public double[][] trainWeights( GeneralDataset<L, F> dataset, double[] initial, boolean bypassTuneSigma, Minimizer<DiffFunction> minimizer) { if (minimizer == null) minimizer = minimizerCreator.create(); if (dataset instanceof RVFDataset) ((RVFDataset<L, F>) dataset).ensureRealValues(); double[] interimWeights = null; if (!bypassTuneSigma) { if (tuneSigmaHeldOut) { interimWeights = heldOutSetSigma( dataset); // the optimum interim weights from held-out training data have already // been found. } else if (tuneSigmaCV) { crossValidateSetSigma( dataset, folds); // TODO: assign optimum interim weights as part of this process. } } LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(dataset, logPrior); if (initial == null && interimWeights != null && !retrainFromScratchAfterSigmaTuning) { // System.err.println("## taking advantage of interim weights as starting point."); initial = interimWeights; } if (initial == null) { initial = objective.initial(); } double[] weights = minimizer.minimize(objective, TOL, initial); return objective.to2D(weights); }
public LinearClassifier createLinearClassifier(double[] weights) { double[][] weights2D; if (objective != null) { weights2D = objective.to2D(weights); } else { weights2D = ArrayUtils.to2D(weights, featureIndex.size(), labelIndex.size()); } return new LinearClassifier<L, F>(weights2D, featureIndex, labelIndex); }
public Classifier<L, F> trainClassifier( GeneralDataset<L, F> dataset, float[] dataWeights, LogPrior prior) { Minimizer<DiffFunction> minimizer = getMinimizer(); if (dataset instanceof RVFDataset) ((RVFDataset<L, F>) dataset).ensureRealValues(); LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(dataset, dataWeights, logPrior); double[] initial = objective.initial(); double[] weights = minimizer.minimize(objective, TOL, initial); LinearClassifier<L, F> classifier = new LinearClassifier<L, F>( objective.to2D(weights), dataset.featureIndex(), dataset.labelIndex()); return classifier; }
public double[][] trainWeightsSemiSup( GeneralDataset<L, F> data, GeneralDataset<L, F> biasedData, double[][] confusionMatrix, double[] initial) { Minimizer<DiffFunction> minimizer = minimizerCreator.create(); LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(data, new LogPrior(LogPrior.LogPriorType.NULL)); BiasedLogConditionalObjectiveFunction biasedObjective = new BiasedLogConditionalObjectiveFunction( biasedData, confusionMatrix, new LogPrior(LogPrior.LogPriorType.NULL)); SemiSupervisedLogConditionalObjectiveFunction semiSupObjective = new SemiSupervisedLogConditionalObjectiveFunction(objective, biasedObjective, logPrior); if (initial == null) { initial = objective.initial(); } double[] weights = minimizer.minimize(semiSupObjective, TOL, initial); return objective.to2D(weights); }
/** * Trains the linear classifier using Generalized Expectation criteria as described in * <tt>Generalized Expectation Criteria for Semi Supervised Learning of Conditional Random * Fields</tt>, Mann and McCallum, ACL 2008. The original algorithm is proposed for CRFs but has * been adopted to LinearClassifier (which is a simpler special case of a CRF). IMPORTANT: the * labeled features that are passed as an argument are assumed to be binary valued, although other * features are allowed to be real valued. */ public LinearClassifier<L, F> trainSemiSupGE( GeneralDataset<L, F> labeledDataset, List<? extends Datum<L, F>> unlabeledDataList, List<F> GEFeatures, double convexComboCoeff) { Minimizer<DiffFunction> minimizer = minimizerCreator.create(); LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>( labeledDataset, new LogPrior(LogPrior.LogPriorType.NULL)); GeneralizedExpectationObjectiveFunction<L, F> geObjective = new GeneralizedExpectationObjectiveFunction<L, F>( labeledDataset, unlabeledDataList, GEFeatures); SemiSupervisedLogConditionalObjectiveFunction semiSupObjective = new SemiSupervisedLogConditionalObjectiveFunction( objective, geObjective, null, convexComboCoeff); double[] initial = objective.initial(); double[] weights = minimizer.minimize(semiSupObjective, TOL, initial); return new LinearClassifier<L, F>( objective.to2D(weights), labeledDataset.featureIndex(), labeledDataset.labelIndex()); }
public Classifier<L, F> trainClassifier(Iterable<Datum<L, F>> dataIterable) { Minimizer<DiffFunction> minimizer = getMinimizer(); Index<F> featureIndex = Generics.newIndex(); Index<L> labelIndex = Generics.newIndex(); for (Datum<L, F> d : dataIterable) { labelIndex.add(d.label()); featureIndex.addAll(d.asFeatures()); // If there are duplicates, it doesn't add them again. } System.err.println( String.format( "Training linear classifier with %d features and %d labels", featureIndex.size(), labelIndex.size())); LogConditionalObjectiveFunction<L, F> objective = new LogConditionalObjectiveFunction<L, F>(dataIterable, logPrior, featureIndex, labelIndex); objective.setPrior(new LogPrior(LogPrior.LogPriorType.QUADRATIC)); double[] initial = objective.initial(); double[] weights = minimizer.minimize(objective, TOL, initial); LinearClassifier<L, F> classifier = new LinearClassifier<L, F>(objective.to2D(weights), featureIndex, labelIndex); return classifier; }