Repository: migalkin/NodePiece Branch: main Commit: 9adc57efe302 Files: 150 Total size: 37.4 MB Directory structure: gitextract_vejh5or5/ ├── .gitignore ├── LICENSE ├── download_data.sh ├── inductive_lp/ │ ├── README.md │ ├── data/ │ │ ├── WN18RR_v1/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v1_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v2/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v2_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v3/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v3_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v4/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── WN18RR_v4_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v1/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v1_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v2/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v2_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v3/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v3_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v4/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── fb237_v4_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v1/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v1_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v2/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v2_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v3/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v3_ind/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ ├── nell_v4/ │ │ │ ├── test.txt │ │ │ ├── train.txt │ │ │ └── valid.txt │ │ └── nell_v4_ind/ │ │ ├── test.txt │ │ ├── train.txt │ │ └── valid.txt │ ├── datasets/ │ │ └── ind_dataset.py │ ├── loops/ │ │ ├── ilp_evaluator.py │ │ ├── inductive_slcwa.py │ │ └── relation_rank_evaluator.py │ ├── model/ │ │ ├── comp_gcn.py │ │ ├── gnn_layer.py │ │ ├── lrga_model.py │ │ └── nodepiece_rotate.py │ ├── nodepiece_tokenizer.py │ ├── run_ilp.py │ └── utils/ │ ├── sample_negatives.py │ └── utils_gcn.py ├── lp_rp/ │ ├── datasets/ │ │ └── codex.py │ ├── loops/ │ │ └── filtered_sampling_loop.py │ ├── nodepiece_tokenizer.py │ ├── patch/ │ │ ├── early_stopping.py │ │ └── evaluator.py │ ├── pykeen105/ │ │ ├── negative_sampler.py │ │ ├── nodepiece_rotate.py │ │ └── relation_rank_evaluator.py │ └── run_lp.py ├── nc/ │ ├── data/ │ │ └── clean/ │ │ └── wd50k/ │ │ ├── statements/ │ │ │ └── wd50k_entity_index.txt │ │ └── triples/ │ │ ├── nc_class_labels.json │ │ ├── nc_edges.txt │ │ ├── nc_entities.txt │ │ ├── nc_rels.txt │ │ ├── nc_test_so_labels.json │ │ ├── nc_train_so_labels.json │ │ ├── nc_val_so_labels.json │ │ ├── test.txt │ │ ├── train.txt │ │ └── valid.txt │ ├── data_loaders/ │ │ ├── __init__.py │ │ └── clean_datasets.py │ ├── loops/ │ │ ├── __init__.py │ │ ├── evaluation.py │ │ ├── loops.py │ │ └── sampler.py │ ├── models/ │ │ ├── __init__.py │ │ ├── gnn_encoder.py │ │ ├── gnn_layer.py │ │ ├── lrga_model.py │ │ └── nc_baselines.py │ ├── models_nc.py │ ├── run_nc.py │ └── utils/ │ ├── __init__.py │ ├── nodepiece_encoder.py │ ├── nodepiece_tokenizer.py │ ├── utils.py │ ├── utils_gcn.py │ └── utils_mytorch.py ├── ogb/ │ ├── download.sh │ ├── ogb_tokenizer.py │ ├── ogb_wikikg2/ │ │ ├── dataloader.py │ │ ├── dummy_factory.py │ │ └── model.py │ ├── readme.md │ ├── requirements.txt │ ├── run_ogb.py │ └── run_ogb.sh ├── oos_lp/ │ └── src/ │ ├── common/ │ │ ├── dataset.py │ │ └── measure.py │ ├── main.py │ ├── model/ │ │ ├── BaseOutKG.py │ │ ├── DisMultOutKG.py │ │ └── dm_tokenized.py │ ├── preprocess/ │ │ └── dataset_prep.py │ ├── tester.py │ ├── trainer.py │ ├── utils.py │ └── vocab/ │ ├── nodepiece_encoder.py │ └── nodepiece_tokenizer.py ├── readme.md └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ .DS_Store ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2021 Michael Galkin 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: download_data.sh ================================================ wget -P ./lp_rp/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/lp/fb15k237_1000_anchors_1000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./lp_rp/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/lp/wn18rr_500_anchors_500_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./lp_rp/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/lp/yago_10000_anchors_10000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./lp_rp/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/lp/codex_l_7000_anchors_7000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./nc/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/nc/wd50k_50_anchors_50_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./nc/data/clean/wd50k/statements/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/nc/wd50k_embs.pkl wget -P ./oos_lp/src/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos-lp/FB15k-237_1000_anchors_1000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./oos_lp/src/data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos-lp/YAGO3-10_10000_anchors_10000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_100sp.pkl wget -P ./oos_lp/datasets/FB15k-237/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/ofb15k237/train.txt wget -P ./oos_lp/datasets/FB15k-237/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/ofb15k237/valid.json wget -P ./oos_lp/datasets/FB15k-237/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/ofb15k237/test.json wget -P ./oos_lp/datasets/YAGO3-10/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/oyago/train.txt wget -P ./oos_lp/datasets/YAGO3-10/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/oyago/valid.json wget -P ./oos_lp/datasets/YAGO3-10/processed/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/oos_datasets/oyago/test.json ================================================ FILE: inductive_lp/README.md ================================================ # Inductive Link Prediction with NodePiece The task is coined in the paper by Teru et al [Inductive relation prediction by subgraph reasoning](https://arxiv.org/abs/1911.06962). Training is executed on one graph, but inference link prediction is executed on a completely new graph with unseen entities (but known relations). For each KG dump (FB15k-237, WN18RR, NELL995) there exist 4 versions of the datasets varying in the size of transductive training and inductive inference graphs. The NodePiece setup here does not use any anchors as inference graphs are disconnected from training graphs. That is, we only use unique relations for node tokenization. ### Results
Dataset HITS@10 (50 samples)
v1 v2 v3 v4
FB15k-237 0.873 0.939 0.944 0.949
WN18RR 0.830 0.886 0.785 0.807
NELL 0.890 0.901 0.936 0.893
Reproduce the reported experiments with the following commands. Add `-wandb True` if you want to log the results to WANDB. ### FB15k-237 * Version 1 ``` python run_ilp.py -loss nssal -margin 25 -epochs 2503 -lr 0.0001 -data fb15k237 -sample_rels 12 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 ``` * Version 2 ``` python run_ilp.py -loss nssal -margin 15 -epochs 2000 -lr 0.0001 -data fb15k237 -sample_rels 12 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -pna False -residual False -jk False -ind_v 2 -eval_bs 2048 ``` * Version 3 ``` python run_ilp.py -loss nssal -margin 15 -epochs 2000 -lr 0.0001 -data fb15k237 -sample_rels 12 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -pna False -residual False -jk False -ind_v 3 -eval_bs 2048 ``` * Version 4 ``` python run_ilp.py -loss nssal -margin 25 -epochs 2000 -lr 0.0001 -data fb15k237 -sample_rels 12 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -pna False -residual False -jk False -ind_v 4 -eval_bs 2048 ``` ### WN18RR * Version 1 ``` python run_ilp.py -loss nssal -margin 15 -epochs 589 -lr 0.0001 -data wn18rr -sample_rels 4 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -pna True -residual True ``` * Version 2 ``` python run_ilp.py -loss nssal -margin 15 -epochs 2000 -lr 0.0001 -data wn18rr -sample_rels 4 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 6 -pna False -residual True -jk True -ind_v 2 ``` * Version 3 ``` python run_ilp.py -loss nssal -margin 5 -epochs 211 -lr 0.0001 -data wn18rr -sample_rels 4 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 6 -pna False -residual True -jk True -ind_v 3 -eval_bs 2048 ``` * Version 4 ``` python run_ilp.py -loss nssal -margin 20 -epochs 2000 -lr 0.0001 -data wn18rr -sample_rels 3 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 10 -pna False -residual True -jk True -ind_v 4 -eval_bs 2048 ``` ### NELL 995 * Version 1 ``` python run_ilp.py -loss nssal -margin 15 -epochs 451 -lr 0.0001 -data nell -sample_rels 4 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -ind_v 1 -eval_bs 2048 ``` * Version 2 ``` python run_ilp.py -loss nssal -margin 20 -epochs 2000 -lr 0.0001 -data nell -sample_rels 6 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 4 -ind_v 2 -eval_bs 2048 ``` * Version 3 ``` python run_ilp.py -loss nssal -margin 30 -epochs 2000 -lr 0.0001 -data nell -sample_rels 4 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -ind_v 3 -eval_bs 2048 ``` * Version 4 ``` python run_ilp.py -loss nssal -margin 20 -epochs 2000 -lr 0.0001 -data nell -sample_rels 30 -negs 32 -gnn True -rp False -gnn_att True -gnn_layers 3 -ind_v 4 -eval_bs 2048 -pool trf ``` ### More More CLI arguments can be found in `run_ilp.py`, eg, you can start the relation prediction task with the `-rp True` flag. ================================================ FILE: inductive_lp/data/WN18RR_v1/test.txt ================================================ 02102002 _hypernym 01835496 02213074 _derivationally_related_form 10003283 13428159 _derivationally_related_form 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concept:sportsteam:bolton_wanderers concept:teamalsoknownas concept:sportsteam:dundee_united concept:stadiumoreventvenue:camp_randall concept:stadiumlocatedincity concept:city:madison concept:agent:linux concept:agentinvolvedwithitem concept:videogamesystem:macintosh concept:sportsteam:baltimore_colts concept:teamplaysagainstteam concept:sportsteam:bolts concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:heart_attack concept:city:livonia concept:proxyfor concept:stateorprovince:michigan concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:phillies concept:invertebrate:larvae concept:animalsuchasinsect concept:insect:aquatic_insects concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:hydrogen concept:sportsteam:minnesota_state_mavericks concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:siena_saints concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:trail_blazers 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concept:teamplaysagainstteam concept:sportsteam:seahawks concept:insect:pests concept:animalsuchasinvertebrate concept:arthropod:spiders concept:mammal:animals concept:animalpreyson concept:mammal:muskrats concept:fruit:vegetables concept:fooddecreasestheriskofdisease concept:disease:diabetes concept:sportsteam:northern_colorado_bears concept:agentcollaborateswithagent concept:personmexico:ncaa concept:mammal:animals concept:animalpreyson concept:animal:badgers concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:pete_times_forum concept:county:detroit concept:organizationhiredperson concept:politician:dave_bing concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:south_dakota_coyotes concept:insect:wasps concept:arthropodandotherarthropod concept:insect:pests concept:arthropod:arthropods concept:animalsuchasinvertebrate concept:arthropod:spiders concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:phillies 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concept:sportsteam:state_university concept:arthropod:arthropods concept:arthropodcalledarthropod concept:arthropod:trilobites concept:sportsteam:ohio_state_university concept:organizationhiredperson concept:coach:thad_matta concept:mammal:wombats concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:northern_iowa_panthers concept:teamplaysinleague concept:sportsleague:ncaa concept:insect:thrips concept:animalthatfeedoninsect concept:insect:pests concept:insect:scale_insects concept:animalthatfeedoninsect concept:insect:insects concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:seahawks concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:north_carolina_state_university concept:arthropod:invertebrates concept:arthropodcalledarthropod concept:arthropod:clams concept:insect:pests concept:animalsuchasinsect concept:insect:midges concept:amphibian:forum concept:agentcompeteswithagent concept:personcanada:search concept:insect:pests concept:arthropodcalledarthropod concept:insect:moth concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:rangers concept:mammal:animals concept:animalistypeofanimal concept:animal:greyhounds concept:insect:insects concept:arthropodandotherarthropod concept:insect:green_lacewings concept:fruit:vegetables concept:fooddecreasestheriskofdisease concept:disease:rheumatoid_arthritis concept:chemical:ozone concept:chemicalistypeofchemical concept:chemical:pollutants concept:sportsteam:cleveland_indians_organization concept:teamplaysagainstteam concept:sportsteam:mariners concept:sportsteam:marquette_golden_eagles concept:teamplaysinleague concept:sportsleague:ncaa concept:city:calgary concept:citylocatedinstate concept:stateorprovince:southern_alberta concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:new_mexico_lobos concept:sportsteam:raptors concept:organizationhiredperson concept:coach:jay_triano concept:sportsteam:new_york_giants 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concept:country:rwanda concept:sportsteam:boston_celtics concept:teamplaysagainstteam concept:sportsteam:charlotte_bobcats concept:insect:pests concept:animalsuchasinsect concept:insect:mosquitoes concept:coach:glen_murray concept:personleadsgeopoliticalorganization concept:city:winnipeg concept:country:republic_of_angola concept:countryalsoknownas concept:country:republic concept:chemical:water concept:chemicalistypeofchemical concept:chemical:flouride concept:website:inktomi concept:subpartoforganization concept:website:yahoo_mail concept:insect:house_flies concept:animalistypeofanimal concept:insect:pests concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:armenia concept:mammal:chimps concept:animalpreyson concept:mammal:animals concept:politicianus:joe_biden concept:agentcollaborateswithagent concept:politician:obama concept:sportsteam:bolton_wanderers concept:teamplaysagainstteam concept:sportsteam:man_utd concept:insect:moths concept:animalsuchasinsect concept:arthropod:pollinators concept:arachnid:kids concept:animaldevelopdisease concept:disease:adhd_ concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:astros concept:sportsteam:sonics concept:teamplaysinleague concept:sportsleague:nba concept:animal:children concept:animaldevelopdisease concept:physiologicalcondition:health_needs concept:sportsteam:anaheim_ducks concept:teamplaysinleague concept:sportsleague:nhl concept:mammal:animals concept:animalistypeofanimal concept:mammal:boars concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:county:towson concept:atlocation concept:city:baltimore concept:animal:carnivores concept:animalistypeofanimal concept:mammal:animals concept:newspaper:the_wall_street concept:headquarteredin concept:city:new_york concept:person:ronaldo concept:worksfor concept:politicsblog:star concept:mammal:fox concept:animalistypeofanimal concept:mammal:wild_animals concept:person:greg001 concept:persondiedatage 3 concept:product:spider concept:synonymfor concept:movie:recluse concept:sportsteam:huskers concept:teamplaysagainstteam concept:sportsteam:buffs concept:sportsteam:cleveland_browns concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:radiostation:cleveland concept:organizationhiredperson concept:athlete:butch_davis concept:arachnid:widow_spider concept:agentcompeteswithagent concept:invertebrate:spider concept:chemical:water concept:chemicalistypeofchemical concept:chemical:chemicals_present concept:sportsgame:games concept:sportsgameteam concept:sportsteam:suns concept:magazine:content concept:agentcompeteswithagent concept:personcanada:search concept:chemical:diesel concept:chemicalistypeofchemical concept:chemical:products concept:sportsteam:georgia_state concept:organizationhiredperson concept:personeurope:curry concept:sportsleague:nba concept:leaguestadiums concept:stadiumoreventvenue:sbc_center concept:sportsteam:new_york_giants concept:teamwontrophy concept:awardtrophytournament:pennant concept:sportsleague:new concept:organizationhiredperson concept:coach:josh_mcdaniels concept:sportsteam:northern_arizona_lumberjacks concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:anaheim_ducks concept:teamplaysagainstteam concept:sportsteam:usc concept:sportsteam:redskins concept:organizationhiredperson concept:coach:vince_lombardi concept:animal:children concept:animaldevelopdisease concept:disease:down_syndrome concept:sportsteam:cincinnati_bearcats concept:organizationhiredperson concept:politician:mark_l__mallory concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:bills concept:sportsteam:pittsburgh_penguins concept:teamplaysagainstteam concept:sportsteam:devils concept:arthropod:invertebrates concept:animalsuchasinvertebrate concept:invertebrate:crustaceans concept:country:republic concept:countryalsoknownas concept:country:zambia concept:sportsteam:charlotte_bobcats concept:teamplaysinleague concept:sportsleague:nba concept:mammal:squirrels concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:trail_blazers concept:teamplaysagainstteam concept:sportsteam:memphis_grizzlies concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:heart_attacks concept:sportsteam:idaho_state_bengals concept:teamplaysincity concept:city:pocatello concept:crustacean:scallops concept:arthropodcalledarthropod concept:agriculturalproduct:oysters concept:company:ryanair001 concept:hasofficeincity concept:city:reus concept:insect:fungus_gnats concept:animalsuchasinsect concept:insect:insects concept:sportsteam:cleveland_indians_organization concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:alumni_stadium concept:sportsteam:usc concept:organizationhiredperson concept:coach:carroll concept:sportsteam:duke_blue_devils concept:teamplaysagainstteam concept:sportsteam:tar_heels concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:oxide_emissions concept:insect:beneficial_insects concept:animalsuchasinsect concept:insect:ladybugs concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:marlins concept:bank:ups concept:competeswith concept:bank:usps_global_priority concept:mammal:animals concept:animalistypeofanimal concept:mammal:fox concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:montana_grizzlies concept:insect:small_rodents concept:animalistypeofanimal concept:animal:animals001 concept:insect:pests concept:invertebratefeedonfood concept:legume:vines concept:arthropod:seafood concept:arthropodcalledarthropod concept:crustacean:fresh_fish concept:animal:voles001 concept:animalpreyson concept:animal:animals001 concept:sportsteam:canucks concept:teamplaysagainstteam concept:sportsteam:leafs concept:sportsteam:buccaneers concept:teamplaysagainstteam concept:sportsteam:redskins concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:legume:groundnut concept:chemical:tritium concept:chemicalistypeofchemical concept:chemical:water concept:animal:children concept:animaldevelopdisease concept:disease:dysfunction concept:sportsteam:cleveland_indians concept:teamplaysagainstteam concept:sportsteam:royals concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:missouri_state_bears concept:animal:children concept:animaldevelopdisease concept:disease:life_limiting_illnesses concept:sportsteam:reds concept:teamplaysagainstteam concept:sportsteam:orioles concept:insect:insects concept:animalthatfeedoninsect concept:insect:ants concept:sportsteam:bowling_green_falcons concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:usc concept:agentcollaborateswithagent concept:personmexico:ncaa concept:coach:sonny_lubick concept:worksfor concept:sportsteam:colorado_state concept:chemical:water concept:chemicalistypeofchemical concept:chemical:ethanol concept:sportsteam:ole_miss concept:teamplaysagainstteam concept:sportsteam:mississippi_state_university concept:coach:nick_saban concept:personbelongstoorganization concept:stateorprovince:ua concept:vertebrate:flocks concept:ismultipleof concept:mammal:sheep_ concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:mercury concept:mammal:animals concept:animalpreyson concept:mammal:ponies concept:animal:children concept:animaldevelopdisease concept:disease:malformation concept:sportsteam:rutgers concept:organizationhiredperson concept:coach:greg_schiano concept:insect:pests concept:arthropodcalledarthropod concept:insect:mealy_bugs concept:sportsteam:bills concept:organizationhiredperson concept:athlete:williams concept:chemical:molecules concept:chemicalistypeofchemical concept:chemical:carbon concept:person:michael002 concept:persongraduatedschool concept:university:harvard_university 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concept:crustacean:shrimp concept:animal:children concept:animaldevelopdisease concept:disease:asthma concept:sportsteam:akron_pros concept:organizationhiredperson concept:coach:j_d__brookhart concept:sportsteam:texas_longhorns concept:teamplaysincity concept:city:lansing concept:politicianus:hillary_rodham_clinton concept:politicianholdsoffice concept:politicaloffice:senate concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:atmospheric_gases concept:sportsteam:morehead_state_eagles concept:teamplaysinleague concept:sportsleague:ncaa concept:chemical:chemical_compounds concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:bobcats concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:sportsteam:man_united concept:teamplaysagainstteam concept:sportsteam:barcelona concept:governmentorganization:template concept:agentcompeteswithagent concept:personcanada:search concept:sportsteam:san_diego_padres concept:organizationhiredperson 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concept:city:athens concept:citylocatedingeopoliticallocation concept:country:greece concept:sportsteam:iupui_jaguars concept:agentcollaborateswithagent concept:personmexico:ncaa concept:automobilemaker:a_m concept:organizationhiredperson concept:person:sherman concept:programminglanguage:system concept:subpartof concept:website:steam concept:bodypart:head001 concept:subpartof concept:videogame:adrenaline concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:eastern_michigan_university concept:politician:obama concept:agentcollaborateswithagent concept:politicianus:al_gore concept:company:lycos concept:agentcompeteswithagent concept:mlauthor:web_search concept:animal:brown_bears concept:animalpreyson concept:mammal:animals concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:jaguars concept:insect:insect_pests concept:animalthatfeedoninsect concept:insect:aphids concept:sport:soccer concept:sportschoolincountry concept:country:germany 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concept:city:edmonton concept:chemical:metals concept:chemicalistypeofchemical concept:chemical:carbon concept:insect:pests concept:animalthatfeedoninsect concept:insect:whitefly concept:sportsgame:games concept:sportsgameteam concept:sportsteam:nashville_predators concept:programminglanguage:system concept:subpartof concept:hallwayitem:loop concept:insect:carpenter_ants concept:animalsuchasinsect concept:insect:insects concept:sportsteam:harvard_divinity_school concept:organizationhiredperson concept:coach:tim_murphy concept:insect:scale concept:animalthatfeedoninsect concept:insect:insects concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:metals concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:long_island_blackbirds concept:animal:greyhounds concept:animalpreyson concept:mammal:animals concept:newspaper:financial_times concept:hasofficeincity concept:city:new_york concept:chemical:sulfur concept:chemicalistypeofchemical 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concept:athlete:paul_brown concept:worksfor concept:sportsteam:eagles concept:mammal:animals concept:animalpreyson concept:mammal:alpacas concept:sportsteam:bruins concept:teamplaysagainstteam concept:sportsteam:san_jose_sharks concept:insect:beetle concept:invertebratefeedonfood concept:legume:bark concept:coach:tony_dungy concept:worksfor concept:sportsteam:buccaneers concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:beneficial_insects concept:insect:aphids concept:animalthatfeedoninsect concept:insect:insect_pests concept:company:post concept:agentactsinlocation concept:city:richmond_hill concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:redskins concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:cal_state_fullerton_titans concept:sportsteam:phillies concept:teamalsoknownas concept:sportsteam:chowan_braves concept:city:appleton concept:citylocatedinstate concept:stateorprovince:wisconsin concept:visualizablething:urine concept:thinghascolor concept:color:orange concept:sportsteam:new_jersey_devils concept:teamplaysinleague concept:sportsleague:nhl concept:person:riley001 concept:worksfor concept:sportsteam:oregon_state_beavers concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:cisco_field concept:sportsteam:trail_blazers concept:teamplaysinleague concept:sportsleague:nba concept:stateorprovince:montana concept:mutualproxyfor concept:city:missoula concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:tampa_bay_lightning concept:programminglanguage:system concept:subpartof concept:beverage:whole_house_water concept:animal:children concept:animaldevelopdisease concept:disease:emotional_disorders concept:bank:express_mail concept:competeswith concept:bank:global_priority concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:diesel concept:insect:small_insects concept:arthropodandotherarthropod 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concept:animalistypeofanimal concept:bird:waterbirds concept:sportsteam:ucla concept:organizationhiredperson concept:coach:karl_dorrell concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:decomposition concept:personcanada:search concept:agentcompeteswithagent concept:personcanada:work concept:animal:cheetahs concept:animalpreyson concept:mammal:predators concept:sportsteam:tampa_bay_lightning concept:teamhomestadium concept:stadiumoreventvenue:pete_times_forum concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:san_diego_padres concept:insect:pests concept:animalsuchasinvertebrate concept:invertebrate:bees concept:invertebrate:black_worms concept:animalsuchasinvertebrate concept:crustacean:shrimp concept:insect:pests concept:invertebratefeedonfood concept:nut:leaves concept:sportsteam:bucks concept:teamplaysagainstteam concept:sportsteam:timberwolves concept:person:mccain concept:agentcollaborateswithagent concept:female:hilary 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concept:teamwontrophy