# -*- coding: utf-8 -*-
"""
@author: Trieu Thi Ly Ly
"""
from ..recommender import Recommender
from .cdl import *
import scipy.sparse as sp
import numpy as np
from ...exception import ScoreException
[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, verbose=False, init_params = None):
Recommender.__init__(self,name=name, trainable = trainable, verbose=verbose)
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, 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 = self.train_set.matrix
X = sp.csr_matrix(X)
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')
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 an item.
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.U[user_id, :].dot(self.V.T)
else:
user_pred = self.U[user_id,:].dot(self.V[user_id,:].T)
user_pred = np.array(user_pred, dtype='float64').flatten()
return user_pred