(8)創建機器學習上下文和加載數據函數

Imports SystemImports System.CollectionsImports System.IOImports System.LinqImports Microsoft.Data.DataViewImports Microsoft.MLImports Microsoft.ML.TrainersImports 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)End SubPublic 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Return NothingEnd FunctionEnd Module

（9）分割并讀入樣本數據集

Imports System Imports System.Collections Imports System.IO Imports System.Linq Imports Microsoft.Data.DataView Imports Microsoft.ML 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)End SubPublic 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 Function End Module

(10)提取特征，確認模型，返回模型

Sub 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) End Sub Public 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 model End Function

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