"""
Module description:
"""
__version__ = '0.3.1'
__author__ = 'Vito Walter Anelli, Claudio Pomo'
__email__ = 'vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
import numpy as np
import pickle
from elliot.recommender.recommender_utils_mixin import RecMixin
from elliot.utils.write import store_recommendation
from elliot.recommender.latent_factor_models.PureSVD.pure_svd_model import PureSVDModel
from elliot.recommender.base_recommender_model import BaseRecommenderModel
from elliot.recommender.base_recommender_model import init_charger
[docs]class PureSVD(RecMixin, BaseRecommenderModel):
r"""
PureSVD
For further details, please refer to the `paper <https://link.springer.com/chapter/10.1007/978-0-387-85820-3_5>`_
Args:
factors: Number of latent factors
seed: Random seed
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
PureSVD:
meta:
save_recs: True
factors: 10
seed: 42
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._params_list = [
("_factors", "factors", "factors", 10, None, None)
]
self.autoset_params()
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._model = PureSVDModel(self._factors, self._data, self._seed)
[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._ratings.keys()}
[docs] def predict(self, u: int, i: int):
"""
Get prediction on the user item pair.
Returns:
A single float vaue.
"""
return self._model.predict(u, i)
@property
def name(self):
return f"PureSVD_{self.get_params_shortcut()}"
[docs] def train(self):
if self._restore:
return self.restore_weights()
self._model.train_step()
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