def __init__(self):
super(BPR, self).__init__()
train_matrix = self.dataset.train_matrix
self.users_num, self.items_num = train_matrix.shape
self.factors_num = self.config["factors_num"]
self.lr = self.config["lr"]
self.reg = self.config["reg"]
self.epochs = self.config["epochs"]
self.batch_size = self.config["batch_size"]
self.user_pos_train = csr_to_user_dict(train_matrix)
self.all_items = np.arange(self.items_num)
self.train_matrix = train_matrix
self._build_model()
self.sess.run(tf.global_variables_initializer())
def __init__(self):
super(CDAE, self).__init__()
self.lr = self.config["lr"]
self.reg = self.config["reg"]
self.batch_size = self.config["batch_size"]
self.hidden_units = self.config["hidden_units"]
self.epochs = self.config["epochs"]
self.neg_num = self.config["neg_num"]
self.corrupt_prob = self.config["corrupt_prob"]
self.loss_function = self.config["loss"]
self.train_matrix = self.dataset.train_matrix.copy()
# convert explicit data to implicit data
self.train_matrix.data[:] = 1
self.users_num, self.items_num = self.train_matrix.shape
self.user_pos_train = csr_to_user_dict(self.train_matrix)
self.all_items = np.arange(self.items_num)
self._build_model()
self.sess.run(tf.global_variables_initializer())
def __init__(self, sess, config, dataset, evaluator):
super(IRGAN, self).__init__(sess, config, dataset, evaluator)
train_matrix = dataset.train_matrix
self.users_num, self.items_num = train_matrix.shape
self.factors_num = config["factors_num"]
self.lr = config["lr"]
self.g_reg = config["g_reg"]
self.d_reg = config["d_reg"]
self.epochs = config["epochs"]
self.batch_size = config["batch_size"]
self.d_tau = config["d_tau"]
self.pretrain_file = config["pretrain_file"]
self.user_pos_train = csr_to_user_dict(train_matrix)
self.all_items = np.arange(self.items_num)
self.evaluator = evaluator
self._build_model()
self.sess = sess
self.sess.run(tf.global_variables_initializer())
def __init__(self, sess, config, dataset, evaluator):
super(BPR, self).__init__(sess, config, dataset, evaluator)
train_matrix = dataset.train_matrix
self.users_num, self.items_num = train_matrix.shape
self.factors_num = config["factors_num"]
self.lr = config["lr"]
self.reg = config["reg"]
self.epochs = config["epochs"]
self.batch_size = config["batch_size"]
self.user_pos_train = csr_to_user_dict(train_matrix)
self.all_items = np.arange(self.items_num)
self.evaluator = evaluator
self.mf = MatrixFactorization(self.users_num,
self.items_num,
self.factors_num,
name=self.__class__.__name__)
self._build_model()
self.sess = sess
self.sess.run(tf.global_variables_initializer())
def __init__(self, train_matrix, test_matrix, top_k=50):
super(FoldOutEvaluator, self).__init__()
self.top_k = top_k
self.user_pos_train = csr_to_user_dict(train_matrix)
self.user_pos_test = csr_to_user_dict(test_matrix)