"""
Module description:
"""
__version__ = '0.3.1'
__author__ = 'Felice Antonio Merra, Vito Walter Anelli, Claudio Pomo'
__email__ = 'felice.merra@poliba.it, vitowalter.anelli@poliba.it, claudio.pomo@poliba.it'
import numpy as np
import pickle
from elliot.recommender.latent_factor_models.NonNegMF.non_negative_matrix_factorization_model import NonNegMFModel
from elliot.recommender.recommender_utils_mixin import RecMixin
from elliot.utils.write import store_recommendation
from elliot.recommender.base_recommender_model import BaseRecommenderModel
from elliot.recommender.base_recommender_model import init_charger
[docs]class NonNegMF(RecMixin, BaseRecommenderModel):
r"""
Non-Negative Matrix Factorization
For further details, please refer to the `paper <https://ieeexplore.ieee.org/document/6748996>`_
Args:
factors: Number of latent factors
lr: Learning rate
reg: Regularization coefficient
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
NonNegMF:
meta:
save_recs: True
epochs: 10
batch_size: 512
factors: 10
lr: 0.001
reg: 0.1
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._params_list = [
("_factors", "factors", "factors", 10, None, None),
("_learning_rate", "lr", "lr", 0.001, None, None),
("_l_w", "reg", "reg", 0.1, None, None),
]
self.autoset_params()
if self._batch_size < 1:
self._batch_size = self._data.transactions
self._ratings = self._data.train_dict
self._global_mean = np.mean(self._data.sp_i_train_ratings)
self._sp_i_train = self._data.sp_i_train
self._i_items_set = list(range(self._num_items))
self._model = NonNegMFModel(self._data,
self._num_users,
self._num_items,
self._global_mean,
self._factors,
self._l_w,
self._learning_rate,
random_seed=self._seed)
@property
def name(self):
return "NonNegMF" \
+ f"_{self.get_base_params_shortcut()}" \
+ f"_{self.get_params_shortcut()}"
[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()}
[docs] def train(self):
print(f"Transactions: {self._data.transactions}")
if self._restore:
return self.restore_weights()
for it in self.iterate(self._epochs):
print(f"\n********** Iteration: {it + 1}")
self._iteration = it
self._model.train_step()
self.evaluate(it)