[Python] Trying to use xgboost for pairwise ranking

Using the python API from the documentation of xgboost I am creating the train data by:

dtrain = xgb.DMatrix(file_path)


Here file_path is of libsvm format txt file. As I am doing pairwise ranking I am also inputting the length of the groups in the dtrain data that we just inputed:

dtrain.set_group(group_len_file)


and now I am training the model:

param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'rankairwise' }
param['nthread'] = 4
param['eval_metric'] = 'ndcg'

bst = xgb.train(param,dtrain,10)


Now I want to use gridsearch. So my question is how to use gridsearch from sklearn when I am inputing DMatrix?

Gennerally:

from sklearn.grid_search import GridSearchCV
param_test1 = {
'max_depth':list(range(3,10,2)),
'min_child_weiht':list(range(1,6,2))
}
gsearch = GridSearchCV(estimator=XGBClassifier(objective='rankairwise'),
param_grid = param_test1)


but gsearch doesnt have train. It only have fit which takes X and y as inputs and not DMatrix. Is there a way to do it?

And if not I can transform the DMatrix to be of format from Dmatrix => X, y but I can't find in documentation how to input set_group in that case? Any idea on it?

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