Catboost with yeti-rank

yeti_rank.py

import mmh3
import pandas as pd
def get_positive_hash(x):
    s =   " ".join(get_unique_tokens(x))
    return mmh3.hash(s) % 2**31

df['group_id'] = df['query_string'].apply(get_positive_hash )


query_groups = df.groupby("group_id")
group_list = list(query_groups.groups)
train_groups, validation_groups = train_test_split(group_list, test_size = 0.2)

def get_group_with_index(x):
    return query_groups.get_group(x)

train_queries = pd.concat([get_group_with_index(x) for x in train_groups], axis = 0 )
validation_queries =pd.concat( [get_group_with_index(x) for x in validation_groups], axis = 0)

y_train = train_queries['target']
y_validation = validation_queries['target']

group_id_train = train_queries['group_id']
group_id_validation = validation_queries['group_id']


X_train = train_queries.drop(non_train_variables, axis=1)
X_validation = validation_queries.drop(non_train_variables, axis=1)


cat_features= [0,1,2,5]

validation_pool = Pool(data=X_validation, label=y_validation , cat_features = cat_features  , group_id = group_id_validation)
train_pool = Pool(data=X_train, label=y_train , cat_features = cat_features , group_id = group_id_train)


from catboost import CatBoost
param = {'loss_function':'YetiRank',
         'learning_rate': .00001,
         'iterations': 1000,
         'depth': 7,
         'use_best_model':True,
         'calc_feature_importance': True,
         'one_hot_max_size' : 20,
         'bagging_temperature' : .7,
         'max_ctr_complexity' : 4 }
model = CatBoost(param)
model.fit(train_pool, eval_set=validation_pool,
    logging_level='Silent',
    plot=True);

Thanks for the example!
Can you describe how the labels look like? from the documentation I understand that they can be 0 or 1 only, but what if I have more than 2 ranks? for example from 1 to 5?
Also, is it possible to work with sample weights as well?

Thanks,
Uriel