"""
This class implements the baseline estimation.
"""
from .algo_base import AlgoBase
[docs]class BaselineOnly(AlgoBase):
r"""Algorithm predicting the baseline estimate for given user and item.
:math:`\hat{r}_{ui} = b_{ui} = \mu + b_u + b_i`
If user :math:`u` is unknown, then the bias :math:`b_u` is assumed to be
zero. The same applies for item :math:`i` with :math:`b_i`.
See section 2.1 of :cite:`Koren:2010` for details.
Args:
bsl_options(dict): A dictionary of options for the baseline estimates
computation. See :ref:`baseline_estimates_configuration` for
accepted options.
verbose(bool): Whether to print trace messages of bias estimation,
similarity, etc. Default is True.
"""
def __init__(self, bsl_options={}, verbose=True):
AlgoBase.__init__(self, bsl_options=bsl_options)
self.verbose = verbose
def fit(self, trainset):
AlgoBase.fit(self, trainset)
self.bu, self.bi = self.compute_baselines()
return self
def estimate(self, u, i):
est = self.trainset.global_mean
if self.trainset.knows_user(u):
est += self.bu[u]
if self.trainset.knows_item(i):
est += self.bi[i]
return est