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src/Microsoft.ML.Data/DataLoadSave/LegacyCompositeDataLoader.cs

@@ -412,7 +412,7 @@ internal TransformerChain<ITransformer> GetTransformer()
                 }
                 else
                 {
-                    ITransformer transformer = new TransformWrapper(_host, transform.Transform, false, true);
+                    ITransformer transformer = new TransformWrapper(_host, transform.Transform);
                     result = result.Append(transformer);
                 }
             }

src/Microsoft.ML.Data/DataLoadSave/TransformWrapper.cs

@@ -27,61 +27,28 @@ internal sealed class TransformWrapper : ITransformer
 
         private readonly IHost _host;
         private readonly IDataView _xf;
-        private readonly bool _allowSave;
         private readonly bool _isRowToRowMapper;
-        private readonly bool _useLastTransformOnly;
 
-        public TransformWrapper(IHostEnvironment env, IDataView xf, bool allowSave = false, bool useLastTransformOnly = false)
+        public TransformWrapper(IHostEnvironment env, IDataView xf)
         {
             Contracts.CheckValue(env, nameof(env));
             _host = env.Register(nameof(TransformWrapper));
             _host.CheckValue(xf, nameof(xf));
             _xf = xf;
-            _allowSave = allowSave;
             _isRowToRowMapper = IsChainRowToRowMapper(_xf);
-            _useLastTransformOnly = useLastTransformOnly;
         }
 
         public DataViewSchema GetOutputSchema(DataViewSchema inputSchema)
         {
             _host.CheckValue(inputSchema, nameof(inputSchema));
 
             var dv = new EmptyDataView(_host, inputSchema);
-            var output = _useLastTransformOnly ? ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, dv) :
-                ApplyTransformUtils.ApplyAllTransformsToData(_host, _xf, dv);
+            var output = ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, dv);
 
             return output.Schema;
         }
 
-        void ICanSaveModel.Save(ModelSaveContext ctx)
-        {
-            if (!_allowSave)
-                throw _host.Except("Saving is not permitted.");
-            ctx.CheckAtModel();
-            ctx.SetVersionInfo(GetVersionInfo());
-
-            var dataPipe = _xf;
-            var transforms = new List<IDataTransform>();
-            while (dataPipe is IDataTransform xf)
-            {
-                // REVIEW: a malicious user could construct a loop in the Source chain, that would
-                // cause this method to iterate forever (and throw something when the list overflows). There's
-                // no way to insulate from ALL malicious behavior.
-                transforms.Add(xf);
-                dataPipe = xf.Source;
-                Contracts.AssertValue(dataPipe);
-            }
-            transforms.Reverse();
-
-            ctx.SaveSubModel("Loader", c => BinaryLoader.SaveInstance(_host, c, dataPipe.Schema));
-
-            ctx.Writer.Write(transforms.Count);
-            for (int i = 0; i < transforms.Count; i++)
-            {
-                var dirName = string.Format(TransformDirTemplate, i);
-                ctx.SaveModel(transforms[i], dirName);
-            }
-        }
+        void ICanSaveModel.Save(ModelSaveContext ctx) => throw _host.Except("Saving is not permitted.");
 
         private static VersionInfo GetVersionInfo()
         {
@@ -100,7 +67,6 @@ private TransformWrapper(IHostEnvironment env, ModelLoadContext ctx)
             Contracts.CheckValue(env, nameof(env));
             _host = env.Register(nameof(TransformWrapper));
             _host.CheckValue(ctx, nameof(ctx));
-            _allowSave = true;
             ctx.CheckAtModel(GetVersionInfo());
             int n = ctx.Reader.ReadInt32();
             _host.CheckDecode(n >= 0);
@@ -119,8 +85,7 @@ private TransformWrapper(IHostEnvironment env, ModelLoadContext ctx)
             _isRowToRowMapper = IsChainRowToRowMapper(_xf);
         }
 
-        public IDataView Transform(IDataView input) => _useLastTransformOnly ? ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, input) :
-            ApplyTransformUtils.ApplyAllTransformsToData(_host, _xf, input);
+        public IDataView Transform(IDataView input) => ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, input);
 
         private static bool IsChainRowToRowMapper(IDataView view)
         {
@@ -137,30 +102,8 @@ private static bool IsChainRowToRowMapper(IDataView view)
         IRowToRowMapper ITransformer.GetRowToRowMapper(DataViewSchema inputSchema)
         {
             _host.CheckValue(inputSchema, nameof(inputSchema));
-            var input = new EmptyDataView(_host, inputSchema);
-            IDataView chain;
-            if (_useLastTransformOnly)
-            {
-                chain = ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, input);
-                return new CompositeRowToRowMapper(inputSchema, new[] { (IRowToRowMapper)chain });
-            }
-            else
-            {
-                var revMaps = new List<IRowToRowMapper>();
-                for (chain = ApplyTransformUtils.ApplyAllTransformsToData(_host, _xf, input);
-                    chain is IDataTransform xf;
-                    chain = xf.Source)
-                {
-                    // Everything in the chain ought to be a row mapper.
-                    _host.Assert(xf is IRowToRowMapper);
-                    revMaps.Add((IRowToRowMapper)xf);
-                }
-
-                // The walkback should have ended at the input.
-                Contracts.Assert(chain == input);
-                revMaps.Reverse();
-                return new CompositeRowToRowMapper(inputSchema, revMaps.ToArray());
-            }
+            return new CompositeRowToRowMapper(inputSchema,
+                new[] { (IRowToRowMapper)ApplyTransformUtils.ApplyTransformToData(_host, (IDataTransform)_xf, new EmptyDataView(_host, inputSchema)) });
         }
     }
 

test/Microsoft.ML.Functional.Tests/ModelFiles.cs

@@ -213,6 +213,43 @@ void AssertIsGam(ITransformer trans)
             Done();
         }
 
+        public class ModelInput
+        {
+#pragma warning disable SA1401
+            public string[] CategoricalFeatures;
+            public float[] NumericalFeatures;
+#pragma warning restore SA1401
+        }
+
+        public class ModelOutput
+        {
+#pragma warning disable SA1401
+            public float[] Score;
+#pragma warning restore SA1401
+        }
+
+
+        [Fact]
+        public void LoadModelWithOptionalColumnTransform()
+        {
+            SchemaDefinition inputSchemaDefinition = SchemaDefinition.Create(typeof(ModelInput));
+            inputSchemaDefinition[nameof(ModelInput.CategoricalFeatures)].ColumnType = new VectorDataViewType(TextDataViewType.Instance, 5);
+            inputSchemaDefinition[nameof(ModelInput.NumericalFeatures)].ColumnType = new VectorDataViewType(NumberDataViewType.Single, 3);
+            var mlContext = new MLContext();
+            ITransformer trainedModel;
+            DataViewSchema dataViewSchema;
+            using (var stream = new FileStream(Path.Join(Directory.GetCurrentDirectory(), @"..\..\..\..\test\data\backcompat\modelwithoptionalcolumntransform.zip"),
+                FileMode.Open, FileAccess.Read, FileShare.Read))
+            {
+                trainedModel = mlContext.Model.Load(stream, out dataViewSchema);
+            }
+
+            var model = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel, inputSchemaDefinition: inputSchemaDefinition);
+            var prediction = model.Predict(new ModelInput() { CategoricalFeatures = new[] { "ABC", "ABC", "ABC", "ABC", "ABC" }, NumericalFeatures = new float [] { 1, 1, 1 } });
+
+            Assert.Equal(1, prediction.Score[0]);
+        }
+
         [Fact]
         public void SaveAndLoadModelWithLoader()
         {

test/Microsoft.ML.Tests/Scenarios/WordBagTest.cs

@@ -31,12 +31,11 @@ public static void WordBags()
             var textPipeline =
                 mlContext.Transforms.Text.ProduceWordBags("Text", "Text",
                 ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf).Append(
-                mlContext.Transforms.Text.ProduceWordBags("Text2", "Text2",
+                mlContext.Transforms.Text.ProduceWordBags("Text2", new[] { "Text2", "Text2" },
                     ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf));
 
 
             var textTransformer = textPipeline.Fit(dataview);
-            var transformedDataView = textTransformer.Transform(dataview);
             var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
             var prediction = predictionEngine.Predict(samples[0]);
             Assert.Equal(prediction.Text, new float[] {
@@ -46,6 +45,35 @@ public static void WordBags()
                 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 });
         }
 
+        [Fact]
+        public static void WordBagsHash()
+        {
+            var mlContext = new MLContext();
+            var samples = new List<TextData>()
+            {
+                new TextData(){ Text = "This is an example to compute bag-of-word features." },
+                new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
+                new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
+                new TextData(){ Text = "computing n-grams and their neumeric values." },
+                new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
+                new TextData(){ Text = "The value at each position corresponds to," },
+                new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
+                new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
+                new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
+            };
+
+            var dataview = mlContext.Data.LoadFromEnumerable(samples);
+            var textPipeline =
+                mlContext.Transforms.Text.ProduceHashedWordBags("Text", "Text", ngramLength: 3, useAllLengths: false).Append(
+                mlContext.Transforms.Text.ProduceHashedWordBags("Text2", new[] { "Text2", "Text2" }, ngramLength: 3, useAllLengths: false));
+
+
+            var textTransformer = textPipeline.Fit(dataview);
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
+            var prediction = predictionEngine.Predict(samples[0]);
+            Assert.Equal(65536, prediction.Text.Length);
+        }
+
         private class TextData
         {
             public string Text { get; set; }