Source code for cornac.models.skm.recom_skmeans

"""
@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