concept:awardtrophytournament:wild_card concept:coach:bill_parcells concept:worksfor concept:sportsteam:new_york_giants concept:invertebrate:corals concept:animalistypeofanimal concept:animal:invertebrates concept:sportsteam:new_york_mets concept:teamplaysagainstteam concept:sportsteam:san_diego_padres concept:animal:hippopotamuses concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:raptors concept:teamplaysagainstteam concept:sportsteam:timberwolves concept:sportsteam:texas_a_m concept:organizationalsoknownas concept:sportsteam:state_university concept:food:oils concept:agriculturalproductincludingagriculturalproduct concept:vegetable:avocado concept:company:post concept:agentactsinlocation concept:trail:in_illinois concept:sportsteam:tampa_bay_lightning concept:teamplaysagainstteam concept:sportsteam:leafs concept:river:oostanaula concept:riveremptiesintoriver concept:river:coosa_river concept:fish:tuna concept:fishservedwithfood 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concept:sportsteam:red_wings concept:teamwontrophy concept:awardtrophytournament:stanley_cup_championship concept:mammal:predators concept:animalsuchasfish concept:fish:tuna concept:sport:soccer concept:sportfansincountry concept:country:wales concept:sportsteam:florida_marlins concept:teamplaysagainstteam concept:sportsteam:cleveland_indians concept:male:world concept:agentcollaborateswithagent concept:politicianus:jim concept:scientist:andres_oppenheimer concept:worksfor concept:newspaper:herald concept:insect:beneficial_insects concept:animalthatfeedoninsect concept:insect:hoverflies concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:cement concept:sportsteam:stanford concept:organizationhiredperson concept:coach:buddy_teevens concept:insect:flies concept:animalthatfeedoninsect concept:insect:midges concept:sportsteam:kansas_city_chiefs concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:sportsteam:villanova_wildcats 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concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:organic_compounds concept:city:kingsport concept:proxyfor concept:stateorprovince:tennessee concept:sportsteam:ball_state_university concept:teamplayssport concept:sport:football concept:person:mark001 concept:personbelongstoorganization concept:sportsteam:state_university concept:insect:insects concept:animalthatfeedoninsect concept:insect:bumblebees concept:mammal:chimps concept:animalistypeofanimal concept:mammal:animals concept:beverage:sugar concept:foodcancausedisease concept:disease:problems concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:chicago_cardinals concept:mammal:predators concept:animalsuchasfish concept:fish:steelhead concept:chemical:organic_compounds concept:chemicalistypeofchemical concept:chemical:pollutants concept:beverage:sugar concept:foodcancausedisease concept:disease:hyperactivity concept:city:oklahoma_city concept:cityliesonriver concept:river:north_canadian 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concept:animalthatfeedoninsect concept:insect:ants concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:dust concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:steelers concept:mammal:animals concept:animalpreyson concept:mammal:goat concept:animal:children concept:animaldevelopdisease concept:disease:aspergers_syndrome concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:portland_state_vikings concept:person:fred concept:agentcollaborateswithagent concept:male:world concept:fish:tuna concept:fishservedwithfood concept:beverage:imports concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:legume:soy_beans concept:invertebrate:cattle concept:specializationof concept:vertebrate:ruminants concept:arthropod:invertebrates concept:animalsuchasinsect concept:insect:ants concept:chemical:chlorine concept:chemicalistypeofchemical concept:chemical:products 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concept:animal:stoats concept:animalistypeofanimal concept:mammal:predators concept:crustacean:shellfish concept:arthropodcalledarthropod concept:agriculturalproduct:oysters concept:mammal:elk concept:animalistypeofanimal concept:mammal:large_mammals concept:sportsteam:minnesota_state_mavericks concept:teamplaysinleague concept:sportsleague:ncaa concept:insect:insects concept:animaleatfood concept:food:plankton concept:sportsteam:northern_illinois_huskies concept:agentcollaborateswithagent concept:personmexico:ncaa concept:coach:al_arbour concept:coachesteam concept:sportsteam:leafs concept:chemical:nitrogen_oxide_emissions concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:cleveland_indians_organization concept:teamplaysagainstteam concept:sportsteam:royals concept:sportsteam:blackhawks concept:teamplaysagainstteam concept:sportsteam:montreal_canadiens concept:insect:insects concept:animalsuchasinsect concept:insect:bed_bugs concept:agriculturalproduct:flowers 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concept:personcanada:search concept:agentcompeteswithagent concept:bird:profile concept:sportsteam:loyola_marymount_lions concept:teamplaysinleague concept:sportsleague:ncaa concept:person:stevens001 concept:persongraduatedfromuniversity concept:university:college concept:coach:paul_wulff concept:coachesteam concept:sportsteam:washington_state concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:cavaliers concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:skin_problems concept:sportsteam:aggies concept:teamplaysagainstteam concept:sportsteam:bulldogs concept:newspaper:daily concept:competeswith concept:website:the_independent concept:sportsteam:wisconsin_badgers concept:teamhomestadium concept:stadiumoreventvenue:camp_randall concept:sportsteam:brown_bears concept:agentcollaborateswithagent concept:personmexico:ncaa concept:insect:spider_mites concept:arthropodandotherarthropod concept:insect:insects concept:sportsteam:marlins 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concept:sportsteam:kansas_state concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:georgia_southern_eagles concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:bolton_wanderers concept:teamplaysagainstteam concept:sportsteam:new_york_giants_in_super_bowl_xlii concept:athlete:rich_rodriguez concept:personbelongstoorganization concept:company:west_virginia concept:invertebrate:bees concept:invertebratefeedonfood concept:beverage:nectar concept:animal:children concept:animaldevelopdisease concept:disease:hiv_infection concept:insect:larva concept:animalsuchasinsect concept:insect:insects concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:boston_celtics concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:flies concept:chemical:water concept:chemicalistypeofchemical concept:visualizablething:lime concept:animal:head concept:agentcollaborateswithagent concept:person:meyer concept:crustacean:shrimp concept:arthropodcalledarthropod concept:crustacean:copepods concept:insect:pests concept:animalsuchasinsect concept:insect:gypsy_moth concept:politician:dodd concept:politicianholdsoffice concept:politicaloffice:senator concept:university:kentucky concept:organizationhiredperson concept:coach:adolph_rupp concept:mammal:caribou concept:animalpreyson concept:mammal:animals concept:personcanada:search concept:agentcompeteswithagent concept:mollusk:phrases concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:nitrogen_oxide concept:sportsteam:alabama_state_hornets concept:agentcollaborateswithagent concept:personmexico:ncaa concept:insect:pests concept:animalpreyson concept:invertebrate:bees concept:politician:obama concept:persongraduatedfromuniversity concept:school:columbia concept:chemical:water concept:chemicalistypeofchemical concept:chemical:sulfur_dioxide concept:sportsteam:minnesota_vikings 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concept:wine:blend concept:beveragemadefrombeverage concept:wine:pinot concept:company:post concept:agentcompeteswithagent concept:university:search concept:insect:flies concept:arthropodcalledarthropod concept:arthropod:invertebrates concept:bird:raptors concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:sonics concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:sportsteam:northwestern concept:teamplaysagainstteam concept:sportsteam:ohio_state_university concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:uc_irvine_anteaters concept:county:towson concept:atlocation concept:stateorprovince:maryland concept:chemical:water concept:chemicalistypeofchemical concept:chemical:fatty_acids concept:bank:ups concept:competeswith concept:bank:priority_mail concept:sportsteam:capitals concept:teamplaysagainstteam concept:sportsteam:red_wings concept:mammal:animals concept:animalpreyson concept:mammal:chipmunks concept:country:slovak_republic 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concept:currency:dinars concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:kansas_city_chiefs concept:agriculturalproduct:cotton concept:agriculturalproductgrowinginstateorprovince concept:stateorprovince:gujarat concept:sportsteam:army_black_knights concept:teamplaysagainstteam concept:sportsteam:eagles concept:person:davis concept:persongraduatedschool concept:university:college concept:sportsteam:south_dakota_coyotes concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsgame:games concept:sportsgameteam concept:sportsteam:mariners concept:crustacean:mollusks concept:arthropodcalledarthropod concept:arthropod:invertebrates concept:sportsteam:penn_state concept:teamplaysagainstteam concept:sportsteam:northwestern concept:sportsgame:games concept:sportsgameteam concept:sportsteam:detroit_tigers concept:insect:insects concept:animalthatfeedoninsect concept:insect:house_flies concept:sportsteam:liverpool concept:teamplaysagainstteam concept:sportsteam:barcelona_dragons concept:agriculturalproduct:grains concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:millet concept:coach:barry_melrose concept:athletehomestadium concept:stadiumoreventvenue:pete_times_forum concept:invertebrate:larvae concept:animalthatfeedoninsect concept:insect:midge concept:chemical:sulfuric_acid concept:chemicalistypeofchemical concept:chemical:sulfur concept:sportsteam:redskins concept:teamplaysagainstteam concept:sportsteam:philadelphia_eagles concept:country:republic concept:countryalsoknownas concept:country:sweden concept:agriculturalproduct:oysters concept:arthropodcalledarthropod concept:crustacean:bivalves concept:vertebrate:herds concept:ismultipleof concept:mammal:caribou concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:bills concept:mammal:small_animals concept:animalistypeofanimal concept:mammal:mice concept:sportsteam:falcons concept:teamplaysagainstteam 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concept:stateorprovince:maine concept:company:charleston concept:proxyfor concept:company:west_virginia concept:sportsteam:ucla concept:agentcollaborateswithagent concept:personmexico:ncaa concept:mammal:animals concept:animalpreyson concept:mammal:stray_dogs concept:crustacean:bivalves concept:animalsuchasinvertebrate concept:crustacean:scallops concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:grain:sweet_corn concept:sport:football concept:sportfansincountry concept:country:republic_of_austria concept:vertebrate:skunks concept:animalistypeofanimal concept:mammal:wild_animals concept:weatherphenomenon:tennessee concept:mutualproxyfor concept:visualizablescene:smyrna concept:sportsteam:oklahoma_city_thunder concept:teamplaysinleague concept:sportsleague:nba concept:crustacean:brine_shrimp concept:arthropodcalledarthropod concept:crustacean:shrimp concept:insect:aphids concept:animalsuchasinsect concept:insect:pests 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concept:countrylocatedingeopoliticallocation concept:geopoliticallocation:european_nations concept:reptile:stock concept:animalistypeofanimal concept:agriculturalproduct:goats concept:invertebrate:infusoria concept:animalistypeofanimal concept:crustacean:shrimp concept:invertebrate:spider concept:synonymfor concept:arachnid:widow_spider concept:city:baltimore concept:citylocatedingeopoliticallocation concept:stateorprovince:md concept:sportsteam:stanford concept:organizationhiredperson concept:coach:johnny_dawkins concept:stateorprovince:tennessee concept:mutualproxyfor concept:city:cookeville concept:country:mongolia concept:countryalsoknownas concept:country:republic concept:sportsleague:nba concept:leaguestadiums concept:stadiumoreventvenue:palace_of_auburn_hills concept:insect:pests concept:arthropodcalledarthropod concept:insect:leafhoppers concept:governmentorganization:results concept:agentcompeteswithagent concept:personcanada:search concept:insect:insects 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concept:sportsteam:byu concept:beverage:sugar concept:foodcancausedisease concept:disease:diabetes concept:arthropod:blood_worms concept:arthropodandotherarthropod concept:crustacean:shrimp concept:country:republic concept:countryalsoknownas concept:country:montenegro concept:mammal:animals concept:animalistypeofanimal concept:mammal:canines concept:animal:children concept:animaldevelopdisease concept:disease:add_adhd concept:city:missoula concept:citylocatedinstate concept:stateorprovince:montana concept:drug:calcium_carbonate concept:chemicalistypeofchemical concept:chemical:carbon concept:arthropod:invertebrates concept:animalsuchasinsect concept:insect:wasps concept:sportsteam:boston_university concept:teamplaysinleague concept:sportsleague:international concept:coach:charlie_weis concept:agentcollaborateswithagent concept:male:notre_dame concept:arachnid:butterflies concept:animalistypeofanimal concept:animal:animals001 concept:sportsteam:boston_celtics 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concept:synonymfor concept:bird:widow concept:sportsteam:army_black_knights concept:agentcollaborateswithagent concept:personus:george_o_leary concept:politicianus:john_kerry concept:agentcollaborateswithagent concept:politician:obama concept:insect:pests concept:animalsuchasinsect concept:arthropod:lice concept:invertebrate:larvae concept:animalthatfeedoninsect concept:insect:dragonfly concept:agriculturalproduct:fruits concept:agriculturalproductcontainchemical concept:chemical:acids concept:animal:gophers concept:animalsuchasinsect concept:insect:pests concept:biotechcompany:apple concept:agentcontrols concept:university:wozniak concept:crustacean:krill concept:animalpreyson concept:crustacean:shrimp concept:sportsteam:miami_dolphins concept:organizationhiredperson concept:coach:todd_bowles concept:insect:small_rodents concept:animalistypeofanimal concept:mammal:rats concept:sportsteam:new_york_giants_in_super_bowl_xlii concept:teamplaysagainstteam concept:sportsteam:dundee_united 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concept:sportsleague:ncaa concept:arachnid:kids concept:animaldevelopdisease concept:disease:developmental_disabilities concept:personcanada:search concept:agentcompeteswithagent concept:model:ads concept:sportsteam:michigan_technological_university concept:teamplaysincity concept:city:houghton concept:country:guinea concept:countryalsoknownas concept:country:republic concept:mammal:hogs concept:animaleatfood concept:agriculturalproduct:fruits concept:sportsteam:liverpool_john_moores_university concept:teamplaysagainstteam concept:sportsteam:a_c__milan concept:country:republic concept:countryalsoknownas concept:country:guinea_bissau concept:invertebrate:amphibians concept:animalpreyson concept:animal:animals001 concept:mammal:animals concept:animalistypeofanimal concept:animal:hamsters concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:bryant_denny concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:sommet_center concept:country:republic concept:countryalsoknownas concept:country:republic_of_guatemala concept:sportsteam:chicago_bulls concept:agentcompeteswithagent concept:animal:animals001 concept:politician:mark_l__mallory concept:worksfor concept:city:cincinnati concept:biotechcompany:apple concept:producesproduct concept:product:powerbook concept:mammal:gorillas concept:animalistypeofanimal concept:mammal:great_apes concept:animal:animals001 concept:animaleatfood concept:food:invasive_plant concept:arthropod:external_parasites concept:animalthatfeedoninsect concept:arthropod:lice concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:niners concept:automobilemaker:catalog concept:agentcompeteswithagent concept:personcanada:search concept:insect:parasitic_wasps concept:animalpreyson concept:insect:insects concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:rangers concept:animal:children concept:animaldevelopdisease concept:disease:adrenal_hyperplasia 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concept:sportsteam:redskins concept:sportsteam:new_york_mets concept:teamplaysagainstteam concept:sportsteam:marlins concept:sportsteam:kansas_city_royals concept:teamhomestadium concept:stadiumoreventvenue:kauffman_stadium concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsteam:buffs concept:teamplaysagainstteam concept:sportsteam:huskers concept:animal:children concept:animaldevelopdisease concept:disease:sickle_cell_disease concept:insect:pests concept:arthropodcalledarthropod concept:insect:mosquitoes concept:agriculturalproduct:goats concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:new_england_revolution concept:teamplaysagainstteam concept:sportsteam:chicago_fire concept:crustacean:shrimp concept:arthropodcalledarthropod concept:arthropod:microworms concept:sportsteam:new_orleans_hornets concept:teamplaysagainstteam concept:sportsteam:mavericks concept:animal:children concept:animaldevelopdisease 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concept:insect:insects concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:mammal:tigers concept:animalpreyson concept:mammal:animals concept:animal:large_animals concept:animalsuchasfish concept:fish:sharks concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:steelers concept:insect:pests concept:arthropodandotherarthropod concept:insect:wasps concept:city:jackson concept:proxyfor concept:newspaper:alabama concept:sportsteam:sacred_heart_pioneers concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:blackhawks concept:teamplaysagainstteam concept:sportsteam:calgary_flames concept:mammal:cheetah concept:mammalsuchasmammal concept:mammal:leopard concept:sportsteam:minnesota_wilds concept:teamplayssport concept:sport:hockey concept:company:express concept:agentcompeteswithagent concept:city:daily concept:sportsleague:new concept:organizationhiredperson concept:coach:scott_linehan concept:insect:insects concept:animalthatfeedoninsect concept:arthropod:lice concept:chemical:water concept:chemicalistypeofchemical concept:chemical:oxygen_content concept:sportsteam:horned_frogs concept:teamplayssport concept:sport:football concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:molecule concept:programminglanguage:system concept:subpartof concept:visualizablething:commercial_water concept:sportsteam:georgia_tech concept:organizationhiredperson concept:coach:chan_gailey concept:sportsteam:buccaneers concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:sportsteam:capitals concept:teamplaysagainstteam concept:sportsteam:bruins concept:agriculturalproduct:fruits concept:agriculturalproductcontainchemical concept:chemical:citric_acid concept:bank:international_priority_mail concept:competeswith concept:company:express concept:chemical:hydrocarbon concept:chemicalistypeofchemical concept:chemical:emissions concept:animal:children concept:animaldevelopdisease concept:disease:developmental_disabilities concept:crustacean:mollusks concept:animalsuchasinvertebrate concept:arthropod:clams concept:mammal:caribou concept:mammalsuchasmammal concept:mammal:sheep concept:country:kosovo concept:countryalsoknownas concept:country:bosnia_herzegovina concept:mammal:gazelles concept:animalistypeofanimal concept:mammal:animals concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:autozone_park concept:chemical:flue_gas concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:leafs concept:teamplaysagainstteam concept:sportsteam:boston_bruins concept:crustacean:shrimp concept:arthropodandotherarthropod concept:arthropod:arthropods concept:sportsteam:leafs concept:teamplaysagainstteam concept:sportsteam:bruins concept:celebrity:feed concept:agentcompeteswithagent concept:personcanada:search concept:crustacean:lobster concept:arthropodcalledarthropod concept:crustacean:crayfish concept:insect:pests concept:arthropodcalledarthropod concept:insect:cockroaches concept:coach:j_d__brookhart concept:worksfor concept:sportsteam:akron_pros concept:personcanada:search concept:agentcompeteswithagent concept:city:tags concept:vertebrate:herds concept:ismultipleof concept:mammal:springbok concept:country:belgium concept:mutualproxyfor concept:visualizablescene:brussels concept:insect:insects concept:arthropodcalledarthropod concept:insect:hoverflies concept:sportsteam:usc concept:teamplaysagainstteam concept:sportsteam:stanford concept:chemical:biodiesel concept:chemicalistypeofchemical concept:chemical:fuels concept:sportsteam:red_wings concept:teamplaysagainstteam concept:sportsteam:l_a__kings concept:sportsteam:new_jersey_devils concept:teamplaysagainstteam concept:sportsteam:rangers concept:stateorprovince:wyoming concept:mutualproxyfor concept:city:laramie concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:metrodome concept:visualizablething:salts concept:chemicalistypeofchemical concept:chemical:carbon concept:politicianus:rodham_clinton concept:agentcollaborateswithagent concept:politician:obama concept:mammal:lions concept:animalistypeofanimal concept:animal:carnivores concept:company:express concept:competeswith concept:bank:express_mail concept:food:oils concept:agriculturalproducttoattractinsect concept:insect:insects concept:insect:insects concept:arthropodcalledarthropod concept:insect:moth concept:insect:pests concept:animalthatfeedoninsect concept:insect:cockroaches concept:university:kentucky concept:mutualproxyfor concept:radiostation:bowling_green concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:moisture concept:bank:usps concept:competeswith concept:bank:ups concept:invertebrate:cattle concept:agentcompeteswithagent concept:comedian:domestic_animals concept:mammal:animals concept:animalpreyson concept:animal:children concept:sportsteam:baltimore_colts concept:teamalsoknownas concept:sportsteam:colts concept:city:orlando concept:proxyfor concept:stadiumoreventvenue:bob_carr_performing_arts_centre concept:arthropod:invertebrates concept:animalsuchasinvertebrate concept:invertebrate:squids concept:mammal:predators concept:animalistypeofanimal concept:mammal:rats concept:animal:children concept:animaldevelopdisease concept:disease:autism concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:belarus concept:sportsteam:texas_christian_university concept:organizationhiredperson concept:coach:gary_patterson concept:university:alabama concept:organizationhiredperson concept:coach:mike_shula concept:chemical:water concept:chemicalistypeofchemical concept:item:bleach concept:sportsteam:utah_jazz concept:teamplaysinleague concept:sportsleague:nba concept:insect:cockroaches concept:arthropodandotherarthropod concept:insect:pests concept:chemical:water concept:chemicalistypeofchemical concept:chemical:harmful_substances concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:republic_of_lithuania concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:arizona_wildcats concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:sunflower concept:stateorprovince:alaska concept:statehascapital concept:city:juneau concept:insect:insects concept:arthropodandotherarthropod concept:insect:leafhoppers concept:chemical:water concept:chemicalistypeofchemical concept:chemical:alum concept:animal:orangutans concept:animalpreyson concept:mammal:animals concept:agriculturalproduct:oysters concept:arthropodcalledarthropod concept:arthropod:clams concept:animal:children concept:animaldevelopdisease concept:disease:hepatitis_a concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:columbia_university concept:chemical:water concept:chemicalistypeofchemical concept:chemical:matter concept:chemical:chlorine concept:chemicalistypeofchemical concept:chemical:disinfectants concept:insect:flies concept:animalpreyson concept:animal:animals001 concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:hubert_h__humphrey_metrodome concept:sportsteam:packers concept:teamplaysagainstteam concept:sportsteam:colts concept:sportsteam:colorado_avalanche concept:teamplaysinleague concept:sportsleague:nhl concept:insect:pests concept:invertebratefeedonfood concept:food:native_plants concept:sportsteam:philadelphia_athletics concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:politician:obama concept:agentcollaborateswithagent concept:politicianus:sens__hillary_clinton concept:animal:head concept:agentcontrols concept:person:taylor concept:arthropod:pollinators concept:animalthatfeedoninsect concept:insect:insects concept:animal:vertebrates concept:animalsuchasfish concept:fish:sharks concept:insect:mosquitoes concept:arthropodandotherarthropod concept:insect:pests concept:programminglanguage:system concept:subpartof concept:visualizableattribute:reliable_water concept:arthropod:pollinators concept:animalthatfeedoninsect concept:insect:wasps concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:arthropod:pollinators concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:colts concept:person:bill concept:personbelongstoorganization concept:sportsteam:harvard_divinity_school concept:sportsteam:iowa_state concept:teamplaysincity concept:city:ames concept:mammal:animals concept:animalpreyson concept:mammal:oxen concept:animal:hyenas concept:animalistypeofanimal concept:animal:scavengers concept:sportsteam:georgia_tech concept:agentcollaborateswithagent concept:personmexico:ncaa concept:chemical:particulate_matter concept:chemicalistypeofchemical concept:chemical:pollutants concept:insect:brown_recluse concept:arthropodcalledarthropod concept:arthropod:spiders concept:animal:children concept:animaldevelopdisease concept:disease:developmental_disorder concept:animal:animals001 concept:animaleatfood concept:food:sea_grasses concept:insect:insects concept:animalsuchasinsect concept:arthropod:roaches concept:chemical:emissions concept:chemicalistypeofchemical concept:product:study concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:chicago_bulls concept:mammal:predators concept:animalsuchasinsect concept:insect:wasps concept:invertebrate:wildebeest concept:animalistypeofanimal concept:animal:animals001 concept:person:mccain concept:agentcollaborateswithagent concept:politicianus:rudy_giuliani concept:country:republic concept:countryalsoknownas concept:country:ukraine concept:biotechcompany:apple concept:agentinvolvedwithitem concept:videogame:mac concept:arthropod:spiders concept:animalsuchasinsect concept:insect:pests