# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import scipy.sparse as sp
from ..recommender import Recommender
from ...exception import ScoreException
[docs]
class SKMeans(Recommender):
"""Spherical k-means based recommender.
Parameters
----------
k: int, optional, default: 5
The number of clusters.
max_iter: int, optional, default: 100
Maximum number of iterations.
name: string, optional, default: 'Skmeans'
The name of the recommender model.
trainable: boolean, optional, default: True
When False, the model is not trained and Cornac assumes that the model is already \
trained.
tol : float, optional, default: 1e-6
Relative tolerance with regards to skmeans' criterion to declare convergence.
verbose: boolean, optional, default: False
When True, some running logs are displayed.
seed: int, optional, default: None
Random seed for parameters initialization.
init_par: numpy 1d array, optional, default: None
The initial object parition, 1d array contaning the cluster label (int type starting from 0) \
of each object (user). If par = None, then skmeans is initialized randomly.
centroids: csc_matrix, shape (k,n_users)
The maxtrix of cluster centroids.
References
----------
* Salah, Aghiles, Nicoleta Rogovschi, and Mohamed Nadif. "A dynamic collaborative filtering system \
via a weighted clustering approach." Neurocomputing 175 (2016): 206-215.
"""
def __init__(
self,
k=5,
max_iter=100,
name="Skmeans",
trainable=True,
tol=1e-6,
verbose=True,
seed=None,
init_par=None,
):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.max_iter = max_iter
self.tol = tol
self.verbose = verbose
self.seed = seed
self.init_par = init_par
self.centroids = None # matrix of cluster centroids
[docs]
def fit(self, train_set, val_set=None):
"""Fit the model to observations.
Parameters
----------
train_set: :obj:`cornac.data.Dataset`, required
User-Item preference data as well as additional modalities.
val_set: :obj:`cornac.data.Dataset`, optional, default: None
User-Item preference data for model selection purposes (e.g., early stopping).
Returns
-------
self : object
"""
Recommender.fit(self, train_set, val_set)
X = train_set.matrix # CSR matrix
# Skmeans requires rows of X to have a unit L2 norm. We therefore need to make a copy of X as we should not modify the latter.
X1 = X.copy()
X1 = X1.multiply(
sp.csc_matrix(1.0 / (np.sqrt(X1.multiply(X1).sum(1).A1) + 1e-20)).T
)
if self.trainable:
from .skmeans import skmeans
res = skmeans(
X1,
k=self.k,
max_iter=self.max_iter,
tol=self.tol,
verbose=self.verbose,
seed=self.seed,
init_par=getattr(self, "final_par", self.init_par),
)
self.centroids = res["centroids"]
self.final_par = res["partition"]
else:
print("%s is trained already (trainable = False)" % (self.name))
self.user_center_sim = (
X1 * self.centroids.T
) # user-centroid cosine similarity matrix
del X1
return self
[docs]
def score(self, user_idx, item_idx=None):
"""Predict the scores/ratings of a user for an item.
Parameters
----------
user_idx: int, required
The index of the user for whom to perform score prediction.
item_idx: int, optional, default: None
The index of the item for which to perform score prediction.
If None, scores for all known items will be returned.
Returns
-------
res : A scalar or a Numpy array
Relative scores that the user gives to the item or to all known items
"""
if self.is_unknown_user(user_idx):
raise ScoreException("Can't make score prediction for user %d" % user_idx)
if item_idx is not None and self.is_unknown_item(item_idx):
raise ScoreException("Can't make score prediction for item %d" % item_idx)
if item_idx is None:
known_item_scores = self.centroids.multiply(
self.user_center_sim[user_idx, :].T
)
known_item_scores = known_item_scores.sum(0).A1 / (
self.user_center_sim[user_idx, :].sum() + 1e-20
) # weighted average of cluster centroids
return known_item_scores
else:
user_pred = self.centroids[item_idx, :].multiply(
self.user_center_sim[user_idx, :].T
)
# transform user_pred to a flatten array
user_pred = user_pred.sum(0).A1 / (
self.user_center_sim[user_idx, :].sum() + 1e-20
) # weighted average of cluster centroids
return user_pred
