LightGBM, Tweedie, and GAM trainers converted to tainerEstimators (#962)

* GAM and LightGBM conversion.
Utility methods for the column shape creations.

* CheckNonEmpty, rather than CheckValue for null checks.

* adjusting ctor visibility

Author: sfilipi

src/Microsoft.ML.Data/Training/TrainerUtils.cs

@@ -2,12 +2,11 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-using Float = System.Single;
-
-using System;
-using System.Collections.Generic;
+using Microsoft.ML.Core.Data;
 using Microsoft.ML.Runtime.Data;
 using Microsoft.ML.Runtime.Internal.Utilities;
+using System;
+using System.Collections.Generic;
 
 namespace Microsoft.ML.Runtime.Training
 {
@@ -238,19 +237,15 @@ private static Func<int, bool> CreatePredicate(RoleMappedData data, CursOpt opt,
         /// This does not verify that the columns exist, but merely activates the ones that do exist.
         /// </summary>
         public static IRowCursor CreateRowCursor(this RoleMappedData data, CursOpt opt, IRandom rand, IEnumerable<int> extraCols = null)
-        {
-            return data.Data.GetRowCursor(CreatePredicate(data, opt, extraCols), rand);
-        }
+            => data.Data.GetRowCursor(CreatePredicate(data, opt, extraCols), rand);
 
         /// <summary>
         /// Create a row cursor set for the RoleMappedData with the indicated standard columns active.
         /// This does not verify that the columns exist, but merely activates the ones that do exist.
         /// </summary>
         public static IRowCursor[] CreateRowCursorSet(this RoleMappedData data, out IRowCursorConsolidator consolidator,
             CursOpt opt, int n, IRandom rand, IEnumerable<int> extraCols = null)
-        {
-            return data.Data.GetRowCursorSet(out consolidator, CreatePredicate(data, opt, extraCols), n, rand);
-        }
+            => data.Data.GetRowCursorSet(out consolidator, CreatePredicate(data, opt, extraCols), n, rand);
 
         private static void AddOpt(HashSet<int> cols, ColumnInfo info)
         {
@@ -260,32 +255,32 @@ private static void AddOpt(HashSet<int> cols, ColumnInfo info)
         }
 
         /// <summary>
-        /// Get the getter for the feature column, assuming it is a vector of Float.
+        /// Get the getter for the feature column, assuming it is a vector of float.
         /// </summary>
-        public static ValueGetter<VBuffer<Float>> GetFeatureFloatVectorGetter(this IRow row, RoleMappedSchema schema)
+        public static ValueGetter<VBuffer<float>> GetFeatureFloatVectorGetter(this IRow row, RoleMappedSchema schema)
         {
             Contracts.CheckValue(row, nameof(row));
             Contracts.CheckValue(schema, nameof(schema));
             Contracts.CheckParam(schema.Schema == row.Schema, nameof(schema), "schemas don't match!");
             Contracts.CheckParam(schema.Feature != null, nameof(schema), "Missing feature column");
 
-            return row.GetGetter<VBuffer<Float>>(schema.Feature.Index);
+            return row.GetGetter<VBuffer<float>>(schema.Feature.Index);
         }
 
         /// <summary>
-        /// Get the getter for the feature column, assuming it is a vector of Float.
+        /// Get the getter for the feature column, assuming it is a vector of float.
         /// </summary>
-        public static ValueGetter<VBuffer<Float>> GetFeatureFloatVectorGetter(this IRow row, RoleMappedData data)
+        public static ValueGetter<VBuffer<float>> GetFeatureFloatVectorGetter(this IRow row, RoleMappedData data)
         {
             Contracts.CheckValue(data, nameof(data));
             return GetFeatureFloatVectorGetter(row, data.Schema);
         }
 
