# -*- coding: utf-8 -*-
"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""
import numpy as np
import scipy.sparse as sp
from scipy.io import loadmat, savemat
from ..recommender import Recommender
import c2pf
# Recommender class for Collaborative Context Poisson Factorization (C2PF)
[docs]class C2PF(Recommender):
"""Collaborative Context Poisson Factorization.
Parameters
----------
k: int, optional, default: 100
The dimension of the latent factors.
max_iter: int, optional, default: 100
Maximum number of iterations for variational C2PF.
aux_info: array, required, shape (n_context_items,3)
The item-context matrix, noted C in the original paper, \
in the triplet sparse format: (row_id, col_id, value).
variant: string, optional, default: 'c2pf'
C2pf's variant: c2pf: 'c2pf', 'tc2pf' (tied-c2pf) or 'rc2pf' (reduced-c2pf). \
Please refer to the original paper for details.
name: string, optional, default: None
The name of the recommender model. If None, \
then "variant" is used as the default name of the model.
trainable: boolean, optional, default: True
When False, the model is not trained and Cornac assumes that the model already \
pre-trained (Theta, Beta and Xi are not None).
init_params: dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None, \
'L2_s':None, 'L2_r':None, 'L3_s':None, 'L3_r':None}
List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r, \
'L2_s':L2_s, 'L2_r':L2_r, 'L3_s':L3_s, 'L3_r':L3_r}, \
where G_s and G_r are of type csc_matrix or np.array with the same shape as Theta, see below). \
They represent respectively the "shape" and "rate" parameters of Gamma distribution over \
Theta. It is the same for L_s, L_r and Beta, L2_s, L2_r and Xi, L3_s, L3_r and Kappa.
Theta: csc_matrix, shape (n_users,k)
The expected user latent factors.
Beta: csc_matrix, shape (n_items,k)
The expected item latent factors.
Xi: csc_matrix, shape (n_items,k)
The expected context item latent factors multiplied by context effects Kappa, \
please refer to the paper below for details.
References
----------
* Salah, Aghiles, and Hady W. Lauw. A Bayesian Latent Variable Model of User Preferences with Item Context. \
In IJCAI, pp. 2667-2674. 2018.
"""
def __init__(self, k=100, max_iter=100, aux_info=None, variant='c2pf', name=None, trainable=True,
init_params={'G_s': None, 'G_r': None, 'L_s': None, 'L_r': None, 'L2_s': None, 'L2_r': None,
'L3_s': None, 'L3_r': None}):
if name is None:
Recommender.__init__(self, name=variant.upper(), trainable=trainable)
else:
Recommender.__init__(self, name=name, trainable=trainable)
self.k = k
self.init_params = init_params
self.max_iter = max_iter
self.ll = np.full(max_iter, 0)
self.eps = 0.000000001
self.Theta = None # user factors
self.Beta = None # item factors
self.Xi = None # context factors Xi multiplied by context effects Kappa
self.aux_info = aux_info # item-context matrix in the triplet sparse format: (row_id, col_id, value)
self.variant = variant
# 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).
"""
# recover the striplet sparse format from csc sparse matrix X (needed to feed c++)
(rid, cid, val) = sp.find(X)
val = np.array(val, dtype='float32')
rid = np.array(rid, dtype='int32')
cid = np.array(cid, dtype='int32')
tX = np.concatenate((np.concatenate(([rid], [cid]), axis=0).T, val.reshape((len(val), 1))), axis=1)
del rid, cid, val
if self.variant == 'c2pf':
res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter,
self.init_params)
elif self.variant == 'tc2pf':
res = c2pf.t_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter,
self.init_params)
elif self.variant == 'rc2pf':
res = c2pf.r_c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter,
self.init_params)
else:
res = c2pf.c2pf(tX, X.shape[0], X.shape[1], self.aux_info, X.shape[1], X.shape[1], self.k, self.max_iter,
self.init_params)
self.Theta = sp.csc_matrix(res['Z']).todense()
self.Beta = sp.csc_matrix(res['W']).todense()
self.Xi = sp.csc_matrix(res['Q']).todense()
[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 self.variant == 'c2pf' or self.variant == 'tc2pf':
if item_indexes is None:
user_pred = self.Beta * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T
else:
user_pred = self.Beta[item_indexes,:] * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T
elif self.variant == 'rc2pf':
if item_indexes is None:
user_pred = self.Xi * self.Theta[user_index, :].T
else:
user_pred = self.Xi[item_indexes,] * self.Theta[user_index, :].T
else:
if item_indexes is None:
user_pred = self.Beta * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T
else:
user_pred = self.Beta[item_indexes,:] * self.Theta[user_index, :].T + self.Xi * self.Theta[user_index, :].T
# transform user_pred to a flatten array,
user_pred = np.array(user_pred, dtype='float64').flatten()
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