"""
The :mod:`surprise.prediction_algorithms.bases` module defines the base class
:class:`AlgoBase` from
which every single prediction algorithm has to inherit.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from .. import similarities as sims
from .predictions import PredictionImpossible
from .predictions import Prediction
from ..six.moves import range
[docs]class AlgoBase:
"""Abstract class where is defined the basic behaviour 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 train(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.dataset.Trainset>`) : A training
set, as returned by the :meth:`folds
<surprise.dataset.Dataset.folds>` method.
"""
self.trainset = trainset
# (re) Initialise baselines
self.bu = self.bi = None
[docs] def predict(self, uid, iid, r=0, 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 (for whatever reason), the prediction is set
to the global mean of all ratings. Also, if :math:`\\hat{r}_{ui}` is
outside the bounds of the rating scale, (e.g. :math:`\\hat{r}_{ui} = 6`
for a rating scale of :math:`[1, 5]`), then it is capped.
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: The true rating :math:`r_{ui}`.
verbose(bool): Whether to print details of the prediction. Default
is False.
Returns:
A :obj:`Prediction\
<surprise.prediction_algorithms.predictions.Prediction>` object.
"""
# 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.trainset.global_mean
details['was_impossible'] = True
details['reason'] = str(e)
# clip estimate into [self.r_min, self.r_max]
est = min(self.trainset.r_max, est)
est = max(self.trainset.r_min, est)
pred = Prediction(uid, iid, r, est, details)
if verbose:
print(pred)
return pred
[docs] def test(self, testset, verbose=False):
"""Test the algorithm on given testset.
Args:
testset: A test set, as returned by the :meth:`folds
<surprise.dataset.Dataset.folds>` 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.
"""
predictions = [self.predict(uid, iid, r, verbose=verbose)
for (uid, iid, r) 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 algoritihm (see
:ref:`baseline_estimates_configuration`).
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
def optimize_sgd():
"""Optimize biases using sgd"""
bu = np.zeros(self.trainset.n_users)
bi = np.zeros(self.trainset.n_items)
reg = self.bsl_options.get('reg', 0.02)
lr = self.bsl_options.get('learning_rate', 0.005)
n_epochs = self.bsl_options.get('n_epochs', 20)
for dummy in range(n_epochs):
for u, i, r in self.trainset.all_ratings():
err = (r - (self.trainset.global_mean + bu[u] + bi[i]))
bu[u] += lr * (err - reg * bu[u])
bi[i] += lr * (err - reg * bi[i])
return bu, bi
def optimize_als():
"""Alternatively optimize user biases and and item biases."""
# This piece of code is largely inspired by that of MyMediaLite:
# https://github.com/zenogantner/MyMediaLite/blob/master/src/MyMediaLite/RatingPrediction/UserItemBaseline.cs
# see also https://www.youtube.com/watch?v=gCaOa3W9kM0&t=32m55s
# (Alex Smola on RS, ML Class 10-701)
bu = np.zeros(self.trainset.n_users)
bi = np.zeros(self.trainset.n_items)
reg_u = self.bsl_options.get('reg_u', 15)
reg_i = self.bsl_options.get('reg_i', 10)
n_epochs = self.bsl_options.get('n_epochs', 10)
for dummy in range(n_epochs):
for i in self.trainset.all_items():
devI = sum(r - self.trainset.global_mean -
bu[u] for (u, r) in self.trainset.ir[i])
bi[i] = devI / (reg_i + len(self.trainset.ir[i]))
for u in self.trainset.all_users():
devU = sum(r - self.trainset.global_mean -
bi[i] for (i, r) in self.trainset.ur[u])
bu[u] = devU / (reg_u + len(self.trainset.ur[u]))
return bu, bi
optimize = dict(als=optimize_als,
sgd=optimize_sgd)
method = self.bsl_options.get('method', 'als')
try:
print('Estimating biases using', method + '...')
self.bu, self.bi = optimize[method]()
return self.bu, self.bi
except KeyError:
raise ValueError('invalid method ' + method + ' for baseline ' +
'computation. Available methods are als, sgd.')
[docs] def compute_similarities(self):
"""Build the simlarity matrix.
The way the similarity matric is computed depends on the
``sim_options`` parameter passed at the creation of the algorithm (see
:ref:`similarity_measures_configuration`).
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]
print('Computing the {0} similarity matrix...'.format(name))
try:
return construction_func[name](*args)
except KeyError:
raise NameError('Wrong sim name ' + name + '. Allowed values ' +
'are ' + ', '.join(construction_func.keys()) + '.')