[
  {
    "path": ".gitignore",
    "content": "# PyCharm\n/.idea\n\n# VS Code\n/.vscode\n\n# Python\n__pycache__\n*.pyc\n\n# macOS\n.DS_Store"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2021 MilaGraph\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE."
  },
  {
    "path": "README.md",
    "content": "# NBFNet: Neural Bellman-Ford Networks #\n\nThis is the official codebase of the paper\n\n[Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction][paper]\n\n[Zhaocheng Zhu](https://kiddozhu.github.io),\n[Zuobai Zhang](https://oxer11.github.io),\n[Louis-Pascal Xhonneux](https://github.com/lpxhonneux),\n[Jian Tang](https://jian-tang.com)\n\n[paper]: https://arxiv.org/pdf/2106.06935.pdf\n\nA PyG re-implementation of NBFNet can be found [here](https://github.com/KiddoZhu/NBFNet-PyG).\n\n## Overview ##\n\nNBFNet is a graph neural network framework inspired by traditional path-based\nmethods. It enjoys the advantages of both traditional path-based methods and modern\ngraph neural networks, including **generalization in the inductive setting**,\n**interpretability**, **high model capacity** and **scalability**. NBFNet can be\napplied to solve link prediction on both homogeneous graphs and knowledge graphs.\n\n![NBFNet](asset/nbfnet.svg)\n\nThis codebase is based on PyTorch and [TorchDrug]. It supports training and inference\nwith multiple GPUs or multiple machines.\n\n[TorchDrug]: https://github.com/DeepGraphLearning/torchdrug\n\n## Installation ##\n\nYou may install the dependencies via either conda or pip. Generally, NBFNet works\nwith Python 3.7/3.8 and PyTorch version >= 1.8.0.\n\n### From Conda ###\n\n```bash\nconda install torchdrug pytorch=1.8.2 cudatoolkit=11.1 -c milagraph -c pytorch-lts -c pyg -c conda-forge\nconda install ogb easydict pyyaml -c conda-forge\n```\n\n### From Pip ###\n\n```bash\npip install torch==1.8.2+cu111 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html\npip install torchdrug\npip install ogb easydict pyyaml\n```\n\n## Reproduction ##\n\nTo reproduce the results of NBFNet, use the following command. Alternatively, you\nmay use `--gpus null` to run NBFNet on a CPU. All the datasets will be automatically\ndownloaded in the code.\n\n```bash\npython script/run.py -c config/inductive/wn18rr.yaml --gpus [0] --version v1\n```\n\nWe provide the hyperparameters for each experiment in configuration files.\nAll the configuration files can be found in `config/*/*.yaml`.\n\nFor experiments on inductive relation prediction, you need to additionally specify\nthe split version with `--version v1`.\n\nTo run NBFNet with multiple GPUs or multiple machines, use the following commands\n\n```bash\npython -m torch.distributed.launch --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3]\n```\n\n```bash\npython -m torch.distributed.launch --nnodes=4 --nproc_per_node=4 script/run.py -c config/inductive/wn18rr.yaml --gpus [0,1,2,3,0,1,2,3,0,1,2,3,0,1,2,3]\n```\n\n### Visualize Interpretations on FB15k-237 ###\n\nOnce you have models trained on FB15k237, you can visualize the path interpretations\nwith the following line. Please replace the checkpoint with your own path.\n\n```bash\npython script/visualize.py -c config/knowledge_graph/fb15k237_visualize.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_20.pth\n```\n\n### Evaluate ogbl-biokg ###\n\nDue to the large size of ogbl-biokg, we only evaluate on a small portion of the\nvalidation set during training. The following line evaluates a model on the full\nvalidation / test sets of ogbl-biokg. Please replace the checkpoint with your own\npath.\n\n```bash\npython script/run.py -c config/knowledge_graph/ogbl-biokg_test.yaml --checkpoint /path/to/nbfnet/experiment/model_epoch_10.pth\n```\n\n## Results ##\n\nHere are the results of NBFNet on standard benchmark datasets. All the results are\nobtained with 4 V100 GPUs (32GB). Note results may be slightly different if the\nmodel is trained with 1 GPU and/or a smaller batch size.\n\n### Knowledge Graph Completion ###\n\n<table>\n    <tr>\n        <th>Dataset</th>\n        <th>MR</th>\n        <th>MRR</th>\n        <th>HITS@1</th>\n        <th>HITS@3</th>\n        <th>HITS@10</th>\n    </tr>\n    <tr>\n        <th>FB15k-237</th>\n        <td>114</td>\n        <td>0.415</td>\n        <td>0.321</td>\n        <td>0.454</td>\n        <td>0.599</td>\n    </tr>\n    <tr>\n        <th>WN18RR</th>\n        <td>636</td>\n        <td>0.551</td>\n        <td>0.497</td>\n        <td>0.573</td>\n        <td>0.666</td>\n    </tr>\n    <tr>\n        <th>ogbl-biokg</th>\n        <td>-</td>\n        <td>0.829</td>\n        <td>0.768</td>\n        <td>0.870</td>\n        <td>0.946</td>\n    </tr>\n</table>\n\n### Homogeneous Graph Link Prediction ###\n\n<table>\n    <tr>\n        <th>Dataset</th>\n        <th>AUROC</th>\n        <th>AP</th>\n    </tr>\n    <tr>\n        <th>Cora</th>\n        <td>0.956</td>\n        <td>0.962</td>\n    </tr>\n    <tr>\n        <th>CiteSeer</th>\n        <td>0.923</td>\n        <td>0.936</td>\n    </tr>\n    <tr>\n        <th>PubMed</th>\n        <td>0.983</td>\n        <td>0.982</td>\n    </tr>\n</table>\n\n### Inductive Relation Prediction ###\n\n<table>\n    <tr>\n        <th rowspan=\"2\">Dataset</th>\n        <th colspan=\"4\">HITS@10 (50 sample)</th>\n    </tr>\n    <tr>\n        <th>v1</th>\n        <th>v2</th>\n        <th>v3</th>\n        <th>v4</th>\n    </tr>\n    <tr>\n        <th>FB15k-237</th>\n        <td>0.834</td>\n        <td>0.949</td>\n        <td>0.951</td>\n        <td>0.960</td>\n    </tr>\n    <tr>\n        <th>WN18RR</th>\n        <td>0.948</td>\n        <td>0.905</td>\n        <td>0.893</td>\n        <td>0.890</td>\n    </tr>\n</table>\n\nFrequently Asked Questions\n--------------------------\n\n1. **The code is stuck at the beginning of epoch 0.**\n\n   This is probably because the JIT cache is broken.\n   Try `rm -r ~/.cache/torch_extensions/*` and run the code again.\n\nCitation\n--------\n\nIf you find this codebase useful in your research, please cite the following paper.\n\n```bibtex\n@article{zhu2021neural,\n  title={Neural bellman-ford networks: A general graph neural network framework for link prediction},\n  author={Zhu, Zhaocheng and Zhang, Zuobai and Xhonneux, Louis-Pascal and Tang, Jian},\n  journal={Advances in Neural Information Processing Systems},\n  volume={34},\n  year={2021}\n}\n```\n"
  },
  {
    "path": "config/homogeneous_graph/citeseer.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: CiteSeerLinkPrediction\n  path: ~/datasets/homogeneous_graphs/\n\ntask:\n  class: LinkPrediction\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: no\n    remove_one_hop: yes\n    symmetric: yes\n  criterion: bce\n  num_negative: 1\n  strict_negative: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 64\n\ntrain:\n  num_epoch: 20\n\nmetric: auroc"
  },
  {
    "path": "config/homogeneous_graph/cora.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: CoraLinkPrediction\n  path: ~/datasets/homogeneous_graphs/\n\ntask:\n  class: LinkPrediction\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: no\n    remove_one_hop: yes\n    symmetric: yes\n  criterion: bce\n  num_negative: 1\n  strict_negative: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 64\n\ntrain:\n  num_epoch: 20\n\nmetric: auroc"
  },
  {
    "path": "config/homogeneous_graph/pubmed.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: PubMedLinkPrediction\n  path: ~/datasets/homogeneous_graphs/\n\ntask:\n  class: LinkPrediction\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: no\n    remove_one_hop: yes\n    symmetric: yes\n  criterion: bce\n  num_negative: 1\n  strict_negative: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 16\n\ntrain:\n  num_epoch: 10\n\nmetric: auroc"
  },
  {
    "path": "config/inductive/fb15k237.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: FB15k237Inductive\n  path: ~/datasets/knowledge_graphs/\n  version: {{ version }}\n\ntask:\n  class: InductiveKnowledgeGraphCompletion\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: yes\n    remove_one_hop: yes\n  criterion: bce\n  num_negative: 32\n  strict_negative: yes\n  adversarial_temperature: 0.5\n  sample_weight: no\n  full_batch_eval: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 64\n\ntrain:\n  num_epoch: 20\n\nmetric: mrr"
  },
  {
    "path": "config/inductive/wn18rr.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: WN18RRInductive\n  path: ~/datasets/knowledge_graphs/\n  version: {{ version }}\n\ntask:\n  class: InductiveKnowledgeGraphCompletion\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: no\n  criterion: bce\n  num_negative: 32\n  strict_negative: yes\n  adversarial_temperature: 1\n  sample_weight: no\n  full_batch_eval: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 32\n\ntrain:\n  num_epoch: 20\n\nmetric: mrr"
  },
  {
    "path": "config/knowledge_graph/fb15k237.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: FB15k237\n  path: ~/datasets/knowledge_graphs/\n\ntask:\n  class: KnowledgeGraphCompletion\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: yes\n    remove_one_hop: yes\n  criterion: bce\n  num_negative: 32\n  strict_negative: yes\n  adversarial_temperature: 0.5\n  sample_weight: no\n  full_batch_eval: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 64\n\ntrain:\n  num_epoch: 20\n\nmetric: mrr"
  },
  {
    "path": "config/knowledge_graph/fb15k237_visualize.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: FB15k237\n  path: ~/datasets/knowledge_graphs/\n\ntask:\n  class: KnowledgeGraphCompletion\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: yes\n    remove_one_hop: yes\n  criterion: bce\n  num_negative: 32\n  strict_negative: yes\n  adversarial_temperature: 0.5\n  sample_weight: no\n  full_batch_eval: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: [0]\n  batch_size: 64\n\ncheckpoint: {{ checkpoint }}\n\nmetric: mrr"
  },
  {
    "path": "config/knowledge_graph/ogbl-biokg.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: OGBLBioKG\n  path: ~/datasets/knowledge_graphs/\n\ntask:\n  class: KnowledgeGraphCompletionOGB\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: yes\n    remove_one_hop: yes\n  criterion: bce\n  evaluator:\n    class: Evaluator\n    name: ogbl-biokg\n  num_negative: 32\n  strict_negative: yes\n  heterogeneous_negative: yes\n  adversarial_temperature: 0.5\n  sample_weight: no\n\noptimizer:\n  class: Adam\n  lr: 2.0e-4\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 8\n  log_interval: 10\n\ntrain:\n  num_epoch: 10\n  batch_per_epoch: 200\n\nmetric: mrr\nfast_test: 1000"
