public virtual void TrainWeightedData(GeneralDataset <L, F> data, float[] dataWeights)
{
//Use LogisticClassifierFactory to train instead.
if (data.labelIndex.Size() != 2)
{
throw new Exception("LogisticClassifier is only for binary classification!");
}
IMinimizer <IDiffFunction> minim;
LogisticObjectiveFunction lof = null;
if (data is Dataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetLabelsArray(), prior, dataWeights);
}
else
{
if (data is RVFDataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetValuesArray(), data.GetLabelsArray(), prior, dataWeights);
}
}
minim = new QNMinimizer(lof);
weights = minim.Minimize(lof, 1e-4, new double[data.NumFeatureTypes()]);
featureIndex = data.featureIndex;
classes[0] = data.labelIndex.Get(0);
classes[1] = data.labelIndex.Get(1);
}
/// <summary>The examples are assumed to be a list of RFVDatum.</summary>
/// <remarks>
/// The examples are assumed to be a list of RFVDatum.
/// The datums are assumed to not contain the zeroes and then they are added to each instance.
/// </remarks>
public virtual NaiveBayesClassifier <L, F> TrainClassifier(GeneralDataset <L, F> examples, ICollection <F> featureSet)
{
int numFeatures = featureSet.Count;
int[][] data = new int[][] { };
int[] labels = new int[examples.Size()];
labelIndex = new HashIndex <L>();
featureIndex = new HashIndex <F>();
foreach (F feat in featureSet)
{
featureIndex.Add(feat);
}
for (int d = 0; d < examples.Size(); d++)
{
RVFDatum <L, F> datum = examples.GetRVFDatum(d);
ICounter <F> c = datum.AsFeaturesCounter();
foreach (F feature in c.KeySet())
{
int fNo = featureIndex.IndexOf(feature);
int value = (int)c.GetCount(feature);
data[d][fNo] = value;
}
labelIndex.Add(datum.Label());
labels[d] = labelIndex.IndexOf(datum.Label());
}
int numClasses = labelIndex.Size();
return(TrainClassifier(data, labels, numFeatures, numClasses, labelIndex, featureIndex));
}
/// <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 virtual LogisticClassifier <L, F> TrainWeightedData(GeneralDataset <L, F> data, float[] dataWeights)
{
if (data is RVFDataset)
{
((RVFDataset <L, F>)data).EnsureRealValues();
}
if (data.labelIndex.Size() != 2)
{
throw new Exception("LogisticClassifier is only for binary classification!");
}
IMinimizer <IDiffFunction> minim;
LogisticObjectiveFunction lof = null;
if (data is Dataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetLabelsArray(), new LogPrior(LogPrior.LogPriorType.Quadratic), dataWeights);
}
else
{
if (data is RVFDataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetValuesArray(), data.GetLabelsArray(), new LogPrior(LogPrior.LogPriorType.Quadratic), dataWeights);
}
}
minim = new QNMinimizer(lof);
weights = minim.Minimize(lof, 1e-4, new double[data.NumFeatureTypes()]);
featureIndex = data.featureIndex;
classes[0] = data.labelIndex.Get(0);
classes[1] = data.labelIndex.Get(1);
return(new LogisticClassifier <L, F>(weights, featureIndex, classes));
}
// public static void main(String[] args) {
// List examples = new ArrayList();
// String leftLight = "leftLight";
// String rightLight = "rightLight";
// String broken = "BROKEN";
// String ok = "OK";
// Counter c1 = new ClassicCounter<>();
// c1.incrementCount(leftLight, 0);
// c1.incrementCount(rightLight, 0);
// RVFDatum d1 = new RVFDatum(c1, broken);
// examples.add(d1);
// Counter c2 = new ClassicCounter<>();
// c2.incrementCount(leftLight, 1);
// c2.incrementCount(rightLight, 1);
