"""
This is an example on datasets-1M demonstrating some additional recommenders
"""

from ml_recsys_tools.datasets.prep_movielense_data import get_and_prep_data
import pandas as pd
from ml_recsys_tools.data_handlers.interaction_handlers_base import ObservationsDF

rating_csv_path, users_csv_path, movies_csv_path = get_and_prep_data()
ratings_df = pd.read_csv(rating_csv_path)

obs = ObservationsDF(ratings_df, uid_col='userid', iid_col='itemid')
train_obs, test_obs = obs.split_train_test(ratio=0.2)

# train and evaluate a Cooccurrence recommender (fast and deterministic)
from ml_recsys_tools.recommenders.cooccurrence_recommenders import ItemCoocRecommender

item_cooc_rec = ItemCoocRecommender()
item_cooc_rec.fit(train_obs)
print(item_cooc_rec.eval_on_test_by_ranking(test_obs, prefix='item cooccurrence '))

# train and evaluate an ALS recommender (fast)
from ml_recsys_tools.recommenders.implib_recommenders import ALSRecommender

als_rec = ALSRecommender()
als_rec.fit(train_obs)
print(als_rec .eval_on_test_by_ranking(test_obs, prefix='als '))

