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Re-train OneVersus all models #4641

srqdev opened this issue Jan 10, 2020 · 2 comments

Re-train OneVersus all models #4641

srqdev opened this issue Jan 10, 2020 · 2 comments


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@srqdev srqdev commented Jan 10, 2020

System information

  • Win 10
  • .NET 4.7.2


Looking at I see that an AveragedPercepton model can be retrained but this only seems to be when used as a binary classifier. Using it as part of a multiclass pipeline (see below) then the fit method does not support model parameters. I may be missing something but it does not appear that you can retrain binary classifiers in this scenario.

Source code / logs

var dataProcessPipeline = 
   ctx.Transforms.Conversion.MapValueToKey("Category", "Prediction")
  .Append(ctx.Transforms.Text.FeaturizeText("Description_tf", textOptions, "Features"))
  .Append(ctx.Transforms.CopyColumns("Features", "Description_tf"))
  .Append(ctx.Transforms.NormalizeMinMax("Features", "Features"))

var trainer = ctx.MulticlassClassification.Trainers
  .AveragedPerceptron("Category", numberOfIterations: iterations), "Category")
  .Append(ctx.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));

var trainingPipeline = dataProcessPipeline.Append(trainer);
return trainingPipeline.Fit(trainingDataView);

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@antoniovs1029 antoniovs1029 commented Jan 10, 2020

Hi, @srqdev . As you have noted, it is not possible to retrain a OneVersusAll trainer, and that's why it doesn't have a Fit() method that accepts both a DataView and a set of pretrained model parameters.

Because of this, I will rephrase the title of your issue, and label it as a feature request.

@antoniovs1029 antoniovs1029 changed the title How can your re-train an AveragedPerceptronTrainer when used in the OneVersus all Multiclass scenario Re-train OneVersus all models Jan 10, 2020
@antoniovs1029 antoniovs1029 added enhancement P2 and removed P3 question labels Jan 10, 2020

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@srqdev srqdev commented Jan 11, 2020

Hi, @antoniovs1029, thanks. The lack of re-trainability has stopped a project moving forward, at least the idea of using model retraining.

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