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Fix for OneVersusAll Multiclass trainer #4698

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Merged
kere-nel merged 5 commits into from
Jan 28, 2020
Merged

Fix for OneVersusAll Multiclass trainer #4698

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d3aea0c
ova fix
kere-nel Jan 23, 2020
e9a7af4
reformatted spacing
kere-nel Jan 23, 2020
d3957ea
reverted offset name -since another PR is resolving this, and toleran…
kere-nel Jan 24, 2020
afa875c
rebase change
kere-nel Jan 28, 2020
3ddcede
rebase change
kere-nel Jan 28, 2020
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2 changes: 1 addition & 1 deletion src/Microsoft.ML.FastTree/FastTree.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -3112,7 +3112,7 @@ private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputNames,
}

string opType = "TreeEnsembleRegressor";
string scoreVarName = (Utils.Size(outputNames) == 2) ? outputNames[1] : outputNames[0]; // Get Score from PredictedLabel and/or Score columns
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());
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2 changes: 1 addition & 1 deletion src/Microsoft.ML.StandardTrainers/Standard/LinearModelParameters.cs
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Expand Up @@ -138,7 +138,7 @@ private protected virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputs, str
Host.CheckValue(ctx, nameof(ctx));
Host.Check(Utils.Size(outputs) >= 1);
string opType = "LinearRegressor";
string scoreVarName = (Utils.Size(outputs) == 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
string scoreVarName = (Utils.Size(outputs) >= 2) ? outputs[1] : outputs[0]; // Get Score from PredictedLabel and/or Score columns
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@harishsk harishsk Jan 24, 2020

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What cases result in the output size being greater than 2?

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@kere-nel kere-nel Jan 24, 2020

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When a calibrator is used, the probability column is added. More specifically, the OVA trainer always creates a probability output column, so the output count will be 3. An alternative would be to distinguish between trainers that need the probability column and those that don't inside OVA, but I think that would be more involved.

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");
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17 changes: 11 additions & 6 deletions src/Microsoft.ML.StandardTrainers/Standard/MulticlassClassification/OneVersusAllTrainer.cs
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Expand Up @@ -563,7 +563,7 @@ public string[] SaveAsOnnxPreProcess(OnnxContext ctx, string featureColumn, bool
var clipNode = ctx.CreateNode(opType, new[] { clipInput, zeroVar }, new[] { outputs[i] }, ctx.GetNodeName(opType), "");
}
else
outputs[i] = predictorOutputNames[2];
outputs[i] = predictorOutputNames[1];
}
return outputs;
}
Expand Down Expand Up @@ -659,7 +659,7 @@ public override JToken SaveAsPfa(BoundPfaContext ctx, JToken input)

public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string featureColumn)
{
var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, true);
var probabilityOutputs = base.SaveAsOnnxPreProcess(ctx, featureColumn, false);

string opType = "Concat";
var concatOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "ConcatOutput", true);
Expand Down Expand Up @@ -794,22 +794,27 @@ public override bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string fe

opType = "Sum";
var sumOutput = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScores", true);
var sumNode = ctx.CreateNode(opType, probabilityOutputs, new[] { sumOutput }, ctx.GetNodeName(opType), "");
ctx.CreateNode(opType, probabilityOutputs, new[] { sumOutput }, ctx.GetNodeName(opType), "");

opType = "Cast";
var castOutput = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZero", true);
var castOutput = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "CastOutput", true);
var castNode = ctx.CreateNode(opType, sumOutput, castOutput, ctx.GetNodeName(opType), "");
var t = InternalDataKindExtensions.ToInternalDataKind(DataKind.Boolean).ToType();
castNode.AddAttribute("to", t);

opType = "Not";
var notOutput = ctx.AddIntermediateVariable(null, "IsSumZero", true);
ctx.CreateNode(opType, castOutput, notOutput, ctx.GetNodeName(opType), "");

opType = "Cast";
var castIsZeroSumToFloat = ctx.AddIntermediateVariable(BooleanDataViewType.Instance, "IsSumZeroAsFloat", true);
var castIsZeroSumToFloatNode = ctx.CreateNode(opType, castOutput, castIsZeroSumToFloat, ctx.GetNodeName(opType), "");
var castIsZeroSumToFloatNode = ctx.CreateNode(opType, notOutput, castIsZeroSumToFloat, ctx.GetNodeName(opType), "");
var t1 = InternalDataKindExtensions.ToInternalDataKind(DataKind.Single).ToType();
castIsZeroSumToFloatNode.AddAttribute("to", t1);

opType = "Sum";
var sumOutputNonZero = ctx.AddIntermediateVariable(NumberDataViewType.Single, "SumOfScoresNonZero", true);
var sumOutputNonZeroNode = ctx.CreateNode(opType, new[] { sumOutput, castIsZeroSumToFloat },
ctx.CreateNode(opType, new[] { sumOutput, castIsZeroSumToFloat },
new[] { sumOutputNonZero }, ctx.GetNodeName(opType), "");

string[] divOutputs = new string[Predictors.Length];
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17 changes: 16 additions & 1 deletion test/Microsoft.ML.Tests/OnnxConversionTest.cs
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Expand Up @@ -1319,8 +1319,22 @@ public void MulticlassTrainersOnnxConversionTest()
{
mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(),
mlContext.MulticlassClassification.Trainers.NaiveBayes(),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.AveragedPerceptron()),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.AveragedPerceptron(), useProbabilities:false),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression()),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(), useProbabilities:false),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.LinearSvm()),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.LinearSvm(), useProbabilities:false),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.FastForest()),
mlContext.MulticlassClassification.Trainers.OneVersusAll(
mlContext.BinaryClassification.Trainers.FastForest(), useProbabilities:false),
mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(),
mlContext.MulticlassClassification.Trainers.SdcaNonCalibrated()
};
Expand Down Expand Up @@ -1357,7 +1371,8 @@ public void MulticlassTrainersOnnxConversionTest()
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
var onnxTransformer = onnxEstimator.Fit(dataView);
var onnxResult = onnxTransformer.Transform(dataView);
CompareSelectedScalarColumns<uint>(transformedData.Schema[5].Name, outputNames[2], transformedData, onnxResult);
CompareSelectedScalarColumns<uint>(transformedData.Schema[5].Name, outputNames[2], transformedData, onnxResult); //compare predicted labels
CompareSelectedR4VectorColumns(transformedData.Schema[6].Name, outputNames[3], transformedData, onnxResult, 4); //compare scores
}
}
Done();
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