/*private static void TrainSentenceDetector(int iterations, int cut)
* {
* // train sentence detector
* var sdTrainingFileDirectory = CurrentDirectory + "Input/SentenceDetect";
* var allTrainingFiles = Directory.GetFiles(sdTrainingFileDirectory);
*
* Console.WriteLine("Starting training...");
* var model = MaximumEntropySentenceDetector.TrainModel(allTrainingFiles, iterations, cut, new CharactersSpecificEndOfSentenceScanner('.','?','!','"'));
* var tokenizer = new MaximumEntropySentenceDetector(model);
*
* // test data
* var trainingLines = new List<string>();
* foreach (var trainingFile in allTrainingFiles)
* {
* trainingLines.AddRange(File.ReadAllLines(trainingFile));
* }
* var testData = new List<TokenizerTestData>();
* foreach (var trainingLine in trainingLines)
* {
* var testDataPoint = new TokenizerTestData(trainingLine, splitMarker.ToString());
* testData.Add(testDataPoint);
* }
* var results = tokenizer.RunAgainstTestData(testData);
* Console.WriteLine("Accuracy of model iteration={0}, cut={1}: {2}", iterations, cut, results.GetAccuracy());
* }*/
private static void TrainTokenizer(int iterations, int cut)
{
// train tokenizer
var tokenizeTrainingFileDirectory = CurrentDirectory + "Input/Tokenize/";
const char splitMarker = '|';
var allTrainingFiles = Directory.GetFiles(tokenizeTrainingFileDirectory);
Console.WriteLine("Starting training...");
var model = MaximumEntropyTokenizer.Train(allTrainingFiles, iterations, cut);
var tokenizer = new MaximumEntropyTokenizer(model);
// test data
var trainingLines = new List <string>();
foreach (var trainingFile in allTrainingFiles)
{
trainingLines.AddRange(File.ReadAllLines(trainingFile));
}
var testData = new List <TokenizerTestData>();
foreach (var trainingLine in trainingLines)
{
var testDataPoint = new TokenizerTestData(trainingLine, splitMarker.ToString());
testData.Add(testDataPoint);
}
var results = tokenizer.RunAgainstTestData(testData);
Console.WriteLine("Accuracy of model iteration={0}, cut={1}: {2}", iterations, cut, results.GetAccuracy());
}
private static void OptimizeTokenizerTraining(bool includeAllCapsExamples = false)
{
// all directories in Input folder
var inputFolderPath = CurrentDirectory + "Input/";
var allDirectories = Directory.GetDirectories(inputFolderPath);
Console.WriteLine("Pick the model to train:");
for (var i = 0; i < allDirectories.Length; i++)
{
Console.WriteLine("{0} - {1}", i, Path.GetFileName(allDirectories[i]));
}
// read directory chosen by user
int directoryIndexPicked = LoopUntilValidUserInput(input => int.Parse(input),
i => i < allDirectories.Length, string.Format("Please enter a number in [0..{0}]", allDirectories.Length - 1));
// read user parameters
Console.WriteLine("Enter the iteration values to test, separated by a comma (ex: 10,100,200)");
var iterations = LoopUntilValidUserInput(input => input.Split(',').Select(s => int.Parse(s.Trim())).ToList(),
li => li != null && li.Any(),
"At least one iteration value is required");
Console.WriteLine("Enter the cut values to test, separated by a comma (ex: 1,2,5)");
var cuts = LoopUntilValidUserInput(input => input.Split(',').Select(s => int.Parse(s.Trim())).ToList(),
li => li != null && li.Any(),
"At least one cut value is required");
// train model file
var directory = allDirectories[directoryIndexPicked];
var allTrainFiles = Directory.GetFiles(directory, "*.train") /*.Where(f => f.Contains("wsj"))*/;
Console.WriteLine("Training model with files: {0}", string.Join(", ", allTrainFiles.Select(f => Path.GetFileNameWithoutExtension(f))));
// load training data
var trainingLines = new List <string>();
foreach (var file in allTrainFiles /*.Where(f => f.Contains("wsj"))*/)
{
trainingLines.AddRange(File.ReadAllLines(file));
}
var bestIterationValue = iterations.First();
var bestCutValue = iterations.First();
var bestAccuracy = 0.0d;
foreach (var iteration in iterations)
{
foreach (var cut in cuts)
{
var model = MaximumEntropyTokenizer.Train(allTrainFiles, iteration, cut, includeAllCapsExamples: includeAllCapsExamples);
// compute accuracy
var tokenizer = new MaximumEntropyTokenizer(model);
// test data
var testData = new List <TokenizerTestData>();
foreach (var trainingLine in trainingLines)
{
var testDataPoint = new TokenizerTestData(trainingLine, "|");
testData.Add(testDataPoint);
}
var results = tokenizer.RunAgainstTestData(testData);
Console.WriteLine("Accuracy of model iteration={0}, cut={1}: {2}", iteration, cut, results.GetAccuracy());
if (results.GetAccuracy() > bestAccuracy)
{
bestAccuracy = results.GetAccuracy();
bestIterationValue = iteration;
bestCutValue = cut;
}
}
}
// Persit model
var outputFilePath = CurrentDirectory + "Output/" + Path.GetFileName(directory) + ".nbin";
Console.WriteLine("Persisting model for iteration={0} and cut={1} to file '{2}'...", bestIterationValue, bestCutValue, outputFilePath);
var bestModel = MaximumEntropyTokenizer.Train(allTrainFiles, bestIterationValue, bestCutValue);
new BinaryGisModelWriter().Persist(bestModel, outputFilePath);
Console.WriteLine("Output file written.");
}
