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Lê Trung Hoàng authored
* Add DNNTSP Model * refactor code * refactor code * Fix scoring function * refactor code * Add random seed * refactor code * refactor code * Fix weight_mse and multi_label loss * Update multi_label loss using sum reduction --------- Co-authored-by:
Quoc-Tuan Truong <tqtg@users.noreply.github.com>
Unverified85ce38cf
dnntsp_tafeng.py 1.60 KiB
# 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,
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# See the License for the specific language governing permissions and
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# ============================================================================
"""Example of Predicting Temporal Sets with Deep Neural Networks (DNNTSP)"""
import cornac
from cornac.eval_methods import NextBasketEvaluation
from cornac.metrics import NDCG, HitRatio, Recall
from cornac.models import DNNTSP
data = cornac.datasets.tafeng.load_basket(
reader=cornac.data.Reader(
min_basket_size=3, max_basket_size=50, min_basket_sequence=2
)
)
next_basket_eval = NextBasketEvaluation(
data=data, fmt="UBITJson", test_size=0.2, val_size=0.08, seed=123, verbose=True
)
models = [
DNNTSP(
emb_dim=32,
loss_type="bpr",
optimizer="adam",
lr=0.001,
weight_decay=0,
batch_size=64,
n_epochs=10,
device="cuda:0",
verbose=True,
)
]
metrics = [
Recall(k=10),
Recall(k=50),
NDCG(k=10),
NDCG(k=50),
HitRatio(k=10),
HitRatio(k=50),
]
cornac.Experiment(eval_method=next_basket_eval, models=models, metrics=metrics).run()