Xgboost Prediction is different for C++ and Python for the same model

Question

I've trained a model in python using the following code(I didnt use a testing set for this example, I was training and predicting using the same dataset, to make the illustration of the problem easier):

params = {'learning_rate':0.1,'obj':'binary:logistic','n_estimators':250, 'scale_pos_weight':0.2, 'max_depth' : 15, 'min_weight' : 1, 'colsample_bytree' : 1, 'gamma' : 0.1, 'subsample':0.95} 

X = np.array(trainingData,dtype = np.uint32) #training data was generated from a csv

X = xgb.DMatrix(np.asmatrix(X), label = Y)

clf = xgb.train(params, X)
clf.save_model('xgb_test.model')
X.save_binary('test.buffer')

answer = clf.predict(X) 

The Prediction generated around 40k zeros and 270k ones

The model is then loaded into c++ with the following code:

const char * fileName = "blahblah/xgb_test.model";
int x = XGBoosterLoadModel(handle, fileName);
if (x == 0) {
    printf("Successfully Loaded Model\n");
}


const char * predictionData = "blahblah/test.buffer";
x = XGDMatrixCreateFromFile(predictionData, 0, &dHandle);


if (x == 0) {
    printf("Successfully Loaded Data\n");
}

bst_ulong  out2;
const float *m_TestResults2;
x = XGBoosterPredict(handle, dHandle, 0, 1, &out2, &m_TestResults2);
if (x == 0) {
    printf("Successful Prediction\n");
}

int zeroCount = 0;
int oneCount = 0;

for (int i = 0; i < out2; i++) {
    if (m_TestResults2[i] < 0.5) {
        zeroCount++;
    }
    else {
        oneCount++;
    }
}
printf("Number of Zeroes: " + zeroCount);
printf("Number of Ones: " + oneCount);

For the c++ prediction I've obtained around 55k zeroes.

I have tried the following:

1. Make sure that the model is trained in python using a dense matrix, since xgboosterpredict takes in a dense matrix(arrived at this assumption from a similar question on stackoverflow)
2. Use xgb.train, instead of Xgbclassifier.fit. Train takes in dmatrix, fit does not
3. Convert training data to np matrix using np.asmatrix(X).

Anyone have any ideas what I've done wrong? Thanks