// RVFDatum d2 = new RVFDatum(c2, ok);
// examples.add(d2);
// Counter c3 = new ClassicCounter<>();
// c3.incrementCount(leftLight, 0);
// c3.incrementCount(rightLight, 1);
// RVFDatum d3 = new RVFDatum(c3, ok);
// examples.add(d3);
// Counter c4 = new ClassicCounter<>();
// c4.incrementCount(leftLight, 1);
// c4.incrementCount(rightLight, 0);
// RVFDatum d4 = new RVFDatum(c4, ok);
// examples.add(d4);
// Dataset data = new Dataset(examples.size());
// data.addAll(examples);
// NaiveBayesClassifier classifier = (NaiveBayesClassifier)
// new NaiveBayesClassifierFactory(200, 200, 1.0,
// LogPrior.LogPriorType.QUADRATIC.ordinal(),
// NaiveBayesClassifierFactory.CL)
// .trainClassifier(data);
// classifier.print();
// //now classifiy
// for (int i = 0; i < examples.size(); i++) {
// RVFDatum d = (RVFDatum) examples.get(i);
// Counter scores = classifier.scoresOf(d);
// System.out.println("for datum " + d + " scores are " + scores.toString());
// System.out.println(" class is " + Counters.topKeys(scores, 1));
// System.out.println(" class should be " + d.label());
// }
// }
// String trainFile = args[0];
// String testFile = args[1];
// NominalDataReader nR = new NominalDataReader();
// Map<Integer, Index<String>> indices = Generics.newHashMap();
// List<RVFDatum<String, Integer>> train = nR.readData(trainFile, indices);
// List<RVFDatum<String, Integer>> test = nR.readData(testFile, indices);
// System.out.println("Constrained conditional likelihood no prior :");
// for (int j = 0; j < 100; j++) {
// NaiveBayesClassifier<String, Integer> classifier = new NaiveBayesClassifierFactory<String, Integer>(0.1, 0.01, 0.6, LogPrior.LogPriorType.NULL.ordinal(), NaiveBayesClassifierFactory.CL).trainClassifier(train);
// classifier.print();
// //now classifiy
//
// float accTrain = classifier.accuracy(train.iterator());
// log.info("training accuracy " + accTrain);
// float accTest = classifier.accuracy(test.iterator());
// log.info("test accuracy " + accTest);
//
// }
// System.out.println("Unconstrained conditional likelihood no prior :");
// for (int j = 0; j < 100; j++) {
// NaiveBayesClassifier<String, Integer> classifier = new NaiveBayesClassifierFactory<String, Integer>(0.1, 0.01, 0.6, LogPrior.LogPriorType.NULL.ordinal(), NaiveBayesClassifierFactory.UCL).trainClassifier(train);
// classifier.print();
// //now classify
//
// float accTrain = classifier.accuracy(train.iterator());
// log.info("training accuracy " + accTrain);
// float accTest = classifier.accuracy(test.iterator());
// log.info("test accuracy " + accTest);
// }
// }
public virtual NaiveBayesClassifier <L, F> TrainClassifier(GeneralDataset <L, F> dataset)
{
if (dataset is RVFDataset)
{
throw new Exception("Not sure if RVFDataset runs correctly in this method. Please update this code if it does.");
}
return(TrainClassifier(dataset.GetDataArray(), dataset.labels, dataset.NumFeatures(), dataset.NumClasses(), dataset.labelIndex, dataset.featureIndex));
}
public CrossValidator(GeneralDataset <L, F> trainData, int kFold)
{
originalTrainData = trainData;
this.kFold = kFold;
savedStates = new CrossValidator.SavedState[kFold];
for (int i = 0; i < savedStates.Length; i++)
{
savedStates[i] = new CrossValidator.SavedState();
}
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data, double l1reg, double tol, LogPrior prior, bool biased)
{
if (data is RVFDataset)
{
