static void Main(string[] args)
{
List <Record> TrainingSet = new List <Record>()
{
new Record("Conservative", false, false, true),
new Record("Conservative", true, true, false),
new Record("Conservative", true, false, false),
new Record("Socialist", false, true, false),
new Record("Socialist", true, true, true),
new Record("Libertarian", true, true, true),
};
var toBeClassifiedRecord = new Record("Unknown", true, true, true);
NaiveBayesClassifier bayesClassifier = new NaiveBayesClassifier(TrainingSet);
toBeClassifiedRecord.classification = bayesClassifier.GetClassification(toBeClassifiedRecord);
}
static void Main(string[] args)
{
var data = DataReader.ReadCsv();
var dataSets = TrainTest.Split(data);
var accuracies = new List <double>();
dataSets.ToList().ForEach(sets =>
{
var model = new NaiveBayesClassifier();
model.Fit(sets.training);
var accuracy = model.Solve(sets.test);
accuracies.Add(accuracy);
});
Console.WriteLine();
Console.WriteLine($"Average accuracy: {accuracies.Sum() / (double)accuracies.Count}");
}
static void Main(string[] args)
{
// Reading data from .txt files
List <_DataSetItem> raw_data_train = ReadInData(Properties.Resources.SMSSpamTrain);
List <_DataSetItem> raw_data_test = ReadInData(Properties.Resources.SMSSpamTest);
List <_DataSetItem> full_dataset = new List <_DataSetItem>(raw_data_test.Count + raw_data_train.Count);
full_dataset.AddRange(raw_data_train);
full_dataset.AddRange(raw_data_test);
// Extracting the vocabulary of the dataset. Only words that occur 3 or more times are considered part of the vocabulary
HashSet <string> vocabulary = ExtractVocabulary(full_dataset, 3);
// Transforming the raw SMS data into a bag of words from the previously extracted vocabulary
DataFeaturesContainer X_train = BagOfWordsFromVocabularyAndSMS(raw_data_train, vocabulary);
DataFeaturesContainer X_test = BagOfWordsFromVocabularyAndSMS(raw_data_test, vocabulary);
// Assigning the target values
List <int> Y_train = raw_data_train.Select(x => x.target == "spam" ? 0 : 1).ToList();
List <int> Y_test = raw_data_test.Select(x => x.target == "spam" ? 0 : 1).ToList();
// Defining the prior
// Unbiased prior, carries no information, every class is as likely to occur
Dictionary <int, double> unbiased_prior = new Dictionary <int, double>()
{
{ 1, 0.5 },
{ 0, 0.5 }
};
// Reasearched prior found on the wikipedia articke
// @"https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering"
Dictionary <int, double> wiki_prior = new Dictionary <int, double>()
{
{ 1, 0.2 },
{ 0, 0.8 }
};
// Calculating the prior from our dataset which includes finding the frequency of
// actuall spam messages vs ham messages
List <string> tartets = full_dataset.Select(x => x.target).ToList();
double spam_prior = CalculateDatasetPrior(tartets);
Dictionary <int, double> dataset_calculated_prior = new Dictionary <int, double>()
{
{ 1, 1.0 - spam_prior },
{ 0, spam_prior }
};
// Training the model and doing the predictions
NaiveBayesClassifier model = new NaiveBayesClassifier();
model.Fit(X_train, Y_train, unbiased_prior);
List <int> predictions = model.Predict(X_test.Data);
// Measuring model performance
ModelMetrics modelMetrics = new ModelMetrics(Y_test, predictions, 0);
modelMetrics.PrintMeasures();
// Saving the results
List <string> test_sms = raw_data_train.Select(x => x.sms.Trim()).ToList();
WriteResults("results.tsv", test_sms, predictions);
}
