Added ONNX export support and tests for {FixedPlatt, Naive}CalibratorEstimators

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

@@ -1158,7 +1158,7 @@ ICalibrator ICalibratorTrainer.FinishTraining(IChannel ch)
     /// <summary>
     /// The naive binning-based calibrator.
     /// </summary>
-    public sealed class NaiveCalibrator : ICalibrator, ICanSaveInBinaryFormat
+    public sealed class NaiveCalibrator : ICalibrator, ICanSaveInBinaryFormat, ISingleCanSaveOnnx
     {
         internal const string LoaderSignature = "NaiveCaliExec";
         internal const string RegistrationName = "NaiveCalibrator";
@@ -1174,6 +1174,12 @@ private static VersionInfo GetVersionInfo()
                 loaderAssemblyName: typeof(NaiveCalibrator).Assembly.FullName);
         }
 
+        /// <summary>
+        /// Bool required by the interface ISingleCanSaveOnnx, returns true if
+        /// and only if calibrator can be exported in ONNX.
+        /// </summary>
+        bool ICanSaveOnnx.CanSaveOnnx(OnnxContext ctx) => true;
+
         private readonly IHost _host;
 
         /// <summary> The bin size.</summary>
@@ -1280,6 +1286,45 @@ internal static int GetBinIdx(float output, float min, float binSize, int numBin
             return binIdx;
         }
 
+        bool ISingleCanSaveOnnx.SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
+        {
+            _host.CheckValue(ctx, nameof(ctx));
+            _host.CheckValue(outputNames, nameof(outputNames));
+            _host.Check(Utils.Size(outputNames) == 2);
+            // outputNames[0] refers to the name of the Score column, which is the input of this graph
+            // outputNames[1] refers to the name of the Probability column, which is the final output of this graph
+
+            const int minimumOpSetVersion = 9;
+            ctx.CheckOpSetVersion(minimumOpSetVersion, "NaiveCalibrator");
+
+            string opType = "Sub";
+            var minVar = ctx.AddInitializer(Min, "Min");
+            var subNodeOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "subNodeOutput");
+            var node = ctx.CreateNode(opType, new[] { outputNames[0], minVar }, new[] { subNodeOutput }, ctx.GetNodeName(opType), "");
+
+            opType = "Div";
+            var binSizeVar = ctx.AddInitializer(BinSize, "BinSize");
+            var divNodeOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "binIndexOutput");
+            node = ctx.CreateNode(opType, new[] { subNodeOutput, binSizeVar }, new[] { divNodeOutput }, ctx.GetNodeName(opType), "");
+
+            opType = "Cast";
+            var castOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "castOutput");
+            node = ctx.CreateNode(opType, divNodeOutput, castOutput, ctx.GetNodeName(opType), "");
+            var toTypeInt = typeof(long);
+            node.AddAttribute("to", toTypeInt);
+
+            opType = "Clip";
+            var zeroVar = ctx.AddInitializer(0, "Zero");
+            var numBinsMinusOneVar = ctx.AddInitializer(_binProbs.Length-1, "NumBinsMinusOne");
+            var binIndexOutput = ctx.AddIntermediateVariable(NumberDataViewType.Int64, "binIndexOutput");
+            node = ctx.CreateNode(opType, new[] { castOutput, zeroVar, numBinsMinusOneVar }, new[] { binIndexOutput }, ctx.GetNodeName(opType), "");
+
+            opType = "GatherElements";
+            var binProbabilitiesVar = ctx.AddInitializer(_binProbs, new long[] { _binProbs.Length, 1 }, "BinProbabilities");
+            node = ctx.CreateNode(opType, new[] { binProbabilitiesVar, binIndexOutput }, new[] { outputNames[1] }, ctx.GetNodeName(opType), "");
+
+            return true;
+        }
     }
 
     /// <summary>

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -261,98 +261,111 @@ public void TestVectorWhiteningOnnxConversionTest()
             Done();
         }
 
-        [Fact]
-        public void PlattCalibratorOnnxConversionTest()
+        private (IDataView, List<IEstimator<ITransformer>>, EstimatorChain<NormalizingTransformer>) GetEstimatorsForOnnxConversionTests()
         {
-            var mlContext = new MLContext(seed: 1);
-            string dataPath = GetDataPath(TestDatasets.breastCancer.trainFilename);
+            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: false);
+            var dataView = ML.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(),
+                ML.BinaryClassification.Trainers.AveragedPerceptron(),
+                ML.BinaryClassification.Trainers.FastForest(),
+                ML.BinaryClassification.Trainers.FastTree(),
+                ML.BinaryClassification.Trainers.LbfgsLogisticRegression(),
+                ML.BinaryClassification.Trainers.LinearSvm(),
+                ML.BinaryClassification.Trainers.Prior(),
+                ML.BinaryClassification.Trainers.SdcaLogisticRegression(),
+                ML.BinaryClassification.Trainers.SdcaNonCalibrated(),
+                ML.BinaryClassification.Trainers.SgdCalibrated(),
+                ML.BinaryClassification.Trainers.SgdNonCalibrated(),
+                ML.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(),
             };
             if (Environment.Is64BitProcess)
             {
-                estimators.Add(mlContext.BinaryClassification.Trainers.LightGbm());
+                estimators.Add(ML.BinaryClassification.Trainers.LightGbm());
             }
 