concept:chemical:particulates concept:chemicalistypeofchemical concept:chemical:nitrogen concept:arthropod:dog concept:animalpreyson concept:animal:pup concept:animal:animals001 concept:animalistypeofanimal concept:arthropod:spiders concept:sportsteam:missouri_tigers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:notre_dame concept:teamplaysagainstteam concept:sportsteam:usc concept:animal:animals001 concept:animalpreyson concept:invertebrate:reptiles concept:mammal:sheep concept:animalistypeofanimal concept:mammal:animals concept:personcanada:search concept:agentcompeteswithagent concept:personaustralia:more_information concept:chemical:water concept:chemicalistypeofchemical concept:chemical:carbon_monoxide concept:mammal:animals concept:animalpreyson concept:mammal:bobcats concept:agriculturalproduct:dairy concept:agriculturalproductcomingfromvertebrate concept:mammal:sheep_ concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:milwaukee_brewers concept:beverage:sugar concept:foodcancausedisease concept:disease:effect concept:arthropod:widow concept:arthropodandotherarthropod concept:arachnid:recluse_spider concept:mammal:felines concept:animalistypeofanimal concept:mammal:cat concept:chemical:zinc concept:chemicalistypeofchemical concept:chemical:water concept:animal:locusts concept:animalsuchasinsect concept:insect:insects concept:person:greg001 concept:personmovedtostateorprovince concept:stateorprovince:alaska concept:company:yahoo001 concept:competeswith concept:radiostation:mobile concept:agriculturalproduct:flowers concept:agriculturalproductgrowninlandscapefeatures concept:landscapefeatures:meadow concept:sportsteam:eagles concept:agentcompeteswithagent concept:sportsteam:arizona_cardinals_27_23 concept:mammal:baboons concept:mammalsuchasmammal concept:mammal:animals concept:insect:flies concept:arthropodandotherarthropod concept:insect:insects concept:weatherphenomenon:tennessee concept:mutualproxyfor concept:physicalaction:clarksville concept:insect:mealy_bugs concept:animalthatfeedoninsect concept:insect:pests concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:flyers_playoff_tickets concept:country:kuwait concept:countrycurrency concept:currency:dinars concept:sportsteam:utah_jazz concept:teamplaysagainstteam concept:sportsteam:mavericks concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:liquid concept:politician:stephen_mandel concept:personhasresidenceingeopoliticallocation concept:city:edmonton concept:stateorprovince:topics concept:agentcompeteswithagent concept:personcanada:search concept:sportsteam:vols concept:teamplayssport concept:sport:golf concept:bank:express_mail concept:competeswith concept:bank:usps concept:sportsteam:demon_deacons concept:teamplaysinleague concept:sportsleague:acc concept:sportsteam:college concept:teamplaysinleague concept:sportsleague:international concept:agriculturalproduct:tree concept:agriculturalproducttoattractinsect concept:insect:insects concept:insect:silverfish concept:arthropodandotherarthropod concept:insect:pests concept:mammal:eland concept:mammalsuchasmammal concept:mammal:antelopes concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:california_golden_bears concept:animal:animals001 concept:animalpreyson concept:arachnid:butterflies concept:arthropod:spider_bite concept:arthropodcalledarthropod concept:arachnid:brown_recluse concept:landscapefeatures:states concept:locationlocatedwithinlocation concept:park:north_eastern concept:chemical:combustion_byproducts concept:chemicalistypeofchemical concept:chemical:carbon concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:water_vapor concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:nitrogen concept:sportsteam:kansas_city_chiefs concept:teamplaysagainstteam concept:sportsteam:miami_dolphins concept:chemical:air_pollutants concept:chemicalistypeofchemical concept:chemical:fuels concept:mammal:laboratory_animals concept:animalistypeofanimal concept:mammal:animals concept:animal:animals001 concept:animalpreyson concept:arthropod:spiders concept:personcanada:search concept:agentcompeteswithagent concept:personafrica:keyword concept:politicianus:chris_dodd concept:agentcollaborateswithagent concept:politician:obama concept:visualizablescene:brussels concept:proxyfor concept:continent:europe concept:person:steven concept:persongraduatedschool concept:university:jamestown_college concept:university:search concept:agentcompeteswithagent concept:company:alta_vista concept:sportsgame:games concept:sportsgameteam concept:sportsteam:capitals concept:beverage:sugar concept:foodcancausedisease concept:disease:tooth_decay concept:invertebrate:cattle concept:animalistypeofanimal concept:animal:large_animals concept:university:kentucky concept:organizationhiredperson concept:coach:joe_b__hall concept:politicianus:mccain concept:politicianholdsoffice concept:politicaloffice:potus concept:sportsteam:skins concept:teamplaysagainstteam concept:sportsteam:eagles concept:mammal:great_apes concept:animalistypeofanimal concept:mammal:animals concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:oral_roberts_golden_eagles concept:politicianus:hillary_rodham_clinton concept:agentcollaborateswithagent concept:politician:obama concept:programminglanguage:system concept:subpartof concept:landscapefeatures:soil concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:donald_w_reynolds_razorback_stadium concept:mammal:pandas concept:animalpreyson concept:mammal:animals concept:food:heart concept:fooddecreasestheriskofdisease concept:physiologicalcondition:degenerative_conditions concept:sportsteam:georgia_bulldogs concept:teamplaysinleague concept:sportsleague:ncaa concept:mammal:elk concept:animalistypeofanimal concept:animal:ungulates concept:animal:children concept:animaldevelopdisease concept:disease:rheumatic_disease concept:sportsteam:georgia_southern_eagles concept:teamplaysinleague concept:sportsleague:ncaa concept:athlete:larry_robinson concept:athletehomestadium concept:stadiumoreventvenue:prudential_center_2 concept:country:namibia concept:countrycurrency concept:currency:dollar concept:animal:animals001 concept:animalsuchasinvertebrate concept:mollusk:arthropods concept:personeurope:margaret_atwood concept:agentcreated concept:musicartist:oryx_and_crake concept:sportsteam:chicago_fire concept:teamplaysagainstteam concept:sportsteam:toronto_fc concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:seahawks concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:bucks concept:insect:larva concept:animaleatfood concept:food:roots concept:chemical:fuels concept:chemicalistypeofchemical concept:chemical:pollutant concept:chemical:moisture concept:chemicalistypeofchemical concept:chemical:carbon concept:crustacean:shrimp concept:animalpreyson concept:crustacean:copepods concept:country:colombia concept:countryalsoknownas concept:country:republic concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:sulfuric_acid concept:sportsteam:marshall_thundering_herd concept:teamplaysinleague concept:sportsleague:ncaa concept:animal:carnivores concept:animalistypeofanimal concept:mammal:wolves concept:animal:children concept:animaldevelopdisease concept:disease:conduct_disorders concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:flour concept:chemical:ozone concept:chemicalistypeofchemical concept:chemical:greenhouse_gases concept:sportsteam:jags concept:teamplaysagainstteam concept:sportsteam:colts concept:person:bill concept:personmovedtostateorprovince concept:stateorprovince:tennessee concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:kansas_city_municipal_stadium concept:sportsleague:new concept:organizationheadquarteredinstateorprovince concept:stateorprovince:pa concept:country:ivory_coast concept:countryalsoknownas concept:country:republic concept:animal:animals001 concept:animalpreyson concept:arthropod:opossums concept:sportsteam:rockies concept:teamplaysagainstteam concept:sportsteam:chowan_braves concept:sportsteam:kansas_city_chiefs concept:teamplaysagainstteam concept:sportsteam:texans concept:insect:jackets concept:animalpreyson concept:insect:insects concept:sportsteam:titans concept:teamplaysagainstteam concept:sportsteam:texans concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:louisiana_state_university concept:sportsteam:bruins concept:teamwontrophy concept:awardtrophytournament:division concept:mollusk:vermin concept:animalistypeofanimal concept:animal:animals001 concept:sportsteam:bolton_wanderers concept:teamplaysinleague concept:sportsleague:fa concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:oil_seeds concept:athlete:matt_ryan concept:athleteplaysforteam concept:sportsteam:falcons concept:coach:mike_stoops concept:worksfor concept:company:arizona concept:sportsteam:florida_panthers concept:teamplaysagainstteam concept:sportsteam:capitals concept:visualizablescene:brussels concept:locationlocatedwithinlocation concept:country:belgium concept:sportsteam:iowa_hawkeyes concept:teamplaysagainstteam concept:sportsteam:ohio_state_buckeyes concept:insect:sparrows concept:agentcompeteswithagent concept:animal:birds003 concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:county_stadium concept:arthropod:hornets concept:arthropodandotherarthropod concept:insect:insects concept:animal:stoats concept:animalsuchasinsect concept:insect:pests concept:sportsteam:fresno_state_bulldogs concept:teamplaysagainstteam concept:sportsteam:colorado_state concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:kansas_city_chiefs concept:sportsteam:steelers concept:teamalsoknownas concept:sportsteam:seahawks concept:insect:beneficial_insects concept:animalsuchasinsect concept:insect:hoverflies concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:crustacean:krill concept:arthropodcalledarthropod concept:crustacean:shrimp concept:person:belichick concept:personbelongstoorganization concept:sportsteam:new_england_patriots concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:fumes concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:joe_aillet_stadium concept:insect:insects concept:arthropodandotherarthropod concept:insect:ladybirds concept:city:somerville concept:proxyfor concept:stateorprovince:massachusetts concept:insect:insects concept:animalsuchasinsect concept:insect:insect_pests concept:professionalorganization:cal concept:organizationhiredperson concept:person:montgomery concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:jarry_park concept:country:philippines concept:countrylocatedingeopoliticallocation concept:country:southeast_asian_countries concept:sportsteam:princeton_theological_seminary concept:teamplayssport concept:sport:football concept:sportsteam:cleveland_browns concept:organizationhiredperson concept:person:belichick concept:mammal:animals concept:animalpreyson concept:animal:hyenas concept:sportsteam:philadelphia_eagles concept:teamplayssport concept:sport:football concept:stateorprovince:massachusetts concept:statehascapital concept:county:berkshire concept:arthropod:yellow_jackets concept:animalsuchasinsect concept:insect:pests concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:philadelphia_eagles concept:chemical:water concept:chemicalistypeofchemical concept:chemical:composition concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:steelers concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:ulcers concept:vertebrate:herds concept:ismultipleof concept:mammal:plains_game concept:invertebrate:crustaceans concept:animalistypeofanimal concept:animal:invertebrates concept:musicartist:availability concept:agentcompeteswithagent concept:personcanada:search concept:politicianus:biden concept:politicianholdsoffice concept:politicaloffice:senator concept:agent:linux concept:synonymfor concept:programminglanguage:unix concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:red_wings concept:sportsteam:cleveland_browns concept:teamplaysagainstteam concept:sportsteam:jaguars concept:coach:vince_lombardi concept:worksfor concept:sportsteam:packers concept:company:ryanair001 concept:hasofficeincity concept:city:girona concept:sportsteam:rangers concept:teamwontrophy concept:awardtrophytournament:division concept:personnorthamerica:bills concept:agentcompeteswithagent concept:sportsteam:new_york_giants concept:sportsteam:redskins concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:chemical:analysis concept:chemicalistypeofchemical concept:chemical:carbon concept:arthropod:bloodworms concept:animalpreyson concept:crustacean:shrimp concept:animal:puppy concept:animalpreyson concept:mammal:puppies concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:hydrocarbon concept:insect:pests concept:animalpreyson concept:invertebrate:possums concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:mental_illnesses concept:animal:children concept:animaldevelopdisease concept:disease:clefts concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:waste_products concept:insect:insects concept:arthropodandotherarthropod concept:insect:cockroaches concept:animal:children concept:animaldevelopdisease concept:disease:deafness concept:animal:animals001 concept:animalsuchasfish concept:fish:tilapia concept:animal:children concept:animaldevelopdisease concept:disease:disabilties concept:county:detroit concept:atlocation concept:stateorprovince:michigan concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:polymers concept:stateorprovince:tennessee concept:organizationhiredperson concept:personmexico:bruce_pearl concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:minnesota_timberwolves concept:animal:children concept:animaldevelopdisease concept:disease:leukemia concept:sport:soccer concept:sportfansincountry concept:country:germany concept:sportsteam:ball_state_university concept:organizationhiredperson concept:coach:brady_hoke concept:country:ukraine concept:countrycurrency concept:currency:dollars concept:sportsteam:seattle_supersonics concept:teamplaysinleague concept:sportsleague:nba concept:city:detroit concept:proxyfor concept:stadiumoreventvenue:palace_of_auburn_hills concept:mammal:jackals concept:animalistypeofanimal concept:mammal:predators concept:country:republic concept:countryalsoknownas concept:country:tunisia concept:bank:fed_ex concept:competeswith concept:bank:priority_mail concept:city:detroit concept:citylocatedingeopoliticallocation concept:geopoliticallocation:wayne concept:arthropod:prey concept:animalsuchasinsect concept:insect:insects concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:orioles concept:sportsteam:penn_state concept:teamalsoknownas concept:sportsteam:college concept:insect:pests concept:arthropodandotherarthropod concept:insect:leafhoppers concept:bank:priority_mail concept:competeswith concept:company:express concept:fish:tuna concept:fishservedwithfood concept:visualizableobject:crostini concept:chemical:hydrocarbons concept:chemicalistypeofchemical concept:chemical:carbon_monoxide concept:insect:pests concept:animalpreyson concept:mammal:rats concept:person:john001 concept:personborninlocation concept:county:york_city concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:hydrogen_chloride concept:insect:flies concept:arthropodandotherarthropod concept:insect:pests concept:fish:tuna concept:fishservedwithfood concept:condiment:wasabi concept:stateorprovince:tennessee concept:organizationhiredperson concept:coach:doug_dickey concept:sportsteam:ole_miss concept:teamplaysinleague concept:sportsleague:ncaa concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:products concept:bank:priority_mail concept:agentcompeteswithagent concept:governmentorganization:usps concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:maize concept:sportsteam:arkansas_state_red_wolves concept:agentcollaborateswithagent concept:personmexico:ncaa concept:coach:mike_sherman concept:worksfor concept:automobilemaker:a_m concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:matter concept:invertebrate:bees concept:invertebratefeedonfood concept:food:pollen concept:chemical:platinum concept:chemicalistypeofchemical concept:chemical:carbon concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:complications concept:bird:pheasants concept:animalistypeofanimal concept:mammal:animals concept:city:baltimore concept:citylocatedingeopoliticallocation concept:hotel:morgan concept:biotechcompany:apple concept:companyeconomicsector concept:economicsector:electronics concept:sportsteam:savannah_state_tigers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:insect:insects concept:animalsuchasinsect concept:insect:gnats concept:chemical:particulates concept:chemicalistypeofchemical concept:chemical:sulfur concept:chemical:end_products concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:la_clippers concept:teamplaysagainstteam concept:sportsteam:bucks concept:chemical:water concept:chemicalistypeofchemical concept:chemical:dioxide concept:mammal:fox concept:animalpreyson concept:mammal:animals concept:insect:insects concept:animalsuchasinsect concept:insect:hoverflies concept:landscapefeatures:states concept:locationlocatedwithinlocation concept:country:germany concept:governmentorganization:copy concept:agentcompeteswithagent concept:personcanada:search concept:sportsteam:boston_celtics concept:organizationhiredperson concept:athlete:rick_pitino concept:sportsteam:wisconsin_badgers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:animal:children concept:animaldevelopdisease concept:physiologicalcondition:attention_deficit concept:sportsteam:florida_gulf_coast_eagles concept:agentcollaborateswithagent concept:personmexico:ncaa concept:vertebrate:herds concept:ismultipleof concept:mammal:impala concept:politicianus:nancy_pelosi concept:politicianholdsoffice concept:politicaloffice:senator concept:chemical:power_plant concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:cleveland_indians concept:teamplaysagainstteam concept:sportsteam:orioles concept:crustacean:shrimp concept:animalpreyson concept:insect:mosquito_larvae concept:coach:gregg_popovich concept:coachesinleague concept:sportsleague:nba concept:arthropod:recluse concept:arthropodandotherarthropod concept:arthropod:spider_bite concept:sportsteam:titans concept:teamplaysagainstteam concept:sportsteam:steelers concept:chemical:water concept:chemicalistypeofchemical concept:visualizablething:alcohols concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:alzheimer_s concept:sportsteam:columbus_crew concept:teamplaysagainstteam concept:sportsteam:toronto_fc concept:county:detroit concept:organizationhiredperson concept:coach:rod_marinelli concept:bank:usps_global_priority concept:competeswith concept:company:express concept:biotechcompany:apple concept:mutualproxyfor concept:island:stephen concept:invertebrate:spider concept:animalistypeofanimal concept:arthropod:widow concept:sportsteam:bills concept:teamplaysagainstteam concept:sportsteam:colts concept:bank:dhl concept:competeswith concept:bank:priority_mail concept:stateorprovince:wisconsin concept:mutualproxyfor concept:city:waukesha concept:invertebrate:larvae concept:invertebratefeedonfood concept:plant:stem concept:insect:insects concept:animalthatfeedoninsect concept:insect:butterfly concept:sportsteam:steelers concept:teamwontrophy concept:awardtrophytournament:afc_north concept:politician:obama concept:persongraduatedfromuniversity concept:university:harvard_university concept:insect:insects concept:arthropodandotherarthropod concept:arthropod:arthropods concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:toxic_fumes concept:arthropod:lice concept:animalsuchasinsect concept:insect:insects concept:chemical:air_emissions concept:chemicalistypeofchemical concept:chemical:fuels concept:stadiumoreventvenue:pier_six_concert_pavilion concept:atlocation concept:city:baltimore concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:water_vapor concept:coach:skip_holtz concept:worksfor concept:sportsteam:east_carolina concept:mammal:elephants concept:animalistypeofanimal concept:animal:large_animals concept:stadiumoreventvenue:lp_field concept:stadiumlocatedincity concept:city:nashville concept:personcanada:search concept:agentcompeteswithagent concept:celebrity:feed concept:arachnid:brown_recluse_spider concept:arthropodandotherarthropod concept:arthropod:spiders concept:sportsteam:red_wings concept:teamplaysagainstteam concept:sportsteam:montreal_canadiens concept:animal:animals001 concept:animalistypeofanimal concept:insect:termites concept:chemical:sulfate concept:chemicalistypeofchemical concept:chemical:carbon concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:oman concept:airport:rinas concept:buildinglocatedincity concept:city:tirana concept:programminglanguage:system concept:subpartof concept:hallwayitem:vacuum concept:city:ann_arbor concept:citylocatedinstate concept:stateorprovince:mi concept:sportsteam:san_antonio concept:teamplaysagainstteam concept:sportsteam:suns concept:sportsteam:northern_arizona_lumberjacks concept:agentcollaborateswithagent concept:personmexico:ncaa concept:city:worcester concept:proxyfor concept:stateorprovince:massachusetts concept:mammal:eland concept:animalistypeofanimal concept:mammal:antelopes concept:insect:insects concept:animalthatfeedoninsect concept:insect:leafhoppers concept:crustacean:shrimp concept:arthropodcalledarthropod concept:arthropod:bloodworms concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:pnc_park concept:city:new_york concept:organizationhiredperson concept:politician:guiliani concept:beverage:sugar concept:foodcancausedisease concept:disease:depression concept:animal:animals001 concept:animalpreyson concept:arthropod:prey concept:sportsteam:florida_state_university concept:teamplayssport concept:sport:football concept:insect:parasitoids concept:animalsuchasinsect concept:insect:insects concept:male:world concept:agentcollaborateswithagent concept:politician:james concept:sportsteam:packers concept:teamplaysagainstteam concept:sportsteam:eagles concept:radiostation:cleveland concept:mutualproxyfor concept:politicianus:carl_b__stokes concept:sportsteam:cameron_aggies concept:agentcollaborateswithagent concept:personmexico:ncaa concept:animal:children concept:animaldevelopdisease concept:disease:disorder_need concept:invertebrate:egrets concept:animalpreyson concept:animal:birds003 concept:programminglanguage:system concept:subpartof concept:televisionshow:pure_water concept:sportsteam:new_york_giants concept:teamalsoknownas concept:sportsteam:bills concept:crustacean:mollusks concept:animalistypeofanimal concept:animal:invertebrates concept:person:anderson 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concept:animalsuchasinvertebrate concept:insect:white_flies concept:mammal:zebras concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:philadelphia_athletics concept:sportsteam:cal_state_fullerton_titans concept:agentcollaborateswithagent concept:personmexico:ncaa concept:coach:philadelphia_eagles concept:agentcompeteswithagent concept:sportsteam:new_york_giants concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:carbon_tetrachloride concept:city:hendersonville concept:mutualproxyfor concept:stateorprovince:tennessee concept:insect:fruit_flies concept:arthropodandotherarthropod concept:insect:insects concept:chemical:hydrocarbons concept:chemicalistypeofchemical concept:chemical:organic_chemicals concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:power_plants concept:chemical:water concept:chemicalistypeofchemical concept:chemical:sulphate 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concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:agriculturalproduct:cereals concept:agriculturalproductincludingagriculturalproduct concept:vegetable:oats concept:coach:carroll concept:worksfor concept:university:usc concept:mammal:sheep concept:mammalsuchasmammal concept:mammal:black_bear concept:insect:insects concept:animalsuchasinsect concept:insect:fruit_flies concept:crustacean:shrimp concept:arthropodcalledarthropod concept:insect:mosquito_larvae concept:plant:roses concept:plantrepresentemotion concept:emotion:deep_love concept:sportsteam:leafs concept:teamplaysagainstteam concept:sportsteam:tampa_bay_lightning concept:country:republic concept:countryalsoknownas concept:country:belgium concept:river:st___croix_river concept:riveremptiesintoriver concept:river:mississippi_river concept:person:jackson001 concept:personbornincity concept:city:orleans concept:sportsteam:lsu_tigers concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:east_carolina_pirates concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:blackhawks concept:teamhomestadium concept:stadiumoreventvenue:wrigley_field concept:mammal:donkey concept:animalistypeofanimal concept:mammal:animals concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:companies concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:red_wings concept:country:republic concept:countryalsoknownas concept:country:malaysia concept:insect:insects concept:arthropodcalledarthropod concept:insect:bug concept:sportsteam:san_diego_padres concept:organizationhiredperson concept:coach:brady_hoke concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:grain:winter_wheat concept:sportsteam:columbus_blue_jackets concept:teamplayssport concept:sport:hockey concept:agriculturalproduct:fruits concept:agriculturalproductcontainchemical concept:chemical:water concept:invertebrate:nymphs concept:invertebratefeedonfood concept:nut:leaves concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:baylor_bears concept:sportsteam:new_york_giants concept:agentcompeteswithagent concept:coach:new_england_patriots concept:sportsteam:tampa_bay_lightning concept:teamplayssport concept:sport:hockey concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:breast_cancer concept:sport:football concept:sportfansincountry concept:country:greece concept:mammal:chimpanzees concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:devils concept:teamplaysagainstteam concept:sportsteam:montreal_canadiens concept:sportsteam:barcelona concept:teamplaysinleague concept:sportsleague:uefa concept:company:arizona concept:organizationhiredperson concept:coach:kevin_o_neill concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:emphysema concept:animal:children concept:animaldevelopdisease concept:physiologicalcondition:health_issues concept:mammal:predators concept:animalpreyson concept:mammal:fox concept:sportsteam:new_york_red_bulls concept:teamplaysagainstteam concept:sportsteam:_skins concept:sportsteam:la_clippers concept:teamplaysagainstteam concept:sportsteam:mavericks concept:wine:merlot concept:beveragemadefrombeverage concept:beverage:cab concept:fish:sharks concept:animalistypeofanimal concept:fish:fishes concept:sportsteam:boston_celtics concept:teamwontrophy concept:awardtrophytournament:nba_title concept:sportsteam:redskins concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:arthropod:conch concept:arthropodcalledarthropod concept:arthropod:seafood concept:sportsteam:uc_santa_barbara_gauchos concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:community