveMethods(this RegressionContext.RegressionTrainers ctx,
+            string label,
+            string features,
             string weights = null,
-            int numLeaves = Defaults.NumLeaves,
-            int numTrees = Defaults.NumTrees,
             int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
             double learningRate = Defaults.LearningRates,
             Action<RegressionGamTrainer.Arguments> advancedSettings = null)
@@ -164,8 +156,8 @@ public static RegressionGamTrainer GeneralizedAdditiveMethods(this BinaryClassif
         /// <param name="learningRate">The learning rate.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionContext.RegressionTrainers ctx,
-            string label = DefaultColumnNames.Label,
-            string features = DefaultColumnNames.Features,
+            string label,
+            string features,
             string weights = null,
             int numLeaves = Defaults.NumLeaves,
             int numTrees = Defaults.NumTrees,
@@ -177,5 +169,59 @@ public static FastTreeTweedieTrainer FastTreeTweedie(this RegressionContext.Regr
             var env = CatalogUtils.GetEnvironment(ctx);
             return new FastTreeTweedieTrainer(env, label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings);
         }
+
+        /// <summary>
+        /// Predict a target using a decision tree regression model trained with the <see cref="FastForestRegression"/>.
+        /// </summary>
+        /// <param name="ctx">The <see cref="RegressionContext"/>.</param>
+        /// <param name="label">The label column.</param>
+        /// <param name="features">The features column.</param>
+        /// <param name="weights">The optional weights column.</param>
+        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
+        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
+        /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
+        /// <param name="learningRate">The learning rate.</param>
+        /// <param name="advancedSettings">Algorithm advanced settings.</param>
+        public static FastForestRegression FastForest(this RegressionContext.RegressionTrainers ctx,
+            string label,
+            string features,
+            string weights = null,
+            int numLeaves = Defaults.NumLeaves,
+            int numTrees = Defaults.NumTrees,
+            int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
+            double learningRate = Defaults.LearningRates,
+            Action<FastForestRegression.Arguments> advancedSettings = null)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new FastForestRegression(env, label, features, weights, numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings);
+        }
+
+        /// <summary>
+        /// Predict a target using a decision tree regression model trained with the <see cref="FastForestClassification"/>.
+        /// </summary>
+        /// <param name="ctx">The <see cref="BinaryClassificationContext"/>.</param>
+        /// <param name="label">The label column.</param>
+        /// <param name="features">The features column.</param>
+        /// <param name="weights">The optional weights column.</param>
+        /// <param name="numTrees">Total number of decision trees to create in the ensemble.</param>
+        /// <param name="numLeaves">The maximum number of leaves per decision tree.</param>
+        /// <param name="minDatapointsInLeafs">The minimal number of datapoints allowed in a leaf of the tree, out of the subsampled data.</param>
+        /// <param name="learningRate">The learning rate.</param>
+        /// <param name="advancedSettings">Algorithm advanced settings.</param>
+        public static FastForestClassification FastForest(this BinaryClassificationContext.BinaryClassificationTrainers ctx,
+            string label,
+            string features,
+            string weights = null,
+            int numLeaves = Defaults.NumLeaves,
+            int numTrees = Defaults.NumTrees,
+            int minDatapointsInLeafs = Defaults.MinDocumentsInLeafs,
+            double learningRate = Defaults.LearningRates,
+            Action<FastForestClassification.Arguments> advancedSettings = null)
+        {
+            Contracts.CheckValue(ctx, nameof(ctx));
+            var env = CatalogUtils.GetEnvironment(ctx);
+            return new FastForestClassification(env, label, features, weights,numLeaves, numTrees, minDatapointsInLeafs, learningRate, advancedSettings);
+        }
     }
 }

src/Microsoft.ML.HalLearners/OlsLinearRegression.cs

@@ -68,13 +68,16 @@ public sealed class Arguments : LearnerInputBaseWithWeight
         /// Initializes a new instance of <see cref="OlsLinearRegressionTrainer"/>
         /// </summary>
         /// <param name="env">The environment to use.</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 example weight column.</param>
+        /// <param name="label">The name of the label column.</param>
+        /// <param name="features">The name of the feature column.</param>
+        /// <param name="weights">The name for the optional example weight column.</param>
         /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
-        public OlsLinearRegressionTrainer(IHostEnvironment env, string featureColumn, string labelColumn,
-            string weightColumn = null, Action<Arguments> advancedSettings = null)
-            : this(env, ArgsInit(featureColumn, labelColumn, weightColumn, advancedSettings))
+        public OlsLinearRegressionTrainer(IHostEnvironment env,
+            string label,
+            string features,
+            string weights = null,
+            Action<Arguments> advancedSettings = null)
+            : this(env, ArgsInit(features, label, weights, advancedSettings))
         {
         }
 

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -154,20 +154,23 @@ protected override TPredictor TrainModelCore(TrainContext context)
         /// Initializes a new instance of <see cref="SymSgdClassificationTrainer"/>
         /// </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="label">The name of the label column.</param>
+        /// <param name="features">The name of the feature column.</param>
         /// <param name="advancedSettings">A delegate to apply all the advanced arguments to the algorithm.</param>
-        public SymSgdClassificationTrainer(IHostEnvironment env, string featureColumn, string labelColumn, Action<Arguments> advancedSettings = null)
-            : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(featureColumn),
-                  TrainerUtils.MakeBoolScalarLabel(labelColumn))
+        public SymSgdClassificationTrainer(IHostEnvironment env,
+            string label,
+            string features,
+            Action<Arguments> advancedSettings = null)
+            : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(features),
+                  TrainerUtils.MakeBoolScalarLabel(label))
         {
             _args = new Arguments();
 
             // Apply the advanced args, if the user supplied any.
             _args.Check(Host);
             advancedSettings?.Invoke(_args);
-            _args.FeatureColumn = featureColumn;
-            _args.LabelColumn = labelColumn;
+            _args.FeatureColumn = features;
+            _args.LabelColumn = label;
 
             Info = new TrainerInfo();
         }

src/Microsoft.ML.KMeansClustering/KMeansCatalog.cs

@@ -23,7 +23,7 @@ public static class KMeansClusteringExtensions
         /// <param name="clustersCount">The number of clusters to use for KMeans.</param>
         /// <param name="advancedSettings">Algorithm advanced settings.</param>
         public static KMeansPlusPlusTrainer KMeans(this ClusteringContext.ClusteringTrainers ctx,
-           string features = DefaultColumnNames.Features,
+           string features,
            string weights = null,
            int clustersCount = KMeansPlusPlusTrainer.Defaults.K,
            Action<KMeansPlusPlusTrainer.Arguments> advancedSettings = null)