-            var initialPipeline = mlContext.Transforms.ReplaceMissingValues("Features").
-                Append(mlContext.Transforms.NormalizeMinMax("Features"));
+            var initialPipeline = ML.Transforms.ReplaceMissingValues("Features").
+                Append(ML.Transforms.NormalizeMinMax("Features"));
+            return (dataView, estimators, initialPipeline);
+        }
+
+        private void CommonCalibratorOnnxConversionTest(IEstimator<ITransformer> calibrator, IEstimator<ITransformer> calibratorNonStandard)
+        {
+            // Initialize variables needed for the ONNX conversion test
+            var (dataView, estimators, initialPipeline) = GetEstimatorsForOnnxConversionTests();
+
+            // Step 1: Test calibrator with binary prediction trainer
             foreach (var estimator in estimators)
             {
-                var pipeline = initialPipeline.Append(estimator).Append(mlContext.BinaryClassification.Calibrators.Platt());
-                var onnxFileName = $"{estimator}-WithPlattCalibrator.onnx";
-
-                TestPipeline(pipeline, dataView, onnxFileName, new ColumnComparison[] { new ColumnComparison("Score", 3), new ColumnComparison("PredictedLabel"), new ColumnComparison("Probability", 3) });
+                var pipelineEstimators = initialPipeline.Append(estimator).Append(calibrator);
+                var onnxFileName = $"{estimator}-With-{calibrator}.onnx";
+                TestPipeline(pipelineEstimators, dataView, onnxFileName, new ColumnComparison[] { new ColumnComparison("Score", 3), new ColumnComparison("PredictedLabel"), new ColumnComparison("Probability", 3) });
             }
+
+            // Step 2: Test calibrator without any binary prediction trainer
+            IDataView dataSoloCalibrator = ML.Data.LoadFromEnumerable(GetCalibratorTestData());
+            var onnxFileNameSoloCalibrator = $"{calibrator}-SoloCalibrator.onnx";
+            TestPipeline(calibrator, dataSoloCalibrator, onnxFileNameSoloCalibrator, new ColumnComparison[] { new ColumnComparison("Probability", 3) });
+
+            // Step 3: Test calibrator with a non-default Score column name and without any binary prediction trainer
+            IDataView dataSoloCalibratorNonStandard = ML.Data.LoadFromEnumerable(GetCalibratorTestDataNonStandard());
+            var onnxFileNameSoloCalibratorNonStandard = $"{calibratorNonStandard}-SoloCalibrator-NonStandard.onnx";
+            TestPipeline(calibratorNonStandard, dataSoloCalibratorNonStandard, onnxFileNameSoloCalibratorNonStandard, new ColumnComparison[] { new ColumnComparison("Probability", 3) });
+
             Done();
         }
 
-        class PlattModelInput
+        [Fact]
+        public void PlattCalibratorOnnxConversionTest()
+        {
+            CommonCalibratorOnnxConversionTest(ML.BinaryClassification.Calibrators.Platt(),
+                ML.BinaryClassification.Calibrators.Platt(scoreColumnName: "ScoreX"));
+        }
+
+        [Fact]
+        public void FixedPlattCalibratorOnnxConversionTest()
+        {
+            // Below, FixedPlattCalibrator is utilized by defining slope and offset in Platt's constructor with sample values.
+            CommonCalibratorOnnxConversionTest(ML.BinaryClassification.Calibrators.Platt(slope: -1f, offset: -0.05f),
+                ML.BinaryClassification.Calibrators.Platt(slope: -1f, offset: -0.05f, scoreColumnName: "ScoreX"));
+        }
+
+        [Fact]
+        public void NaiveCalibratorOnnxConversionTest()
+        {
+            CommonCalibratorOnnxConversionTest(ML.BinaryClassification.Calibrators.Naive(),
+                ML.BinaryClassification.Calibrators.Naive(scoreColumnName: "ScoreX"));
+        }
+
+        class CalibratorInput
         {
             public bool Label { get; set; }
             public float Score { get; set; }
         }
 
-        class PlattModelInput2
+        class CalibratorInputNonStandard
         {
             public bool Label { get; set; }
             public float ScoreX { get; set; }
         }
 
-        static IEnumerable<PlattModelInput> PlattGetData()
+        static IEnumerable<CalibratorInput> GetCalibratorTestData()
         {
             for (int i = 0; i < 100; i++)
             {
-                yield return new PlattModelInput { Score = i, Label = i % 2 == 0 };
+                yield return new CalibratorInput { Score = i, Label = i % 2 == 0 };
             }
         }
 
-        static IEnumerable<PlattModelInput2> PlattGetData2()
+        static IEnumerable<CalibratorInputNonStandard> GetCalibratorTestDataNonStandard()
         {
             for (int i = 0; i < 100; i++)
             {
-                yield return new PlattModelInput2 { ScoreX = i, Label = i % 2 == 0 };
+                yield return new CalibratorInputNonStandard { ScoreX = 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 pipeline = mlContext.BinaryClassification.Calibrators
-                .Platt();
-            var onnxFileName = $"{pipeline}.onnx";
-
-            TestPipeline(pipeline, data, onnxFileName, new ColumnComparison[] { new ColumnComparison("Probability", 3) });
-
-            // Test PlattCalibrator with a non-default Score column name, and without any binary prediction trainer
-            IDataView data2 = mlContext.Data.LoadFromEnumerable(PlattGetData2());
-
-            var pipeline2 = mlContext.BinaryClassification.Calibrators
-                .Platt(scoreColumnName: "ScoreX");
-            var onnxFileName2 = $"{pipeline2}.onnx";
-
-            TestPipeline(pipeline2, data2, onnxFileName2, new ColumnComparison[] { new ColumnComparison("Probability", 3) });
-
-            Done();
-        }
-
         [Fact]
         public void TextNormalizingOnnxConversionTest()
         {