code: package zemberek.embedding.fasttext;
  
  import static zemberek.embedding.fasttext.AutomaticLabelingExperiment.saveSets;
  
  import java.io.IOException;
  import java.io.PrintWriter;
  import java.nio.charset.StandardCharsets;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.nio.file.Paths;
  import java.util.ArrayList;
  import java.util.Comparator;
  import java.util.LinkedHashSet;
  import java.util.List;
  import java.util.Locale;
  import java.util.stream.Collectors;
  import zemberek.core.ScoredItem;
  import zemberek.core.collections.Histogram;
  import zemberek.core.embeddings.Args;
  import zemberek.core.embeddings.FastText;
  import zemberek.core.embeddings.FastText.EvaluationResult;
  import zemberek.core.embeddings.FastTextTrainer;
  import zemberek.core.logging.Log;
  import zemberek.core.turkish.Turkish;
  import zemberek.corpus.WebCorpus;
  import zemberek.corpus.WebDocument;
  import zemberek.morphology.TurkishMorphology;
  import zemberek.morphology.analysis.SentenceAnalysis;
  import zemberek.morphology.analysis.SentenceWordAnalysis;
  import zemberek.morphology.analysis.SingleAnalysis;
  import zemberek.tokenization.TurkishTokenizer;
  import zemberek.tokenization.Token;
  
  public class CategoryPredictionExperiment {
  
    private Path experimentRoot;
    private Path rawCorpusRoot;
  
    private CategoryPredictionExperiment(Path experimentRoot, Path rawCorpusRoot) {
      this.experimentRoot = experimentRoot;
      this.rawCorpusRoot = rawCorpusRoot;
    }
  
    /**
     * run with -Xmx8G or more.
     */
    public static void main(String[] args) throws Exception {
  
      Path expRoot = Paths.get("/home/ahmetaa/data/fasttext/cat-exp");
  
      new CategoryPredictionExperiment(
          expRoot,
          expRoot.resolve("www.cnnturk.com")).runExperiment();
    }
  
    private void runExperiment() throws Exception {
      Path corpusPath = experimentRoot.resolve("category.corpus");
      Path train = experimentRoot.resolve("category.train");
      Path test = experimentRoot.resolve("category.test");
      Path titleRaw = experimentRoot.resolve("category.title");
      Path modelPath = experimentRoot.resolve("category.model");
      Path predictionPath = experimentRoot.resolve("category.predictions");
      extractCategoryDocuments(rawCorpusRoot, corpusPath);
      boolean useOnlyTitles = true;
      boolean useLemmas = true;
      generateSets(corpusPath, train, test, useOnlyTitles, useLemmas);
      generateRawSet(corpusPath, titleRaw);
  
      FastText fastText;
  
      if (modelPath.toFile().exists()) {
        Log.info("Reusing existing model %s", modelPath);
        fastText = FastText.load(modelPath);
      } else {
        Args argz = Args.forSupervised();
        argz.thread = 4;
        argz.model = Args.model_name.supervised;
        argz.loss = Args.loss_name.softmax;
        argz.epoch = 50;
        argz.wordNgrams = 2;
        argz.minCount = 0;
        argz.lr = 0.5;
        argz.dim = 100;
        argz.bucket = 5_000_000;
  
        fastText = new FastTextTrainer(argz).train(train);
        fastText.saveModel(modelPath);
      }
  
      EvaluationResult result = fastText.test(test, 1);
      Log.info(result.toString());
  
