Source code for cornac.models.context_cf.recom_c2pf

# -*- 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