Question

我希望我将问题表述为好，请原谅我对如果措辞不够准确的知识，如果您对如何更好地提出该问题有任何建议，请告诉我，我将其重述。

我正在按照Microsoft的新Microsoft.ML软件包上的指南进行操作：https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/sentiment-analysis

该指南基于C＃构建，我正尝试转换为VB.NET。本指南的完整C＃代码位于：https://github.com/dotnet/samples/blob/master/machine-learning/tutorials/SentimentAnalysis/Program.cs

除了几行内容外，我已经对所有内容进行了转换，而我只是缺乏有关如何完成此转换的知识：

第220行：

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

接着是＃224至＃228行：

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} ");

            }

我以前从未使用过它，有人知道如何转换此代码吗？

作为最后的手段，我还尝试在converter.telerik.com上使用Telerik的转换工具，并收到以下错误消息：

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

Answer 1

我终于能够弄清楚了，不是最简单的转换，但是它可以工作。如果有人对完整代码感兴趣，请参见下文。

    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: " & result.Item1.SentimentText & " | Prediction: " & If(Convert.ToBoolean(result.Item2.Prediction), "Positive", "Negative"))
    Next

请注意，在上面的代码中，我必须通过

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

进入Zip函数，然后在For语句中使用.ToTuple函数将该项转换为Tuple。

完整代码：

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: " & result.Item1.SentimentText & " | Prediction: " & 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

使用元组将IENUMERABLE从C＃转换为VB

1 个答案: