Added Onnx Export to PlattCalibratorTransformer

src/Microsoft.ML.Data/Prediction/Calibrator.cs

@@ -1750,7 +1750,7 @@ bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] scoreProbablityColu
             var node = ctx.CreateNode(opType, new[] { scoreProbablityColumnNames[0], slopVar }, new[] { mulNodeOutput }, ctx.GetNodeName(opType), "");
 
             opType = "Add";
-            var betaVar = ctx.AddInitializer(-0.0000001f, "Slope");
+            var betaVar = ctx.AddInitializer((float)(-Offset), "Offset");
             var linearOutput = ctx.AddIntermediateVariable(null, "linearOutput", true);
             node = ctx.CreateNode(opType, new[] { mulNodeOutput, betaVar }, new[] { linearOutput }, ctx.GetNodeName(opType), "");
 

src/Microsoft.ML.Data/Prediction/CalibratorCatalog.cs

@@ -8,6 +8,7 @@
 using Microsoft.ML;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
+using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Trainers;
 
@@ -220,13 +221,15 @@ private protected VersionInfo GetVersionInfo()
                 loaderAssemblyName: typeof(CalibratorTransformer<>).Assembly.FullName);
         }
 
-        private sealed class Mapper<TCalibrator> : MapperBase
+        private sealed class Mapper<TCalibrator> : MapperBase, ISaveAsOnnx
             where TCalibrator : class, ICalibrator
         {
             private TCalibrator _calibrator;
             private readonly int _scoreColIndex;
             private CalibratorTransformer<TCalibrator> _parent;
 
+            bool ICanSaveOnnx.CanSaveOnnx(OnnxContext ctx) => _calibrator is ICanSaveOnnx onnxMapper ? onnxMapper.CanSaveOnnx(ctx) : false;
+
             internal Mapper(CalibratorTransformer<TCalibrator> parent, TCalibrator calibrator, DataViewSchema inputSchema) :
                 base(parent.Host, inputSchema, parent)
             {
@@ -243,6 +246,20 @@ private protected override Func<int, bool> GetDependenciesCore(Func<int, bool> a
 
             private protected override void SaveModel(ModelSaveContext ctx) => _parent.SaveModel(ctx);
 
+            void ISaveAsOnnx.SaveAsOnnx(OnnxContext ctx)
+            {
+                var scoreName = InputSchema[_scoreColIndex].Name;
+                var probabilityName = GetOutputColumnsCore()[0].Name;
+                Host.CheckValue(ctx, nameof(ctx));
+                if (_calibrator is ISingleCanSaveOnnx onnx)
+                {
+                    Host.Check(onnx.CanSaveOnnx(ctx), "Cannot be saved as ONNX.");
+                    scoreName = ctx.GetVariableName(scoreName);
+                    probabilityName = ctx.AddIntermediateVariable(NumberDataViewType.Single, probabilityName);
+                    onnx.SaveAsOnnx(ctx, new[] { scoreName, probabilityName }, ""); // No need for featureColumn
+                }
+            }
+
             protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore()
             {
                 var builder = new DataViewSchema.Annotations.Builder();

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt

@@ -393,7 +393,7 @@
       {
         "input": [
           "MulNodeOutput",
-          "Slope0"
+          "Offset"
         ],
         "output": [
           "linearOutput"
@@ -489,9 +489,9 @@
       {
         "dataType": 1,
         "floatData": [
-          -1E-07
+          0
         ],
-        "name": "Slope0"
+        "name": "Offset"
       }
     ],
     "input": [

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt

@@ -719,7 +719,7 @@
       {
         "input": [
           "MulNodeOutput",
-          "Slope0"
+          "Offset"
         ],
         "output": [
           "linearOutput"
@@ -815,9 +815,9 @@
       {
         "dataType": 1,
         "floatData": [
-          -1E-07
+          0
         ],
-        "name": "Slope0"
+        "name": "Offset"
       }
     ],
     "input": [

test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt

@@ -346,7 +346,7 @@
       {
         "input": [
           "MulNodeOutput",
-          "Slope0"
+          "Offset"
         ],
         "output": [
           "linearOutput"
@@ -482,9 +482,9 @@
       {
         "dataType": 1,
         "floatData": [
-          -1E-07
+          0
         ],
-        "name": "Slope0"
+        "name": "Offset"
       }
     ],
     "input": [

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -343,6 +343,109 @@ public void TestVectorWhiteningOnnxConversionTest()
             Done();
         }
 
