"""
Module description:
"""
__version__ = '0.3'
__author__ = 'Massimo Quadrana, Vito Walter Anelli, Claudio Pomo, Felice Antonio Merra'
__email__ = 'mquadrana@pandora.com, vitowalter.anelli@poliba.it, claudio.pomo@poliba.it, felice.merra@poliba.it'
import pickle
from elliot.recommender.base_recommender_model import BaseRecommenderModel
from elliot.recommender.base_recommender_model import init_charger
from elliot.recommender.latent_factor_models.Slim.slim_model import SlimModel
from elliot.recommender.recommender_utils_mixin import RecMixin
from elliot.utils.write import store_recommendation
[docs]class Slim(RecMixin, BaseRecommenderModel):
r"""
Train a Sparse Linear Methods (SLIM) item similarity model.
NOTE: ElasticNet solver is parallel, a single intance of SLIM_ElasticNet will
make use of half the cores available
See:
Efficient Top-N Recommendation by Linear Regression,
M. Levy and K. Jack, LSRS workshop at RecSys 2013.
SLIM: Sparse linear methods for top-n recommender systems,
X. Ning and G. Karypis, ICDM 2011.
For further details, please refer to the `paper <http://glaros.dtc.umn.edu/gkhome/fetch/papers/SLIM2011icdm.pdf>`_
Args:
l1_ratio:
alpha:
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
Slim:
meta:
save_recs: True
l1_ratio: 0.001
alpha: 0.001
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
self._params_list = [
("_l1_ratio", "l1_ratio", "l1", 0.001, float, None),
("_alpha", "alpha", "alpha", 0.001, float, None),
("_neighborhood", "neighborhood", "neighborhood", 10, int, None)
]
self.autoset_params()
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._i_items_set = list(range(self._num_items))
self._model = SlimModel(self._data, self._num_users, self._num_items, self._l1_ratio, self._alpha,
self._epochs, self._neighborhood, self._seed)
@property
def name(self):
return "Slim" \
+ f"_{self.get_base_params_shortcut()}" \
+ f"_{self.get_params_shortcut()}"
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 get_recommendations(self, k: int = 10):
self._model.prepare_predictions()
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 predict(self, u: int, i: int):
"""
Get prediction on the user item pair.
Returns:
A single float vaue.
"""
return self._model.predict(u, i)
[docs] def train(self):
if self._restore:
return self.restore_weights()
self._model.train(self._verbose)
self.evaluate()