Using XGBOOST in c++

Question

How can I use XGBOOST https://github.com/dmlc/xgboost/ library in c++? I have founded Python and Java API, but I can\'t found API for c++

Answer 1

Use XGBoost C API.

  BoosterHandle booster;
  const char *model_path = "/path/of/model";

  // create booster handle first
  XGBoosterCreate(NULL, 0, &booster);

  // by default, the seed will be set 0
  XGBoosterSetParam(booster, "seed", "0");

  // load model
  XGBoosterLoadModel(booster, model_path);

  const int feat_size = 100;
  const int num_row = 1;
  float feat[num_row][feat_size];

  // create some fake data for predicting
  for (int i = 0; i < num_row; ++i) {
    for(int j = 0; j < feat_size; ++j) {
      feat[i][j] = (i + 1) * (j + 1)
    }
  }

  // convert 2d array to DMatrix
  DMatrixHandle dtest;
  XGDMatrixCreateFromMat(reinterpret_cast(feat),
                         num_row, feat_size, NAN, &dtest);

  // predict
  bst_ulong out_len;
  const float *f;
  XGBoosterPredict(booster, dtest, 0, 0, &out_len, &f);
  assert(out_len == num_row);
  std::cout << f[0] << std::endl;

  // free memory
  XGDMatrixFree(dtest);
  XGBoosterFree(booster);

Note when you want to load an existing model(like above code shows), you have to ensure the data format in training is the same as in predicting. So, if you predict with XGBoosterPredict, which accepts a dense matrix as parameter, you have to use dense matrix in training.

Training with libsvm format and predict with dense matrix may cause wrong predictions, as XGBoost FAQ says:

“Sparse” elements are treated as if they were “missing” by the tree booster, and as zeros by the linear booster. For tree models, it is important to use consistent data formats during training and scoring.