Java Code Examples for weka.core.converters.CSVSaver

The following are top voted examples for showing how to use weka.core.converters.CSVSaver. These examples are extracted from open source projects. You can vote up the examples you like and your votes will be used in our system to generate more good examples.
Example 1
Project: SAIL   File: SentiNets.java   Source Code and License 7 votes vote down vote up
public void writePredictions(Instances ins, String filePrefix) {
	try {
		BufferedWriter writer = new BufferedWriter(new FileWriter(outputDir
				+ "/" + filePrefix + ".arff"));
		writer.write(ins.toString());
		writer.newLine();
		writer.flush();
		writer.close();
		CSVSaver s = new CSVSaver();

		s.setFile(new File(outputDir + "/" + filePrefix + ".tsv"));
		s.setInstances(ins);
		s.setFieldSeparator("\t");
		s.writeBatch();

	} catch (IOException e) {
		// TODO Auto-generated catch block
		e.printStackTrace();
	}
}
 
Example 2
Project: bestconf   File: DataIOFile.java   Source Code and License 6 votes vote down vote up
/**
 * Save @param data to the CSV file at @param path
 */
public static void saveDataToCsvFile(String path, Instances data) throws IOException{
	    System.out.println("\nSaving to file " + path + "...");
	    CSVSaver saver = new CSVSaver();
	    saver.setInstances(data);
	    saver.setFile(new File(path));
	    saver.writeBatch();
}
 
Example 3
Project: wekaDeeplearning4j   File: AbstractTextEmbeddingIterator.java   Source Code and License 6 votes vote down vote up
/**
 * Load the embedding from a given arff file. First converts the ARFF to a temporary CSV file and
 * continues the loading mechanism with the CSV file afterwards
 *
 * @param path Path to the ARFF file
 */
private void loadEmbeddingFromArff(String path) {
  // Try loading ARFF file
  try {
    Instances insts = new Instances(new FileReader(path));
    CSVSaver saver = new CSVSaver();
    saver.setFieldSeparator(" ");
    saver.setInstances(insts);
    final File tmpFile =
        Paths.get(System.getProperty("java.io.tmpdir"), UUID.randomUUID().toString(), ".csv")
            .toFile();
    saver.setFile(tmpFile);
    saver.setNoHeaderRow(true);
    saver.writeBatch();
    loadEmbeddingFromCSV(tmpFile);
    tmpFile.delete();
  } catch (Exception e) {
    throw new RuntimeException(
        "ARFF file could not be read (" + wordVectorLocation.getAbsolutePath() + ")", e);
  }
}
 
Example 4
Project: SAIL   File: Prediction.java   Source Code and License 6 votes vote down vote up
public int writePredictions(Instances ins, String filePrefix) {
	try {
		System.out.println("Trying to create the following files:");
		System.out.println(outputDir+ "/" + filePrefix + ".arff");
		System.out.println(outputDir+ "/" + filePrefix + ".tsv");
		BufferedWriter writer = new BufferedWriter(new FileWriter(outputDir
				+ "/" + filePrefix + ".arff"));
		writer.write(ins.toString());
		writer.newLine();
		writer.flush();
		writer.close();
		CSVSaver s = new CSVSaver();

		s.setFile(new File(outputDir + "/" + filePrefix + ".tsv"));
		s.setInstances(ins);
		s.setFieldSeparator("\t");
		s.writeBatch();

	} catch (IOException e) {
		// TODO Auto-generated catch block
		e.printStackTrace();
		return 1;
	}
	return 0;
}
 
Example 5
Project: BestConfig   File: DataIOFile.java   Source Code and License 5 votes vote down vote up
/**
 * Save @param data to the CSV file at @param path
 */
public static void saveDataToCsvFile(String path, Instances data) throws IOException{
	    System.out.println("\nSaving to file " + path + "...");
	    CSVSaver saver = new CSVSaver();
	    saver.setInstances(data);
	    saver.setFile(new File(path));
	    saver.writeBatch();
}
 
