Xgboost: XGBoosterLoadModel() in c api have Segmentation Fault

Created on 15 Aug 2017 · 4Comments · Source: dmlc/xgboost

XGBoosterLoadModel() in c api have Segmentation Fault.
I train a model with python and want to load the model with c api,but get Segmentation Fault in XGBoosterLoadModel().

Environment info

Operating System:ubuntu 16.04

Compiler:gcc 5.4.0 g++ 5.4.0

Package used (python/R/jvm/C++):python and C++

xgboost version used:master branch

Steps to reproduce

1.compile the source of master branch to get the libxgboost.so,librabit.so,libdmlccore.a.
2.set up a new peoject,
CMakeList.txt to link the .so
target_link_libraries(main xgboost rabit dmlccore)

include "xgboost/c_api.h"

BoosterHandle test_booster;
XGBoosterLoadModel(test_booster,"test.model");

to get the model.but return Segmentation Fault.

What have you tried?

1.try other functions in c_api.h.everything is ok，like predict and save model.so the compile and link was successful.

int cols=3,rows=5;
    float train[rows][cols];
    for (int i=0;i<rows;i++)
        for (int j=0;j<cols;j++)
              train[i][j] = (i+1) * (j+1);

    float train_labels[rows];
    for (int i=0;i<rows;i++)
            train_labels[i] = 1+i*i*i;


    // convert to DMatrix
    DMatrixHandle h_train[1];
    XGDMatrixCreateFromMat((float *) train, rows, cols, -1, &h_train[0]);

    // load the labels
    XGDMatrixSetFloatInfo(h_train[0], "label", train_labels, rows);

    // read back the labels, just a sanity check
    bst_ulong bst_result;
    const float *out_floats;
    XGDMatrixGetFloatInfo(h_train[0], "label" , &bst_result, &out_floats);
    for (unsigned int i=0;i<bst_result;i++)
        std::cout << "label[" << i << "]=" << out_floats[i] << std::endl;

// create the booster and load some parameters
BoosterHandle h_booster;
XGBoosterCreate(h_train, 1, &h_booster);
XGBoosterSetParam(h_booster, "booster", "gbtree");
XGBoosterSetParam(h_booster, "objective", "reg:linear");
XGBoosterSetParam(h_booster, "max_depth", "5");
XGBoosterSetParam(h_booster, "eta", "0.1");
XGBoosterSetParam(h_booster, "min_child_weight", "1");
XGBoosterSetParam(h_booster, "subsample", "0.5");
XGBoosterSetParam(h_booster, "colsample_bytree", "1");
XGBoosterSetParam(h_booster, "num_parallel_tree", "1");

// perform 200 learning iterations
for (int iter=0; iter<200; iter++)
    XGBoosterUpdateOneIter(h_booster, iter, h_train[0]);

// predict
const int sample_rows = 5;
float test[sample_rows][cols];
for (int i=0;i<sample_rows;i++)
    for (int j=0;j<cols;j++)
        test[i][j] = (i+1) * (j+1);
DMatrixHandle h_test;
XGDMatrixCreateFromMat((float *) test, sample_rows, cols, -1, &h_test);
bst_ulong out_len;
const float *f;
XGBoosterPredict(h_booster, h_test, 0,0,&out_len,&f);

for (unsigned int i=0;i<out_len;i++)
    std::cout << "prediction[" << i << "]=" << f[i] << std::endl;

for (unsigned int i=0;i<out_len;i++)
    std::cout << "prediction[" << i << "]=" << f[i] << std::endl;
XGDMatrixFree(h_train[0]);
XGDMatrixFree(h_test);
XGBoosterFree(h_booster);

2.I'm afraid there might be some wrong with my Python generated model.
so I save the model training by c++ and then load.I get the same error(Segmentation fault)in the XGBoosterLoadModel().

XGBoosterSaveModel(h_booster,"test.model");
BoosterHandle test_booster;
XGBoosterLoadModel(test_booster,"test.model");
XGBoosterPredict(test_booster, h_test, 0,0,&out_len,&f);

Source