((RVFDataset <L, F>)data).EnsureRealValues();
}
if (data.labelIndex.Size() != 2)
{
throw new Exception("LogisticClassifier is only for binary classification!");
}
IMinimizer <IDiffFunction> minim;
if (!biased)
{
LogisticObjectiveFunction lof = null;
if (data is Dataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetLabelsArray(), prior);
}
else
{
if (data is RVFDataset <object, object> )
{
lof = new LogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetValuesArray(), data.GetLabelsArray(), prior);
}
}
if (l1reg > 0.0)
{
minim = ReflectionLoading.LoadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
}
else
{
minim = new QNMinimizer(lof);
}
weights = minim.Minimize(lof, tol, new double[data.NumFeatureTypes()]);
}
else
{
BiasedLogisticObjectiveFunction lof = new BiasedLogisticObjectiveFunction(data.NumFeatureTypes(), data.GetDataArray(), data.GetLabelsArray(), prior);
if (l1reg > 0.0)
{
minim = ReflectionLoading.LoadByReflection("edu.stanford.nlp.optimization.OWLQNMinimizer", l1reg);
}
else
{
minim = new QNMinimizer(lof);
}
weights = minim.Minimize(lof, tol, new double[data.NumFeatureTypes()]);
}
featureIndex = data.featureIndex;
classes[0] = data.labelIndex.Get(0);
classes[1] = data.labelIndex.Get(1);
return(new LogisticClassifier <L, F>(weights, featureIndex, classes));
}
public GeneralizedExpectationObjectiveFunction(GeneralDataset <L, F> labeledDataset, IList <IDatum <L, F> > unlabeledDataList, IList <F> geFeatures)
{
System.Console.Out.WriteLine("Number of labeled examples:" + labeledDataset.size + "\nNumber of unlabeled examples:" + unlabeledDataList.Count);
System.Console.Out.WriteLine("Number of GE features:" + geFeatures.Count);
this.numFeatures = labeledDataset.NumFeatures();
this.numClasses = labeledDataset.NumClasses();
this.labeledDataset = labeledDataset;
this.unlabeledDataList = unlabeledDataList;
this.geFeatures = geFeatures;
this.classifier = new LinearClassifier <L, F>(null, labeledDataset.featureIndex, labeledDataset.labelIndex);
ComputeEmpiricalStatistics(geFeatures);
}
/// <summary>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C.
/// </summary>
/// <remarks>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C. The scorer is how you determine what to
/// optimize for (F-score, accuracy, etc). The C is then saved, so that
/// if you train a classifier after calling this method, that C will be used.
/// </remarks>
public virtual void HeldOutSetC(GeneralDataset <L, F> trainSet, GeneralDataset <L, F> devSet, IScorer <L> scorer, ILineSearcher minimizer)
{
useAlphaFile = true;
bool oldUseSigmoid = useSigmoid;
useSigmoid = false;
IDoubleUnaryOperator negativeScorer = null;
C = minimizer.Minimize(negativeScorer);
useAlphaFile = false;
useSigmoid = oldUseSigmoid;
}
public virtual SVMLightClassifier <L, F> TrainClassifier(GeneralDataset <L, F> dataset)
{
if (tuneHeldOut)
{
HeldOutSetC(dataset, heldOutPercent, scorer, tuneMinimizer);
}
else
{
if (tuneCV)
{
CrossValidateSetC(dataset, folds, scorer, tuneMinimizer);
}
}
return(TrainClassifierBasic(dataset));
}
/// <summary>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C.
/// </summary>
/// <remarks>
/// This method will cross validate on the given data and number of folds
/// to find the optimal C. The scorer is how you determine what to
/// optimize for (F-score, accuracy, etc). The C is then saved, so that
/// if you train a classifier after calling this method, that C will be used.
