Я наконец смог понять это, не самое лучшее преобразование, но оно работает.Если кого-то интересует полный код, см. 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: " & result.Item1.SentimentText & " | Prediction: " & 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 поле For.оператор преобразует элемент в кортеж с помощью функции .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: " & 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