+        [Fact]
+        public void PlattCalibratorOnnxConversionTest()
+        {
+            var mlContext = new MLContext(seed: 1);
+            string dataPath = GetDataPath("breast-cancer.txt");
+            // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
+            var dataView = mlContext.Data.LoadFromTextFile<BreastCancerBinaryClassification>(dataPath, separatorChar: '\t', hasHeader: true);
+            List<IEstimator<ITransformer>> estimators = new List<IEstimator<ITransformer>>()
+            {
+                mlContext.BinaryClassification.Trainers.AveragedPerceptron(),
+                mlContext.BinaryClassification.Trainers.FastForest(),
+                mlContext.BinaryClassification.Trainers.FastTree(),
+                mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(),
+                mlContext.BinaryClassification.Trainers.LinearSvm(),
+                mlContext.BinaryClassification.Trainers.Prior(),
+                mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(),
+                mlContext.BinaryClassification.Trainers.SdcaNonCalibrated(),
+                mlContext.BinaryClassification.Trainers.SgdCalibrated(),
+                mlContext.BinaryClassification.Trainers.SgdNonCalibrated(),
+                mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(),
+            };
+            if (Environment.Is64BitProcess)
+            {
+                estimators.Add(mlContext.BinaryClassification.Trainers.LightGbm());
+            }
+
+            var initialPipeline = mlContext.Transforms.ReplaceMissingValues("Features").
+                Append(mlContext.Transforms.NormalizeMinMax("Features"));
+            foreach (var estimator in estimators)
+            {
+                var pipeline = initialPipeline.Append(estimator).Append(mlContext.BinaryClassification.Calibrators.Platt());
+                var model = pipeline.Fit(dataView);
+                var outputSchema = model.GetOutputSchema(dataView.Schema);
+                var transformedData = model.Transform(dataView);
+                var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
+
+                // Compare model scores produced by ML.NET and ONNX's runtime. 
+                if (IsOnnxRuntimeSupported())
+                {
+                    var onnxFileName = $"{estimator.ToString()}-WithPlattCalibrator.onnx";
+                    var onnxModelPath = GetOutputPath(onnxFileName);
+                    SaveOnnxModel(onnxModel, onnxModelPath, null);
+                    // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
+                    string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                    string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                    var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                    var onnxTransformer = onnxEstimator.Fit(dataView);
+                    var onnxResult = onnxTransformer.Transform(dataView);
+                    CompareSelectedR4ScalarColumns(transformedData.Schema[5].Name, outputNames[3], transformedData, onnxResult, 3); //compare scores
+                    CompareSelectedScalarColumns<Boolean>(transformedData.Schema[4].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
+                    CompareSelectedR4ScalarColumns(transformedData.Schema.Last().Name, outputNames.Last(), transformedData, onnxResult, 3); //compare probabilities
+                }
+            }
+            Done();
+        }
+
+        class PlattModelInput
+        {
+            public bool Label { get; set; }
+            public float Score { get; set; }
+        }
+
+        static IEnumerable<PlattModelInput> PlattGetData()
+        {
+            for (int i = 0; i < 100; i++)
+            {
+                yield return new PlattModelInput { Score = i, Label = i % 2 == 0 };
+            }
+        }
+
+        [Fact]
+        public void PlattCalibratorOnnxConversionTest2()
+        {
+            // Test PlattCalibrator without any binary prediction trainer
+            var mlContext = new MLContext(seed: 0);
+
+            IDataView data = mlContext.Data.LoadFromEnumerable(PlattGetData());
+
+            var calibratorEstimator = mlContext.BinaryClassification.Calibrators
+                .Platt();
+
+            var calibratorTransformer = calibratorEstimator.Fit(data);
+            var transformedData = calibratorTransformer.Transform(data);
+            var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(calibratorTransformer, data);
+
+            // Compare model scores produced by ML.NET and ONNX's runtime.
+            if (IsOnnxRuntimeSupported())
+            {
+                var onnxFileName = $"{calibratorTransformer.ToString()}.onnx";
+                var onnxModelPath = GetOutputPath(onnxFileName);
+                SaveOnnxModel(onnxModel, onnxModelPath, null);
+
+                // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
+                string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
+                var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
+                var onnxTransformer = onnxEstimator.Fit(data);
+                var onnxResult = onnxTransformer.Transform(data);
+                CompareSelectedR4ScalarColumns(transformedData.Schema.Last().Name, outputNames.Last(), transformedData, onnxResult, 3); //compare probabilities
+            }
+            Done();
+        }
+
         private class DataPoint
         {
             [VectorType(3)]