  },
  {
    "path": "config/knowledge_graph/ogbl-biokg_test.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: OGBLBioKG\n  path: ~/datasets/knowledge_graphs/\n\ntask:\n  class: KnowledgeGraphCompletionOGB\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: yes\n    remove_one_hop: yes\n  criterion: bce\n  evaluator:\n    class: Evaluator\n    name: ogbl-biokg\n  num_negative: 32\n  strict_negative: yes\n  heterogeneous_negative: yes\n  adversarial_temperature: 0.5\n  sample_weight: no\n\noptimizer:\n  class: Adam\n  lr: 2.0e-4\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 32\n  log_interval: 10\n\ntrain:\n  num_epoch: 0\n  batch_per_epoch: 200\n\ncheckpoint: {{ checkpoint }}\nmetric: mrr"
  },
  {
    "path": "config/knowledge_graph/wn18rr.yaml",
    "content": "output_dir: ~/experiments/\n\ndataset:\n  class: WN18RR\n  path: ~/datasets/knowledge_graphs/\n\ntask:\n  class: KnowledgeGraphCompletion\n  model:\n    class: NBFNet\n    input_dim: 32\n    hidden_dims: [32, 32, 32, 32, 32, 32]\n    message_func: distmult\n    aggregate_func: pna\n    short_cut: yes\n    layer_norm: yes\n    dependent: no\n  criterion: bce\n  num_negative: 32\n  strict_negative: yes\n  adversarial_temperature: 1\n  sample_weight: no\n  full_batch_eval: yes\n\noptimizer:\n  class: Adam\n  lr: 5.0e-3\n\nengine:\n  gpus: {{ gpus }}\n  batch_size: 32\n\ntrain:\n  num_epoch: 20\n\nmetric: mrr"
  },
  {
    "path": "data/fb15k237_entity.txt",
    "content": "/m/027rn\tlanguages of dominican republic (Q786)\n/m/06cx9\trepublic (government) (Q7270)\n/m/017dcd\trangers in reverse (Q513809)\n/m/06v8s0\telyse floyd (Q2642437)\n/m/07s9rl0\tdrama movie (Q130232)\n/m/0170z3\tdanny vinyard (Q208572)\n/m/01sl1q\tmichelle rodríguez (Q184219)\n/m/044mz_\tnaveen andrews (Q208374)\n/m/0cnk2q\taustralia national soccer team (Q268208)\n/m/02nzb8\tright winger (football) (Q193592)\n/m/04nrcg\tmdv (Q387312)\n/m/02sdk9v\toutside forward (Q280658)\n/m/07nznf\tbryan singer/comments (Q220751)\n/m/014lc_\tstar trek 11 (cancelled film) (Q388319)\n/m/02qyp19\tbafta award for best original screenplay 1983–1999 (Q41375)\n/m/02d413\tphiladelphia (film) (Q204057)\n/m/0q9kd\tdaniel michael devito jr. (Q26806)\n/m/0184jc\tguy pearce (Q223745)\n/m/03q5t\tharpischord (Q81982)\n/m/07y_7\tviolin tuning (Q8355)\n/m/0gqng\tacademy award for best foreign language film (Q105304)\n/m/073hkh\t61th academy awards (Q924339)\n/m/0b76d_m\tanonymous (film) (Q313819)\n/m/029j_\tdual layer (Q5294)\n/m/014_x2\tcity of angels (film) (Q168010)\n/m/012ljv\tgabriel yared (Q502957)\n/m/0ds35l9\tbridesmaids (2011 film) (Q837264)\n/m/05r4w\trepublica portuguesa (Q45)\n/m/015qsq\tserpico (film) (Q657079)\n/m/02bjrlw\thistory of the italian language (Q652)\n/m/04bdxl\tcatherine keener (Q230378)\n/m/02s2ft\tchris cooper (actor) (Q273208)\n/m/09c7w0\tusofa (Q30)\n/m/0rs6x\tsaint augustine (fl) (Q487988)\n/m/079vf\tstanley lee (Q181900)\n/m/0d90m\tx-men i (Q106182)\n/m/015zyd\tlos angeles conservatory of music (Q1026827)\n/m/05vsxz\tchiwetel ejiofor (Q317343)\n/m/06qgvf\tcarla gugino (Q228871)\n/m/04ljl_l\tgolden raspberry award for worst supporting actor - motion picture (Q641316)\n/m/03qcfvw\tg.i. joe: rise of cobra (film) (Q288645)\n/m/0grwj\toprah winfery (Q55800)\n/m/05sxg2\ttheatre producer (Q1759246)\n/m/0rh6k\tdistrict of columbia county, dc (Q61)\n/m/01lp8\tkristendom (Q5043)\n/m/05d7rk\tsaeed jaffery (Q979990)\n/m/027dtxw\tbafta award for best supporting actor 2010-2034 (Q548389)\n/m/05hs4r\theartland rock (Q747717)\n/m/01pbxb\tbob seger & the silver bullet band (Q364131)\n/m/03qcq\thunter s thomson (Q219780)\n/m/084w8\tfaulkner william (Q38392)\n/m/01lxd4\tavant-garde jazz (Q790188)\n/m/0f0y8\tjowcol music (Q7346)\n/m/08815\tyale model united nations (Q49112)\n/m/05bnp0\tjames franco (Q306403)\n/m/0g56t9t\tfrankenweenie (full-length) (Q1051023)\n/m/0160w\tgreater bahamas (Q778)\n/m/02vk52z\tworld bank publications (Q7164)\n/m/0dbpyd\tburnett, alan (Q360036)\n/m/053y0s\tboat drunks (Q862359)\n/m/0dz3r\tlist of record producers (Q183945)\n/m/016qtt\tbill ray cyrus (Q231259)\n/m/08mbj5d\t/m/08mbj5d\n/m/09sh8k\tfantastic four (2007 film) (Q390063)\n/m/09zzb8\tmake-up artist 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(Q8787)\n/m/0h9xl\tpeople’s action party (Q371395)\n/m/01803s\teverlasting life (Q193942)\n/m/01lys3\tabuse, substance (Q4134457)\n/m/01730d\tchicago blues (Q1071873)\n/m/057ph\tmickey (disney character) (Q11934)\n/m/03ykjs9\t/m/03ykjs9\n/m/060m4\tpm of canada (Q839078)\n/m/04p4r\tliberal party australia (Q241149)\n/m/017yzc\thanoverian britain (Q157217)\n/m/021yyx\tfederation of bosnia & herzegovina (Q11198)\n/m/01_30_\tstrategic simulations, inc (Q1268871)\n/m/02p4pt3\tthe plantagenets (Q106151)\n/m/09kqc\tfacetious (Q35874)\n/m/04y41\t/m/04y41\n/m/01tv5c\tQ13526807\n/m/0gslw\tte wai pounamu (Q120755)\n/m/01x4x4\t@skyteam (Q212282)\n/m/01lk31\tgovernor (massachusetts) (Q887117)\n/m/04z288\tnew york state senate (Q1722258)\n/m/0jq47\tlibyan mercenaries (Q178197)\n/m/09cmq\tpublic museum (Q33506)\n/m/0dng4\tdaffy (Q319918)\n/m/05qc_\tplanetary mass objects (Q634)\n/m/08hbxv\tkosi zone (Q9170)\n/m/05hyf\tnazidom (Q7310)\n/m/0qb7t\tfriends of (Q43229)\n/m/027752\thistory of the united national party (Q1321770)\n/m/03f_jk\talbert lasker clinical medical research award (Q136696)\n/m/027qb1\tcommissioners (Q524778)\n/m/01x8f6\tondp (Q827589)\n/m/025x7g_\t/m/025x7g_\n/m/080v2\tvice presidency of the united states (Q11699)\n/m/037vqt\tjames e sullivan award (Q2720648)\n/m/026y05\t/m/026y05\n/m/05k8m5\tspinefarm records (Q1124153)\n/m/0hsph\tnorthern dancer (horse) (Q1969034)\n/m/02rxd26\t2009 tour de france (Q217263)\n/m/07hn5\ttour of france (Q33881)\n/m/03tp4\tQ131736\n/m/04gbl3\tdavid ward (programmer) (Q1408498)\n/m/01664_\tnatural historian (Q484591)\n/m/0171c7\tideology of the australian greens (Q781486)\n/m/0zq7r\tvertigo (company) (Q122669)\n/m/047vnfs\tfootball manager (Q628099)\n/m/022qqh\tconservator (art) (Q674426)\n/m/0jvq\ticeteroid (Q3863)\n/m/0lyb_\tpulitzer prize for music (Q1783509)\n/m/027yjnv\t2008 tdf (Q221530)\n/m/024030\tpadma vibhushan (Q672392)\n/m/024tv_\tpakistani peoples party (Q186591)\n/m/0q96\talp (Q216082)\n"
  },
  {
    "path": "nbfnet/__init__.py",
    "content": ""
  },
  {
    "path": "nbfnet/dataset.py",
    "content": "import os\nimport csv\nimport glob\nfrom tqdm import tqdm\nfrom ogb import linkproppred\n\nimport torch\nfrom torch.utils import data as torch_data\n\nfrom torchdrug import data, datasets, utils\nfrom torchdrug.core import Registry as R\n\n\nclass InductiveKnowledgeGraphDataset(data.KnowledgeGraphDataset):\n\n    def load_inductive_tsvs(self, train_files, test_files, verbose=0):\n        assert len(train_files) == len(test_files) == 2\n        inv_train_entity_vocab = {}\n        inv_test_entity_vocab = {}\n        inv_relation_vocab = {}\n        triplets = []\n        num_samples = []\n\n        for txt_file in train_files:\n            with open(txt_file, \"r\") as fin:\n                reader = csv.reader(fin, delimiter=\"\\t\")\n                if verbose:\n                    reader = tqdm(reader, \"Loading %s\" % txt_file, utils.get_line_count(txt_file))\n\n                num_sample = 0\n                for tokens in reader:\n                    h_token, r_token, t_token = tokens\n                    if h_token not in inv_train_entity_vocab:\n                        inv_train_entity_vocab[h_token] = len(inv_train_entity_vocab)\n                    h = inv_train_entity_vocab[h_token]\n                    if r_token not in inv_relation_vocab:\n                        inv_relation_vocab[r_token] = len(inv_relation_vocab)\n                    r = inv_relation_vocab[r_token]\n                    if t_token not in inv_train_entity_vocab:\n                        inv_train_entity_vocab[t_token] = len(inv_train_entity_vocab)\n                    t = inv_train_entity_vocab[t_token]\n                    triplets.append((h, t, r))\n                    num_sample += 1\n            num_samples.append(num_sample)\n\n        for txt_file in test_files:\n            with open(txt_file, \"r\") as fin:\n                reader = csv.reader(fin, delimiter=\"\\t\")\n                if verbose:\n                    reader = tqdm(reader, \"Loading %s\" % txt_file, utils.get_line_count(txt_file))\n\n                num_sample = 0\n                for tokens in reader:\n                    h_token, r_token, t_token = tokens\n                    if h_token not in inv_test_entity_vocab:\n                        inv_test_entity_vocab[h_token] = len(inv_test_entity_vocab)\n                    h = inv_test_entity_vocab[h_token]\n                    assert r_token in inv_relation_vocab\n                    r = inv_relation_vocab[r_token]\n                    if t_token not in inv_test_entity_vocab:\n                        inv_test_entity_vocab[t_token] = len(inv_test_entity_vocab)\n                    t = inv_test_entity_vocab[t_token]\n                    triplets.append((h, t, r))\n                    num_sample += 1\n            num_samples.append(num_sample)\n\n        train_entity_vocab, inv_train_entity_vocab = self._standarize_vocab(None, inv_train_entity_vocab)\n        test_entity_vocab, inv_test_entity_vocab = self._standarize_vocab(None, inv_test_entity_vocab)\n        relation_vocab, inv_relation_vocab = self._standarize_vocab(None, inv_relation_vocab)\n\n        self.train_graph = data.Graph(triplets[:num_samples[0]],\n                                      num_node=len(train_entity_vocab), num_relation=len(relation_vocab))\n        self.valid_graph = self.train_graph\n        self.test_graph = data.Graph(triplets[sum(num_samples[:2]): sum(num_samples[:3])],\n                                     num_node=len(test_entity_vocab), num_relation=len(relation_vocab))\n        self.graph = self.train_graph\n        self.triplets = torch.tensor(triplets[:sum(num_samples[:2])] + triplets[sum(num_samples[:3]):])\n        self.num_samples = num_samples[:2] + num_samples[3:]\n        self.train_entity_vocab = train_entity_vocab\n        self.test_entity_vocab = test_entity_vocab\n        self.relation_vocab = relation_vocab\n        self.inv_train_entity_vocab = inv_train_entity_vocab\n        self.inv_test_entity_vocab = inv_test_entity_vocab\n        self.inv_relation_vocab = inv_relation_vocab\n\n    def __getitem__(self, index):\n        return self.triplets[index]\n\n    def split(self):\n        offset = 0\n        splits = []\n        for num_sample in self.num_samples:\n            split = torch_data.Subset(self, range(offset, offset + num_sample))\n            splits.append(split)\n            offset += num_sample\n        return splits\n\n\n@R.register(\"datasets.CoraLinkPrediction\")\nclass CoraLinkPrediction(datasets.Cora):\n\n    def __init__(self, **kwargs):\n        super(CoraLinkPrediction, self).