concept:sportsteam:buccaneers concept:teamplaysagainstteam concept:sportsteam:jaguars concept:crustacean:lobster concept:arthropodcalledarthropod concept:crustacean:shellfish concept:sportsteam:texas_state_bobcats concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:bruins concept:animal:polar_bears concept:animalpreyson concept:mammal:animals concept:sportsteam:cleveland_browns concept:teamplaysagainstteam concept:sportsteam:eagles concept:athlete:lorenzen_wright concept:athleteledsportsteam concept:sportsteam:reds concept:insect:pests concept:animalthatfeedoninsect concept:insect:mite concept:journalist:dan concept:persongraduatedschool concept:university:college concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:products concept:insect:pests concept:animalthatfeedoninsect concept:insect:armyworms concept:animal:carnivores concept:animalpreyson concept:mammal:animals concept:personmexico:ncaa concept:agentcompeteswithagent concept:company:west_virginia concept:chemical:pollutants concept:chemicalistypeofchemical concept:chemical:carbon concept:mammal:mice concept:animalistypeofanimal concept:mammal:predators concept:insect:moths concept:arthropodandotherarthropod concept:insect:insects concept:politicianus:john_f__kennedy concept:persongraduatedfromuniversity concept:university:harvard concept:person:button concept:agentcompeteswithagent concept:personcanada:search concept:insect:trojans concept:agentcontrols concept:coach:carroll concept:personcanada:search concept:agentcompeteswithagent concept:automobilemaker:firefox concept:country:republic concept:countryalsoknownas concept:country:scandinavian_countries concept:invertebrate:larvae concept:animalthatfeedoninsect concept:insect:small_insects concept:bird:domestic_birds concept:animalistypeofanimal concept:mammal:animals concept:sportsgame:games concept:sportsgameteam concept:sportsteam:hokies concept:insect:insects concept:animalthatfeedoninsect concept:insect:ladybugs concept:chemical:ammonia concept:chemicalistypeofchemical concept:chemical:molecules concept:female:lot concept:agentcompeteswithagent concept:personcanada:search concept:river:nile concept:riveremptiesintoriver concept:river:lake_nasser concept:sportsteam:sacramento_kings concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:steam concept:county:baltimore concept:locationlocatedwithinlocation concept:stateorprovince:maryland concept:insect:flies concept:animalsuchasinsect concept:insect:pests concept:sportsteam:oklahoma_state_university concept:teamplaysinleague concept:sportsleague:ncaa concept:bird:raptors concept:animalistypeofanimal concept:mammal:predators concept:product:spider concept:synonymfor concept:insect:brown_recluse concept:mammal:elephants concept:animalpreyson concept:mammal:wild_animals concept:crustacean:daphnia concept:arthropodcalledarthropod concept:crustacean:shrimp concept:stateorprovince:tennessee concept:organizationhiredperson concept:coach:pat_summitt concept:arthropod:mantids concept:animalsuchasinvertebrate concept:insect:insects concept:sportsteam:los_angeles_angels_of_anaheim concept:teamplayssport concept:sport:baseball concept:chemical:fuels concept:chemicalistypeofchemical concept:chemical:nitrogen_oxides concept:arthropod:spiders concept:arthropodcalledarthropod concept:arthropod:recluse concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:vegetable:wheat concept:sportsteam:royals concept:teamplayssport concept:sport:hockey concept:visualartist:award concept:agentcompeteswithagent concept:personcanada:search concept:crustacean:prawns concept:animalsuchasfish concept:fish:shellfish concept:animal:scavengers concept:animalthatfeedoninsect concept:insect:insects concept:mammal:predators concept:animalsuchasfish concept:fish:larger_fish concept:food:oil concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:canola_oil concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:cataracts concept:sportsteam:pittsburgh_penguins concept:teamhomestadium concept:stadiumoreventvenue:mellon_arena concept:insect:honeybees concept:agentcompeteswithagent concept:insect:insects concept:biotechcompany:apple concept:producesproduct concept:product:mac_pro concept:sportsteam:phillies concept:teamplaysagainstteam concept:sportsteam:milwaukee_brewers concept:mammal:mountain_lions concept:animalistypeofanimal concept:mammal:animals concept:coach:carroll concept:coachesteam concept:sportsteam:usc concept:chemical:fuels concept:chemicalistypeofchemical concept:chemical:harmful_emissions concept:vertebrate:herds concept:ismultipleof concept:mammal:oxen concept:personcanada:search concept:agentcompeteswithagent concept:newspaper:msn_com concept:personcanada:search concept:agentcompeteswithagent concept:politicalparty:way concept:sportsteam:chelsea concept:teamwontrophy concept:awardtrophytournament:league_title concept:food:large_seeds concept:agriculturalproductincludingagriculturalproduct concept:vegetable:beans concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:cleveland_indians_organization concept:insect:insects concept:arthropodandotherarthropod concept:insect:carpenter_ants concept:chemical:water concept:chemicalistypeofchemical concept:visualizablething:lipids concept:animal:children concept:animaldevelopdisease concept:disease:hyperactivity_disorders concept:insect:pests concept:animalsuchasinvertebrate concept:invertebrate:larvae concept:animal:children concept:animaldevelopdisease concept:disease:hemophilia concept:sportsleague:nba concept:leaguestadiums concept:stadiumoreventvenue:bradley_center concept:mammal:squirrels concept:animalsuchasinvertebrate concept:insect:pests concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:deterioration concept:stateorprovince:vermont concept:stateorprovinceisborderedbystateorprovince concept:stateorprovince:massachusetts concept:politicianus:al_gore concept:agentcollaborateswithagent concept:politician:obama concept:politicianus:hilary_clinton concept:agentcollaborateswithagent concept:politician:obama concept:animal:hamsters concept:animalpreyson concept:mammal:small_animals concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:nippert_stadium concept:plant:trees concept:thinghascolor concept:color:yellow concept:sportsteam:liverpool_university concept:subpartof concept:geopoliticallocation:fa concept:sportsgame:games concept:sportsgameteam concept:sportsteam:mavericks concept:arthropod:spider_bite concept:arthropodcalledarthropod concept:arthropod:recluse concept:company:alta_vista concept:agentcompeteswithagent concept:university:search concept:sportsteam:pittsburgh_penguins concept:teamplaysagainstteam concept:sportsteam:flyers_playoff_tickets concept:mammal:predators 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concept:sportsteam:nevada_wolfpack concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:carolina concept:insect:insects concept:animalpreyson concept:insect:jackets concept:crustacean:crab concept:arthropodcalledarthropod concept:arthropod:seafood concept:university:alabama concept:organizationhiredperson concept:coach:gene_stallings concept:animal:children concept:animaldevelopdisease concept:disease:condition concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:hydrogen concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:rexall_place concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:nitrogen_oxides concept:arachnid:recluse_spider concept:arthropodcalledarthropod concept:arthropod:spider_bite concept:radiostation:grand_rapids concept:proxyfor concept:creditunion:michigan concept:insect:insects concept:animalthatfeedoninsect concept:insect:predatory_mites concept:sportsteam:falcons concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:biotechcompany:apple concept:agentcollaborateswithagent concept:university:wozniak concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:organic_compounds concept:country:albania concept:countryalsoknownas concept:country:republic concept:mammal:elk concept:animalistypeofanimal concept:mammal:large_herbivores concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:st_bonaventure_bonnies concept:agriculturalproduct:oysters concept:arthropodcalledarthropod concept:crustacean:crab concept:sportsteam:indiana_university concept:teamplaysinleague concept:sportsleague:international concept:insect:insects concept:animalthatfeedoninsect concept:insect:whiteflies concept:politician:obama concept:agentcollaborateswithagent concept:person:bill concept:politicsblog:guardian concept:competeswith concept:newspaper:tribune concept:politician:obama concept:politicianholdsoffice 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concept:geopoliticallocation:sterling_heights concept:proxyfor concept:stateorprovince:michigan concept:sportsteam:idaho_state_bengals concept:teamplayssport concept:sport:football concept:sportsteam:spartans concept:teamalsoknownas concept:sportsteam:buckeyes concept:insect:insects concept:animalthatfeedoninsect concept:insect:mealy_bugs concept:insect:insects concept:arthropodcalledarthropod concept:insect:thrips concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:emissions concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:western_kentucky_hilltoppers concept:city:jakarta concept:citycapitalofcountry concept:country:republic concept:sportsteam:la_clippers concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:chemical:nickel concept:chemicalistypeofchemical concept:chemical:metals concept:politicianus:miller concept:worksfor concept:website:new_york_times concept:sportsteam:notre_dame concept:organizationhiredperson 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concept:competeswith concept:company:express concept:biotechcompany:apple concept:agentinvolvedwithitem concept:musicinstrument:mac_os_x concept:animal:children concept:animaldevelopdisease concept:disease:growth_hormone_deficiency concept:insect:hoverflies concept:animalpreyson concept:insect:insects concept:country:republic concept:countryalsoknownas concept:country:drc concept:vertebrate:ruminants concept:animalpreyson concept:agriculturalproduct:goats concept:insect:insects concept:arthropodandotherarthropod concept:arachnid:scorpions concept:university:haverford_college concept:agentcontrols concept:book:tim concept:company:priority concept:competeswith concept:bank:fed_ex concept:visualizablething:tree concept:plantrepresentemotion concept:emotion:friendship concept:sportsteam:falcons concept:organizationhiredperson concept:coach:bobby_petrino concept:vertebrate:crayfish concept:animalpreyson concept:mammal:animals concept:animal:roosters concept:specializationof concept:mammal:animals concept:personus:andruw_jones concept:personbelongstoorganization concept:sportsteam:chowan_braves concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:acid_gases concept:insect:insects concept:animalsuchasinsect concept:arthropod:mantids concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:new_jersey_devils concept:radiostation:cleveland concept:organizationhiredperson concept:person:belichick concept:animal:hyenas concept:animalistypeofanimal concept:mammal:predators concept:university:search concept:agentcompeteswithagent concept:company:post concept:chemical:greenhouse_gas_emissions concept:chemicalistypeofchemical concept:chemical:pollutants concept:athlete:larry_robinson concept:coachwontrophy concept:awardtrophytournament:stanley_cup concept:mammal:predators concept:animalsuchasinvertebrate concept:insect:ground_beetles concept:insect:insects concept:animalsuchasinsect concept:insect:mealy_bugs 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concept:animal:children concept:animaldevelopdisease concept:disease:behavior_problems concept:insect:bug concept:animalthatfeedoninsect concept:insect:insects concept:sportsteam:rangers concept:teamwontrophy concept:awardtrophytournament:stanley_cup concept:animal:children concept:animaldevelopdisease concept:disease:seizures concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:packers concept:animal:children concept:animaldevelopdisease concept:disease:leukaemia concept:sportsteam:oregon_state_beavers concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:wisconsin_milwaukee_panthers concept:teamplaysinleague concept:sportsleague:ncaa concept:animal:children concept:animaldevelopdisease concept:disease:diseases concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:nationals_park concept:sport:football concept:sportfansincountry concept:country:sweden concept:chemical:acid_rain concept:chemicalistypeofchemical concept:chemical:pollutants concept:plant:orchids concept:plantrepresentemotion concept:emotion:passion concept:agriculturalproduct:cereals concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:millet concept:mlauthor:web_search concept:agentcompeteswithagent concept:company:lycos concept:mammal:lions concept:animalpreyson concept:mammal:wild_animals concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:steelers concept:country:romania concept:countryalsoknownas concept:country:republic concept:sportsteam:phillies concept:teamplaysagainstteam concept:sportsteam:reds concept:vertebrate:herds concept:ismultipleof concept:mammal:gazelles concept:animal:children concept:animaldevelopdisease concept:physiologicalcondition:handicaps concept:sportsteam:florida_marlins concept:teamplaysincity concept:city:miami concept:stadiumoreventvenue:bridgestone_arena concept:stadiumlocatedincity concept:city:nashville concept:personus:tiger_woods concept:agentcollaborateswithagent concept:politician:obama concept:bank:fed_ex concept:competeswith concept:company:express concept:sportsteam:new_york_giants concept:teamwontrophy concept:awardtrophytournament:superbowl concept:sportsteam:suns concept:teamplaysagainstteam concept:sportsteam:la_clippers concept:insect:ants concept:animalpreyson concept:insect:small_insects concept:sportsteam:maine_black_bears concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:idaho_state_bengals concept:teamplaysinleague concept:sportsleague:ncaa concept:insect:insects concept:animalthatfeedoninsect concept:arthropod:mantids concept:city:orleans concept:cityliesonriver concept:river:loire concept:sportsteam:stanford concept:teamplaysagainstteam concept:sportsteam:uconn concept:coach:marc_iavaroni concept:worksfor concept:sportsteam:memphis_grizzlies concept:sportsteam:redskins concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:wright_state_raiders concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:organic_matter concept:sportsteam:capitals concept:teamplaysagainstteam concept:sportsteam:tampa_bay_lightning concept:biotechcompany:apple concept:organizationterminatedperson concept:ceo:tim_cook concept:sportsteam:baltimore_colts concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsleague:new concept:organizationhiredperson concept:coach:cam_cameron concept:invertebrate:cattle concept:animalpreyson concept:animal:animals001 concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:denver_nuggets concept:invertebrate:molluscs concept:animalsuchasinvertebrate concept:arthropod:invertebrates concept:agriculturalproduct:berries concept:agriculturalproductgrowninlandscapefeatures concept:landscapefeatures:mountains concept:personcanada:search concept:agentcompeteswithagent concept:criminal:web_sites concept:sportsteam:flyers_playoff_tickets concept:teamplaysagainstteam concept:sportsteam:devils concept:mammal:predators concept:animalsuchasfish concept:fish:sunfish concept:insect:insects concept:animalsuchasinvertebrate concept:insect:ground_beetles concept:insect:widow_spiders concept:arthropodcalledarthropod concept:arthropod:spiders concept:sportsteam:arizona_cardinals_27_23 concept:agentcompeteswithagent concept:sportsteam:steelers concept:sportsteam:liverpool_john_moores_university concept:teamwontrophy concept:awardtrophytournament:league_title concept:coach:san_antonio_spurs concept:agentbelongstoorganization concept:sportsleague:nba concept:sportsteam:red_wings concept:teamplaysagainstteam concept:sportsteam:anaheim_ducks concept:food:oil concept:agriculturalproductcookedwithagriculturalproduct concept:agriculturalproduct:oysters concept:animal:birds003 concept:agentcompeteswithagent concept:journalist:eagles concept:chemical:oxide concept:chemicalistypeofchemical concept:chemical:nitrogen concept:personcanada:search concept:agentcompeteswithagent concept:insect:goggle concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:tucson_electric_park concept:museum:franklin concept:proxyfor concept:weatherphenomenon:tennessee concept:insect:insects concept:arthropodandotherarthropod concept:insect:ants concept:sportsteam:memphis_grizzlies concept:teamplaysagainstteam concept:sportsteam:san_antonio concept:mammal:tigers concept:mammalsuchasmammal concept:mammal:elephants concept:insect:mosquitoes concept:arthropodandotherarthropod concept:arthropod:arthropods concept:automobilemaker:information concept:agentcompeteswithagent concept:personcanada:search concept:chemical:fumes concept:chemicalistypeofchemical concept:chemical:nitrogen concept:newspaper:tribune concept:agentcompeteswithagent concept:website:new_york_times concept:crustacean:shrimp concept:arthropodcalledarthropod concept:arthropod:clams concept:sportsteam:steelers concept:teamwontrophy concept:awardtrophytournament:super_bowl_xliii concept:chemical:toxic_fumes concept:chemicalistypeofchemical concept:chemical:carbon concept:mammal:cougars concept:animalistypeofanimal concept:animal:large_predators concept:sportsteam:chelsea_football_club concept:teamplaysagainstteam concept:sportsteam:liverpool_university concept:country:republic concept:countrylocatedingeopoliticallocation concept:geopoliticallocation:european_nations concept:arthropod:seafood concept:arthropodcalledarthropod concept:arthropod:calamari concept:animal:mantises concept:animalsuchasinvertebrate concept:insect:insects concept:insect:insects concept:animalsuchasinsect concept:arthropod:pollinators concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:ballpark_in_arlington concept:sport:golf concept:sportschoolincountry concept:country:taiwan concept:insect:pests concept:arthropodcalledarthropod concept:arthropod:spiders concept:sport:golf concept:sportschoolincountry concept:country:germany concept:sportsteam:reds concept:teamplaysagainstteam concept:sportsteam:new_york_mets concept:chemical:hydrogen_chloride concept:chemicalistypeofchemical concept:chemical:nitrogen concept:crustacean:lobster concept:arthropodcalledarthropod concept:crustacean:langostino concept:mammal:animals concept:animalpreyson concept:mammal:canines concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:butterfly concept:wine:blend concept:beveragemadefrombeverage concept:beverage:cab concept:mammal:animals concept:animalpreyson concept:animal:house_pets concept:fish:large_fish concept:animalsuchasfish concept:fish:skate concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:kansas_city_chiefs concept:country:tanzania concept:countryalsoknownas concept:country:republic concept:mammal:mice concept:animalistypeofanimal concept:mammal:small_animals concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:kansas_city_royals concept:city:new_york concept:proxyfor concept:stadiumoreventvenue:hammerstein_ballroom concept:sport:baseball concept:sportfansincountry concept:country:germany concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:particulates concept:mammal:sheep concept:animalistypeofanimal concept:vertebrate:ruminants concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:colts concept:fish:fishes concept:animaleatfood concept:food:worm concept:animal:children concept:animaldevelopdisease concept:disease:obsessive_compulsive_disorder concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:edison_field concept:politicianus:president_bush concept:politicianholdsoffice concept:politicaloffice:senator concept:plant:roses concept:plantrepresentemotion concept:emotion:hope concept:newspaper:tribune concept:agentcollaborateswithagent concept:ceo:sam_zell concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:packers concept:ceo:site_usage concept:agentcompeteswithagent concept:personcanada:search concept:programminglanguage:system concept:subpartof concept:visualizableattribute:portable_water concept:agriculturalproduct:varieties concept:agriculturalproducttoattractinsect concept:insect:insects concept:insect:insects concept:animalsuchasinsect concept:insect:fungus_gnats concept:city:silver_spring concept:atlocation concept:stateorprovince:maryland concept:sportsteam:reds concept:teamplaysagainstteam concept:sportsteam:chowan_braves concept:chemical:nitrogen concept:chemicalistypeofchemical concept:chemical:inert_gas concept:insect:midges concept:animalsuchasinsect concept:insect:flies concept:coach:reggie_theus concept:coachesteam concept:sportsteam:sacramento_kings concept:sportsteam:o_s concept:organizationhiredperson concept:coach:doc_holliday concept:sportsteam:jacksonville_state_gamecocks concept:teamplaysinleague concept:sportsleague:ncaa concept:creditunion:michigan concept:mutualproxyfor concept:musicalbum:holland concept:geometricshape:right concept:agentcompeteswithagent concept:personcanada:search concept:sportsteam:packers concept:teamplaysagainstteam concept:sportsteam:steelers concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:schoellkopf_field concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:columbus_crew concept:insect:insects concept:animalsuchasinsect concept:insect:blackflies concept:agriculturalproduct:flowers concept:agriculturalproductgrowninlandscapefeatures concept:landscapefeatures:prairies concept:fish:albacore concept:animalistypeofanimal concept:fish:tunas concept:mammal:grizzly_bears concept:animaleatfood concept:food:vegetation concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:criteria_pollutants concept:sportsteam:eastern_illinois_panthers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:arthropod:rotifers concept:arthropodandotherarthropod concept:crustacean:shrimp concept:creditunion:michigan concept:mutualproxyfor concept:radiostation:flint concept:mammal:ponies concept:animalpreyson concept:mammal:animals concept:sportsteam:akron_zips concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:kent_state concept:teamplaysinleague concept:sportsleague:ncaa concept:stateorprovince:maine concept:statehascapital concept:city:augusta concept:coach:dennis_franchione concept:coachesteam concept:sportsteam:texas_a_m concept:mammal:small_animals concept:animalpreyson concept:mammal:ferrets concept:plant:roses concept:plantrepresentemotion concept:emotion:thanks concept:animal:children concept:animaldevelopdisease concept:disease:hyperactivity concept:comedian:image concept:agentcreated concept:book:print concept:sportsteam:leafs concept:teamplaysagainstteam concept:sportsteam:carolina_hurricanes concept:chemical:diesel_exhaust concept:chemicalistypeofchemical concept:chemical:carbon concept:mammal:animals concept:animalistypeofanimal concept:mammal:elephants concept:sportsteam:northern_arizona concept:synonymfor concept:sportsteam:state_university concept:city:charleroi concept:locationlocatedwithinlocation concept:country:belgium concept:politicianus:miller concept:persongraduatedfromuniversity concept:university:high_school concept:arthropod:secondary_pests concept:arthropodandotherarthropod concept:arachnid:mites concept:insect:beneficial_insects concept:animalthatfeedoninsect concept:insect:lacewings concept:insect:insects concept:animalpreyson concept:mammal:small_animals concept:coach:ed_orgeron concept:worksfor concept:stateorprovince:ole_miss concept:biotechcompany:apple concept:agentinvolvedwithitem concept:software:quicktime_player concept:sportsteam:northern_colorado_bears concept:teamplaysinleague concept:sportsleague:ncaa concept:bank:ups_air concept:competeswith concept:company:express concept:mammal:marmots concept:animalistypeofanimal concept:mammal:animals concept:person:ryan_church concept:personbelongstoorganization concept:sportsteam:new_york_mets concept:mammal:animals concept:animalpreyson concept:mammal:reindeer concept:mammal:lions concept:mammalsuchasmammal concept:mammal:elephant concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:greenhouse_gas_emissions concept:mammal:animals concept:animalistypeofanimal concept:mammal:camels concept:creditunion:wisconsin concept:organizationhiredperson concept:coach:dick_bennett concept:insect:honey_bees concept:invertebratefeedonfood concept:beverage:nectar concept:insect:insects concept:arthropodandotherarthropod concept:arthropod:pollinators concept:chemical:soot concept:chemicalistypeofchemical concept:chemical:emissions concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:vegetable:oats concept:agriculturalproduct:fruits concept:agriculturalproductcontainchemical concept:chemical:fats concept:agriculturalproduct:butter concept:agriculturalproductcutintogeometricshape concept:geometricshape:dice concept:mammal:lions concept:mammalsuchasmammal concept:mammal:zebra concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:eagles concept:mammal:animals concept:animalpreyson concept:vertebrate:koalas concept:sportsteam:bruins concept:teamalsoknownas concept:sportsteam:ucla concept:personcanada:search concept:agentcompeteswithagent concept:city:photos concept:bird:pheasants concept:animalistypeofanimal concept:bird:upland_game_birds concept:city:tirana concept:proxyfor concept:country:albania concept:sportsteam:kennesaw_state_owls concept:agentcollaborateswithagent concept:personmexico:ncaa concept:athlete:vinny_del_negro concept:worksfor concept:sportsteam:chicago_bulls concept:sportsteam:memphis_grizzlies concept:teamplaysagainstteam concept:sportsteam:mavericks concept:sportsgame:games concept:sportsgameteam concept:sportsteam:flyers_playoff_tickets concept:arthropod:spiders concept:synonymfor concept:arachnid:brown_recluse concept:stadiumoreventvenue:joe_louis_arena concept:stadiumlocatedincity concept:city:detroit concept:bird:herons concept:animalistypeofanimal concept:bird:shorebirds concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:angel_stadium concept:invertebrate:leeches concept:invertebratefeedonfood concept:fungus:blood concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:winston_salem_state_rams concept:insect:insects concept:animalthatfeedoninsect concept:insect:black_flies concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:grains concept:sportsteam:miami_dolphins concept:agentcompeteswithagent concept:sportsteam:new_england_patriots concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:texans concept:arthropod:invertebrates concept:arthropodcalledarthropod concept:arthropod:spiders concept:politician:obama concept:politicianholdsoffice concept:politicaloffice:senate concept:country:slovakia concept:countryalsoknownas concept:country:republic concept:country:madagascar concept:countrycurrency concept:currency:ariary concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:eagles concept:sportsteam:tampa_bay_lightning concept:teamwontrophy concept:awardtrophytournament:n2004_stanley_cup