Repository: RUCAIBox/RecBole-GNN Branch: main Commit: 632ef8885899 Files: 74 Total size: 361.6 KB Directory structure: gitextract_g58jlyza/ ├── .github/ │ ├── ISSUE_TEMPLATE/ │ │ ├── bug_report.md │ │ ├── bug_report_CN.md │ │ ├── feature_request.md │ │ └── feature_request_CN.md │ └── workflows/ │ └── python-package.yml ├── .gitignore ├── LICENSE ├── README.md ├── recbole_gnn/ │ ├── config.py │ ├── data/ │ │ ├── __init__.py │ │ ├── dataloader.py │ │ ├── dataset.py │ │ └── transform.py │ ├── model/ │ │ ├── abstract_recommender.py │ │ ├── general_recommender/ │ │ │ ├── __init__.py │ │ │ ├── directau.py │ │ │ ├── hmlet.py │ │ │ ├── lightgcl.py │ │ │ ├── lightgcn.py │ │ │ ├── ncl.py │ │ │ ├── ngcf.py │ │ │ ├── sgl.py │ │ │ ├── simgcl.py │ │ │ ├── ssl4rec.py │ │ │ └── xsimgcl.py │ │ ├── layers.py │ │ ├── sequential_recommender/ │ │ │ ├── __init__.py │ │ │ ├── gcegnn.py │ │ │ ├── gcsan.py │ │ │ ├── lessr.py │ │ │ ├── niser.py │ │ │ ├── sgnnhn.py │ │ │ ├── srgnn.py │ │ │ └── tagnn.py │ │ └── social_recommender/ │ │ ├── __init__.py │ │ ├── diffnet.py │ │ ├── mhcn.py │ │ └── sept.py │ ├── properties/ │ │ ├── model/ │ │ │ ├── DiffNet.yaml │ │ │ ├── DirectAU.yaml │ │ │ ├── GCEGNN.yaml │ │ │ ├── GCSAN.yaml │ │ │ ├── HMLET.yaml │ │ │ ├── LESSR.yaml │ │ │ ├── LightGCL.yaml │ │ │ ├── LightGCN.yaml │ │ │ ├── MHCN.yaml │ │ │ ├── NCL.yaml │ │ │ ├── NGCF.yaml │ │ │ ├── NISER.yaml │ │ │ ├── SEPT.yaml │ │ │ ├── SGL.yaml │ │ │ ├── SGNNHN.yaml │ │ │ ├── SRGNN.yaml │ │ │ ├── SSL4REC.yaml │ │ │ ├── SimGCL.yaml │ │ │ ├── TAGNN.yaml │ │ │ └── XSimGCL.yaml │ │ └── quick_start_config/ │ │ ├── sequential_base.yaml │ │ └── social_base.yaml │ ├── quick_start.py │ ├── trainer.py │ └── utils.py ├── results/ │ ├── README.md │ ├── general/ │ │ └── ml-1m.md │ ├── sequential/ │ │ └── diginetica.md │ └── social/ │ └── lastfm.md ├── run_hyper.py ├── run_recbole_gnn.py ├── run_test.sh └── tests/ ├── test_data/ │ └── test/ │ ├── test.inter │ └── test.net ├── test_model.py └── test_model.yaml ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report.md ================================================ --- name: Bug report about: Create a report to help us improve title: "[\U0001F41BBUG] Describe your problem in one sentence." labels: bug assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. extra yaml file 2. your code 3. script for running **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. **Colab Links** If applicable, add links to Colab or other Jupyter laboratory platforms that can reproduce the bug. **Desktop (please complete the following information):** - OS: [e.g. Linux, macOS or Windows] - RecBole Version [e.g. 0.1.0] - Python Version [e.g. 3.79] - PyTorch Version [e.g. 1.60] - cudatoolkit Version [e.g. 9.2, none] ================================================ FILE: .github/ISSUE_TEMPLATE/bug_report_CN.md ================================================ --- name: Bug 报告 about: 提交一份 bug 报告，帮助 RecBole-GNN 变得更好 title: "[\U0001F41BBUG] 用一句话描述您的问题。" labels: bug assignees: '' --- **描述这个 bug** 对 bug 作一个清晰简明的描述。 **如何复现** 复现这个 bug 的步骤： 1. 您引入的额外 yaml 文件 2. 您的代码 3. 您的运行脚本 **预期** 对您的预期作清晰简明的描述。 **屏幕截图** 添加屏幕截图以帮助解释您的问题。（可选） **链接** 添加能够复现 bug 的代码链接，如 Colab 或者其他在线 Jupyter 平台。（可选） **实验环境（请补全下列信息）：** - 操作系统: [如 Linux, macOS 或 Windows] - RecBole 版本 [如 0.1.0] - Python 版本 [如 3.79] - PyTorch 版本 [如 1.60] - cudatoolkit 版本 [如 9.2, none] ================================================ FILE: .github/ISSUE_TEMPLATE/feature_request.md ================================================ --- name: Feature request about: Suggest an idea for this project title: "[\U0001F4A1SUG] Description of what you want to happen in one sentence" labels: enhancement assignees: '' --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. ================================================ FILE: .github/ISSUE_TEMPLATE/feature_request_CN.md ================================================ --- name: 请求添加新功能 about: 提出一个关于本项目新功能/新特性的建议 title: "[\U0001F4A1SUG] 一句话描述您希望新增的功能或特性" labels: enhancement assignees: '' --- **您希望添加的功能是否与某个问题相关？** 关于这个问题的简洁清晰的描述，例如，当 [...] 时，我总是很沮丧。 **描述您希望的解决方案** 关于解决方案的简洁清晰的描述。 **描述您考虑的替代方案** 关于您考虑的，能实现这个功能的其他替代方案的简洁清晰的描述。 **其他** 您可以添加其他任何的资料、链接或者屏幕截图，以帮助我们理解这个新功能。 ================================================ FILE: .github/workflows/python-package.yml ================================================ name: RecBole-GNN tests # Controls when the action will run. on: # Triggers the workflow on push or pull request events but only for the master branch push: pull_request: # Allows you to run this workflow manually from the Actions tab workflow_dispatch: jobs: build: runs-on: ubuntu-latest strategy: matrix: python-version: [3.9] torch-version: [2.0.0] defaults: run: shell: bash -l {0} steps: - uses: actions/checkout@v2 - name: Setup Miniconda uses: conda-incubator/setup-miniconda@v2 with: python-version: ${{ matrix.python-version }} channels: conda-forge channel-priority: true auto-activate-base: true # install setuptools as a interim solution for bugs in PyTorch 1.10.2 (#69904) - name: Install dependencies run: | python -m pip install --upgrade pip pip install pytest pip install dgl pip install torch==${{ matrix.torch-version}}+cpu -f https://download.pytorch.org/whl/torch_stable.html pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-${{ matrix.torch-version }}+cpu.html pip install recbole==1.1.1 conda install -c conda-forge faiss-cpu # Use "python -m pytest" instead of "pytest" to fix imports - name: Test model run: | python -m pytest -v tests/test_model.py ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # RecBole log_tensorboard/ saved/ dataset/ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2021 RUCAIBox Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # RecBole-GNN ![](asset/recbole-gnn-logo.png) ----- *Updates*: * [Oct 29, 2023] Add [SSL4Rec](https://github.com/RUCAIBox/RecBole-GNN/blob/main/recbole_gnn/model/general_recommender/ssl4rec.py). (https://github.com/RUCAIBox/RecBole-GNN/pull/76, by [@downeykking](https://github.com/downeykking)) * [Oct 23, 2023] Add sparse tensor support, accelerating LightGCN & NGCF by ~5x, with 1/6 GPU memories. (https://github.com/RUCAIBox/RecBole-GNN/pull/75, by [@downeykking](https://github.com/downeykking)) * [Oct 20, 2023] Add [DirectAU](https://github.com/RUCAIBox/RecBole-GNN/blob/main/recbole_gnn/model/general_recommender/directau.py). (https://github.com/RUCAIBox/RecBole-GNN/pull/74, by [@downeykking](https://github.com/downeykking)) * [Oct 16, 2023] Add [XSimGCL](https://github.com/RUCAIBox/RecBole-GNN/blob/main/recbole_gnn/model/general_recommender/xsimgcl.py). (https://github.com/RUCAIBox/RecBole-GNN/pull/72, by [@downeykking](https://github.com/downeykking)) * [Apr 12, 2023] Add [LightGCL](https://github.com/RUCAIBox/RecBole-GNN/blob/main/recbole_gnn/model/general_recommender/lightgcl.py). (https://github.com/RUCAIBox/RecBole-GNN/pull/63, by [@wending0417](https://github.com/wending0417)) * [Oct 29, 2022] Adaptation to RecBole 1.1.1. (https://github.com/RUCAIBox/RecBole-GNN/pull/53) * [Jun 15, 2022] Add [MultiBehaviorDataset](https://github.com/RUCAIBox/RecBole-GNN/blob/8c61463451b294dce9af2d1939a5e054f7955e0f/recbole_gnn/data/dataset.py#L145). (https://github.com/RUCAIBox/RecBole-GNN/pull/43, by [@Tokkiu](https://github.com/Tokkiu)) ----- **RecBole-GNN** is a library built upon [PyTorch](https://pytorch.org) and [RecBole](https://github.com/RUCAIBox/RecBole) for reproducing and developing recommendation algorithms based on graph neural networks (GNNs). Our library includes algorithms covering three major categories: * **General Recommendation** with user-item interaction graphs; * **Sequential Recommendation** with session/sequence graphs; * **Social Recommendation** with social networks. ![](asset/arch.png) ## Highlights * **Easy-to-use and unified API**: Our library shares unified API and input (atomic files) as RecBole. * **Efficient and reusable graph processing**: We provide highly efficient and reusable basic datasets, dataloaders and layers for graph processing and learning. * **Extensive graph library**: Graph neural networks from widely-used library like [PyG](https://github.com/pyg-team/pytorch_geometric) are incorporated. Recently proposed graph algorithms can be easily equipped and compared with existing methods. ## Requirements ``` recbole==1.1.1 pyg>=2.0.4 pytorch>=1.7.0 python>=3.7.0 ``` > If you are using `recbole==1.0.1`, please refer to our `recbole1.0.1` branch [[link]](https://github.com/hyp1231/RecBole-GNN/tree/recbole1.0.1). ## Quick-Start With the source code, you can use the provided script for initial usage of our library: ```bash python run_recbole_gnn.py ``` If you want to change the models or datasets, just run the script by setting additional command parameters: ```bash python run_recbole_gnn.py -m [model] -d [dataset] ``` ## Implemented Models We list currently supported models according to category: **General Recommendation**: * **[NGCF](recbole_gnn/model/general_recommender/ngcf.py)** from Wang *et al.*: [Neural Graph Collaborative Filtering](https://arxiv.org/abs/1905.08108) (SIGIR 2019). * **[LightGCN](recbole_gnn/model/general_recommender/lightgcn.py)** from He *et al.*: [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/abs/2002.02126) (SIGIR 2020). * **[SSL4Rec](recbole_gnn/model/general_recommender/ssl4rec.py)** from Yao *et al.*: [Self-supervised Learning for Large-scale Item Recommendations](https://arxiv.org/abs/2007.12865) (CIKM 2021). * **[SGL](recbole_gnn/model/general_recommender/sgl.py)** from Wu *et al.*: [Self-supervised Graph Learning for Recommendation](https://arxiv.org/abs/2010.10783) (SIGIR 2021). * **[HMLET](recbole_gnn/model/general_recommender/hmlet.py)** from Kong *et al.*: [Linear, or Non-Linear, That is the Question!](https://arxiv.org/abs/2111.07265) (WSDM 2022). * **[NCL](recbole_gnn/model/general_recommender/ncl.py)** from Lin *et al.*: [Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning](https://arxiv.org/abs/2202.06200) (TheWebConf 2022). * **[DirectAU](recbole_gnn/model/general_recommender/directau.py)** from Wang *et al.*: [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://arxiv.org/abs/2206.12811) (KDD 2022). * **[SimGCL](recbole_gnn/model/general_recommender/simgcl.py)** from Yu *et al.*: [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://arxiv.org/abs/2112.08679) (SIGIR 2022). * **[XSimGCL](recbole_gnn/model/general_recommender/xsimgcl.py)** from Yu *et al.*: [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://arxiv.org/abs/2209.02544) (TKDE 2023). * **[LightGCL](recbole_gnn/model/general_recommender/lightgcl.py)** from Cai *et al.*: [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation ](https://arxiv.org/abs/2302.08191) (ICLR 2023). **Sequential Recommendation**: * **[SR-GNN](recbole_gnn/model/sequential_recommender/srgnn.py)** from Wu *et al.*: [Session-based Recommendation with Graph Neural Networks](https://arxiv.org/abs/1811.00855) (AAAI 2019). * **[GC-SAN](recbole_gnn/model/sequential_recommender/gcsan.py)** from Xu *et al.*: [Graph Contextualized Self-Attention Network for Session-based Recommendation](https://www.ijcai.org/proceedings/2019/547) (IJCAI 2019). * **[NISER+](recbole_gnn/model/sequential_recommender/niser.py)** from Gupta *et al.*: [NISER: Normalized Item and Session Representations to Handle Popularity Bias](https://arxiv.org/abs/1909.04276) (GRLA, CIKM 2019 workshop). * **[LESSR](recbole_gnn/model/sequential_recommender/lessr.py)** from Chen *et al.*: [Handling Information Loss of Graph Neural Networks for Session-based Recommendation](https://dl.acm.org/doi/10.1145/3394486.3403170) (KDD 2020). * **[TAGNN](recbole_gnn/model/sequential_recommender/tagnn.py)** from Yu *et al.*: [TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation](https://arxiv.org/abs/2005.02844) (SIGIR 2020 short). * **[GCE-GNN](recbole_gnn/model/sequential_recommender/gcegnn.py)** from Wang *et al.*: [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://arxiv.org/abs/2106.05081) (SIGIR 2020). * **[SGNN-HN](recbole_gnn/model/sequential_recommender/sgnnhn.py)** from Pan *et al.*: [Star Graph Neural Networks for Session-based Recommendation](https://dl.acm.org/doi/10.1145/3340531.3412014) (CIKM 2020). **Social Recommendation**: > Note that datasets for social recommendation methods can be downloaded from [Social-Datasets](https://github.com/Sherry-XLL/Social-Datasets). * **[DiffNet](recbole_gnn/model/social_recommender/diffnet.py)** from Wu *et al.*: [A Neural Influence Diffusion Model for Social Recommendation](https://arxiv.org/abs/1904.10322) (SIGIR 2019). * **[MHCN](recbole_gnn/model/social_recommender/mhcn.py)** from Yu *et al.*: [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://doi.org/10.1145/3442381.3449844) (WWW 2021). * **[SEPT](recbole_gnn/model/social_recommender/sept.py)** from Yu *et al.*: [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://doi.org/10.1145/3447548.3467340) (KDD 2021). ## Result ### Leaderboard We carefully tune the hyper-parameters of the implemented models of each research field and release the corresponding leaderboards for reference: - **General** recommendation on `MovieLens-1M` dataset [[link]](results/general/ml-1m.md); - **Sequential** recommendation on `Diginetica` dataset [[link]](results/sequential/diginetica.md); - **Social** recommendation on `LastFM` dataset [[link]](results/social/lastfm.md); ### Efficiency With the sequential/session graphs preprocessing technique, as well as efficient GNN layers, we speed up the training process of our sequential recommenders a lot. ## The Team RecBole-GNN is initially developed and maintained by members from [RUCAIBox](http://aibox.ruc.edu.cn/), the main developers are Yupeng Hou ([@hyp1231](https://github.com/hyp1231)), Lanling Xu ([@Sherry-XLL](https://github.com/Sherry-XLL)) and Changxin Tian ([@ChangxinTian](https://github.com/ChangxinTian)). We also thank Xinzhou ([@downeykking](https://github.com/downeykking)), Wanli ([@wending0417](https://github.com/wending0417)), and Jingqi ([@Tokkiu](https://github.com/Tokkiu)) for their great contribution! ❤️ ## Acknowledgement The implementation is based on the open-source recommendation library [RecBole](https://github.com/RUCAIBox/RecBole). RecBole-GNN is part of [RecBole 2.0](https://github.com/RUCAIBox/RecBole2.0) now! Please cite the following paper as the reference if you use our code or processed datasets. ```bibtex @inproceedings{zhao2022recbole2, author={Wayne Xin Zhao and Yupeng Hou and Xingyu Pan and Chen Yang and Zeyu Zhang and Zihan Lin and Jingsen Zhang and Shuqing Bian and Jiakai Tang and Wenqi Sun and Yushuo Chen and Lanling Xu and Gaowei Zhang and Zhen Tian and Changxin Tian and Shanlei Mu and Xinyan Fan and Xu Chen and Ji-Rong Wen}, title={RecBole 2.0: Towards a More Up-to-Date Recommendation Library}, booktitle = {{CIKM}}, year={2022} } @inproceedings{zhao2021recbole, author = {Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Yushuo Chen and Xingyu Pan and Kaiyuan Li and Yujie Lu and Hui Wang and Changxin Tian and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji{-}Rong Wen}, title = {RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms}, booktitle = {{CIKM}}, pages = {4653--4664}, publisher = {{ACM}}, year = {2021} } ``` ================================================ FILE: recbole_gnn/config.py ================================================ import os import recbole from recbole.config.configurator import Config as RecBole_Config from recbole.utils import ModelType as RecBoleModelType from recbole_gnn.utils import get_model, ModelType class Config(RecBole_Config): def __init__(self, model=None, dataset=None, config_file_list=None, config_dict=None): """ Args: model (str/AbstractRecommender): the model name or the model class, default is None, if it is None, config will search the parameter 'model' from the external input as the model name or model class. dataset (str): the dataset name, default is None, if it is None, config will search the parameter 'dataset' from the external input as the dataset name. config_file_list (list of str): the external config file, it allows multiple config files, default is None. config_dict (dict): the external parameter dictionaries, default is None. """ if recbole.__version__ == "1.1.1": self.compatibility_settings() super(Config, self).__init__(model, dataset, config_file_list, config_dict) def compatibility_settings(self): import numpy as np np.bool = np.bool_ np.int = np.int_ np.float = np.float_ np.complex = np.complex_ np.object = np.object_ np.str = np.str_ np.long = np.int_ np.unicode = np.unicode_ def _get_model_and_dataset(self, model, dataset): if model is None: try: model = self.external_config_dict['model'] except KeyError: raise KeyError( 'model need to be specified in at least one of the these ways: ' '[model variable, config file, config dict, command line] ' ) if not isinstance(model, str): final_model_class = model final_model = model.__name__ else: final_model = model final_model_class = get_model(final_model) if dataset is None: try: final_dataset = self.external_config_dict['dataset'] except KeyError: raise KeyError( 'dataset need to be specified in at least one of the these ways: ' '[dataset variable, config file, config dict, command line] ' ) else: final_dataset = dataset return final_model, final_model_class, final_dataset def _load_internal_config_dict(self, model, model_class, dataset): super()._load_internal_config_dict(model, model_class, dataset) current_path = os.path.dirname(os.path.realpath(__file__)) model_init_file = os.path.join(current_path, './properties/model/' + model + '.yaml') quick_start_config_path = os.path.join(current_path, './properties/quick_start_config/') sequential_base_init = os.path.join(quick_start_config_path, 'sequential_base.yaml') social_base_init = os.path.join(quick_start_config_path, 'social_base.yaml') if os.path.isfile(model_init_file): config_dict = self._update_internal_config_dict(model_init_file) self.internal_config_dict['MODEL_TYPE'] = model_class.type if self.internal_config_dict['MODEL_TYPE'] == RecBoleModelType.SEQUENTIAL: self._update_internal_config_dict(sequential_base_init) if self.internal_config_dict['MODEL_TYPE'] == ModelType.SOCIAL: self._update_internal_config_dict(social_base_init) ================================================ FILE: recbole_gnn/data/__init__.py ================================================ ================================================ FILE: recbole_gnn/data/dataloader.py ================================================ import numpy as np import torch from recbole.data.interaction import cat_interactions from recbole.data.dataloader.general_dataloader import TrainDataLoader, NegSampleEvalDataLoader, FullSortEvalDataLoader from recbole_gnn.data.transform import gnn_construct_transform class CustomizedTrainDataLoader(TrainDataLoader): def __init__(self, config, dataset, sampler, shuffle=False): super().__init__(config, dataset, sampler, shuffle=shuffle) if config['gnn_transform'] is not None: self.transform = gnn_construct_transform(config) class CustomizedNegSampleEvalDataLoader(NegSampleEvalDataLoader): def __init__(self, config, dataset, sampler, shuffle=False): super().__init__(config, dataset, sampler, shuffle=shuffle) if config['gnn_transform'] is not None: self.transform = gnn_construct_transform(config) def collate_fn(self, index): index = np.array(index) if ( self.neg_sample_args["distribution"] != "none" and self.neg_sample_args["sample_num"] != "none" ): uid_list = self.uid_list[index] data_list = [] idx_list = [] positive_u = [] positive_i = torch.tensor([], dtype=torch.int64) for idx, uid in enumerate(uid_list): index = self.uid2index[uid] data_list.append(self._neg_sampling(self._dataset[index])) idx_list += [idx for i in range(self.uid2items_num[uid] * self.times)] positive_u += [idx for i in range(self.uid2items_num[uid])] positive_i = torch.cat( (positive_i, self._dataset[index][self.iid_field]), 0 ) cur_data = cat_interactions(data_list) idx_list = torch.from_numpy(np.array(idx_list)).long() positive_u = torch.from_numpy(np.array(positive_u)).long() return self.transform(self._dataset, cur_data), idx_list, positive_u, positive_i else: data = self._dataset[index] transformed_data = self.transform(self._dataset, data) cur_data = self._neg_sampling(transformed_data) return cur_data, None, None, None class CustomizedFullSortEvalDataLoader(FullSortEvalDataLoader): def __init__(self, config, dataset, sampler, shuffle=False): super().__init__(config, dataset, sampler, shuffle=shuffle) if config['gnn_transform'] is not None: self.transform = gnn_construct_transform(config) ================================================ FILE: recbole_gnn/data/dataset.py ================================================ import os import torch import numpy as np import pandas as pd from tqdm import tqdm from torch_geometric.nn.conv.gcn_conv import gcn_norm from torch_geometric.utils import degree try: from torch_sparse import SparseTensor is_sparse = True except ImportError: is_sparse = False from recbole.data.dataset import SequentialDataset from recbole.data.dataset import Dataset as RecBoleDataset from recbole.utils import set_color, FeatureSource import recbole import pickle from recbole.utils import ensure_dir class GeneralGraphDataset(RecBoleDataset): def __init__(self, config): super().__init__(config) if recbole.__version__ == "1.1.1": def save(self): """Saving this :class:`Dataset` object to :attr:`config['checkpoint_dir']`.""" save_dir = self.config["checkpoint_dir"] ensure_dir(save_dir) file = os.path.join(save_dir, f'{self.config["dataset"]}-{self.__class__.__name__}.pth') self.logger.info( set_color("Saving filtered dataset into ", "pink") + f"[{file}]" ) with open(file, "wb") as f: pickle.dump(self, f) @staticmethod def edge_index_to_adj_t(edge_index, edge_weight, m_num_nodes, n_num_nodes): adj = SparseTensor(row=edge_index[0], col=edge_index[1], value=edge_weight, sparse_sizes=(m_num_nodes, n_num_nodes)) return adj.t() def get_norm_adj_mat(self, enable_sparse=False): self.is_sparse = is_sparse r"""Get the normalized interaction matrix of users and items. Construct the square matrix from the training data and normalize it using the laplace matrix. .. math:: A_{hat} = D^{-0.5} \times A \times D^{-0.5} Returns: The normalized interaction matrix in Tensor. """ row = self.inter_feat[self.uid_field] col = self.inter_feat[self.iid_field] + self.user_num edge_index1 = torch.stack([row, col]) edge_index2 = torch.stack([col, row]) edge_index = torch.cat([edge_index1, edge_index2], dim=1) edge_weight = torch.ones(edge_index.size(1)) num_nodes = self.user_num + self.item_num if enable_sparse: if not is_sparse: self.logger.warning( "Import `torch_sparse` error, please install corrsponding version of `torch_sparse`. Now we will use dense edge_index instead of SparseTensor in dataset.") else: adj_t = self.edge_index_to_adj_t(edge_index, edge_weight, num_nodes, num_nodes) adj_t = gcn_norm(adj_t, None, num_nodes, add_self_loops=False) return adj_t, None edge_index, edge_weight = gcn_norm(edge_index, edge_weight, num_nodes, add_self_loops=False) return edge_index, edge_weight def get_bipartite_inter_mat(self, row='user', row_norm=True): r"""Get the row-normalized bipartite interaction matrix of users and items. """ if row == 'user': row_field, col_field = self.uid_field, self.iid_field else: row_field, col_field = self.iid_field, self.uid_field row = self.inter_feat[row_field] col = self.inter_feat[col_field] edge_index = torch.stack([row, col]) if row_norm: deg = degree(edge_index[0], self.num(row_field)) norm_deg = 1. / torch.where(deg == 0, torch.ones([1]), deg) edge_weight = norm_deg[edge_index[0]] else: row_deg = degree(edge_index[0], self.num(row_field)) col_deg = degree(edge_index[1], self.num(col_field)) row_norm_deg = 1. / torch.sqrt(torch.where(row_deg == 0, torch.ones([1]), row_deg)) col_norm_deg = 1. / torch.sqrt(torch.where(col_deg == 0, torch.ones([1]), col_deg)) edge_weight = row_norm_deg[edge_index[0]] * col_norm_deg[edge_index[1]] return edge_index, edge_weight class SessionGraphDataset(SequentialDataset): def __init__(self, config): super().__init__(config) def session_graph_construction(self): # Default session graph dataset follows the graph construction operator like SR-GNN. self.logger.info('Constructing session graphs.') item_seq = self.inter_feat[self.item_id_list_field] item_seq_len = self.inter_feat[self.item_list_length_field] x = [] edge_index = [] alias_inputs = [] for i, seq in enumerate(tqdm(list(torch.chunk(item_seq, item_seq.shape[0])))): seq, idx = torch.unique(seq, return_inverse=True) x.append(seq) alias_seq = idx.squeeze(0)[:item_seq_len[i]] alias_inputs.append(alias_seq) # No repeat click edge = torch.stack([alias_seq[:-1], alias_seq[1:]]).unique(dim=-1) edge_index.append(edge) self.inter_feat.interaction['graph_idx'] = torch.arange(item_seq.shape[0]) self.graph_objs = { 'x': x, 'edge_index': edge_index, 'alias_inputs': alias_inputs } def build(self): datasets = super().build() for dataset in datasets: dataset.session_graph_construction() return datasets class MultiBehaviorDataset(SessionGraphDataset): def session_graph_construction(self): self.logger.info('Constructing multi-behavior session graphs.') self.item_behavior_list_field = self.config['ITEM_BEHAVIOR_LIST_FIELD'] self.behavior_id_field = self.config['BEHAVIOR_ID_FIELD'] item_seq = self.inter_feat[self.item_id_list_field] item_seq_len = self.inter_feat[self.item_list_length_field] if self.item_behavior_list_field == None or self.behavior_id_field == None: # To be compatible with existing datasets item_behavior_seq = torch.tensor([0] * len(item_seq)) self.behavior_id_field = 'behavior_id' self.field2id_token[self.behavior_id_field] = {0: 'interaction'} else: item_behavior_seq = self.inter_feat[self.item_list_length_field] edge_index = [] alias_inputs = [] behaviors = torch.unique(item_behavior_seq) x = {} for behavior in behaviors: x[behavior.item()] = [] behavior_seqs = list(torch.chunk(item_behavior_seq, item_seq.shape[0])) for i, seq in enumerate(tqdm(list(torch.chunk(item_seq, item_seq.shape[0])))): bseq = behavior_seqs[i] for behavior in behaviors: bidx = torch.where(bseq == behavior) subseq = torch.index_select(seq, 0, bidx[0]) subseq, _ = torch.unique(subseq, return_inverse=True) x[behavior.item()].append(subseq) seq, idx = torch.unique(seq, return_inverse=True) alias_seq = idx.squeeze(0)[:item_seq_len[i]] alias_inputs.append(alias_seq) # No repeat click edge = torch.stack([alias_seq[:-1], alias_seq[1:]]).unique(dim=-1) edge_index.append(edge) nx = {} for k, v in x.items(): behavior_name = self.id2token(self.behavior_id_field, k) nx[behavior_name] = v self.inter_feat.interaction['graph_idx'] = torch.arange(item_seq.shape[0]) self.graph_objs = { 'x': nx, 'edge_index': edge_index, 'alias_inputs': alias_inputs } class LESSRDataset(SessionGraphDataset): def session_graph_construction(self): self.logger.info('Constructing LESSR session graphs.') item_seq = self.inter_feat[self.item_id_list_field] item_seq_len = self.inter_feat[self.item_list_length_field] empty_edge = torch.stack([torch.LongTensor([]), torch.LongTensor([])]) x = [] edge_index_EOP = [] edge_index_shortcut = [] is_last = [] for i, seq in enumerate(tqdm(list(torch.chunk(item_seq, item_seq.shape[0])))): seq, idx = torch.unique(seq, return_inverse=True) x.append(seq) alias_seq = idx.squeeze(0)[:item_seq_len[i]] edge = torch.stack([alias_seq[:-1], alias_seq[1:]]) edge_index_EOP.append(edge) last = torch.zeros_like(seq, dtype=torch.bool) last[alias_seq[-1]] = True is_last.append(last) sub_edges = [] for j in range(1, item_seq_len[i]): sub_edges.append(torch.stack([alias_seq[:-j], alias_seq[j:]])) shortcut_edge = torch.cat(sub_edges, dim=-1).unique(dim=-1) if len(sub_edges) > 0 else empty_edge edge_index_shortcut.append(shortcut_edge) self.inter_feat.interaction['graph_idx'] = torch.arange(item_seq.shape[0]) self.graph_objs = { 'x': x, 'edge_index_EOP': edge_index_EOP, 'edge_index_shortcut': edge_index_shortcut, 'is_last': is_last } self.node_attr = ['x', 'is_last'] class GCEGNNDataset(SequentialDataset): def __init__(self, config): super().