/// <summary>
/// Trains a
/// <see cref="IClassifier{L, F}"/>
/// on a
/// <see cref="Dataset{L, F}"/>
/// .
/// </summary>
/// <returns>
/// A
/// <see cref="IClassifier{L, F}"/>
/// trained on the data.
/// </returns>
public virtual LinearClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data)
{
labelIndex = data.LabelIndex();
featureIndex = data.FeatureIndex();
double[][] weights = TrainWeights(data);
return(new LinearClassifier <L, F>(weights, featureIndex, labelIndex));
}
public static Edu.Stanford.Nlp.Classify.OneVsAllClassifier <L, F> Train <L, F>(IClassifierFactory <string, F, IClassifier <string, F> > classifierFactory, GeneralDataset <L, F> dataset, ICollection <L> trainLabels)
{
IIndex <L> labelIndex = dataset.LabelIndex();
IIndex <F> featureIndex = dataset.FeatureIndex();
IDictionary <L, IClassifier <string, F> > classifiers = Generics.NewHashMap();
foreach (L label in trainLabels)
{
int i = labelIndex.IndexOf(label);
logger.Info("Training " + label + " = " + i + ", posIndex = " + posIndex);
// Create training data for training this classifier
IDictionary <L, string> posLabelMap = new ArrayMap <L, string>();
posLabelMap[label] = PosLabel;
GeneralDataset <string, F> binaryDataset = dataset.MapDataset(dataset, binaryIndex, posLabelMap, NegLabel);
IClassifier <string, F> binaryClassifier = classifierFactory.TrainClassifier(binaryDataset);
classifiers[label] = binaryClassifier;
}
Edu.Stanford.Nlp.Classify.OneVsAllClassifier <L, F> classifier = new Edu.Stanford.Nlp.Classify.OneVsAllClassifier <L, F>(featureIndex, labelIndex, classifiers);
return(classifier);
}
public virtual SVMLightClassifier <L, F> TrainClassifierBasic(GeneralDataset <L, F> dataset)
{
IIndex <L> labelIndex = dataset.LabelIndex();
IIndex <F> featureIndex = dataset.featureIndex;
bool multiclass = (dataset.NumClasses() > 2);
try
{
// this is the file that the model will be saved to
File modelFile = File.CreateTempFile("svm-", ".model");
if (deleteTempFilesOnExit)
{
modelFile.DeleteOnExit();
}
// this is the file that the svm light formated dataset
// will be printed to
File dataFile = File.CreateTempFile("svm-", ".data");
if (deleteTempFilesOnExit)
{
dataFile.DeleteOnExit();
}
// print the dataset
PrintWriter pw = new PrintWriter(new FileWriter(dataFile));
dataset.PrintSVMLightFormat(pw);
pw.Close();
// -v 0 makes it not verbose
// -m 400 gives it a larger cache, for faster training
string cmd = (multiclass ? svmStructLearn : (useSVMPerf ? svmPerfLearn : svmLightLearn)) + " -v " + svmLightVerbosity + " -m 400 ";
// set the value of C if we have one specified
if (C > 0.0)
{
cmd = cmd + " -c " + C + " ";
}
else
{
// C value
if (useSVMPerf)
{
cmd = cmd + " -c " + 0.01 + " ";
}
}
//It's required to specify this parameter for SVM perf
// Alpha File
if (useAlphaFile)
{
File newAlphaFile = File.CreateTempFile("svm-", ".alphas");
if (deleteTempFilesOnExit)
{
newAlphaFile.DeleteOnExit();
}
cmd = cmd + " -a " + newAlphaFile.GetAbsolutePath();
if (alphaFile != null)
{
cmd = cmd + " -y " + alphaFile.GetAbsolutePath();
}
alphaFile = newAlphaFile;
}
// File and Model Data
cmd = cmd + " " + dataFile.GetAbsolutePath() + " " + modelFile.GetAbsolutePath();
if (verbose)
{
logger.Info("<< " + cmd + " >>");
}
/*Process p = Runtime.getRuntime().exec(cmd);
*
* p.waitFor();
*
* if (p.exitValue() != 0) throw new RuntimeException("Error Training SVM Light exit value: " + p.exitValue());
* p.destroy(); */
SystemUtils.Run(new ProcessBuilder(whitespacePattern.Split(cmd)), new PrintWriter(System.Console.Error), new PrintWriter(System.Console.Error));
if (doEval)
{
File predictFile = File.CreateTempFile("svm-", ".pred");
if (deleteTempFilesOnExit)
{
predictFile.DeleteOnExit();
}
string evalCmd = (multiclass ? svmStructClassify : (useSVMPerf ? svmPerfClassify : svmLightClassify)) + " " + dataFile.GetAbsolutePath() + " " + modelFile.GetAbsolutePath() + " " + predictFile.GetAbsolutePath();
if (verbose)
{
logger.Info("<< " + evalCmd + " >>");
}
SystemUtils.Run(new ProcessBuilder(whitespacePattern.Split(evalCmd)), new PrintWriter(System.Console.Error), new PrintWriter(System.Console.Error));
}
// read in the model file
Pair <double, ClassicCounter <int> > weightsAndThresh = ReadModel(modelFile, multiclass);
double threshold = weightsAndThresh.First();
ClassicCounter <Pair <F, L> > weights = ConvertWeights(weightsAndThresh.Second(), featureIndex, labelIndex, multiclass);
ClassicCounter <L> thresholds = new ClassicCounter <L>();
if (!multiclass)
{
thresholds.SetCount(labelIndex.Get(0), -threshold);
thresholds.SetCount(labelIndex.Get(1), threshold);
}
SVMLightClassifier <L, F> classifier = new SVMLightClassifier <L, F>(weights, thresholds);
if (doEval)
{
File predictFile = File.CreateTempFile("svm-", ".pred2");
if (deleteTempFilesOnExit)
{
predictFile.DeleteOnExit();
}
PrintWriter pw2 = new PrintWriter(predictFile);
NumberFormat nf = NumberFormat.GetNumberInstance();
nf.SetMaximumFractionDigits(5);
foreach (IDatum <L, F> datum in dataset)
{
ICounter <L> scores = classifier.ScoresOf(datum);
pw2.Println(Counters.ToString(scores, nf));
}
pw2.Close();
}
if (useSigmoid)
{
if (verbose)
{
System.Console.Out.Write("fitting sigmoid...");
}
classifier.SetPlatt(FitSigmoid(classifier, dataset));
if (verbose)
{
System.Console.Out.WriteLine("done");
}
}
return(classifier);
}
catch (Exception e)
{
throw new Exception(e);
}
}
public static Edu.Stanford.Nlp.Classify.OneVsAllClassifier <L, F> Train <L, F>(IClassifierFactory <string, F, IClassifier <string, F> > classifierFactory, GeneralDataset <L, F> dataset)
{
IIndex <L> labelIndex = dataset.LabelIndex();
return(Train(classifierFactory, dataset, labelIndex.ObjectsList()));
}
