Java 类weka.core.converters.CSVSaver 实例源码

/**
 * 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);
  }
}

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();
    }
}

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;
}

/**
 * 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();
}

/**
 * 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();
}

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;
    }