diff --git a/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs b/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs
index 9ef81f9047..ba015ebe6f 100644
--- a/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs
+++ b/src/Microsoft.ML.Data/Scorers/BinaryClassifierScorer.cs
@@ -184,6 +184,7 @@ private protected override void SaveAsOnnxCore(OnnxContext ctx)
{
Host.CheckValue(ctx, nameof(ctx));
Host.Assert(Bindable is IBindableCanSaveOnnx);
+ Host.Assert(Bindings.InfoCount >= 2);
if (!ctx.ContainsColumn(DefaultColumnNames.Features))
return;
@@ -197,15 +198,28 @@ private protected override void SaveAsOnnxCore(OnnxContext ctx)
for (int iinfo = 0; iinfo < Bindings.InfoCount; ++iinfo)
outColumnNames[iinfo] = Bindings.GetColumnName(Bindings.MapIinfoToCol(iinfo));
- //Check if "Probability" column was generated by the base class, only then
- //label can be predicted.
- if (Bindings.InfoCount >= 3 && ctx.ContainsColumn(outColumnNames[2]))
+ /* If the probability column was generated, then the classification threshold is set to 0.5. Otherwise,
+ the predicted label is based on the sign of the score.
+ */
+ string opType = "Binarizer";
+ OnnxNode node;
+ var binarizerOutput = ctx.AddIntermediateVariable(null, "BinarizerOutput", true);
+
+ if (Bindings.InfoCount >= 3)
{
- string opType = "Binarizer";
- var node = ctx.CreateNode(opType, new[] { ctx.GetVariableName(outColumnNames[2]) },
- new[] { ctx.GetVariableName(outColumnNames[0]) }, ctx.GetNodeName(opType));
+ Host.Assert(ctx.ContainsColumn(outColumnNames[2]));
+ node = ctx.CreateNode(opType, ctx.GetVariableName(outColumnNames[2]), binarizerOutput, ctx.GetNodeName(opType));
node.AddAttribute("threshold", 0.5);
}
+ else
+ {
+ node = ctx.CreateNode(opType, ctx.GetVariableName(outColumnNames[1]), binarizerOutput, ctx.GetNodeName(opType));
+ node.AddAttribute("threshold", 0.0);
+ }
+ opType = "Cast";
+ node = ctx.CreateNode(opType, binarizerOutput, ctx.GetVariableName(outColumnNames[0]), ctx.GetNodeName(opType), "");
+ var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Boolean).ToType();
+ node.AddAttribute("to", t);
}
private protected override IDataTransform ApplyToDataCore(IHostEnvironment env, IDataView newSource)
diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs
index a84d18792c..77a3b15fbf 100644
--- a/src/Microsoft.ML.FastTree/FastTree.cs
+++ b/src/Microsoft.ML.FastTree/FastTree.cs
@@ -3049,6 +3049,7 @@ private enum AggregateFunction
private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
Host.CheckValue(ctx, nameof(ctx));
+ Host.Check(Utils.Size(outputNames) >= 1);
//Nodes.
var nodesTreeids = new List<long>();
@@ -3111,7 +3112,8 @@ private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputNames,
}
string opType = "TreeEnsembleRegressor";
- var node = ctx.CreateNode(opType, new[] { featureColumn }, outputNames, ctx.GetNodeName(opType));
+ string scoreVarName = (Utils.Size(outputNames) == 2) ? outputNames[1] : outputNames[0]; // Get Score from PredictedLabel and/or Score columns
+ var node = ctx.CreateNode(opType, new[] { featureColumn }, new[] { scoreVarName }, ctx.GetNodeName(opType));
node.AddAttribute("post_transform", PostTransform.None.GetDescription());
node.AddAttribute("n_targets", 1);
diff --git a/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs b/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
index 1ab5d8b6be..d2dea6f60e 100644
--- a/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
+++ b/src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
@@ -136,9 +136,10 @@ internal LinearModelParameters(IHostEnvironment env, string name, in VBuffer<flo
private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputs, string featureColumn)
{
Host.CheckValue(ctx, nameof(ctx));
- Host.Check(Utils.Size(outputs) == 1);
+ Host.Check(Utils.Size(outputs) >= 1);
string opType = "LinearRegressor";
- var node = ctx.CreateNode(opType, new[] { featureColumn }, outputs, ctx.GetNodeName(opType));
+ string scoreVarName = (Utils.Size(outputs) == 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
+ var node = ctx.CreateNode(opType, new[] { featureColumn }, new[] { scoreVarName }, ctx.GetNodeName(opType));
// Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
node.AddAttribute("post_transform", "NONE");
node.AddAttribute("targets", 1);
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
index 3a56e96dd7..c7ddd56e30 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt
@@ -416,7 +416,7 @@
"Probability"
],
"output": [
- "PredictedLabel"
+ "BinarizerOutput"
],
"name": "Binarizer",
"opType": "Binarizer",
@@ -429,6 +429,23 @@
],
"domain": "ai.onnx.ml"
},
+ {
+ "input": [
