"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""
import numpy as np
from ..recommender import Recommender
from .skmeans import *
[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: True
When True, the skmeans criterion (likelihood) is displayed after each iteration.
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)
self.k = k
self.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, X):
"""Fit the model to observations.
Parameters
----------
X: scipy sparse matrix, required
the user-item preference matrix (traning data), in a scipy sparse format\
(e.g., csc_matrix).
"""
X1 = X.copy()
X1 = X1.multiply(sp.csc_matrix(1. / (np.sqrt(X1.multiply(X1).sum(1).A1) + 1e-20)).T)
if self.trainable:
# 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.
res = skmeans(X1, k=self.k, max_iter=self.max_iter, tol=self.tol, verbose=self.verbose, init_par=self.par)
self.centroids = res['centroids']
self.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_index, item_indexes = None):
"""Predict the scores/ratings of a user for a list of items.
Parameters
----------
user_index: int, required
The index of the user for whom to perform score predictions.
item_indexes: 1d array, optional, default: None
A list of item indexes for which to predict the rating score.\
When "None", score prediction is performed for all test items of the given user.
Returns
-------
Numpy 1d array
Array containing the predicted values for the items of interest
"""
if item_indexes is None:
user_pred = self.centroids.multiply(self.user_center_sim[user_index, :].T)
# transform user_pred to a flatten array
user_pred = user_pred.sum(0).A1 / (
self.user_center_sim[user_index, :].sum() + 1e-20) # weighted average of cluster centroids
else:
user_pred = self.centroids[item_indexes,:].multiply(self.user_center_sim[user_index, item_indexes].T)
# transform user_pred to a flatten array
user_pred = user_pred.sum(0).A1 / (
self.user_center_sim[user_index, item_indexes].sum() + 1e-20) # weighted average of cluster centroids
return user_pred
[docs] def rank(self, user_index, known_items = None):
"""Rank all test items for a given user.
Parameters
----------
user_index: int, required
The index of the user for whom to perform item raking.
known_items: 1d array, optional, default: None
A list of item indices already known by the user
Returns
-------
Numpy 1d array
Array of item indices sorted (in decreasing order) relative to some user preference scores.
"""
u_pref_score = np.array(self.score(user_index))
if known_items is not None:
u_pref_score[known_items] = None
rank_item_list = (-u_pref_score).argsort() # ordering the items (in decreasing order) according to the preference score
return rank_item_list