List of usage examples for weka.core SerializationHelper writeAll
public static void writeAll(OutputStream stream, Object[] o) throws Exception
From source file:homemadeWEKA.java
public static void save_modelWithEval(Classifier cls, Evaluation eval) throws Exception { Object[] o = new Object[2]; o[0] = cls;/*from ww w. j a va 2s. co m*/ o[1] = eval; SerializationHelper.writeAll("j48_10folds.model", o); }
From source file:meka.classifiers.multilabel.Evaluation.java
License:Open Source License
/** * RunExperiment - Build and evaluate a model with command-line options. * @param h multi-label classifier * @param options command line options *///from www . j a va 2s . c om public static void runExperiment(MultiLabelClassifier h, String options[]) throws Exception { // Help if (Utils.getOptionPos('h', options) >= 0) { System.out.println("\nHelp requested"); Evaluation.printOptions(h.listOptions()); return; } h.setOptions(options); if (h.getDebug()) System.out.println("Loading and preparing dataset ..."); // Load Instances from a file Instances D_train = loadDataset(options); Instances D_full = D_train; // Try extract and set a class index from the @relation name MLUtils.prepareData(D_train); // Override the number of classes with command-line option (optional) if (Utils.getOptionPos('C', options) >= 0) { int L = Integer.parseInt(Utils.getOption('C', options)); D_train.setClassIndex(L); } // We we still haven't found -C option, we can't continue (don't know how many labels) int L = D_train.classIndex(); if (L <= 0) { throw new Exception( "[Error] Number of labels not specified.\n\tYou must set the number of labels with the -C option, either inside the @relation tag of the Instances file, or on the command line."); // apparently the dataset didn't contain the '-C' flag, check in the command line options ... } // Randomize (Instances) int seed = (Utils.getOptionPos('s', options) >= 0) ? Integer.parseInt(Utils.getOption('s', options)) : 0; if (Utils.getFlag('R', options)) { D_train.randomize(new Random(seed)); } boolean Threaded = false; if (Utils.getOptionPos("Thr", options) >= 0) { Threaded = Utils.getFlag("Thr", options); } // Verbosity Option String voption = "1"; if (Utils.getOptionPos("verbosity", options) >= 0) { voption = Utils.getOption("verbosity", options); } // Save for later? //String fname = null; //if (Utils.getOptionPos('f',options) >= 0) { // fname = Utils.getOption('f',options); //} // Dump for later? String dname = null; if (Utils.getOptionPos('d', options) >= 0) { dname = Utils.getOption('d', options); } // Load from file? String lname = null; Instances dataHeader = null; if (Utils.getOptionPos('l', options) >= 0) { lname = Utils.getOption('l', options); Object[] data = SerializationHelper.readAll(lname); h = (MultiLabelClassifier) data[0]; if (data.length > 1) dataHeader = (Instances) data[1]; //Object o[] = SerializationHelper.readAll(lname); //h = (MultilabelClassifier)o[0]; } try { Result r = null; // Threshold OPtion String top = "PCut1"; // default if (Utils.getOptionPos("threshold", options) >= 0) top = Utils.getOption("threshold", options); if (Utils.getOptionPos('x', options) >= 0) { // CROSS-FOLD-VALIDATION int numFolds = MLUtils.getIntegerOption(Utils.getOption('x', options), 10); // default 10 // Check for remaining options Utils.checkForRemainingOptions(options); r = Evaluation.cvModel(h, D_train, numFolds, top, voption); System.out.println(r.toString()); } else { // TRAIN-TEST SPLIT Instances D_test = null; if (Utils.getOptionPos('T', options) >= 0) { // load separate test set try { D_test = loadDataset(options, 'T'); MLUtils.prepareData(D_test); } catch (Exception e) { throw new Exception("[Error] Failed to Load Test Instances from file.", e); } } else { // split training set into train and test sets // default split int N_T = (int) (D_train.numInstances() * 0.60); if (Utils.getOptionPos("split-percentage", options) >= 0) { // split by percentage double percentTrain = Double.parseDouble(Utils.getOption("split-percentage", options)); N_T = (int) Math.round((D_train.numInstances() * (percentTrain / 100.0))); } else if (Utils.getOptionPos("split-number", options) >= 0) { // split by number N_T = Integer.parseInt(Utils.getOption("split-number", options)); } int N_t = D_train.numInstances() - N_T; D_test = new Instances(D_train, N_T, N_t); D_train = new Instances(D_train, 0, N_T); } // Invert the split? if (Utils.getFlag('i', options)) { //boolean INVERT = Utils.getFlag('i',options); Instances temp = D_test; D_test = D_train; D_train = temp; } // Check for remaining options Utils.checkForRemainingOptions(options); if (h.getDebug()) System.out.println(":- Dataset -: " + MLUtils.getDatasetName(D_train) + "\tL=" + L + "\tD(t:T)=(" + D_train.numInstances() + ":" + D_test.numInstances() + ")\tLC(t:T)=" + Utils.roundDouble(MLUtils.labelCardinality(D_train, L), 2) + ":" + Utils.roundDouble(MLUtils.labelCardinality(D_test, L), 2) + ")"); if (lname != null) { // h is already built, and loaded from a file, test it! r = testClassifier(h, D_test); String t = top; if (top.startsWith("PCut")) { // if PCut is specified we need the training data, // so that we can calibrate the threshold! t = MLEvalUtils.getThreshold(r.predictions, D_train, top); } r = evaluateModel(h, D_test, t, voption); } else { //check if train and test set size are > 0 if (D_train.numInstances() > 0 && D_test.numInstances() > 0) { if (Threaded) { r = evaluateModelM(h, D_train, D_test, top, voption); } else { r = evaluateModel(h, D_train, D_test, top, voption); } } else { // otherwise just train on full set. Maybe better throw an exception. h.buildClassifier(D_full); } } // @todo, if D_train==null, assume h is already trained if (D_train.numInstances() > 0 && D_test.numInstances() > 0) { System.out.println(r.toString()); } } // Save model to file? if (dname != null) { dataHeader = new Instances(D_train, 0); SerializationHelper.writeAll(dname, new Object[] { h, dataHeader }); } } catch (Exception e) { e.printStackTrace(); Evaluation.printOptions(h.listOptions()); System.exit(1); } System.exit(0); }
From source file:meka.gui.explorer.classify.SaveModel.java
License:Open Source License
/** * Returns the action lister to use in the menu. * * @param history the current history//from www.j a va 2 s . c om * @param index the selected history item * @return the listener */ @Override public ActionListener getActionListener(final ResultHistoryList history, final int index) { return new ActionListener() { @Override public void actionPerformed(ActionEvent e) { int retVal = getFileChooser().showSaveDialog(null); if (retVal != MekaFileChooser.APPROVE_OPTION) return; File file = getFileChooser().getSelectedFile(); try { SerializationHelper.writeAll(file.getAbsolutePath(), (Object[]) history.getPayloadAt(index)); } catch (Exception ex) { String msg = "Failed to write model to '" + file + "'!"; System.err.println(msg); ex.printStackTrace(); JOptionPane.showMessageDialog(null, msg + "\n" + e); } } }; }