      WebCorpus corpus = new WebCorpus("corpus", "labeled");
      corpus.addDocuments(WebCorpus.loadDocuments(corpusPath));
      Log.info("Testing started.");
      List<String> testLines = Files.readAllLines(test, StandardCharsets.UTF_8);
      try (PrintWriter pw = new PrintWriter(predictionPath.toFile(), "utf-8")) {
        for (String testLine : testLines) {
          String id = testLine.substring(0, testLine.indexOf(' ')).substring(1);
          WebDocument doc = corpus.getDocument(id);
          List<ScoredItem<String>> res = fastText.predict(testLine, 3);
          List<String> predictedCategories = new ArrayList<>();
          for (ScoredItem<String> re : res) {
            if (re.score < -10) {
              continue;
            }
            predictedCategories.add(String.format(Locale.ENGLISH, "%s (%.2f)",
                re.item.replaceAll("__label__", "").replaceAll("_", " "), re.score));
          }
          pw.println("id = " + id);
          pw.println();
          pw.println(doc.getTitle());
          pw.println();
          pw.println("Actual Category = " + doc.getCategory());
          pw.println("Predictions   = " + String.join(", ", predictedCategories));
          pw.println();
          pw.println("------------------------------------------------------");
          pw.println();
        }
      }
      Log.info("Done.");
    }
  
    private void generateRawSet(
        Path input,
        Path train) throws IOException {
  
      WebCorpus corpus = new WebCorpus("category", "category");
      Log.info("Loading corpus from %s", input);
      corpus.addDocuments(WebCorpus.loadDocuments(input));
      List<String> set = new ArrayList<>(corpus.documentCount());
  
      Histogram<String> categoryCounts = new Histogram<>();
      for (WebDocument document : corpus.getDocuments()) {
        String category = document.getCategory();
        if (category.length() > 0) {
          categoryCounts.add(category);
        }
      }
  
      Log.info("All category count = %d", categoryCounts.size());
      categoryCounts.removeSmaller(20);
      for (String c : categoryCounts.getSortedList()) {
        System.out.println(c + " " + categoryCounts.getCount(c));
      }
      Log.info("Reduced label count = %d", categoryCounts.size());
  
      Log.info("Extracting data from %d documents ", corpus.documentCount());
      int c = 0;
  
      for (WebDocument document : corpus.getDocuments()) {
        if (document.getCategory().length() == 0) {
          continue;
        }
        if (document.getTitle().length() == 0) {
          continue;
        }
  
        String title = document.getTitle();
  
        String category = document.getCategory();
        if (category.contains("CNN") ||
            category.contains("Güncel") ||
            category.contains("Euro 2016") ||
            category.contains("Yazarlar") ||
            category.contains("Ajanda")
  
            ) {
          continue;
        }
        if (category.equals("İyilik Sağlık")) {
          category = "Sağlık";
        }
        if (category.equals("Spor Diğer")) {
          category = "Spor";
        }
        if (category.equals("İyilik Sağlık")) {
          category = "Sağlık";
        }
  
        if (categoryCounts.contains(category)) {
          category = "__label__" + category.replaceAll("[ ]+", "_")
              .toLowerCase(Turkish.LOCALE);
        } else {
          continue;
        }
  
        set.add(category + " " + title);
  
        if (c++ % 1000 == 0) {
          Log.info("%d of %d processed.", c, corpus.documentCount());
        }
      }
  
      Log.info("Generate raw set.");
  
      Files.write(train, set, StandardCharsets.UTF_8);
    }
  
  
    private void generateSets(
        Path input,
        Path train,
        Path test,
        boolean useOnlyTitle,
        boolean useLemmas) throws IOException {
  
      TurkishMorphology morphology = TurkishMorphology.createWithDefaults();
  
      WebCorpus corpus = new WebCorpus("category", "category");
      Log.info("Loading corpus from %s", input);
      corpus.addDocuments(WebCorpus.loadDocuments(input));
      List<String> set = new ArrayList<>(corpus.documentCount());
  
      TurkishTokenizer lexer = TurkishTokenizer.DEFAULT;
  
      Histogram<String> categoryCounts = new Histogram<>();
      for (WebDocument document : corpus.getDocuments()) {
        String category = document.getCategory();
        if (category.length() > 0) {
          categoryCounts.add(category);
        }
      }
  