__init__(config) def reverse_session(self): self.logger.info('Reversing sessions.') item_seq = self.inter_feat[self.item_id_list_field] item_seq_len = self.inter_feat[self.item_list_length_field] for i in tqdm(range(item_seq.shape[0])): item_seq[i, :item_seq_len[i]] = item_seq[i, :item_seq_len[i]].flip(dims=[0]) def bidirectional_edge(self, edge_index): seq_len = edge_index.shape[1] ed = edge_index.T ed2 = edge_index.T.flip(dims=[1]) idc = ed.unsqueeze(1).expand(-1, seq_len, 2) == ed2.unsqueeze(0).expand(seq_len, -1, 2) return torch.logical_and(idc[:, :, 0], idc[:, :, 1]).any(dim=-1) def session_graph_construction(self): self.logger.info('Constructing session graphs.') item_seq = self.inter_feat[self.item_id_list_field] item_seq_len = self.inter_feat[self.item_list_length_field] x = [] edge_index = [] edge_attr = [] alias_inputs = [] for i, seq in enumerate(tqdm(list(torch.chunk(item_seq, item_seq.shape[0])))): seq, idx = torch.unique(seq, return_inverse=True) x.append(seq) alias_seq = idx.squeeze(0)[:item_seq_len[i]] alias_inputs.append(alias_seq) edge_index_backward = torch.stack([alias_seq[:-1], alias_seq[1:]]) edge_attr_backward = torch.where(self.bidirectional_edge(edge_index_backward), 3, 1) edge_backward = torch.cat([edge_index_backward, edge_attr_backward.unsqueeze(0)], dim=0) edge_index_forward = torch.stack([alias_seq[1:], alias_seq[:-1]]) edge_attr_forward = torch.where(self.bidirectional_edge(edge_index_forward), 3, 2) edge_forward = torch.cat([edge_index_forward, edge_attr_forward.unsqueeze(0)], dim=0) edge_index_selfloop = torch.stack([alias_seq, alias_seq]) edge_selfloop = torch.cat([edge_index_selfloop, torch.zeros([1, edge_index_selfloop.shape[1]])], dim=0) edge = torch.cat([edge_backward, edge_forward, edge_selfloop], dim=-1).long() edge = edge.unique(dim=-1) cur_edge_index = edge[:2] cur_edge_attr = edge[2] edge_index.append(cur_edge_index) edge_attr.append(cur_edge_attr) self.inter_feat.interaction['graph_idx'] = torch.arange(item_seq.shape[0]) self.graph_objs = { 'x': x, 'edge_index': edge_index, 'edge_attr': edge_attr, 'alias_inputs': alias_inputs } def build(self): datasets = super().build() for dataset in datasets: dataset.reverse_session() dataset.session_graph_construction() return datasets class SocialDataset(GeneralGraphDataset): """:class:`SocialDataset` is based on :class:`~recbole_gnn.data.dataset.GeneralGraphDataset`, and load ``.net``. All users in ``.inter`` and ``.net`` are remapped into the same ID sections. Users that only exist in social network will be filtered. It also provides several interfaces to transfer ``.net`` features into coo sparse matrix, csr sparse matrix, :class:`DGL.Graph` or :class:`PyG.Data`. Attributes: net_src_field (str): The same as ``config['NET_SOURCE_ID_FIELD']``. net_tgt_field (str): The same as ``config['NET_TARGET_ID_FIELD']``. net_feat (pandas.DataFrame): Internal data structure stores the users' social network relations. It's loaded from file ``.net``. """ def __init__(self, config): super().__init__(config) def _get_field_from_config(self): super()._get_field_from_config() self.net_src_field = self.config['NET_SOURCE_ID_FIELD'] self.net_tgt_field = self.config['NET_TARGET_ID_FIELD'] self.filter_net_by_inter = self.config['filter_net_by_inter'] self.undirected_net = self.config['undirected_net'] self._check_field('net_src_field', 'net_tgt_field') self.logger.debug(set_color('net_src_field', 'blue') + f': {self.net_src_field}') self.logger.debug(set_color('net_tgt_field', 'blue') + f': {self.net_tgt_field}') def _data_filtering(self): super()._data_filtering() if self.filter_net_by_inter: self._filter_net_by_inter() def _filter_net_by_inter(self): """Filter users in ``net_feat`` that don't occur in interactions. """ inter_uids = set(self.inter_feat[self.uid_field]) self.net_feat.drop(self.net_feat.index[~self.net_feat[self.net_src_field].isin(inter_uids)], inplace=True) self.net_feat.drop(self.net_feat.index[~self.net_feat[self.net_tgt_field].isin(inter_uids)], inplace=True) def _load_data(self, token, dataset_path): super()._load_data(token, dataset_path) self.net_feat = self._load_net(self.dataset_name, self.dataset_path) @property def net_num(self): """Get the number of social network records. Returns: int: Number of social network records. """ return len(self.net_feat) def __str__(self): info = [ super().__str__(), set_color('The number of social network relations', 'blue') + f': {self.net_num}' ] # yapf: disable return '\n'.join(info) def _build_feat_name_list(self): feat_name_list = super()._build_feat_name_list() if self.net_feat is not None: feat_name_list.append('net_feat') return feat_name_list def _load_net(self, token, dataset_path): self.logger.debug(set_color(f'Loading social network from [{dataset_path}].', 'green')) net_path = os.path.join(dataset_path, f'{token}.net') if not os.path.isfile(net_path): raise ValueError(f'[{token}.net] not found in [{dataset_path}].') df = self._load_feat(net_path, FeatureSource.NET) if self.undirected_net: row = df[self.net_src_field] col = df[self.net_tgt_field] df_net_src = pd.concat([row, col], axis=0) df_net_tgt = pd.concat([col, row], axis=0) df_net_src.name = self.net_src_field df_net_tgt.name = self.net_tgt_field df = pd.concat([df_net_src, df_net_tgt], axis=1) self._check_net(df) return df def _check_net(self, net): net_warn_message = 'net data requires field [{}]' assert self.net_src_field in net, net_warn_message.format(self.net_src_field) assert self.net_tgt_field in net, net_warn_message.format(self.net_tgt_field) def _init_alias(self): """Add :attr:`alias_of_user_id`. """ self._set_alias('user_id', [self.uid_field, self.net_src_field, self.net_tgt_field]) self._set_alias('item_id', [self.iid_field]) for alias_name_1, alias_1 in self.alias.items(): for alias_name_2, alias_2 in self.alias.items(): if alias_name_1 != alias_name_2: intersect = np.intersect1d(alias_1, alias_2, assume_unique=True) if len(intersect) > 0: raise ValueError( f'`alias_of_{alias_name_1}` and `alias_of_{alias_name_2}` ' f'should not have the same field {list(intersect)}.' ) self._rest_fields = self.token_like_fields for alias_name, alias in self.alias.items(): isin = np.isin(alias, self._rest_fields, assume_unique=True) if isin.all() is False: raise ValueError( f'`alias_of_{alias_name}` should not contain ' f'non-token-like field {list(alias[~isin])}.' ) self._rest_fields = np.setdiff1d(self._rest_fields, alias, assume_unique=True) def get_norm_net_adj_mat(self, row_norm=False): r"""Get the normalized socail matrix of users and users. Construct the square matrix from the social network data and normalize it using the laplace matrix. .. math:: A_{hat} = D^{-0.5} \times A \times D^{-0.5} Returns: The normalized social network matrix in Tensor. """ row = self.net_feat[self.net_src_field] col = self.net_feat[self.net_tgt_field] edge_index = torch.stack([row, col]) deg = degree(edge_index[0], self.user_num) if row_norm: norm_deg = 1. / torch.where(deg == 0, torch.ones([1]), deg) edge_weight = norm_deg[edge_index[0]] else: norm_deg = 1. / torch.sqrt(torch.where(deg == 0, torch.ones([1]), deg)) edge_weight = norm_deg[edge_index[0]] * norm_deg[edge_index[1]] return edge_index, edge_weight def net_matrix(self, form='coo', value_field=None): """Get sparse matrix that describe social relations between user_id and user_id. Sparse matrix has shape (user_num, user_num). Returns: scipy.sparse: Sparse matrix in form ``coo`` or ``csr``. """ return self._create_sparse_matrix(self.net_feat, self.net_src_field, self.net_tgt_field, form, value_field) ================================================ FILE: recbole_gnn/data/transform.py ================================================ from logging import getLogger import torch from torch.nn.utils.rnn import pad_sequence from recbole.data.interaction import Interaction def gnn_construct_transform(config): if config['gnn_transform'] is None: raise ValueError('config["gnn_transform"] is None but trying to construct transform.') str2transform = { 'sess_graph': SessionGraph, } return str2transform[config['gnn_transform']](config) class SessionGraph: def __init__(self, config): self.logger = getLogger() self.logger.info('SessionGraph Transform in DataLoader.') def __call__(self, dataset, interaction): graph_objs = dataset.graph_objs index = interaction['graph_idx'] graph_batch = { k: [graph_objs[k][_.item()] for _ in index] for k in graph_objs } graph_batch['batch'] = [] tot_node_num = torch.ones([1], dtype=torch.long) for i in range(index.shape[0]): for k in graph_batch: if 'edge_index' in k: graph_batch[k][i] = graph_batch[k][i] + tot_node_num if 'alias_inputs' in graph_batch: graph_batch['alias_inputs'][i] = graph_batch['alias_inputs'][i] + tot_node_num graph_batch['batch'].append(torch.full_like(graph_batch['x'][i], i)) tot_node_num += graph_batch['x'][i].shape[0] if hasattr(dataset, 'node_attr'): node_attr = ['batch'] + dataset.node_attr else: node_attr = ['x', 'batch'] for k in node_attr: graph_batch[k] = [torch.zeros([1], dtype=graph_batch[k][-1].dtype)] + graph_batch[k] for k in graph_batch: if k == 'alias_inputs': graph_batch[k] = pad_sequence(graph_batch[k], batch_first=True) else: graph_batch[k] = torch.cat(graph_batch[k], dim=-1) interaction.update(Interaction(graph_batch)) return interaction ================================================ FILE: recbole_gnn/model/abstract_recommender.py ================================================ from recbole.model.abstract_recommender import GeneralRecommender from recbole.utils import ModelType as RecBoleModelType from recbole_gnn.utils import ModelType class GeneralGraphRecommender(GeneralRecommender): """This is an abstract general graph recommender. All the general graph models should implement in this class. The base general graph recommender class provide the basic U-I graph dataset and parameters information. """ type = RecBoleModelType.GENERAL def __init__(self, config, dataset): super(GeneralGraphRecommender, self).__init__(config, dataset) self.edge_index, self.edge_weight = dataset.get_norm_adj_mat(enable_sparse=config["enable_sparse"]) self.use_sparse = config["enable_sparse"] and dataset.is_sparse if self.use_sparse: self.edge_index, self.edge_weight = self.edge_index.to(self.device), None else: self.edge_index, self.edge_weight = self.edge_index.to(self.device), self.edge_weight.to(self.device) class SocialRecommender(GeneralRecommender): """This is an abstract social recommender. All the social graph model should implement this class. The base social recommender class provide the basic social graph dataset and parameters information. """ type = ModelType.SOCIAL def __init__(self, config, dataset): super(SocialRecommender, self).__init__(config, dataset) ================================================ FILE: recbole_gnn/model/general_recommender/__init__.py ================================================ from recbole_gnn.model.general_recommender.lightgcn import LightGCN from recbole_gnn.model.general_recommender.hmlet import HMLET from recbole_gnn.model.general_recommender.ncl import NCL from recbole_gnn.model.general_recommender.ngcf import NGCF from recbole_gnn.model.general_recommender.sgl import SGL from recbole_gnn.model.general_recommender.lightgcl import LightGCL from recbole_gnn.model.general_recommender.simgcl import SimGCL from recbole_gnn.model.general_recommender.xsimgcl import XSimGCL from recbole_gnn.model.general_recommender.directau import DirectAU from recbole_gnn.model.general_recommender.ssl4rec import SSL4REC ================================================ FILE: recbole_gnn/model/general_recommender/directau.py ================================================ # r""" # DiretAU # ################################################ # Reference: # Chenyang Wang et al. "Towards Representation Alignment and Uniformity in Collaborative Filtering." in KDD 2022. # Reference code: # https://github.com/THUwangcy/DirectAU # """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from recbole.model.init import xavier_normal_initialization from recbole.utils import InputType from recbole.model.general_recommender import BPR from recbole_gnn.model.general_recommender import LightGCN from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender class DirectAU(GeneralGraphRecommender): input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(DirectAU, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.gamma = config['gamma'] self.encoder_name = config['encoder'] # define encoder if self.encoder_name == 'MF': self.encoder = MFEncoder(config, dataset) elif self.encoder_name == 'LightGCN': self.encoder = LGCNEncoder(config, dataset) else: raise ValueError('Non-implemented Encoder.') # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_normal_initialization) def forward(self, user, item): user_e, item_e = self.encoder(user, item) return F.normalize(user_e, dim=-1), F.normalize(item_e, dim=-1) @staticmethod def alignment(x, y, alpha=2): return (x - y).norm(p=2, dim=1).pow(alpha).mean() @staticmethod def uniformity(x, t=2): return torch.pdist(x, p=2).pow(2).mul(-t).exp().mean().log() def calculate_loss(self, interaction): if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_e, item_e = self.forward(user, item) align = self.alignment(user_e, item_e) uniform = self.gamma * (self.uniformity(user_e) + self.uniformity(item_e)) / 2 return align, uniform def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_e = self.user_embedding(user) item_e = self.item_embedding(item) return torch.mul(user_e, item_e).sum(dim=1) def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.encoder_name == 'LightGCN': if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.encoder.get_all_embeddings() user_e = self.restore_user_e[user] all_item_e = self.restore_item_e else: user_e = self.encoder.user_embedding(user) all_item_e = self.encoder.item_embedding.weight score = torch.matmul(user_e, all_item_e.transpose(0, 1)) return score.view(-1) class MFEncoder(BPR): def __init__(self, config, dataset): super(MFEncoder, self).__init__(config, dataset) def forward(self, user_id, item_id): return super().forward(user_id, item_id) def get_all_embeddings(self): user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight return user_embeddings, item_embeddings class LGCNEncoder(LightGCN): def __init__(self, config, dataset): super(LGCNEncoder, self).__init__(config, dataset) def forward(self, user_id, item_id): user_all_embeddings, item_all_embeddings = self.get_all_embeddings() u_embed = user_all_embeddings[user_id] i_embed = item_all_embeddings[item_id] return u_embed, i_embed def get_all_embeddings(self): return super().forward() ================================================ FILE: recbole_gnn/model/general_recommender/hmlet.py ================================================ # @Time : 2022/3/21 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" HMLET ################################################ Reference: Taeyong Kong et al. "Linear, or Non-Linear, That is the Question!." in WSDM 2022. Reference code: https://github.com/qbxlvnf11/HMLET """ import torch import torch.nn as nn import torch.nn.functional as F from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.layers import activation_layer from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender from recbole_gnn.model.layers import LightGCNConv class Gating_Net(nn.Module): def __init__(self, embedding_dim, mlp_dims, dropout_p): super(Gating_Net, self).__init__() self.embedding_dim = embedding_dim fc_layers = [] for i in range(len(mlp_dims)): if i == 0: fc = nn.Linear(embedding_dim*2, mlp_dims[i]) fc_layers.append(fc) else: fc = nn.Linear(mlp_dims[i-1], mlp_dims[i]) fc_layers.append(fc) if i != len(mlp_dims) - 1: fc_layers.append(nn.BatchNorm1d(mlp_dims[i])) fc_layers.append(nn.Dropout(p=dropout_p)) fc_layers.append(nn.ReLU(inplace=True)) self.mlp = nn.Sequential(*fc_layers) def gumbel_softmax(self, logits, temperature, hard): """Sample from the Gumbel-Softmax distribution and optionally discretize. Args: logits: [batch_size, n_class] unnormalized log-probs temperature: non-negative scalar hard: if True, take argmax, but differentiate w.r.t. soft sample y Returns: [batch_size, n_class] sample from the Gumbel-Softmax distribution. If hard=True, then the returned sample will be one-hot, otherwise it will be a probabilitiy distribution that sums to 1 across classes """ y = self.gumbel_softmax_sample(logits, temperature) ## (0.6, 0.2, 0.1,..., 0.11) if hard: k = logits.size(1) # k is numb of classes # y_hard = tf.cast(tf.one_hot(tf.argmax(y,1),k), y.dtype) ## (1, 0, 0, ..., 0) y_hard = torch.eq(y, torch.max(y, dim=1, keepdim=True)[0]).type_as(y) y = (y_hard - y).detach() + y return y def gumbel_softmax_sample(self, logits, temperature): """ Draw a sample from the Gumbel-Softmax distribution""" noise = self.sample_gumbel(logits) y = (logits + noise) / temperature return F.softmax(y, dim=1) def sample_gumbel(self, logits): """Sample from Gumbel(0, 1)""" noise = torch.rand(logits.size()) eps = 1e-20 noise.add_(eps).log_().neg_() noise.add_(eps).log_().neg_() return torch.Tensor(noise.float()).to(logits.device) def forward(self, feature, temperature, hard): x = self.mlp(feature) out = self.gumbel_softmax(x, temperature, hard) out_value = out.unsqueeze(2) gating_out = out_value.repeat(1, 1, self.embedding_dim) return gating_out class HMLET(GeneralGraphRecommender): r"""HMLET combines both linear and non-linear propagation layers for general recommendation and yields better performance. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(HMLET, self).__init__(config, dataset) # load parameters info self.latent_dim = config['embedding_size'] # int type:the embedding size of lightGCN self.n_layers = config['n_layers'] # int type:the layer num of lightGCN self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization self.require_pow = config['require_pow'] # bool type: whether to require pow when regularization self.gate_layer_ids = config['gate_layer_ids'] # list type: layer ids for non-linear gating self.gating_mlp_dims = config['gating_mlp_dims'] # list type: list of mlp dimensions in gating module self.dropout_ratio = config['dropout_ratio'] # dropout ratio for mlp in gating module self.gum_temp = config['ori_temp'] self.logger.info(f'Model initialization, gumbel softmax temperature: {self.gum_temp}') # define layers and loss self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.gcn_conv = LightGCNConv(dim=self.latent_dim) self.activation = nn.ELU() if config['activation_function'] == 'elu' else activation_layer(config['activation_function']) self.gating_nets = nn.ModuleList([ Gating_Net(self.latent_dim, self.gating_mlp_dims, self.dropout_ratio) for _ in range(len(self.gate_layer_ids)) ]) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e', 'gum_temp'] for gating in self.gating_nets: self._gating_freeze(gating, False) def _gating_freeze(self, model, freeze_flag): for name, child in model.named_children(): for param in child.parameters(): param.requires_grad = freeze_flag def __choosing_one(self, features, gumbel_out): feature = torch.sum(torch.mul(features, gumbel_out), dim=1) # batch x embedding_dim (or batch x embedding_dim x layer_num) return feature def __where(self, idx, lst): for i in range(len(lst)): if lst[i] == idx: return i raise ValueError(f'{idx} not in {lst}.') def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim] """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings def forward(self): all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] non_lin_emb_list = [all_embeddings] for layer_idx in range(self.n_layers): linear_embeddings = self.gcn_conv(all_embeddings, self.edge_index, self.edge_weight) if layer_idx not in self.gate_layer_ids: all_embeddings = linear_embeddings else: non_lin_id = self.__where(layer_idx, self.gate_layer_ids) last_non_lin_emb = non_lin_emb_list[non_lin_id] non_lin_embeddings = self.activation(self.gcn_conv(last_non_lin_emb, self.edge_index, self.edge_weight)) stack_embeddings = torch.stack([linear_embeddings, non_lin_embeddings], dim=1) concat_embeddings = torch.cat((linear_embeddings, non_lin_embeddings), dim=-1) gumbel_out = self.gating_nets[non_lin_id](concat_embeddings, self.gum_temp, not self.training) all_embeddings = self.__choosing_one(stack_embeddings, gumbel_out) non_lin_emb_list.append(all_embeddings) embeddings_list.append(all_embeddings) hmlet_all_embeddings = torch.stack(embeddings_list, dim=1) hmlet_all_embeddings = torch.mean(hmlet_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(hmlet_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings, require_pow=self.require_pow) loss = mf_loss + self.reg_weight * reg_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/general_recommender/lightgcl.py ================================================ # -*- coding: utf-8 -*- # @Time : 2023/04/12 # @Author : Wanli Yang # @Email : 2013774@mail.nankai.edu.cn r""" LightGCL ################################################ Reference: Xuheng Cai et al. "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation" in ICLR 2023. Reference code: https://github.com/HKUDS/LightGCL """ import numpy as np import scipy.sparse as sp import torch import torch.nn as nn from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import EmbLoss from recbole.utils import InputType import torch.nn.functional as F class LightGCL(GeneralRecommender): r"""LightGCL is a GCN-based recommender model. LightGCL guides graph augmentation by singular value decomposition (SVD) to not only distill the useful information of user-item interactions but also inject the global collaborative context into the representation alignment of contrastive learning. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(LightGCL, self).