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_
import scipy.sparse as sp
from ...exception import ScoreException




# 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: see "cornac/examples/pcrl_example.py" in the GitHub repo for an example of how to use \ cornac's graph module provide item auxiliary data (e.g., context, text, etc.) for PCRL. 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, name = "pcrl", trainable = True, verbose=False, w_determinist = True, init_params = {'G_s':None, 'G_r':None, 'L_s':None, 'L_r':None}): Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose) 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, train_set): """Fit the model to observations. Parameters ---------- train_set: object of type TrainSet, required An object contraining the user-item preference in csr scipy sparse format,\ as well as some useful attributes such as mappings to the original user/item ids.\ Please refer to the class TrainSet in the "data" module for details. """ Recommender.fit(self, train_set) X = sp.csc_matrix(self.train_set.matrix) if self.trainable: # intanciate pcrl train_aux_info = train_set.item_graph.matrix[:self.train_set.num_items, :self.train_set.num_items] pcrl_ = PCRL_(cf_data=X, aux_data=train_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) elif self.verbose: print('%s is trained already (trainable = False)' % (self.name))
[docs] def score(self, user_id, item_id=None): """Predict the scores/ratings of a user for a list of items. Parameters ---------- user_id: int, required The index of the user for whom to perform score prediction. item_id: int, optional, default: None The index of the item for that to perform score prediction. If None, scores for all known items will be returned. Returns ------- res : A scalar or a Numpy array Relative scores that the user gives to the item or to all known items """ if item_id is None: user_pred = self.Beta * self.Theta[user_id, :].T else: user_pred = self.Beta[item_id, :] * self.Theta[user_id, :].T # transform user_pred to a flatten array user_pred = np.array(user_pred, dtype='float64').flatten() return user_pred