__init__(**kwargs)\n        self.transform = None\n\n    def __getitem__(self, index):\n        return self.graph.edge_list[index]\n\n    def __len__(self):\n        return self.graph.num_edge\n\n    def split(self, ratios=(85, 5, 10)):\n        length = self.graph.num_edge\n        norm = sum(ratios)\n        lengths = [int(r / norm * length) for r in ratios]\n        lengths[-1] = length - sum(lengths[:-1])\n\n        g = torch.Generator()\n        g.manual_seed(0)\n        return torch_data.random_split(self, lengths, generator=g)\n\n\n@R.register(\"datasets.CiteSeerLinkPrediction\")\nclass CiteSeerLinkPrediction(datasets.CiteSeer):\n\n    def __init__(self, **kwargs):\n        super(CiteSeerLinkPrediction, self).__init__(**kwargs)\n        self.transform = None\n\n    def __getitem__(self, index):\n        return self.graph.edge_list[index]\n\n    def __len__(self):\n        return self.graph.num_edge\n\n    def split(self, ratios=(85, 5, 10)):\n        length = self.graph.num_edge\n        norm = sum(ratios)\n        lengths = [int(r / norm * length) for r in ratios]\n        lengths[-1] = length - sum(lengths[:-1])\n\n        g = torch.Generator()\n        g.manual_seed(0)\n        return torch_data.random_split(self, lengths, generator=g)\n\n\n@R.register(\"datasets.PubMedLinkPrediction\")\nclass PubMedLinkPrediction(datasets.PubMed):\n\n    def __init__(self, **kwargs):\n        super(PubMedLinkPrediction, self).__init__(**kwargs)\n        self.transform = None\n\n    def __getitem__(self, index):\n        return self.graph.edge_list[index]\n\n    def __len__(self):\n        return self.graph.num_edge\n\n    def split(self, ratios=(85, 5, 10)):\n        length = self.graph.num_edge\n        norm = sum(ratios)\n        lengths = [int(r / norm * length) for r in ratios]\n        lengths[-1] = length - sum(lengths[:-1])\n\n        g = torch.Generator()\n        g.manual_seed(0)\n        return torch_data.random_split(self, lengths, generator=g)\n\n\n@R.register(\"datasets.FB15k237Inductive\")\nclass FB15k237Inductive(InductiveKnowledgeGraphDataset):\n\n    train_urls = [\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s/train.txt\",\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s/valid.txt\",\n    ]\n\n    test_urls = [\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s_ind/train.txt\",\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/fb237_%s_ind/test.txt\",\n    ]\n\n    def __init__(self, path, version=\"v1\", verbose=1):\n        path = os.path.expanduser(path)\n        if not os.path.exists(path):\n            os.makedirs(path)\n        self.path = path\n\n        train_files = []\n        for url in self.train_urls:\n            url = url % version\n            save_file = \"fb15k237_%s_%s\" % (version, os.path.basename(url))\n            txt_file = os.path.join(path, save_file)\n            if not os.path.exists(txt_file):\n                txt_file = utils.download(url, self.path, save_file=save_file)\n            train_files.append(txt_file)\n        test_files = []\n        for url in self.test_urls:\n            url = url % version\n            save_file = \"fb15k237_%s_ind_%s\" % (version, os.path.basename(url))\n            txt_file = os.path.join(path, save_file)\n            if not os.path.exists(txt_file):\n                txt_file = utils.download(url, self.path, save_file=save_file)\n            test_files.append(txt_file)\n\n        self.load_inductive_tsvs(train_files, test_files, verbose=verbose)\n\n\n@R.register(\"datasets.WN18RRInductive\")\nclass WN18RRInductive(InductiveKnowledgeGraphDataset):\n\n    train_urls = [\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s/train.txt\",\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s/valid.txt\",\n    ]\n\n    test_urls = [\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s_ind/train.txt\",\n        \"https://raw.githubusercontent.com/kkteru/grail/master/data/WN18RR_%s_ind/test.txt\",\n    ]\n\n    def __init__(self, path, version=\"v1\", verbose=1):\n        path = os.path.expanduser(path)\n        if not os.path.exists(path):\n            os.makedirs(path)\n        self.path = path\n\n        train_files = []\n        for url in self.train_urls:\n            url = url % version\n            save_file = \"wn18rr_%s_%s\" % (version, os.path.basename(url))\n            txt_file = os.path.join(path, save_file)\n            if not os.path.exists(txt_file):\n                txt_file = utils.download(url, self.path, save_file=save_file)\n            train_files.append(txt_file)\n        test_files = []\n        for url in self.test_urls:\n            url = url % version\n            save_file = \"wn18rr_%s_ind_%s\" % (version, os.path.basename(url))\n            txt_file = os.path.join(path, save_file)\n            if not os.path.exists(txt_file):\n                txt_file = utils.download(url, self.path, save_file=save_file)\n            test_files.append(txt_file)\n\n        self.load_inductive_tsvs(train_files, test_files, verbose=verbose)\n\n\n@R.register(\"datasets.OGBLBioKG\")\nclass OGBLBioKG(data.KnowledgeGraphDataset):\n\n    def __init__(self, path, verbose=1):\n        path = os.path.expanduser(path)\n        self.path = path\n\n        dataset = linkproppred.LinkPropPredDataset(\"ogbl-biokg\", path)\n        self.load_ogb(dataset, verbose=verbose)\n\n    def load_ogb(self, dataset, verbose=1):\n        entity_vocab = []\n        relation_vocab = []\n        entity_type_vocab = []\n        inv_entity_type_offset = {}\n        entity_type2num = []\n\n        zip_files = glob.glob(os.path.join(dataset.root, \"mapping/*.gz\"))\n        for zip_file in zip_files:\n            csv_file = utils.extract(zip_file)\n            type = os.path.basename(csv_file).split(\"_\")[0]\n            with open(csv_file, \"r\") as fin:\n                reader = csv.reader(fin)\n                if verbose:\n                    reader = iter(tqdm(reader, \"Loading %s\" % csv_file, utils.get_line_count(csv_file)))\n                fields = next(reader)\n                if \"relidx\" in csv_file:\n                    for index, token in reader:\n                        relation_vocab.append(token)\n                else:\n                    entity_type_vocab.append(type)\n                    inv_entity_type_offset[type] = len(entity_vocab)\n                    num_entity = 0\n                    for index, token in reader:\n                        entity_vocab.append(\"%s (%s)\" % (type, token))\n                        num_entity += 1\n                    entity_type2num.append(num_entity)\n\n        edge_split = dataset.get_edge_split()\n        triplets = []\n        num_samples = []\n        num_samples_with_neg = []\n        negative_heads = []\n        negative_tails = []\n        for key in [\"train\", \"valid\", \"test\"]:\n            split_dict = edge_split[key]\n            h = torch.as_tensor(split_dict[\"head\"])\n            t = torch.as_tensor(split_dict[\"tail\"])\n            r = torch.as_tensor(split_dict[\"relation\"])\n            h_type = torch.tensor([inv_entity_type_offset[h] for h in split_dict[\"head_type\"]])\n            t_type = torch.tensor([inv_entity_type_offset[t] for t in split_dict[\"tail_type\"]])\n\n            h = h + h_type\n            t = t + t_type\n            triplet = torch.stack([h, t, r], dim=-1)\n            triplets.append(triplet)\n            num_samples.append(len(h))\n            if \"head_neg\" in split_dict:\n                neg_h = torch.as_tensor(split_dict[\"head_neg\"])\n                neg_t = torch.as_tensor(split_dict[\"tail_neg\"])\n                neg_h = neg_h + h_type.unsqueeze(-1)\n                neg_t = neg_t + t_type.unsqueeze(-1)\n                negative_heads.append(neg_h)\n                negative_tails.append(neg_t)\n                num_samples_with_neg.append(len(h))\n            else:\n                num_samples_with_neg.append(0)\n        triplets = torch.cat(triplets)\n\n        self.load_triplet(triplets, entity_vocab=entity_vocab, relation_vocab=relation_vocab)\n        entity_type_vocab, inv_entity_type_vocab = self._standarize_vocab(entity_type_vocab, None)\n        self.entity_type_vocab = entity_type_vocab\n        self.inv_entity_type_vocab = inv_entity_type_vocab\n        self.num_samples = num_samples\n        self.num_samples_with_neg = num_samples_with_neg\n        self.negative_heads = torch.cat(negative_heads)\n        self.negative_tails = torch.cat(negative_tails)\n\n        node_type = []\n        for i, num_entity in enumerate(entity_type2num):\n            node_type += [i] * num_entity\n        with self.graph.node():\n            self.graph.node_type = torch.tensor(node_type)\n\n    def split(self, test_negative=True):\n        offset = 0\n        neg_offset = 0\n        splits = []\n        for num_sample, num_sample_with_neg in zip(self.num_samples, self.num_samples_with_neg):\n            if test_negative and num_sample_with_neg:\n                pos_h, pos_t, pos_r = self[offset: offset + num_sample].t()\n                neg_h = self.negative_heads[neg_offset: neg_offset + num_sample_with_neg]\n                neg_t = self.negative_tails[neg_offset: neg_offset + num_sample_with_neg]\n                num_negative = neg_h.shape[-1]\n                h = pos_h.unsqueeze(-1).repeat(2, num_negative + 1)\n                t = pos_t.unsqueeze(-1).repeat(2, num_negative + 1)\n                r = pos_r.unsqueeze(-1).repeat(2, num_negative + 1)\n                t[:num_sample_with_neg, 1:] = neg_t\n                h[num_sample_with_neg:, 1:] = neg_h\n                split = torch.stack([h, t, r], dim=-1)\n            else:\n                split = torch_data.Subset(self, range(offset, offset + num_sample))\n            splits.append(split)\n            offset += num_sample\n            neg_offset += num_sample_with_neg\n        return splits\n"
  },
  {
