# -*- coding: utf-8 -*-
"""
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
"""
import numpy as np
from .ibpr import *
from ..recommender import Recommender
from ...exception import ScoreException
[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).
verbose: boolean, optional, default: False
When True, some running logs are displayed.
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,
verbose=False, init_params = None):
Recommender.__init__(self, name=name, trainable=trainable, verbose=verbose)
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, 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
#change the data to original user Id item Id and rating format
cooX = X.tocoo()
data = np.ndarray(shape=(len(cooX.data), 3), dtype=float)
data[:, 0] = cooX.row
data[:, 1] = cooX.col
data[:, 2] = cooX.data
if self.verbose:
print('Learning...')
res = ibpr(X, data, 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 = np.asarray(res['U'])
self.V = np.asarray(res['V'])
if self.verbose:
print('Learning completed')
[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:
if self.train_set.is_unk_user(user_id):
raise ScoreException("Can't make score prediction for (user_id=%d)" % user_id)
known_item_scores = self.V.dot(self.U[user_id, :])
return known_item_scores
else:
if self.train_set.is_unk_user(user_id) or self.train_set.is_unk_item(item_id):
raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_id, item_id))
user_pred = self.V[item_id, :].dot(self.U[user_id, :])
return user_pred