__init__(config, dataset) self._user = dataset.inter_feat[dataset.uid_field] self._item = dataset.inter_feat[dataset.iid_field] # load parameters info self.embed_dim = config["embedding_size"] self.n_layers = config["n_layers"] self.dropout = config["dropout"] self.temp = config["temp"] self.lambda_1 = config["lambda1"] self.lambda_2 = config["lambda2"] self.q = config["q"] self.act = nn.LeakyReLU(0.5) self.reg_loss = EmbLoss() # get the normalized adjust matrix self.adj_norm = self.coo2tensor(self.create_adjust_matrix()) # perform svd reconstruction svd_u, s, svd_v = torch.svd_lowrank(self.adj_norm, q=self.q) self.u_mul_s = svd_u @ (torch.diag(s)) self.v_mul_s = svd_v @ (torch.diag(s)) del s self.ut = svd_u.T self.vt = svd_v.T self.E_u_0 = nn.Parameter(nn.init.xavier_uniform_(torch.empty(self.n_users, self.embed_dim))) self.E_i_0 = nn.Parameter(nn.init.xavier_uniform_(torch.empty(self.n_items, self.embed_dim))) self.E_u_list = [None] * (self.n_layers + 1) self.E_i_list = [None] * (self.n_layers + 1) self.E_u_list[0] = self.E_u_0 self.E_i_list[0] = self.E_i_0 self.Z_u_list = [None] * (self.n_layers + 1) self.Z_i_list = [None] * (self.n_layers + 1) self.G_u_list = [None] * (self.n_layers + 1) self.G_i_list = [None] * (self.n_layers + 1) self.G_u_list[0] = self.E_u_0 self.G_i_list[0] = self.E_i_0 self.E_u = None self.E_i = None self.restore_user_e = None self.restore_item_e = None self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def create_adjust_matrix(self): r"""Get the normalized interaction matrix of users and items. Returns: coo_matrix of the normalized interaction matrix. """ ratings = np.ones_like(self._user, dtype=np.float32) matrix = sp.csr_matrix( (ratings, (self._user, self._item)), shape=(self.n_users, self.n_items), ).tocoo() rowD = np.squeeze(np.array(matrix.sum(1)), axis=1) colD = np.squeeze(np.array(matrix.sum(0)), axis=0) for i in range(len(matrix.data)): matrix.data[i] = matrix.data[i] / pow(rowD[matrix.row[i]] * colD[matrix.col[i]], 0.5) return matrix def coo2tensor(self, matrix: sp.coo_matrix): r"""Convert coo_matrix to tensor. Args: matrix (scipy.coo_matrix): Sparse matrix to be converted. Returns: torch.sparse.FloatTensor: Transformed sparse matrix. """ indices = torch.from_numpy( np.vstack((matrix.row, matrix.col)).astype(np.int64)) values = torch.from_numpy(matrix.data) shape = torch.Size(matrix.shape) x = torch.sparse.FloatTensor(indices, values, shape).coalesce().to(self.device) return x def sparse_dropout(self, matrix, dropout): if dropout == 0.0: return matrix indices = matrix.indices() values = F.dropout(matrix.values(), p=dropout) size = matrix.size() return torch.sparse.FloatTensor(indices, values, size) def forward(self): for layer in range(1, self.n_layers + 1): # GNN propagation self.Z_u_list[layer] = torch.spmm(self.sparse_dropout(self.adj_norm, self.dropout), self.E_i_list[layer - 1]) self.Z_i_list[layer] = torch.spmm(self.sparse_dropout(self.adj_norm, self.dropout).transpose(0, 1), self.E_u_list[layer - 1]) # aggregate self.E_u_list[layer] = self.Z_u_list[layer] self.E_i_list[layer] = self.Z_i_list[layer] # aggregate across layer self.E_u = sum(self.E_u_list) self.E_i = sum(self.E_i_list) return self.E_u, self.E_i def calculate_loss(self, interaction): if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user_list = interaction[self.USER_ID] pos_item_list = interaction[self.ITEM_ID] neg_item_list = interaction[self.NEG_ITEM_ID] E_u_norm, E_i_norm = self.forward() bpr_loss = self.calc_bpr_loss(E_u_norm, E_i_norm, user_list, pos_item_list, neg_item_list) ssl_loss = self.calc_ssl_loss(E_u_norm, E_i_norm, user_list, pos_item_list) total_loss = bpr_loss + ssl_loss return total_loss def calc_bpr_loss(self, E_u_norm, E_i_norm, user_list, pos_item_list, neg_item_list): r"""Calculate the pairwise Bayesian Personalized Ranking (BPR) loss and parameter regularization loss. Args: E_u_norm (torch.Tensor): Ego embedding of all users after forwarding. E_i_norm (torch.Tensor): Ego embedding of all items after forwarding. user_list (torch.Tensor): List of the user. pos_item_list (torch.Tensor): List of positive examples. neg_item_list (torch.Tensor): List of negative examples. Returns: torch.Tensor: Loss of BPR tasks and parameter regularization. """ u_e = E_u_norm[user_list] pi_e = E_i_norm[pos_item_list] ni_e = E_i_norm[neg_item_list] pos_scores = torch.mul(u_e, pi_e).sum(dim=1) neg_scores = torch.mul(u_e, ni_e).sum(dim=1) loss1 = -(pos_scores - neg_scores).sigmoid().log().mean() # reg loss loss_reg = 0 for param in self.parameters(): loss_reg += param.norm(2).square() loss_reg *= self.lambda_2 return loss1 + loss_reg def calc_ssl_loss(self, E_u_norm, E_i_norm, user_list, pos_item_list): r"""Calculate the loss of self-supervised tasks. Args: E_u_norm (torch.Tensor): Ego embedding of all users in the original graph after forwarding. E_i_norm (torch.Tensor): Ego embedding of all items in the original graph after forwarding. user_list (torch.Tensor): List of the user. pos_item_list (torch.Tensor): List of positive examples. Returns: torch.Tensor: Loss of self-supervised tasks. """ # calculate G_u_norm&G_i_norm for layer in range(1, self.n_layers + 1): # svd_adj propagation vt_ei = self.vt @ self.E_i_list[layer - 1] self.G_u_list[layer] = self.u_mul_s @ vt_ei ut_eu = self.ut @ self.E_u_list[layer - 1] self.G_i_list[layer] = self.v_mul_s @ ut_eu # aggregate across layer G_u_norm = sum(self.G_u_list) G_i_norm = sum(self.G_i_list) neg_score = torch.log(torch.exp(G_u_norm[user_list] @ E_u_norm.T / self.temp).sum(1) + 1e-8).mean() neg_score += torch.log(torch.exp(G_i_norm[pos_item_list] @ E_i_norm.T / self.temp).sum(1) + 1e-8).mean() pos_score = (torch.clamp((G_u_norm[user_list] * E_u_norm[user_list]).sum(1) / self.temp, -5.0, 5.0)).mean() + ( torch.clamp((G_i_norm[pos_item_list] * E_i_norm[pos_item_list]).sum(1) / self.temp, -5.0, 5.0)).mean() ssl_loss = -pos_score + neg_score return self.lambda_1 * ssl_loss def predict(self, interaction): if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() user = self.restore_user_e[interaction[self.USER_ID]] item = self.restore_item_e[interaction[self.ITEM_ID]] return torch.sum(user * item, dim=1) def full_sort_predict(self, interaction): if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() user = self.restore_user_e[interaction[self.USER_ID]] return user.matmul(self.restore_item_e.T) ================================================ FILE: recbole_gnn/model/general_recommender/lightgcn.py ================================================ # @Time : 2022/3/8 # @Author : Lanling Xu # @Email : xulanling_sherry@163.com r""" LightGCN ################################################ Reference: Xiangnan He et al. "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation." in SIGIR 2020. Reference code: https://github.com/kuandeng/LightGCN """ import numpy as np import torch from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender from recbole_gnn.model.layers import LightGCNConv class LightGCN(GeneralGraphRecommender): r"""LightGCN is a GCN-based recommender model, implemented via PyG. LightGCN includes only the most essential component in GCN — neighborhood aggregation — for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(LightGCN, self).__init__(config, dataset) # load parameters info self.latent_dim = config['embedding_size'] # int type:the embedding size of lightGCN self.n_layers = config['n_layers'] # int type:the layer num of lightGCN self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization self.require_pow = config['require_pow'] # bool type: whether to require pow when regularization # define layers and loss self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.gcn_conv = LightGCNConv(dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim] """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings def forward(self): all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for layer_idx in range(self.n_layers): all_embeddings = self.gcn_conv(all_embeddings, self.edge_index, self.edge_weight) embeddings_list.append(all_embeddings) lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings, require_pow=self.require_pow) loss = mf_loss + self.reg_weight * reg_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/general_recommender/ncl.py ================================================ # -*- coding: utf-8 -*- r""" NCL ################################################ Reference: Zihan Lin*, Changxin Tian*, Yupeng Hou*, Wayne Xin Zhao. "Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning." in WWW 2022. """ import torch import torch.nn.functional as F from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender from recbole_gnn.model.layers import LightGCNConv class NCL(GeneralGraphRecommender): input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(NCL, self).__init__(config, dataset) # load parameters info self.latent_dim = config['embedding_size'] # int type: the embedding size of the base model self.n_layers = config['n_layers'] # int type: the layer num of the base model self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization self.ssl_temp = config['ssl_temp'] self.ssl_reg = config['ssl_reg'] self.hyper_layers = config['hyper_layers'] self.alpha = config['alpha'] self.proto_reg = config['proto_reg'] self.k = config['num_clusters'] # define layers and loss self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.gcn_conv = LightGCNConv(dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] self.user_centroids = None self.user_2cluster = None self.item_centroids = None self.item_2cluster = None def e_step(self): user_embeddings = self.user_embedding.weight.detach().cpu().numpy() item_embeddings = self.item_embedding.weight.detach().cpu().numpy() self.user_centroids, self.user_2cluster = self.run_kmeans(user_embeddings) self.item_centroids, self.item_2cluster = self.run_kmeans(item_embeddings) def run_kmeans(self, x): """Run K-means algorithm to get k clusters of the input tensor x """ import faiss kmeans = faiss.Kmeans(d=self.latent_dim, k=self.k, gpu=True) kmeans.train(x) cluster_cents = kmeans.centroids _, I = kmeans.index.search(x, 1) # convert to cuda Tensors for broadcast centroids = torch.Tensor(cluster_cents).to(self.device) centroids = F.normalize(centroids, p=2, dim=1) node2cluster = torch.LongTensor(I).squeeze().to(self.device) return centroids, node2cluster def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim] """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings def forward(self): all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for layer_idx in range(max(self.n_layers, self.hyper_layers * 2)): all_embeddings = self.gcn_conv(all_embeddings, self.edge_index, self.edge_weight) embeddings_list.append(all_embeddings) lightgcn_all_embeddings = torch.stack(embeddings_list[:self.n_layers + 1], dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings, embeddings_list def ProtoNCE_loss(self, node_embedding, user, item): user_embeddings_all, item_embeddings_all = torch.split(node_embedding, [self.n_users, self.n_items]) user_embeddings = user_embeddings_all[user] # [B, e] norm_user_embeddings = F.normalize(user_embeddings) user2cluster = self.user_2cluster[user] # [B,] user2centroids = self.user_centroids[user2cluster] # [B, e] pos_score_user = torch.mul(norm_user_embeddings, user2centroids).sum(dim=1) pos_score_user = torch.exp(pos_score_user / self.ssl_temp) ttl_score_user = torch.matmul(norm_user_embeddings, self.user_centroids.transpose(0, 1)) ttl_score_user = torch.exp(ttl_score_user / self.ssl_temp).sum(dim=1) proto_nce_loss_user = -torch.log(pos_score_user / ttl_score_user).sum() item_embeddings = item_embeddings_all[item] norm_item_embeddings = F.normalize(item_embeddings) item2cluster = self.item_2cluster[item] # [B, ] item2centroids = self.item_centroids[item2cluster] # [B, e] pos_score_item = torch.mul(norm_item_embeddings, item2centroids).sum(dim=1) pos_score_item = torch.exp(pos_score_item / self.ssl_temp) ttl_score_item = torch.matmul(norm_item_embeddings, self.item_centroids.transpose(0, 1)) ttl_score_item = torch.exp(ttl_score_item / self.ssl_temp).sum(dim=1) proto_nce_loss_item = -torch.log(pos_score_item / ttl_score_item).sum() proto_nce_loss = self.proto_reg * (proto_nce_loss_user + proto_nce_loss_item) return proto_nce_loss def ssl_layer_loss(self, current_embedding, previous_embedding, user, item): current_user_embeddings, current_item_embeddings = torch.split(current_embedding, [self.n_users, self.n_items]) previous_user_embeddings_all, previous_item_embeddings_all = torch.split(previous_embedding, [self.n_users, self.n_items]) current_user_embeddings = current_user_embeddings[user] previous_user_embeddings = previous_user_embeddings_all[user] norm_user_emb1 = F.normalize(current_user_embeddings) norm_user_emb2 = F.normalize(previous_user_embeddings) norm_all_user_emb = F.normalize(previous_user_embeddings_all) pos_score_user = torch.mul(norm_user_emb1, norm_user_emb2).sum(dim=1) ttl_score_user = torch.matmul(norm_user_emb1, norm_all_user_emb.transpose(0, 1)) pos_score_user = torch.exp(pos_score_user / self.ssl_temp) ttl_score_user = torch.exp(ttl_score_user / self.ssl_temp).sum(dim=1) ssl_loss_user = -torch.log(pos_score_user / ttl_score_user).sum() current_item_embeddings = current_item_embeddings[item] previous_item_embeddings = previous_item_embeddings_all[item] norm_item_emb1 = F.normalize(current_item_embeddings) norm_item_emb2 = F.normalize(previous_item_embeddings) norm_all_item_emb = F.normalize(previous_item_embeddings_all) pos_score_item = torch.mul(norm_item_emb1, norm_item_emb2).sum(dim=1) ttl_score_item = torch.matmul(norm_item_emb1, norm_all_item_emb.transpose(0, 1)) pos_score_item = torch.exp(pos_score_item / self.ssl_temp) ttl_score_item = torch.exp(ttl_score_item / self.ssl_temp).sum(dim=1) ssl_loss_item = -torch.log(pos_score_item / ttl_score_item).sum() ssl_loss = self.ssl_reg * (ssl_loss_user + self.alpha * ssl_loss_item) return ssl_loss def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings, embeddings_list = self.forward() center_embedding = embeddings_list[0] context_embedding = embeddings_list[self.hyper_layers * 2] ssl_loss = self.ssl_layer_loss(context_embedding, center_embedding, user, pos_item) proto_loss = self.ProtoNCE_loss(center_embedding, user, pos_item) u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) return mf_loss + self.reg_weight * reg_loss, ssl_loss, proto_loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings, embeddings_list = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e, embedding_list = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/general_recommender/ngcf.py ================================================ # @Time : 2022/3/8 # @Author : Changxin Tian # @Email : cx.tian@outlook.com r""" NGCF ################################################ Reference: Xiang Wang et al. "Neural Graph Collaborative Filtering." in SIGIR 2019. Reference code: https://github.com/xiangwang1223/neural_graph_collaborative_filtering """ import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.utils import dropout_adj from recbole.model.init import xavier_normal_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender from recbole_gnn.model.layers import BiGNNConv class NGCF(GeneralGraphRecommender): r"""NGCF is a model that incorporate GNN for recommendation. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(NGCF, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.hidden_size_list = config['hidden_size_list'] self.hidden_size_list = [self.embedding_size] + self.hidden_size_list self.node_dropout = config['node_dropout'] self.message_dropout = config['message_dropout'] self.reg_weight = config['reg_weight'] # define layers and loss self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.GNNlayers = torch.nn.ModuleList() for input_size, output_size in zip(self.hidden_size_list[:-1], self.hidden_size_list[1:]): self.GNNlayers.append(BiGNNConv(input_size, output_size)) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_normal_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of (n_items+n_users, embedding_dim) """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings def forward(self): if self.node_dropout == 0: edge_index, edge_weight = self.edge_index, self.edge_weight else: edge_index, edge_weight = self.edge_index, self.edge_weight if self.use_sparse: row, col, edge_weight = edge_index.t().coo() edge_index = torch.stack([row, col], 0) edge_index, edge_weight = dropout_adj(edge_index=edge_index, edge_attr=edge_weight, p=self.node_dropout, training=self.training) from torch_sparse import SparseTensor edge_index = SparseTensor(row=edge_index[0], col=edge_index[1], value=edge_weight, sparse_sizes=(self.n_users + self.n_items, self.n_users + self.n_items)) edge_index = edge_index.t() edge_weight = None else: edge_index, edge_weight = dropout_adj(edge_index=edge_index, edge_attr=edge_weight, p=self.node_dropout, training=self.training) all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for gnn in self.GNNlayers: all_embeddings = gnn(all_embeddings, edge_index, edge_weight) all_embeddings = nn.LeakyReLU(negative_slope=0.2)(all_embeddings) all_embeddings = nn.Dropout(self.message_dropout)(all_embeddings) all_embeddings = F.normalize(all_embeddings, p=2, dim=1) embeddings_list += [all_embeddings] # storage output embedding of each layer ngcf_all_embeddings = torch.cat(embeddings_list, dim=1) user_all_embeddings, item_all_embeddings = torch.split(ngcf_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) # L2 regularization of embeddings return mf_loss + self.reg_weight * reg_loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/general_recommender/sgl.py ================================================ # -*- coding: utf-8 -*- # @Time : 2022/3/8 # @Author : Changxin Tian # @Email : cx.tian@outlook.com r""" SGL ################################################ Reference: Jiancan Wu et al. "SGL: Self-supervised Graph Learning for Recommendation" in SIGIR 2021. Reference code: https://github.com/wujcan/SGL """ import numpy as np import torch import torch.nn.functional as F from torch_geometric.utils import degree from torch_geometric.nn.conv.gcn_conv import gcn_norm from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender from recbole_gnn.model.layers import LightGCNConv class SGL(GeneralGraphRecommender): r"""SGL is a GCN-based recommender model. SGL supplements the classical supervised task of recommendation with an auxiliary self supervised task, which reinforces node representation learning via self- discrimination.Specifically,SGL generates multiple views of a node, maximizing the agreement between different views of the same node compared to that of other nodes. SGL devises three operators to generate the views — node dropout, edge dropout, and random walk — that change the graph structure in different manners. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(SGL, self).__init__(config, dataset) # load parameters info self.latent_dim = config["embedding_size"] self.n_layers = int(config["n_layers"]) self.aug_type = config["type"] self.drop_ratio = config["drop_ratio"] self.ssl_tau = config["ssl_tau"] self.reg_weight = config["reg_weight"] self.ssl_weight = config["ssl_weight"] self._user = dataset.inter_feat[dataset.uid_field] self._item = dataset.inter_feat[dataset.iid_field] self.dataset = dataset # define layers and loss self.user_embedding = torch.nn.Embedding(self.n_users, self.latent_dim) self.item_embedding = torch.nn.Embedding(self.n_items, self.latent_dim) self.gcn_conv = LightGCNConv(dim=self.latent_dim) self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def train(self, mode: bool = True): r"""Override train method of base class. The subgraph is reconstructed each time it is called. """ T = super().train(mode=mode) if mode: self.graph_construction() return T def graph_construction(self): r"""Devise three operators to generate the views — node dropout, edge dropout, and random walk of a node. """ if self.aug_type == "ND" or self.aug_type == "ED": self.sub_graph1 = [self.random_graph_augment()] * self.n_layers self.sub_graph2 = [self.random_graph_augment()] * self.n_layers elif self.aug_type == "RW": self.sub_graph1 = [self.random_graph_augment() for _ in range(self.n_layers)] self.sub_graph2 = [self.random_graph_augment() for _ in range(self.n_layers)] def random_graph_augment(self): def rand_sample(high, size=None, replace=True): return np.random.choice(np.arange(high), size=size, replace=replace) if self.aug_type == "ND": drop_user = rand_sample(self.n_users, size=int(self.n_users * self.drop_ratio), replace=False) drop_item = rand_sample(self.n_items, size=int(self.n_items * self.drop_ratio), replace=False) mask = np.isin(self._user.numpy(), drop_user) mask |= np.isin(self._item.numpy(), drop_item) keep = np.where(~mask) row = self._user[keep] col = self._item[keep] + self.n_users elif self.aug_type == "ED" or self.aug_type == "RW": keep = rand_sample(len(self._user), size=int(len(self._user) * (1 - self.drop_ratio)), replace=False) row = self._user[keep] col = self._item[keep] + self.n_users edge_index1 = torch.stack([row, col]) edge_index2 = torch.stack([col, row]) edge_index = torch.cat([edge_index1, edge_index2], dim=1) edge_weight = torch.ones(edge_index.size(1)) num_nodes = self.n_users + self.n_items if self.use_sparse: adj_t = self.dataset.edge_index_to_adj_t(edge_index, edge_weight, num_nodes, num_nodes) adj_t = gcn_norm(adj_t, None, num_nodes, add_self_loops=False) return adj_t.to(self.device), None edge_index, edge_weight = gcn_norm(edge_index, edge_weight, num_nodes, add_self_loops=False) return edge_index.to(self.device), edge_weight.to(self.device) def forward(self, graph=None): all_embeddings = torch.cat([self.user_embedding.weight, self.item_embedding.weight]) embeddings_list = [all_embeddings] if graph is None: # for the original graph for _ in range(self.n_layers): all_embeddings = self.gcn_conv(all_embeddings, self.edge_index, self.edge_weight) embeddings_list.append(all_embeddings) else: # for the augmented graph for graph_edge_index, graph_edge_weight in graph: all_embeddings = self.gcn_conv(all_embeddings, graph_edge_index, graph_edge_weight) embeddings_list.append(all_embeddings) embeddings_list = torch.stack(embeddings_list, dim=1) embeddings_list = torch.mean(embeddings_list, dim=1, keepdim=False) user_all_embeddings, item_all_embeddings = torch.split(embeddings_list, [self.n_users, self.n_items], dim=0) return user_all_embeddings, item_all_embeddings def calc_bpr_loss(self, user_emd, item_emd, user_list, pos_item_list, neg_item_list): r"""Calculate the the pairwise Bayesian Personalized Ranking (BPR) loss and parameter regularization loss. Args: user_emd (torch.Tensor): Ego embedding of all users after forwarding. item_emd (torch.Tensor): Ego embedding of all items after forwarding. user_list (torch.Tensor): List of the user. pos_item_list (torch.Tensor): List of positive examples. neg_item_list (torch.Tensor): List of negative examples. Returns: torch.Tensor: Loss of BPR tasks and parameter regularization. """ u_e = user_emd[user_list] pi_e = item_emd[pos_item_list] ni_e = item_emd[neg_item_list] p_scores = torch.mul(u_e, pi_e).sum(dim=1) n_scores = torch.mul(u_e, ni_e).sum(dim=1) l1 = torch.sum(-F.logsigmoid(p_scores - n_scores)) u_e_p = self.user_embedding(user_list) pi_e_p = self.item_embedding(pos_item_list) ni_e_p = self.item_embedding(neg_item_list) l2 = self.reg_loss(u_e_p, pi_e_p, ni_e_p) return l1 + l2 * self.reg_weight def calc_ssl_loss(self, user_list, pos_item_list, user_sub1, user_sub2, item_sub1, item_sub2): r"""Calculate the loss of self-supervised tasks. Args: user_list (torch.Tensor): List of the user. pos_item_list (torch.Tensor): List of positive examples. user_sub1 (torch.Tensor): Ego embedding of all users in the first subgraph after forwarding. user_sub2 (torch.Tensor): Ego embedding of all users in the second subgraph after forwarding. item_sub1 (torch.Tensor): Ego embedding of all items in the first subgraph after forwarding. item_sub2 (torch.Tensor): Ego embedding of all items in the second subgraph after forwarding. Returns: torch.Tensor: Loss of self-supervised tasks. """ u_emd1 = F.normalize(user_sub1[user_list], dim=1) u_emd2 = F.normalize(user_sub2[user_list], dim=1) all_user2 = F.normalize(user_sub2, dim=1) v1 = torch.sum(u_emd1 * u_emd2, dim=1) v2 = u_emd1.matmul(all_user2.T) v1 = torch.exp(v1 / self.ssl_tau) v2 = torch.sum(torch.exp(v2 / self.ssl_tau), dim=1) ssl_user = -torch.sum(torch.log(v1 / v2)) i_emd1 = F.normalize(item_sub1[pos_item_list], dim=1) i_emd2 = F.normalize(item_sub2[pos_item_list], dim=1) all_item2 = F.normalize(item_sub2, dim=1) v3 = torch.sum(i_emd1 * i_emd2, dim=1) v4 = i_emd1.matmul(all_item2.T) v3 = torch.exp(v3 / self.ssl_tau) v4 = torch.sum(torch.exp(v4 / self.ssl_tau), dim=1) ssl_item = -torch.sum(torch.log(v3 / v4)) return (ssl_item + ssl_user) * self.ssl_weight def calculate_loss(self, interaction): if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user_list = interaction[self.USER_ID] pos_item_list = interaction[self.ITEM_ID] neg_item_list = interaction[self.NEG_ITEM_ID] user_emd, item_emd = self.forward() user_sub1, item_sub1 = self.forward(self.sub_graph1) user_sub2, item_sub2 = self.forward(self.sub_graph2) total_loss = self.calc_bpr_loss(user_emd, item_emd, user_list, pos_item_list, neg_item_list) + \ self.calc_ssl_loss(user_list, pos_item_list, user_sub1, user_sub2, item_sub1, item_sub2) return total_loss def predict(self, interaction): if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() user = self.restore_user_e[interaction[self.USER_ID]] item = self.restore_item_e[interaction[self.ITEM_ID]] return torch.sum(user * item, dim=1) def full_sort_predict(self, interaction): if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() user = self.restore_user_e[interaction[self.USER_ID]] return user.matmul(self.restore_item_e.T) ================================================ FILE: recbole_gnn/model/general_recommender/simgcl.py ================================================ # -*- coding: utf-8 -*- r""" SimGCL ################################################ Reference: Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, Quoc Viet Hung Nguyen. "Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation." in SIGIR 2022. """ import torch import torch.nn.functional as F from recbole_gnn.model.general_recommender import LightGCN class SimGCL(LightGCN): def __init__(self, config, dataset): super(SimGCL, self).__init__(config, dataset) self.cl_rate = config['lambda'] self.eps = config['eps'] self.temperature = config['temperature'] def forward(self, perturbed=False): all_embs = self.get_ego_embeddings() embeddings_list = [] for layer_idx in range(self.n_layers): all_embs = self.gcn_conv(all_embs, self.edge_index, self.edge_weight) if perturbed: random_noise = torch.rand_like(all_embs, device=all_embs.device) all_embs = all_embs + torch.sign(all_embs) * F.normalize(random_noise, dim=-1) * self.eps embeddings_list.append(all_embs) lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_cl_loss(self, x1, x2): x1, x2 = F.normalize(x1, dim=-1), F.normalize(x2, dim=-1) pos_score = (x1 * x2).sum(dim=-1) pos_score = torch.exp(pos_score / self.temperature) ttl_score = torch.matmul(x1, x2.transpose(0, 1)) ttl_score = torch.exp(ttl_score / self.temperature).sum(dim=1) return -torch.log(pos_score / ttl_score).sum() def calculate_loss(self, interaction): loss = super().calculate_loss(interaction) user = torch.unique(interaction[self.USER_ID]) pos_item = torch.unique(interaction[self.ITEM_ID]) perturbed_user_embs_1, perturbed_item_embs_1 = self.forward(perturbed=True) perturbed_user_embs_2, perturbed_item_embs_2 = self.forward(perturbed=True) user_cl_loss = self.calculate_cl_loss(perturbed_user_embs_1[user], perturbed_user_embs_2[user]) item_cl_loss = self.calculate_cl_loss(perturbed_item_embs_1[pos_item], perturbed_item_embs_2[pos_item]) return loss + self.cl_rate * (user_cl_loss + item_cl_loss) ================================================ FILE: recbole_gnn/model/general_recommender/ssl4rec.py ================================================ r""" SSL4REC ################################################ Reference: Tiansheng Yao et al. "Self-supervised Learning for Large-scale Item Recommendations." in CIKM 2021. Reference code: https://github.com/Coder-Yu/SELFRec/model/graph/SSL4Rec.py """ import torch import torch.nn as nn import torch.nn.functional as F from recbole.model.loss import EmbLoss from recbole.utils import InputType from recbole.model.init import xavier_uniform_initialization from recbole_gnn.model.abstract_recommender import GeneralGraphRecommender class SSL4REC(GeneralGraphRecommender): input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(SSL4REC, self).