Source code for cornac.models.pcrl.recom_pcrl

# -*- coding: utf-8 -*-
"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""



import numpy as np
from ..recommender import Recommender
from .pcrl import PCRL_

#Recommender class for Probabilistic Collaborative Representation Learning (PCRL)
[docs]class PCRL(Recommender): """Probabilistic Collaborative Representation Learning. Parameters ---------- k: int, optional, default: 100 The dimension of the latent factors. z_dims: Numpy 1d array, optional, default: [300] The dimensions of the hidden intermdiate layers 'z' in the order \ [dim(z_L), ...,dim(z_1)], please refer to Figure 1 in the orginal paper for more details. max_iter: int, optional, default: 300 Maximum number of iterations (number of epochs) for variational PCRL. batch_size: int, optional, default: 300 The batch size for SGD. learning_rate: float, optional, default: 0.001 The learning rate for SGD. aux_info: csc sparse matrix, required The item auxiliary information matrix, item-context in the PCRL's paper, \ in the scipy csc sparse format. name: string, optional, default: 'PCRL' 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, Beta and Xi are not None). w_determinist: boolean, optional, default: True When True, determinist wheights "W" are used for the generator network, \ otherwise "W" is stochastic as in the original paper. init_params: dictionary, optional, default: {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':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. It is the same for L_s, L_r and 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 ---------- * Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. \ In UAI 2018. """ def __init__(self, k=100, z_dims = [300], max_iter=300, batch_size = 300,learning_rate = 0.001,aux_info = None, name = "pcrl", trainable = True,w_determinist = True, init_params = {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None}): Recommender.__init__(self, name=name, trainable = trainable) self.aux_info = aux_info self.k = k self.z_dims = z_dims #the dimension of the second hidden layer (we consider a 2-layers PCRL) self.max_iter = max_iter self.batch_size = batch_size self.learning_rate = learning_rate self.init_params = init_params self.w_determinist = w_determinist #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: #intanciate pcrl pcrl_ = PCRL_(cf_data = X, aux_data = self.aux_info, k =self.k, z_dims = self.z_dims, n_epoch=self.max_iter, batch_size = self.batch_size, learning_rate = self.learning_rate, B = 1, w_determinist = self.w_determinist, init_params = self.init_params) pcrl_.learn() self.Theta = np.array(pcrl_.Gs)/np.array(pcrl_.Gr) self.Beta = np.array(pcrl_.Ls)/np.array(pcrl_.Lr) 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.Beta*self.Theta[user_index,:].T else: user_pred = self.Beta[item_indexes,:]*self.Theta[user_index,:].T #transform user_pred to a flatten array user_pred = np.array(user_pred,dtype='float64').flatten() 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