43 lines
1.7 KiB
C#
43 lines
1.7 KiB
C#
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using Microsoft.ML;
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namespace RhSolutions.ML.Lib;
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public class RhSolutionsMLBuilder
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{
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private static string _appPath = Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]) ?? ".";
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private static MLContext _mlContext = new MLContext(seed: 0);
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public static void RebuildModel()
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{
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var _trainDataView = _mlContext.Data.LoadFromTextFile<Product>(
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Path.Combine(_appPath, "..", "..", "..", "..", "Data", "*"), hasHeader: false);
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var pipeline = ProcessData();
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BuildAndTrainModel(_trainDataView, pipeline, out ITransformer trainedModel);
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SaveModelAsFile(_mlContext, _trainDataView.Schema, trainedModel);
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}
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private static IEstimator<ITransformer> ProcessData()
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{
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var pipeline = _mlContext.Transforms.Conversion.MapValueToKey(inputColumnName: "Type", outputColumnName: "Label")
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.Append(_mlContext.Transforms.Text.FeaturizeText(inputColumnName: "Name", outputColumnName: "NameFeaturized"))
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.Append(_mlContext.Transforms.Concatenate("Features", "NameFeaturized"))
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.AppendCacheCheckpoint(_mlContext);
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return pipeline;
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}
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private static IEstimator<ITransformer> BuildAndTrainModel(IDataView trainingDataView, IEstimator<ITransformer> pipeline, out ITransformer trainedModel)
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{
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var trainingPipeline = pipeline.Append(_mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features"))
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.Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));
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trainedModel = trainingPipeline.Fit(trainingDataView);
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return trainingPipeline;
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}
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private static void SaveModelAsFile(MLContext mlContext, DataViewSchema trainingDataViewSchema, ITransformer model)
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{
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mlContext.Model.Save(model, trainingDataViewSchema,
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Path.Combine(_appPath, "..", "..", "..", "..", "Models", "model.zip"));
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}
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}
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