（11）評估模型，保存模型

Imports System Imports System.Collections 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.TextModule ProgramPrivate ReadOnly _dataPath As String = Path.Combine(Environment.CurrentDirectory, "data", "yelp_labelled.txt")Private ReadOnly _modelPath As String = Path.Combine(Environment.CurrentDirectory, "data", "model.zip")Public ReadOnly Property DataPath As StringGetReturn _dataPathEnd GetEnd PropertyPublic ReadOnly Property ModelPath As StringGetReturn _modelPathEnd GetEnd PropertySub Main(args As String())'創建上下文ML作業Dim mlConText As New MLContextDim splitDataView As TrainCatalogBase.TrainTestData = LoadData(mlConText)Dim model As ITransformer = BuildAndTrainModel(mlConText, splitDataView.TrainSet)Evaluate(mlConText, model, splitDataView.TestSet)End SubPublic Function BuildAndTrainModel(mlContext As MLContext, splitTrainSet As IDataView) As ITransformer'將文本列特征化為機器學習算法使用的名為Features的數值向量的FeaturizeText，再將決策樹算法追加到管道Dim pipleline = mlContext.Transforms.Text.FeaturizeText(outputColumnName:=DefaultColumnNames.Features, inputColumnName:=NameOf(SentimentData.SentimentText)).Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves:=50, numTrees:=50, minDatapointsInLeaves:=20))Dim model = pipleline.Fit(splitTrainSet)Return modelEnd FunctionPublic Function LoadData(mlContext As MLContext) As TrainCatalogBase.TrainTestData'加載數據，將數據集分為訓練集與測試集并返回'加載數據集通過基本的數據管道dataviewDim dataView As IDataView = mlContext.Data.LoadFromTextFile(Of SentimentData)(_dataPath, hasHeader:=False)'拆分數據集進行模型訓練和測試,20%的測試集Dim splitDataView As TrainCatalogBase.TrainTestData = mlContext.BinaryClassification.TrainTestSplit(dataView, testFraction:=0.2)Return splitDataViewEnd FunctionPublic Sub Evaluate(mlContext As MLContext, model As ITransformer, splitTestSet As IDataView)'加載測試數據集，創建分類計算器，評估模型并創建指標，顯示效果指標Console.WriteLine("===用測試數據評估模型正確率===")'返回預測Dim predictions As IDataView = model.Transform(splitTestSet)'計算預測模型質量指標Dim metrics As CalibratedBinaryClassificationMetrics = mlContext.BinaryClassification.Evaluate(predictions, "label")'顯示模型驗證指標Console.WriteLine("正確率：" & metrics.Accuracy)Console.WriteLine("AUC：" & metrics.Auc)Console.WriteLine("F1Score：" & metrics.F1Score)SaveModelAsFile(mlContext, model)End SubPrivate Sub SaveModelAsFile(mlContext As MLContext, model As ITransformer)Throw New NotImplementedException()Using fs As New FileStream(ModelPath, FileMode.Create, FileAccess.Write, FileShare.Write))mlContext.Model.Save(model, fs)Console.WriteLine("模型存入" & ModelPath)End UsingEnd Sub End Module

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