/// </remarks>
public virtual void CrossValidateSetC(GeneralDataset <L, F> dataset, int numFolds, IScorer <L> scorer, ILineSearcher minimizer)
{
System.Console.Out.WriteLine("in Cross Validate");
useAlphaFile = true;
bool oldUseSigmoid = useSigmoid;
useSigmoid = false;
CrossValidator <L, F> crossValidator = new CrossValidator <L, F>(dataset, numFolds);
IToDoubleFunction <Triple <GeneralDataset <L, F>, GeneralDataset <L, F>, CrossValidator.SavedState> > score = null;
//train(trainSet,true,true);
IDoubleUnaryOperator negativeScorer = null;
C = minimizer.Minimize(negativeScorer);
useAlphaFile = false;
useSigmoid = oldUseSigmoid;
}
public virtual ICounter <L> ScoresOf(IDatum <L, F> example)
{
ICounter <L> scores = new ClassicCounter <L>();
foreach (L label in labelIndex)
{
IDictionary <L, string> posLabelMap = new ArrayMap <L, string>();
posLabelMap[label] = PosLabel;
IDatum <string, F> binDatum = GeneralDataset.MapDatum(example, posLabelMap, NegLabel);
IClassifier <string, F> binaryClassifier = GetBinaryClassifier(label);
ICounter <string> binScores = binaryClassifier.ScoresOf(binDatum);
double score = binScores.GetCount(PosLabel);
scores.SetCount(label, score);
}
return(scores);
}
public virtual MultinomialLogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> dataset)
{
numClasses = dataset.NumClasses();
numFeatures = dataset.NumFeatures();
data = dataset.GetDataArray();
if (dataset is RVFDataset <object, object> )
{
dataValues = dataset.GetValuesArray();
}
else
{
dataValues = LogisticUtils.InitializeDataValues(data);
}
AugmentFeatureMatrix(data, dataValues);
labels = dataset.GetLabelsArray();
return(new MultinomialLogisticClassifier <L, F>(TrainWeights(), dataset.featureIndex, dataset.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 AdaptedGaussianPriorObjectiveFunction(GeneralDataset <L, F> dataset, LogPrior prior, double[][] weights)
: base(dataset, prior)
{
this.weights = To1D(weights);
}
public _IEnumerator_596(GeneralDataset <L, F> _enclosing)
{
this._enclosing = _enclosing;
}
public CrossValidator(GeneralDataset <L, F> trainData)
: this(trainData, 10)
{
}
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()));
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data, double l1reg, double tol, bool biased)
{
return(TrainClassifier(data, l1reg, tol, new LogPrior(LogPrior.LogPriorType.Quadratic), biased));
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data, double l1reg, double tol, LogPrior prior)
{
return(TrainClassifier(data, l1reg, tol, prior, false));
}
/// <summary>Builds a sigmoid model to turn the classifier outputs into probabilities.</summary>
private LinearClassifier <L, L> FitSigmoid(SVMLightClassifier <L, F> classifier, GeneralDataset <L, F> dataset)
{
RVFDataset <L, L> plattDataset = new RVFDataset <L, L>();
for (int i = 0; i < dataset.Size(); i++)
{
RVFDatum <L, F> d = dataset.GetRVFDatum(i);
ICounter <L> scores = classifier.ScoresOf((IDatum <L, F>)d);
scores.IncrementCount(null);
plattDataset.Add(new RVFDatum <L, L>(scores, d.Label()));
}
LinearClassifierFactory <L, L> factory = new LinearClassifierFactory <L, L>();
factory.SetPrior(new LogPrior(LogPrior.LogPriorType.Null));
return(factory.TrainClassifier(plattDataset));
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data, LogPrior prior, bool biased)
{
return(TrainClassifier(data, 0.0, 1e-4, prior, biased));
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data)
{
return(TrainClassifier(data, 0.0));
}
public BiasedLogConditionalObjectiveFunction(GeneralDataset <object, object> dataset, double[][] confusionMatrix, LogPrior prior)
: this(dataset.NumFeatures(), dataset.NumClasses(), dataset.GetDataArray(), dataset.GetLabelsArray(), confusionMatrix, prior)
{
}
public BiasedLogConditionalObjectiveFunction(GeneralDataset <object, object> dataset, double[][] confusionMatrix)
: this(dataset, confusionMatrix, new LogPrior(LogPrior.LogPriorType.Quadratic))
{
}
protected internal abstract double[][] TrainWeights(GeneralDataset <L, F> dataset);
public virtual void Train(GeneralDataset <L, F> data)
{
//Use LogisticClassifierFactory to train instead.
Train(data, 0.0, 1e-4);
}
public virtual LogisticClassifier <L, F> TrainClassifier(GeneralDataset <L, F> data, double l1reg)
{
return(TrainClassifier(data, l1reg, 1e-4));
}
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 virtual void HeldOutSetC(GeneralDataset <L, F> train, double percentHeldOut, IScorer <L> scorer, ILineSearcher minimizer)
{
Pair <GeneralDataset <L, F>, GeneralDataset <L, F> > data = train.Split(percentHeldOut);
HeldOutSetC(data.First(), data.Second(), scorer, minimizer);
}