+ "BinarizerOutput"
+ ],
+ "output": [
+ "PredictedLabel"
+ ],
+ "name": "Cast1",
+ "opType": "Cast",
+ "attribute": [
+ {
+ "name": "to",
+ "i": "9",
+ "type": "INT"
+ }
+ ]
+ },
{
"input": [
"PredictedLabel"
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
index 5045719a2d..166b713fe7 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt
@@ -742,7 +742,7 @@
"Probability"
],
"output": [
- "PredictedLabel"
+ "BinarizerOutput"
],
"name": "Binarizer",
"opType": "Binarizer",
@@ -755,6 +755,23 @@
],
"domain": "ai.onnx.ml"
},
+ {
+ "input": [
+ "BinarizerOutput"
+ ],
+ "output": [
+ "PredictedLabel"
+ ],
+ "name": "Cast1",
+ "opType": "Cast",
+ "attribute": [
+ {
+ "name": "to",
+ "i": "9",
+ "type": "INT"
+ }
+ ]
+ },
{
"input": [
"PredictedLabel"
diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
index 36ae7a2fd3..d007cd95c0 100644
--- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
+++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt
@@ -369,7 +369,7 @@
"Probability"
],
"output": [
- "PredictedLabel"
+ "BinarizerOutput"
],
"name": "Binarizer",
"opType": "Binarizer",
@@ -382,6 +382,23 @@
],
"domain": "ai.onnx.ml"
},
+ {
+ "input": [
+ "BinarizerOutput"
+ ],
+ "output": [
+ "PredictedLabel"
+ ],
+ "name": "Cast1",
+ "opType": "Cast",
+ "attribute": [
+ {
+ "name": "to",
+ "i": "9",
+ "type": "INT"
+ }
+ ]
+ },
{
"input": [
"Label"
diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
index 6c794904b7..0a61508491 100644
--- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs
+++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs
@@ -134,6 +134,15 @@ private class BreastCancerMulticlassExample
public float[] Features;
}
+ private class BreastCancerBinaryClassification
+ {
+ [LoadColumn(0)]
+ public bool Label;
+
+ [LoadColumn(2, 9), VectorType(8)]
+ public float[] Features;
+ }
+
[LessThanNetCore30OrNotNetCoreFact("netcoreapp3.0 output differs from Baseline. Tracked by https://github.com/dotnet/machinelearning/issues/2087")]
public void KmeansOnnxConversionTest()
{
@@ -202,14 +211,15 @@ public void RegressionTrainersOnnxConversionTest()
List<IEstimator<ITransformer>> estimators = new List<IEstimator<ITransformer>>()
{
mlContext.Regression.Trainers.Sdca("Target","FeatureVector"),
- mlContext.Regression.Trainers.Ols("Target","FeatureVector"),
+ mlContext.Regression.Trainers.Ols("Target","FeatureVector"),
mlContext.Regression.Trainers.OnlineGradientDescent("Target","FeatureVector"),
mlContext.Regression.Trainers.FastForest("Target", "FeatureVector"),
mlContext.Regression.Trainers.FastTree("Target", "FeatureVector"),
mlContext.Regression.Trainers.FastTreeTweedie("Target", "FeatureVector"),
mlContext.Regression.Trainers.LbfgsPoissonRegression("Target", "FeatureVector"),
};
- if (Environment.Is64BitProcess) {
+ if (Environment.Is64BitProcess)
+ {
estimators.Add(mlContext.Regression.Trainers.LightGbm("Target", "FeatureVector"));
}
foreach (var estimator in estimators)
@@ -232,7 +242,7 @@ public void RegressionTrainersOnnxConversionTest()
CompareSelectedR4ScalarColumns(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult, 3); // compare score results
}
// Compare the Onnx graph to a baseline if OnnxRuntime is not supported
- else
+ else
{
var onnxFileName = $"{estimator.ToString()}.txt";
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Regression", "Adult");
@@ -244,6 +254,58 @@ public void RegressionTrainersOnnxConversionTest()
Done();
}
+ [Fact]
+ public void BinaryClassificationTrainersOnnxConversionTest()
+ {
+ 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.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);
+ var model = pipeline.Fit(dataView);
+ 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()}.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);
+ CompareSelectedScalarColumns<Boolean>(transformedData.Schema[4].Name, outputNames[2], transformedData, onnxResult);
+ }
+ }
+ Done();
+ }
+
private class DataPoint
{
[VectorType(3)]
@@ -1225,7 +1287,8 @@ private void CreateDummyExamplesToMakeComplierHappy()
var dummyExample = new BreastCancerFeatureVector() { Features = null };
var dummyExample1 = new BreastCancerCatFeatureExample() { Label = false, F1 = 0, F2 = "Amy" };
var dummyExample2 = new BreastCancerMulticlassExample() { Label = "Amy", Features = null };
- var dummyExample3 = new SmallSentimentExample() { Tokens = null };
+ var dummyExample3 = new BreastCancerBinaryClassification() { Label = false, Features = null };
+ var dummyExample4 = new SmallSentimentExample() { Tokens = null };
}
private void CompareResults(string leftColumnName, string rightColumnName, IDataView left, IDataView right)