concept:city:baltimore concept:proxyfor concept:stadiumoreventvenue:pier_six_concert_pavilion concept:crustacean:mollusks concept:animalsuchasinsect concept:arthropod:invertebrates concept:wine:pinot_gris concept:beveragemadefrombeverage concept:wine:pinot concept:invertebrate:larvae concept:animalsuchasinsect concept:insect:insects concept:sportsteam:bulldogs 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concept:person:house concept:chemical:water concept:chemicalistypeofchemical concept:chemical:nitrogen_oxides concept:chemical:organic_matter concept:chemicalistypeofchemical concept:chemical:carbon concept:fish:swordfish concept:animalistypeofanimal concept:fish:billfish concept:insect:beneficial_insects concept:animalsuchasinsect concept:arthropod:mantis concept:sportsteam:colts concept:teamwontrophy concept:awardtrophytournament:division concept:sportsteam:san_diego_padres concept:teamhomestadium concept:stadiumoreventvenue:qualcomm_stadium concept:insect:pests concept:animalsuchasinsect concept:insect:houseflies concept:fish:whales concept:animalsuchasfish concept:fish:whale concept:coach:phil_jackson concept:coachesinleague concept:sportsleague:nba concept:sportsteam:atlanta_thrashers concept:teamplayssport concept:sport:hockey concept:food:oil concept:agriculturalproductcookedwithagriculturalproduct concept:crustacean:prawns concept:mammal:porcupines concept:animalpreyson concept:mammal:animals concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:td_garden concept:insect:insects concept:animalsuchasinsect concept:insect:moths concept:sportsteam:tennessee_chattanooga_mocs concept:teamplaysinleague concept:sportsleague:ncaa concept:governmentorganization:usps concept:agentcompeteswithagent concept:company:express concept:animal:animals001 concept:animalsuchasinvertebrate concept:mollusk:clams concept:county:york_city concept:subpartof concept:city:new_york concept:mammal:wild_animals concept:animalpreyson concept:mammal:squirrels concept:chemical:power_plant_emissions concept:chemicalistypeofchemical concept:chemical:carbon concept:animal:orangutans concept:specializationof concept:mammal:animals concept:mammal:warthogs concept:animalistypeofanimal concept:mammal:animals concept:arthropod:pollinators concept:arthropodcalledarthropod concept:insect:wasps concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:products concept:sportsteam:ohio_state_university concept:teamplaysagainstteam concept:sportsteam:usc concept:country:united_arab_emirates concept:countryalsoknownas concept:country:republic concept:sportsteam:nebraska_cornhuskers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:person:michael002 concept:persongraduatedfromuniversity concept:university:law_school concept:sportsteam:florida_state_university concept:teamplaysinleague concept:sportsleague:international concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:egypt concept:chemical:nickel concept:chemicalistypeofchemical concept:chemical:minerals concept:sportsteam:coastal_carolina_chanticleers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:mammal:predators concept:animalthatfeedoninsect concept:insect:mosquito_larvae concept:chemical:mercury concept:chemicalistypeofchemical concept:chemical:minerals concept:sportsteam:idaho_vandals concept:teamplaysinleague concept:sportsleague:ncaa concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:kansas_city_chiefs concept:sportsteam:san_diego_padres concept:teamplaysagainstteam concept:sportsteam:mariners concept:coach:mitch_williams concept:worksfor concept:sportsteam:raptors concept:mammal:animals concept:animalpreyson concept:mammal:apes concept:insect:insects concept:arthropodandotherarthropod concept:insect:thrips concept:chemical:bromine concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsleague:new concept:organizationhiredperson concept:person:anderson concept:arthropod:crustaceans concept:arthropodcalledarthropod concept:agriculturalproduct:shrimps concept:person:greg001 concept:persongraduatedschool concept:university:college concept:sportsteam:florida_international_golden_panthers concept:teamplaysinleague concept:sportsleague:ncaa concept:female:hillary concept:politicianholdsoffice concept:politicaloffice:u_s__president concept:mammal:mongooses concept:animalistypeofanimal concept:mammal:predators concept:chemical:hydrochloric_acid concept:chemicalistypeofchemical concept:chemical:carbon concept:animal:hyenas concept:animalistypeofanimal concept:mammal:animals concept:city:edmonton concept:organizationhiredperson concept:politician:stephen_mandel concept:mammal:animals concept:animalpreyson concept:mammal:yaks concept:sportsteam:pats concept:teamwontrophy concept:awardtrophytournament:superbowl concept:stateorprovince:montana concept:mutualproxyfor concept:city:great_falls concept:company:west_virginia concept:organizationhiredperson concept:personmexico:stewart concept:agriculturalproduct:varieties concept:beveragemadefrombeverage concept:beverage:grenache concept:sportsteam:buckeyes concept:teamplayssport concept:sport:golf concept:agriculturalproduct:tree concept:agriculturalproducttoattractinsect concept:insect:ants concept:insect:scale concept:animalsuchasinsect concept:insect:pests concept:mammal:animals concept:animalistypeofanimal concept:animal:orangutans concept:magazine:story concept:agentcompeteswithagent concept:university:search concept:sportsleague:new concept:organizationhiredperson concept:coach:jim_schwartz concept:sportsteam:duke_blue_devils concept:teamplaysagainstteam concept:sportsteam:eagles concept:sportsteam:usc concept:teamplaysagainstteam concept:sportsteam:oregon_state_beavers concept:stateorprovince:tennessee concept:mutualproxyfor concept:city:kingsport concept:mammal:wild_horses concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:st_bonaventure_bonnies concept:teamplaysinleague concept:sportsleague:ncaa concept:person:mark001 concept:persongraduatedfromuniversity concept:university:college concept:sportsteam:blackhawks concept:teamplaysagainstteam concept:sportsteam:anaheim_ducks concept:arthropod:clams concept:arthropodcalledarthropod concept:arthropod:seafood concept:sportsteam:new_york_red_bulls concept:teamplaysagainstteam concept:sportsteam:philadelphia_athletics concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:food:soy concept:sportsteam:cleveland_browns concept:teamplaysagainstteam concept:sportsteam:bills concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:raptors concept:chemical:emissions concept:chemicalistypeofchemical concept:chemical:_ concept:sportsteam:bradley_braves concept:agentcollaborateswithagent concept:personmexico:ncaa concept:agriculturalproduct:cereals concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:maize concept:insect:insects concept:animalsuchasinsect concept:insect:green_lacewings concept:sportsteam:santa_clara_broncos concept:teamplaysinleague concept:sportsleague:ncaa concept:mammal:goat concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:east_carolina concept:teamplaysincity concept:city:greenville concept:food:oil concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:soya concept:agriculturalproduct:varieties concept:beveragemadefrombeverage concept:wine:merlot concept:personaustralia:more_information concept:agentcompeteswithagent concept:personcanada:search concept:agriculturalproduct:berries concept:agriculturalproductgrowninlandscapefeatures concept:landscapefeatures:forest concept:fish:albacore concept:animalpreyson concept:fish:tunas concept:mammal:animals concept:animalpreyson concept:mammal:elephants concept:arachnid:kids concept:animaldevelopdisease concept:disease:problems concept:animal:badgers concept:animalistypeofanimal concept:mammal:predators concept:chemical:dioxide concept:chemicalistypeofchemical concept:visualizablething:glucose concept:sportsteam:marlins concept:teamplaysagainstteam concept:sportsteam:phillies concept:chemical:carbon concept:specializationof concept:chemical:gas_emissions concept:animal:social_insects concept:animalistypeofanimal concept:insect:wasps concept:city:internet concept:agentcreated concept:programminglanguage:contact concept:chemical:ammonia concept:chemicalistypeofchemical concept:chemical:nitrogen concept:chemical:water concept:chemicalistypeofchemical concept:chemical:metals concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:miami_dolphins concept:chemical:water concept:chemicalistypeofchemical concept:chemical:molecule concept:athlete:niklas_backstrom concept:athleteplaysforteam concept:sportsteam:minnesota_wilds concept:country:democratic_republic_of_congo concept:countryalsoknownas concept:country:sudan concept:invertebrate:molluscs concept:animalistypeofanimal concept:animal:animals001 concept:mammal:sheep concept:mammalsuchasmammal concept:mammal:lions concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:aerosols concept:agriculturalproduct:oysters concept:arthropodcalledarthropod concept:crustacean:scallops concept:city:hagerstown concept:proxyfor concept:stateorprovince:maryland concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:cement concept:arthropod:centipedes concept:arthropodandotherarthropod concept:arachnid:scorpions concept:bank:ups concept:competeswith concept:bank:fed_ex concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:hydrogen_sulfide concept:mammal:animals concept:animalpreyson concept:animal:gophers concept:invertebrate:hedgehogs concept:animalpreyson concept:animal:animals001 concept:crustacean:copepods concept:animalsuchasinvertebrate concept:crustacean:shrimp concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:greenhouse_gases concept:chemical:greenhouse_gases concept:chemicalistypeofchemical concept:chemical:fuels concept:sportsteam:orioles concept:teamplaysagainstteam concept:sportsteam:cleveland_indians concept:creditunion:michigan concept:organizationhiredperson concept:athlete:rich_rodriguez concept:person:michael002 concept:agentcollaborateswithagent concept:male:world concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:cardinals_27_23 concept:chemical:water concept:chemicalistypeofchemical concept:chemical:fuels concept:invertebrate:larvae concept:animalthatfeedoninsect concept:insect:aquatic_insects concept:politician:obama concept:agentcollaborateswithagent concept:politicianus:bill_clinton concept:invertebrate:bees concept:animaleatfood concept:plant:wildflowers concept:sportsteam:wright_state_raiders concept:teamplaysinleague concept:sportsleague:ncaa concept:person:lane_kiffin concept:personbelongstoorganization concept:sportsteam:vols concept:chemical:fuels concept:chemicalistypeofchemical concept:chemical:greenhouse_gases concept:sportsteam:east_carolina concept:teamplaysagainstteam concept:sportsteam:nc_state_wolfpack_basketball concept:tradeunion:disk concept:agentcompeteswithagent concept:personcanada:search concept:insect:mosquito_larvae concept:arthropodcalledarthropod concept:crustacean:shrimp concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:buffalo_state concept:sportsteam:buccaneers concept:organizationhiredperson concept:coach:john_gruden concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:uzbekistan concept:female:hillary concept:agentcollaborateswithagent concept:politician:obama concept:arachnid:brown_recluse_spider concept:arthropodandotherarthropod concept:arthropod:spider_bite concept:country:darfur concept:countryalsoknownas concept:country:republic concept:mammal:animals concept:animalpreyson concept:animal:sugar_gliders concept:sportsgame:games concept:sportsgameteam concept:sportsteam:new_orleans_hornets concept:city:portland concept:proxyfor concept:stadiumoreventvenue:cumberland_county_civic_center concept:athlete:herman_edwards concept:worksfor concept:sportsteam:kansas_city_chiefs concept:insect:long_tongued_bees concept:animaleatfood concept:food:pollen concept:crustacean:crayfish concept:animalsuchasinsect concept:arthropod:invertebrates concept:sportsteam:bruins concept:teamplaysagainstteam concept:sportsteam:sun_devils concept:city:athens concept:cityliesonriver concept:river:hudson concept:sportsteam:mariners concept:teamplaysagainstteam concept:sportsteam:rangers concept:city:download concept:subpartoforganization concept:tradeunion:web_site concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:chicago_white_sox concept:sportsgame:games concept:sportsgameteam concept:sportsteam:florida_panthers concept:sportsteam:chowan_braves concept:teamplaysagainstteam concept:sportsteam:new_york_mets concept:university:kentucky concept:organizationhiredperson concept:athlete:rick_pitino concept:country:armenia concept:countryalsoknownas concept:country:republic concept:politician:dave_bing concept:personhasresidenceingeopoliticallocation concept:county:detroit concept:arthropod:spiders concept:arthropodcalledarthropod concept:insect:widow_spiders concept:agriculturalproduct:shrimps concept:arthropodcalledarthropod concept:arthropod:invertebrates concept:mammal:mountain_lions concept:animalpreyson concept:mammal:predators concept:sportsteam:long_island_blackbirds concept:teamplaysinleague concept:sportsleague:ncaa concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:organic_matter concept:coach:tressel concept:worksfor concept:city:buckeye concept:sportsteam:usc concept:teamplaysagainstteam concept:sportsteam:bruins concept:arthropod:invertebrates concept:animalsuchasinsect concept:insect:insects concept:stateorprovince:michigan concept:mutualproxyfor concept:city:houghton concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:_skins concept:chemical:water concept:chemicalistypeofchemical concept:chemical:plastics concept:mammal:lab_animals concept:animalistypeofanimal concept:mammal:rats concept:politicianus:bill_clinton concept:agentcollaborateswithagent concept:politician:obama concept:chemical:atoms concept:chemicalistypeofchemical concept:chemical:oxygen concept:insect:termites concept:arthropodandotherarthropod concept:insect:insects concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:organic_material concept:insect:aphids concept:animalsuchasinvertebrate concept:insect:pests concept:stadiumoreventvenue:metrodome concept:stadiumlocatedincity concept:city:minneapolis concept:animal:carnivores concept:animalpreyson concept:mammal:lions concept:arthropod:prey concept:arthropodcalledarthropod concept:crustacean:shrimp concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:company:express concept:competeswith concept:bank:priority_mail concept:chemical:oxide_emissions concept:chemicalistypeofchemical concept:chemical:sulfur concept:bank:usps_global_priority concept:competeswith concept:bank:international_priority_mail concept:stateorprovince:south_carolina concept:mutualproxyfor concept:city:charleston concept:animal:carnivores concept:animalpreyson concept:mammal:wolves concept:sportsteam:chicago_bulls concept:organizationhiredperson concept:athlete:vinny_del_negro concept:stadiumoreventvenue:n1st_mariner_arena concept:atlocation concept:city:baltimore concept:animal:animals001 concept:animalpreyson concept:invertebrate:amphibians concept:stateorprovince:michigan concept:mutualproxyfor concept:city:saginaw concept:sportsteam:north_texas_mean_green concept:teamplaysinleague concept:sportsleague:ncaa concept:mammal:leopard concept:animalistypeofanimal concept:animal:large_predators concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:camp_randall concept:mammal:warthogs concept:agentcompeteswithagent concept:mammal:animals concept:arachnid:recluse_spider concept:arthropodandotherarthropod concept:arthropod:recluse concept:sportsteam:oakland_athletics concept:teamplayssport concept:sport:baseball concept:sportsteam:creighton_bluejays concept:teamplaysinleague concept:sportsleague:ncaa concept:fish:large_fish concept:animalsuchasfish concept:fish:tuna concept:vertebrate:herds concept:ismultipleof concept:mammal:sheep concept:sportsteam:blackhawks concept:teamplaysagainstteam concept:sportsteam:red_wings concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:chemical:water concept:chemicalistypeofchemical concept:chemical:carbonic_acid concept:sportsteam:cleveland_browns concept:organizationhiredperson concept:athlete:butch_davis concept:sportsteam:tar_heels concept:teamplaysagainstteam concept:sportsteam:terps concept:person:belichick concept:personbelongstoorganization concept:sportsteam:cleveland_browns concept:sportsteam:minnesota_duluth_bulldogs concept:agentcollaborateswithagent concept:personmexico:ncaa concept:country:kuwait concept:countrylocatedingeopoliticallocation concept:country:middle concept:plant:lilies concept:plantrepresentemotion concept:emotion:devotion concept:mammal:wild_animals concept:animalistypeofanimal concept:mammal:wolves concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:belize concept:city:mount_pleasant concept:cityliesonriver concept:company:cooper concept:animal:children concept:animaldevelopdisease concept:disease:risk_factors concept:animal:roosters concept:animalpreyson concept:mammal:animals concept:arthropod:invertebrates concept:arthropodcalledarthropod concept:insect:flies concept:sportsteam:bruins concept:teamplayssport concept:sport:hockey concept:sportsgame:games concept:sportsgameteam concept:sportsteam:chowan_braves concept:sportsteam:toronto_blue_jays concept:teamplaysagainstteam concept:sportsteam:chowan_braves concept:sportsteam:oregon_state_beavers concept:organizationhiredperson concept:person:riley001 concept:sportsteam:western_kentucky concept:organizationalsoknownas concept:sportsteam:state_university concept:sportsteam:capitals concept:teamplaysagainstteam concept:sportsteam:leafs concept:arthropod:invertebrates concept:animalsuchasinvertebrate concept:agriculturalproduct:shrimps concept:fish:otters concept:animalpreyson concept:animal:animals001 concept:sportsteam:northwestern concept:organizationhiredperson concept:coach:randy_walker concept:insect:insects concept:animalpreyson concept:animal:animals001 concept:agriculturalproduct:flowers concept:agriculturalproductcamefromcountry concept:country:germany concept:mammal:bison concept:agentcompeteswithagent concept:mammal:animals concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:tennessee_state_tigers concept:sportsteam:liverpool_john_moores_university concept:subpartoforganization concept:sportsleague:fa concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:bills concept:arthropod:arthropods concept:arthropodcalledarthropod concept:arachnid:scorpions concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:memphis_tigers concept:arthropod:blood_worms concept:arthropodcalledarthropod concept:crustacean:shrimp concept:politicianus:john_engler concept:personleadsorganization concept:stateorprovince:michigan concept:person:don_shula concept:worksfor concept:sportsteam:miami_dolphins concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:air_canada_center concept:coach:carroll concept:worksfor concept:recordlabel:trojan concept:invertebrate:larvae concept:invertebratefeedonfood concept:agriculturalproduct:tree concept:mammal:alpacas concept:animalistypeofanimal concept:mammal:animals concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:kauffman_stadium concept:university:ask_jeeves concept:agentcompeteswithagent concept:personcanada:search concept:chemical:smog concept:chemicalistypeofchemical concept:chemical:pollutants concept:biotechcompany:apple concept:competeswith concept:company:yahoo001 concept:sportsteam:tampa_bay_rays concept:teamwontrophy concept:awardtrophytournament:n2008_world_series concept:mammal:animals concept:animalistypeofanimal concept:mammal:predators concept:sportsteam:dundee_united concept:teamplaysagainstteam concept:sportsteam:new_york_giants_in_super_bowl_xlii concept:insect:gnats concept:animalsuchasinsect concept:insect:insects concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:charcoal concept:animal:cheetahs concept:animalistypeofanimal concept:mammal:predators concept:vertebrate:koalas concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:packers concept:animal:social_insects concept:animalsuchasinsect concept:insect:termites concept:company:west_virginia concept:organizationhiredperson concept:coach:bob_huggins concept:mammal:animals concept:animalistypeofanimal concept:mammal:porcupines concept:animal:animals001 concept:animalsuchasinsect concept:animal:moles001 concept:chemical:carbon_monoxide_levels concept:chemicalistypeofchemical concept:chemical:carbon concept:insect:damselflies concept:animalsuchasinsect concept:insect:insects concept:sportsteam:state_university concept:organizationalsoknownas concept:sportsteam:idaho_state_bengals concept:insect:mealybugs concept:animalsuchasinsect concept:insect:pests concept:sportsgame:games concept:sportsgameteam concept:sportsteam:ucla concept:city:cookeville concept:subpartof concept:weatherphenomenon:tennessee concept:insect:recluse_spider concept:agentcompeteswithagent concept:insect:brown_recluse concept:mammal:animals concept:animalpreyson concept:mammal:mules concept:geopoliticallocation:sterling_heights concept:atlocation concept:stateorprovince:michigan concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:boise_state_broncos concept:sportsteam:mavericks concept:teamplaysagainstteam concept:sportsteam:la_clippers concept:river:mississippi_river concept:riveremptiesintoriver concept:river:red_river concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:wayne_state_warriors concept:sportsteam:capitals concept:teamhomestadium concept:stadiumoreventvenue:verizon_center concept:mammal:animals concept:animalpreyson concept:mammal:swine concept:sport:hockey concept:sportusesstadium concept:stadiumoreventvenue:mellon_arena concept:chemical:dust concept:chemicalistypeofchemical concept:chemical:emissions concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:boston_university concept:person:caldwell concept:worksfor concept:sportsteam:colts concept:sportsgame:games concept:sportsgameteam concept:sportsteam:buccaneers concept:sportsteam:boston_bruins concept:teamplaysagainstteam concept:sportsteam:leafs concept:insect:parasitic_wasps concept:arthropodandotherarthropod concept:insect:insects concept:chemical:harmful_emissions concept:chemicalistypeofchemical concept:chemical:carbon concept:mammal:gazelles concept:agentcompeteswithagent concept:mammal:animals concept:sportsteam:indiana_state_sycamores concept:teamplaysincity concept:city:terre_haute concept:food:sugars concept:foodcancausedisease concept:disease:symptoms concept:sportsleague:nba concept:leaguestadiums concept:stadiumoreventvenue:rose_garden concept:county:detroit concept:subpartof concept:creditunion:michigan concept:animal:hamsters concept:specializationof concept:mammal:animals concept:sportsteam:grand_valley_state_lakers concept:teamplaysinleague concept:sportsleague:ncaa concept:insect:arachnids concept:arthropodandotherarthropod concept:arthropod:arthropods concept:stateorprovince:tennessee concept:organizationhiredperson concept:person:lane_kiffin concept:insect:aphids concept:animalsuchasinsect concept:insect:insect_pests concept:country:democratic_republic_of_congo concept:countryalsoknownas concept:country:namibia concept:sportsteam:charleston_cougars concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:packers concept:coach:urls concept:agentcompeteswithagent concept:personcanada:search concept:insect:ants concept:animalsuchasinvertebrate concept:insect:pests concept:coach:lou_holtz concept:worksfor concept:stateorprovince:south_carolina concept:mammal:animals concept:animalistypeofanimal concept:animal:nonhuman_primates concept:sportsteam:kansas_city_chiefs concept:organizationhiredperson concept:athlete:herman_edwards concept:mammal:sloths concept:animalistypeofanimal concept:mammal:animals concept:insect:insects concept:animalsuchasinvertebrate concept:insect:larva concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:colts concept:politicianus:john_engler concept:personhasresidenceingeopoliticallocation concept:stateorprovince:michigan concept:sportsteam:man_utd concept:teamplaysagainstteam concept:sportsteam:derby_county concept:city:orleans concept:citylocatedinstate concept:stateorprovince:vermont concept:insect:pests concept:animalpreyson concept:animal:gophers concept:fish:sharks concept:animalpreyson concept:mammal:animals concept:stateorprovince:tennessee concept:mutualproxyfor concept:city:oak_ridge concept:chemical:carbonate concept:chemicalistypeofchemical concept:chemical:carbon concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:grain:buckwheat concept:bank:express_mail concept:competeswith concept:bank:ups concept:sportsteam:cleveland_indians concept:teamplayssport concept:sport:baseball concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:small_bees concept:agriculturalproduct:crops concept:agriculturalproductincludingagriculturalproduct concept:agriculturalproduct:oilseeds concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:kansas_city_chiefs concept:animal:animals001 concept:animalsuchasinvertebrate concept:invertebrate:bees concept:country:qatar concept:countryalsoknownas concept:country:republic concept:personcanada:search concept:agentcompeteswithagent concept:magazine:picture concept:sportsteam:bobcats concept:teamplaysinleague concept:sportsleague:nba concept:coach:jim_zorn concept:athletehomestadium concept:stadiumoreventvenue:fedex_field concept:country:republic concept:countryalsoknownas concept:country:slovenia concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:appalachian_state_mo concept:chemical:sulfuric_acid concept:chemicalistypeofchemical concept:chemical:hydrogen concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:penn_state concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:eagles concept:city:oxford concept:citylocatedinstate concept:stateorprovince:maine concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:hokies concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:east_tennessee_state_buccaneers concept:chemical:groundwater concept:chemicalistypeofchemical concept:chemical:pressure concept:city:bozeman concept:citylocatedinstate concept:stateorprovince:montana concept:sportsteam:maple_leafs concept:teamplaysagainstteam concept:sportsteam:rangers concept:animal:deer001 concept:animalistypeofanimal concept:arthropod:prey concept:food:oil concept:agriculturalproductincludingagriculturalproduct concept:nut:almond concept:person:kevin concept:agentcollaborateswithagent concept:male:world concept:mammal:animals concept:animalistypeofanimal concept:mammal:chimpanzees concept:city:muskegon concept:proxyfor concept:stateorprovince:michigan concept:sportsteam:east_carolina concept:teamplaysagainstteam concept:sportsteam:hokies concept:person:wilson concept:persongraduatedfromuniversity concept:university:college concept:winery:federal_express concept:competeswith concept:company:express concept:politician:obama concept:agentcollaborateswithagent concept:personnorthamerica:hillary_rodham concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:psoriasis concept:sportsteam:red_wings concept:organizationhiredperson