"""
The :mod:`surprise.prediction_algorithms.algo_base` module defines the base
class :class:`AlgoBase` from which every single prediction algorithm has to
inherit.
"""
import heapq
from .. import similarities as sims
from .optimize_baselines import baseline_als, baseline_sgd
from .predictions import Prediction, PredictionImpossible
[docs]class AlgoBase:
"""Abstract class where is defined the basic behavior of a prediction
algorithm.
Keyword Args:
baseline_options(dict, optional): If the algorithm needs to compute a
baseline estimate, the ``baseline_options`` parameter is used to
configure how they are computed. See
:ref:`baseline_estimates_configuration` for usage.
"""
def __init__(self, **kwargs):
self.bsl_options = kwargs.get("bsl_options", {})
self.sim_options = kwargs.get("sim_options", {})
if "user_based" not in self.sim_options:
self.sim_options["user_based"] = True
[docs] def fit(self, trainset):
"""Train an algorithm on a given training set.
This method is called by every derived class as the first basic step
for training an algorithm. It basically just initializes some internal
structures and set the self.trainset attribute.
Args:
trainset(:obj:`Trainset <surprise.Trainset>`) : A training
set, as returned by the :meth:`folds
<surprise.dataset.Dataset.folds>` method.
Returns:
self
"""
self.trainset = trainset
# (re) Initialise baselines
self.bu = self.bi = None
return self
[docs] def predict(self, uid, iid, r_ui=None, clip=True, verbose=False):
"""Compute the rating prediction for given user and item.
The ``predict`` method converts raw ids to inner ids and then calls the
``estimate`` method which is defined in every derived class. If the
prediction is impossible (e.g. because the user and/or the item is
unknown), the prediction is set according to
:meth:`default_prediction()
<surprise.prediction_algorithms.algo_base.AlgoBase.default_prediction>`.
Args:
uid: (Raw) id of the user. See :ref:`this note<raw_inner_note>`.
iid: (Raw) id of the item. See :ref:`this note<raw_inner_note>`.
r_ui(float): The true rating :math:`r_{ui}`. Optional, default is
``None``.
clip(bool): Whether to clip the estimation into the rating scale.
For example, if :math:`\\hat{r}_{ui}` is :math:`5.5` while the
rating scale is :math:`[1, 5]`, then :math:`\\hat{r}_{ui}` is
set to :math:`5`. Same goes if :math:`\\hat{r}_{ui} < 1`.
Default is ``True``.
verbose(bool): Whether to print details of the prediction. Default
is False.
Returns:
A :obj:`Prediction\
<surprise.prediction_algorithms.predictions.Prediction>` object
containing:
- The (raw) user id ``uid``.
- The (raw) item id ``iid``.
- The true rating ``r_ui`` (:math:`r_{ui}`).
- The estimated rating (:math:`\\hat{r}_{ui}`).
- Some additional details about the prediction that might be useful
for later analysis.
"""
# Convert raw ids to inner ids
try:
iuid = self.trainset.to_inner_uid(uid)
except ValueError:
iuid = "UKN__" + str(uid)
try:
iiid = self.trainset.to_inner_iid(iid)
except ValueError:
iiid = "UKN__" + str(iid)
details = {}
try:
est = self.estimate(iuid, iiid)
# If the details dict was also returned
if isinstance(est, tuple):
est, details = est
details["was_impossible"] = False
except PredictionImpossible as e:
est = self.default_prediction()
details["was_impossible"] = True
details["reason"] = str(e)
# clip estimate into [lower_bound, higher_bound]
if clip:
lower_bound, higher_bound = self.trainset.rating_scale
est = min(higher_bound, est)
est = max(lower_bound, est)
pred = Prediction(uid, iid, r_ui, est, details)
if verbose:
print(pred)
return pred
[docs] def default_prediction(self):
"""Used when the ``PredictionImpossible`` exception is raised during a
call to :meth:`predict()
<surprise.prediction_algorithms.algo_base.AlgoBase.predict>`. By
default, return the global mean of all ratings (can be overridden in
child classes).
Returns:
(float): The mean of all ratings in the trainset.
"""
return self.trainset.global_mean
[docs] def test(self, testset, verbose=False):
"""Test the algorithm on given testset, i.e. estimate all the ratings
in the given testset.
