"""
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.WRMF.wrmf_model import WRMFModel
from elliot.recommender.base_recommender_model import BaseRecommenderModel
from elliot.recommender.base_recommender_model import init_charger
[docs]class WRMF(RecMixin, BaseRecommenderModel):
r"""
Weighted XXX Matrix Factorization
For further details, please refer to the `paper <https://archive.siam.org/meetings/sdm06/proceedings/059zhangs2.pdf>`_
Args:
factors: Number of latent factors
lr: Learning rate
alpha:
reg: Regularization coefficient
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
WRMF:
meta:
save_recs: True
epochs: 10
factors: 50
alpha: 1
reg: 0.1
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._params_list = [
("_factors", "factors", "factors", 10, None, None),
("_alpha", "alpha", "alpha", 1, None, None),
("_reg", "reg", "reg", 0.1, None, None)
]
self.autoset_params()
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._model = WRMFModel(self._factors, self._data, self._nprandom, self._alpha, self._reg)
[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 "WRMF" \
+ f"_{self.get_base_params_shortcut()}" \
+ f"_{self.get_params_shortcut()}"
[docs] def train(self):
if self._restore:
return self.restore_weights()
for it in self.iterate(self._epochs):
self._model.train_step()
print("Iteration Finished")
self.evaluate(it)