      Log.info("All category count = %d", categoryCounts.size());
      categoryCounts.removeSmaller(20);
      for (String c : categoryCounts.getSortedList()) {
        System.out.println(c + " " + categoryCounts.getCount(c));
      }
      Log.info("Reduced label count = %d", categoryCounts.size());
  
      Log.info("Extracting data from %d documents ", corpus.documentCount());
      int c = 0;
  
      for (WebDocument document : corpus.getDocuments()) {
        if (document.getCategory().length() == 0) {
          continue;
        }
        if (useOnlyTitle && document.getTitle().length() == 0) {
          continue;
        }
  
        String content = document.getContentAsString();
        String title = document.getTitle();
  
        List<Token> docTokens = useOnlyTitle ? lexer.tokenize(title) : lexer.tokenize(content);
        List<String> reduced = new ArrayList<>(docTokens.size());
  
        String category = document.getCategory();
        if (categoryCounts.contains(category)) {
          category = "__label__" + document.getCategory().replaceAll("[ ]+", "_")
              .toLowerCase(Turkish.LOCALE);
        } else {
          continue;
        }
  
        for (Token token : docTokens) {
          if (
              token.getType() == Token.Type.PercentNumeral ||
                  token.getType() == Token.Type.Number ||
                  token.getType() == Token.Type.Punctuation ||
                  token.getType() == Token.Type.RomanNumeral ||
                  token.getType() == Token.Type.Time ||
                  token.getType() == Token.Type.UnknownWord ||
                  token.getType() == Token.Type.Unknown) {
            continue;
          }
          String tokenStr = token.getText();
          reduced.add(tokenStr);
        }
  
        String join = String.join(" ", reduced);
        if (join.trim().isEmpty()) {
          continue;
        }
  
        if (useLemmas) {
          SentenceAnalysis analysis = morphology.analyzeAndDisambiguate(join);
          List<String> res = new ArrayList<>();
          for (SentenceWordAnalysis e : analysis) {
            SingleAnalysis best = e.getBestAnalysis();
            if (best.isUnknown()) {
              res.add(e.getWordAnalysis().getInput());
              continue;
            }
            List<String> lemmas = best.getLemmas();
            if (lemmas.size() == 0) {
              continue;
            }
            res.add(lemmas.get(lemmas.size() - 1));
          }
          join = String.join(" ", res);
        }
  
        set.add("#" + document.getId() + " " + category + " " + join.replaceAll("[']", "")
            .toLowerCase(Turkish.LOCALE));
  
        if (c++ % 1000 == 0) {
          Log.info("%d of %d processed.", c, corpus.documentCount());
        }
      }
  
      Log.info("Generate train and test set.");
  
      saveSets(train, test, new LinkedHashSet<>(set));
    }
  
    private void extractCategoryDocuments(Path root, Path categoryFile) throws IOException {
  
      List<Path> files = Files.walk(root).filter(s -> s.toFile().isFile())
          .sorted(Comparator.comparing(Path::toString))
          .collect(Collectors.toList());
      WebCorpus corpus = new WebCorpus("category", "category");
      for (Path file : files) {
        if (file.toFile().isDirectory()) {
          continue;
        }
        Log.info("Adding %s", file);
        List<WebDocument> doc = WebCorpus.loadDocuments(file);
        List<WebDocument> labeled = doc.stream()
            .filter(s -> s.getCategory().length() > 0 && s.getContentAsString().length() > 200)
            .collect(Collectors.toList());
        corpus.addDocuments(labeled);
      }
      Log.info("Total amount of files = %d", corpus.getDocuments().size());
      WebCorpus noDuplicates = corpus.copyNoDuplicates();
      Log.info("Corpus size = %d, After removing duplicates = %d",
          corpus.documentCount(),
          noDuplicates.documentCount());
      Log.info("Saving corpus to %s", categoryFile);
      noDuplicates.save(categoryFile, false);
    }
  
  }
  