concept:athlete:lidstrom concept:mammal:cheetah concept:mammalsuchasmammal concept:mammal:lion concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:stephen_f_austin_lumberjacks concept:city:cincinnati concept:organizationhiredperson concept:politician:mark_l__mallory concept:crustacean:mysis_shrimp concept:arthropodcalledarthropod concept:crustacean:shrimp concept:coach:vince_lombardi concept:coachesteam concept:sportsteam:packers concept:insect:pests concept:animalsuchasinsect concept:insect:mealybugs concept:sportsteam:trail_blazers concept:teamplaysagainstteam concept:sportsteam:golden_state_warriors concept:newspaper:the_wall_street concept:organizationheadquarteredincity concept:city:new_york concept:insect:raccoon concept:animalistypeofanimal concept:animal:animals001 concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:air_pollutants concept:arthropod:spiders concept:arthropodcalledarthropod concept:arachnid:scorpions concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:sulphur_dioxide concept:chemical:organic_material concept:chemicalistypeofchemical concept:chemical:carbon concept:insect:aquatic_insects concept:arthropodcalledarthropod concept:arthropod:invertebrates concept:arthropod:blood_worms concept:animalsuchasinvertebrate concept:crustacean:shrimp concept:city:download concept:subpartoforganization concept:city:internet concept:mammal:rhinoceros concept:animalpreyson concept:mammal:animals concept:company:express concept:competeswith concept:newspaper:guardian concept:mammal:animals concept:animalistypeofanimal concept:mammal:lions concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:pats concept:sportsteam:louisville_cardinals concept:teamplaysagainstteam concept:sportsteam:new_york_mets concept:sportsteam:man_utd concept:teamplaysagainstteam concept:sportsteam:chelsea_football_club concept:mammal:animals concept:animalistypeofanimal concept:mammal:alpacas concept:chemical:oxide concept:chemicalistypeofchemical concept:chemical:oxygen concept:sportsteam:utah_jazz concept:teamplaysagainstteam concept:sportsteam:timberwolves concept:mammal:wolves concept:animalpreyson concept:animal:large_predators concept:sportsgame:games concept:sportsgameteam concept:sportsteam:colts concept:insect:insects concept:animalpreyson concept:insect:mayflies concept:invertebrate:spider concept:animalistypeofanimal concept:arachnid:widow_spider concept:insect:pests concept:animalthatfeedoninsect concept:insect:midges concept:animal:animals001 concept:animalpreyson concept:insect:mosquitoes concept:sportsteam:miami_dolphins concept:teamplaysagainstteam concept:sportsteam:buffalo_bills concept:mammal:tigers concept:animalpreyson concept:mammal:predators concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:camden_yards concept:food:roots concept:agriculturalproducttoattractinsect concept:insect:white_grubs concept:sportsteam:hokies concept:teamplaysagainstteam concept:sportsteam:boston_college concept:radiostation:cleveland concept:organizationhiredperson concept:politicianus:carl_b__stokes concept:sportsteam:falcons concept:teamplaysagainstteam concept:sportsteam:carolina concept:male:world concept:agentcollaborateswithagent concept:stateorprovince:author concept:sportsteam:cleveland_indians_organization concept:teamplayssport concept:sport:baseball concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:northern_iowa_panthers concept:sportsteam:sam_houston_state_bearkats concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:cincinnati_bearcats concept:teamplaysinleague concept:sportsleague:ncaa concept:food:oils concept:agriculturalproductincludingagriculturalproduct concept:food:soy concept:sportsteam:kansas_state concept:teamplaysagainstteam concept:sportsteam:usc concept:sportsteam:saints concept:organizationhiredperson concept:coach:sean_payton concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:noxious_gases concept:chemical:water concept:chemicalistypeofchemical concept:chemical:_ concept:sportsteam:columbus_blue_jackets concept:teamplaysinleague concept:sportsleague:nhl concept:mammal:weasels concept:animalpreyson concept:mammal:animals concept:vertebrate:ruminants concept:animalistypeofanimal concept:mammal:sheep_ concept:city:somerville concept:atlocation concept:stateorprovince:massachusetts concept:mammal:equines concept:animalistypeofanimal concept:mammal:animals concept:chemical:atoms concept:chemicalistypeofchemical concept:chemical:nitrogen concept:country:bahrain concept:countrylocatedingeopoliticallocation concept:country:middle concept:mammal:wolves concept:animalpreyson concept:animal:carnivores concept:stateorprovince:wyoming concept:atlocation concept:stateorprovince:michigan concept:chemical:molecules concept:chemicalistypeofchemical concept:chemical:sulfur concept:arthropod:invertebrates concept:animalsuchasinsect concept:insect:cockroaches concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:dundee_united concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:molecules concept:chemical:sulfur concept:chemicalistypeofchemical concept:chemical:hydrogen concept:arthropod:spiders concept:arthropodandotherarthropod concept:arachnid:black_widow_spiders concept:sportsteam:cleveland_browns concept:teamplaysagainstteam concept:sportsteam:titans concept:animal:gophers concept:animalistypeofanimal concept:insect:pests concept:chemical:ozone concept:chemicalistypeofchemical concept:chemical:emissions concept:chemical:water_vapor concept:chemicalistypeofchemical concept:chemical:greenhouse_gases concept:sportsteam:seahawks concept:teamplaysagainstteam concept:sportsteam:kansas_city_chiefs concept:chemical:pressure concept:chemicalistypeofchemical concept:chemical:carbon concept:insect:armyworms concept:invertebratefeedonfood concept:nut:leaves concept:mammal:wild_animals concept:animaldevelopdisease concept:disease:rabies concept:arachnid:recluse_spider concept:arthropodandotherarthropod concept:arthropod:spider_bite concept:sportsteam:colts concept:teamplaysagainstteam concept:sportsteam:titans concept:sportsteam:elon_phoenix concept:agentcollaborateswithagent concept:personmexico:ncaa concept:person:andrew001 concept:agentcollaborateswithagent concept:male:world concept:sportsteam:uconn concept:teamplaysagainstteam concept:sportsteam:stanford concept:animal:animals001 concept:animalsuchasinsect concept:insect:mosquitoes concept:sportsteam:miami_hurricanes concept:teamplaysincity concept:city:miami concept:coach:gregg_popovich concept:coachesteam concept:sportsteam:san_antonio concept:coach:pat_fitzgerald concept:worksfor concept:sportsteam:northwestern concept:sportsteam:florida_state concept:organizationalsoknownas concept:sportsteam:state_university concept:animal:social_insects concept:animalpreyson concept:invertebrate:bees concept:sportsteam:eastern_michigan_eagles concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:italy concept:teamplayssport concept:sport:golf concept:animal:animals001 concept:animaleatfood concept:beverage:nectar concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:bills concept:city:new_york concept:mutualproxyfor concept:politicianus:rudy_giuliani concept:city:jackson concept:proxyfor concept:creditunion:michigan concept:insect:mealybugs concept:invertebratefeedonfood concept:plant:plants concept:fish:yellowfin concept:animalistypeofanimal concept:fish:tunas concept:sportsteam:rhode_island_rams concept:agentcollaborateswithagent concept:personmexico:ncaa concept:coach:jeff_jagodzinski concept:worksfor concept:sportsteam:boston_college concept:chemical:hydrogen_chloride concept:chemicalistypeofchemical concept:chemical:carbon concept:sportsteam:l_a__kings concept:teamplaysagainstteam concept:sportsteam:red_wings concept:sportsteam:rice_owls concept:agentcollaborateswithagent concept:personmexico:ncaa concept:stateorprovince:tennessee concept:organizationhiredperson concept:coach:jeff_fisher concept:city:portland concept:proxyfor concept:stadiumoreventvenue:mcmenamins_crystal_ballroom concept:insect:pests concept:animalpreyson concept:insect:house_flies concept:animal:mice001 concept:specializationof concept:insect:pests concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:san_antonio concept:governmentorganization:usps concept:agentcompeteswithagent concept:bank:global_priority concept:chemical:nitrogen_oxides concept:chemicalistypeofchemical concept:chemical:greenhouse_gases concept:sportsteam:pittsburgh_penguins concept:teamalsoknownas concept:sportsteam:flyers_playoff_tickets concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:sportsteam:utah_state concept:teamplaysinleague concept:sportsleague:ncaa concept:mammal:small_animals concept:animalistypeofanimal concept:mammal:animals concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:honeybees concept:mammal:predators concept:animalpreyson concept:fish:sharks concept:sportsteam:mariners concept:teamplaysagainstteam concept:sportsteam:orioles concept:arachnid:mites concept:arthropodandotherarthropod concept:arthropod:external_parasites concept:insect:pests concept:arthropodcalledarthropod concept:insect:thrips concept:coach:cam_cameron concept:worksfor concept:sportsleague:new concept:sportsteam:seahawks concept:teamhomestadium concept:stadiumoreventvenue:husky_stadium concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:cleveland_indians concept:crustacean:shrimp concept:arthropodcalledarthropod concept:arthropod:seafood concept:chemical:mercury concept:chemicalistypeofchemical concept:chemical:fuels concept:animal:children concept:animaldevelopdisease concept:disease:high_blood_pressure concept:animal:children concept:animaldevelopdisease concept:disease:disorder concept:chemical:greenhouses_gases concept:chemicalistypeofchemical concept:chemical:carbon concept:country:burkina concept:countrylocatedingeopoliticallocation concept:country:western_africa concept:personeurope:owen_hargreaves concept:athletehomestadium concept:stadiumoreventvenue:old_trafford concept:stadiumoreventvenue:pro_player_stadium concept:stadiumlocatedincity concept:city:miami concept:insect:small_insects concept:animalthatfeedoninsect concept:insect:ants concept:sportsteam:liverpool_university concept:teamplaysagainstteam concept:sportsteam:barcelona concept:crustacean:prawns concept:arthropodcalledarthropod concept:crustacean:crab concept:mammal:predators concept:animalthatfeedoninsect concept:insect:moths concept:sportsteam:blackhawks concept:teamplaysagainstteam concept:sportsteam:minnesota_wilds concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:montreal_canadiens concept:sportsteam:boise_state_broncos concept:agentcollaborateswithagent concept:personmexico:ncaa concept:mammal:buffalo concept:mammalsuchasmammal concept:mammal:lion concept:stateorprovince:south_carolina concept:mutualproxyfor concept:city:florence concept:coach:jeff_fisher concept:worksfor concept:sportsteam:titans concept:sportsteam:san_antonio concept:organizationhiredperson concept:coach:gregg_popovich concept:chemical:carbon concept:chemicalistypeofchemical concept:chemical:emissions concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:limestone concept:mammal:elephants concept:mammalsuchasmammal concept:mammal:tigers concept:bank:usps concept:competeswith concept:bank:global_priority concept:sportsteam:liverpool_university concept:teamplaysagainstteam concept:sportsteam:man_united concept:mammal:puppies concept:animalistypeofanimal concept:mammal:animals concept:sportsteam:florida_marlins concept:atlocation concept:county:miami concept:arthropod:spiders concept:arthropodcalledarthropod concept:arachnid:black_widows concept:sportsteam:new_mexico_state_aggies concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:mavericks concept:teamplaysagainstteam concept:sportsteam:san_antonio concept:insect:aphids concept:invertebratefeedonfood concept:plant:trees concept:mammal:squirrels concept:animalpreyson concept:insect:pests concept:sportsteam:georgia_tech concept:teamplaysagainstteam concept:sportsteam:tar_heels concept:insect:insects concept:animalthatfeedoninsect concept:insect:spider_mites concept:insect:small_insects concept:arthropodcalledarthropod concept:insect:flies concept:arthropod:invertebrates concept:arthropodcalledarthropod concept:crustacean:crayfish concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:utah_jazz concept:sport:baseball concept:sportusesstadium concept:stadiumoreventvenue:ebbets_field concept:stadiumoreventvenue:saint_andrews_hall concept:locationlocatedwithinlocation concept:city:detroit concept:agriculturalproduct:flowers concept:agriculturalproducttoattractinsect concept:insect:weevils concept:sportsteam:philadelphia_eagles concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:sportsteam:stanford concept:teamplaysagainstteam concept:sportsteam:anaheim_ducks concept:politician:obama concept:agentcollaborateswithagent concept:personnorthamerica:john_mccain concept:sportsteam:colts concept:organizationhiredperson concept:coach:jim_caldwell concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:western_michigan_university concept:insect:moths concept:animalsuchasinsect concept:insect:insects concept:sportsteam:pepperdine_waves concept:agentcollaborateswithagent concept:personmexico:ncaa concept:animal:animals001 concept:agentcompeteswithagent concept:musicartist:type concept:fish:pelagics concept:animalsuchasfish concept:fish:rays concept:chemical:hydrogen_gas concept:chemicalistypeofchemical concept:chemical:carbon concept:arachnid:recluse_spiders concept:arthropodandotherarthropod concept:arthropod:recluse concept:food:heart concept:fooddecreasestheriskofdisease concept:disease:illness concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:florida_panthers concept:animal:children concept:animaldevelopdisease concept:disease:mental_illnesses concept:chemical:soot concept:chemicalistypeofchemical concept:chemical:pollutants concept:coach:bill_belichick concept:worksfor concept:sportsteam:new_england_patriots concept:mammal:fox 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concept:chemical:carbon concept:mammal:predators concept:animalpreyson concept:mammal:rats concept:beverage:sugar concept:foodcancausedisease concept:disease:inflammation concept:chemical:chlorine concept:chemicalistypeofchemical concept:chemical:systems concept:athlete:rick_pitino concept:personbelongstoorganization concept:university:kentucky concept:insect:pests concept:arthropodcalledarthropod concept:insect:insects concept:politician:obama concept:agentcollaborateswithagent concept:politician:george_bush concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:bills concept:sportsgame:games concept:sportsgameteam concept:sportsteam:blue_jackets concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:redskins concept:chemical:greenhouse_effect concept:chemicalistypeofchemical concept:chemical:fuels concept:sportsgame:games concept:sportsgameteam concept:sportsteam:notre_dame concept:crustacean:scallops concept:arthropodcalledarthropod concept:crustacean:mollusks concept:country:republic concept:countryalsoknownas concept:country:equator__guinea concept:automobilemaker:video concept:competeswith concept:company:yahoo001 concept:insect:ladybugs concept:animalsuchasinsect concept:insect:insects concept:sportsteam:spartans concept:teamplaysagainstteam concept:sportsteam:falcons concept:insect:mealworms concept:animalthatfeedoninsect concept:insect:insects concept:animal:animals001 concept:animalpreyson concept:invertebrate:bees concept:insect:insect_pests concept:animalsuchasinsect concept:insect:aphids concept:beverage:mobile concept:mutualproxyfor concept:university:alabama concept:sportsgame:championship concept:sportsgameteam concept:sportsteam:rangers concept:musicalbum:chattanooga concept:subpartof concept:weatherphenomenon:tennessee concept:stadiumoreventvenue:rose_garden concept:stadiumlocatedincity concept:city:portland concept:sportsteam:arizona_cardinals_27_23 concept:agentcompeteswithagent concept:sportsteam:eagles concept:personcanada:search concept:agentcompeteswithagent concept:automobilemaker:catalog concept:sportsteam:raptors concept:teamplaysagainstteam concept:sportsteam:boston_celtics concept:chemical:sulphuric_acid concept:chemicalistypeofchemical concept:chemical:carbon concept:journalist:paul_sheehan concept:worksfor concept:website:sydney_morning_herald concept:sportsteam:bolton_wanderers concept:teamplaysagainstteam concept:sportsteam:new_york_red_bulls concept:country:albania concept:mutualproxyfor concept:city:tirana concept:mammal:big_cats concept:animalpreyson concept:mammal:tigers concept:personcanada:search concept:agentcompeteswithagent concept:person:button concept:arthropod:external_parasites concept:animalsuchasinsect concept:arthropod:lice concept:mammal:small_pets concept:animalistypeofanimal concept:mammal:ferrets concept:insect:gnats concept:arthropodandotherarthropod concept:insect:insects concept:coach:new_jersey_nets concept:agentbelongstoorganization concept:sportsleague:nba concept:mammal:lions concept:mammalsuchasmammal concept:mammal:rhinos concept:mammal:weasels concept:animalistypeofanimal concept:animal:carnivores concept:vertebrate:koalas concept:animalpreyson concept:mammal:animals concept:personaustralia:smith concept:worksfor concept:sportsteam:falcons concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:carbon_monoxide concept:insect:sandflies concept:arthropodandotherarthropod concept:insect:insects concept:athlete:paul_brown concept:athletehomestadium concept:stadiumoreventvenue:cleveland_browns_stadium concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:sam_houston_state_bearkats concept:arthropod:mantids concept:animalpreyson concept:insect:insects concept:crustacean:bivalves concept:animalsuchasinvertebrate concept:mollusk:oysters concept:insect:insects concept:animalthatfeedoninsect concept:insect:pests concept:personcanada:search concept:agentcompeteswithagent concept:university:message concept:animal:animals001 concept:animaldevelopdisease concept:disease:tetanus concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:insect:fruit_flies concept:arthropodandotherarthropod concept:insect:pests concept:crustacean:crab concept:arthropodcalledarthropod concept:crustacean:lobster concept:sportsteam:state_university concept:teamalsoknownas concept:sportsteam:indiana_university concept:sportsteam:phillies concept:teamplaysagainstteam concept:sportsteam:chowan_braves concept:insect:insects concept:animalsuchasinsect concept:insect:leafhoppers concept:sportsteam:bulldogs concept:teamplaysagainstteam concept:sportsteam:army_black_knights concept:chemical:hydrogen concept:chemicalistypeofchemical concept:chemical:organic_molecules concept:chemical:oxides concept:chemicalistypeofchemical concept:chemical:criteria_pollutants concept:sport:soccer concept:sportfansincountry concept:country:the_united_kingdom concept:sportsteam:liverpool_john_moores_university concept:teamplaysagainstteam concept:sportsteam:chelsea concept:company:express concept:competeswith concept:bank:international_priority_mail concept:animal:head concept:agentcontrols concept:politicianus:miller concept:bank:iowa_state concept:organizationhiredperson concept:coach:gene_chizik concept:chemical:dioxide concept:chemicalistypeofchemical concept:chemical:hydrogen concept:coach:barry_alvarez concept:worksfor concept:creditunion:wisconsin concept:sportsteam:leafs concept:teamwontrophy concept:awardtrophytournament:stanley_cup concept:mammal:animals concept:animalpreyson concept:mammal:jaguars concept:sportsteam:cavaliers concept:teamwontrophy concept:awardtrophytournament:nba_finals concept:athlete:lidstrom concept:athleteplaysforteam concept:sportsteam:red_wings concept:city:missoula concept:proxyfor concept:stateorprovince:montana concept:mammal:animals concept:animalpreyson 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concept:coach:tim_floyd concept:worksfor concept:university:usc concept:sportsteam:dallas_cowboys concept:teamplaysagainstteam concept:sportsteam:miami_dolphins concept:sportsteam:tennessee_chattanooga_mocs concept:agentcollaborateswithagent concept:personmexico:ncaa concept:website:the_san_francisco_chronicle concept:hasofficeincity concept:city:new_york concept:coach:eric_mangini concept:worksfor concept:sportsteam:new_york_jets concept:sportsteam:washington_nationals concept:teamplaysagainstteam concept:sportsteam:chowan_braves concept:sportsteam:florida_gators concept:teamplaysagainstteam concept:sportsteam:wildcats concept:sportsteam:miami_dolphins concept:teamplaysagainstteam concept:sportsteam:redskins concept:stateorprovince:oklahoma concept:mutualproxyfor concept:city:tulsa concept:university:state_university concept:synonymfor concept:county:san_francisco_state concept:sportsteam:kansas_city_chiefs concept:teamplaysagainstteam concept:sportsteam:oakland_raiders concept:city:phoenix concept:organizationhiredperson concept:coach:terry_porter concept:city:washington_d_c concept:organizationhiredperson concept:person:ty_willingham concept:politicaloffice:office concept:atdate concept:year:n1952 concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:city:abc concept:agentcollaborateswithagent concept:person:jake_tapper concept:stateorprovince:pa concept:statehascapital concept:city:philadelphia concept:sportsteam:redskins concept:teamplaysagainstteam concept:sportsteam:miami_dolphins concept:sportsgame:finals concept:sportsgameteam concept:sportsteam:lee_flames concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:chicago_white_sox concept:sportsteam:columbus_blue_jackets concept:teamplaysagainstteam concept:sportsteam:red_wings concept:sportsteam:buccaneers concept:teamhomestadium concept:stadiumoreventvenue:raymond_james_stadium concept:organization:tigers concept:organizationhiredperson concept:coach:gary_pinkel concept:sportsteam:new_york_islanders concept:organizationhiredperson concept:coach:ted_nolan concept:geopoliticallocation:national concept:atlocation concept:city:bell_gardens concept:sportsteam:suns concept:teamplaysagainstteam concept:sportsteam:dallas_mavericks concept:university:state_university concept:synonymfor concept:geopoliticallocation:carnegie_mellon concept:county:brooklyn concept:agentparticipatedinevent concept:sportsgame:series concept:sportsteam:virginia_tech_hokies concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:charlotte_bobcats concept:company:bbc concept:companyeconomicsector concept:academicfield:news concept:sportsteam:oakland_raiders concept:teamplaysagainstteam concept:sportsteam:bills concept:sociopolitical:independence concept:subpartof concept:company:missouri concept:sportsteam:los_angeles_angels_of_anaheim concept:teamplaysagainstteam concept:sportsteam:red_sox_this_season concept:sportsteam:wildcats concept:teamplaysagainstteam concept:sportsteam:pirates concept:sportsteam:florida_state concept:teamplaysagainstteam concept:sportsteam:hokies concept:sportsteam:new_york_jets concept:teamplaysagainstteam concept:sportsteam:oilers concept:sportsteam:ravens concept:teamplaysagainstteam concept:sportsteam:steelers concept:musicartist:american concept:agentactsinlocation concept:city:irvington concept:sportsteam:penn_state concept:teamplaysagainstteam concept:sportsteam:hawkeyes concept:sportsteam:broncos concept:agentcontrols concept:sportsleague:nfl concept:sportsteam:chowan_braves concept:teamplaysagainstteam concept:sportsteam:toronto_blue_jays concept:politicaloffice:new concept:mutualproxyfor concept:politicsbill:state_law concept:company:missouri concept:mutualproxyfor concept:sociopolitical:independence concept:sportsteam:maple_leafs concept:teamplaysagainstteam concept:sportsteam:capitals concept:sportsteam:tampa concept:teamplaysagainstteam concept:sportsteam:new_york_mets concept:musicartist:american concept:agentactsinlocation concept:city:clinton concept:sportsteam:hawks concept:organizationhiredperson concept:coach:mike_woodson concept:person:mccain concept:agentcollaborateswithagent concept:politicianus:mitt_romney concept:retailstore:burlington concept:proxyfor concept:beach:vermont concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:broncos concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:new_orleans_saints concept:sportsteam:tar_heels concept:organizationhiredperson concept:athlete:roy_williams concept:company:ryanair001 concept:organizationhiredperson concept:person:john_o_leary concept:sportsteam:grand_valley_state_lakers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:city:columbus concept:cityliesonriver concept:river:yellowstone concept:sportsteam:dallas_cowboys concept:teamplaysagainstteam concept:sportsteam:eagles concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:hokies concept:musicartist:american concept:atlocation concept:island:san_antonio concept:sportsteam:astros concept:teamplaysagainstteam concept:sportsteam:white_sox concept:sportsteam:boston_celtics concept:teamplaysagainstteam concept:sportsteam:hawks concept:sportsteam:carolina concept:teamplaysagainstteam concept:sportsteam:buccaneers concept:jobposition:agency concept:subpartof concept:weatherphenomenon:water concept:sportsteam:st___louis_cardinals concept:teamplaysagainstteam concept:sportsteam:phillies concept:university:ohio concept:organizationhiredperson concept:coach:frank_solich concept:company:los_angeles concept:agentcompeteswithagent concept:male:sun concept:school:johns_hopkins concept:organizationalsoknownas concept:university:state_university concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:eagles concept:sportsleague:nfl concept:leaguestadiums concept:stadiumoreventvenue:pro_player_stadium concept:sportsteam:spurs concept:teamplaysagainstteam concept:sportsteam:chelsea concept:sportsteam:trail_blazers concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:geometricshape:property concept:atdate concept:year:n1994 concept:sportsteam:new_york_giants concept:agentcompeteswithagent concept:personnorthamerica:bills concept:musicartist:american concept:agentactsinlocation concept:street:azusa concept:musicartist:delaware concept:atdate concept:date:n1996 concept:sportsteam:leafs concept:organizationhiredperson concept:coach:al_arbour concept:city:charlotte concept:proxyfor concept:stadiumoreventvenue:time_warner_cable_arena concept:sportsteam:buffalo_sabres concept:teamplaysagainstteam concept:sportsteam:philadelphia_flyers concept:sportsteam:ravens concept:teamplaysagainstteam concept:sportsteam:pats concept:musicartist:american concept:atlocation concept:hotel:salisbury concept:city:phoenix concept:atdate concept:date:n2009 concept:coach:frank_beamer concept:coachesteam concept:sportsteam:hokies concept:sportsgame:series concept:sportsgameteam concept:sportsteam:red_sox_this_season concept:sportsleague:nfl concept:agentcollaborateswithagent concept:sportsteam:arizona_cardinals_27_23 concept:sportsteam:usc concept:teamplaysagainstteam concept:sportsteam:kansas_state concept:sportsteam:trail_blazers concept:teamplaysagainstteam concept:sportsteam:cavaliers concept:sportsteam:iowa_state concept:organizationhiredperson concept:coach:larry_eustachy concept:terroristorganization:state