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Я надеюсь, что я правильно сформулировал вопрос, простите за мое незнание, если не точно сформулировано, и если у вас есть предложения о том, как лучше задать вопрос, пожалуйста, дайте мне знать, и я перефразирую его.

Я следую этому руководству от Microsoft по новой Microsoft.Пакет ML: https://learn.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis

Руководство построено на C #, и я пытаюсь преобразовать в VB.NET . Полный код C # для этого руководства находится по адресу: https://github.com/dotnet/samples/blob/master/machine-learning/tutorials/SentimentAnalysis/Program.cs

Я преобразовал все, за исключением нескольких строк, и мне просто не хватает знаний о том, как выполнить это преобразование:

Строка # 220:

 IEnumerable<(SentimentData sentiment, SentimentPrediction prediction)> sentimentsAndPredictions = sentiments.Zip(predictedResults, (sentiment, prediction) => (sentiment, prediction));
  

Далее следуют строки с #224 по #228:

 foreach ((SentimentData sentiment, SentimentPrediction prediction) item in sentimentsAndPredictions)
            {
                Console.WriteLine($"Sentiment: {item.sentiment.SentimentText} | Prediction: {(Convert.ToBoolean(item.prediction.Prediction) ? "Positive" : "Negative")} | Probability: {item.prediction.Probability} ");

            }
  

Я никогда раньше с этим не работал, кто-нибудь знает, как преобразовать этот код?

В качестве последнего средства я также попробовал инструмент преобразования Telerik в converter.telerik.com и получите следующую ошибку:

 ''' Cannot convert ForEachStatementSyntax, CONVERSION ERROR: Conversion for TupleType not implemented, please report this issue in '(SentimentData sentiment, S...'
  

Я, наконец, смог понять это, не самое аккуратное преобразование, но оно работает. Если кого-то интересует полный код, смотрите ниже.

     Dim sentimentsAndPredictions = sentiments.Zip(predictedResults, Function(sentiment As SentimentData, prediction As SentimentPrediction) (sentiment, prediction))

    For Each item In sentimentsAndPredictions
        Dim result = item.ToTuple
        Console.WriteLine("Sentiment: " amp; result.Item1.SentimentText amp; " | Prediction: " amp; If(Convert.ToBoolean(result.Item2.Prediction), "Positive", "Negative"))
    Next
  

Обратите внимание, что в приведенном выше коде мне пришлось передать

 Function(sentiment As SentimentData, prediction As SentimentPrediction) (sentiment, prediction)
  

В функцию Zip, а затем в операторе For преобразуйте элемент в кортеж, используя .Функция ToTuple.

Полный код:

 Imports System
Imports System.Collections.Generic
Imports System.IO
Imports System.Linq
Imports Microsoft.Data.DataView
Imports Microsoft.ML
Imports Microsoft.ML.Data
Imports Microsoft.ML.Trainers
Imports Microsoft.ML.Transforms.Text

Module Module1

    Public _dataPath As String = Path.Combine(Environment.CurrentDirectory, "Data", "yelp_labelled.txt")
    Public _modelPath As String = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip")

    Sub Main()

        Dim mlcontext As MLContext = New MLContext()
        Dim splitDataView As TrainCatalogBase.TrainTestData = LoadData(mlcontext)
        Dim model As ITransformer = BuildAndTrainModel(mlcontext, splitDataView.TrainSet)
        Evaluate(mlcontext, model, splitDataView.TestSet)
        UseModelWithSingleItem(mlcontext, model)

        UseLoadedModelWithBatchItems(mlcontext)

        Console.WriteLine()
        Console.WriteLine("=============== End of process ===============")

        Console.ReadLine()

    End Sub

    Public Function LoadData(ByVal mlContext As MLContext) As TrainCatalogBase.TrainTestData
        Dim dataView As IDataView = mlContext.Data.LoadFromTextFile(Of SentimentData)(_dataPath, hasHeader:=False)
        Dim splitDataView As TrainCatalogBase.TrainTestData = mlContext.BinaryClassification.TrainTestSplit(dataView, testFraction:=0.2)
        Return splitDataView
    End Function

