MLNetUsingWordEmbeddings.cs

internal static class Program
{
    private static PredictionModel<SentimentData, SentimentPrediction> _model;
    private static PredictionModel<SentimentData, SentimentPrediction> _modelWordEmbeddings;
    private static string AppPath => Path.GetDirectoryName(Environment.GetCommandLineArgs()[0]);
    private static string TrainDataPath => Path.Combine(AppPath, "datasets", "sentiment-imdb-train.txt");
    private static string TestDataPath => Path.Combine(AppPath, "datasets", "sentiment-yelp-test.txt");
    private static string ModelPath => Path.Combine(AppPath, "SentimentModel.zip");

    private static void Main(string[] args)
    {
        TrainModel();
        TrainModelWordEmbeddings();

        Evaluate(_model, "normal");
        Evaluate(_modelWordEmbeddings, "using WordEmbeddings");

        Console.ReadLine();
    }

    public static void TrainModel()
    {
        var pipeline = new LearningPipeline();
        pipeline.Add(new TextLoader(TrainDataPath).CreateFrom<SentimentData>());
        pipeline.Add(new TextFeaturizer("Features", "SentimentText"));
        pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
        Console.WriteLine("=============== Training model ===============");
        var model = pipeline.Train<SentimentData, SentimentPrediction>();
        Console.WriteLine("=============== End training ===============");
        _model = model;
    }

    public static void TrainModelWordEmbeddings()
    {
        var pipeline = new LearningPipeline();
        pipeline.Add(new TextLoader(TrainDataPath).CreateFrom<SentimentData>());
        pipeline.Add(new TextFeaturizer("FeaturesA", "SentimentText") { OutputTokens = true });
        pipeline.Add(new WordEmbeddings(("FeaturesA_TransformedText", "FeaturesB")));
        pipeline.Add(new ColumnConcatenator("Features", "FeaturesA", "FeaturesB"));
        pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 });
        Console.WriteLine("=============== Training model with Word Embeddings ===============");
        var model = pipeline.Train<SentimentData, SentimentPrediction>();
        Console.WriteLine("=============== End training ===============");
        _modelWordEmbeddings = model;
    }

    private static void Evaluate(PredictionModel<SentimentData, SentimentPrediction> model, string name)
    {
        var testData = new TextLoader(TestDataPath).CreateFrom<SentimentData>();
        var evaluator = new BinaryClassificationEvaluator();
        Console.WriteLine("=============== Evaluating model {0} ===============", name);
        var metrics = evaluator.Evaluate(model, testData);
        Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
        Console.WriteLine($"Auc: {metrics.Auc:P2}");
        Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
        Console.WriteLine("=============== End evaluating ===============");
        Console.WriteLine();
    }
}