__init__(config, dataset) # load parameters info self.tau = config["tau"] self.reg_weight = config["reg_weight"] self.cl_rate = config["ssl_weight"] self.require_pow = config["require_pow"] self.reg_loss = EmbLoss() self.encoder = DNN_Encoder(config, dataset) # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def forward(self, user, item): user_e, item_e = self.encoder(user, item) return user_e, item_e def calculate_batch_softmax_loss(self, user_emb, item_emb, temperature): user_emb, item_emb = F.normalize(user_emb, dim=1), F.normalize(item_emb, dim=1) pos_score = (user_emb * item_emb).sum(dim=-1) pos_score = torch.exp(pos_score / temperature) ttl_score = torch.matmul(user_emb, item_emb.transpose(0, 1)) ttl_score = torch.exp(ttl_score / temperature).sum(dim=1) loss = -torch.log(pos_score / ttl_score + 10e-6) return torch.mean(loss) def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] user_embeddings, item_embeddings = self.forward(user, pos_item) rec_loss = self.calculate_batch_softmax_loss(user_embeddings, item_embeddings, self.tau) cl_loss = self.encoder.calculate_cl_loss(pos_item) reg_loss = self.reg_loss(user_embeddings, item_embeddings, require_pow=self.require_pow) loss = rec_loss + self.cl_rate * cl_loss + self.reg_weight * reg_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_embeddings, item_embeddings = self.forward(user, item) u_embeddings = user_embeddings[user] i_embeddings = item_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward(torch.arange( self.n_users, device=self.device), torch.arange(self.n_items, device=self.device)) # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) class DNN_Encoder(nn.Module): def __init__(self, config, dataset): super(DNN_Encoder, self).__init__() self.emb_size = config["embedding_size"] self.drop_ratio = config["drop_ratio"] self.tau = config["tau"] self.USER_ID = config["USER_ID_FIELD"] self.ITEM_ID = config["ITEM_ID_FIELD"] self.n_users = dataset.num(self.USER_ID) self.n_items = dataset.num(self.ITEM_ID) self.user_tower = nn.Sequential( nn.Linear(self.emb_size, 1024), nn.ReLU(True), nn.Linear(1024, 128), nn.Tanh() ) self.item_tower = nn.Sequential( nn.Linear(self.emb_size, 1024), nn.ReLU(True), nn.Linear(1024, 128), nn.Tanh() ) self.dropout = nn.Dropout(self.drop_ratio) self.initial_user_emb = nn.Embedding(self.n_users, self.emb_size) self.initial_item_emb = nn.Embedding(self.n_items, self.emb_size) self.reset_parameters() def reset_parameters(self): nn.init.xavier_uniform_(self.initial_user_emb.weight) nn.init.xavier_uniform_(self.initial_item_emb.weight) def forward(self, q, x): q_emb = self.initial_user_emb(q) i_emb = self.initial_item_emb(x) q_emb = self.user_tower(q_emb) i_emb = self.item_tower(i_emb) return q_emb, i_emb def item_encoding(self, x): i_emb = self.initial_item_emb(x) i1_emb = self.dropout(i_emb) i2_emb = self.dropout(i_emb) i1_emb = self.item_tower(i1_emb) i2_emb = self.item_tower(i2_emb) return i1_emb, i2_emb def calculate_cl_loss(self, idx): x1, x2 = self.item_encoding(idx) x1, x2 = F.normalize(x1, dim=-1), F.normalize(x2, dim=-1) pos_score = (x1 * x2).sum(dim=-1) pos_score = torch.exp(pos_score / self.tau) ttl_score = torch.matmul(x1, x2.transpose(0, 1)) ttl_score = torch.exp(ttl_score / self.tau).sum(dim=1) return -torch.log(pos_score / ttl_score).mean() ================================================ FILE: recbole_gnn/model/general_recommender/xsimgcl.py ================================================ # -*- coding: utf-8 -*- r""" XSimGCL ################################################ Reference: Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, Hongzhi Yin. "XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation" in TKDE 2023. Reference code: https://github.com/Coder-Yu/SELFRec/blob/main/model/graph/XSimGCL.py """ import torch import torch.nn.functional as F from recbole_gnn.model.general_recommender import LightGCN class XSimGCL(LightGCN): def __init__(self, config, dataset): super(XSimGCL, self).__init__(config, dataset) self.cl_rate = config['lambda'] self.eps = config['eps'] self.temperature = config['temperature'] self.layer_cl = config['layer_cl'] def forward(self, perturbed=False): all_embs = self.get_ego_embeddings() all_embs_cl = all_embs embeddings_list = [] for layer_idx in range(self.n_layers): all_embs = self.gcn_conv(all_embs, self.edge_index, self.edge_weight) if perturbed: random_noise = torch.rand_like(all_embs, device=all_embs.device) all_embs = all_embs + torch.sign(all_embs) * F.normalize(random_noise, dim=-1) * self.eps embeddings_list.append(all_embs) if layer_idx == self.layer_cl - 1: all_embs_cl = all_embs lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) user_all_embeddings_cl, item_all_embeddings_cl = torch.split(all_embs_cl, [self.n_users, self.n_items]) if perturbed: return user_all_embeddings, item_all_embeddings, user_all_embeddings_cl, item_all_embeddings_cl return user_all_embeddings, item_all_embeddings def calculate_cl_loss(self, x1, x2): x1, x2 = F.normalize(x1, dim=-1), F.normalize(x2, dim=-1) pos_score = (x1 * x2).sum(dim=-1) pos_score = torch.exp(pos_score / self.temperature) ttl_score = torch.matmul(x1, x2.transpose(0, 1)) ttl_score = torch.exp(ttl_score / self.temperature).sum(dim=1) return -torch.log(pos_score / ttl_score).mean() def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings, user_all_embeddings_cl, item_all_embeddings_cl = self.forward(perturbed=True) u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings, require_pow=self.require_pow) user = torch.unique(interaction[self.USER_ID]) pos_item = torch.unique(interaction[self.ITEM_ID]) # calculate CL Loss user_cl_loss = self.calculate_cl_loss(user_all_embeddings[user], user_all_embeddings_cl[user]) item_cl_loss = self.calculate_cl_loss(item_all_embeddings[pos_item], item_all_embeddings_cl[pos_item]) return mf_loss, self.reg_weight * reg_loss, self.cl_rate * (user_cl_loss + item_cl_loss) ================================================ FILE: recbole_gnn/model/layers.py ================================================ import numpy as np import torch import torch.nn as nn from torch_geometric.nn import MessagePassing from torch_sparse import matmul class LightGCNConv(MessagePassing): def __init__(self, dim): super(LightGCNConv, self).__init__(aggr='add') self.dim = dim def forward(self, x, edge_index, edge_weight): return self.propagate(edge_index, x=x, edge_weight=edge_weight) def message(self, x_j, edge_weight): return edge_weight.view(-1, 1) * x_j def message_and_aggregate(self, adj_t, x): return matmul(adj_t, x, reduce=self.aggr) def __repr__(self): return '{}({})'.format(self.__class__.__name__, self.dim) class BipartiteGCNConv(MessagePassing): def __init__(self, dim): super(BipartiteGCNConv, self).__init__(aggr='add') self.dim = dim def forward(self, x, edge_index, edge_weight, size): return self.propagate(edge_index, x=x, edge_weight=edge_weight, size=size) def message(self, x_j, edge_weight): return edge_weight.view(-1, 1) * x_j def __repr__(self): return '{}({})'.format(self.__class__.__name__, self.dim) class BiGNNConv(MessagePassing): r"""Propagate a layer of Bi-interaction GNN .. math:: output = (L+I)EW_1 + LE \otimes EW_2 """ def __init__(self, in_channels, out_channels): super().__init__(aggr='add') self.in_channels, self.out_channels = in_channels, out_channels self.lin1 = torch.nn.Linear(in_features=in_channels, out_features=out_channels) self.lin2 = torch.nn.Linear(in_features=in_channels, out_features=out_channels) def forward(self, x, edge_index, edge_weight): x_prop = self.propagate(edge_index, x=x, edge_weight=edge_weight) x_trans = self.lin1(x_prop + x) x_inter = self.lin2(torch.mul(x_prop, x)) return x_trans + x_inter def message(self, x_j, edge_weight): return edge_weight.view(-1, 1) * x_j def message_and_aggregate(self, adj_t, x): return matmul(adj_t, x, reduce=self.aggr) def __repr__(self): return '{}({},{})'.format(self.__class__.__name__, self.in_channels, self.out_channels) class SRGNNConv(MessagePassing): def __init__(self, dim): # mean aggregation to incorporate weight naturally super(SRGNNConv, self).__init__(aggr='mean') self.lin = torch.nn.Linear(dim, dim) def forward(self, x, edge_index): x = self.lin(x) return self.propagate(edge_index, x=x) class SRGNNCell(nn.Module): def __init__(self, dim): super(SRGNNCell, self).__init__() self.dim = dim self.incomming_conv = SRGNNConv(dim) self.outcomming_conv = SRGNNConv(dim) self.lin_ih = nn.Linear(2 * dim, 3 * dim) self.lin_hh = nn.Linear(dim, 3 * dim) self._reset_parameters() def forward(self, hidden, edge_index): input_in = self.incomming_conv(hidden, edge_index) reversed_edge_index = torch.flip(edge_index, dims=[0]) input_out = self.outcomming_conv(hidden, reversed_edge_index) inputs = torch.cat([input_in, input_out], dim=-1) gi = self.lin_ih(inputs) gh = self.lin_hh(hidden) i_r, i_i, i_n = gi.chunk(3, -1) h_r, h_i, h_n = gh.chunk(3, -1) reset_gate = torch.sigmoid(i_r + h_r) input_gate = torch.sigmoid(i_i + h_i) new_gate = torch.tanh(i_n + reset_gate * h_n) hy = (1 - input_gate) * hidden + input_gate * new_gate return hy def _reset_parameters(self): stdv = 1.0 / np.sqrt(self.dim) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) ================================================ FILE: recbole_gnn/model/sequential_recommender/__init__.py ================================================ from recbole_gnn.model.sequential_recommender.gcegnn import GCEGNN from recbole_gnn.model.sequential_recommender.gcsan import GCSAN from recbole_gnn.model.sequential_recommender.lessr import LESSR from recbole_gnn.model.sequential_recommender.niser import NISER from recbole_gnn.model.sequential_recommender.sgnnhn import SGNNHN from recbole_gnn.model.sequential_recommender.srgnn import SRGNN from recbole_gnn.model.sequential_recommender.tagnn import TAGNN ================================================ FILE: recbole_gnn/model/sequential_recommender/gcegnn.py ================================================ # @Time : 2022/3/22 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" GCE-GNN ################################################ Reference: Ziyang Wang et al. "Global Context Enhanced Graph Neural Networks for Session-based Recommendation." in SIGIR 2020. Reference code: https://github.com/CCIIPLab/GCE-GNN """ import numpy as np from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import MessagePassing from torch_geometric.utils import softmax from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender class LocalAggregator(MessagePassing): def __init__(self, dim, alpha): super().__init__(aggr='add') self.edge_emb = nn.Embedding(4, dim) self.leakyrelu = nn.LeakyReLU(alpha) def forward(self, x, edge_index, edge_attr): return self.propagate(edge_index, x=x, edge_attr=edge_attr) def message(self, x_j, x_i, edge_attr, index, ptr, size_i): x = x_j * x_i a = self.edge_emb(edge_attr) e = (x * a).sum(dim=-1) e = self.leakyrelu(e) e = softmax(e, index, ptr, size_i) return e.unsqueeze(-1) * x_j class GlobalAggregator(nn.Module): def __init__(self, dim, dropout, act=torch.relu): super(GlobalAggregator, self).__init__() self.dropout = dropout self.act = act self.dim = dim self.w_1 = nn.Parameter(torch.Tensor(self.dim + 1, self.dim)) self.w_2 = nn.Parameter(torch.Tensor(self.dim, 1)) self.w_3 = nn.Parameter(torch.Tensor(2 * self.dim, self.dim)) self.bias = nn.Parameter(torch.Tensor(self.dim)) def forward(self, self_vectors, neighbor_vector, batch_size, masks, neighbor_weight, extra_vector=None): if extra_vector is not None: alpha = torch.matmul(torch.cat([extra_vector.unsqueeze(2).repeat(1, 1, neighbor_vector.shape[2], 1)*neighbor_vector, neighbor_weight.unsqueeze(-1)], -1), self.w_1).squeeze(-1) alpha = F.leaky_relu(alpha, negative_slope=0.2) alpha = torch.matmul(alpha, self.w_2).squeeze(-1) alpha = torch.softmax(alpha, -1).unsqueeze(-1) neighbor_vector = torch.sum(alpha * neighbor_vector, dim=-2) else: neighbor_vector = torch.mean(neighbor_vector, dim=2) # self_vectors = F.dropout(self_vectors, 0.5, training=self.training) output = torch.cat([self_vectors, neighbor_vector], -1) output = F.dropout(output, self.dropout, training=self.training) output = torch.matmul(output, self.w_3) output = output.view(batch_size, -1, self.dim) output = self.act(output) return output class GCEGNN(SequentialRecommender): def __init__(self, config, dataset): super(GCEGNN, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.leakyrelu_alpha = config['leakyrelu_alpha'] self.dropout_local = config['dropout_local'] self.dropout_global = config['dropout_global'] self.dropout_gcn = config['dropout_gcn'] self.device = config['device'] self.loss_type = config['loss_type'] self.build_global_graph = config['build_global_graph'] self.sample_num = config['sample_num'] self.hop = config['hop'] self.max_seq_length = dataset.field2seqlen[self.ITEM_SEQ] # global graph construction self.global_graph = None if self.build_global_graph: self.global_adj, self.global_weight = self.construct_global_graph(dataset) # item embedding self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.pos_embedding = nn.Embedding(self.max_seq_length, self.embedding_size) # define layers and loss # Aggregator self.local_agg = LocalAggregator(self.embedding_size, self.leakyrelu_alpha) global_agg_list = [] for i in range(self.hop): global_agg_list.append(GlobalAggregator(self.embedding_size, self.dropout_gcn)) self.global_agg = nn.ModuleList(global_agg_list) self.w_1 = nn.Linear(2 * self.embedding_size, self.embedding_size, bias=False) self.w_2 = nn.Linear(self.embedding_size, 1, bias=False) self.glu1 = nn.Linear(self.embedding_size, self.embedding_size) self.glu2 = nn.Linear(self.embedding_size, self.embedding_size, bias=False) if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") self.reset_parameters() self.other_parameter_name = ['global_adj', 'global_weight'] def reset_parameters(self): stdv = 1.0 / np.sqrt(self.embedding_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def _add_edge(self, graph, sid, tid): if tid not in graph[sid]: graph[sid][tid] = 0 graph[sid][tid] += 1 def construct_global_graph(self, dataset): self.logger.info('Constructing global graphs.') item_id_list = dataset.inter_feat['item_id_list'] src_item_ids = item_id_list[:,:4].tolist() tgt_itme_id = dataset.inter_feat['item_id'].tolist() global_graph = [{} for _ in range(self.n_items)] for i in tqdm(range(len(tgt_itme_id)), desc='Converting: '): tid = tgt_itme_id[i] for sid in src_item_ids[i]: if sid > 0: self._add_edge(global_graph, tid, sid) self._add_edge(global_graph, sid, tid) global_adj = [[] for _ in range(self.n_items)] global_weight = [[] for _ in range(self.n_items)] for i in tqdm(range(self.n_items), desc='Sorting: '): sorted_out_edges = [v for v in sorted(global_graph[i].items(), reverse=True, key=lambda x: x[1])] global_adj[i] = [v[0] for v in sorted_out_edges[:self.sample_num]] global_weight[i] = [v[1] for v in sorted_out_edges[:self.sample_num]] if len(global_adj[i]) < self.sample_num: for j in range(self.sample_num - len(global_adj[i])): global_adj[i].append(0) global_weight[i].append(0) return torch.LongTensor(global_adj).to(self.device), torch.FloatTensor(global_weight).to(self.device) def fusion(self, hidden, mask): batch_size = hidden.shape[0] length = hidden.shape[1] pos_emb = self.pos_embedding.weight[:length] pos_emb = pos_emb.unsqueeze(0).expand(batch_size, -1, -1) hs = torch.sum(hidden * mask, -2) / torch.sum(mask, 1) hs = hs.unsqueeze(-2).expand(-1, length, -1) nh = self.w_1(torch.cat([pos_emb, hidden], -1)) nh = torch.tanh(nh) nh = torch.sigmoid(self.glu1(nh) + self.glu2(hs)) beta = self.w_2(nh) beta = beta * mask final_h = torch.sum(beta * hidden, 1) return final_h def forward(self, x, edge_index, edge_attr, alias_inputs, item_seq_len): batch_size = alias_inputs.shape[0] mask = alias_inputs.gt(0).unsqueeze(-1) h = self.item_embedding(x) # local h_local = self.local_agg(h, edge_index, edge_attr) # global item_neighbors = [F.pad(x[alias_inputs], (0, self.max_seq_length - x[alias_inputs].shape[1]), "constant", 0)] weight_neighbors = [] support_size = self.max_seq_length for i in range(self.hop): item_sample_i, weight_sample_i = self.global_adj[item_neighbors[-1].view(-1)], self.global_weight[item_neighbors[-1].view(-1)] support_size *= self.sample_num item_neighbors.append(item_sample_i.view(batch_size, support_size)) weight_neighbors.append(weight_sample_i.view(batch_size, support_size)) entity_vectors = [self.item_embedding(i) for i in item_neighbors] weight_vectors = weight_neighbors session_info = [] item_emb = h[alias_inputs] * mask # mean sum_item_emb = torch.sum(item_emb, 1) / torch.sum(mask.float(), 1) # sum # sum_item_emb = torch.sum(item_emb, 1) sum_item_emb = sum_item_emb.unsqueeze(-2) for i in range(self.hop): session_info.append(sum_item_emb.repeat(1, entity_vectors[i].shape[1], 1)) for n_hop in range(self.hop): entity_vectors_next_iter = [] shape = [batch_size, -1, self.sample_num, self.embedding_size] for hop in range(self.hop - n_hop): aggregator = self.global_agg[n_hop] vector = aggregator(self_vectors=entity_vectors[hop], neighbor_vector=entity_vectors[hop + 1].view(shape), masks=None, batch_size=batch_size, neighbor_weight=weight_vectors[hop].view(batch_size, -1, self.sample_num), extra_vector=session_info[hop]) entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter h_global = entity_vectors[0].view(batch_size, self.max_seq_length, self.embedding_size) h_global = h_global[:,:alias_inputs.shape[1],:] h_local = F.dropout(h_local, self.dropout_local, training=self.training) h_global = F.dropout(h_global, self.dropout_global, training=self.training) h_local = h_local[alias_inputs] h_session = h_local + h_global h_session = self.fusion(h_session, mask) return h_session def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] edge_attr = interaction['edge_attr'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, edge_attr, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) neg_items_emb = self.item_embedding(neg_items) pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index = interaction['edge_index'] edge_attr = interaction['edge_attr'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, edge_attr, alias_inputs, item_seq_len) test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] edge_attr = interaction['edge_attr'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, edge_attr, alias_inputs, item_seq_len) test_items_emb = self.item_embedding.weight scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/gcsan.py ================================================ # @Time : 2022/3/7 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" GCSAN ################################################ Reference: Chengfeng Xu et al. "Graph Contextualized Self-Attention Network for Session-based Recommendation." in IJCAI 2019. """ import torch from torch import nn from recbole.model.layers import TransformerEncoder from recbole.model.loss import EmbLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender from recbole_gnn.model.layers import SRGNNCell class GCSAN(SequentialRecommender): r"""GCSAN captures rich local dependencies via graph neural network, and learns long-range dependencies by applying the self-attention mechanism. Note: In the original paper, the attention mechanism in the self-attention layer is a single head, for the reusability of the project code, we use a unified transformer component. According to the experimental results, we only applied regularization to embedding. """ def __init__(self, config, dataset): super(GCSAN, self).__init__(config, dataset) # load parameters info self.n_layers = config['n_layers'] self.n_heads = config['n_heads'] self.hidden_size = config['hidden_size'] # same as embedding_size self.inner_size = config['inner_size'] # the dimensionality in feed-forward layer self.hidden_dropout_prob = config['hidden_dropout_prob'] self.attn_dropout_prob = config['attn_dropout_prob'] self.hidden_act = config['hidden_act'] self.layer_norm_eps = config['layer_norm_eps'] self.step = config['step'] self.device = config['device'] self.weight = config['weight'] self.reg_weight = config['reg_weight'] self.loss_type = config['loss_type'] self.initializer_range = config['initializer_range'] # item embedding self.item_embedding = nn.Embedding(self.n_items, self.hidden_size, padding_idx=0) # define layers and loss self.gnncell = SRGNNCell(self.hidden_size) self.self_attention = TransformerEncoder( n_layers=self.n_layers, n_heads=self.n_heads, hidden_size=self.hidden_size, inner_size=self.inner_size, hidden_dropout_prob=self.hidden_dropout_prob, attn_dropout_prob=self.attn_dropout_prob, hidden_act=self.hidden_act, layer_norm_eps=self.layer_norm_eps ) self.reg_loss = EmbLoss() if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self.apply(self._init_weights) def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, (nn.Linear, nn.Embedding)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.initializer_range) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() def get_attention_mask(self, item_seq): """Generate left-to-right uni-directional attention mask for multi-head attention.""" attention_mask = (item_seq > 0).long() extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) # torch.int64 # mask for left-to-right unidirectional max_len = attention_mask.size(-1) attn_shape = (1, max_len, max_len) subsequent_mask = torch.triu(torch.ones(attn_shape), diagonal=1) # torch.uint8 subsequent_mask = (subsequent_mask == 0).unsqueeze(1) subsequent_mask = subsequent_mask.long().to(item_seq.device) extended_attention_mask = extended_attention_mask * subsequent_mask extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def forward(self, x, edge_index, alias_inputs, item_seq_len): hidden = self.item_embedding(x) for i in range(self.step): hidden = self.gnncell(hidden, edge_index) seq_hidden = hidden[alias_inputs] # fetch the last hidden state of last timestamp ht = self.gather_indexes(seq_hidden, item_seq_len - 1) attention_mask = self.get_attention_mask(alias_inputs) outputs = self.self_attention(seq_hidden, attention_mask, output_all_encoded_layers=True) output = outputs[-1] at = self.gather_indexes(output, item_seq_len - 1) seq_output = self.weight * at + (1 - self.weight) * ht return seq_output def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) neg_items_emb = self.item_embedding(neg_items) pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) else: # self.loss_type = 'CE' test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) reg_loss = self.reg_loss(self.item_embedding.weight) total_loss = loss + self.reg_weight * reg_loss return total_loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_items_emb = self.item_embedding.weight scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/lessr.py ================================================ # @Time : 2022/3/11 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" LESSR ################################################ Reference: Tianwen Chen and Raymond Chi-Wing Wong. "Handling Information Loss of Graph Neural Networks for Session-based Recommendation." in KDD 2020. Reference code: https://github.com/twchen/lessr """ import torch from torch import nn from torch_geometric.utils import softmax from torch_geometric.nn import global_add_pool from recbole.model.abstract_recommender import SequentialRecommender class EOPA(nn.Module): def __init__( self, input_dim, output_dim, batch_norm=True, feat_drop=0.0, activation=None ): super().__init__() self.batch_norm = nn.BatchNorm1d(input_dim) if batch_norm else None self.feat_drop = nn.Dropout(feat_drop) self.gru = nn.GRU(input_dim, input_dim, batch_first=True) self.fc_self = nn.Linear(input_dim, output_dim, bias=False) self.fc_neigh = nn.Linear(input_dim, output_dim, bias=False) self.activation = activation def reducer(self, nodes): m = nodes.mailbox['m'] # (num_nodes, deg, d) # m[i]: the messages passed to the i-th node with in-degree equal to 'deg' # the order of messages follows the order of incoming edges # since the edges are sorted by occurrence time when the EOP multigraph is built # the messages are in the order required by EOPA _, hn = self.gru(m) # hn: (1, num_nodes, d) return {'neigh': hn.squeeze(0)} def forward(self, mg, feat): import dgl.function as fn with mg.local_scope(): if self.batch_norm is not None: feat = self.batch_norm(feat) mg.ndata['ft'] = self.feat_drop(feat) if mg.number_of_edges() > 0: mg.update_all(fn.copy_u('ft', 'm'), self.reducer) neigh = mg.ndata['neigh'] rst = self.fc_self(feat) + self.fc_neigh(neigh) else: rst = self.fc_self(feat) if self.activation is not None: rst = self.activation(rst) return rst class SGAT(nn.Module): def __init__( self, input_dim, hidden_dim, output_dim, batch_norm=True, feat_drop=0.0, activation=None, ): super().__init__() self.batch_norm = nn.BatchNorm1d(input_dim) if batch_norm else None self.feat_drop = nn.Dropout(feat_drop) self.fc_q = nn.Linear(input_dim, hidden_dim, bias=True) self.fc_k = nn.Linear(input_dim, hidden_dim, bias=False) self.fc_v = nn.Linear(input_dim, output_dim, bias=False) self.fc_e = nn.Linear(hidden_dim, 1, bias=False) self.activation = activation def forward(self, sg, feat): import dgl.ops as F if self.batch_norm is not None: feat = self.batch_norm(feat) feat = self.feat_drop(feat) q = self.fc_q(feat) k = self.fc_k(feat) v = self.fc_v(feat) e = F.u_add_v(sg, q, k) e = self.fc_e(torch.sigmoid(e)) a = F.edge_softmax(sg, e) rst = F.u_mul_e_sum(sg, v, a) if self.activation is not None: rst = self.activation(rst) return rst class AttnReadout(nn.Module): def __init__( self, input_dim, hidden_dim, output_dim, batch_norm=True, feat_drop=0.0, activation=None, ): super().__init__() self.batch_norm = nn.BatchNorm1d(input_dim) if batch_norm else None self.feat_drop = nn.Dropout(feat_drop) self.fc_u = nn.Linear(input_dim, hidden_dim, bias=False) self.fc_v = nn.Linear(input_dim, hidden_dim, bias=True) self.fc_e = nn.Linear(hidden_dim, 1, bias=False) self.fc_out = ( nn.Linear(input_dim, output_dim, bias=False) if output_dim != input_dim else None ) self.activation = activation def forward(self, g, feat, last_nodes, batch): if self.batch_norm is not None: feat = self.batch_norm(feat) feat = self.feat_drop(feat) feat_u = self.fc_u(feat) feat_v = self.fc_v(feat[last_nodes]) feat_v = torch.index_select(feat_v, dim=0, index=batch) e = self.fc_e(torch.sigmoid(feat_u + feat_v)) alpha = softmax(e, batch) feat_norm = feat * alpha rst = global_add_pool(feat_norm, batch) if self.fc_out is not None: rst = self.fc_out(rst) if self.activation is not None: rst = self.activation(rst) return rst class LESSR(SequentialRecommender): r"""LESSR analyzes the information losses when constructing session graphs, and emphasises lossy session encoding problem and the ineffective long-range dependency capturing problem. To solve the first problem, authors propose a lossless encoding scheme and an edge-order preserving aggregation layer. To solve the second problem, authors propose a shortcut graph attention layer that effectively captures long-range dependencies. Note: We follow the original implementation, which requires DGL package. We find it difficult to implement these functions via PyG, so we remain them. If you would like to test this model, please install DGL. """ def __init__(self, config, dataset): super().