Fixed model saving and loading of OneVersusAllTrainer to include Soft…

…Max (dotnet#4472)

* Fixed model saving and loading of OneVersusAllTrainer to include SoftMax

* Modified existing test to include SoftMax option

* Modified test to verify both cases: when UseSoftmax is true and false

src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs

@@ -364,23 +364,29 @@ private OneVersusAllModelParameters(IHostEnvironment env, ModelLoadContext ctx)
                 : base(env, RegistrationName, ctx)
         {
             // *** Binary format ***
-            // bool: useDist
+            // byte: OutputFormula as byte
             // int: predictor count
-            bool useDist = ctx.Reader.ReadBoolByte();
+            OutputFormula outputFormula = (OutputFormula)ctx.Reader.ReadByte();
             int len = ctx.Reader.ReadInt32();
             Host.CheckDecode(len > 0);
 
-            if (useDist)
+            if (outputFormula == OutputFormula.Raw)
+            {
+                var predictors = new TScalarPredictor[len];
+                LoadPredictors(Host, predictors, ctx);
+                _impl = new ImplRaw(predictors);
+            }
+            else if (outputFormula == OutputFormula.ProbabilityNormalization)
             {
                 var predictors = new IValueMapperDist[len];
                 LoadPredictors(Host, predictors, ctx);
                 _impl = new ImplDist(predictors);
             }
-            else
+            else if (outputFormula == OutputFormula.Softmax)
             {
                 var predictors = new TScalarPredictor[len];
                 LoadPredictors(Host, predictors, ctx);
-                _impl = new ImplRaw(predictors);
+                _impl = new ImplSoftmax(predictors);
             }
 
             DistType = new VectorDataViewType(NumberDataViewType.Single, _impl.Predictors.Length);
@@ -409,9 +415,10 @@ private protected override void SaveCore(ModelSaveContext ctx)
             var preds = _impl.Predictors;
 
             // *** Binary format ***
-            // bool: useDist
+            // byte: _impl.OutputFormula as byte
             // int: predictor count
-            ctx.Writer.WriteBoolByte(_impl is ImplDist);
+            byte[] outputFormula = { (byte)_impl.OutputFormula };
+            ctx.Writer.WriteBytesNoCount(outputFormula, 1);
             ctx.Writer.Write(preds.Length);
 
             // Save other streams.
@@ -485,6 +492,7 @@ void ICanSaveInTextFormat.SaveAsText(TextWriter writer, RoleMappedSchema schema)
 
         private abstract class ImplBase : ISingleCanSavePfa
         {
+            public OutputFormula OutputFormula;
             public abstract DataViewType InputType { get; }
             public abstract IValueMapper[] Predictors { get; }
             public abstract bool CanSavePfa { get; }
@@ -536,6 +544,7 @@ internal ImplRaw(TScalarPredictor[] predictors)
                 CanSavePfa = Predictors.All(m => (m as ISingleCanSavePfa)?.CanSavePfa == true);
                 Contracts.AssertValue(inputType);
                 InputType = inputType;
+                OutputFormula = OutputFormula.Raw;
             }
 
             public override ValueMapper<VBuffer<float>, VBuffer<float>> GetMapper()
@@ -601,6 +610,7 @@ internal ImplDist(IValueMapperDist[] predictors)
                 CanSavePfa = Predictors.All(m => (m as IDistCanSavePfa)?.CanSavePfa == true);
                 Contracts.AssertValue(inputType);
                 InputType = inputType;
+                OutputFormula = OutputFormula.ProbabilityNormalization;
             }
 
             private bool IsValid(IValueMapperDist mapper, ref VectorDataViewType inputType)
@@ -712,6 +722,7 @@ internal ImplSoftmax(TScalarPredictor[] predictors)
                 CanSavePfa = false;
                 Contracts.AssertValue(inputType);
                 InputType = inputType;
+                OutputFormula = OutputFormula.Softmax;
             }
 
             public override ValueMapper<VBuffer<float>, VBuffer<float>> GetMapper()

test/Microsoft.ML.TestFramework/Attributes/LightGBMTheoryAttribute.cs

@@ -0,0 +1,26 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// 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 System.Runtime.InteropServices;
+using Microsoft.ML.TestFrameworkCommon.Attributes;
+
+namespace Microsoft.ML.TestFramework.Attributes
+{
+    /// <summary>
+    /// A theory for tests requiring LightGBM.
+    /// </summary>
+    public sealed class LightGBMTheoryAttribute : EnvironmentSpecificTheoryAttribute
+    {
+        public LightGBMTheoryAttribute() : base("LightGBM is 64-bit only")
+        {
+        }
+
+        /// <inheritdoc />
+        protected override bool IsEnvironmentSupported()
+        {
+            return Environment.Is64BitProcess;
+        }
+    }
+}

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

@@ -303,12 +303,15 @@ public void LightGbmMulticlassEstimator()
         /// <summary>
         /// LightGbmMulticlass TrainerEstimator test with options
         /// </summary>
-        [LightGBMFact]
-        public void LightGbmMulticlassEstimatorWithOptions()
+        [LightGBMTheory]
+        [InlineData(true)]
+        [InlineData(false)]
+        public void LightGbmMulticlassEstimatorWithOptions(bool softMax)
         {
             var options = new LightGbmMulticlassTrainer.Options
             {
-                EvaluationMetric = LightGbmMulticlassTrainer.Options.EvaluateMetricType.Default
+                EvaluationMetric = LightGbmMulticlassTrainer.Options.EvaluateMetricType.Default,
+                UseSoftmax = softMax
             };
 
             var (pipeline, dataView) = GetMulticlassPipeline();