    "path": "nbfnet/layer.py",
    "content": "import torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom torch_scatter import scatter_add, scatter_mean, scatter_max, scatter_min\n\nfrom torchdrug import layers\nfrom torchdrug.layers import functional\n\n\nclass GeneralizedRelationalConv(layers.MessagePassingBase):\n\n    eps = 1e-6\n\n    message2mul = {\n        \"transe\": \"add\",\n        \"distmult\": \"mul\",\n    }\n\n    def __init__(self, input_dim, output_dim, num_relation, query_input_dim, message_func=\"distmult\",\n                 aggregate_func=\"pna\", layer_norm=False, activation=\"relu\", dependent=True):\n        super(GeneralizedRelationalConv, self).__init__()\n        self.input_dim = input_dim\n        self.output_dim = output_dim\n        self.num_relation = num_relation\n        self.query_input_dim = query_input_dim\n        self.message_func = message_func\n        self.aggregate_func = aggregate_func\n        self.dependent = dependent\n\n        if layer_norm:\n            self.layer_norm = nn.LayerNorm(output_dim)\n        else:\n            self.layer_norm = None\n        if isinstance(activation, str):\n            self.activation = getattr(F, activation)\n        else:\n            self.activation = activation\n\n        if self.aggregate_func == \"pna\":\n            self.linear = nn.Linear(input_dim * 13, output_dim)\n        else:\n            self.linear = nn.Linear(input_dim * 2, output_dim)\n        if dependent:\n            self.relation_linear = nn.Linear(query_input_dim, num_relation * input_dim)\n        else:\n            self.relation = nn.Embedding(num_relation, input_dim)\n\n    def message(self, graph, input):\n        assert graph.num_relation == self.num_relation\n\n        batch_size = len(graph.query)\n        node_in, node_out, relation = graph.edge_list.t()\n        if self.dependent:\n            relation_input = self.relation_linear(graph.query).view(batch_size, self.num_relation, self.input_dim)\n        else:\n            relation_input = self.relation.weight.expand(batch_size, -1, -1)\n        relation_input = relation_input.transpose(0, 1)\n        node_input = input[node_in]\n        edge_input = relation_input[relation]\n\n        if self.message_func == \"transe\":\n            message = edge_input + node_input\n        elif self.message_func == \"distmult\":\n            message = edge_input * node_input\n        elif self.message_func == \"rotate\":\n            node_re, node_im = node_input.chunk(2, dim=-1)\n            edge_re, edge_im = edge_input.chunk(2, dim=-1)\n            message_re = node_re * edge_re - node_im * edge_im\n            message_im = node_re * edge_im + node_im * edge_re\n            message = torch.cat([message_re, message_im], dim=-1)\n        else:\n            raise ValueError(\"Unknown message function `%s`\" % self.message_func)\n        message = torch.cat([message, graph.boundary])\n\n        return message\n\n    def aggregate(self, graph, message):\n        node_out = graph.edge_list[:, 1]\n        node_out = torch.cat([node_out, torch.arange(graph.num_node, device=graph.device)])\n        edge_weight = torch.cat([graph.edge_weight, torch.ones(graph.num_node, device=graph.device)])\n        edge_weight = edge_weight.unsqueeze(-1).unsqueeze(-1)\n        degree_out = graph.degree_out.unsqueeze(-1).unsqueeze(-1) + 1\n\n        if self.aggregate_func == \"sum\":\n            update = scatter_add(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)\n        elif self.aggregate_func == \"mean\":\n            update = scatter_mean(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)\n        elif self.aggregate_func == \"max\":\n            update = scatter_max(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]\n        elif self.aggregate_func == \"pna\":\n            mean = scatter_mean(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)\n            sq_mean = scatter_mean(message ** 2 * edge_weight, node_out, dim=0, dim_size=graph.num_node)\n            max = scatter_max(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]\n            min = scatter_min(message * edge_weight, node_out, dim=0, dim_size=graph.num_node)[0]\n            std = (sq_mean - mean ** 2).clamp(min=self.eps).sqrt()\n            features = torch.cat([mean.unsqueeze(-1), max.unsqueeze(-1), min.unsqueeze(-1), std.unsqueeze(-1)], dim=-1)\n            features = features.flatten(-2)\n            scale = degree_out.log()\n            scale = scale / scale.mean()\n            scales = torch.cat([torch.ones_like(scale), scale, 1 / scale.clamp(min=1e-2)], dim=-1)\n            update = (features.unsqueeze(-1) * scales.unsqueeze(-2)).flatten(-2)\n        else:\n            raise ValueError(\"Unknown aggregation function `%s`\" % self.aggregate_func)\n\n        return update\n\n    def message_and_aggregate(self, graph, input):\n        if graph.requires_grad or self.message_func == \"rotate\":\n            return super(GeneralizedRelationalConv, self).message_and_aggregate(graph, input)\n\n        assert graph.num_relation == self.num_relation\n\n        batch_size = len(graph.query)\n        input = input.flatten(1)\n        boundary = graph.boundary.flatten(1)\n\n        degree_out = graph.degree_out.unsqueeze(-1) + 1\n        if self.dependent:\n            relation_input = self.relation_linear(graph.query).view(batch_size, self.num_relation, self.input_dim)\n            relation_input = relation_input.transpose(0, 1).flatten(1)\n        else:\n            relation_input = self.relation.weight.repeat(1, batch_size)\n        adjacency = graph.adjacency.transpose(0, 1)\n\n        if self.message_func in self.message2mul:\n            mul = self.message2mul[self.message_func]\n        else:\n            raise ValueError(\"Unknown message function `%s`\" % self.message_func)\n        if self.aggregate_func == \"sum\":\n            update = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"add\", mul=mul)\n            update = update + boundary\n        elif self.aggregate_func == \"mean\":\n            update = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"add\", mul=mul)\n            update = (update + boundary) / degree_out\n        elif self.aggregate_func == \"max\":\n            update = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"max\", mul=mul)\n            update = torch.max(update, boundary)\n        elif self.aggregate_func == \"pna\":\n            sum = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"add\", mul=mul)\n            sq_sum = functional.generalized_rspmm(adjacency, relation_input ** 2, input ** 2, sum=\"add\", mul=mul)\n            max = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"max\", mul=mul)\n            min = functional.generalized_rspmm(adjacency, relation_input, input, sum=\"min\", mul=mul)\n            mean = (sum + boundary) / degree_out\n            sq_mean = (sq_sum + boundary ** 2) / degree_out\n            max = torch.max(max, boundary)\n            min = torch.min(min, boundary)\n            std = (sq_mean - mean ** 2).clamp(min=self.eps).sqrt()\n            features = torch.cat([mean.unsqueeze(-1), max.unsqueeze(-1), min.unsqueeze(-1), std.unsqueeze(-1)], dim=-1)\n            features = features.flatten(-2)\n            scale = degree_out.log()\n            scale = scale / scale.mean()\n            scales = torch.cat([torch.ones_like(scale), scale, 1 / scale.clamp(min=1e-2)], dim=-1)\n            update = (features.unsqueeze(-1) * scales.unsqueeze(-2)).flatten(-2)\n        else:\n            raise ValueError(\"Unknown aggregation function `%s`\" % self.aggregate_func)\n\n        return update.view(len(update), batch_size, -1)\n\n    def combine(self, input, update):\n        output = self.linear(torch.cat([input, update], dim=-1))\n        if self.layer_norm:\n            output = self.layer_norm(output)\n        if self.activation:\n            output = self.activation(output)\n        return output"
  },
  {
    "path": "nbfnet/model.py",
    "content": "from collections.abc import Sequence\n\nimport torch\nfrom torch import nn\nfrom torch import autograd\n\nfrom torch_scatter import scatter_add\n\nfrom torchdrug import core, layers\nfrom torchdrug.layers import functional\nfrom torchdrug.core import Registry as R\n\nfrom . import layer\n\n\n@R.register(\"model.NBFNet\")\nclass NeuralBellmanFordNetwork(nn.Module, core.Configurable):\n\n    def __init__(self, input_dim, hidden_dims, num_relation=None, symmetric=False,\n                 message_func=\"distmult\", aggregate_func=\"pna\", short_cut=False, layer_norm=False, activation=\"relu\",\n                 concat_hidden=False, num_mlp_layer=2, dependent=True, remove_one_hop=False,\n                 num_beam=10, path_topk=10):\n        super(NeuralBellmanFordNetwork, self).__init__()\n\n        if not isinstance(hidden_dims, Sequence):\n            hidden_dims = [hidden_dims]\n        if num_relation is None:\n            double_relation = 1\n        else:\n            num_relation = int(num_relation)\n            double_relation = num_relation * 2\n        self.dims = [input_dim] + list(hidden_dims)\n        self.num_relation = num_relation\n        self.symmetric = symmetric\n        self.short_cut = short_cut\n        self.concat_hidden = concat_hidden\n        self.remove_one_hop = remove_one_hop\n        self.num_beam = num_beam\n        self.path_topk = path_topk\n\n        self.layers = nn.ModuleList()\n        for i in range(len(self.dims) - 1):\n            self.layers.append(layer.GeneralizedRelationalConv(self.dims[i], self.dims[i + 1], double_relation,\n                                                               self.dims[0], message_func, aggregate_func, layer_norm,\n                                                               activation, dependent))\n\n        feature_dim = hidden_dims[-1] * (len(hidden_dims) if concat_hidden else 1) + input_dim\n        self.query = nn.Embedding(double_relation, input_dim)\n        self.mlp = layers.MLP(feature_dim, [feature_dim] * (num_mlp_layer - 1) + [1])\n\n    def remove_easy_edges(self, graph, h_index, t_index, r_index=None):\n        if self.remove_one_hop:\n            h_index_ext = torch.cat([h_index, t_index], dim=-1)\n            t_index_ext = torch.cat([t_index, h_index], dim=-1)\n            if r_index is not None:\n                any = -torch.ones_like(h_index_ext)\n                pattern = torch.stack([h_index_ext, t_index_ext, any], dim=-1)\n            else:\n                pattern = torch.stack([h_index_ext, t_index_ext], dim=-1)\n        else:\n            if r_index is not None:\n                pattern = torch.stack([h_index, t_index, r_index], dim=-1)\n            else:\n                pattern = torch.stack([h_index, t_index], dim=-1)\n        pattern = pattern.flatten(0, -2)\n        edge_index = graph.match(pattern)[0]\n        edge_mask = ~functional.as_mask(edge_index, graph.num_edge)\n        return graph.edge_mask(edge_mask)\n\n    def negative_sample_to_tail(self, h_index, t_index, r_index):\n        # convert p(h | t, r) to p(t' | h', r')\n        # h' = t, r' = r^{-1}, t' = h\n        is_t_neg = (h_index == h_index[:, [0]]).all(dim=-1, keepdim=True)\n        new_h_index = torch.where(is_t_neg, h_index, t_index)\n        new_t_index = torch.where(is_t_neg, t_index, h_index)\n        new_r_index = torch.where(is_t_neg, r_index, r_index + self.num_relation)\n        return new_h_index, new_t_index, new_r_index\n\n    def as_relational_graph(self, graph, self_loop=True):\n        # add self loop\n        # convert homogeneous graphs to knowledge graphs with 1 relation\n        edge_list = graph.edge_list\n        edge_weight = graph.edge_weight\n        if self_loop:\n            node_in = node_out = torch.arange(graph.num_node, device=self.device)\n            loop = torch.stack([node_in, node_out], dim=-1)\n            edge_list = torch.cat([edge_list, loop])\n            edge_weight = torch.cat([edge_weight, torch.ones(graph.num_node, device=self.device)])\n        relation = torch.zeros(len(edge_list), 1, dtype=torch.long, device=self.device)\n        edge_list = torch.cat([edge_list, relation], dim=-1)\n        graph = type(graph)(edge_list, edge_weight=edge_weight, num_node=graph.num_node,\n                            num_relation=1, meta_dict=graph.meta_dict, **graph.data_dict)\n        return graph\n\n    def bellmanford(self, graph, h_index, r_index, separate_grad=False):\n        query = self.query(r_index)\n        index = h_index.unsqueeze(-1).expand_as(query)\n        boundary = torch.zeros(graph.num_node, *query.shape, device=self.device)\n        boundary.scatter_add_(0, index.unsqueeze(0), query.unsqueeze(0))\n        with graph.graph():\n            graph.query = query\n        with graph.node():\n            graph.boundary = boundary\n\n        hiddens = []\n        step_graphs = []\n        layer_input = boundary\n\n        for layer in self.layers:\n            if separate_grad:\n                step_graph = graph.clone().requires_grad_()\n            else:\n                step_graph = graph\n            hidden = layer(step_graph, layer_input)\n            if self.short_cut and hidden.shape == layer_input.shape:\n                hidden = hidden + layer_input\n            hiddens.append(hidden)\n            step_graphs.append(step_graph)\n            layer_input = hidden\n\n        node_query = query.expand(graph.num_node, -1, -1)\n        if self.concat_hidden:\n            output = torch.cat(hiddens + [node_query], dim=-1)\n        else:\n            output = torch.cat([hiddens[-1], node_query], dim=-1)\n\n        return {\n            \"node_feature\": output,\n            \"step_graphs\": step_graphs,\n        }\n\n    def forward(self, graph, h_index, t_index, r_index=None, all_loss=None, metric=None):\n        if all_loss is not None:\n            graph = self.remove_easy_edges(graph, h_index, t_index, r_index)\n\n        shape = h_index.shape\n        if graph.num_relation:\n            graph = graph.undirected(add_inverse=True)\n            h_index, t_index, r_index = self.negative_sample_to_tail(h_index, t_index, r_index)\n        else:\n            graph = self.as_relational_graph(graph)\n            h_index = h_index.view(-1, 1)\n            t_index = t_index.view(-1, 1)\n            r_index = torch.zeros_like(h_index)\n\n        assert (h_index[:, [0]] == h_index).all()\n        assert (r_index[:, [0]] == r_index).all()\n        output = self.bellmanford(graph, h_index[:, 0], r_index[:, 0])\n        feature = output[\"node_feature\"].transpose(0, 1)\n        index = t_index.unsqueeze(-1).expand(-1, -1, feature.shape[-1])\n        feature = feature.gather(1, index)\n\n        if self.symmetric:\n            assert (t_index[:, [0]] == t_index).all()\n            output = self.bellmanford(graph, t_index[:, 0], r_index[:, 0])\n            inv_feature = output[\"node_feature\"].transpose(0, 1)\n            index = h_index.unsqueeze(-1).expand(-1, -1, inv_feature.shape[-1])\n            inv_feature = inv_feature.gather(1, index)\n            feature = (feature + inv_feature) / 2\n\n        score = self.mlp(feature).squeeze(-1)\n        return score.view(shape)\n\n    def visualize(self, graph, h_index, t_index, r_index):\n        assert h_index.numel() == 1 and h_index.ndim == 1\n        graph = graph.undirected(add_inverse=True)\n\n        output = self.bellmanford(graph, h_index, r_index, separate_grad=True)\n        feature = output[\"node_feature\"]\n        step_graphs = output[\"step_graphs\"]\n\n        index = t_index.unsqueeze(0).unsqueeze(-1).expand(-1, -1, feature.shape[-1])\n        feature = feature.gather(0, index).squeeze(0)\n        score = self.mlp(feature).squeeze(-1)\n\n        edge_weights = [graph.edge_weight for graph in step_graphs]\n        edge_grads = autograd.grad(score, edge_weights)\n        for graph, edge_grad in zip(step_graphs, edge_grads):\n            with graph.edge():\n                graph.edge_grad = edge_grad\n        distances, back_edges = self.beam_search_distance(step_graphs, h_index, t_index, self.num_beam)\n        paths, weights = self.topk_average_length(distances, back_edges, t_index, self.path_topk)\n\n        return paths, weights\n\n    @torch.no_grad()\n    def beam_search_distance(self, graphs, h_index, t_index, num_beam=10):\n        num_node = graphs[0].num_node\n        input = torch.full((num_node, num_beam), float(\"-inf\"), device=self.device)\n        input[h_index, 0] = 0\n\n        distances = []\n        back_edges = []\n        for graph in graphs:\n            graph = graph.edge_mask(graph.edge_list[:, 0] != t_index)\n            node_in, node_out = graph.edge_list.t()[:2]\n\n            message = input[node_in] + graph.edge_grad.unsqueeze(-1)\n            msg_source = graph.edge_list.unsqueeze(1).expand(-1, num_beam, -1)\n\n            is_duplicate = torch.isclose(message.unsqueeze(-1), message.unsqueeze(-2)) & \\\n                           (msg_source.unsqueeze(-2) == msg_source.unsqueeze(-3)).all(dim=-1)\n            is_duplicate = is_duplicate.float() - \\\n                           torch.arange(num_beam, dtype=torch.float, device=self.device) / (num_beam + 1)\n            # pick the first occurrence as the previous state\n            prev_rank = is_duplicate.argmax(dim=-1, keepdim=True)\n            msg_source = torch.cat([msg_source, prev_rank], dim=-1)\n\n            node_out, order = node_out.sort()\n            node_out_set = torch.unique(node_out)\n            # sort message w.r.t. node_out\n            message = message[order].flatten()\n            msg_source = msg_source[order].flatten(0, -2)\n            size = scatter_add(torch.ones_like(node_out), node_out, dim_size=num_node)\n            msg2out = torch.repeat_interleave(size[node_out_set] * num_beam)\n            # deduplicate\n            is_duplicate = (msg_source[1:] == msg_source[:-1]).all(dim=-1)\n            is_duplicate = torch.cat([torch.zeros(1, dtype=torch.bool, device=self.device), is_duplicate])\n            message = message[~is_duplicate]\n            msg_source = msg_source[~is_duplicate]\n            msg2out = msg2out[~is_duplicate]\n            size = scatter_add(torch.ones_like(msg2out), msg2out, dim_size=len(node_out_set))\n\n            if not torch.isinf(message).all():\n                distance, rel_index = functional.variadic_topk(message, size, k=num_beam)\n                abs_index = rel_index + (size.cumsum(0) - size).unsqueeze(-1)\n                back_edge = msg_source[abs_index]\n                distance = distance.view(len(node_out_set), num_beam)\n                back_edge = back_edge.view(len(node_out_set), num_beam, 4)\n                distance = scatter_add(distance, node_out_set, dim=0, dim_size=num_node)\n                back_edge = scatter_add(back_edge, node_out_set, dim=0, dim_size=num_node)\n            else:\n                distance = torch.full((num_node, num_beam), float(\"-inf\"), device=self.device)\n                back_edge = torch.zeros(num_node, num_beam, 4, dtype=torch.long, device=self.device)\n\n            distances.append(distance)\n            back_edges.append(back_edge)\n            input = distance\n\n        return distances, back_edges\n\n    def topk_average_length(self, distances, back_edges, t_index, k=10):\n        paths = []\n        average_lengths = []\n\n        for i in range(len(distances)):\n            distance, order = distances[i][t_index].flatten(0, -1).sort(descending=True)\n            back_edge = back_edges[i][t_index].flatten(0, -2)[order]\n            for d, (h, t, r, prev_rank) in zip(distance[:k].tolist(), back_edge[:k].tolist()):\n                if d == float(\"-inf\"):\n                    break\n                path = [(h, t, r)]\n                for j in range(i - 1, -1, -1):\n                    h, t, r, prev_rank = back_edges[j][h, prev_rank].tolist()\n                    path.append((h, t, r))\n                paths.append(path[::-1])\n                average_lengths.append(d / len(path))\n\n        if paths:\n            average_lengths, paths = zip(*sorted(zip(average_lengths, paths), reverse=True)[:k])\n\n        return paths, average_lengths"
  },
  {
    "path": "nbfnet/task.py",