__init__(config, dataset) embedding_dim = config['embedding_size'] self.num_layers = config['n_layers'] batch_norm = config['batch_norm'] feat_drop = config['feat_drop'] self.loss_type = config['loss_type'] self.item_embedding = nn.Embedding(self.n_items, embedding_dim, max_norm=1) self.layers = nn.ModuleList() input_dim = embedding_dim for i in range(self.num_layers): if i % 2 == 0: layer = EOPA( input_dim, embedding_dim, batch_norm=batch_norm, feat_drop=feat_drop, activation=nn.PReLU(embedding_dim), ) else: layer = SGAT( input_dim, embedding_dim, embedding_dim, batch_norm=batch_norm, feat_drop=feat_drop, activation=nn.PReLU(embedding_dim), ) input_dim += embedding_dim self.layers.append(layer) self.readout = AttnReadout( input_dim, embedding_dim, embedding_dim, batch_norm=batch_norm, feat_drop=feat_drop, activation=nn.PReLU(embedding_dim), ) input_dim += embedding_dim self.batch_norm = nn.BatchNorm1d(input_dim) if batch_norm else None self.feat_drop = nn.Dropout(feat_drop) self.fc_sr = nn.Linear(input_dim, embedding_dim, bias=False) if self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['CE']!") def forward(self, x, edge_index_EOP, edge_index_shortcut, batch, is_last): import dgl mg = dgl.graph((edge_index_EOP[0], edge_index_EOP[1]), num_nodes=batch.shape[0]) sg = dgl.graph((edge_index_shortcut[0], edge_index_shortcut[1]), num_nodes=batch.shape[0]) feat = self.item_embedding(x) for i, layer in enumerate(self.layers): if i % 2 == 0: out = layer(mg, feat) else: out = layer(sg, feat) feat = torch.cat([out, feat], dim=1) sr_g = self.readout(mg, feat, is_last, batch) sr_l = feat[is_last] sr = torch.cat([sr_l, sr_g], dim=1) if self.batch_norm is not None: sr = self.batch_norm(sr) sr = self.fc_sr(self.feat_drop(sr)) return sr def calculate_loss(self, interaction): x = interaction['x'] edge_index_EOP = interaction['edge_index_EOP'] edge_index_shortcut = interaction['edge_index_shortcut'] batch = interaction['batch'] is_last = interaction['is_last'] seq_output = self.forward(x, edge_index_EOP, edge_index_shortcut, batch, is_last) pos_items = interaction[self.POS_ITEM_ID] test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index_EOP = interaction['edge_index_EOP'] edge_index_shortcut = interaction['edge_index_shortcut'] batch = interaction['batch'] is_last = interaction['is_last'] seq_output = self.forward(x, edge_index_EOP, edge_index_shortcut, batch, is_last) test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index_EOP = interaction['edge_index_EOP'] edge_index_shortcut = interaction['edge_index_shortcut'] batch = interaction['batch'] is_last = interaction['is_last'] seq_output = self.forward(x, edge_index_EOP, edge_index_shortcut, batch, is_last) test_items_emb = self.item_embedding.weight scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/niser.py ================================================ # @Time : 2022/3/7 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" NISER ################################################ Reference: Priyanka Gupta et al. "NISER: Normalized Item and Session Representations to Handle Popularity Bias." in CIKM 2019 GRLA workshop. """ import numpy as np import torch from torch import nn import torch.nn.functional as F from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender from recbole_gnn.model.layers import SRGNNCell class NISER(SequentialRecommender): r"""NISER+ is a GNN-based model that normalizes session and item embeddings to handle popularity bias. """ def __init__(self, config, dataset): super(NISER, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.step = config['step'] self.device = config['device'] self.loss_type = config['loss_type'] self.sigma = config['sigma'] self.max_seq_length = dataset.field2seqlen[self.ITEM_SEQ] # item embedding self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.pos_embedding = nn.Embedding(self.max_seq_length, self.embedding_size) self.item_dropout = nn.Dropout(config['item_dropout']) # define layers and loss self.gnncell = SRGNNCell(self.embedding_size) self.linear_one = nn.Linear(self.embedding_size, self.embedding_size) self.linear_two = nn.Linear(self.embedding_size, self.embedding_size) self.linear_three = nn.Linear(self.embedding_size, 1, bias=False) self.linear_transform = nn.Linear(self.embedding_size * 2, self.embedding_size) if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self._reset_parameters() def _reset_parameters(self): stdv = 1.0 / np.sqrt(self.embedding_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, x, edge_index, alias_inputs, item_seq_len): mask = alias_inputs.gt(0) hidden = self.item_embedding(x) # Dropout in NISER+ hidden = self.item_dropout(hidden) # Normalize item embeddings hidden = F.normalize(hidden, dim=-1) for i in range(self.step): hidden = self.gnncell(hidden, edge_index) seq_hidden = hidden[alias_inputs] batch_size = seq_hidden.shape[0] pos_emb = self.pos_embedding.weight[:seq_hidden.shape[1]] pos_emb = pos_emb.unsqueeze(0).expand(batch_size, -1, -1) seq_hidden = seq_hidden + pos_emb # fetch the last hidden state of last timestamp ht = self.gather_indexes(seq_hidden, item_seq_len - 1) q1 = self.linear_one(ht).view(ht.size(0), 1, ht.size(1)) q2 = self.linear_two(seq_hidden) alpha = self.linear_three(torch.sigmoid(q1 + q2)) a = torch.sum(alpha * seq_hidden * mask.view(mask.size(0), -1, 1).float(), 1) seq_output = self.linear_transform(torch.cat([a, ht], dim=1)) # Normalize session embeddings seq_output = F.normalize(seq_output, dim=-1) return seq_output def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = F.normalize(self.item_embedding(pos_items), dim=-1) neg_items_emb = F.normalize(self.item_embedding(neg_items), dim=-1) pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(self.sigma * pos_score, self.sigma * neg_score) return loss else: # self.loss_type = 'CE' test_item_emb = F.normalize(self.item_embedding.weight, dim=-1) logits = self.sigma * torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_item_emb = F.normalize(self.item_embedding(test_item), dim=-1) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_items_emb = F.normalize(self.item_embedding.weight, dim=-1) scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/sgnnhn.py ================================================ # @Time : 2022/3/28 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" SRGNN ################################################ Reference: Zhiqiang Pan et al. "Star Graph Neural Networks for Session-based Recommendation." in CIKM 2020. Reference code: https://bitbucket.org/nudtpanzq/sgnn-hn """ import math import numpy as np import torch from torch import nn from torch_geometric.nn import global_mean_pool, global_add_pool from torch_geometric.utils import softmax from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.loss import BPRLoss from recbole_gnn.model.layers import SRGNNCell def layer_norm(x): ave_x = torch.mean(x, -1).unsqueeze(-1) x = x - ave_x norm_x = torch.sqrt(torch.sum(x**2, -1)).unsqueeze(-1) y = x / norm_x return y class SGNNHN(SequentialRecommender): r"""SGNN-HN applies a star graph neural network to model the complex transition relationship between items in an ongoing session. To avoid overfitting, it applies highway networks to adaptively select embeddings from item representations. """ def __init__(self, config, dataset): super(SGNNHN, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.step = config['step'] self.device = config['device'] self.loss_type = config['loss_type'] self.scale = config['scale'] # item embedding self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.max_seq_length = dataset.field2seqlen[self.ITEM_SEQ] self.pos_embedding = nn.Embedding(self.max_seq_length, self.embedding_size) # define layers and loss self.gnncell = SRGNNCell(self.embedding_size) self.linear_one = nn.Linear(self.embedding_size, self.embedding_size) self.linear_two = nn.Linear(self.embedding_size, self.embedding_size) self.linear_three = nn.Linear(self.embedding_size, self.embedding_size) self.linear_four = nn.Linear(self.embedding_size, 1, bias=False) self.linear_transform = nn.Linear(self.embedding_size * 2, self.embedding_size) if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self._reset_parameters() def _reset_parameters(self): stdv = 1.0 / np.sqrt(self.embedding_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def att_out(self, hidden, star_node, batch): star_node_repeat = torch.index_select(star_node, 0, batch) sim = (hidden * star_node_repeat).sum(dim=-1) sim = softmax(sim, batch) att_hidden = sim.unsqueeze(-1) * hidden output = global_add_pool(att_hidden, batch) return output def forward(self, x, edge_index, batch, alias_inputs, item_seq_len): mask = alias_inputs.gt(0) hidden = self.item_embedding(x) star_node = global_mean_pool(hidden, batch) for i in range(self.step): hidden = self.gnncell(hidden, edge_index) star_node_repeat = torch.index_select(star_node, 0, batch) sim = (hidden * star_node_repeat).sum(dim=-1, keepdim=True) / math.sqrt(self.embedding_size) alpha = torch.sigmoid(sim) hidden = (1 - alpha) * hidden + alpha * star_node_repeat star_node = self.att_out(hidden, star_node, batch) seq_hidden = hidden[alias_inputs] bs, item_num, _ = seq_hidden.shape pos_emb = self.pos_embedding.weight[:item_num] pos_emb = pos_emb.unsqueeze(0).expand(bs, -1, -1) seq_hidden = seq_hidden + pos_emb # fetch the last hidden state of last timestamp ht = self.gather_indexes(seq_hidden, item_seq_len - 1) q1 = self.linear_one(ht).view(ht.size(0), 1, ht.size(1)) q2 = self.linear_two(seq_hidden) q3 = self.linear_three(star_node).view(star_node.shape[0], 1, star_node.shape[1]) alpha = self.linear_four(torch.sigmoid(q1 + q2 + q3)) a = torch.sum(alpha * seq_hidden * mask.view(mask.size(0), -1, 1).float(), 1) seq_output = self.linear_transform(torch.cat([a, ht], dim=1)) return layer_norm(seq_output) def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] batch = interaction['batch'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, batch, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = layer_norm(self.item_embedding(pos_items)) neg_items_emb = layer_norm(self.item_embedding(neg_items)) pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) * self.scale # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) * self.scale # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' test_item_emb = layer_norm(self.item_embedding.weight) logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) * self.scale loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index = interaction['edge_index'] batch = interaction['batch'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, batch, alias_inputs, item_seq_len) test_item_emb = layer_norm(self.item_embedding(test_item)) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) * self.scale # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] batch = interaction['batch'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, batch, alias_inputs, item_seq_len) test_items_emb = layer_norm(self.item_embedding.weight) scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) * self.scale # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/srgnn.py ================================================ # @Time : 2022/3/7 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" SRGNN ################################################ Reference: Shu Wu et al. "Session-based Recommendation with Graph Neural Networks." in AAAI 2019. Reference code: https://github.com/CRIPAC-DIG/SR-GNN """ import numpy as np import torch from torch import nn from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender from recbole_gnn.model.layers import SRGNNCell class SRGNN(SequentialRecommender): r"""SRGNN regards the conversation history as a directed graph. In addition to considering the connection between the item and the adjacent item, it also considers the connection with other interactive items. Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connection matrix A Outgoing edges: === ===== ===== ===== ===== \ 1 2 3 4 === ===== ===== ===== ===== 1 0 1 0 0 2 0 0 1/2 1/2 3 0 1 0 0 4 0 0 0 0 === ===== ===== ===== ===== Incoming edges: === ===== ===== ===== ===== \ 1 2 3 4 === ===== ===== ===== ===== 1 0 0 0 0 2 1/2 0 1/2 0 3 0 1 0 0 4 0 1 0 0 === ===== ===== ===== ===== """ def __init__(self, config, dataset): super(SRGNN, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.step = config['step'] self.device = config['device'] self.loss_type = config['loss_type'] # item embedding self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) # define layers and loss self.gnncell = SRGNNCell(self.embedding_size) self.linear_one = nn.Linear(self.embedding_size, self.embedding_size) self.linear_two = nn.Linear(self.embedding_size, self.embedding_size) self.linear_three = nn.Linear(self.embedding_size, 1, bias=False) self.linear_transform = nn.Linear(self.embedding_size * 2, self.embedding_size) if self.loss_type == 'BPR': self.loss_fct = BPRLoss() elif self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self._reset_parameters() def _reset_parameters(self): stdv = 1.0 / np.sqrt(self.embedding_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, x, edge_index, alias_inputs, item_seq_len): mask = alias_inputs.gt(0) hidden = self.item_embedding(x) for i in range(self.step): hidden = self.gnncell(hidden, edge_index) seq_hidden = hidden[alias_inputs] # fetch the last hidden state of last timestamp ht = self.gather_indexes(seq_hidden, item_seq_len - 1) q1 = self.linear_one(ht).view(ht.size(0), 1, ht.size(1)) q2 = self.linear_two(seq_hidden) alpha = self.linear_three(torch.sigmoid(q1 + q2)) a = torch.sum(alpha * seq_hidden * mask.view(mask.size(0), -1, 1).float(), 1) seq_output = self.linear_transform(torch.cat([a, ht], dim=1)) return seq_output def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) neg_items_emb = self.item_embedding(neg_items) pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): test_item = interaction[self.ITEM_ID] x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(x, edge_index, alias_inputs, item_seq_len) test_items_emb = self.item_embedding.weight scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores ================================================ FILE: recbole_gnn/model/sequential_recommender/tagnn.py ================================================ # @Time : 2022/3/17 # @Author : Yupeng Hou # @Email : houyupeng@ruc.edu.cn r""" TAGNN ################################################ Reference: Feng Yu et al. "TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation." in SIGIR 2020 short. Implemented using PyTorch Geometric. Reference code: https://github.com/CRIPAC-DIG/TAGNN """ import numpy as np import torch from torch import nn import torch.nn.functional as F from recbole.model.abstract_recommender import SequentialRecommender from recbole_gnn.model.layers import SRGNNCell class TAGNN(SequentialRecommender): r"""TAGNN introduces target-aware attention and adaptively activates different user interests with respect to varied target items. """ def __init__(self, config, dataset): super(TAGNN, self).__init__(config, dataset) # load parameters info self.embedding_size = config['embedding_size'] self.step = config['step'] self.device = config['device'] self.loss_type = config['loss_type'] # item embedding self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) # define layers and loss self.gnncell = SRGNNCell(self.embedding_size) self.linear_one = nn.Linear(self.embedding_size, self.embedding_size) self.linear_two = nn.Linear(self.embedding_size, self.embedding_size) self.linear_three = nn.Linear(self.embedding_size, 1, bias=False) self.linear_transform = nn.Linear(self.embedding_size * 2, self.embedding_size) self.linear_t = nn.Linear(self.embedding_size, self.embedding_size, bias=False) #target attention if self.loss_type == 'CE': self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") # parameters initialization self._reset_parameters() def _reset_parameters(self): stdv = 1.0 / np.sqrt(self.embedding_size) for weight in self.parameters(): weight.data.uniform_(-stdv, stdv) def forward(self, x, edge_index, alias_inputs, item_seq_len): mask = alias_inputs.gt(0) hidden = self.item_embedding(x) for i in range(self.step): hidden = self.gnncell(hidden, edge_index) seq_hidden = hidden[alias_inputs] # fetch the last hidden state of last timestamp ht = self.gather_indexes(seq_hidden, item_seq_len - 1) q1 = self.linear_one(ht).view(ht.size(0), 1, ht.size(1)) q2 = self.linear_two(seq_hidden) alpha = self.linear_three(torch.sigmoid(q1 + q2)) alpha = F.softmax(alpha, 1) a = torch.sum(alpha * seq_hidden * mask.view(mask.size(0), -1, 1).float(), 1) seq_output = self.linear_transform(torch.cat([a, ht], dim=1)) seq_hidden = seq_hidden * mask.view(mask.shape[0], -1, 1).float() qt = self.linear_t(seq_hidden) b = self.item_embedding.weight beta = F.softmax(b @ qt.transpose(1,2), -1) target = beta @ seq_hidden a = seq_output.view(ht.shape[0], 1, ht.shape[1]) # b,1,d a = a + target # b,n,d scores = torch.sum(a * b, -1) # b,n return scores def calculate_loss(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] logits = self.forward(x, edge_index, alias_inputs, item_seq_len) pos_items = interaction[self.POS_ITEM_ID] loss = self.loss_fct(logits, pos_items) return loss def predict(self, interaction): pass def full_sort_predict(self, interaction): x = interaction['x'] edge_index = interaction['edge_index'] alias_inputs = interaction['alias_inputs'] item_seq_len = interaction[self.ITEM_SEQ_LEN] scores = self.forward(x, edge_index, alias_inputs, item_seq_len) return scores ================================================ FILE: recbole_gnn/model/social_recommender/__init__.py ================================================ from recbole_gnn.model.social_recommender.diffnet import DiffNet from recbole_gnn.model.social_recommender.mhcn import MHCN from recbole_gnn.model.social_recommender.sept import SEPT ================================================ FILE: recbole_gnn/model/social_recommender/diffnet.py ================================================ # @Time : 2022/3/15 # @Author : Lanling Xu # @Email : xulanling_sherry@163.com r""" DiffNet ################################################ Reference: Le Wu et al. "A Neural Influence Diffusion Model for Social Recommendation." in SIGIR 2019. Reference code: https://github.com/PeiJieSun/diffnet """ import numpy as np import torch import torch.nn as nn from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import SocialRecommender from recbole_gnn.model.layers import BipartiteGCNConv class DiffNet(SocialRecommender): r"""DiffNet is a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(DiffNet, self).__init__(config, dataset) # load dataset info self.edge_index, self.edge_weight = dataset.get_bipartite_inter_mat(row='user') self.edge_index, self.edge_weight = self.edge_index.to(self.device), self.edge_weight.to(self.device) self.net_edge_index, self.net_edge_weight = dataset.get_norm_net_adj_mat(row_norm=True) self.net_edge_index, self.net_edge_weight = self.net_edge_index.to(self.device), self.net_edge_weight.to(self.device) # load parameters info self.embedding_size = config['embedding_size'] # int type:the embedding size of DiffNet self.n_layers = config['n_layers'] # int type:the GCN layer num of DiffNet for social net self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization self.pretrained_review = config['pretrained_review'] # bool type:whether to load pre-trained review vectors of users and items # define layers and loss self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.embedding_size) self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.embedding_size) self.bipartite_gcn_conv = BipartiteGCNConv(dim=self.embedding_size) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] if self.pretrained_review: # handle review information, map the origin review into the new space self.user_review_embedding = nn.Embedding(self.n_users, self.embedding_size, padding_idx=0) self.user_review_embedding.weight.requires_grad = False self.user_review_embedding.weight.data.copy_(self.convertDistribution(dataset.user_feat['user_review_emb'])) self.item_review_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.item_review_embedding.weight.requires_grad = False self.item_review_embedding.weight.data.copy_(self.convertDistribution(dataset.item_feat['item_review_emb'])) self.user_fusion_layer = nn.Linear(self.embedding_size, self.embedding_size) self.item_fusion_layer = nn.Linear(self.embedding_size, self.embedding_size) self.activation = nn.Sigmoid() def convertDistribution(self, x): mean, std = torch.mean(x), torch.std(x) y = (x - mean) * 0.2 / std return y def forward(self): user_embedding = self.user_embedding.weight final_item_embedding = self.item_embedding.weight if self.pretrained_review: user_reduce_dim_vector_matrix = self.activation(self.user_fusion_layer(self.user_review_embedding.weight)) item_reduce_dim_vector_matrix = self.activation(self.item_fusion_layer(self.item_review_embedding.weight)) user_review_vector_matrix = self.convertDistribution(user_reduce_dim_vector_matrix) item_review_vector_matrix = self.convertDistribution(item_reduce_dim_vector_matrix) user_embedding = user_embedding + user_review_vector_matrix final_item_embedding = final_item_embedding + item_review_vector_matrix user_embedding_from_consumed_items = self.bipartite_gcn_conv(x=(final_item_embedding, user_embedding), edge_index=self.edge_index.flip([0]), edge_weight=self.edge_weight, size=(self.n_items, self.n_users)) embeddings_list = [user_embedding] for layer_idx in range(self.n_layers): user_embedding = self.bipartite_gcn_conv((user_embedding, user_embedding), self.net_edge_index.flip([0]), self.net_edge_weight, size=(self.n_users, self.n_users)) embeddings_list.append(user_embedding) final_user_embedding = torch.stack(embeddings_list, dim=1) final_user_embedding = torch.sum(final_user_embedding, dim=1) + user_embedding_from_consumed_items return final_user_embedding, final_item_embedding def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/social_recommender/mhcn.py ================================================ # @Time : 2022/4/5 # @Author : Lanling Xu # @Email : xulanling_sherry@163.com r""" MHCN ################################################ Reference: Junliang Yu et al. "Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation." in WWW 2021. Reference code: https://github.com/Coder-Yu/QRec """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from scipy.sparse import coo_matrix from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import SocialRecommender from recbole_gnn.model.layers import BipartiteGCNConv class GatingLayer(nn.Module): def __init__(self, dim): super(GatingLayer, self).__init__() self.dim = dim self.linear = nn.Linear(self.dim, self.dim) self.activation = nn.Sigmoid() def forward(self, emb): embedding = self.linear(emb) embedding = self.activation(embedding) embedding = torch.mul(emb, embedding) return embedding class AttLayer(nn.Module): def __init__(self, dim): super(AttLayer, self).__init__() self.dim = dim self.attention_mat = nn.Parameter(torch.randn([self.dim, self.dim])) self.attention = nn.Parameter(torch.randn([1, self.dim])) def forward(self, *embs): weights = [] emb_list = [] for embedding in embs: weights.append(torch.sum(torch.mul(self.attention, torch.matmul(embedding, self.attention_mat)), dim=1)) emb_list.append(embedding) score = torch.nn.Softmax(dim=0)(torch.stack(weights, dim=0)) embeddings = torch.stack(emb_list, dim=0) mixed_embeddings = torch.mul(embeddings, score.unsqueeze(dim=2).repeat(1, 1, self.dim)).sum(dim=0) return mixed_embeddings class MHCN(SocialRecommender): r"""MHCN fuses hypergraph modeling and graph neural networks in social recommendation by exploiting multiple types of high-order user relations under a multi-channel setting. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(MHCN, self).