Source code for cornac.models.ibpr.recom_ibpr

# -*- coding: utf-8 -*-
"""
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
"""

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


[docs]class IBPR(Recommender): """Indexable Bayesian Personalized Ranking. Parameters ---------- k: int, optional, default: 20 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: 0.05 The learning rate for SGD. lamda: float, optional, default: 0.001 The regularization parameter. batch_size: int, optional, default: 100 The batch size for SGD. name: string, optional, default: 'IBRP' 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 (U and V are not None). init_params: dictionary, optional, default: None List of initial parameters, e.g., init_params = {'U':U, 'V':V} \ please see below the definition of U and V. U: csc_matrix, shape (n_users,k) The user latent factors, optional initialization via init_params. V: csc_matrix, shape (n_items,k) The item latent factors, optional initialization via init_params. References ---------- * Le, D. D., & Lauw, H. W. (2017, November). Indexable Bayesian personalized ranking for efficient top-k recommendation.\ In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1389-1398). ACM. """ def __init__(self, k=20, max_iter=100, learning_rate = 0.05, lamda = 0.001, batch_size = 100, name="ibpr",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.name = name self.learning_rate = learning_rate self.lamda = lamda self.batch_size = batch_size self.U = init_params['U'] # matrix of user factors self.V = init_params['V'] # 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). """ #change the data to original user Id item Id and rating format #X = X.tocoo() # convert sparse matrix to COOrdiante format #data = np.ndarray(shape=(len(X.data), 3), dtype=float) #data[:, 0] = X.row #data[:, 1] = X.col #data[:, 2] = X.data print('Learning...') res = ibpr(X, k=self.k, n_epochs=self.max_iter,lamda = self.lamda, learning_rate= self.learning_rate, batch_size = self.batch_size, init_params=self.init_params) self.U = res['U'] self.V = res['V'] print('Learning completed')
[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.U[user_index, :].dot(self.V.T) else: user_pred = self.U[user_index, :].dot(self.V[item_indexes,:].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