Args:
testset: A test set, as returned by a :ref:`cross-validation
itertor<use_cross_validation_iterators>` or by the
:meth:`build_testset() <surprise.Trainset.build_testset>`
method.
verbose(bool): Whether to print details for each predictions.
Default is False.
Returns:
A list of :class:`Prediction\
<surprise.prediction_algorithms.predictions.Prediction>` objects
that contains all the estimated ratings.
"""
# The ratings are translated back to their original scale.
predictions = [
self.predict(uid, iid, r_ui_trans, verbose=verbose)
for (uid, iid, r_ui_trans) in testset
]
return predictions
[docs] def compute_baselines(self):
"""Compute users and items baselines.
The way baselines are computed depends on the ``bsl_options`` parameter
passed at the creation of the algorithm (see
:ref:`baseline_estimates_configuration`).
This method is only relevant for algorithms using :func:`Pearson
baseline similarity<surprise.similarities.pearson_baseline>` or the
:class:`BaselineOnly
<surprise.prediction_algorithms.baseline_only.BaselineOnly>` algorithm.
Returns:
A tuple ``(bu, bi)``, which are users and items baselines."""
# Firt of, if this method has already been called before on the same
# trainset, then just return. Indeed, compute_baselines may be called
# more than one time, for example when a similarity metric (e.g.
# pearson_baseline) uses baseline estimates.
if self.bu is not None:
return self.bu, self.bi
method = dict(als=baseline_als, sgd=baseline_sgd)
method_name = self.bsl_options.get("method", "als")
try:
if getattr(self, "verbose", False):
print("Estimating biases using", method_name + "...")
self.bu, self.bi = method[method_name](self)
return self.bu, self.bi
except KeyError:
raise ValueError(
"Invalid method "
+ method_name
+ " for baseline computation."
+ " Available methods are als and sgd."
)
[docs] def compute_similarities(self):
"""Build the similarity matrix.
The way the similarity matrix is computed depends on the
``sim_options`` parameter passed at the creation of the algorithm (see
:ref:`similarity_measures_configuration`).
This method is only relevant for algorithms using a similarity measure,
such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`.
Returns:
The similarity matrix."""
construction_func = {
"cosine": sims.cosine,
"msd": sims.msd,
"pearson": sims.pearson,
"pearson_baseline": sims.pearson_baseline,
}
if self.sim_options["user_based"]:
n_x, yr = self.trainset.n_users, self.trainset.ir
else:
n_x, yr = self.trainset.n_items, self.trainset.ur
min_support = self.sim_options.get("min_support", 1)
args = [n_x, yr, min_support]
name = self.sim_options.get("name", "msd").lower()
if name == "pearson_baseline":
shrinkage = self.sim_options.get("shrinkage", 100)
bu, bi = self.compute_baselines()
if self.sim_options["user_based"]:
bx, by = bu, bi
else:
bx, by = bi, bu
args += [self.trainset.global_mean, bx, by, shrinkage]
try:
if getattr(self, "verbose", False):
print(f"Computing the {name} similarity matrix...")
sim = construction_func[name](*args)
if getattr(self, "verbose", False):
print("Done computing similarity matrix.")
return sim
except KeyError:
raise NameError(
"Wrong sim name "
+ name
+ ". Allowed values "
+ "are "
+ ", ".join(construction_func.keys())
+ "."
)
[docs] def get_neighbors(self, iid, k):
"""Return the ``k`` nearest neighbors of ``iid``, which is the inner id
of a user or an item, depending on the ``user_based`` field of
``sim_options`` (see :ref:`similarity_measures_configuration`).
As the similarities are computed on the basis of a similarity measure,
this method is only relevant for algorithms using a similarity measure,
such as the :ref:`k-NN algorithms <pred_package_knn_inpired>`.
For a usage example, see the :ref:`FAQ <get_k_nearest_neighbors>`.
Args:
iid(int): The (inner) id of the user (or item) for which we want
the nearest neighbors. See :ref:`this note<raw_inner_note>`.
k(int): The number of neighbors to retrieve.
Returns:
The list of the ``k`` (inner) ids of the closest users (or items)
to ``iid``.
"""
if self.sim_options["user_based"]:
all_instances = self.trainset.all_users
else:
all_instances = self.trainset.all_items
others = [(x, self.sim[iid, x]) for x in all_instances() if x != iid]
others = heapq.nlargest(k, others, key=lambda tple: tple[1])
k_nearest_neighbors = [j for (j, _) in others]
return k_nearest_neighbors