# -*- coding: utf-8 -*-
"""
@author: Aghiles Salah <salahaghiles@gmail.com>
"""
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: 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 = 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()
#get prefiction for a single user (predictions for one user at a time for efficiency purposes)
#predictions are not stored for the same efficiency reasons
[docs] def predict(self,index_user):
"""Predic the scores (ratings) of a user for all items.
Parameters
----------
index_user: int, required
The index of the user for whom to perform predictions.
Returns
-------
Numpy 1d array
Array containing the predicted values for all items
"""
if self.variant == 'c2pf' or self.variant == 'tc2pf':
user_pred = self.Beta*self.Theta[index_user,:].T + self.Xi*self.Theta[index_user,:].T
elif self.variant == 'rc2pf':
user_pred = self.Xi*self.Theta[index_user,:].T
else:
user_pred = self.Beta*self.Theta[index_user,:].T + self.Xi*self.Theta[index_user,:].T
#transform user_pred to a flatten array, but keep thinking about another possible format
user_pred = np.array(user_pred,dtype='float64').flatten()
return user_pred