How fit pairwise ranking models in xgBoost?

As far as I know, to train learning to rank models, you need to have three things in the dataset:

• label or relevance
• group or query id
• feature vector

For example, the Microsoft Learning to Rank dataset uses this format (label, group id and features).

1 qid:10 1:0.031310 2:0.666667 ...
0 qid:10 1:0.078682 2:0.166667 ...

I am trying out xgBoost that utilizes GBMs to do pairwise ranking. They have an example for a ranking task that uses the C++ program to learn on the Microsoft dataset like above.

However, I am using their Python wrapper and cannot seem to find where I can input the group id (qid above). I can train the model using just the features and relevance scores but I feel like I am missing something.

Here is a sample script.

gbm = XGBRegressor(objective="rank:pairwise")

X =  np.random.normal(0, 1, 1000).reshape(100, 10)
y = np.random.randint(0, 5, 100)

gbm.fit(X, y) ### --- no group id needed???

print gbm.predict(X)

# should be in reverse order of relevance score
print y[gbm.predict_proba(X)[:, 1].argsort()][::-1]