__init__(config, dataset) # load dataset info self.R_user_edge_index, self.R_user_edge_weight, self.R_item_edge_index, self.R_item_edge_weight = self.get_bipartite_inter_mat(dataset) H_s, H_j, H_p = self.get_motif_adj_matrix(dataset) # transform matrix to edge index and edge weight for convolution self.H_s_edge_index, self.H_s_edge_weight = self.get_edge_index_weight(H_s) self.H_j_edge_index, self.H_j_edge_weight = self.get_edge_index_weight(H_j) self.H_p_edge_index, self.H_p_edge_weight = self.get_edge_index_weight(H_p) # load parameters info self.embedding_size = config['embedding_size'] self.n_layers = config['n_layers'] self.ssl_reg = config['ssl_reg'] self.reg_weight = config['reg_weight'] # define embedding and loss self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.bipartite_gcn_conv = BipartiteGCNConv(dim=self.embedding_size) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # define gating layers self.gating_c1 = GatingLayer(self.embedding_size) self.gating_c2 = GatingLayer(self.embedding_size) self.gating_c3 = GatingLayer(self.embedding_size) self.gating_simple = GatingLayer(self.embedding_size) # define self supervised gating layers self.ss_gating_c1 = GatingLayer(self.embedding_size) self.ss_gating_c2 = GatingLayer(self.embedding_size) self.ss_gating_c3 = GatingLayer(self.embedding_size) # define attention layers self.attention_layer = AttLayer(self.embedding_size) # storage variables for full sort evaluation acceleration self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def get_bipartite_inter_mat(self, dataset): R_user_edge_index, R_user_edge_weight = dataset.get_bipartite_inter_mat(row='user', row_norm=False) R_item_edge_index, R_item_edge_weight = dataset.get_bipartite_inter_mat(row='item', row_norm=False) return R_user_edge_index.to(self.device), R_user_edge_weight.to(self.device), R_item_edge_index.to(self.device), R_item_edge_weight.to(self.device) def get_edge_index_weight(self, matrix): matrix = coo_matrix(matrix) edge_index = torch.stack([torch.LongTensor(matrix.row), torch.LongTensor(matrix.col)]) edge_weight = torch.FloatTensor(matrix.data) return edge_index.to(self.device), edge_weight.to(self.device) def get_motif_adj_matrix(self, dataset): S = dataset.net_matrix() Y = dataset.inter_matrix() B = S.multiply(S.T) U = S - B C1 = (U.dot(U)).multiply(U.T) A1 = C1 + C1.T C2 = (B.dot(U)).multiply(U.T) + (U.dot(B)).multiply(U.T) + (U.dot(U)).multiply(B) A2 = C2 + C2.T C3 = (B.dot(B)).multiply(U) + (B.dot(U)).multiply(B) + (U.dot(B)).multiply(B) A3 = C3 + C3.T A4 = (B.dot(B)).multiply(B) C5 = (U.dot(U)).multiply(U) + (U.dot(U.T)).multiply(U) + (U.T.dot(U)).multiply(U) A5 = C5 + C5.T A6 = (U.dot(B)).multiply(U) + (B.dot(U.T)).multiply(U.T) + (U.T.dot(U)).multiply(B) A7 = (U.T.dot(B)).multiply(U.T) + (B.dot(U)).multiply(U) + (U.dot(U.T)).multiply(B) A8 = (Y.dot(Y.T)).multiply(B) A9 = (Y.dot(Y.T)).multiply(U) A9 = A9 + A9.T A10 = Y.dot(Y.T) - A8 - A9 # addition and row-normalization H_s = sum([A1, A2, A3, A4, A5, A6, A7]) # add epsilon to avoid divide by zero Warning H_s = H_s.multiply(1.0 / (H_s.sum(axis=1) + 1e-7).reshape(-1, 1)) H_j = sum([A8, A9]) H_j = H_j.multiply(1.0 / (H_j.sum(axis=1) + 1e-7).reshape(-1, 1)) H_p = A10 H_p = H_p.multiply(H_p > 1) H_p = H_p.multiply(1.0 / (H_p.sum(axis=1) + 1e-7).reshape(-1, 1)) return H_s, H_j, H_p def forward(self): # get ego embeddings user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight # self-gating user_embeddings_c1 = self.gating_c1(user_embeddings) user_embeddings_c2 = self.gating_c2(user_embeddings) user_embeddings_c3 = self.gating_c3(user_embeddings) simple_user_embeddings = self.gating_simple(user_embeddings) all_embeddings_c1 = [user_embeddings_c1] all_embeddings_c2 = [user_embeddings_c2] all_embeddings_c3 = [user_embeddings_c3] all_embeddings_simple = [simple_user_embeddings] all_embeddings_i = [item_embeddings] for layer_idx in range(self.n_layers): mixed_embedding = self.attention_layer(user_embeddings_c1, user_embeddings_c2, user_embeddings_c3) + simple_user_embeddings / 2 # Channel S user_embeddings_c1 = self.bipartite_gcn_conv((user_embeddings_c1, user_embeddings_c1), self.H_s_edge_index.flip([0]), self.H_s_edge_weight, size=(self.n_users, self.n_users)) norm_embeddings = F.normalize(user_embeddings_c1, p=2, dim=1) all_embeddings_c1 += [norm_embeddings] # Channel J user_embeddings_c2 = self.bipartite_gcn_conv((user_embeddings_c2, user_embeddings_c2), self.H_j_edge_index.flip([0]), self.H_j_edge_weight, size=(self.n_users, self.n_users)) norm_embeddings = F.normalize(user_embeddings_c2, p=2, dim=1) all_embeddings_c2 += [norm_embeddings] # Channel P user_embeddings_c3 = self.bipartite_gcn_conv((user_embeddings_c3, user_embeddings_c3), self.H_p_edge_index.flip([0]), self.H_p_edge_weight, size=(self.n_users, self.n_users)) norm_embeddings = F.normalize(user_embeddings_c3, p=2, dim=1) all_embeddings_c3 += [norm_embeddings] # item convolution new_item_embeddings = self.bipartite_gcn_conv((mixed_embedding, item_embeddings), self.R_item_edge_index.flip([0]), self.R_item_edge_weight, size=(self.n_users, self.n_items)) norm_embeddings = F.normalize(new_item_embeddings, p=2, dim=1) all_embeddings_i += [norm_embeddings] simple_user_embeddings = self.bipartite_gcn_conv((item_embeddings, simple_user_embeddings), self.R_user_edge_index.flip([0]), self.R_user_edge_weight, size=(self.n_items, self.n_users)) norm_embeddings = F.normalize(simple_user_embeddings, p=2, dim=1) all_embeddings_simple += [norm_embeddings] item_embeddings = new_item_embeddings # averaging the channel-specific embeddings user_embeddings_c1 = torch.stack(all_embeddings_c1, dim=0).sum(dim=0) user_embeddings_c2 = torch.stack(all_embeddings_c2, dim=0).sum(dim=0) user_embeddings_c3 = torch.stack(all_embeddings_c3, dim=0).sum(dim=0) simple_user_embeddings = torch.stack(all_embeddings_simple, dim=0).sum(dim=0) item_all_embeddings = torch.stack(all_embeddings_i, dim=0).sum(dim=0) # aggregating channel-specific embeddings user_all_embeddings = self.attention_layer(user_embeddings_c1, user_embeddings_c2, user_embeddings_c3) user_all_embeddings += simple_user_embeddings / 2 return user_all_embeddings, item_all_embeddings def hierarchical_self_supervision(self, user_embeddings, edge_index, edge_weight): def row_shuffle(embedding): shuffled_embeddings = embedding[torch.randperm(embedding.size(0))] return shuffled_embeddings def row_column_shuffle(embedding): shuffled_embeddings = embedding[:, torch.randperm(embedding.size(1))] shuffled_embeddings = shuffled_embeddings[torch.randperm(embedding.size(0))] return shuffled_embeddings def score(x1, x2): return torch.sum(torch.mul(x1, x2), dim=1) # For Douban, normalization is needed. # user_embeddings = F.normalize(user_embeddings, p=2, dim=1) edge_embeddings = self.bipartite_gcn_conv((user_embeddings, user_embeddings), edge_index.flip([0]), edge_weight, size=(self.n_users, self.n_users)) # Local MIM pos = score(user_embeddings, edge_embeddings) neg1 = score(row_shuffle(user_embeddings), edge_embeddings) neg2 = score(row_column_shuffle(edge_embeddings), user_embeddings) local_loss = torch.sum(-torch.log(torch.sigmoid(pos - neg1)) - torch.log(torch.sigmoid(neg1 - neg2))) # Global MIM graph = torch.mean(edge_embeddings, dim=0, keepdim=True) pos = score(edge_embeddings, graph) neg1 = score(row_column_shuffle(edge_embeddings), graph) global_loss = torch.sum(-torch.log(torch.sigmoid(pos - neg1))) return global_loss + local_loss def calculate_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate self-supervised loss ss_loss = self.hierarchical_self_supervision(self.ss_gating_c1(user_all_embeddings), self.H_s_edge_index, self.H_s_edge_weight) ss_loss += self.hierarchical_self_supervision(self.ss_gating_c2(user_all_embeddings), self.H_j_edge_index, self.H_j_edge_weight) ss_loss += self.hierarchical_self_supervision(self.ss_gating_c3(user_all_embeddings), self.H_p_edge_index, self.H_p_edge_weight) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) loss = mf_loss + self.ssl_reg * ss_loss + self.reg_weight * reg_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/model/social_recommender/sept.py ================================================ # @Time : 2022/3/29 # @Author : Lanling Xu # @Email : xulanling_sherry@163.com r""" SEPT ################################################ Reference: Junliang Yu et al. "Socially-Aware Self-Supervised Tri-Training for Recommendation." in KDD 2021. Reference code: https://github.com/Coder-Yu/QRec """ import numpy as np import torch import torch.nn.functional as F from scipy.sparse import coo_matrix, eye from torch_geometric.utils import degree from recbole.model.init import xavier_uniform_initialization from recbole.model.loss import BPRLoss, EmbLoss from recbole.utils import InputType from recbole_gnn.model.abstract_recommender import SocialRecommender from recbole_gnn.model.layers import LightGCNConv class SEPT(SocialRecommender): r"""SEPT is a socially-aware GCN-based SSL framework that integrates tri-training. Under the regime of tri-training for multi-view encoding, the framework builds three graph encoders (one for recommendation) upon the augmented views and iteratively improves each encoder with self-supervision signals from other users, generated by the other two encoders. We implement the model following the original author with a pairwise training mode. """ input_type = InputType.PAIRWISE def __init__(self, config, dataset): super(SEPT, self).__init__(config, dataset) # load dataset info self.edge_index, self.edge_weight = dataset.get_norm_adj_mat() self.edge_index, self.edge_weight = self.edge_index.to(self.device), self.edge_weight.to(self.device) # generate intermediate data self.social_edge_index, self.social_edge_weight, self.sharing_edge_index, \ self.sharing_edge_weight = self.get_user_view_matrix(dataset) self._user = dataset.inter_feat[dataset.uid_field] self._item = dataset.inter_feat[dataset.iid_field] self._src_user = dataset.net_feat[dataset.net_src_field] self._tgt_user = dataset.net_feat[dataset.net_tgt_field] # load parameters info self.latent_dim = config["embedding_size"] self.n_layers = int(config["n_layers"]) self.drop_ratio = config["drop_ratio"] self.instance_cnt = config["instance_cnt"] self.reg_weight = config["reg_weight"] self.ssl_weight = config["ssl_weight"] self.ssl_tau = config["ssl_tau"] # define layers and loss self.user_embedding = torch.nn.Embedding(self.n_users, self.latent_dim) self.item_embedding = torch.nn.Embedding(self.n_items, self.latent_dim) self.gcn_conv = LightGCNConv(dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() # storage variables for full sort evaluation acceleration self.user_all_embeddings = None self.restore_user_e = None self.restore_item_e = None # parameters initialization self.apply(xavier_uniform_initialization) self.other_parameter_name = ['restore_user_e', 'restore_item_e'] def get_norm_edge_weight(self, edge_index, node_num): r"""Get normalized edge weight using the laplace matrix. """ deg = degree(edge_index[0], node_num) norm_deg = 1. / torch.sqrt(torch.where(deg == 0, torch.ones([1]), deg)) edge_weight = norm_deg[edge_index[0]] * norm_deg[edge_index[1]] return edge_weight def get_user_view_matrix(self, dataset): # Friend View: A_f = (SS) ⊙ S social_mat = dataset.net_matrix() social_matrix = social_mat.dot(social_mat) social_matrix = social_matrix.toarray() * social_mat.toarray() + eye(self.n_users) social_matrix = coo_matrix(social_matrix) social_edge_index = torch.stack([torch.LongTensor(social_matrix.row), torch.LongTensor(social_matrix.col)]) social_edge_weight = self.get_norm_edge_weight(social_edge_index, self.n_users) # Sharing View: A_s = (RR^T) ⊙ S rating_mat = dataset.inter_matrix() sharing_matrix = rating_mat.dot(rating_mat.T) sharing_matrix = sharing_matrix.toarray() * social_mat.toarray() + eye(self.n_users) sharing_matrix = coo_matrix(sharing_matrix) sharing_edge_index = torch.stack([torch.LongTensor(sharing_matrix.row), torch.LongTensor(sharing_matrix.col)]) sharing_edge_weight = self.get_norm_edge_weight(sharing_edge_index, self.n_users) return social_edge_index.to(self.device), social_edge_weight.to(self.device), \ sharing_edge_index.to(self.device), sharing_edge_weight.to(self.device) def subgraph_construction(self): r"""Perturb the joint graph to construct subgraph for integrated self-supervision signals. """ def rand_sample(high, size=None, replace=True): return np.random.choice(np.arange(high), size=size, replace=replace) # perturb the raw graph with edge dropout keep = rand_sample(len(self._user), size=int(len(self._user) * (1 - self.drop_ratio)), replace=False) row = self._user[keep] col = self._item[keep] + self.n_users # perturb the social graph with edge dropout net_keep = rand_sample(len(self._src_user), size=int(len(self._src_user) * (1 - self.drop_ratio)), replace=False) net_row = self._src_user[net_keep] net_col = self._tgt_user[net_keep] # concatenation and normalization edge_index1 = torch.stack([row, col]) edge_index2 = torch.stack([col, row]) edge_index3 = torch.stack([net_row, net_col]) edge_index = torch.cat([edge_index1, edge_index2, edge_index3], dim=1) edge_weight = self.get_norm_edge_weight(edge_index, self.n_users + self.n_items) self.sub_graph = edge_index.to(self.device), edge_weight.to(self.device) def get_ego_embeddings(self): r"""Get the embedding of users and items and combine to an embedding matrix. Returns: Tensor of the embedding matrix. Shape of [n_items+n_users, embedding_dim] """ user_embeddings = self.user_embedding.weight item_embeddings = self.item_embedding.weight ego_embeddings = torch.cat([user_embeddings, item_embeddings], dim=0) return ego_embeddings def forward(self, graph=None): all_embeddings = torch.cat([self.user_embedding.weight, self.item_embedding.weight]) embeddings_list = [all_embeddings] if graph is None: # for the original graph edge_index, edge_weight = self.edge_index, self.edge_weight else: # for the augmented graph edge_index, edge_weight = graph for _ in range(self.n_layers): all_embeddings = self.gcn_conv(all_embeddings, edge_index, edge_weight) norm_embeddings = F.normalize(all_embeddings, p=2, dim=1) embeddings_list.append(norm_embeddings) all_embeddings = torch.stack(embeddings_list, dim=1) all_embeddings = torch.sum(all_embeddings, dim=1) user_all_embeddings, item_all_embeddings = torch.split(all_embeddings, [self.n_users, self.n_items], dim=0) return user_all_embeddings, item_all_embeddings def user_view_forward(self): all_social_embeddings = self.user_embedding.weight all_sharing_embeddings = self.user_embedding.weight social_embeddings_list = [all_social_embeddings] sharing_embeddings_list = [all_sharing_embeddings] for _ in range(self.n_layers): # friend view all_social_embeddings = self.gcn_conv(all_social_embeddings, self.social_edge_index, self.social_edge_weight) norm_social_embeddings = F.normalize(all_social_embeddings, p=2, dim=1) social_embeddings_list.append(norm_social_embeddings) # sharing view all_sharing_embeddings = self.gcn_conv(all_sharing_embeddings, self.sharing_edge_index, self.sharing_edge_weight) norm_sharing_embeddings = F.normalize(all_sharing_embeddings, p=2, dim=1) sharing_embeddings_list.append(norm_sharing_embeddings) social_all_embeddings = torch.stack(social_embeddings_list, dim=1) social_all_embeddings = torch.sum(social_all_embeddings, dim=1) sharing_all_embeddings = torch.stack(sharing_embeddings_list, dim=1) sharing_all_embeddings = torch.sum(sharing_all_embeddings, dim=1) return social_all_embeddings, sharing_all_embeddings def label_prediction(self, emb, aug_emb): prob = torch.matmul(emb, aug_emb.transpose(0, 1)) prob = F.softmax(prob, dim=1) return prob def sampling(self, logits): return torch.topk(logits, k=self.instance_cnt)[1] def generate_pesudo_labels(self, prob1, prob2): positive = (prob1 + prob2) / 2 pos_examples = self.sampling(positive) return pos_examples def calculate_ssl_loss(self, aug_emb, positive, emb): pos_emb = aug_emb[positive] pos_score = torch.sum(emb.unsqueeze(dim=1).repeat(1, self.instance_cnt, 1) * pos_emb, dim=2) ttl_score = torch.matmul(emb, aug_emb.transpose(0, 1)) pos_score = torch.sum(torch.exp(pos_score / self.ssl_tau), dim=1) ttl_score = torch.sum(torch.exp(ttl_score / self.ssl_tau), dim=1) ssl_loss = - torch.sum(torch.log(pos_score / ttl_score)) return ssl_loss def calculate_rec_loss(self, interaction): # clear the storage variable when training if self.restore_user_e is not None or self.restore_item_e is not None: self.restore_user_e, self.restore_item_e = None, None user = interaction[self.USER_ID] pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] self.user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = self.user_all_embeddings[user] pos_embeddings = item_all_embeddings[pos_item] neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate regularization Loss u_ego_embeddings = self.user_embedding(user) pos_ego_embeddings = self.item_embedding(pos_item) neg_ego_embeddings = self.item_embedding(neg_item) reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss def calculate_loss(self, interaction): # preference view rec_loss = self.calculate_rec_loss(interaction) # unlabeled sample view aug_user_embeddings, _ = self.forward(graph=self.sub_graph) # friend and sharing views friend_view_embeddings, sharing_view_embeddings = self.user_view_forward() user = interaction[self.USER_ID] aug_u_embeddings = aug_user_embeddings[user] social_u_embeddings = friend_view_embeddings[user] sharing_u_embeddings = sharing_view_embeddings[user] rec_u_embeddings = self.user_all_embeddings[user] aug_u_embeddings = F.normalize(aug_u_embeddings, p=2, dim=1) social_u_embeddings = F.normalize(social_u_embeddings, p=2, dim=1) sharing_u_embeddings = F.normalize(sharing_u_embeddings, p=2, dim=1) rec_u_embeddings = F.normalize(rec_u_embeddings, p=2, dim=1) # self-supervision prediction social_prediction = self.label_prediction(social_u_embeddings, aug_u_embeddings) sharing_prediction = self.label_prediction(sharing_u_embeddings, aug_u_embeddings) rec_prediction = self.label_prediction(rec_u_embeddings, aug_u_embeddings) # find informative positive examples for each encoder friend_pos = self.generate_pesudo_labels(sharing_prediction, rec_prediction) sharing_pos = self.generate_pesudo_labels(social_prediction, rec_prediction) rec_pos = self.generate_pesudo_labels(social_prediction, sharing_prediction) # neighbor-discrimination based contrastive learning ssl_loss = self.calculate_ssl_loss(aug_u_embeddings, friend_pos, social_u_embeddings) ssl_loss += self.calculate_ssl_loss(aug_u_embeddings, sharing_pos, sharing_u_embeddings) ssl_loss += self.calculate_ssl_loss(aug_u_embeddings, rec_pos, rec_u_embeddings) # L = L_r + β * L_{ssl} loss = rec_loss + self.ssl_weight * ssl_loss return loss def predict(self, interaction): user = interaction[self.USER_ID] item = interaction[self.ITEM_ID] user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] i_embeddings = item_all_embeddings[item] scores = torch.mul(u_embeddings, i_embeddings).sum(dim=1) return scores def full_sort_predict(self, interaction): user = interaction[self.USER_ID] if self.restore_user_e is None or self.restore_item_e is None: self.restore_user_e, self.restore_item_e = self.forward() # get user embedding from storage variable u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) ================================================ FILE: recbole_gnn/properties/model/DiffNet.yaml ================================================ embedding_size: 64 n_layers: 2 reg_weight: 1e-05 pretrained_review: False ================================================ FILE: recbole_gnn/properties/model/DirectAU.yaml ================================================ embedding_size: 64 encoder: "MF" # "MF" or "lightGCN" gamma: 0.5 weight_decay: 1e-6 train_batch_size: 256 # n_layers: 3 # needed for LightGCN ================================================ FILE: recbole_gnn/properties/model/GCEGNN.yaml ================================================ embedding_size: 64 leakyrelu_alpha: 0.2 dropout_local: 0. dropout_global: 0.5 dropout_gcn: 0. loss_type: CE gnn_transform: sess_graph # global build_global_graph: True sample_num: 12 hop: 1 ================================================ FILE: recbole_gnn/properties/model/GCSAN.yaml ================================================ n_layers: 1 n_heads: 1 hidden_size: 64 inner_size: 256 hidden_dropout_prob: 0.2 attn_dropout_prob: 0.2 hidden_act: 'gelu' layer_norm_eps: 1e-12 initializer_range: 0.02 step: 1 weight: 0.6 reg_weight: 5e-5 loss_type: 'CE' gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/HMLET.yaml ================================================ embedding_size: 64 n_layers: 4 reg_weight: 1e-05 require_pow: True gate_layer_ids: [2,3] gating_mlp_dims: [64,16,2] dropout_ratio: 0.2 activation_function: elu warm_up_epochs: 50 ori_temp: 0.7 min_temp: 0.01 gum_temp_decay: 0.005 epoch_temp_decay: 1 ================================================ FILE: recbole_gnn/properties/model/LESSR.yaml ================================================ embedding_size: 64 n_layers: 4 batch_norm: True feat_drop: 0.2 loss_type: CE gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/LightGCL.yaml ================================================ embedding_size: 64 # (int) The embedding size of users and items. n_layers: 2 # (int) The number of layers in LightGCL. dropout: 0.0 # (float) The dropout ratio. temp: 0.8 # (float) The temperature in softmax. lambda1: 0.01 # (float) The hyperparameter to control the strengths of SSL. lambda2: 1e-05 # (float) The L2 regularization weight. q: 5 # (int) A slightly overestimated rank of the adjacency matrix. ================================================ FILE: recbole_gnn/properties/model/LightGCN.yaml ================================================ embedding_size: 64 n_layers: 2 reg_weight: 1e-05 require_pow: True ================================================ FILE: recbole_gnn/properties/model/MHCN.yaml ================================================ embedding_size: 64 n_layers: 2 ssl_reg: 1e-05 reg_weight: 1e-05 ================================================ FILE: recbole_gnn/properties/model/NCL.yaml ================================================ embedding_size: 64 n_layers: 3 reg_weight: 1e-4 ssl_temp: 0.1 ssl_reg: 1e-7 hyper_layers: 1 alpha: 1 proto_reg: 8e-8 num_clusters: 1000 m_step: 1 warm_up_step: 20 ================================================ FILE: recbole_gnn/properties/model/NGCF.yaml ================================================ embedding_size: 64 hidden_size_list: [64,64,64] node_dropout: 0.0 message_dropout: 0.1 reg_weight: 1e-5 ================================================ FILE: recbole_gnn/properties/model/NISER.yaml ================================================ embedding_size: 64 step: 1 sigma: 16 item_dropout: 0.1 loss_type: 'CE' gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/SEPT.yaml ================================================ warm_up_epochs: 100 embedding_size: 64 n_layers: 2 drop_ratio: 0.3 instance_cnt: 10 reg_weight: 1e-05 ssl_weight: 1e-07 ssl_tau: 0.1 ================================================ FILE: recbole_gnn/properties/model/SGL.yaml ================================================ type: "ED" n_layers: 3 ssl_tau: 0.5 reg_weight: 1e-5 ssl_weight: 0.05 drop_ratio: 0.1 embedding_size: 64 ================================================ FILE: recbole_gnn/properties/model/SGNNHN.yaml ================================================ embedding_size: 64 step: 6 scale: 12 loss_type: 'CE' gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/SRGNN.yaml ================================================ embedding_size: 64 step: 1 loss_type: 'CE' gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/SSL4REC.yaml ================================================ embedding_size: 64 drop_ratio: 0.1 tau: 0.1 reg_weight: 1e-04 ssl_weight: 1e-05 require_pow: True ================================================ FILE: recbole_gnn/properties/model/SimGCL.yaml ================================================ embedding_size: 64 n_layers: 2 reg_weight: 1e-4 lambda: 0.5 eps: 0.1 temperature: 0.2 ================================================ FILE: recbole_gnn/properties/model/TAGNN.yaml ================================================ embedding_size: 64 step: 1 loss_type: 'CE' gnn_transform: sess_graph ================================================ FILE: recbole_gnn/properties/model/XSimGCL.yaml ================================================ embedding_size: 64 n_layers: 2 reg_weight: 0.0001 lambda: 0.1 eps: 0.2 temperature: 0.2 layer_cl: 1 require_pow: True ================================================ FILE: recbole_gnn/properties/quick_start_config/sequential_base.yaml ================================================ train_neg_sample_args: ~ ================================================ FILE: recbole_gnn/properties/quick_start_config/social_base.yaml ================================================ NET_SOURCE_ID_FIELD: source_id NET_TARGET_ID_FIELD: target_id load_col: inter: ['user_id', 'item_id', 'rating', 'timestamp'] net: [source_id, target_id] filter_net_by_inter: True undirected_net: True ================================================ FILE: recbole_gnn/quick_start.py ================================================ import logging from logging import getLogger from recbole.utils import init_logger, init_seed, set_color from recbole_gnn.config import Config from recbole_gnn.utils import create_dataset, data_preparation, get_model, get_trainer def run_recbole_gnn(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True): r""" A fast running api, which includes the complete process of training and testing a model on a specified dataset Args: model (str, optional): Model name. Defaults to ``None``. dataset (str, optional): Dataset name. Defaults to ``None``. config_file_list (list, optional): Config files used to modify experiment parameters. Defaults to ``None``. config_dict (dict, optional): Parameters dictionary used to modify experiment parameters. Defaults to ``None``. saved (bool, optional): Whether to save the model. Defaults to ``True``. """ # configurations initialization config = Config(model=model, dataset=dataset, config_file_list=config_file_list, config_dict=config_dict) try: assert config["enable_sparse"] in [True, False, None] except AssertionError: raise ValueError("Your config `enable_sparse` must be `True` or `False` or `None`") init_seed(config['seed'], config['reproducibility']) # logger initialization init_logger(config) logger = getLogger() logger.info(config) # dataset filtering dataset = create_dataset(config) logger.info(dataset) # dataset splitting train_data, valid_data, test_data = data_preparation(config, dataset) # model loading and initialization init_seed(config['seed'], config['reproducibility']) model = get_model(config['model'])(config, train_data.dataset).to(config['device']) logger.info(model) # trainer loading and initialization trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) # model training best_valid_score, best_valid_result = trainer.fit( train_data, valid_data, saved=saved, show_progress=config['show_progress'] ) # model evaluation test_result = trainer.evaluate(test_data, load_best_model=saved, show_progress=config['show_progress']) logger.info(set_color('best valid ', 'yellow') + f': {best_valid_result}') logger.info(set_color('test result', 'yellow') + f': {test_result}') return { 'best_valid_score': best_valid_score, 'valid_score_bigger': config['valid_metric_bigger'], 'best_valid_result': best_valid_result, 'test_result': test_result } def objective_function(config_dict=None, config_file_list=None, saved=True): r""" The default objective_function used in HyperTuning Args: config_dict (dict, optional): Parameters dictionary used to modify experiment parameters. Defaults to ``None``. config_file_list (list, optional): Config files used to modify experiment parameters. Defaults to ``None``. saved (bool, optional): Whether to save the model. Defaults to ``True``. """ config = Config(config_dict=config_dict, config_file_list=config_file_list) try: assert config["enable_sparse"] in [True, False, None] except AssertionError: raise ValueError("Your config `enable_sparse` must be `True` or `False` or `None`") init_seed(config['seed'], config['reproducibility']) logging.basicConfig(level=logging.ERROR) dataset = create_dataset(config) train_data, valid_data, test_data = data_preparation(config, dataset) init_seed(config['seed'], config['reproducibility']) model = get_model(config['model'])(config, train_data.dataset).to(config['device']) trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, verbose=False, saved=saved) test_result = trainer.evaluate(test_data, load_best_model=saved) return { 'model': config['model'], 'best_valid_score': best_valid_score, 'valid_score_bigger': config['valid_metric_bigger'], 'best_valid_result': best_valid_result, 'test_result': test_result } ================================================ FILE: recbole_gnn/trainer.py ================================================ from time import time import math from torch.nn.utils.clip_grad import clip_grad_norm_ from tqdm import tqdm from recbole.trainer import Trainer from recbole.utils import early_stopping, dict2str, set_color, get_gpu_usage class NCLTrainer(Trainer): def __init__(self, config, model): super(NCLTrainer, self).