Source code for cornac.models.cf.recom_hpf

# -*- coding: utf-8 -*-

"""
@author: Aghiles Salah
"""

import numpy as np
from ..recommender import Recommender
from .hpf import *

#HierarchicalPoissonFactorization: Hpf
[docs]class Hpf(Recommender): """Hierarchical Poisson Factorization. Parameters ---------- k: int, optional, default: 5 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations. name: string, optional, default: 'HPF' The name of the recommender model. trainable: boolean, optional, default: True When False, the model is not trained and Cornac assumes that the model already \ pre-trained (Theta and Beta 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}, \ 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. Similarly, L_s, L_r are the shape and rate parameters of the Gamma over Beta. Theta: csc_matrix, shape (n_users,k) The expected user latent factors. Beta: csc_matrix, shape (n_items,k) The expected item latent factors. References ---------- * Gopalan, Prem, Jake M. Hofman, and David M. Blei. Scalable Recommendation with \ Hierarchical Poisson Factorization. In UAI, pp. 326-335. 2015. """ def __init__(self, k=5, max_iter=100,name = "HPF",trainable = True, init_params = None): 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.etp_r = np.full(max_iter, 0) self.etp_c = np.full(max_iter, 0) self.eps = 0.000000001 self.Theta = None #matrix of user factors self.Beta = None #matrix of item factors #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). """ if self.trainable: res = pf(X,k = self.k, max_iter = self.max_iter,init_param = self.init_params) self.Theta = res['Z'] self.Beta = res['W'] else: print('%s is trained already (trainable = False)' % (self.name))
#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 """ user_pred = self.Beta*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