# -*- 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