# -*- coding: utf-8 -*-
"""
@author: Aghiles Salah
"""
import numpy as np
import scipy.sparse as sp
import pmf
from ..recommender import Recommender
from ...utils.util_functions import sigmoid
from ...utils.util_functions import which_
from ...utils.util_functions import map_to
from ...utils.util_functions import clipping
[docs]class PMF(Recommender):
"""Probabilistic Matrix Factorization.
Parameters
----------
k: int, optional, default: 5
The dimension of the latent factors.
max_iter: int, optional, default: 100
Maximum number of iterations or the number of epochs for SGD.
learning_rate: float, optional, default: 0.001
The learning rate for SGD_RMSProp.
gamma: float, optional, default: 0.9
The weight for previous/current gradient in RMSProp.
lamda: float, optional, default: 0.001
The regularization parameter.
name: string, optional, default: 'PMF'
The name of the recommender model.
variant: {"linear","non_linear"}, optional, default: 'non_linear'
Pmf variant. If 'non_linear', the Gaussian mean is the output of a Sigmoid function.\
If 'linear' the Gaussian mean is the output of the identity function.
trainable: boolean, optional, default: True
When False, the model is not trained and Cornac assumes that the model already \
pre-trained (U and V are not None).
rating_range: 1d array, optional, default: [None,None]
The minimum and maximum rating values, e.g., [1,5].
init_params: dictionary, optional, default: {'U':None,'V':None}
List of initial parameters, e.g., init_params = {'U':U, 'V':V}. \
U: a csc_matrix of shape (n_users,k), containing the user latent factors. \
V: a csc_matrix of shape (n_items,k), containing the item latent factors.
References
----------
* Mnih, Andriy, and Ruslan R. Salakhutdinov. Probabilistic matrix factorization. \
In NIPS, pp. 1257-1264. 2008.
"""
def __init__(self, k=5, max_iter=100, learning_rate = 0.001,gamma = 0.9, lamda = 0.001, name = "pmf", variant ='non_linear', trainable = True, rating_range = [None,None] ,init_params = {'U':None,'V':None}):
Recommender.__init__(self,name=name, trainable = trainable)
self.k = k
self.init_params = init_params
self.max_iter = max_iter
self.learning_rate = learning_rate
self.gamma = gamma
self.lamda = lamda
self.variant = variant
self.ll = np.full(max_iter, 0)
self.eps = 0.000000001
self.U = init_params['U'] #matrix of user factors
self.V = init_params['V'] #matrix of item factors
self.min_rating = rating_range[0]
self.max_rating = rating_range[1]
#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.min_rating is None:
self.min_rating = np.min(X.data)
if self.max_rating is None:
self.max_rating = np.max(X.data)
if self.trainable:
#converting data to the triplet format (needed for cython function pmf)
(rid,cid,val)=sp.find(X)
val = np.array(val,dtype='float32')
if self.variant == 'non_linear': #need to map the ratings to [0,1]
if[self.min_rating,self.max_rating] != [0,1]:
val = map_to(val,0.,1.,self.min_rating,self.max_rating)
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
print('Learning...')
if self.variant == 'linear':
res = pmf.pmf_linear(tX,k = self.k,n_X= X.shape[0], d_X = X.shape[1], n_epochs = self.max_iter,lamda = self.lamda, learning_rate= self.learning_rate,gamma = self.gamma, init_params = self.init_params)
elif self.variant == 'non_linear':
res = pmf.pmf_non_linear(tX,k = self.k,n_X= X.shape[0], d_X = X.shape[1], n_epochs = self.max_iter,lamda = self.lamda, learning_rate= self.learning_rate,gamma = self.gamma, init_params = self.init_params)
else:
raise ValueError('variant must be one of {"linear","non_linear"}')
self.U = sp.csc_matrix(res['U'])
self.V = sp.csc_matrix(res['V'])
print('Learning completed')
else:
print('%s is trained already (trainable = False)' % (self.name))
[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 item_indexes is None:
user_pred = self.V.todense()*self.U[user_index,:].T.todense()
else:
user_pred = self.V[item_indexes,:].todense()*self.U[user_index,:].T.todense()
user_pred = np.array(user_pred,dtype='float64').flatten()
if self.variant == "non_linear":
user_pred = sigmoid(user_pred)
user_pred = map_to(user_pred,self.min_rating,self.max_rating,0.,1.)
else:
#perform clipping to enforce the predictions to lie in the same range as the original ratings
user_pred = clipping(user_pred,self.min_rating,self.max_rating)
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