concept:agentcontrols concept:buildingfeature:gear concept:stateorprovince:auburn concept:subpartof concept:city:washington_d_c concept:politicaloffice:office concept:atdate concept:year:n1994 concept:sportsteam:titans concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsteam:los_angeles_dodgers concept:teamplaysagainstteam concept:sportsteam:padres concept:geometricshape:activities concept:atdate concept:date:n1999 concept:sportsteam:packers concept:teamplaysagainstteam concept:sportsteam:broncos concept:nongovorganization:u_s_ concept:proxyfor concept:emotion:new concept:sportsteam:yankees concept:teamplaysagainstteam concept:sportsteam:royals concept:sportsteam:pirates concept:organizationhiredperson concept:coach:skip_holtz concept:terroristorganization:state concept:agentcontrols concept:politicsissue:processes concept:sportsteam:new_york_mets concept:agentcollaborateswithagent concept:personus:david_wright concept:sportsleague:new concept:organizationhiredperson concept:coach:josh_mcdaniels concept:politician:jobs concept:agentcontrols concept:website:amazon concept:sportsteam:oakland_athletics concept:teamplaysagainstteam concept:sportsteam:twins concept:sportsteam:raptors concept:teamplaysagainstteam concept:sportsteam:boston_celtics concept:musicartist:american concept:atlocation concept:highway:gardena concept:sportsteam:red_wings concept:teamplaysagainstteam concept:sportsteam:flyers_playoff_tickets concept:winery:mansfield concept:proxyfor concept:university:ohio concept:musicartist:american concept:agentactsinlocation concept:county:lafayette concept:sportsteam:anaheim_ducks concept:teamplaysagainstteam concept:sportsteam:ottawa_senators concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:seahawks concept:company:new_york concept:competeswith concept:website:the_houston_chronicle concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:white_sox concept:university:utah_state concept:synonymfor concept:university:state_university concept:company:new_york concept:agentcollaborateswithagent concept:personus:joe_dimaggio concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:buffalo_sabres concept:company:los_angeles concept:agentparticipatedinevent concept:sportsgame:series concept:athlete:herman_edwards concept:worksfor concept:sportsteam:kansas_city_chiefs concept:sportsleague:nfl concept:organizationhiredperson concept:coach:bill_walsh concept:sportsteam:raptors concept:teamplaysagainstteam concept:sportsteam:washington_wizards concept:sportsteam:kings_college concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:sportsteam:los_angeles_lakers concept:organizationhiredperson concept:person:riley001 concept:politicianus:president_bush concept:personchargedwithcrime concept:crimeorcharge:crimes concept:sportsleague:ncaa concept:leaguestadiums concept:stadiumoreventvenue:tiger_stadium concept:sportsteam:tampa_bay_rays concept:teamplaysagainstteam concept:sportsteam:yankees concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:jaguars concept:sportsteam:d_backs concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsteam:new_england_patriots concept:organizationhiredperson concept:coach:carroll concept:sportsteam:spurs concept:teamplaysagainstteam concept:sportsteam:heat concept:coach:nick_saban concept:worksfor concept:sportsteam:michigan_state concept:sportsteam:cavaliers concept:teamplaysagainstteam concept:sportsteam:indiana_pacers concept:university:unc concept:organizationhiredperson concept:coach:dean_smith concept:sportsteam:arizona_diamond_backs concept:teamhomestadium concept:stadiumoreventvenue:chase_field concept:sportsteam:broncos concept:teamplaysagainstteam concept:sportsteam:ravens concept:university:state_university concept:synonymfor concept:university:oregon_st concept:sportsteam:titans concept:teamplaysagainstteam concept:sportsteam:steelers concept:sportsteam:steelers concept:organizationhiredperson concept:coach:ken_whisenhunt concept:sportsteam:atlanta_braves concept:teamplaysagainstteam concept:sportsteam:red_sox concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:titans concept:sportsteam:magic concept:organizationhiredperson concept:coach:van_gundy concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:texas_rangers concept:musicartist:american concept:agentparticipatedinevent concept:eventoutcome:result concept:website:the_washington_times concept:hasofficeincity concept:city:new_york concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:washington_senators concept:sportsteam:minnesota_vikings concept:teamalsoknownas concept:sportsteam:falcons concept:sportsteam:new_orleans_saints concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsteam:florida_gators concept:organizationhiredperson concept:coach:steve_spurrier concept:sportsteam:knicks concept:teamplaysagainstteam concept:sportsteam:washington_wizards concept:university:national concept:agentactsinlocation concept:retailstore:albertson concept:newspaper:la_times concept:newspaperincity concept:county:los_angeles_county concept:sportsteam:toronto_blue_jays concept:teamplaysagainstteam concept:sportsteam:red_sox_this_season concept:sportsteam:phillies concept:teamplaysagainstteam concept:sportsteam:houston_astros concept:sportsteam:arkansas_state_red_wolves concept:agentcollaborateswithagent concept:personmexico:ncaa concept:company:new_york concept:agentcompeteswithagent concept:musicartist:times concept:geopoliticallocation:national concept:atlocation concept:city:clementon concept:geopoliticallocation:national concept:atlocation concept:city:la_habra concept:sportsteam:bengals concept:teamplaysagainstteam concept:sportsteam:redskins concept:sportsteam:northern_illinois_huskies concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:cleveland_indians_organization concept:teamplaysagainstteam concept:sportsteam:former_san_francisco_giants concept:company:oracle concept:competeswith concept:company:ibm concept:personmexico:ncaa concept:agentcontrols concept:sportsteam:loyola_marymount_lions concept:sportsteam:buffalo_sabres concept:teamplaysagainstteam concept:sportsteam:capitals concept:athlete:vernon_wells concept:athleteplaysinleague concept:sportsleague:mlb concept:terroristorganization:state concept:agentcontrols concept:politicsissue:requirements concept:person:mccain concept:agentcollaborateswithagent concept:politicianus:hillary_clinton concept:sportsteam:atlanta_braves concept:teamplaysagainstteam concept:sportsteam:astros concept:musicartist:american concept:agentactsinlocation concept:city:cranford concept:sportsgame:series concept:sportsgameteam concept:sportsteam:canucks concept:sportsteam:pirates concept:teamplaysagainstteam concept:sportsteam:louisville_cardinals concept:sportsteam:steelers concept:teamplaysagainstteam concept:sportsteam:texans concept:sportsteam:bucks concept:teamplaysagainstteam concept:sportsteam:cavaliers concept:sportsteam:magic concept:teamplaysagainstteam concept:sportsteam:chicago_bulls concept:newspaper:the_international_herald_tribune concept:hasofficeincity concept:city:new_york concept:city:washington_d_c concept:organizationhiredperson concept:person:willingham concept:city:washington_d_c concept:mutualproxyfor concept:county:olympia concept:sportsteam:chicago_bulls concept:teamplaysagainstteam concept:sportsteam:sacramento_kings concept:athlete:lidstrom concept:worksfor concept:sportsteam:red_wings concept:sportsteam:yankees concept:teamplaysagainstteam concept:sportsteam:detroit_tigers concept:county:edison concept:subpartof concept:radiostation:new_jersey concept:sportsteam:boston_celtics concept:teamplaysagainstteam concept:sportsteam:heat concept:biotechcompany:apple concept:agentcollaborateswithagent concept:personus:jobs concept:sportsteam:montreal_canadiens concept:teamplaysagainstteam concept:sportsteam:maple_leafs concept:stadiumoreventvenue:ford_field concept:stadiumlocatedincity concept:city:detroit concept:coach:linehan concept:coachesinleague concept:sportsleague:nfl concept:coach:skip_holtz concept:worksfor concept:sportsteam:pirates concept:musicartist:american concept:agentactsinlocation concept:geopoliticallocation:grand_junction concept:sportsteam:bills concept:teamplaysagainstteam concept:sportsteam:pats concept:university:google concept:agentcompeteswithagent concept:website:amazon concept:coach:cowher concept:coachesteam concept:sportsteam:steelers concept:politician:jobs concept:agentcontrols concept:retailstore:apple_computers concept:building:norwich concept:buildinglocatedincity concept:city:norwich concept:sportsteam:ottawa_senators concept:teamplaysagainstteam concept:sportsteam:anaheim_ducks concept:actor:scott_mcnealy concept:worksfor concept:blog:sun concept:programminglanguage:state concept:subpartof concept:weatherphenomenon:air concept:sportsteam:dallas_cowboys concept:teamplaysagainstteam concept:sportsteam:titans concept:company:north_carolina concept:subpartof concept:sportsteam:ncaa_mens_midwest_regionals concept:university:state_university concept:synonymfor concept:geopoliticallocation:arizona_state concept:sportsteam:padres concept:teamplaysagainstteam concept:sportsteam:yankees concept:sportsteam:oakland_raiders concept:teamplaysagainstteam concept:sportsteam:minnesota_vikings concept:sportsteam:bobcats concept:teamplaysagainstteam concept:sportsteam:washington_wizards concept:sportsleague:new concept:organizationhiredperson concept:person:anderson concept:sportsteam:ravens concept:teamplaysagainstteam concept:sportsteam:broncos concept:sportsteam:byu_hawaii concept:teamalsoknownas concept:sportsteam:state_university concept:sportsteam:red_sox concept:teamplaysagainstteam concept:sportsteam:minnesota_twins concept:sportsteam:bills concept:teamplaysagainstteam concept:sportsteam:redskins concept:coach:green_bay_packers concept:subpartof concept:sportsleague:nfl concept:company:arizona concept:companyeconomicsector concept:economicsector:insurance_coverage concept:musicartist:american concept:agentactsinlocation concept:company:national_city concept:county:florence concept:subpartof concept:newspaper:alabama concept:coach:randy_walker concept:coachesteam concept:sportsteam:northwestern concept:sportsteam:white_sox concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:sportsteam:bills concept:teamplaysagainstteam concept:sportsteam:oilers concept:sportsteam:new_york_jets concept:teamplaysagainstteam concept:sportsteam:new_york_giants concept:athlete:mike_smith concept:athletehomestadium concept:stadiumoreventvenue:pete_times_forum concept:sportsleague:nhl concept:leaguestadiums concept:stadiumoreventvenue:mellon_arena concept:sportsteam:esu_hornets concept:teamplaysagainstteam concept:sportsteam:los_angeles_lakers concept:musicartist:american concept:agentactsinlocation concept:city:phoenix concept:county:penn_state concept:organizationhiredperson concept:coach:joe_paterno concept:politician:clinton concept:agentcollaborateswithagent concept:politicianus:barack concept:geopoliticallocation:yale concept:synonymfor concept:university:state_university concept:politicianus:bush concept:personchargedwithcrime concept:crimeorcharge:treason concept:coach:jeff_fisher concept:worksfor concept:sportsteam:titans concept:university:state_university concept:synonymfor concept:bank:iowa_state concept:sportsteam:tampa_bay_devil_rays concept:teamplaysagainstteam concept:sportsteam:dodgers concept:university:national concept:agentactsinlocation concept:building:nation concept:highway:lakewood concept:mutualproxyfor concept:radiostation:new_jersey concept:athlete:john_brady concept:worksfor concept:sportsteam:lsu concept:sportsteam:minnesota_vikings concept:teamplaysagainstteam concept:sportsteam:sd_chargers concept:sportsteam:kansas_city_chiefs concept:organizationhiredperson concept:coach:todd_haley concept:sportsteam:new_york_giants concept:teamplaysagainstteam concept:sportsteam:dodgers concept:sportsgame:finals concept:sportsgameteam concept:sportsteam:boston_celtics concept:sportsteam:bills concept:teamplaysagainstteam concept:sportsteam:cleveland_browns concept:sportsteam:marlins concept:teamplaysagainstteam concept:sportsteam:astros concept:coach:cam_cameron concept:coachesteam concept:sportsteam:miami_dolphins concept:sportsgame:series concept:sportsgameteam concept:sportsteam:astros concept:sportsteam:penn_state concept:teamplaysagainstteam concept:sportsteam:usc concept:sportsteam:philadelphia_phillies concept:teamalsoknownas concept:sportsteam:chowan_braves concept:sportsteam:royals concept:teamplaysagainstteam concept:sportsteam:yankees concept:sportsteam:los_angeles_lakers concept:teamplaysagainstteam concept:sportsteam:bucks concept:website:amazon concept:competeswith concept:website:facebook concept:profession:division concept:subpartof concept:weatherphenomenon:water concept:sportsteam:maryland concept:agentcollaborateswithagent concept:personmexico:ncaa concept:politician:jobs concept:personbelongstoorganization concept:sportsteam:state_university concept:sportsteam:dallas_mavericks concept:teamplaysagainstteam concept:sportsteam:golden_state_warriors concept:sportsteam:eastern_illinois_panthers concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:twins concept:teamplaysagainstteam concept:sportsteam:rangers concept:sportsteam:oakland_raiders concept:teamplaysagainstteam concept:sportsteam:new_york_jets concept:sportsteam:eagles concept:teamplaysagainstteam concept:sportsteam:new_orleans_saints concept:sportsteam:rockets concept:teamplaysagainstteam concept:sportsteam:mavs concept:personmexico:texas_rangers concept:agentparticipatedinevent concept:sportsgame:series concept:sportsteam:wildcats concept:teamplaysagainstteam concept:sportsteam:florida_gators concept:person:macintosh concept:atdate concept:year:n1984 concept:radiostation:new_jersey concept:mutualproxyfor concept:hotel:somerset concept:politicsblog:journal concept:competeswith concept:company:fortune001 concept:personus:jason_giambi concept:personbelongstoorganization concept:sportsteam:yankees concept:magazine:charlotte concept:subpartof concept:company:north_carolina concept:sportsteam:buffalo_state concept:agentcollaborateswithagent concept:personmexico:ncaa concept:sportsteam:reds concept:teamplaysagainstteam concept:sportsteam:phillies concept:sportsleague:nfl concept:agentcontrols concept:coach:new_york_jets concept:clothing:positions concept:atdate concept:date:n2004 concept:sportsteam:saints concept:teamplaysagainstteam concept:sportsteam:texans concept:sportsteam:texans concept:teamplaysagainstteam concept:sportsteam:new_york_jets concept:sportsteam:rangers concept:teamplaysagainstteam concept:sportsteam:buffalo_sabres concept:sportsteam:orioles concept:teamplaysagainstteam concept:sportsteam:pirates concept:profession:camden concept:proxyfor concept:radiostation:new_jersey concept:sportsteam:nuggets concept:teamplaysagainstteam concept:sportsteam:hawks concept:city:houston concept:citylocatedinstate concept:stateorprovince:tx concept:sportsteam:red_sox_this_season concept:teamplaysagainstteam concept:sportsteam:louisville_cardinals concept:politicianus:stephen_goldsmith concept:atlocation concept:city:indianapolis concept:coach:chris_quinn concept:coachesteam concept:sportsteam:heat concept:politicsissue:nebraska concept:atdate concept:date:n2009 concept:sportsgame:finals concept:sportsgameteam concept:sportsteam:bruins concept:sportsleague:nfl concept:leaguestadiums concept:stadiumoreventvenue:sun_devil_stadium ================================================ FILE: inductive_lp/datasets/ind_dataset.py ================================================ from pykeen.datasets import PathDataSet, TriplesFactory, DataSet class InductiveDataset(DataSet): def __init__(self, transductive: str, inductive: str, create_inverse_triples: bool = True,): self.cache_root = "./data/" self.transductive_part = TriplesFactory( path=self.cache_root + transductive + "/train.txt", create_inverse_triples=create_inverse_triples ) self.inductive_part = inductive self.inductive_inference = TriplesFactory( path=self.cache_root + inductive + "/train.txt", relation_to_id=self.transductive_part.relation_to_id, create_inverse_triples=create_inverse_triples ) self.inductive_val = TriplesFactory( path=self.cache_root + inductive + "/valid.txt", entity_to_id=self.inductive_inference.entity_to_id, relation_to_id=self.transductive_part.relation_to_id ) self.inductive_test = TriplesFactory( path=self.cache_root + inductive + "/test.txt", entity_to_id=self.inductive_inference.entity_to_id, relation_to_id=self.transductive_part.relation_to_id ) class Ind_FB15k237(InductiveDataset): def __init__(self, version: int = 1, create_inverse_triples: bool = True,): super().__init__(transductive=f"fb237_v{version}", inductive=f"fb237_v{version}_ind", create_inverse_triples=create_inverse_triples) class Ind_WN18RR(InductiveDataset): def __init__(self, version: int = 1, create_inverse_triples: bool = True, ): super().__init__(transductive=f"WN18RR_v{version}", inductive=f"WN18RR_v{version}_ind", create_inverse_triples=create_inverse_triples) class Ind_NELL(InductiveDataset): def __init__(self, version: int = 1, create_inverse_triples: bool = True, ): super().__init__(transductive=f"nell_v{version}", inductive=f"nell_v{version}_ind", create_inverse_triples=create_inverse_triples) ================================================ FILE: inductive_lp/loops/ilp_evaluator.py ================================================ import torch from torch import nn from typing import Optional, Iterable, Union, Dict, Tuple, List from collections import defaultdict from pykeen.evaluation import RankBasedEvaluator from pykeen.evaluation.rank_based_evaluator import compute_rank_from_scores class ILPRankBasedEvaluator(RankBasedEvaluator): def __init__( self, ks: Optional[Iterable[Union[int, float]]] = None, filtered: bool = True, head_samples: torch.Tensor = None, # shape: [num_valid_triples, n] tail_samples: torch.Tensor = None, # shape: [num_valid_triples, n] ): """Initialize rank-based evaluator. :param ks: The values for which to calculate hits@k. Defaults to {1,3,5,10}. :param filtered: Whether to use the filtered evaluation protocol. If enabled, ranking another true triple higher than the currently considered one will not decrease the score. """ super().__init__(filtered=filtered) self.ks = tuple(ks) if ks is not None else (1, 3, 5, 10) for k in self.ks: if isinstance(k, float) and not (0 < k < 1): raise ValueError( 'If k is a float, it should represent a relative rank, i.e. a value between 0 and 1 (excl.)', ) self.ranks: Dict[Tuple[str, str], List[float]] = defaultdict(list) self.num_entities = None self.head_samples = head_samples self.tail_samples = tail_samples def _update_ranks_( self, true_scores: torch.FloatTensor, all_scores: torch.FloatTensor, side: str, ) -> None: """Shared code for updating the stored ranks for head/tail scores. :param true_scores: shape: (batch_size,) :param all_scores: shape: (batch_size, num_entities) """ sampled_entities = self.head_samples if side == "head" else self.tail_samples batch_ranks = compute_rank_from_scores( true_score=true_scores, all_scores=all_scores.gather(1, sampled_entities.to(all_scores.device)), ) self.num_entities = all_scores.shape[1] for k, v in batch_ranks.items(): self.ranks[side, k].extend(v.detach().cpu().tolist()) ================================================ FILE: inductive_lp/loops/inductive_slcwa.py ================================================ # -*- coding: utf-8 -*- """Training KGE models based on the sLCWA.""" import logging from typing import Any, Mapping, Optional, Type import torch from torch.optim.optimizer import Optimizer from pykeen.training.training_loop import TrainingLoop from pykeen.training.utils import apply_label_smoothing from pykeen.losses import CrossEntropyLoss from pykeen.models.base import Model from pykeen.sampling import BasicNegativeSampler, NegativeSampler from pykeen.triples import SLCWAInstances from pykeen.typing import MappedTriples from pykeen.datasets import TriplesFactory __all__ = [ 'InductiveSLCWATrainingLoop', ] logger = logging.getLogger(__name__) class InductiveSLCWATrainingLoop(TrainingLoop): """A training loop that uses the stochastic local closed world assumption training approach.""" negative_sampler: NegativeSampler loss_blacklist = [CrossEntropyLoss] def __init__( self, model: Model, optimizer: Optional[Optimizer] = None, negative_sampler_cls: Optional[Type[NegativeSampler]] = None, negative_sampler_kwargs: Optional[Mapping[str, Any]] = None, ): """Initialize the training loop. :param model: The model to train :param optimizer: The optimizer to use while training the model :param negative_sampler_cls: The class of the negative sampler :param negative_sampler_kwargs: Keyword arguments to pass to the negative sampler class on instantiation for every positive one """ super().__init__( model=model, optimizer=optimizer, ) if negative_sampler_cls is None: negative_sampler_cls = BasicNegativeSampler self.negative_sampler = negative_sampler_cls( triples_factory=self.triples_factory, **(negative_sampler_kwargs or {}), ) @property def triples_factory(self) -> TriplesFactory: # noqa: D401 """The triples factory in the model.""" return self.model.transductive_triples_factory @property def num_negs_per_pos(self) -> int: """Return number of negatives per positive from the sampler. Property for API compatibility """ return self.negative_sampler.num_negs_per_pos def _create_instances(self, use_tqdm: Optional[bool] = None) -> SLCWAInstances: # noqa: D102 return self.triples_factory.create_slcwa_instances() @staticmethod def _get_batch_size(batch: MappedTriples) -> int: # noqa: D102 return batch.shape[0] def _process_batch( self, batch: MappedTriples, start: int, stop: int, label_smoothing: float = 0.0, slice_size: Optional[int] = None, ) -> torch.FloatTensor: # noqa: D102 # Slicing is not possible in sLCWA training loops if slice_size is not None: raise AttributeError('Slicing is not possible for sLCWA training loops.') # Send positive batch to device positive_batch = batch[start:stop].to(device=self.device) # Create negative samples neg_samples = self.negative_sampler.sample(positive_batch=positive_batch) # Ensure they reside on the device (should hold already for most simple negative samplers, e.g. # BasicNegativeSampler, BernoulliNegativeSampler negative_batch = neg_samples.to(self.device) # Make it negative batch broadcastable (required for num_negs_per_pos > 1). negative_batch = negative_batch.view(-1, 3) # Compute negative and positive scores positive_scores = self.model.score_hrt(positive_batch) negative_scores = self.model.score_hrt(negative_batch) loss = self._loss_helper( positive_scores, negative_scores, label_smoothing, ) return loss def _mr_loss_helper( self, positive_scores: torch.FloatTensor, negative_scores: torch.FloatTensor, _label_smoothing=None, ) -> torch.FloatTensor: # Repeat positives scores (necessary for more than one negative per positive) if self.num_negs_per_pos > 1: positive_scores = positive_scores.repeat(self.num_negs_per_pos, 1) return self.model.compute_mr_loss( positive_scores=positive_scores, negative_scores=negative_scores, ) def _self_adversarial_negative_sampling_loss_helper( self, positive_scores: torch.FloatTensor, negative_scores: torch.FloatTensor, _label_smoothing=None, ) -> torch.FloatTensor: """Compute self adversarial negative sampling loss.""" return self.model.compute_self_adversarial_negative_sampling_loss( positive_scores=positive_scores, negative_scores=negative_scores, ) def _label_loss_helper( self, positive_scores: torch.FloatTensor, negative_scores: torch.FloatTensor, label_smoothing: float, ) -> torch.FloatTensor: # Stack predictions predictions = torch.cat([positive_scores, negative_scores], dim=0) # Create target ones = torch.ones_like(positive_scores, device=self.device) zeros = torch.zeros_like(negative_scores, device=self.device) labels = torch.cat([ones, zeros], dim=0) if label_smoothing > 0.: labels = apply_label_smoothing( labels=labels, epsilon=label_smoothing, num_classes=self.model.num_entities, ) # Normalize the loss to have the average loss per positive triple # This allows comparability of sLCWA and LCWA losses return self.model.compute_label_loss( predictions=predictions, labels=labels, ) def _slice_size_search( self, batch_size: int, sub_batch_size: int, supports_sub_batching: bool, ) -> None: # noqa: D102 # Slicing is not possible for sLCWA if supports_sub_batching: report = "This model supports sub-batching, but it also requires slicing, which is not possible for sLCWA" else: report = "This model doesn't support sub-batching and slicing is not possible for sLCWA" logger.warning(report) raise MemoryError("The current model can't be trained on this hardware with these parameters.") ================================================ FILE: inductive_lp/loops/relation_rank_evaluator.py ================================================ from pykeen.evaluation import Evaluator, MetricResults, RankBasedEvaluator from pykeen.models.base import Model, MappedTriples from pykeen.evaluation.evaluator import create_dense_positive_mask_, filter_scores_, timeit, split_list_in_batches_iter, tqdm, optional_context_manager, logger from pykeen.evaluation.rank_based_evaluator import pd, RankBasedMetricResults, dataclass, field, dataclass_json, fix_dataclass_init_docs, compute_rank_from_scores, RANK_TYPES, RANK_BEST, RANK_WORST, RANK_AVERAGE, RANK_AVERAGE_ADJUSTED from collections import defaultdict import numpy as np from typing import Any, Collection, List, Mapping, Optional, Tuple, Union, Dict import torch import pdb @fix_dataclass_init_docs @dataclass_json @dataclass class RelationPredictionRankBasedMetricResults(RankBasedMetricResults): #: The mean over all ranks: mean_i r_i. Lower is better. mean_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all ranks: mean_i r_i. Lower is better.', )) #: The mean over all reciprocal ranks: mean_i (1/r_i). Higher is better. mean_reciprocal_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all reciprocal ranks: mean_i (1/r_i). Higher is better.', )) #: The hits at k for different values of k, i.e. the relative frequency of ranks not larger than k. #: Higher is better. hits_at_k: Dict[str, Dict[int, float]] = field(metadata=dict( doc='The hits at k for different values of k, i.e. the relative frequency of ranks not larger than k.' ' Higher is better.', )) #: The mean over all chance-adjusted ranks: mean_i (2r_i / (num_entities+1)). Lower is better. #: Described by [berrendorf2020]_. adjusted_mean_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all chance-adjusted ranks: mean_i (2r_i / (num_entities+1)). Lower is better.', )) def get_metric(self, name: str) -> float: # noqa: D102 dot_count = name.count('.') if 0 == dot_count: # assume average by default rank_type, metric = 'avg', name elif 1 == dot_count: rank_type, metric = name.split('.') else: raise ValueError(f'Malformed metric name: {name}') if rank_type not in RANK_AVERAGE and metric in {'adjusted_mean_rank'}: raise ValueError(f'Invalid rank type for adjusted mean rank: {rank_type}. Allowed type: {RANK_AVERAGE}') elif rank_type not in RANK_TYPES: raise ValueError(f'Invalid rank type: {rank_type}. Allowed types: {RANK_TYPES}') if metric in {'mean_rank', 'mean_reciprocal_rank'}: return getattr(self, metric)[rank_type] elif metric in {'adjusted_mean_rank'}: return getattr(self, metric) rank_type_hits_at_k = self.hits_at_k[rank_type] for prefix in ('hits_at_', 'hits@'): if not metric.startswith(prefix): continue k = metric[len(prefix):] k = 10 if k == 'k' else int(k) return rank_type_hits_at_k[k] raise ValueError(f'Invalid metric name: {name}') def to_flat_dict(self): # noqa: D102 r = {'avg.adjusted_mean_rank': self.adjusted_mean_rank} for rank_type in RANK_TYPES: r[f'{rank_type}.mean_rank'] = self.mean_rank[rank_type] r[f'{rank_type}.mean_reciprocal_rank'] = self.mean_reciprocal_rank[rank_type] for k, v in self.hits_at_k[rank_type].items(): r[f'{rank_type}.hits_at_{k}'] = v return r def to_df(self): """Output the metrics as a pandas dataframe.""" rows = [ ('avg', 'adjusted_mean_rank', self.adjusted_mean_rank) ] for rank_type in RANK_TYPES: rows.append((rank_type, 'mean_rank', self.mean_rank[rank_type])) rows.append((rank_type, 'mean_reciprocal_rank', self.mean_reciprocal_rank[rank_type])) for k, v in self.hits_at_k[rank_type].items(): rows.append((rank_type, f'hits_at_{k}', v)) return pd.DataFrame(rows, columns=['Type', 'Metric', 'Value']) class RelationPredictionRankBasedEvaluator(Evaluator): def __init__( self, ks = None, filtered: bool = True, ): super().