Example 6
Project: VirtaMarketAnalyzer   File: RetailSalePrediction.java   Source Code and License 4 votes vote down vote up
public static LinearRegressionSummary createCommonPrediction(final String productID) throws IOException, GitAPIException {
        logger.info("productID = {}", productID);
        final Set<RetailAnalytics> set = getAllRetailAnalytics(RETAIL_ANALYTICS_ + productID)
                .filter(ra -> productID.isEmpty() || ra.getProductId().equals(productID))
                //.filter(ra -> ra.getShopSize() == 100 || ra.getShopSize() == 500 || ra.getShopSize() == 1_000 || ra.getShopSize() == 10_000 || ra.getShopSize() == 100_000)
//                .filter(ra -> ra.getShopSize() > 0)
//                .filter(ra -> ra.getSellVolumeNumber() > 0)
//                .filter(ra -> ra.getDemography() > 0)
//                .filter(ra -> ra.getMarketIdx().isEmpty() || ra.getMarketIdx().equals("E"))
                .collect(toSet());
        logger.info("set.size() = {}", set.size());

        if (!set.isEmpty()) {
            //группируем аналитику по товарам и сохраняем
//            final Map<String, List<RetailAnalytics>> retailAnalyticsHist = set.parallelStream()
//                    .filter(ra -> ra.getNotoriety() >= 100)
//                    .collect(Collectors.groupingBy(RetailAnalytics::getProductId));

//            final ExclusionStrategy es = new HistAnalytExclStrat();
//            for (final Map.Entry<String, List<RetailAnalytics>> entry : retailAnalyticsHist.entrySet()) {
//                final String fileNamePath = GitHubPublisher.localPath + RetailSalePrediction.predict_retail_sales + File.separator
//                        + RetailSalePrediction.RETAIL_ANALYTICS_HIST + File.separator + entry.getKey() + ".json";
//                Utils.writeToGson(fileNamePath, squeeze(entry.getValue()), es);
//            }
            final Set<String> productIds = set.parallelStream().map(RetailAnalytics::getProductId).collect(Collectors.toSet());
            final Set<String> productCategories = set.parallelStream().map(RetailAnalytics::getProductCategory).collect(Collectors.toSet());
            try {
                logger.info("createTrainingSet");
                final Instances trainingSet = createTrainingSet(set, productIds, productCategories);

//                final Standardize standardize = new Standardize();
//                standardize.setInputFormat(trainingSetRaw);
//                final Instances trainingSet = Filter.useFilter(trainingSetRaw, standardize);

                logger.info("ArffSaver");
                final ArffSaver saver = new ArffSaver();
                saver.setInstances(trainingSet);
                saver.setFile(new File(Utils.getDir() + WEKA + File.separator + "common_" + productID + ".arff"));
                saver.writeBatch();

                logger.info("CSVSaver");
                final CSVSaver saverCsv = new CSVSaver();
                saverCsv.setInstances(trainingSet);
                saverCsv.setFile(new File(Utils.getDir() + WEKA + File.separator + "common_" + productID + ".csv"));
                saverCsv.writeBatch();
//                final File file = new File(GitHubPublisher.localPath + RetailSalePrediction.predict_retail_sales + File.separator + WEKA + File.separator + "common.arff");
//                file.delete();

                final LinearRegressionSummary summary = trainLinearRegression(trainingSet, productID);
//                trainRandomCommittee(trainingSet);
//                trainDecisionTable(trainingSet);
//                trainMultilayerPerceptron(trainingSet);

//                trainRandomForest(trainingSet);
//                trainRandomTree(trainingSet);
//                trainLibSvm(trainingSet);
//                logger.info("begin trainJ48BySet");
//                trainJ48BySet(trainingSet);
//                logger.info("end trainJ48BySet");
//
//                logger.info("begin trainJ48CrossValidation");
//                trainJ48CrossValidation(trainingSet);
//                logger.info("end trainJ48CrossValidation");

                //запоминаем дату обновления данных
                final DateFormat df = new SimpleDateFormat("dd.MM.yyyy");
                Utils.writeToGson(GitHubPublisher.localPath + RetailSalePrediction.predict_retail_sales + File.separator + "updateDate.json", new UpdateDate(df.format(new Date())));

                return summary;
            } catch (final Exception e) {
                logger.info("productID = {}", productID);
                logger.error(e.getLocalizedMessage(), e);
            }
        }
        return null;
    }