         /// <summary>
-        /// Get a getter for the label as a Float. This assumes that the label column type
+        /// Get a getter for the label as a float. This assumes that the label column type
         /// has already been validated as appropriate for the kind of training being done.
         /// </summary>
-        public static ValueGetter<Float> GetLabelFloatGetter(this IRow row, RoleMappedSchema schema)
+        public static ValueGetter<float> GetLabelFloatGetter(this IRow row, RoleMappedSchema schema)
         {
             Contracts.CheckValue(row, nameof(row));
             Contracts.CheckValue(schema, nameof(schema));
@@ -296,10 +291,10 @@ public static ValueGetter<Float> GetLabelFloatGetter(this IRow row, RoleMappedSc
         }
 
         /// <summary>
-        /// Get a getter for the label as a Float. This assumes that the label column type
+        /// Get a getter for the label as a float. This assumes that the label column type
         /// has already been validated as appropriate for the kind of training being done.
         /// </summary>
-        public static ValueGetter<Float> GetLabelFloatGetter(this IRow row, RoleMappedData data)
+        public static ValueGetter<float> GetLabelFloatGetter(this IRow row, RoleMappedData data)
         {
             Contracts.CheckValue(data, nameof(data));
             return GetLabelFloatGetter(row, data.Schema);
@@ -308,7 +303,7 @@ public static ValueGetter<Float> GetLabelFloatGetter(this IRow row, RoleMappedDa
         /// <summary>
         /// Get the getter for the weight column, or null if there is no weight column.
         /// </summary>
-        public static ValueGetter<Float> GetOptWeightFloatGetter(this IRow row, RoleMappedSchema schema)
+        public static ValueGetter<float> GetOptWeightFloatGetter(this IRow row, RoleMappedSchema schema)
         {
             Contracts.CheckValue(row, nameof(row));
             Contracts.CheckValue(schema, nameof(schema));
@@ -318,10 +313,10 @@ public static ValueGetter<Float> GetOptWeightFloatGetter(this IRow row, RoleMapp
             var col = schema.Weight;
             if (col == null)
                 return null;
-            return RowCursorUtils.GetGetterAs<Float>(NumberType.Float, row, col.Index);
+            return RowCursorUtils.GetGetterAs<float>(NumberType.Float, row, col.Index);
         }
 
-        public static ValueGetter<Float> GetOptWeightFloatGetter(this IRow row, RoleMappedData data)
+        public static ValueGetter<float> GetOptWeightFloatGetter(this IRow row, RoleMappedData data)
         {
             Contracts.CheckValue(data, nameof(data));
             return GetOptWeightFloatGetter(row, data.Schema);
@@ -348,6 +343,45 @@ public static ValueGetter<ulong> GetOptGroupGetter(this IRow row, RoleMappedData
             Contracts.CheckValue(data, nameof(data));
             return GetOptGroupGetter(row, data.Schema);
         }
+
+        /// <summary>
+        /// The <see cref="SchemaShape.Column"/> for the label column for binary classification tasks.
+        /// </summary>
+        /// <param name="labelColumn">name of the label column</param>
+        public static SchemaShape.Column MakeBoolScalarLabel(string labelColumn)
+            => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, BoolType.Instance, false);
+
+        /// <summary>
+        /// The <see cref="SchemaShape.Column"/> for the label column for regression tasks.
+        /// </summary>
+        /// <param name="labelColumn">name of the weight column</param>
+        public static SchemaShape.Column MakeR4ScalarLabel(string labelColumn)
+            => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false);
+
+        /// <summary>
+        /// The <see cref="SchemaShape.Column"/> for the label column for regression tasks.
+        /// </summary>
+        /// <param name="labelColumn">name of the weight column</param>
+        public static SchemaShape.Column MakeU4ScalarLabel(string labelColumn)
+            => new SchemaShape.Column(labelColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.U4, true);
+
+        /// <summary>
+        /// The <see cref="SchemaShape.Column"/> for the feature column.
+        /// </summary>
+        /// <param name="featureColumn">name of the feature column</param>
+        public static SchemaShape.Column MakeR4VecFeature(string featureColumn)
+            => new SchemaShape.Column(featureColumn, SchemaShape.Column.VectorKind.Vector, NumberType.R4, false);
+
+        /// <summary>
+        /// The <see cref="SchemaShape.Column"/> for the weight column.
+        /// </summary>
+        /// <param name="weightColumn">name of the weight column</param>
+        public static SchemaShape.Column MakeR4ScalarWeightColumn(string weightColumn)
+        {
+            if (weightColumn == null)
+                return null;
+            return new SchemaShape.Column(weightColumn, SchemaShape.Column.VectorKind.Scalar, NumberType.R4, false);
+        }
     }
 
