Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
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();
}
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.