"""
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 pickle
import numpy as np
from tqdm import tqdm
from elliot.dataset.samplers import custom_sampler as cs
from elliot.recommender import BaseRecommenderModel
from elliot.recommender.latent_factor_models.CML.CML_model import CML_model
from elliot.recommender.recommender_utils_mixin import RecMixin
from elliot.utils.write import store_recommendation
from elliot.recommender.base_recommender_model import init_charger
[docs]class CML(RecMixin, BaseRecommenderModel):
r"""
Collaborative Metric Learning
For further details, please refer to the `paper <https://www.cs.cornell.edu/~ylongqi/paper/HsiehYCLBE17.pdf>`_
Args:
factors: Number of latent factors
lr: Learning rate
l_w: Regularization coefficient for latent factors
l_b: Regularization coefficient for bias
margin: Safety margin size
To include the recommendation model, add it to the config file adopting the following pattern:
.. code:: yaml
models:
CML:
meta:
save_recs: True
epochs: 10
batch_size: 512
factors: 10
lr: 0.001
l_w: 0.001
l_b: 0.001
margin: 0.5
"""
@init_charger
def __init__(self, data, config, params, *args, **kwargs):
"""
Create a CML instance.
(see https://vision.cornell.edu/se3/wp-content/uploads/2017/03/WWW-fp0554-hsiehA.pdf for details about the algorithm design choices).
Args:
data: data loader object
params: model parameters {embed_k: embedding size,
[l_w, l_b]: regularization,
lr: learning rate}
"""
self._params_list = [
("_user_factors", "factors", "factors", 100, None, None),
("_learning_rate", "lr", "lr", 0.001, None, None),
("_l_w", "l_w", "l_w", 0.001, None, None),
("_l_b", "l_b", "l_b", 0.001, None, None),
("_margin", "margin", "margin", 0.5, None, None),
]
self.autoset_params()
self._item_factors = self._user_factors
if self._batch_size < 1:
self._batch_size = self._data.transactions
self._ratings = self._data.train_dict
self._sampler = cs.Sampler(self._data.i_train_dict)
self._model = CML_model(self._user_factors,
self._item_factors,
self._learning_rate,
self._l_w,
self._l_b,
self._margin,
self._num_users,
self._num_items,
self._seed)
@property
def name(self):
return "CML" \
+ 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):
loss = 0
steps = 0
with tqdm(total=int(self._data.transactions // self._batch_size), disable=not self._verbose) as t:
for batch in self._sampler.step(self._data.transactions, self._batch_size):
steps += 1
loss += self._model.train_step(batch)
t.set_postfix({'loss': f'{loss.numpy() / steps:.5f}'})
t.update()
self.evaluate(it, loss.numpy()/(it + 1))
[docs] def get_recommendations(self, k: int = 100):
predictions_top_k_test = {}
predictions_top_k_val = {}
for index, offset in enumerate(range(0, self._num_users, self._batch_size)):
offset_stop = min(offset + self._batch_size, self._num_users)
predictions = self._model.predict(offset, offset_stop)
recs_val, recs_test = self.process_protocol(k, predictions, offset, offset_stop)
predictions_top_k_val.update(recs_val)
predictions_top_k_test.update(recs_test)
return predictions_top_k_val, predictions_top_k_test