     /// <summary>
@@ -593,11 +627,11 @@ public void Signal(CursOpt opt)
     }
 
     /// <summary>
-    /// This supports Weight (Float), Group (ulong), and Id (UInt128) columns.
+    /// This supports Weight (float), Group (ulong), and Id (UInt128) columns.
     /// </summary>
     public class StandardScalarCursor : TrainingCursorBase
     {
-        private readonly ValueGetter<Float> _getWeight;
+        private readonly ValueGetter<float> _getWeight;
         private readonly ValueGetter<ulong> _getGroup;
         private readonly ValueGetter<UInt128> _getId;
         private readonly bool _keepBadWeight;
@@ -608,7 +642,7 @@ public class StandardScalarCursor : TrainingCursorBase
         public long BadWeightCount { get { return _badWeightCount; } }
         public long BadGroupCount { get { return _badGroupCount; } }
 
-        public Float Weight;
+        public float Weight;
         public ulong Group;
         public UInt128 Id;
 
@@ -655,7 +689,7 @@ public override bool Accept()
             if (_getWeight != null)
             {
                 _getWeight(ref Weight);
-                if (!_keepBadWeight && !(0 < Weight && Weight < Float.PositiveInfinity))
+                if (!_keepBadWeight && !(0 < Weight && Weight < float.PositiveInfinity))
                 {
                     _badWeightCount++;
                     return false;
@@ -683,9 +717,7 @@ public Factory(RoleMappedData data, CursOpt opt)
             }
 
             protected override StandardScalarCursor CreateCursorCore(IRowCursor input, RoleMappedData data, CursOpt opt, Action<CursOpt> signal)
-            {
-                return new StandardScalarCursor(input, data, opt, signal);
-            }
+                => new StandardScalarCursor(input, data, opt, signal);
         }
     }
 
@@ -695,13 +727,13 @@ protected override StandardScalarCursor CreateCursorCore(IRowCursor input, RoleM
     /// </summary>
     public class FeatureFloatVectorCursor : StandardScalarCursor
     {
-        private readonly ValueGetter<VBuffer<Float>> _get;
+        private readonly ValueGetter<VBuffer<float>> _get;
         private readonly bool _keepBad;
 
         private long _badCount;
         public long BadFeaturesRowCount { get { return _badCount; } }
 
-        public VBuffer<Float> Features;
+        public VBuffer<float> Features;
 
         public FeatureFloatVectorCursor(RoleMappedData data, CursOpt opt = CursOpt.Features,
             IRandom rand = null, params int[] extraCols)
@@ -758,18 +790,18 @@ protected override FeatureFloatVectorCursor CreateCursorCore(IRowCursor input, R
     }
 
