public async Task <bool> Predict(Agent agent, NlpDoc doc, PipeModel meta)
{
var options = new ClassifyOptions
{
ModelFilePath = Path.Combine(Settings.ModelDir, meta.Model)
};
var classifier = new ClassifierFactory <NaiveBayesClassifier, SentenceFeatureExtractor>(options, SupportedLanguage.English);
var sentence = doc.Sentences.Select(s => new Sentence
{
Text = s.Text,
Words = s.Tokens
}).First();
var result = classifier.Classify(sentence);
doc.Sentences[0].Intent = new TextClassificationResult
{
Classifier = "BotSharpNBayesClassifier",
Label = result.First().Item1,
Confidence = (decimal)result.First().Item2
};
return(true);
}
public void GenderTest()
{
var options = new ClassifyOptions
{
TrainingCorpusDir = Path.Combine(Configuration.GetValue <String>("MachineLearning:dataDir"), "Gender")
};
var classifier = new ClassifierFactory <WordFeatureExtractor>(options, SupportedLanguage.English);
var corpus = GetLabeledCorpus(options);
var tokenizer = new TokenizerFactory(new TokenizationOptions
{
Pattern = RegexTokenizer.WORD_PUNC
}, SupportedLanguage.English);
tokenizer.GetTokenizer <RegexTokenizer>();
corpus.ForEach(x => x.Words = tokenizer.Tokenize(x.Text));
classifier.Train(corpus);
string text = "Bridget";
classifier.Classify(new Sentence {
Text = text, Words = tokenizer.Tokenize(text)
});
corpus.Shuffle();
var trainingData = corpus.Skip(2000).ToList();
classifier.Train(trainingData);
var testData = corpus.Take(2000).ToList();
int correct = 0;
testData.ForEach(td =>
{
var classes = classifier.Classify(td);
if (td.Label == classes[0].Item1)
{
correct++;
}
});
var accuracy = (float)correct / testData.Count;
}
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);
}
public void SpookyAuthorIdentification()
{
var reader = new KaggleTextDataReader();
var sentences = reader.Read(new ReaderOptions {
FileName = "train.csv"
});
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].Id = sentences[i].Id;
newSentences[i].Label = sentences[i].Label;
}
sentences = newSentences.ToList();
sentences.Shuffle();
var dataset = sentences.Take(2000).ToList().Split(0.7M);
var options = new ClassifyOptions
{
ModelDir = AppContext.BaseDirectory,
ModelFilePath = Path.Combine(AppContext.BaseDirectory, "nb.model"),
Dimension = 300
};
var classifier = new ClassifierFactory <SentenceFeatureExtractor>(options, SupportedLanguage.English);
classifier.GetClassifer("NaiveBayesClassifier");
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);
}
public async Task <bool> Predict(AgentBase agent, NlpDoc doc, PipeModel meta)
{
Init(meta);
var sentence = doc.Sentences.Select(s => new Sentence
{
Text = s.Text,
Words = s.Tokens
}).First();
var result = _classifier.Classify(sentence);
doc.Sentences[0].Intent = new TextClassificationResult
{
Classifier = "BotSharpIntentClassifier",
Label = result.First().Item1,
Confidence = result.First().Item2
};
return(true);
}