    Public Function BuildAndTrainModel(ByVal mlContext As MLContext, ByVal splitTrainSet As IDataView) As ITransformer
        Dim pipeline = mlContext.Transforms.Text.FeaturizeText(outputColumnName:=DefaultColumnNames.Features, inputColumnName:=NameOf(SentimentData.SentimentText)).Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves:=50, numTrees:=50, minDatapointsInLeaves:=20))
        Console.WriteLine("=============== Create and Train the Model ===============")
        Dim model = pipeline.Fit(splitTrainSet)
        Console.WriteLine("=============== End of training ===============")
        Console.WriteLine()
        Return model
    End Function

    Public Sub Evaluate(ByVal mlContext As MLContext, ByVal model As ITransformer, ByVal splitTestSet As IDataView)
        Console.WriteLine("=============== Evaluating Model accuracy with Test data===============")
        Dim predictions As IDataView = model.Transform(splitTestSet)
        Dim metrics As CalibratedBinaryClassificationMetrics = mlContext.BinaryClassification.Evaluate(predictions, "Label")
        Console.WriteLine()
        Console.WriteLine("Model quality metrics evaluation")
        Console.WriteLine("--------------------------------")
        Console.WriteLine($"Accuracy: {metrics.Accuracy}")
        Console.WriteLine($"Auc: {metrics.Auc}")
        Console.WriteLine($"F1Score: {metrics.F1Score}")
        Console.WriteLine("=============== End of model evaluation ===============")
        SaveModelAsFile(mlContext, model)
    End Sub

    Private Sub UseModelWithSingleItem(ByVal mlContext As MLContext, ByVal model As ITransformer)
        Dim predictionFunction As PredictionEngine(Of SentimentData, SentimentPrediction) = model.CreatePredictionEngine(Of SentimentData, SentimentPrediction)(mlContext)
        Dim sampleStatement As SentimentData = New SentimentData With {
            .SentimentText = "This was a very bad steak"
        }
        Dim resultprediction = predictionFunction.Predict(sampleStatement)
        Console.WriteLine()
        Console.WriteLine("=============== Prediction Test of model with a single sample and test dataset ===============")
        Console.WriteLine()
        Console.WriteLine($"Sentiment: {sampleStatement.SentimentText} | Prediction: {(If(Convert.ToBoolean(resultprediction.Prediction), "Positive", "Negative"))} | Probability: {resultprediction.Probability} ")
        Console.WriteLine("=============== End of Predictions ===============")
        Console.WriteLine()
    End Sub

    Public Sub UseLoadedModelWithBatchItems(ByVal mlContext As MLContext)
        Dim sentiments As IEnumerable(Of SentimentData) = {New SentimentData With {
            .SentimentText = "This was a horrible meal"
        }, New SentimentData With {
            .SentimentText = "I love this spaghetti."
        }}

        Dim loadedModel As ITransformer
        Using s1 As IO.FileStream = New FileStream(_modelPath, FileMode.Open, FileAccess.Read, FileShare.Read)
            loadedModel = mlContext.Model.Load(s1)
        End Using

        Dim sentimentStreamingDataView As IDataView = mlContext.Data.LoadFromEnumerable(sentiments)
        Dim predictions As IDataView = loadedModel.Transform(sentimentStreamingDataView)
        Dim predictedResults As IEnumerable(Of SentimentPrediction) = mlContext.Data.CreateEnumerable(Of SentimentPrediction)(predictions, reuseRowObject:=False)
        Console.WriteLine()

        Console.WriteLine("=============== Prediction Test of loaded model with a multiple samples ===============")

        Console.WriteLine()

        Dim sentimentsAndPredictions = sentiments.Zip(predictedResults, Function(sentiment As SentimentData, prediction As SentimentPrediction) (sentiment, prediction))

        For Each item In sentimentsAndPredictions
            Dim result = item.ToTuple
            Console.WriteLine("Sentiment: " amp; result.Item1.SentimentText amp; " | Prediction: " amp; If(Convert.ToBoolean(result.Item2.Prediction), "Positive", "Negative"))
        Next

    End Sub

    Private Sub SaveModelAsFile(ByVal mlContext As MLContext, ByVal model As ITransformer)
        Using fs = New FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write)
            mlContext.Model.Save(model, fs)
        End Using

        Console.WriteLine("The model is saved to {0}", _modelPath)
    End Sub

End Module