"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""
import numpy as np
import scipy.sparse as sp
from ..recommender import Recommender
from .skmeans import *
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.
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, init_par=None):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
self.k = k
self.init_par = init_par
self.max_iter = max_iter
self.tol = tol
self.verbose = verbose
self.centroids = None # matrix of cluster centroids
# fit the recommender model to the traning data
[docs] def fit(self, train_set):
"""Fit the model to observations.
Parameters
----------
train_set: object of type TrainSet, required
An object contraining the user-item preference in csr scipy sparse format,\
as well as some useful attributes such as mappings to the original user/item ids.\
Please refer to the class TrainSet in the "data" module for details.
"""
Recommender.fit(self, train_set)
X = self.train_set.matrix
X = sp.csr_matrix(X)
# 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. / (np.sqrt(X1.multiply(X1).sum(1).A1) + 1e-20)).T)
if self.trainable:
res = skmeans(X1, k=self.k, max_iter=self.max_iter, tol=self.tol, verbose=self.verbose,
init_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)
[docs] def score(self, user_id, item_id=None):
"""Predict the scores/ratings of a user for an item.
Parameters
----------
user_id: int, required
The index of the user for whom to perform score prediction.
item_id: int, optional, default: None
The index of the item for that 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 item_id is None:
if self.train_set.is_unk_user(user_id):
raise ScoreException("Can't make score prediction for (user_id=%d)" % user_id)
known_item_scores = self.centroids.multiply(self.user_center_sim[user_id, :].T)
known_item_scores = known_item_scores.sum(0).A1 / (
self.user_center_sim[user_id, :].sum() + 1e-20) # weighted average of cluster centroids
return known_item_scores
else:
if self.train_set.is_unk_user(user_id) or self.train_set.is_unk_item(item_id):
raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_id, item_id))
user_pred = self.centroids[item_id, :].multiply(self.user_center_sim[user_id, :].T)
# transform user_pred to a flatten array
user_pred = user_pred.sum(0).A1 / (
self.user_center_sim[user_id, :].sum() + 1e-20) # weighted average of cluster centroids
return user_pred