Hyperparameter tuning with XGBRanker - xgboost

Question

I am trying to figure how to tune my hyperparameter through RandomizedSearchCV with an XGBRanker model.
I could split the data into groups, feed it into the model and make predictions. However I am not sure how to set up the Search object, namely 2 specific things - how to inform it about the groups and also what kind of score I need to supply.
model = xg.XGBRanker(
tree_method='exact',
booster='gbtree',
objective='rank:pairwise',
random_state=42,
learning_rate=0.06,
max_depth=5,
n_estimators=700,
subsample=0.75,
#colsample_bytree=0.9,
#subsample=0.75
min_child_weight=0.06
)
model.fit(x_train, y_train, group=train_groups, verbose=True)
This works fine.
This is where I need some help
param_dist = {'n_estimators': stats.randint(40, 1000),
'learning_rate': stats.uniform(0.01, 0.59),
'subsample': stats.uniform(0.3, 0.6),
'max_depth': [3, 4, 5, 6, 7, 8, 9],
'colsample_bytree': stats.uniform(0.5, 0.4),
'min_child_weight': [0.05, 0.1, 0.02]
}
clf = RandomizedSearchCV(model,
param_distributions=param_dist,
cv=5,
n_iter=5,
scoring=???, #
error_score=0,
verbose=3,
n_jobs=-1)
#also what about the groups?

Answer 1

i had tried something similar. for scoring however i used the ndcg_scorer from sklearn.
i added
scoring = sklearn.metrics.make_scorer(sklearn.metrics.ndcg_score, greater_is_better=True)
for groups u can add to the fit_params in RandomizedSearchCV.
fit_params = {"model__groups": group}
clf = RandomizedSearchCV(model,
param_distributions=param_dist,
cv=5,
n_iter=5,
scoring=scoring,
error_score=0,
verbose=3,
n_jobs=-1,fit_params = fit_params)