__init__(config, model) self.num_m_step = config['m_step'] assert self.num_m_step is not None def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): r"""Train the model based on the train data and the valid data. Args: train_data (DataLoader): the train data valid_data (DataLoader, optional): the valid data, default: None. If it's None, the early_stopping is invalid. verbose (bool, optional): whether to write training and evaluation information to logger, default: True saved (bool, optional): whether to save the model parameters, default: True show_progress (bool): Show the progress of training epoch and evaluate epoch. Defaults to ``False``. callback_fn (callable): Optional callback function executed at end of epoch. Includes (epoch_idx, valid_score) input arguments. Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) """ if saved and self.start_epoch >= self.epochs: self._save_checkpoint(-1) self.eval_collector.data_collect(train_data) for epoch_idx in range(self.start_epoch, self.epochs): # only differences from the original trainer if epoch_idx % self.num_m_step == 0: self.logger.info("Running E-step ! ") self.model.e_step() # train training_start_time = time() train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss) if verbose: self.logger.info(train_loss_output) self._add_train_loss_to_tensorboard(epoch_idx, train_loss) # eval if self.eval_step <= 0 or not valid_data: if saved: self._save_checkpoint(epoch_idx) update_output = set_color('Saving current', 'blue') + ': %s' % self.saved_model_file if verbose: self.logger.info(update_output) continue if (epoch_idx + 1) % self.eval_step == 0: valid_start_time = time() valid_score, valid_result = self._valid_epoch(valid_data, show_progress=show_progress) self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping( valid_score, self.best_valid_score, self.cur_step, max_step=self.stopping_step, bigger=self.valid_metric_bigger ) valid_end_time = time() valid_score_output = (set_color("epoch %d evaluating", 'green') + " [" + set_color("time", 'blue') + ": %.2fs, " + set_color("valid_score", 'blue') + ": %f]") % \ (epoch_idx, valid_end_time - valid_start_time, valid_score) valid_result_output = set_color('valid result', 'blue') + ': \n' + dict2str(valid_result) if verbose: self.logger.info(valid_score_output) self.logger.info(valid_result_output) self.tensorboard.add_scalar('Vaild_score', valid_score, epoch_idx) if update_flag: if saved: self._save_checkpoint(epoch_idx) update_output = set_color('Saving current best', 'blue') + ': %s' % self.saved_model_file if verbose: self.logger.info(update_output) self.best_valid_result = valid_result if callback_fn: callback_fn(epoch_idx, valid_score) if stop_flag: stop_output = 'Finished training, best eval result in epoch %d' % \ (epoch_idx - self.cur_step * self.eval_step) if verbose: self.logger.info(stop_output) break self._add_hparam_to_tensorboard(self.best_valid_score) return self.best_valid_score, self.best_valid_result def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): r"""Train the model in an epoch Args: train_data (DataLoader): The train data. epoch_idx (int): The current epoch id. loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be :attr:`self.model.calculate_loss`. Defaults to ``None``. show_progress (bool): Show the progress of training epoch. Defaults to ``False``. Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, it will return a tuple which includes the sum of loss in each part. """ self.model.train() loss_func = loss_func or self.model.calculate_loss total_loss = None iter_data = ( tqdm( train_data, total=len(train_data), ncols=100, desc=set_color(f"Train {epoch_idx:>5}", 'pink'), ) if show_progress else train_data ) for batch_idx, interaction in enumerate(iter_data): interaction = interaction.to(self.device) self.optimizer.zero_grad() losses = loss_func(interaction) if isinstance(losses, tuple): if epoch_idx < self.config['warm_up_step']: losses = losses[:-1] loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) loss.backward() if self.clip_grad_norm: clip_grad_norm_(self.model.parameters(), **self.clip_grad_norm) self.optimizer.step() if self.gpu_available and show_progress: iter_data.set_postfix_str(set_color('GPU RAM: ' + get_gpu_usage(self.device), 'yellow')) return total_loss class HMLETTrainer(Trainer): def __init__(self, config, model): super(HMLETTrainer, self).__init__(config, model) self.warm_up_epochs = config['warm_up_epochs'] self.ori_temp = config['ori_temp'] self.min_temp = config['min_temp'] self.gum_temp_decay = config['gum_temp_decay'] self.epoch_temp_decay = config['epoch_temp_decay'] def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): if epoch_idx > self.warm_up_epochs: # Temp decay gum_temp = self.ori_temp * math.exp(-self.gum_temp_decay*(epoch_idx - self.warm_up_epochs)) self.model.gum_temp = max(gum_temp, self.min_temp) self.logger.info(f'Current gumbel softmax temperature: {self.model.gum_temp}') for gating in self.model.gating_nets: self.model._gating_freeze(gating, True) return super()._train_epoch(train_data, epoch_idx, loss_func, show_progress) class SEPTTrainer(Trainer): def __init__(self, config, model): super(SEPTTrainer, self).__init__(config, model) self.warm_up_epochs = config['warm_up_epochs'] def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): if epoch_idx < self.warm_up_epochs: loss_func = self.model.calculate_rec_loss else: self.model.subgraph_construction() return super()._train_epoch(train_data, epoch_idx, loss_func, show_progress) ================================================ FILE: recbole_gnn/utils.py ================================================ import os import pickle import importlib from logging import getLogger from recbole.data.utils import load_split_dataloaders, create_samplers, save_split_dataloaders from recbole.data.utils import create_dataset as create_recbole_dataset from recbole.data.utils import data_preparation as recbole_data_preparation from recbole.utils import set_color, Enum from recbole.utils import get_model as get_recbole_model from recbole.utils import get_trainer as get_recbole_trainer from recbole.utils.argument_list import dataset_arguments from recbole_gnn.data.dataloader import CustomizedTrainDataLoader, CustomizedNegSampleEvalDataLoader, CustomizedFullSortEvalDataLoader def create_dataset(config): """Create dataset according to :attr:`config['model']` and :attr:`config['MODEL_TYPE']`. If :attr:`config['dataset_save_path']` file exists and its :attr:`config` of dataset is equal to current :attr:`config` of dataset. It will return the saved dataset in :attr:`config['dataset_save_path']`. Args: config (Config): An instance object of Config, used to record parameter information. Returns: Dataset: Constructed dataset. """ model_type = config['MODEL_TYPE'] dataset_module = importlib.import_module('recbole_gnn.data.dataset') gen_graph_module_path = '.'.join(['recbole_gnn.model.general_recommender', config['model'].lower()]) seq_module_path = '.'.join(['recbole_gnn.model.sequential_recommender', config['model'].lower()]) if hasattr(dataset_module, config['model'] + 'Dataset'): dataset_class = getattr(dataset_module, config['model'] + 'Dataset') elif importlib.util.find_spec(gen_graph_module_path, __name__): dataset_class = getattr(dataset_module, 'GeneralGraphDataset') elif importlib.util.find_spec(seq_module_path, __name__): dataset_class = getattr(dataset_module, 'SessionGraphDataset') elif model_type == ModelType.SOCIAL: dataset_class = getattr(dataset_module, 'SocialDataset') else: return create_recbole_dataset(config) default_file = os.path.join(config['checkpoint_dir'], f'{config["dataset"]}-{dataset_class.__name__}.pth') file = config['dataset_save_path'] or default_file if os.path.exists(file): with open(file, 'rb') as f: dataset = pickle.load(f) dataset_args_unchanged = True for arg in dataset_arguments + ['seed', 'repeatable']: if config[arg] != dataset.config[arg]: dataset_args_unchanged = False break if dataset_args_unchanged: logger = getLogger() logger.info(set_color('Load filtered dataset from', 'pink') + f': [{file}]') return dataset dataset = dataset_class(config) if config['save_dataset']: dataset.save() return dataset def get_model(model_name): r"""Automatically select model class based on model name Args: model_name (str): model name Returns: Recommender: model class """ model_submodule = [ 'general_recommender', 'sequential_recommender', 'social_recommender' ] model_file_name = model_name.lower() model_module = None for submodule in model_submodule: module_path = '.'.join(['recbole_gnn.model', submodule, model_file_name]) if importlib.util.find_spec(module_path, __name__): model_module = importlib.import_module(module_path, __name__) break if model_module is None: model_class = get_recbole_model(model_name) else: model_class = getattr(model_module, model_name) return model_class def _get_customized_dataloader(config, phase): if phase == 'train': return CustomizedTrainDataLoader else: eval_mode = config["eval_args"]["mode"] if eval_mode == 'full': return CustomizedFullSortEvalDataLoader else: return CustomizedNegSampleEvalDataLoader def data_preparation(config, dataset): """Split the dataset by :attr:`config['eval_args']` and create training, validation and test dataloader. Note: If we can load split dataloaders by :meth:`load_split_dataloaders`, we will not create new split dataloaders. Args: config (Config): An instance object of Config, used to record parameter information. dataset (Dataset): An instance object of Dataset, which contains all interaction records. Returns: tuple: - train_data (AbstractDataLoader): The dataloader for training. - valid_data (AbstractDataLoader): The dataloader for validation. - test_data (AbstractDataLoader): The dataloader for testing. """ seq_module_path = '.'.join(['recbole_gnn.model.sequential_recommender', config['model'].lower()]) if importlib.util.find_spec(seq_module_path, __name__): # Special condition for sequential models of RecBole-Graph dataloaders = load_split_dataloaders(config) if dataloaders is not None: train_data, valid_data, test_data = dataloaders else: built_datasets = dataset.build() train_dataset, valid_dataset, test_dataset = built_datasets train_sampler, valid_sampler, test_sampler = create_samplers(config, dataset, built_datasets) train_data = _get_customized_dataloader(config, 'train')(config, train_dataset, train_sampler, shuffle=True) valid_data = _get_customized_dataloader(config, 'evaluation')(config, valid_dataset, valid_sampler, shuffle=False) test_data = _get_customized_dataloader(config, 'evaluation')(config, test_dataset, test_sampler, shuffle=False) if config['save_dataloaders']: save_split_dataloaders(config, dataloaders=(train_data, valid_data, test_data)) logger = getLogger() logger.info( set_color('[Training]: ', 'pink') + set_color('train_batch_size', 'cyan') + ' = ' + set_color(f'[{config["train_batch_size"]}]', 'yellow') + set_color(' negative sampling', 'cyan') + ': ' + set_color(f'[{config["train_neg_sample_args"]}]', 'yellow') ) logger.info( set_color('[Evaluation]: ', 'pink') + set_color('eval_batch_size', 'cyan') + ' = ' + set_color(f'[{config["eval_batch_size"]}]', 'yellow') + set_color(' eval_args', 'cyan') + ': ' + set_color(f'[{config["eval_args"]}]', 'yellow') ) return train_data, valid_data, test_data else: return recbole_data_preparation(config, dataset) def get_trainer(model_type, model_name): r"""Automatically select trainer class based on model type and model name Args: model_type (ModelType): model type model_name (str): model name Returns: Trainer: trainer class """ try: return getattr(importlib.import_module('recbole_gnn.trainer'), model_name + 'Trainer') except AttributeError: return get_recbole_trainer(model_type, model_name) class ModelType(Enum): """Type of models. - ``Social``: Social-based Recommendation """ SOCIAL = 7 ================================================ FILE: results/README.md ================================================ ## General Model Results * [ml-1m](general/ml-1m.md) ## Sequential Model Results * [diginetica](sequential/diginetica.md) ## Social-aware Model Results * [lastfm](social/lastfm.md) ================================================ FILE: results/general/ml-1m.md ================================================ # Experimental Setting **Dataset:** [MovieLens-1M](https://grouplens.org/datasets/movielens/) **Filtering:** Remove interactions with a rating score of less than 3 **Evaluation:** ratio-based 8:1:1, full sort **Metrics:** Recall@10, NGCG@10, MRR@10, Hit@10, Precision@10 **Properties:** ```yaml # dataset config field_separator: "\t" seq_separator: " " USER_ID_FIELD: user_id ITEM_ID_FIELD: item_id RATING_FIELD: rating NEG_PREFIX: neg_ LABEL_FIELD: label load_col: inter: [user_id, item_id, rating] val_interval: rating: "[3,inf)" unused_col: inter: [rating] # training and evaluation epochs: 500 train_batch_size: 4096 valid_metric: MRR@10 eval_batch_size: 4096000 ``` For fairness, we restrict users' and items' embedding dimension as following. Please adjust the name of the corresponding args of different models. ``` embedding_size: 64 ``` # Dataset Statistics | Dataset | #Users | #Items | #Interactions | Sparsity | | ---------- | ------ | ------ | ------------- | -------- | | ml-1m | 6,040 | 3,629 | 836,478 | 96.18% | # Evaluation Results | Method | Recall@10 | MRR@10 | NDCG@10 | Hit@10 | Precision@10 | |--------------|-----------|--------|---------|--------|--------------| | **BPR** | 0.1776 | 0.4187 | 0.2401 | 0.7199 | 0.1779 | | **NeuMF** | 0.1651 | 0.4020 | 0.2271 | 0.7029 | 0.1700 | | **NGCF** | 0.1814 | 0.4354 | 0.2508 | 0.7239 | 0.1850 | | **LightGCN** | 0.1861 | 0.4388 | 0.2538 | 0.7330 | 0.1863 | | **LightGCL** | 0.1867 | 0.4283 | 0.2479 | 0.7370 | 0.1815 | | **SGL** | 0.1889 | 0.4315 | 0.2505 | 0.7392 | 0.1843 | | **HMLET** | 0.1847 | 0.4297 | 0.2490 | 0.7305 | 0.1836 | | **NCL** | 0.2021 | 0.4599 | 0.2702 | 0.7565 | 0.1962 | | **SimGCL** | 0.2029 | 0.4550 | 0.2667 | 0.7640 | 0.1933 | | **XSimGCL** | 0.2116 | 0.4638 | 0.2750 | 0.7743 | 0.1987 | # Hyper-parameters | | Best hyper-parameters | Tuning range | |--------------|------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | **BPR** | learning_rate=0.001 | learning_rate choice [0.05, 0.02, 0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002, 0.0001, 0.00005, 0.00002, 0.00001] | | **NeuMF** | learning_rate=0.0001
mlp_hidden_size=[32,16,8]
dropout_prob=0 | learning_rate choice [0.005, 0.002, 0.001, 0.0005, 0.0002, 0.0001, 0.00005]
mlp_hidden_size choice ['[64,64]', '[64,32]', '[64,32,16]','[32,16,8]']
dropout_prob choice [0, 0.1, 0.2] | | **NGCF** | learning_rate=0.0002
message_dropout=0.0
node_dropout=0.0 | learning_rate choice [0.001, 0.0005, 0.0002]
node_dropout choice [0.0, 0.1]
message_dropout choice [0.0, 0.1] | | **LightGCN** | learning_rate=0.002
n_layers=3
reg_weight=0.0001 | learning_rate choice [0.005, 0.002, 0.001]
n_layers choice [2, 3]
reg_weight choice [1e-4, 1e-5] | | **LightGCL** | learning_rate=0.001
n_layers=2
lambda1=0.0001
temp=2
lambda2=1e-7
dropout=0.1 | learning_rate choice [0.001]
n_layers choice [2, 3]
lambda1 choice [0.01, 0.005, 0.001, 0.0001, 1e-5, 1e-7]
temp choice [0.5, 0.8, 2, 3]
lambda2 choice [1e-4, 1e-5, 1e-7]
dropout choice [0.0, 0.1, 0.25] | | **SGL** | learning_rate=0.002
n_layers=3
reg_weight=0.0001
ssl_tau=0.5
drop_ratio=0.1
ssl_weight=0.005 | learning_rate choice [0.002]
n_layers choice [3]
reg_weight choice [1e-4]
ssl_tau choice [0.1, 0.5]
drop_ratio choice [0.1, 0.3]
ssl_weight choice [1e-5, 1e-6, 1e-7, 0.005, 0.01, 0.05] | | **HMLET** | learning_rate=0.002
n_layers=4
activation_function=leakyrelu | learning_rate choice [0.002, 0.001, 0.0005]
n_layers choice [3, 4]
activation_function choice ['elu', 'leakyrelu'] | | **NCL** | learning_rate=0.002
n_layers=3
reg_weight=0.0001
ssl_temp=0.1
ssl_reg=1e-06
hyper_layers=1
alpha=1.5 | learning_rate choice [0.002]
n_layers choice [3]
reg_weight choice [1e-4]
ssl_temp choice [0.1, 0.05]
ssl_reg choice [1e-7, 1e-6]
hyper_layers choice [1]
alpha choice [1, 0.8, 1.5] | | **SimGCL** | learning_rate=0.002
n_layers=2
reg_weight=0.0001
temperature=0.05
lambda=1e-5
eps=0.1 | learning_rate choice [0.002]
n_layers choice [2, 3]
reg_weight choice [1e-4]
temperature choice [0.05, 0.1, 0.2]
lambda choice [1e-5, 1e-6, 1e-7, 0.005, 0.01, 0.05]
eps choice [0.1, 0.2] | | **XSimGCL** | learning_rate=0.002
n_layers=2
reg_weight=0.0001
temperature=0.2
lambda=0.1
eps=0.2
layer_cl=1 | learning_rate choice [0.002]
n_layers choice [2, 3]
reg_weight choice [1e-4]
temperature choice [0.05, 0.1, 0.2]
lambda choice [1e-5, 1e-6, 1e-7, 1e-4, 0.005, 0.01, 0.05, 0.1]
eps choice [0.1, 0.2]
layer_cl choice [1] | ================================================ FILE: results/sequential/diginetica.md ================================================ # Experimental Setting **Dataset:** diginetica-not-merged **Filtering:** Remove users and items with less than 5 interactions **Evaluation:** leave one out, full sort **Metrics:** Recall@10, NGCG@10, MRR@10, Hit@10, Precision@10 **Properties:** ```yaml # dataset config field_separator: "\t" seq_separator: " " USER_ID_FIELD: session_id ITEM_ID_FIELD: item_id TIME_FIELD: timestamp NEG_PREFIX: neg_ ITEM_LIST_LENGTH_FIELD: item_length LIST_SUFFIX: _list MAX_ITEM_LIST_LENGTH: 20 POSITION_FIELD: position_id load_col: inter: [session_id, item_id, timestamp] user_inter_num_interval: "[5,inf)" item_inter_num_interval: "[5,inf)" # training and evaluation epochs: 500 train_batch_size: 4096 eval_batch_size: 2000 valid_metric: MRR@10 eval_args: split: {'LS':"valid_and_test"} mode: full order: TO train_neg_sample_args: ~ ``` For fairness, we restrict users' and items' embedding dimension as following. Please adjust the name of the corresponding args of different models. ``` embedding_size: 64 ``` # Dataset Statistics | Dataset | #Users | #Items | #Interactions | Sparsity | | ---------- | ------ | ------ | ------------- | -------- | | diginetica | 72,014 | 29,454 | 580,490 | 99.97% | # Evaluation Results | Method | Recall@10 | MRR@10 | NDCG@10 | Hit@10 | Precision@10 | | -------------------- | --------- | ------ | ------- | ------ | ------------ | | **GRU4Rec** | 0.3691 | 0.1632 | 0.2114 | 0.3691 | 0.0369 | | **NARM** | 0.3801 | 0.1695 | 0.2188 | 0.3801 | 0.0380 | | **SASRec** | 0.4144 | 0.1857 | 0.2393 | 0.4144 | 0.0414 | | **SR-GNN** | 0.3881 | 0.1754 | 0.2253 | 0.3881 | 0.0388 | | **GC-SAN** | 0.4127 | 0.1881 | 0.2408 | 0.4127 | 0.0413 | | **NISER+** | 0.4144 | 0.1904 | 0.2430 | 0.4144 | 0.0414 | | **LESSR** | 0.3964 | 0.1763 | 0.2279 | 0.3964 | 0.0396 | | **TAGNN** | 0.3894 | 0.1763 | 0.2263 | 0.3894 | 0.0389 | | **GCE-GNN** | 0.4284 | 0.1961 | 0.2507 | 0.4284 | 0.0428 | | **SGNN-HN** | 0.4183 | 0.1877 | 0.2418 | 0.4183 | 0.0418 | # Hyper-parameters | | Best hyper-parameters | Tuning range | | -------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | | **GRU4Rec** | learning_rate=0.01
hidden_size=128
dropout_prob=0.3
num_layers=1 | learning_rate in [1e-2, 1e-3, 3e-3]
num_layers in [1, 2, 3]
hidden_size in [128]
dropout_prob in [0.1, 0.2, 0.3] | | **SASRec** | learning_rate=0.001
n_layers=2
attn_dropout_prob=0.2
hidden_dropout_prob=0.2 | learning_rate in [0.001, 0.0001]
n_layers in [1, 2]
hidden_dropout_prob in [0.2, 0.5]
attn_dropout_prob in [0.2, 0.5] | | **NARM** | learning_rate=0.001
hidden_size=128
n_layers=1
dropout_probs=[0.25, 0.5] | learning_rate in [0.001, 0.01, 0.03]
hidden_size in [128]
n_layers in [1, 2]
dropout_probs in ['[0.25,0.5]', '[0.2,0.2]', '[0.1,0.2]'] | | **SR-GNN** | learning_rate=0.001
step=1 | learning_rate in [0.01, 0.001, 0.0001]
step in [1, 2] | | **GC-SAN** | learning_rate=0.001
step=1 | learning_rate in [0.01, 0.001, 0.0001]
step in [1, 2] | | **NISER+** | learning_rate=0.001
sigma=16 | learning_rate in [0.01, 0.001, 0.003]
sigma in [10, 16, 20] | | **LESSR** | learning_rate=0.001
n_layers=4 | learning_rate in [0.01, 0.001, 0.003]
n_layers in [2, 4] | | **TAGNN** | learning_rate=0.001 | learning_rate in [0.01, 0.001, 0.003]
train_batch_size=512 | | **GCE-GNN** | learning_rate=0.001
dropout_global=0.5 | learning_rate in [0.01, 0.001, 0.003]
dropout_global in [0.2, 0.5] | | **SGNN-HN** | learning_rate=0.003
scale=12
step=2 | learning_rate in [0.01, 0.001, 0.003]
scale in [12, 16, 20]
step in [2, 4, 6] | ================================================ FILE: results/social/lastfm.md ================================================ # Experimental Setting **Dataset:** [LastFM](http://files.grouplens.org/datasets/hetrec2011/) > Note that datasets for social recommendation methods can be downloaded from [Social-Datasets](https://github.com/Sherry-XLL/Social-Datasets). **Filtering:** None **Evaluation:** ratio-based 8:1:1, full sort **Metrics:** Recall@10, NGCG@10, MRR@10, Hit@10, Precision@10 **Properties:** ```yaml # dataset config field_separator: "\t" seq_separator: " " USER_ID_FIELD: user_id ITEM_ID_FIELD: artist_id NET_SOURCE_ID_FIELD: source_id NET_TARGET_ID_FIELD: target_id LABEL_FIELD: label NEG_PREFIX: neg_ load_col: inter: [user_id, artist_id] net: [source_id, target_id] # social network config filter_net_by_inter: True undirected_net: True # training and evaluation epochs: 5000 train_batch_size: 4096 eval_batch_size: 409600000 valid_metric: NDCG@10 stopping_step: 50 ``` For fairness, we restrict users' and items' embedding dimension as following. Please adjust the name of the corresponding args of different models. ``` embedding_size: 64 ``` # Dataset Statistics | Dataset | #Users | #Items | #Interactions | Sparsity | | ---------- | ------ | ------ | ------------- | -------- | | lastfm | 1,892 | 17,632 | 92,834 | 99.72% | # Evaluation Results | Method | Recall@10 | MRR@10 | NDCG@10 | Hit@10 | Precision@10 | | -------------------- | --------- | ------ | ------- | ------ | ------------ | | **BPR** | 0.1761 | 0.3026 | 0.1674 | 0.5573 | 0.0858 | | **NeuMF** | 0.1696 | 0.2924 | 0.1604 | 0.5456 | 0.0828 | | **NGCF** | 0.1960 | 0.3479 | 0.1898 | 0.6141 | 0.0961 | | **LightGCN** | 0.2064 | 0.3559 | 0.1972 | 0.6322 | 0.1009 | | **DiffNet** | 0.1757 | 0.3117 | 0.1694 | 0.5621 | 0.0857 | | **MHCN** | 0.2123 | 0.3782 | 0.2068 | 0.6523 | 0.1042 | | **SEPT** | 0.2127 | 0.3703 | 0.2057 | 0.6465 | 0.1044 | # Hyper-parameters | | Best hyper-parameters | Tuning range | | -------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | | **BPR** | learning_rate=0.0005 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001] | | **NeuMF** | learning_rate=0.0005
dropout_prob=0.1 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
dropout_prob in [0.1, 0.2, 0.3] | | **NGCF** | learning_rate=0.0005
hidden_size_list=[64,64,64] | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
hidden_size_list in ['[64]', '[64,64]', '[64,64,64]'] | | **LightGCN** | learning_rate=0.001
n_layers=3 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
n_layers in [1, 2, 3] | | **DiffNet** | learning_rate=0.0005
n_layers=1 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
n_layers in [1, 2, 3] | | **MHCN** | learning_rate=0.0005
n_layers=2
ssl_reg=1e-05 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
n_layers in [1, 2, 3]
ssl_reg in [1e-04, 1e-05, 1e-06] | | **SEPT** | learning_rate=0.0005
n_layers=2
ssl_weight=1e-07 | learning_rate in [0.01, 0.005, 0.001, 0.0005, 0.0001]
n_layers in [1, 2, 3]
