# -*- coding: utf-8 -*-
"""
@author: Dung D. Le (Andrew) <ddle.2015@smu.edu.sg>
"""
import numpy as np
from .coe import *
from ..recommender import Recommender
[docs]class COE(Recommender):
"""Collaborative Ordinal Embedding.
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. (2016, June). Euclidean co-embedding of ordinal data for multi-type visualization.\
In Proceedings of the 2016 SIAM International Conference on Data Mining (pp. 396-404). Society for Industrial and Applied Mathematics.
"""
def __init__(self, k=20, max_iter=100, learning_rate = 0.05, lamda = 0.001, batch_size = 1000, name="coe",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 = coe(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')
#get prefiction for a single user (predictions for one user at a time for efficiency purposes)
#predictions are not stored for the same efficiency reasons"""
[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 = np.sum(np.abs(self.V - self.U[user_index, :])**2,axis=-1)**(1./2)
else:
user_pred = np.sum(np.abs(self.V[item_indexes,] - self.U[user_index, :])**2,axis=-1)**(1./2)
# 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