# Copyright 2023 The Cornac Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from ..recommender import NextBasketRecommender
[docs]
class DNNTSP(NextBasketRecommender):
"""Deep Neural Network for Temporal Sets Prediction (DNNTSP).
Parameters
----------
name: string, default: 'DNNTSP'
The name of the recommender model.
emb_dim: int, optional, default: 32
Number of hidden factors
loss_type: string, optional, default: "bpr"
Loss type. Including
"bpr": BPRLoss
"mse": MSELoss
"weight_mse": WeightMSELoss
"multi_label_soft_margin": MultiLabelSoftMarginLoss
optimizer: string, optional, default: "adam"
Optimizer
lr: string, optional, default: 0.001
Learning rate
weight_decay: float, optional, default: 0
Weight decay for adaptive optimizer
n_epochs: int, optional, default: 100
Number of epochs
batch_size: int, optional, default: 64
Batch size
device: string, optional, default: "cpu"
Device for learning and evaluation. Using cpu as default.
Use "cuda:0" for using gpu.
trainable: boolean, optional, default: True
When False, the model will not be re-trained, and input of pre-trained parameters are required.
verbose: boolean, optional, default: True
When True, running logs are displayed.
seed: int, optional, default: None
Random seed
References
----------
Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Hui Xiong, and Weifeng Lv. 2020.
Predicting Temporal Sets with Deep Neural Networks.
In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '20). Association for Computing Machinery, New York, NY, USA, 1083–1091. https://doi.org/10.1145/3394486.3403152
"""
def __init__(
self,
name="DNNTSP",
emb_dim=32,
loss_type="bpr",
optimizer="adam",
lr=0.001,
weight_decay=0,
n_epochs=100,
batch_size=64,
device="cpu",
trainable=True,
verbose=False,
seed=None,
):
super().__init__(name=name, trainable=trainable, verbose=verbose)
self.emb_dim = emb_dim
self.loss_type = loss_type
self.optimizer = optimizer
self.lr = lr
self.weight_decay = weight_decay
self.n_epochs = n_epochs
self.batch_size = batch_size
self.seed = seed
self.device = device
[docs]
def fit(self, train_set, val_set=None):
super().fit(train_set=train_set, val_set=val_set)
from .dnntsp import TemporalSetPrediction, learn
self.model = TemporalSetPrediction(
n_items=self.total_items,
emb_dim=self.emb_dim,
seed=self.seed,
)
learn(
model=self.model,
train_set=train_set,
total_items=self.total_items,
val_set=val_set,
n_epochs=self.n_epochs,
batch_size=self.batch_size,
lr=self.lr,
weight_decay=self.weight_decay,
loss_type=self.loss_type,
optimizer=self.optimizer,
device=self.device,
verbose=self.verbose,
)
return self
[docs]
def score(self, user_idx, history_baskets, **kwargs):
from .dnntsp import transform_data
self.model.eval()
(g, nodes_feature, edges_weight, lengths, nodes, _) = transform_data(
[history_baskets],
item_embedding=self.model.embedding_matrix,
total_items=self.total_items,
device=self.device,
is_test=True,
)
preds = self.model(g, nodes_feature, edges_weight, lengths, nodes)
return preds.cpu().detach().numpy()
