"""
Module description:
Lemire, Daniel, and Anna Maclachlan. "Slope one predictors for online rating-based collaborative filtering."
Proceedings of the 2005 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
import pickle
import numpy as np
from elliot.recommender.algebric.slope_one.slope_one_model import SlopeOneModel
from elliot.recommender.base_recommender_model import BaseRecommenderModel, init_charger
from elliot.recommender.recommender_utils_mixin import RecMixin
from elliot.utils.write import store_recommendation
[docs]class SlopeOne(RecMixin, BaseRecommenderModel):
r"""
Slope One Predictors for Online Rating-Based Collaborative Filtering
For further details, please refer to the `paper <https://arxiv.org/abs/cs/0702144>`_
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
SlopeOne:
meta:
save_recs: True
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
super().__init__(data, config, params, *args, **kwargs)
self._restore = getattr(self._params, "restore", False)
self._num_items = self._data.num_items
self._num_users = self._data.num_users
self._ratings = self._data.train_dict
self._i_ratings = self._data.i_train_dict
self._model = SlopeOneModel(self._data)
[docs] def get_recommendations(self, k: int = 10):
predictions_top_k_val = {}
predictions_top_k_test = {}
recs_val, recs_test = self.process_protocol(k)
predictions_top_k_val.update(recs_val)
predictions_top_k_test.update(recs_test)
return predictions_top_k_val, predictions_top_k_test
[docs] def get_single_recommendation(self, mask, k, *args):
return {u: self._model.get_user_recs(u, mask, k) for u in self._data.train_dict.keys()}
@property
def name(self):
return f"SlopeOne"
[docs] def train(self):
if self._restore:
return self.restore_weights()
self._model.initialize()
self.evaluate()
[docs] def restore_weights(self):
try:
with open(self._saving_filepath, "rb") as f:
self._model.set_model_state(pickle.load(f))
print(f"Model correctly Restored")
recs = self.get_recommendations(self.evaluator.get_needed_recommendations())
result_dict = self.evaluator.eval(recs)
self._results.append(result_dict)
print("******************************************")
if self._save_recs:
store_recommendation(recs, self._config.path_output_rec_result + f"{self.name}.tsv")
return True
except Exception as ex:
print(f"Error in model restoring operation! {ex}")
return False