    "content": "import math\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.utils import data as torch_data\n\nfrom ogb import linkproppred\n\nfrom torchdrug import core, tasks, metrics\nfrom torchdrug.layers import functional\nfrom torchdrug.core import Registry as R\n\n\nEvaluator = core.make_configurable(linkproppred.Evaluator)\nEvaluator = R.register(\"ogb.linkproppred.Evaluator\")(Evaluator)\nsetattr(linkproppred, \"Evaluator\", Evaluator)\n\n\n@R.register(\"tasks.KnowledgeGraphCompletionExt\")\nclass KnowledgeGraphCompletionExt(tasks.KnowledgeGraphCompletion, core.Configurable):\n\n    def __init__(self, model, criterion=\"bce\",\n                 metric=(\"mr\", \"mrr\", \"hits@1\", \"hits@3\", \"hits@10\", \"1-to-1\", \"1-to-n\", \"n-to-1\", \"n-to-n\"),\n                 num_negative=128, margin=6, adversarial_temperature=0, strict_negative=True, fact_ratio=None,\n                 sample_weight=True, filtered_ranking=True, full_batch_eval=False):\n        super(KnowledgeGraphCompletionExt, self).__init__(\n            model, criterion, metric, num_negative, margin, adversarial_temperature, strict_negative, fact_ratio,\n            sample_weight, filtered_ranking, full_batch_eval)\n\n    def preprocess(self, train_set, valid_set, test_set):\n        super(KnowledgeGraphCompletionExt, self).preprocess(train_set, valid_set, test_set)\n\n        degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n        degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n        for h, t, r in train_set:\n            degree_hr[h, r] += 1\n            degree_tr[t, r] += 1\n\n        has_category = False\n        for _metric in self.metric:\n            if _metric in [\"1-to-1\", \"1-to-n\", \"n-to-1\", \"n-to-n\"]:\n                has_category = True\n        if has_category:\n            is_to_one = degree_hr.sum(dim=0).float() / (degree_hr > 0).sum(dim=0) < 1.5\n            is_one_to = degree_tr.sum(dim=0).float() / (degree_tr > 0).sum(dim=0) < 1.5\n            self.register_buffer(\"is_one_to_one\", is_one_to & is_to_one)\n            self.register_buffer(\"is_one_to_many\", is_one_to & ~is_to_one)\n            self.register_buffer(\"is_many_to_one\", ~is_one_to & is_to_one)\n            self.register_buffer(\"is_many_to_many\", ~is_one_to & ~is_to_one)\n            assert self.is_one_to_one.sum() + self.is_one_to_many.sum() + \\\n                   self.is_many_to_one.sum() + self.is_many_to_many.sum() == self.num_relation\n            assert (self.is_one_to_one | self.is_one_to_many | self.is_many_to_one | self.is_many_to_many).all()\n\n    def target(self, batch):\n        mask, target = super(KnowledgeGraphCompletionExt, self).target(batch)\n        relation = batch[:, 2]\n        # in case of GPU OOM\n        return mask, target, relation.cpu()\n\n    def evaluate(self, pred, target):\n        mask, target, relation = target\n\n        pos_pred = pred.gather(-1, target.unsqueeze(-1))\n        if self.filtered_ranking:\n            ranking = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1\n        else:\n            ranking = torch.sum(pos_pred <= pred, dim=-1) + 1\n\n        metric = {}\n        for _metric in self.metric:\n            if _metric == \"mr\":\n                score = ranking.float().mean()\n            elif _metric == \"mrr\":\n                score = (1 / ranking.float()).mean()\n            elif _metric.startswith(\"hits@\"):\n                threshold = int(_metric[5:])\n                score = (ranking <= threshold).float().mean()\n            elif _metric == \"1-to-1\":\n                score = (1 / ranking[self.is_one_to_one[relation]].float()).mean()\n                metric[\"1-to-1 tail\"] = (1 / ranking[self.is_one_to_one[relation]].float()).mean(dim=0)[0]\n                metric[\"1-to-1 head\"] = (1 / ranking[self.is_one_to_one[relation]].float()).mean(dim=0)[1]\n            elif _metric == \"1-to-n\":\n                score = (1 / ranking[self.is_one_to_many[relation]].float()).mean()\n                metric[\"1-to-n tail\"] = (1 / ranking[self.is_one_to_many[relation]].float()).mean(dim=0)[0]\n                metric[\"1-to-n head\"] = (1 / ranking[self.is_one_to_many[relation]].float()).mean(dim=0)[1]\n            elif _metric == \"n-to-1\":\n                score = (1 / ranking[self.is_many_to_one[relation]].float()).mean()\n                metric[\"n-to-1 tail\"] = (1 / ranking[self.is_many_to_one[relation]].float()).mean(dim=0)[0]\n                metric[\"n-to-1 head\"] = (1 / ranking[self.is_many_to_one[relation]].float()).mean(dim=0)[1]\n            elif _metric == \"n-to-n\":\n                score = (1 / ranking[self.is_many_to_many[relation]].float()).mean()\n                metric[\"n-to-n tail\"] = (1 / ranking[self.is_many_to_many[relation]].float()).mean(dim=0)[0]\n                metric[\"n-to-n head\"] = (1 / ranking[self.is_many_to_many[relation]].float()).mean(dim=0)[1]\n            else:\n                raise ValueError(\"Unknown metric `%s`\" % _metric)\n\n            name = tasks._get_metric_name(_metric)\n            metric[name] = score\n\n        return metric\n\n\n@R.register(\"tasks.LinkPrediction\")\nclass LinkPrediction(tasks.Task, core.Configurable):\n\n    _option_members = [\"criterion\", \"metric\"]\n\n    def __init__(self, model, criterion=\"bce\", metric=(\"auroc\", \"ap\"), num_negative=128, strict_negative=True):\n        super(LinkPrediction, self).__init__()\n        self.model = model\n        self.criterion = criterion\n        self.metric = metric\n        self.num_negative = num_negative\n        self.strict_negative = strict_negative\n\n    def preprocess(self, train_set, valid_set, test_set):\n        if isinstance(train_set, torch_data.Subset):\n            dataset = train_set.dataset\n        else:\n            dataset = train_set\n        self.num_node = dataset.num_node\n        train_mask = train_set.indices\n        valid_mask = train_set.indices + valid_set.indices\n        train_graph = dataset.graph.edge_mask(train_mask)\n        valid_graph = dataset.graph.edge_mask(valid_mask)\n        self.register_buffer(\"train_graph\", train_graph.undirected())\n        self.register_buffer(\"valid_graph\", valid_graph.undirected())\n        self.register_buffer(\"test_graph\", dataset.graph.undirected())\n\n    def forward(self, batch):\n        all_loss = torch.tensor(0, dtype=torch.float32, device=self.device)\n        metric = {}\n\n        pred, target = self.predict_and_target(batch, all_loss, metric)\n        metric.update(self.evaluate(pred, target))\n\n        for criterion, weight in self.criterion.items():\n            if criterion == \"bce\":\n                loss = F.binary_cross_entropy_with_logits(pred, target, reduction=\"none\")\n                neg_weight = torch.ones_like(pred)\n                neg_weight[:, 1:] = 1 / self.num_negative\n                loss = (loss * neg_weight).sum(dim=-1) / neg_weight.sum(dim=-1)\n            else:\n                raise ValueError(\"Unknown criterion `%s`\" % criterion)\n\n            loss = loss.mean()\n            name = tasks._get_criterion_name(criterion)\n            metric[name] = loss\n            all_loss += loss * weight\n\n        return all_loss, metric\n\n    @torch.no_grad()\n    def _strict_negative(self, count, split=\"train\"):\n        graph = getattr(self, \"%s_graph\" % split)\n\n        node_in = graph.edge_list[:, 0]\n        degree_in = torch.bincount(node_in, minlength=self.num_node)\n        prob = (graph.num_node - degree_in - 1).float()\n\n        neg_h_index = functional.multinomial(prob, count, replacement=True)\n        any = -torch.ones_like(neg_h_index)\n        pattern = torch.stack([neg_h_index, any], dim=-1)\n        edge_index, num_t_truth = graph.match(pattern)\n        t_truth_index = graph.edge_list[edge_index, 1]\n        pos_index = torch.repeat_interleave(num_t_truth)\n        t_mask = torch.ones(count, self.num_node, dtype=torch.bool, device=self.device)\n        t_mask[pos_index, t_truth_index] = 0\n        t_mask.scatter_(1, neg_h_index.unsqueeze(-1), 0)\n        neg_t_candidate = t_mask.nonzero()[:, 1]\n        num_t_candidate = t_mask.sum(dim=-1)\n        neg_t_index = functional.variadic_sample(neg_t_candidate, num_t_candidate, 1).squeeze(-1)\n\n        return neg_h_index, neg_t_index\n\n    def predict_and_target(self, batch, all_loss=None, metric=None):\n        batch_size = len(batch)\n        pos_h_index, pos_t_index = batch.t()\n\n        if self.split == \"train\":\n            num_negative = self.num_negative\n        else:\n            num_negative = 1\n        if self.strict_negative or self.split != \"train\":\n            neg_h_index, neg_t_index = self._strict_negative(batch_size * num_negative, self.split)\n        else:\n            neg_h_index, neg_t_index = torch.randint(self.num_node, (2, batch_size * num_negative), device=self.device)\n        neg_h_index = neg_h_index.view(batch_size, num_negative)\n        neg_t_index = neg_t_index.view(batch_size, num_negative)\n        h_index = pos_h_index.unsqueeze(-1).repeat(1, num_negative + 1)\n        t_index = pos_t_index.unsqueeze(-1).repeat(1, num_negative + 1)\n        h_index[:, 1:] = neg_h_index\n        t_index[:, 1:] = neg_t_index\n\n        pred = self.model(self.train_graph, h_index, t_index, all_loss=all_loss, metric=metric)\n        target = torch.zeros_like(pred)\n        target[:, 0] = 1\n        return pred, target\n\n    def evaluate(self, pred, target):\n        pred = pred.flatten()\n        target = target.flatten()\n\n        metric = {}\n        for _metric in self.metric:\n            if _metric == \"auroc\":\n                score = metrics.area_under_roc(pred, target)\n            elif _metric == \"ap\":\n                score = metrics.area_under_prc(pred, target)\n            else:\n                raise ValueError(\"Unknown metric `%s`\" % _metric)\n\n            name = tasks._get_metric_name(_metric)\n            metric[name] = score\n\n        return metric\n\n\n@R.register(\"tasks.InductiveKnowledgeGraphCompletion\")\nclass InductiveKnowledgeGraphCompletion(tasks.KnowledgeGraphCompletion, core.Configurable):\n\n    def __init__(self, model, criterion=\"bce\", metric=(\"mr\", \"mrr\", \"hits@1\", \"hits@3\", \"hits@10\", \"hits@10_50\"),\n                 num_negative=128, margin=6, adversarial_temperature=0, strict_negative=True, sample_weight=True,\n                 full_batch_eval=False):\n        super(InductiveKnowledgeGraphCompletion, self).