     /// <summary>
-    /// This derives from the FeatureFloatVectorCursor and adds the Label (Float) column.
+    /// This derives from the FeatureFloatVectorCursor and adds the Label (float) column.
     /// </summary>
     public class FloatLabelCursor : FeatureFloatVectorCursor
     {
-        private readonly ValueGetter<Float> _get;
+        private readonly ValueGetter<float> _get;
         private readonly bool _keepBad;
 
         private long _badCount;
 
         public long BadLabelCount { get { return _badCount; } }
 
-        public Float Label;
+        public float Label;
 
         public FloatLabelCursor(RoleMappedData data, CursOpt opt = CursOpt.Label,
             IRandom rand = null, params int[] extraCols)
@@ -831,13 +863,13 @@ protected override FloatLabelCursor CreateCursorCore(IRowCursor input, RoleMappe
     public class MultiClassLabelCursor : FeatureFloatVectorCursor
     {
         private readonly int _classCount;
-        private readonly ValueGetter<Float> _get;
+        private readonly ValueGetter<float> _get;
         private readonly bool _keepBad;
 
         private long _badCount;
         public long BadLabelCount { get { return _badCount; } }
 
-        private Float _raw;
+        private float _raw;
         public int Label;
 
         public MultiClassLabelCursor(int classCount, RoleMappedData data, CursOpt opt = CursOpt.Label,

src/Microsoft.ML.FastTree/FastTree.cs

@@ -2,16 +2,6 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-using Float = System.Single;
-
-using System;
-using System.Collections;
-using System.Collections.Generic;
-using System.ComponentModel;
-using System.Diagnostics;
-using System.IO;
-using System.Linq;
-using System.Text;
 using Microsoft.ML.Core.Data;
 using Microsoft.ML.Runtime.CommandLine;
 using Microsoft.ML.Runtime.Data;
@@ -26,6 +16,15 @@
 using Microsoft.ML.Runtime.Training;
 using Microsoft.ML.Runtime.TreePredictor;
 using Newtonsoft.Json.Linq;
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Diagnostics;
+using System.IO;
+using System.Linq;
+using System.Text;
+using Float = System.Single;
 
 // All of these reviews apply in general to fast tree and random forest implementations.
 //REVIEW: Decouple train method in Application.cs to have boosting and random forest logic seperate.
@@ -90,7 +89,7 @@ public abstract class FastTreeTrainerBase<TArgs, TTransformer, TModel> :
         private protected virtual bool NeedCalibration => false;
 
         /// <summary>
-        /// Constructor to use when instantiating the classing deriving from here through the API.
+        /// Constructor to use when instantiating the classes deriving from here through the API.
         /// </summary>
         private protected FastTreeTrainerBase(IHostEnvironment env, SchemaShape.Column label, string featureColumn,
             string weightColumn = null, string groupIdColumn = null, Action<TArgs> advancedSettings = null)
@@ -101,6 +100,7 @@ private protected FastTreeTrainerBase(IHostEnvironment env, SchemaShape.Column l
             //apply the advanced args, if the user supplied any
             advancedSettings?.Invoke(Args);
             Args.LabelColumn = label.Name;
+            Args.FeatureColumn = featureColumn;
 
             if (weightColumn != null)
                 Args.WeightColumn = weightColumn;
@@ -120,7 +120,7 @@ private protected FastTreeTrainerBase(IHostEnvironment env, SchemaShape.Column l
         }
 
         /// <summary>
-        /// Legacy constructor that is used when invoking the classsing deriving from this, through maml.
+        /// Legacy constructor that is used when invoking the classes deriving from this, through maml.
         /// </summary>
         private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args, SchemaShape.Column label)
             : base(Contracts.CheckRef(env, nameof(env)).Register(RegisterName), MakeFeatureColumn(args.FeatureColumn), label, MakeWeightColumn(args.WeightColumn))

src/Microsoft.ML.FastTree/FastTreeArguments.cs

@@ -2,11 +2,11 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-using System;
 using Microsoft.ML.Runtime.CommandLine;
 using Microsoft.ML.Runtime.EntryPoints;
 using Microsoft.ML.Runtime.FastTree;
 using Microsoft.ML.Runtime.Internal.Internallearn;
+using System;
 
 [assembly: EntryPointModule(typeof(FastTreeBinaryClassificationTrainer.Arguments))]
 [assembly: EntryPointModule(typeof(FastTreeRegressionTrainer.Arguments))]