public void OneHotTest()
{
var reader = new FasttextDataReader();
var sentences = reader.Read(new ReaderOptions
{
DataDir = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Text Classification", "cooking.stackexchange"),
FileName = "cooking.stackexchange.txt"
});
var tokenizer = new TokenizerFactory(new TokenizationOptions {
}, SupportedLanguage.English);
tokenizer.GetTokenizer <TreebankTokenizer>();
var newSentences = tokenizer.Tokenize(sentences.Select(x => x.Text).ToList());
for (int i = 0; i < newSentences.Count; i++)
{
newSentences[i].Label = sentences[i].Label;
}
sentences = newSentences.ToList();
var encoder = new OneHotEncoder();
encoder.Sentences = sentences;
encoder.EncodeAll();
}
public void CookingTest()
{
var reader = new FasttextDataReader();
var sentences = reader.Read(new ReaderOptions
{
DataDir = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Text Classification", "cooking.stackexchange"),
FileName = "cooking.stackexchange.txt"
});
var tokenizer = new TokenizerFactory(new TokenizationOptions {
}, SupportedLanguage.English);
tokenizer.GetTokenizer <TreebankTokenizer>();
var newSentences = tokenizer.Tokenize(sentences.Select(x => x.Text).ToList());
for (int i = 0; i < newSentences.Count; i++)
{
newSentences[i].Label = sentences[i].Label;
}
sentences = newSentences.ToList();
sentences.Shuffle();
var options = new ClassifyOptions
{
ModelFilePath = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Text Classification", "cooking.stackexchange", "nb.model"),
TrainingCorpusDir = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Text Classification", "cooking.stackexchange"),
Dimension = 100
};
var classifier = new ClassifierFactory <SentenceFeatureExtractor>(options, SupportedLanguage.English);
var dataset = sentences.Split(0.7M);
classifier.Train(dataset.Item1);
int correct = 0;
int total = 0;
dataset.Item2.ForEach(td =>
{
var classes = classifier.Classify(td);
if (td.Label == classes[0].Item1)
{
correct++;
}
total++;
});
var accuracy = (float)correct / total;
Assert.IsTrue(accuracy > 0.5);
}