__init__(\n            model, criterion, metric, num_negative, margin, adversarial_temperature, strict_negative,\n            sample_weight=sample_weight, filtered_ranking=True, full_batch_eval=full_batch_eval)\n\n    def preprocess(self, train_set, valid_set, test_set):\n        if isinstance(train_set, torch_data.Subset):\n            dataset = train_set.dataset\n        else:\n            dataset = train_set\n        self.num_entity = dataset.num_entity\n        self.num_relation = dataset.num_relation\n        self.register_buffer(\"fact_graph\", dataset.graph)\n\n        if self.sample_weight:\n            degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n            degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n            for h, t, r in train_set:\n                degree_hr[h, r] += 1\n                degree_tr[t, r] += 1\n            self.register_buffer(\"degree_hr\", degree_hr)\n            self.register_buffer(\"degree_tr\", degree_tr)\n\n        self.register_buffer(\"train_graph\", dataset.train_graph)\n        self.register_buffer(\"valid_graph\", dataset.valid_graph)\n        self.register_buffer(\"test_graph\", dataset.test_graph)\n\n        return train_set, valid_set, test_set\n\n    def predict(self, batch, all_loss=None, metric=None):\n        pos_h_index, pos_t_index, pos_r_index = batch.t()\n        batch_size = len(batch)\n        graph = getattr(self, \"%s_graph\" % self.split)\n\n        if all_loss is None:\n            # test\n            all_index = torch.arange(graph.num_node, device=self.device)\n            t_preds = []\n            h_preds = []\n            num_negative = graph.num_node if self.full_batch_eval else self.num_negative\n            for neg_index in all_index.split(num_negative):\n                r_index = pos_r_index.unsqueeze(-1).expand(-1, len(neg_index))\n                h_index, t_index = torch.meshgrid(pos_h_index, neg_index)\n                t_pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)\n                t_preds.append(t_pred)\n            t_pred = torch.cat(t_preds, dim=-1)\n            for neg_index in all_index.split(num_negative):\n                r_index = pos_r_index.unsqueeze(-1).expand(-1, len(neg_index))\n                t_index, h_index = torch.meshgrid(pos_t_index, neg_index)\n                h_pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)\n                h_preds.append(h_pred)\n            h_pred = torch.cat(h_preds, dim=-1)\n            pred = torch.stack([t_pred, h_pred], dim=1)\n            # in case of GPU OOM\n            pred = pred.cpu()\n        else:\n            # train\n            if self.strict_negative:\n                neg_index = self._strict_negative(pos_h_index, pos_t_index, pos_r_index)\n            else:\n                neg_index = torch.randint(self.num_entity, (batch_size, self.num_negative), device=self.device)\n            h_index = pos_h_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            t_index = pos_t_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            r_index = pos_r_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            t_index[:batch_size // 2, 1:] = neg_index[:batch_size // 2]\n            h_index[batch_size // 2:, 1:] = neg_index[batch_size // 2:]\n            pred = self.model(graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)\n\n        return pred\n\n    def target(self, batch):\n        # test target\n        batch_size = len(batch)\n        graph = getattr(self, \"%s_graph\" % self.split)\n        pos_h_index, pos_t_index, pos_r_index = batch.t()\n        any = -torch.ones_like(pos_h_index)\n\n        pattern = torch.stack([pos_h_index, any, pos_r_index], dim=-1)\n        edge_index, num_t_truth = graph.match(pattern)\n        t_truth_index = graph.edge_list[edge_index, 1]\n        pos_index = torch.repeat_interleave(num_t_truth)\n        t_mask = torch.ones(batch_size, graph.num_node, dtype=torch.bool, device=self.device)\n        t_mask[pos_index, t_truth_index] = 0\n\n        pattern = torch.stack([any, pos_t_index, pos_r_index], dim=-1)\n        edge_index, num_h_truth = graph.match(pattern)\n        h_truth_index = graph.edge_list[edge_index, 0]\n        pos_index = torch.repeat_interleave(num_h_truth)\n        h_mask = torch.ones(batch_size, graph.num_node, dtype=torch.bool, device=self.device)\n        h_mask[pos_index, h_truth_index] = 0\n\n        mask = torch.stack([t_mask, h_mask], dim=1)\n        target = torch.stack([pos_t_index, pos_h_index], dim=1)\n\n        # in case of GPU OOM\n        return mask.cpu(), target.cpu()\n\n    def evaluate(self, pred, target):\n        mask, target = target\n\n        pos_pred = pred.gather(-1, target.unsqueeze(-1))\n        ranking = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1\n\n        metric = {}\n        for _metric in self.metric:\n            if _metric == \"mr\":\n                score = ranking.float().mean()\n            elif _metric == \"mrr\":\n                score = (1 / ranking.float()).mean()\n            elif _metric.startswith(\"hits@\"):\n                values = _metric[5:].split(\"_\")\n                threshold = int(values[0])\n                if len(values) > 1:\n                    num_sample = int(values[1])\n                    # unbiased estimation\n                    fp_rate = (ranking - 1).float() / mask.sum(dim=-1)\n                    score = 0\n                    for i in range(threshold):\n                        # choose i false positive from num_sample negatives\n                        num_comb = math.factorial(num_sample) / math.factorial(i) / math.factorial(num_sample - i)\n                        score += num_comb * (fp_rate ** i) * ((1 - fp_rate) ** (num_sample - i))\n                    score = score.mean()\n                else:\n                    score = (ranking <= threshold).float().mean()\n            else:\n                raise ValueError(\"Unknown metric `%s`\" % _metric)\n\n            name = tasks._get_metric_name(_metric)\n            metric[name] = score\n\n        return metric\n\n\n@R.register(\"tasks.KnowledgeGraphCompletionOGB\")\nclass KnowledgeGraphCompletionOGB(tasks.KnowledgeGraphCompletion, core.Configurable):\n\n    def __init__(self, model, criterion=\"bce\", evaluator=None, num_negative=128, margin=6, adversarial_temperature=0,\n                 strict_negative=True, heterogeneous_negative=False, fact_ratio=None, sample_weight=True):\n        super(KnowledgeGraphCompletionOGB, self).__init__(\n            model, criterion, None, num_negative, margin, adversarial_temperature, strict_negative, fact_ratio,\n            sample_weight)\n\n        self.evaluator = evaluator\n        self.heterogeneous_negative = heterogeneous_negative\n\n    def preprocess(self, train_set, valid_set, test_set):\n        if isinstance(train_set, torch_data.Subset):\n            dataset = train_set.dataset\n        else:\n            dataset = train_set\n        self.num_entity = dataset.num_entity\n        self.num_relation = dataset.num_relation\n        self.register_buffer(\"graph\", dataset.graph)\n        fact_mask = torch.zeros(len(dataset), dtype=torch.bool)\n        fact_mask[train_set.indices] = 1\n        if self.fact_ratio:\n            length = int(len(train_set) * self.fact_ratio)\n            index = torch.randperm(len(train_set))[length:]\n            train_indices = torch.tensor(train_set.indices)\n            fact_mask[train_indices[index]] = 0\n            train_set = torch_data.Subset(train_set, index)\n        self.register_buffer(\"fact_graph\", dataset.graph.edge_mask(fact_mask))\n\n        if self.sample_weight:\n            degree_hr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n            degree_tr = torch.zeros(self.num_entity, self.num_relation, dtype=torch.long)\n            for h, t, r in train_set:\n                degree_hr[h, r] += 1\n                degree_tr[t, r] += 1\n            self.register_buffer(\"degree_hr\", degree_hr)\n            self.register_buffer(\"degree_tr\", degree_tr)\n\n        return train_set, valid_set, test_set\n\n    @torch.no_grad()\n    def _strict_negative(self, pos_h_index, pos_t_index, pos_r_index):\n        batch_size = len(pos_h_index)\n        any = -torch.ones_like(pos_h_index)\n        node_type = self.fact_graph.node_type\n\n        pattern = torch.stack([pos_h_index, any, pos_r_index], dim=-1)\n        pattern = pattern[:batch_size // 2]\n        edge_index, num_t_truth = self.fact_graph.match(pattern)\n        t_truth_index = self.fact_graph.edge_list[edge_index, 1]\n        pos_index = torch.repeat_interleave(num_t_truth)\n        if self.heterogeneous_negative:\n            pos_t_type = node_type[pos_t_index[:batch_size // 2]]\n            t_mask = pos_t_type.unsqueeze(-1) == node_type.unsqueeze(0)\n        else:\n            t_mask = torch.ones(len(pattern), self.num_entity, dtype=torch.bool, device=self.device)\n        t_mask[pos_index, t_truth_index] = 0\n        neg_t_candidate = t_mask.nonzero()[:, 1]\n        num_t_candidate = t_mask.sum(dim=-1)\n        neg_t_index = functional.variadic_sample(neg_t_candidate, num_t_candidate, self.num_negative)\n\n        pattern = torch.stack([any, pos_t_index, pos_r_index], dim=-1)\n        pattern = pattern[batch_size // 2:]\n        edge_index, num_h_truth = self.fact_graph.match(pattern)\n        h_truth_index = self.fact_graph.edge_list[edge_index, 0]\n        pos_index = torch.repeat_interleave(num_h_truth)\n        if self.heterogeneous_negative:\n            pos_h_type = node_type[pos_h_index[batch_size // 2:]]\n            h_mask = pos_h_type.unsqueeze(-1) == node_type.unsqueeze(0)\n        else:\n            h_mask = torch.ones(len(pattern), self.num_entity, dtype=torch.bool, device=self.device)\n        h_mask[pos_index, h_truth_index] = 0\n        neg_h_candidate = h_mask.nonzero()[:, 1]\n        num_h_candidate = h_mask.sum(dim=-1)\n        neg_h_index = functional.variadic_sample(neg_h_candidate, num_h_candidate, self.num_negative)\n\n        neg_index = torch.cat([neg_t_index, neg_h_index])\n\n        return neg_index\n\n    def predict(self, batch, all_loss=None, metric=None):\n        batch_size = len(batch)\n\n        if all_loss is None:\n            # test\n            h_index, t_index, r_index = batch.unbind(-1)\n            pattern = batch[:, 0, :]\n            num_match = self.fact_graph.match(pattern)[1]\n            assert (num_match == 0).all()\n            pred = self.model(self.fact_graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)\n            # in case of GPU OOM\n            pred = pred.cpu()\n        else:\n            # train\n            pos_h_index, pos_t_index, pos_r_index = batch.t()\n            if self.strict_negative:\n                neg_index = self._strict_negative(pos_h_index, pos_t_index, pos_r_index)\n            else:\n                neg_index = torch.randint(self.num_entity, (batch_size, self.num_negative), device=self.device)\n            h_index = pos_h_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            t_index = pos_t_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            r_index = pos_r_index.unsqueeze(-1).repeat(1, self.num_negative + 1)\n            t_index[:batch_size // 2, 1:] = neg_index[:batch_size // 2]\n            h_index[batch_size // 2:, 1:] = neg_index[batch_size // 2:]\n            pred = self.model(self.fact_graph, h_index, t_index, r_index, all_loss=all_loss, metric=metric)\n\n        return pred\n\n    def target(self, batch):\n        # test target\n        batch_size = len(batch)\n        target = torch.zeros(batch_size, dtype=torch.long, device=self.device)\n        # in case of GPU OOM\n        return target.cpu()\n\n    def evaluate(self, pred, target):\n        is_positive = torch.zeros(pred.shape, dtype=torch.bool)\n        is_positive.scatter_(-1, target.unsqueeze(-1), 1)\n        pos_pred = pred[is_positive]\n        neg_pred = pred[~is_positive].view(len(pos_pred), -1)\n        metric = self.evaluator.eval({\"y_pred_pos\": pos_pred, \"y_pred_neg\": neg_pred})\n\n        new_metric = {}\n        for key in metric:\n            new_key = key.split(\"_\")[0]\n            new_metric[new_key] = metric[key].mean()\n\n        return new_metric"
