Source code for cornac.models.cdl.recom_cdl

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

"""
@author: Trieu Thi Ly Ly 
"""

from ..recommender import Recommender
from .cdl import *

[docs]class CDL(Recommender): """Collaborative Deep Learning. Parameters ---------- k: int, optional, default: 50 The dimension of the latent factors. max_iter: int, optional, default: 100 Maximum number of iterations or the number of epochs for SGD. text_information:ndarray, shape (n_items, n_vocabularies), optional, default:None Bag-of-words features of items autoencoder_structure:array, optional, default: [200] The number of neurons of encoder/ decoder layer for SDAE learning_rate: float, optional, default: 0.001 The learning rate for AdamOptimizer. lambda_u: float, optional, default: 0.1 The regularization parameter for users. lambda_v: float, optional, default: 10 The regularization parameter for items. lambda_w: float, optional, default: 0.1 The regularization parameter for SDAE weights. lambda_n: float, optional, default: 1000 The regularization parameter for SDAE output. a: float, optional, default: 1 The confidence of observed ratings. b: float, optional, default: 0.01 The confidence of unseen ratings. autoencoder_corruption: float, optional, default: 0.3 The corruption ratio for SDAE. keep_prob: float, optional, default: 1.0 The probability that each element is kept in dropout of SDAE. batch_size: int, optional, default: 100 The batch size for SGD. name: string, optional, default: 'CDL' 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: ndarray, shape (n_users,k) The user latent factors, optional initialization via init_params. V: ndarray, shape (n_items,k) The item latent factors, optional initialization via init_params. References ---------- * Hao Wang, Naiyan Wang, Dit-Yan Yeung. CDL: Collaborative Deep Learning for Recommender Systems. In : SIGKDD. 2015. p. 1235-1244. """ def __init__(self, k=50, text_information = None, autoencoder_structure = None ,lambda_u = 0.1, lambda_v = 0.01,lambda_w = 0.01, lambda_n = 0.01, a = 1, b = 0.01, autoencoder_corruption = 0.3, learning_rate=0.001, keep_prob = 1.0, batch_size = 100, max_iter=100, name = "CDL",trainable = True, init_params = None): Recommender.__init__(self,name=name, trainable = trainable) self.k = k self.text_information = text_information self.lambda_u = lambda_u self.lambda_v = lambda_v self.lambda_w = lambda_w self.lambda_n = lambda_n self.a = a self.b = b self.autoencoder_corruption = autoencoder_corruption self.keep_prob = keep_prob self.learning_rate = learning_rate self.name = name self.init_params = init_params self.max_iter = max_iter self.autoencoder_structure = autoencoder_structure 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 (training data), in a scipy sparse format\ (e.g., csc_matrix). (e.g., csc_matrix). """ if self.trainable: res = cdl(X, self.text_information, self.autoencoder_structure, k = self.k, lambda_u = self.lambda_u, lambda_v = self.lambda_v, lambda_w = self.lambda_w, lambda_n = self.lambda_n , a = self.a, b = self.b, autoencoder_corruption = self.autoencoder_corruption, n_epochs=self.max_iter, learning_rate= self.learning_rate, keep_prob = self.keep_prob, batch_size = self.batch_size, init_params = self.init_params) self.U = res['U'] self.V = 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.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, but keep thinking about another possible format 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