Source code for cornac.models.hpf.recom_hpf

# -*- coding: utf-8 -*-

"""
@author: Aghiles Salah <asalah@smu.edu.sg>
"""

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 is already \ pre-trained (Theta and Beta are not None). 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. 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={'G_s': None, 'G_r': None, 'L_s': None, 'L_r': 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))
[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