  },
  {
    "path": "nbfnet/util.py",
    "content": "import os\nimport time\nimport logging\nimport argparse\n\nimport yaml\nimport jinja2\nfrom jinja2 import meta\nimport easydict\n\nimport torch\nfrom torch.utils import data as torch_data\nfrom torch import distributed as dist\n\nfrom torchdrug import core, utils\nfrom torchdrug.utils import comm\n\n\nlogger = logging.getLogger(__file__)\n\n\ndef get_root_logger(file=True):\n    logger = logging.getLogger(\"\")\n    logger.setLevel(logging.INFO)\n    format = logging.Formatter(\"%(asctime)-10s %(message)s\", \"%H:%M:%S\")\n\n    if file:\n        handler = logging.FileHandler(\"log.txt\")\n        handler.setFormatter(format)\n        logger.addHandler(handler)\n\n    return logger\n\n\ndef create_working_directory(cfg):\n    file_name = \"working_dir.tmp\"\n    world_size = comm.get_world_size()\n    if world_size > 1 and not dist.is_initialized():\n        comm.init_process_group(\"nccl\", init_method=\"env://\")\n\n    working_dir = os.path.join(os.path.expanduser(cfg.output_dir),\n                               cfg.task[\"class\"], cfg.dataset[\"class\"], cfg.task.model[\"class\"],\n                               time.strftime(\"%Y-%m-%d-%H-%M-%S\"))\n\n    # synchronize working directory\n    if comm.get_rank() == 0:\n        with open(file_name, \"w\") as fout:\n            fout.write(working_dir)\n        os.makedirs(working_dir)\n    comm.synchronize()\n    if comm.get_rank() != 0:\n        with open(file_name, \"r\") as fin:\n            working_dir = fin.read()\n    comm.synchronize()\n    if comm.get_rank() == 0:\n        os.remove(file_name)\n\n    os.chdir(working_dir)\n    return working_dir\n\n\ndef detect_variables(cfg_file):\n    with open(cfg_file, \"r\") as fin:\n        raw = fin.read()\n    env = jinja2.Environment()\n    ast = env.parse(raw)\n    vars = meta.find_undeclared_variables(ast)\n    return vars\n\n\ndef load_config(cfg_file, context=None):\n    with open(cfg_file, \"r\") as fin:\n        raw = fin.read()\n    template = jinja2.Template(raw)\n    instance = template.render(context)\n    cfg = yaml.safe_load(instance)\n    cfg = easydict.EasyDict(cfg)\n    return cfg\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-c\", \"--config\", help=\"yaml configuration file\", required=True)\n    parser.add_argument(\"-s\", \"--seed\", help=\"random seed for PyTorch\", type=int, default=1024)\n\n    args, unparsed = parser.parse_known_args()\n    # get dynamic arguments defined in the config file\n    vars = detect_variables(args.config)\n    parser = argparse.ArgumentParser()\n    for var in vars:\n        parser.add_argument(\"--%s\" % var, required=True)\n    vars = parser.parse_known_args(unparsed)[0]\n    vars = {k: utils.literal_eval(v) for k, v in vars._get_kwargs()}\n\n    return args, vars\n\n\ndef build_solver(cfg, dataset):\n    train_set, valid_set, test_set = dataset.split()\n    if comm.get_rank() == 0:\n        logger.warning(dataset)\n        logger.warning(\"#train: %d, #valid: %d, #test: %d\" % (len(train_set), len(valid_set), len(test_set)))\n\n    if \"fast_test\" in cfg:\n        if comm.get_rank() == 0:\n            logger.warning(\"Quick test mode on. Only evaluate on %d samples for valid / test.\" % cfg.fast_test)\n        g = torch.Generator()\n        g.manual_seed(1024)\n        valid_set = torch_data.random_split(valid_set, [cfg.fast_test, len(valid_set) - cfg.fast_test], generator=g)[0]\n        test_set = torch_data.random_split(test_set, [cfg.fast_test, len(test_set) - cfg.fast_test], generator=g)[0]\n    if hasattr(dataset, \"num_relation\"):\n        cfg.task.model.num_relation = dataset.num_relation\n\n    task = core.Configurable.load_config_dict(cfg.task)\n    cfg.optimizer.params = task.parameters()\n    optimizer = core.Configurable.load_config_dict(cfg.optimizer)\n    solver = core.Engine(task, train_set, valid_set, test_set, optimizer, **cfg.engine)\n\n    if \"checkpoint\" in cfg:\n        solver.load(cfg.checkpoint)\n\n    return solver"
  },
  {
    "path": "requirements.txt",
    "content": "torch>=1.8.0\ntorch-scatter>=2.0.8\ntorchdrug>=0.1.2\nogb>=1.3.0\neasydict\npyyaml"
  },
  {
    "path": "script/run.py",
    "content": "import os\nimport sys\nimport math\nimport pprint\n\nimport torch\n\nfrom torchdrug import core\nfrom torchdrug.utils import comm\n\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom nbfnet import dataset, layer, model, task, util\n\n\ndef train_and_validate(cfg, solver):\n    if cfg.train.num_epoch == 0:\n        return\n\n    step = math.ceil(cfg.train.num_epoch / 10)\n    best_result = float(\"-inf\")\n    best_epoch = -1\n\n    for i in range(0, cfg.train.num_epoch, step):\n        kwargs = cfg.train.copy()\n        kwargs[\"num_epoch\"] = min(step, cfg.train.num_epoch - i)\n        solver.model.split = \"train\"\n        solver.train(**kwargs)\n        solver.save(\"model_epoch_%d.pth\" % solver.epoch)\n        solver.model.split = \"valid\"\n        metric = solver.evaluate(\"valid\")\n        result = metric[cfg.metric]\n        if result > best_result:\n            best_result = result\n            best_epoch = solver.epoch\n\n    solver.load(\"model_epoch_%d.pth\" % best_epoch)\n    return solver\n\n\ndef test(cfg, solver):\n    solver.model.split = \"valid\"\n    solver.evaluate(\"valid\")\n    solver.model.split = \"test\"\n    solver.evaluate(\"test\")\n\n\nif __name__ == \"__main__\":\n    args, vars = util.parse_args()\n    cfg = util.load_config(args.config, context=vars)\n    working_dir = util.create_working_directory(cfg)\n\n    torch.manual_seed(args.seed + comm.get_rank())\n\n    logger = util.get_root_logger()\n    if comm.get_rank() == 0:\n        logger.warning(\"Config file: %s\" % args.config)\n        logger.warning(pprint.pformat(cfg))\n\n    dataset = core.Configurable.load_config_dict(cfg.dataset)\n    solver = util.build_solver(cfg, dataset)\n\n    train_and_validate(cfg, solver)\n    test(cfg, solver)\n"
  },
  {
    "path": "script/visualize.py",
    "content": "import os\nimport sys\nimport pprint\n\nimport torch\n\nfrom torchdrug import core\nfrom torchdrug.utils import comm\n\nsys.path.append(os.path.dirname(os.path.dirname(__file__)))\nfrom nbfnet import dataset, layer, model, task, util\n\n\nvocab_file = os.path.join(os.path.dirname(__file__), \"../data/fb15k237_entity.txt\")\nvocab_file = os.path.abspath(vocab_file)\n\n\ndef load_vocab(dataset):\n    entity_mapping = {}\n    with open(vocab_file, \"r\") as fin:\n        for line in fin:\n            k, v = line.strip().split(\"\\t\")\n            entity_mapping[k] = v\n    entity_vocab = [entity_mapping[t] for t in dataset.entity_vocab]\n    relation_vocab = [\"%s (%d)\" % (t[t.rfind(\"/\") + 1:].replace(\"_\", \" \"), i)\n                      for i, t in enumerate(dataset.relation_vocab)]\n\n    return entity_vocab, relation_vocab\n\n\ndef visualize_path(solver, triplet, entity_vocab, relation_vocab):\n    num_relation = len(relation_vocab)\n    h, t, r = triplet.tolist()\n    triplet = torch.as_tensor([[h, t, r]], device=solver.device)\n    inverse = torch.as_tensor([[t, h, r + num_relation]], device=solver.device)\n    solver.model.eval()\n    pred, (mask, target) = solver.model.predict_and_target(triplet)\n    pos_pred = pred.gather(-1, target.unsqueeze(-1))\n    rankings = torch.sum((pos_pred <= pred) & mask, dim=-1) + 1\n    rankings = rankings.squeeze(0)\n\n    logger.warning(\"\")\n    samples = (triplet, inverse)\n    for sample, ranking in zip(samples, rankings):\n        h, t, r = sample.squeeze(0).tolist()\n        h_name = entity_vocab[h]\n        t_name = entity_vocab[t]\n        r_name = relation_vocab[r % num_relation]\n        if r >= num_relation:\n            r_name += \"^(-1)\"\n        logger.warning(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n        logger.warning(\"rank(%s | %s, %s) = %g\" % (t_name, h_name, r_name, ranking))\n\n        paths, weights = solver.model.visualize(sample)\n        for path, weight in zip(paths, weights):\n            triplets = []\n            for h, t, r in path:\n                h_name = entity_vocab[h]\n                t_name = entity_vocab[t]\n                r_name = relation_vocab[r % num_relation]\n                if r >= num_relation:\n                    r_name += \"^(-1)\"\n                triplets.append(\"<%s, %s, %s>\" % (h_name, r_name, t_name))\n            logger.warning(\"weight: %g\\n\\t%s\" % (weight, \" ->\\n\\t\".join(triplets)))\n\n\nif __name__ == \"__main__\":\n    args, vars = util.parse_args()\n    cfg = util.load_config(args.config, context=vars)\n    working_dir = util.create_working_directory(cfg)\n\n    torch.manual_seed(args.seed + comm.get_rank())\n\n    logger = util.get_root_logger()\n    logger.warning(\"Config file: %s\" % args.config)\n    logger.warning(pprint.pformat(cfg))\n\n    if cfg.dataset[\"class\"] != \"FB15k237\":\n        raise ValueError(\"Visualization is only implemented for FB15k237\")\n\n    dataset = core.Configurable.load_config_dict(cfg.dataset)\n    solver = util.build_solver(cfg, dataset)\n\n    entity_vocab, relation_vocab = load_vocab(dataset)\n\n    for i in range(500):\n        visualize_path(solver, solver.test_set[i], entity_vocab, relation_vocab)\n"
  }
]