ssl_weight in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7] | ================================================ FILE: run_hyper.py ================================================ import argparse from recbole.trainer import HyperTuning from recbole_gnn.quick_start import objective_function def main(): parser = argparse.ArgumentParser() parser.add_argument('--config_files', type=str, default=None, help='fixed config files') parser.add_argument('--params_file', type=str, default=None, help='parameters file') parser.add_argument('--output_file', type=str, default='hyper_example.result', help='output file') args, _ = parser.parse_known_args() # plz set algo='exhaustive' to use exhaustive search, in this case, max_evals is auto set config_file_list = args.config_files.strip().split(' ') if args.config_files else None hp = HyperTuning(objective_function, algo='exhaustive', params_file=args.params_file, fixed_config_file_list=config_file_list) hp.run() hp.export_result(output_file=args.output_file) print('best params: ', hp.best_params) print('best result: ') print(hp.params2result[hp.params2str(hp.best_params)]) if __name__ == '__main__': main() ================================================ FILE: run_recbole_gnn.py ================================================ import argparse from recbole_gnn.quick_start import run_recbole_gnn if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', '-m', type=str, default='BPR', help='name of models') parser.add_argument('--dataset', '-d', type=str, default='ml-100k', help='name of datasets') parser.add_argument('--config_files', type=str, default=None, help='config files') args, _ = parser.parse_known_args() config_file_list = args.config_files.strip().split(' ') if args.config_files else None run_recbole_gnn(model=args.model, dataset=args.dataset, config_file_list=config_file_list) ================================================ FILE: run_test.sh ================================================ #!/bin/bash python -m pytest -v tests/test_model.py echo "model tests finished" ================================================ FILE: tests/test_data/test/test.inter ================================================ user_id:token item_id:token rating:float timestamp:float 196 242 3 881250949 186 302 3 891717742 22 377 1 878887116 244 51 2 880606923 166 346 1 886397596 298 474 4 884182806 115 265 2 881171488 253 465 5 891628467 305 451 3 886324817 6 86 3 883603013 62 257 2 879372434 286 1014 5 879781125 200 222 5 876042340 210 40 3 891035994 224 29 3 888104457 303 785 3 879485318 122 387 5 879270459 194 274 2 879539794 291 1042 4 874834944 234 1184 2 892079237 119 392 4 886176814 167 486 4 892738452 299 144 4 877881320 291 118 2 874833878 308 1 4 887736532 95 546 2 879196566 38 95 5 892430094 102 768 2 883748450 63 277 4 875747401 160 234 5 876861185 50 246 3 877052329 301 98 4 882075827 225 193 4 879539727 290 88 4 880731963 97 194 3 884238860 157 274 4 886890835 181 1081 1 878962623 278 603 5 891295330 276 796 1 874791932 7 32 4 891350932 10 16 4 877888877 284 304 4 885329322 201 979 2 884114233 276 564 3 874791805 287 327 5 875333916 246 201 5 884921594 242 1137 5 879741196 249 241 5 879641194 99 4 5 886519097 178 332 3 882823437 251 100 4 886271884 81 432 2 876535131 260 322 4 890618898 25 181 5 885853415 59 196 5 888205088 72 679 2 880037164 87 384 4 879877127 290 143 5 880474293 42 423 5 881107687 292 515 4 881103977 115 20 3 881171009 20 288 1 879667584 201 219 4 884112673 13 526 3 882141053 246 919 4 884920949 138 26 5 879024232 167 232 1 892738341 60 427 5 883326620 57 304 5 883698581 223 274 4 891550094 189 512 4 893277702 243 15 3 879987440 92 1049 1 890251826 246 416 3 884923047 194 165 4 879546723 241 690 2 887249482 178 248 4 882823954 254 1444 3 886475558 293 5 3 888906576 127 229 5 884364867 225 237 5 879539643 299 229 3 878192429 225 480 5 879540748 276 54 3 874791025 291 144 5 874835091 222 366 4 878183381 267 518 5 878971773 42 403 3 881108684 11 111 4 891903862 95 625 4 888954412 8 338 4 879361873 162 25 4 877635573 87 1016 4 879876194 279 154 5 875296291 145 275 2 885557505 119 1153 5 874781198 62 498 4 879373848 62 382 3 879375537 28 209 4 881961214 135 23 4 879857765 32 294 3 883709863 90 382 5 891383835 286 208 4 877531942 293 685 3 888905170 216 144 4 880234639 166 328 5 886397722 250 496 4 878090499 271 132 5 885848672 160 174 5 876860807 265 118 4 875320714 198 498 3 884207492 42 96 5 881107178 168 151 5 884288058 110 307 4 886987260 58 144 4 884304936 90 648 4 891384754 271 346 4 885844430 62 21 3 879373460 279 832 3 881375854 237 514 4 879376641 94 789 4 891720887 128 485 3 879966895 298 317 4 884182806 44 195 5 878347874 264 200 5 886122352 194 385 2 879524643 72 195 5 880037702 222 750 5 883815120 250 264 3 878089182 41 265 3 890687042 224 245 3 888082216 82 135 3 878769629 262 1147 4 879791710 293 471 3 888904884 216 658 3 880245029 250 140 3 878092059 59 23 5 888205300 286 379 5 877533771 244 815 4 880605185 7 479 4 891352010 174 368 1 886434402 87 274 4 879876734 194 1211 2 879551380 82 1134 2 884714402 13 836 2 882139746 13 272 4 884538403 244 756 2 880605157 305 427 5 886323090 95 787 2 888954930 43 14 2 883955745 299 955 4 889502823 57 419 3 883698454 84 405 3 883452363 269 504 4 891449922 299 111 3 877878184 194 466 4 879525876 160 135 4 876860807 99 268 3 885678247 10 486 4 877886846 259 117 4 874724988 85 427 3 879456350 303 919 4 879467295 213 273 5 878870987 121 514 3 891387947 90 98 5 891383204 49 559 2 888067405 42 794 3 881108425 155 323 2 879371261 68 117 4 876973939 172 177 4 875537965 19 4 4 885412840 268 231 4 875744136 5 2 3 875636053 305 117 2 886324028 44 294 4 883612356 43 137 4 875975656 279 1336 1 875298353 80 466 5 887401701 254 164 4 886472768 298 281 3 884183336 279 1240 1 892174404 66 298 4 883601324 18 443 3 880130193 268 1035 2 875542174 99 79 4 885680138 13 98 4 881515011 26 258 3 891347949 7 455 4 891353086 222 755 4 878183481 200 673 5 884128554 119 328 4 876923913 213 172 5 878955442 276 322 3 874786392 94 1217 3 891723086 130 379 4 875801662 38 328 4 892428688 160 719 3 876857977 293 1267 3 888906966 26 930 2 891385985 130 216 4 875216545 92 1079 3 886443455 256 452 4 882164999 1 61 4 878542420 72 48 4 880036718 56 755 3 892910207 13 360 4 882140926 15 405 2 879455957 92 77 3 875654637 207 476 2 884386343 292 174 5 881105481 232 483 5 888549622 251 748 2 886272175 224 26 3 888104153 181 220 4 878962392 259 255 4 874724710 305 471 4 886323648 52 280 3 882922806 161 202 5 891170769 148 408 5 877399018 125 235 2 892838559 97 228 5 884238860 58 1098 4 884304936 83 234 4 887665548 90 347 4 891383319 272 178 5 879455113 194 181 3 879521396 125 478 4 879454628 110 688 1 886987605 299 14 4 877877775 151 10 5 879524921 269 127 4 891446165 6 14 5 883599249 54 106 3 880937882 303 69 5 879467542 16 944 1 877727122 301 790 4 882078621 276 1091 3 874793035 305 214 2 886323068 194 1028 2 879541148 91 323 2 891438397 87 554 4 879875940 294 109 4 877819599 286 171 4 877531791 200 318 5 884128458 229 328 1 891632142 178 568 4 882826555 303 842 2 879484804 62 65 4 879374686 207 591 3 876018608 92 172 4 875653271 301 401 4 882078040 36 339 5 882157581 70 746 3 884150257 63 242 3 875747190 28 201 3 881961671 279 68 4 875307407 250 7 4 878089716 14 98 3 890881335 299 1018 3 889502324 194 54 3 879525876 303 815 3 879485532 119 237 5 874775038 295 218 5 879966498 268 930 2 875742942 268 2 2 875744173 66 258 4 883601089 233 202 5 879394264 83 623 4 880308578 214 334 3 891542540 192 476 2 881368243 100 344 4 891374868 268 145 1 875744501 301 56 4 882076587 307 89 5 879283786 234 141 3 892334609 83 576 4 880308755 181 264 2 878961624 297 133 4 875240090 38 153 5 892430369 7 382 4 891352093 264 813 4 886122952 181 872 1 878961814 201 146 1 884140579 85 507 4 879456199 269 367 3 891450023 59 468 3 888205855 286 143 4 889651549 193 96 1 889124507 113 595 5 875936424 292 11 5 881104093 130 1014 3 876250718 275 98 4 875155140 189 520 5 893265380 219 82 1 889452455 218 209 5 877488546 123 427 3 879873020 119 222 5 874775311 158 177 4 880134407 222 118 4 877563802 302 322 2 879436875 279 501 3 875308843 301 79 5 882076403 181 3 2 878963441 201 695 1 884140115 13 198 3 881515193 1 189 3 888732928 145 237 5 875270570 23 385 4 874786462 201 767 4 884114505 296 705 5 884197193 42 546 3 881105817 33 872 3 891964230 301 554 3 882078830 16 64 5 877720297 95 135 3 879197562 154 357 4 879138713 77 484 5 884733766 296 508 5 884196584 302 303 2 879436785 244 673 3 880606667 222 77 4 878183616 13 215 5 882140588 16 705 5 877722736 270 452 4 876956264 145 15 2 875270655 187 64 5 879465631 200 304 5 876041644 170 749 5 887646170 101 829 3 877136138 184 218 3 889909840 128 204 4 879967478 181 1295 1 878961781 184 153 3 889911285 1 33 4 878542699 1 160 4 875072547 184 321 5 889906967 54 595 3 880937813 94 343 4 891725009 128 508 4 879967767 23 323 2 874784266 301 227 3 882077222 301 191 3 882075672 112 903 1 892440172 82 183 3 878769848 222 724 3 878181976 218 430 3 877488316 308 1197 4 887739521 303 134 5 879467959 133 751 3 890588547 215 212 2 891435680 69 256 5 882126156 254 662 4 887347350 276 2 4 874792436 104 984 1 888442575 63 1067 3 875747514 267 410 4 878970785 13 56 5 881515011 240 879 3 885775745 286 237 2 875806800 294 271 5 889241426 90 1086 4 891384424 18 26 4 880129731 92 229 3 875656201 308 649 4 887739292 144 89 3 888105691 191 302 4 891560253 59 951 3 888206409 200 96 5 884129409 16 197 5 877726146 61 678 3 892302309 271 199 4 885848448 271 709 3 885849325 142 169 5 888640356 275 597 3 876197678 222 151 3 878182109 87 40 3 879876917 207 258 4 877879172 272 1393 2 879454663 177 333 4 880130397 207 1115 2 879664906 299 577 3 889503806 271 378 4 885849447 305 425 4 886324486 49 959 2 888068912 94 1224 3 891722802 130 1017 3 874953895 10 175 3 877888677 203 321 3 880433418 191 286 4 891560842 43 323 3 875975110 21 558 5 874951695 197 96 5 891409839 13 344 2 888073635 194 66 3 879527264 234 206 4 892334543 308 402 4 887740700 308 640 4 887737036 269 522 5 891447773 94 265 4 891721889 268 62 3 875310824 272 12 5 879455254 121 291 3 891390477 296 20 5 884196921 134 286 3 891732334 180 462 5 877544218 234 612 3 892079140 104 117 2 888465972 38 758 1 892434626 269 845 1 891456255 7 163 4 891353444 234 1451 3 892078343 275 405 2 876197645 52 250 3 882922661 102 823 3 888801465 13 186 4 890704999 178 731 4 882827532 236 71 3 890116671 256 781 5 882165296 263 176 5 891299752 244 186 3 880605697 279 1181 4 875314001 43 815 4 883956189 83 78 2 880309089 151 197 5 879528710 254 436 2 886474216 109 631 3 880579371 297 716 3 875239422 249 188 4 879641067 144 699 4 888106106 301 604 4 882075994 64 392 3 889737542 92 501 2 875653665 222 97 4 878181739 268 436 3 875310745 293 135 5 888905550 213 173 5 878955442 160 460 2 876861185 13 498 4 882139901 59 715 5 888205921 5 17 4 875636198 125 163 5 879454956 174 315 5 886432749 114 505 3 881260203 213 515 4 878870518 23 196 2 874786926 128 15 4 879968827 239 56 4 889179478 181 279 1 878962955 291 80 4 875086354 250 238 4 878089963 201 649 3 884114275 60 60 5 883327734 181 325 2 878961814 119 407 3 887038665 287 1 5 875334088 216 228 3 880245642 216 531 4 880233810 203 471 4 880434463 92 587 3 875660408 13 892 3 882774224 213 176 4 878956338 286 288 5 875806672 117 1047 2 881009697 99 111 1 885678886 11 558 3 891904214 65 47 2 879216672 295 194 4 879517412 269 217 2 891451610 85 259 2 881705026 250 596 5 878089921 137 144 5 881433689 201 960 2 884112077 257 137 4 882049932 111 328 4 891679939 91 480 4 891438875 215 211 4 891436202 181 938 1 878961586 189 1060 5 893264301 1 20 4 887431883 303 404 4 879468375 299 305 3 879737314 187 210 4 879465242 222 278 2 877563913 214 568 4 892668197 293 770 3 888906655 285 191 4 890595859 303 252 3 879544791 96 156 4 884402860 72 1110 3 880037334 115 1067 4 881171009 7 430 3 891352178 116 350 3 886977926 73 480 4 888625753 269 246 5 891457067 263 419 5 891299514 70 431 3 884150257 221 475 4 875244204 72 182 5 880036515 25 357 4 885852757 290 50 5 880473582 189 526 4 893266205 299 303 3 877618584 264 294 3 886121516 200 365 5 884129962 187 135 4 879465653 184 187 4 889909024 63 289 2 875746985 13 229 4 882397650 298 486 3 884183063 235 185 4 889655435 62 712 4 879376178 246 94 2 884923505 54 742 5 880934806 63 762 3 875747688 11 732 3 891904596 92 168 4 875653723 8 550 3 879362356 307 174 4 879283480 303 200 4 879468459 256 849 2 882164603 72 54 3 880036854 164 406 2 889402389 117 150 4 880125101 224 77 4 888103872 193 869 3 889127811 94 184 2 891720862 281 338 2 881200457 130 109 3 874953794 128 371 1 879966954 94 720 1 891723593 182 845 3 885613067 129 873 1 883245452 254 229 4 886474580 64 381 4 879365491 151 176 2 879524293 45 25 4 881014015 193 879 3 889123257 276 922 4 889174849 276 57 3 874787526 234 187 4 892079140 181 306 1 878962006 21 370 1 874951293 293 249 3 888905229 264 721 5 886123656 10 611 5 877886722 197 346 3 891409070 276 142 3 874792945 308 427 4 887736584 221 943 4 875246759 131 126 4 883681514 268 824 2 876518557 109 8 3 880572642 198 58 3 884208173 230 680 4 880484286 181 741 1 878962918 192 1061 4 881368891 234 448 3 892335501 90 900 4 891382309 193 941 4 889124890 128 603 5 879966839 126 905 2 887855283 244 265 4 880606634 90 289 3 891382310 157 25 3 886890787 305 71 3 886323684 119 382 5 874781742 21 222 2 874951382 231 181 4 888605273 280 508 3 891700453 288 132 3 886374129 279 1497 2 890780576 301 33 4 882078228 72 699 3 880036783 90 259 2 891382392 308 55 3 887738760 59 742 3 888203053 94 744 4 891721462 130 642 4 875216933 26 1015 3 891352136 56 121 5 892679480 82 508 2 884714249 62 12 4 879373613 276 40 3 874791871 181 1015 1 878963121 152 301 3 880147407 178 845 4 882824291 217 597 4 889070087 79 303 4 891271203 138 484 4 879024127 308 81 5 887737293 75 284 2 884050393 269 198 4 891447062 307 94 3 877122695 222 781 3 881059677 121 740 3 891390544 269 22 1 891448072 13 864 4 882141924 230 742 5 880485043 269 507 4 891448800 239 1099 5 889179253 245 1028 5 888513447 56 546 3 892679460 295 961 5 879519556 271 1028 2 885848102 222 812 2 881059117 69 240 3 882126156 10 7 4 877892210 22 376 3 878887112 294 931 3 889242857 82 717 1 884714492 279 399 4 875313859 269 234 1 891449406 6 98 5 883600680 243 1039 4 879988184 298 181 4 884125629 282 325 1 881703044 78 323 1 879633567 118 200 5 875384647 283 1114 5 879297545 171 292 4 891034835 70 217 4 884151119 10 100 5 877891747 245 181 4 888513664 107 333 3 891264267 246 561 1 884923445 13 901 1 883670672 276 70 4 874790826 244 17 2 880607205 189 56 5 893265263 226 242 5 883888671 62 1016 4 879373008 276 417 4 874792907 214 478 4 891544052 306 235 4 876504354 222 26 3 878183043 280 631 5 891700751 60 430 5 883326122 56 71 4 892683275 42 274 5 881105817 1 202 5 875072442 13 809 4 882397582 173 289 4 877556988 15 749 1 879455311 185 23 4 883524249 280 540 3 891702304 244 381 4 880604077 150 293 4 878746946 7 497 4 891352134 178 317 4 882826915 178 742 3 882823833 95 1217 3 880572658 234 1462 3 892333865 97 222 5 884238887 109 127 2 880563471 117 268 5 880124306 269 705 2 891448850 130 1246 3 876252497 264 655 4 886123530 207 13 3 875506839 42 588 5 881108147 246 409 2 884923372 87 367 4 879876702 101 304 3 877135677 256 127 4 882164406 92 794 3 875654798 181 762 2 878963418 213 235 1 878955115 92 739 2 876175582 292 661 5 881105561 246 665 4 884922831 274 845 5 878945579 188 692 5 875072583 18 86 4 880129731 5 439 1 878844423 236 632 3 890116254 193 407 4 889127921 144 709 4 888105940 90 1198 5 891383866 48 609 4 879434819 5 225 2 875635723 22 128 5 878887983 311 432 4 884365485 8 22 5 879362183 276 188 4 874792547 222 173 5 878183043 72 866 4 880035887 299 134 4 878192311 1 171 5 889751711 308 295 3 887741461 165 216 4 879525778 222 49 3 878183512 181 121 4 878962623 200 11 5 884129542 234 626 4 892336358 244 707 4 880606243 90 25 5 891384789 208 216 5 883108324 263 96 4 891298336 134 323 4 891732335 279 586 4 892864663 2 292 4 888550774 288 593 2 886892127 49 302 4 888065432 286 153 5 877531406 205 304 3 888284313 22 80 4 878887227 234 318 4 892078890 223 328 3 891548959 15 25 3 879456204 268 147 4 876514002 94 1220 3 891722678 274 405 4 878945840 7 492 5 891352010 268 217 2 875744501 16 55 5 877717956 164 620 3 889402298 290 161 4 880474293 92 515 4 875640800 239 1070 5 889179032 56 449 5 892679308 248 234 4 884534968 234 10 3 891227851 280 1049 2 891702486 308 187 5 887738760 276 64 5 874787441 192 948 3 881368302 122 509 4 879270511 85 588 3 880838306 262 931 2 879790874 201 272 3 886013700 181 870 2 878962623 295 739 4 879518319 263 568 4 891299387 295 39 4 879518279 201 1100 4 884112800 93 820 3 888705966 159 1028 5 880557539 158 665 2 880134532 293 423 3 888906070 82 597 3 878768882 276 181 5 874786488 13 823 5 882397833 217 2 3 889069782 83 660 4 880308256 189 20 5 893264466 222 796 4 878183684 146 1022 5 891458193 267 121 3 878970681 126 294 3 887855087 181 1060 1 878962675 125 80 4 892838865 43 120 4 884029430 13 780 1 882142057 253 259 2 891628883 42 44 3 881108548 77 518 4 884753202 291 686 5 874835165 268 21 3 875742822 262 28 3 879792220 234 81 3 892334680 29 245 3 882820803 236 57 5 890116575 158 729 3 880133116 156 661 4 888185947 232 52 5 888550130 168 866 5 884287927 37 288 4 880915258 141 245 3 884584426 235 230 4 889655162 102 70 3 888803537 77 172 3 884752562 90 506 5 891383319 186 566 5 879023663 44 660 5 878347915 118 774 5 875385198 7 661 5 891351624 49 1003 2 888068651 62 68 1 879374969 42 1028 4 881106072 178 433 4 882827834 85 51 2 879454782 77 474 5 884732407 58 1099 2 892243079 56 1047 4 892911290 197 688 1 891409564 286 99 4 878141681 90 258 3 891382121 181 1288 1 878962349 295 190 4 879517062 224 69 4 888082495 272 317 4 879454977 221 1010 3 875246662 66 877 1 883601089 207 318 5 877124871 234 487 3 892079237 7 648 5 891351653 87 82 5 879875774 195 1052 1 877835102 44 449 5 883613334 306 287 4 876504442 194 172 3 879521474 94 62 3 891722933 167 659 4 892738277 108 100 4 879879720 230 304 5 880484286 181 927 1 878962675 54 302 4 880928519 90 22 4 891384357 181 696 2 878962997 286 357 4 877531537 14 269 4 892242403 311 179 2 884365357 92 121 5 875640679 21 440 1 874951798 244 550 1 880602264 181 405 4 878962919 65 806 4 879216529 37 540 2 880916070 44 443 5 878348289 244 183 4 880606043 1 265 4 878542441 270 25 5 876954456 299 387 2 889502756 94 572 3 891723883 286 746 4 877533058 239 272 5 889181247 216 55 5 880245145 254 121 3 886472369 62 665 2 879376483 178 385 4 882826982 194 23 4 879522819 268 955 3 875745160 188 143 5 875072674 276 294 4 874786366 158 1098 4 880135069 207 845 3 881681663 161 48 1 891170745 305 654 4 886323937 47 324 3 879439078 64 736 4 889739212 191 751 3 891560753 7 378 5 891353011 59 92 5 888204997 69 268 5 882027109 10 461 3 877888944 21 129 4 874951382 58 9 4 884304328 194 152 3 879549996 7 200 5 891353543 113 126 5 875076827 173 328 5 877557028 95 233 4 879196354 16 194 5 877720733 59 323 4 888206809 311 654 3 884365075 292 589 4 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197 328 4 891409290 184 15 3 889907812 313 482 5 891016193 109 823 3 880572296 152 167 5 882477430 297 629 3 875410013 167 1147 4 892738384 264 524 3 886123596 280 571 3 891702338 222 577 1 878185137 21 591 3 874951382 210 501 4 887736998 280 230 3 891702153 86 286 3 879569555 320 174 4 884749255 144 50 5 888103929 256 97 4 882165103 65 427 5 879216734 198 429 4 884207691 184 217 3 889910394 151 709 5 879524778 18 530 4 880129877 43 724 4 875981390 86 319 3 879569555 242 305 5 879741340 97 28 5 884238778 114 195 4 881260861 188 69 4 875072009 301 230 4 882077033 85 241 3 882995340 129 313 3 883243934 106 77 4 881451716 261 748 3 890454310 188 7 5 875073477 13 208 5 882140624 342 288 5 875318267 299 286 4 877618524 311 204 5 884365617 125 813 1 879455184 276 463 4 874792839 13 421 2 882140389 141 472 5 884585274 222 550 3 878184623 191 896 3 891562090 144 516 2 888105197 216 1047 3 881428365 151 213 5 879524849 144 845 4 888104191 4 356 3 892003459 96 64 5 884403336 160 79 4 876859413 49 369 1 888069329 110 332 3 886987287 209 351 2 883589546 178 1004 4 882827375 344 97 3 884901156 11 203 4 891905856 241 307 4 887249795 239 312 2 889181247 276 719 3 877935336 18 191 4 880130193 141 535 5 884585195 18 971 4 880131878 162 42 3 877636675 342 591 3 875318629 278 525 5 891295330 102 217 2 888803149 16 447 5 877724066 343 82 5 876405735 109 357 2 880572528 301 732 4 882077351 303 202 5 879468149 250 378 4 878092059 234 507 4 892334803 217 68 3 889069974 87 523 5 879875649 95 26 3 880571951 245 94 2 888513081 95 289 2 879191590 334 1008 4 891545126 201 896 3 884110766 126 323 3 887853568 150 475 5 878746764 59 871 2 888203865 227 9 3 879035431 169 603 5 891359171 293 553 3 888907453 ================================================ FILE: tests/test_data/test/test.net ================================================ source_id:token target_id:token 187 100 119 40 96 119 12 52 153 131 259 232 191 307 83 150 86 255 177 4 210 192 25 323 90 298 38 47 201 283 93 63 115 190 143 293 147 265 320 68 188 273 332 321 212 203 326 98 74 270 4 333 87 261 163 207 18 175 127 77 296 179 17 101 24 30 102 288 345 269 270 188 235 297 68 303 313 43 239 109 28 76 108 227 78 218 96 30 180 301 211 12 234 34 178 53 3 243 179 73 98 92 310 116 154 271 293 3 80 297 329 254 198 134 341 238 75 185 166 64 205 142 317 163 261 91 314 322 4 33 71 73 289 182 21 12 248 49 255 32 261 170 257 314 159 118 212 221 5 177 204 57 132 120 13 275 340 252 245 251 334 15 130 103 280 187 232 153 242 341 219 123 6 290 49 289 46 347 185 231 57 254 134 248 24 234 57 207 147 295 191 274 340 54 280 150 190 4 238 198 72 123 122 178 7 334 11 90 232 78 16 77 41 190 108 101 212 66 258 18 321 250 126 280 271 85 11 176 22 69 129 159 235 193 129 88 221 315 308 329 103 83 180 43 208 87 64 75 92 36 298 151 56 103 162 268 81 252 344 115 67 282 132 17 83 307 299 82 321 227 48 13 212 57 344 280 195 81 112 122 345 346 65 18 269 3 131 123 185 311 124 330 347 297 321 251 196 135 65 122 322 197 334 160 129 64 38 17 289 256 51 286 107 260 300 101 290 281 192 170 42 2 54 260 126 1 326 294 119 14 48 172 133 191 332 157 311 99 115 123 160 201 269 267 302 184 262 168 11 80 317 155 163 310 290 32 90 239 246 129 105 189 336 8 266 100 153 311 7 20 329 94 135 38 216 331 291 89 121 253 246 82 113 325 99 313 226 188 319 60 195 280 245 319 168 291 63 127 316 280 67 69 40 143 177 18 239 253 213 304 218 315 18 312 165 6 324 232 167 156 295 275 42 110 25 226 114 104 172 305 66 26 51 303 247 110 245 18 335 307 325 95 289 81 166 141 4 39 171 16 79 145 187 65 102 105 234 70 321 104 62 179 171 122 225 239 283 315 121 107 154 297 309 170 3 38 78 345 164 238 92 142 339 4 251 61 223 240 167 39 223 8 61 253 220 256 139 247 199 267 344 264 336 56 110 235 75 90 93 321 345 277 119 260 214 10 15 86 102 5 34 213 223 238 243 169 107 223 106 175 218 104 28 82 267 37 331 124 16 146 186 289 226 304 109 34 124 73 165 286 260 70 94 159 151 257 151 210 263 288 276 218 222 79 48 133 67 218 282 250 127 195 222 316 19 272 238 43 71 240 208 65 219 300 338 29 75 86 86 269 91 100 273 248 202 9 190 33 84 92 124 306 284 70 281 341 247 302 306 230 320 279 319 41 91 160 323 201 305 194 41 156 220 264 296 310 183 131 232 21 239 218 302 49 250 287 200 109 96 263 225 221 123 263 329 256 136 344 338 76 233 245 347 198 99 83 240 81 238 291 78 331 56 225 21 93 24 293 28 155 245 19 225 198 90 235 191 35 146 28 303 194 203 276 189 49 265 232 204 198 283 217 306 44 133 175 256 80 345 215 97 13 25 287 104 48 20 50 155 340 202 57 343 263 135 293 152 266 232 182 86 217 73 72 143 44 299 162 277 324 154 124 307 210 210 226 323 293 55 97 52 8 32 163 312 307 271 171 204 34 64 282 311 315 174 58 56 84 217 275 86 180 342 84 340 174 13 80 100 197 189 341 5 86 9 40 210 329 260 188 236 261 94 282 105 188 141 258 132 285 17 156 70 213 204 5 344 74 34 202 347 263 121 312 146 219 31 48 53 291 213 203 125 9 279 301 247 140 217 2 298 83 315 311 165 209 169 270 259 40 174 285 21 276 58 229 165 84 48 29 222 257 38 209 336 30 53 63 269 243 36 324 252 138 113 155 123 290 10 253 346 15 217 36 15 102 264 149 143 122 300 178 25 220 58 231 19 250 11 147 73 186 90 109 248 104 196 55 308 298 316 7 160 208 173 323 196 176 147 168 168 293 274 328 6 133 177 226 49 336 173 7 307 1 85 128 63 241 39 323 167 173 298 253 171 42 196 326 53 329 221 307 51 194 192 231 13 23 308 117 324 84 228 13 231 156 314 286 321 314 140 30 143 288 55 340 192 264 119 220 28 226 248 309 227 122 157 227 81 178 143 329 327 170 199 308 297 27 28 101 317 179 176 293 328 265 64 256 176 316 336 315 137 189 290 209 243 232 305 233 28 26 216 306 155 65 246 166 148 218 28 343 31 148 6 38 43 267 85 30 5 212 328 157 93 65 158 179 315 256 261 210 8 234 137 163 261 9 247 231 32 266 118 191 107 34 87 153 132 81 41 235 80 103 13 167 31 166 290 32 53 125 131 163 188 82 68 38 94 325 254 129 99 63 267 164 1 46 175 36 99 72 328 80 84 221 164 80 232 264 172 70 227 346 183 44 208 184 120 317 20 154 76 315 52 200 231 46 343 241 42 284 229 345 213 75 155 135 28 261 22 255 106 169 310 347 212 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def test_lightgcn(self): config_dict = { 'model': 'LightGCN', } quick_test(config_dict) def test_sgl(self): config_dict = { 'model': 'SGL', } quick_test(config_dict) def test_hmlet(self): config_dict = { 'model': 'HMLET', } quick_test(config_dict) def test_ncl(self): config_dict = { 'model': 'NCL', 'num_clusters': 10 } quick_test(config_dict) def test_simgcl(self): config_dict = { 'model': 'SimGCL' } quick_test(config_dict) def test_xsimgcl(self): config_dict = { 'model': 'XSimGCL' } quick_test(config_dict) def test_lightgcl(self): config_dict = { 'model': 'LightGCL' } quick_test(config_dict) def test_directau(self): config_dict = { 'model': 'DirectAU' } quick_test(config_dict) def test_ssl4rec(self): config_dict = { 'model': 'SSL4REC' } quick_test(config_dict) class TestSequentialRecommender(unittest.TestCase): def test_gru4rec(self): config_dict = { 'model': 'GRU4Rec', } quick_test(config_dict) def test_narm(self): config_dict = { 'model': 'NARM', } quick_test(config_dict) def test_sasrec(self): config_dict = { 'model': 'SASRec', } quick_test(config_dict) def test_srgnn(self): config_dict = { 'model': 'SRGNN', } quick_test(config_dict) def test_srgnn_uni100(self): config_dict = { 'model': 'SRGNN', 'eval_args': { 'split': {'LS': "valid_and_test"}, 'mode': 'uni100', 'order': 'TO' } } quick_test(config_dict) def test_gcsan(self): config_dict = { 'model': 'GCSAN', } quick_test(config_dict) def test_niser(self): config_dict = { 'model': 'NISER', } quick_test(config_dict) def test_lessr(self): config_dict = { 'model': 'LESSR' } quick_test(config_dict) def test_tagnn(self): config_dict = { 'model': 'TAGNN' } quick_test(config_dict) def test_gcegnn(self): config_dict = { 'model': 'GCEGNN' } quick_test(config_dict) def test_sgnnhn(self): config_dict = { 'model': 'SGNNHN' } quick_test(config_dict) class TestSocialRecommender(unittest.TestCase): def test_diffnet(self): config_dict = { 'model': 'DiffNet', } quick_test(config_dict) def test_mhcn(self): config_dict = { 'model': 'MHCN', } quick_test(config_dict) def test_sept(self): config_dict = { 'model': 'SEPT', } quick_test(config_dict) if __name__ == '__main__': unittest.main() ================================================ FILE: tests/test_model.yaml ================================================ dataset: test epochs: 1 state: ERROR data_path: tests/test_data/ # Atomic File Format field_separator: "\t" seq_separator: " " # Common Features USER_ID_FIELD: user_id ITEM_ID_FIELD: item_id RATING_FIELD: rating TIME_FIELD: timestamp seq_len: ~ # Label for Point-wise DataLoader LABEL_FIELD: label # NegSample Prefix for Pair-wise DataLoader NEG_PREFIX: neg_ # Sequential Model Needed ITEM_LIST_LENGTH_FIELD: item_length LIST_SUFFIX: _list MAX_ITEM_LIST_LENGTH: 50 POSITION_FIELD: position_id # social network config NET_SOURCE_ID_FIELD: source_id NET_TARGET_ID_FIELD: target_id filter_net_by_inter: True undirected_net: True # Selectively Loading load_col: inter: [user_id, item_id, rating, timestamp] net: [source_id, target_id] unload_col: ~ # Preprocessing alias_of_user_id: ~ alias_of_item_id: ~ alias_of_entity_id: ~ alias_of_relation_id: ~ preload_weight: ~ normalize_field: ~ normalize_all: True