void FastText::trainThread()

in src/fasttext.cc [632:678]

• 45 lines of code
• 12 McCabe index (conditional complexity)

void FastText::trainThread(int32_t threadId, const TrainCallback& callback) {
  std::ifstream ifs(args_->input);
  utils::seek(ifs, threadId * utils::size(ifs) / args_->thread);

  Model::State state(args_->dim, output_->size(0), threadId + args_->seed);

  const int64_t ntokens = dict_->ntokens();
  int64_t localTokenCount = 0;
  std::vector<int32_t> line, labels;
  uint64_t callbackCounter = 0;
  try {
    while (keepTraining(ntokens)) {
      real progress = real(tokenCount_) / (args_->epoch * ntokens);
      if (callback && ((callbackCounter++ % 64) == 0)) {
        double wst;
        double lr;
        int64_t eta;
        std::tie<double, double, int64_t>(wst, lr, eta) =
            progressInfo(progress);
        callback(progress, loss_, wst, lr, eta);
      }
      real lr = args_->lr * (1.0 - progress);
      if (args_->model == model_name::sup) {
        localTokenCount += dict_->getLine(ifs, line, labels);
        supervised(state, lr, line, labels);
      } else if (args_->model == model_name::cbow) {
        localTokenCount += dict_->getLine(ifs, line, state.rng);
        cbow(state, lr, line);
      } else if (args_->model == model_name::sg) {
        localTokenCount += dict_->getLine(ifs, line, state.rng);
        skipgram(state, lr, line);
      }
      if (localTokenCount > args_->lrUpdateRate) {
        tokenCount_ += localTokenCount;
        localTokenCount = 0;
        if (threadId == 0 && args_->verbose > 1) {
          loss_ = state.getLoss();
        }
      }
    }
  } catch (DenseMatrix::EncounteredNaNError&) {
    trainException_ = std::current_exception();
  }
  if (threadId == 0)
    loss_ = state.getLoss();
  ifs.close();
}