__init__(filtered=filtered) self.ks = tuple(ks) if ks is not None else (1, 3, 5, 10) for k in self.ks: if isinstance(k, float) and not (0 < k < 1): raise ValueError( 'If k is a float, it should represent a relative rank, i.e. a value between 0 and 1 (excl.)', ) self.ranks = defaultdict(list) self.num_relations = None def process_tail_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: pass def process_head_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: pass def _update_ranks_(self, true_scores, all_scores): batch_ranks = compute_rank_from_scores( true_score=true_scores, all_scores=all_scores, ) self.num_relations = all_scores.shape[1] for k, v in batch_ranks.items(): self.ranks[k].extend(v.detach().cpu().tolist()) def evaluate( self, model, mapped_triples = None, batch_size = None, slice_size = None, device = None, use_tqdm = True, tqdm_kwargs = None, restrict_entities_to = None, do_time_consuming_checks = True, additional_filter_triples=None, ): if mapped_triples is None: mapped_triples = model.triples_factory.mapped_triples if batch_size is None and model.automatic_memory_optimization: batch_size, slice_size = self.batch_and_slice( model=model, mapped_triples=mapped_triples, batch_size=batch_size, device=device, use_tqdm=False, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) # The batch_size and slice_size should be accessible to outside objects for re-use, e.g. early stoppers. self.batch_size = batch_size self.slice_size = slice_size # Clear the ranks from the current evaluator self.finalize() return evaluate( model=model, mapped_triples=mapped_triples, additional_filtered_triples=additional_filter_triples, evaluators=self, batch_size=batch_size, slice_size=slice_size, device=device, squeeze=True, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, restrict_relations_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) def _get_ranks(self, rank_type): return np.asarray(self.ranks.get(rank_type, []), dtype=np.float64) def finalize(self) -> MetricResults: mean_rank = {} mean_reciprocal_rank = {} hits_at_k = {} adjusted_mean_rank = {} for rank_type in RANK_TYPES: ranks = self._get_ranks(rank_type=rank_type) if len(ranks) < 1: continue hits_at_k[rank_type] = { k: np.mean(ranks <= k) if isinstance(k, int) else np.mean(ranks <= int(self.num_relations * k)) for k in self.ks } mean_rank[rank_type] = np.mean(ranks) mean_reciprocal_rank[rank_type] = np.mean(np.reciprocal(ranks)) adjusted_ranks = self._get_ranks(rank_type=RANK_AVERAGE_ADJUSTED) if len(adjusted_ranks) >= 1: adjusted_mean_rank = float(np.mean(adjusted_ranks)) self.ranks.clear() return RelationPredictionRankBasedMetricResults( mean_rank=dict(mean_rank), mean_reciprocal_rank=dict(mean_reciprocal_rank), hits_at_k=dict(hits_at_k), adjusted_mean_rank=adjusted_mean_rank, ) def process_relation_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: # noqa: D102 self._update_ranks_(true_scores=true_scores, all_scores=scores) def get_unique_relation_ids_from_triples_tensor(mapped_triples: MappedTriples) -> torch.LongTensor: """Return the unique entity IDs used in a tensor of triples.""" return mapped_triples[:, 1].unique() def evaluate( model: Model, mapped_triples: MappedTriples, evaluators: Union[Evaluator, Collection[Evaluator]], additional_filtered_triples: Union[None, MappedTriples, List[MappedTriples]] = None, only_size_probing: bool = False, batch_size: Optional[int] = None, slice_size: Optional[int] = None, device: Optional[torch.device] = None, squeeze: bool = True, use_tqdm: bool = True, tqdm_kwargs: Optional[Mapping[str, str]] = None, restrict_relations_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> Union[MetricResults, List[MetricResults]]: """Evaluate metrics for model on mapped triples. The model is used to predict scores for all tails and all heads for each triple. Subsequently, each abstract evaluator is applied to the scores, also receiving the batch itself (e.g. to compute entity-specific metrics). Thereby, the (potentially) expensive score computation against all entities is done only once. The metric evaluators are expected to maintain their own internal buffers. They are returned after running the evaluation, and should offer a possibility to extract some final metrics. :param model: The model to evaluate. :param mapped_triples: The triples on which to evaluate. :param evaluators: An evaluator or a list of evaluators working on batches of triples and corresponding scores. :param only_size_probing: The evaluation is only performed for two batches to test the memory footprint, especially on GPUs. :param batch_size: >0 A positive integer used as batch size. Generally chosen as large as possible. Defaults to 1 if None. :param slice_size: >0 The divisor for the scoring function when using slicing. :param device: The device on which the evaluation shall be run. If None is given, use the model's device. :param squeeze: Return a single instance of :class:`MetricResults` if only one evaluator was given. :param use_tqdm: Should a progress bar be displayed? :param restrict_relations_to: Optionally restrict the evaluation to the given relations IDs. This may be useful if one is only interested in a part of the relations, e.g. due to type constraints, but wants to train on all available data. For ranking the entities, we still compute all scores for all possible replacement entities to avoid irregular access patterns which might decrease performance, but the scores with afterwards be filtered to only keep those of interest. If provided, we assume that the triples are already filtered, such that it only contains the entities of interest. :param do_time_consuming_checks: Whether to perform some time consuming checks on the provided arguments. Currently, this encompasses: - If restrict_entities_to is not None, check whether the triples have been filtered. Disabling this option can accelerate the method. """ if isinstance(evaluators, Evaluator): # upgrade a single evaluator to a list evaluators = [evaluators] start = timeit.default_timer() # verify that the triples have been filtered if restrict_relations_to is not None and do_time_consuming_checks: present_relation_ids = set(get_unique_relation_ids_from_triples_tensor(mapped_triples=mapped_triples).tolist()) unwanted = present_relation_ids.difference(restrict_relations_to.tolist()) if len(unwanted) > 0: raise ValueError(f'mapped_triples contains IDs of entities which are not contained in restrict_relations_to:' f'{unwanted}. This will invalidate the evaluation results.') # Send to device if device is not None: model = model.to(device) device = model.device # Ensure evaluation mode model.eval() # Split evaluators into those which need unfiltered results, and those which require filtered ones filtered_evaluators = list(filter(lambda e: e.filtered, evaluators)) unfiltered_evaluators = list(filter(lambda e: not e.filtered, evaluators)) # Check whether we need to be prepared for filtering filtering_necessary = len(filtered_evaluators) > 0 # Check whether an evaluator needs access to the masks # This can only be an unfiltered evaluator. positive_masks_required = any(e.requires_positive_mask for e in unfiltered_evaluators) # Prepare for result filtering if filtering_necessary or positive_masks_required: #all_pos_triples = torch.cat([model.triples_factory.mapped_triples, mapped_triples], dim=0) if additional_filtered_triples is None: logger.warning("The filtered setting was enabled, but there were no `additional_filtered_triples" "given. This means you probably forgot to pass (at least) the training triples. Try:" "additional_filtered_triples=[dataset.training.mapped_triples]" "Or if you want to use the Bordes et al. (2013) approach to filtering, do:" "additional_filtered_triples=[dataset.training.mapped_triples,dataset.validation.mapped_triples,]") all_pos_triples = mapped_triples elif isinstance(additional_filtered_triples, (list, tuple)): all_pos_triples = torch.cat([*additional_filtered_triples, mapped_triples], dim=0) else: all_pos_triples = torch.cat([additional_filtered_triples, mapped_triples], dim=0) all_pos_triples = all_pos_triples.to(device=device) else: all_pos_triples = None # Send tensors to device mapped_triples = mapped_triples.to(device=device) # Prepare batches if batch_size is None: batch_size = 1 batches = split_list_in_batches_iter(input_list=mapped_triples, batch_size=batch_size) # Show progressbar num_triples = mapped_triples.shape[0] # Flag to check when to quit the size probing evaluated_once = False # Disable gradient tracking _tqdm_kwargs = dict( desc=f'Evaluating on {model.device}', total=num_triples, unit='triple', unit_scale=True, # Choosing no progress bar (use_tqdm=False) would still show the initial progress bar without disable=True disable=not use_tqdm, ) if tqdm_kwargs: _tqdm_kwargs.update(tqdm_kwargs) with optional_context_manager(use_tqdm, tqdm(**_tqdm_kwargs)) as progress_bar, torch.no_grad(): # batch-wise processing for batch in batches: batch_size = batch.shape[0] _evaluate_batch( batch=batch, model=model, filtered_evaluators=filtered_evaluators, unfiltered_evaluators=unfiltered_evaluators, slice_size=slice_size, all_pos_triples=all_pos_triples, restrict_relations_to=restrict_relations_to, positive_masks_required=positive_masks_required, filtering_necessary=filtering_necessary, ) # If we only probe sizes we do not need more than one batch if only_size_probing and evaluated_once: break evaluated_once = True if use_tqdm: progress_bar.update(batch_size) # Finalize results = [evaluator.finalize() for evaluator in evaluators] stop = timeit.default_timer() if only_size_probing: logger.debug("Evaluation took %.2fs seconds", stop - start) else: logger.info("Evaluation took %.2fs seconds", stop - start) if squeeze and len(results) == 1: return results[0] return results def _evaluate_batch( batch, model, filtered_evaluators: Collection[Evaluator], unfiltered_evaluators: Collection[Evaluator], slice_size: Optional[int], all_pos_triples: Optional[MappedTriples], restrict_relations_to: Optional[torch.LongTensor], positive_masks_required: bool, filtering_necessary: bool, ) -> torch.BoolTensor: """ Evaluate batch for all head predictions(column=0), or all tail predictions (column=2). :param batch: shape: (batch_size, 3) The batch of currently evaluated triples. :param model: The model to evaluate. :param column: The column which to evaluate. Either 0 for head prediction, or 2 for tail prediction. :param filtered_evaluators: The evaluators which work on filtered scores. :param unfiltered_evaluators: The evaluators which work on unfiltered scores. :param slice_size: An optional slice size for computing the scores. :param all_pos_triples: All positive triples (required if filtering is necessary). :param restrict_relations_to: Restriction to evaluate only for these relations. :param positive_masks_required: Whether dense positive masks are required (by any unfiltered evaluator). :param filtering_necessary: Whether filtering is necessary. :return: The relation filter, which can be re-used for the same batch. """ # Predict scores once batch_scores_of_corrupted = model.predict_scores_all_relations(batch[:, [0, 2]], slice_size=slice_size) # Select scores of true batch_scores_of_true = batch_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, 1], ] # Create positive filter for all corrupted if filtering_necessary or positive_masks_required: # Needs all positive triples if all_pos_triples is None: raise ValueError('If filtering_necessary of positive_masks_required is True, all_pos_triples has to be ' 'provided, but is None.') # Create filter positive_filter = create_sparse_positive_filter_( hrt_batch=batch, all_pos_triples=all_pos_triples, ) # Create a positive mask with the size of the scores from the positive filter if positive_masks_required: positive_mask = create_dense_positive_mask_( zero_tensor=torch.zeros_like(batch_scores_of_corrupted), filter_batch=positive_filter, ) else: positive_mask = None # Restrict to entities of interest if restrict_relations_to is not None: batch_scores_of_corrupted_ = batch_scores_of_corrupted[:, restrict_relations_to] positive_mask = positive_mask[:, restrict_relations_to] else: batch_scores_of_corrupted_ = batch_scores_of_corrupted # Evaluate metrics on these *unfiltered* scores for unfiltered_evaluator in unfiltered_evaluators: unfiltered_evaluator.process_relation_scores_( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_scores_of_corrupted_, dense_positive_mask=positive_mask, ) # Filter if filtering_necessary: batch_filtered_scores_of_corrupted = filter_scores_( scores=batch_scores_of_corrupted, filter_batch=positive_filter, ) # The scores for the true triples have to be rewritten to the scores tensor batch_filtered_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, 1], ] = batch_scores_of_true # Restrict to entities of interest if restrict_relations_to is not None: batch_filtered_scores_of_corrupted = batch_filtered_scores_of_corrupted[:, restrict_relations_to] # Evaluate metrics on these *filtered* scores for filtered_evaluator in filtered_evaluators: filtered_evaluator.process_relation_scores_( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_filtered_scores_of_corrupted, ) def create_sparse_positive_filter_( hrt_batch: MappedTriples, all_pos_triples: torch.LongTensor ) -> Tuple[torch.LongTensor, torch.BoolTensor]: # Split batch batch_heads, batch_tails = hrt_batch[:, 0:1], hrt_batch[:, 2:3] head_filter = (all_pos_triples[:, 0]).view(1, -1) == batch_heads tail_filter = (all_pos_triples[:, 2]).view(1, -1) == batch_tails filter_batch = (head_filter & tail_filter).nonzero(as_tuple=False) filter_batch[:, 1] = all_pos_triples[:, 1].view(1, -1)[:, filter_batch[:, 1]] return filter_batch ================================================ FILE: inductive_lp/model/comp_gcn.py ================================================ import torch import numpy as np from typing import Dict from torch import nn from torch.nn import Parameter import torch.nn.functional as F from utils.utils_gcn import weight_init from .gnn_layer import StarEConvLayer from .lrga_model import LowRankAttention from torch_geometric.data import Data class StarE_PyG_Encoder(nn.Module): def __init__(self, emb_dim: int, device: torch.device, num_rel: int, layer_config: dict, num_layers: int = 2, use_lrga: bool = False, lrga_k: int = 50, lrga_drop: float = 0.1, hid_drop: float = 0.1, drop1: float = 0.1, triple_mode: bool = True, residual: bool = False, jk: bool = False, ): super(StarE_PyG_Encoder, self).__init__() self.act = torch.relu # was tanh before self.layer_config = layer_config self.emb_dim = emb_dim self.gcn_dim = emb_dim self.hid_drop = hid_drop self.drop1 = nn.Dropout(drop1) self.triple_mode = triple_mode self.num_rel = num_rel self.device = device self.residual = residual self.jk = jk self.num_layers = num_layers """ LRGA params """ self.use_lrga = use_lrga self.lrga_k = lrga_k self.lrga_drop = lrga_drop self.convs = nn.ModuleList() if self.use_lrga: self.attention = nn.ModuleList() self.dim_reduction = nn.ModuleList() # populating manually first and last layers, otherwise in a loop self.convs.append(StarEConvLayer(self.emb_dim, self.gcn_dim, self.num_rel, act=self.act, config=layer_config)) if self.use_lrga: self.attention.append(LowRankAttention(self.lrga_k, self.emb_dim, self.lrga_drop)) self.dim_reduction.append(nn.Sequential(nn.Linear(2 * self.lrga_k + self.gcn_dim + self.emb_dim, self.gcn_dim))) self.bns = nn.ModuleList([nn.BatchNorm1d(self.gcn_dim) for _ in range(self.num_layers - 1)]) for _ in range(self.num_layers - 1): self.convs.append(StarEConvLayer(self.gcn_dim, self.gcn_dim, self.num_rel, act=self.act, config=layer_config)) if self.use_lrga: self.attention.append(LowRankAttention(self.lrga_k, self.gcn_dim, self.lrga_drop)) self.dim_reduction.append(nn.Sequential(nn.Linear(2 * (self.lrga_k + self.gcn_dim), self.gcn_dim))) # self.register_parameter('bias', Parameter(torch.zeros(self.num_ent))) if self.jk: self.linear = nn.Sequential( nn.Linear(self.gcn_dim * self.num_layers, self.gcn_dim), nn.ReLU(), nn.Linear(self.gcn_dim, self.gcn_dim) ) def reset_parameters(self): #torch.nn.init.constant_(self.bias.data, 0) for conv in self.convs: conv.reset_parameters() if self.use_lrga: for att in self.attention: att.apply(weight_init) for dim_r in self.dim_reduction: dim_r.apply(weight_init) for bnorm in self.bns: bnorm.reset_parameters() if self.jk: for module in self.linear: if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() def forward_base(self, graph): x, edge_index, edge_type, r = graph['x'], graph['edge_index'], graph['edge_type'], graph['rels'] # Add reverse stuff # reverse_index = torch.zeros_like(edge_index) # reverse_index[1, :] = edge_index[0, :] # reverse_index[0, :] = edge_index[1, :] # rev_edge_type = edge_type + self.num_rel # # edge_index = torch.cat([edge_index, reverse_index], dim=1) # edge_type = torch.cat([edge_type, rev_edge_type], dim=0) if not self.triple_mode: raise NotImplementedError if self.jk: outputs = [] for i, conv in enumerate(self.convs[:-1]): x_local, r = conv(x=x, edge_index=edge_index, edge_type=edge_type, rel_embed=r, quals=None) x_local = self.drop1(x_local) if self.use_lrga: x_global = self.attention[i](x) x = self.dim_reduction[i](torch.cat((x_global, x_local, x), dim=1)) x = F.relu(x) x = self.bns[i](x) else: if self.residual: x = x + x_local else: x = x_local if self.jk: outputs.append(x) # last layer x_local, r = self.convs[-1](x=x, edge_index=edge_index, edge_type=edge_type, rel_embed=r, quals=None) x_local = self.drop1(x_local) if self.use_lrga: x_global = self.attention[-1](x) x = self.dim_reduction[-1](torch.cat((x_global, x_local, x), dim=1)) else: if self.residual: x = x + x_local else: x = x_local if self.jk: outputs.append(x) x = self.linear(torch.cat(outputs, dim=-1)) return x, r ================================================ FILE: inductive_lp/model/gnn_layer.py ================================================ import torch import numpy as np import torch.nn.functional as F from utils.utils_gcn import get_param, ccorr, rotate, softmax from torch_scatter import scatter_add, scatter_mean from torch_geometric.nn import MessagePassing from torch.nn import Parameter from torch import Tensor, nn from typing import Optional from torch_scatter import scatter from torch_geometric.utils import degree class StarEConvLayer(MessagePassing): """ The important stuff. """ def __init__(self, in_channels, out_channels, num_rels, act=lambda x: x, config=None): self.pna = config['STAREARGS']['PNA'] super(self.__class__, self).__init__(flow='target_to_source', aggr='add' if not self.pna else None) self.p = config self.in_channels = in_channels self.out_channels = out_channels self.num_rels = num_rels self.act = act self.device = None self.w_loop = get_param((in_channels, out_channels)) # (100,200) self.w_in = get_param((in_channels, out_channels)) # (100,200) self.w_out = get_param((in_channels, out_channels)) # (100,200) self.w_rel = get_param((in_channels, out_channels)) # (100,200) if self.p['STATEMENT_LEN'] != 3: if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'sum' or self.p['STAREARGS']['QUAL_AGGREGATE'] == 'mul' or \ self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': self.w_q = get_param((in_channels, in_channels)) # new for quals setup elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'concat': self.w_q = get_param((2 * in_channels, in_channels)) # need 2x size due to the concat operation self.loop_rel = get_param((1, in_channels)) # (1,100) self.loop_ent = get_param((1, in_channels)) # new self.drop = torch.nn.Dropout(self.p['STAREARGS']['GCN_DROP']) self.bn = torch.nn.BatchNorm1d(out_channels) if self.p['STAREARGS']['ATTENTION']: assert self.p['STAREARGS']['GCN_DIM'] == self.p['EMBEDDING_DIM'], "Current attn implementation requires those tto be identical" assert self.p['EMBEDDING_DIM'] % self.p['STAREARGS']['ATTENTION_HEADS'] == 0, "should be divisible" self.heads = self.p['STAREARGS']['ATTENTION_HEADS'] self.attn_dim = self.out_channels // self.heads self.negative_slope = self.p['STAREARGS']['ATTENTION_SLOPE'] self.attn_drop = self.p['STAREARGS']['ATTENTION_DROP'] self.att = get_param((1, self.heads, 2 * self.attn_dim)) if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': assert self.p['STAREARGS']['GCN_DIM'] == self.p[ 'EMBEDDING_DIM'], "Current attn implementation requires those tto be identical" assert self.p['EMBEDDING_DIM'] % self.p['STAREARGS']['ATTENTION_HEADS'] == 0, "should be divisible" if not self.p['STAREARGS']['ATTENTION']: self.heads = self.p['STAREARGS']['ATTENTION_HEADS'] self.attn_dim = self.out_channels // self.heads self.negative_slope = self.p['STAREARGS']['ATTENTION_SLOPE'] self.attn_drop = self.p['STAREARGS']['ATTENTION_DROP'] self.att_qual = get_param((1, self.heads, 2 * self.attn_dim)) if self.p['STAREARGS']['BIAS']: self.register_parameter('bias', Parameter( torch.zeros(out_channels))) if self.pna: self.linear = nn.Linear(out_channels * 30, out_channels) def reset_parameters(self): torch.nn.init.xavier_normal_(self.w_loop.data) torch.nn.init.xavier_normal_(self.w_in.data) torch.nn.init.xavier_normal_(self.w_out.data) torch.nn.init.xavier_normal_(self.w_rel.data) if self.p['STATEMENT_LEN'] != 3: torch.nn.init.xavier_normal_(self.w_q.data) torch.nn.init.xavier_normal_(self.loop_rel.data) torch.nn.init.xavier_normal_(self.loop_ent.data) self.bn.reset_parameters() if self.p['STAREARGS']['ATTENTION']: torch.nn.init.xavier_normal_(self.att.data) if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': torch.nn.init.xavier_normal_(self.att_qual.data) if self.pna: self.linear.reset_parameters() def forward(self, x, edge_index, edge_type, rel_embed, qualifier_ent=None, qualifier_rel=None, quals=None): """ See end of doc string for explaining. :param x: all entities*dim_of_entities (for jf17k -> 28646*200) :param edge_index: COO matrix (2 list each having nodes with index [1,2,3,4,5] [3,4,2,5,4] Here node 1 and node 3 are connected with edge. And the type of edge can be found using edge_type. Note that there are twice the number of edges as each edge is also reversed. ) :param edge_type: The type of edge connecting the COO matrix :param rel_embed: 2 Times Total relation * emb_dim (200 in our case and 2 Times because of inverse relations) :param qualifier_ent: :param qualifier_rel: :param quals: Another sparse matrix where quals[0] --> qualifier relations type quals[1] --> qualifier entity quals[2] --> index of the original COO matrix that states for which edge this qualifier exists () For argument sake if a knowledge graph has following statements [e1,p1,e4,qr1,qe1,qr2,qe2] [e1,p1,e2,qr1,qe1,qr2,qe3] [e1,p2,e3,qr3,qe3,qr2,qe2] [e1,p2,e5,qr1,qe1] [e2,p1,e4] [e4,p3,e3,qr4,qe1,qr2,qe4] [e1,p1,e5] (incoming) (outgoing) <----(regular)------><---(inverse)-------> Edge index would be : [e1,e1,e1,e1,e2,e4,e1,e4,e2,e3,e5,e4,e3,e5] [e4,e2,e3,e5,e4,e3,e5,e1,e1,e1,e1,e2,e4,e1] Edge Type would be : [p1,p1,p2,p2,p1,p3,p1,p1_inv,p1_inv,p2_inv,p2_inv,p1_inv,p3_inv,p1_inv] <-------on incoming-----------------><---------on outgoing--------------> quals would be : [qr1,qr2,qr1,qr2,qr3,qr2,qr1,qr4,qr2,qr1,qr2,qr1,qr2,qr3,qr2,qr1,qr4,qr2] [qe1,qe2,qe1,qe3,qe3,qe2,qe1,qe1,qe4,qe1,qe2,qe1,qe3,qe3,qe2,qe1,qe1,qe4] [0,0,1,1,2,2,3,5,5,0,0,1,1,2,2,3,5,5] <--on incoming---><--outgoing-------> Note that qr1,qr2... and qe1, qe2, ... all belong to the same space :return: """ if self.device is None: self.device = edge_index.device rel_embed = torch.cat([rel_embed, self.loop_rel], dim=0) num_edges = edge_index.size(1) // 2 num_ent = x.size(0) self.in_index, self.out_index = edge_index[:, :num_edges], edge_index[:, num_edges:] self.in_type, self.out_type = edge_type[:num_edges], edge_type[num_edges:] if self.p['STATEMENT_LEN'] != 3: num_quals = quals.size(1) // 2 self.in_index_qual_ent, self.out_index_qual_ent = quals[1, :num_quals], quals[1, num_quals:] self.in_index_qual_rel, self.out_index_qual_rel = quals[0, :num_quals], quals[0, num_quals:] self.quals_index_in, self.quals_index_out = quals[2, :num_quals], quals[2, num_quals:] ''' Adding self loop by creating a COO matrix. Thus \ loop index [1,2,3,4,5] [1,2,3,4,5] loop type [10,10,10,10,10] --> assuming there are 9 relations ''' # Self edges between all the nodes self.loop_index = torch.stack([torch.arange(num_ent), torch.arange(num_ent)]).to(self.device) self.loop_type = torch.full((num_ent,), rel_embed.size(0) - 1, dtype=torch.long).to(self.device) # if rel meb is 500, the index of the self emb is # 499 .. which is just added here self.in_norm = self.compute_norm(self.in_index, num_ent) self.out_norm = self.compute_norm(self.out_index, num_ent) if self.p['STATEMENT_LEN'] != 3: in_res = self.propagate(self.in_index, x=x, edge_type=self.in_type, rel_embed=rel_embed, edge_norm=self.in_norm, mode='in', ent_embed=x, qualifier_ent=self.in_index_qual_ent, qualifier_rel=self.in_index_qual_rel, qual_index=self.quals_index_in, source_index=self.in_index[0]) loop_res = self.propagate(self.loop_index, x=x, edge_type=self.loop_type, rel_embed=rel_embed, edge_norm=None, mode='loop', ent_embed=None, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None) out_res = self.propagate(self.out_index, x=x, edge_type=self.out_type, rel_embed=rel_embed, edge_norm=self.out_norm, mode='out', ent_embed=x, qualifier_ent=self.out_index_qual_ent, qualifier_rel=self.out_index_qual_rel, qual_index=self.quals_index_out, source_index=self.out_index[0]) else: in_res = self.propagate(self.in_index, x=x, edge_type=self.in_type, rel_embed=rel_embed, edge_norm=self.in_norm, mode='in', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=self.in_index[0]) loop_res = self.propagate(self.loop_index, x=x, edge_type=self.loop_type, rel_embed=rel_embed, edge_norm=None, mode='loop', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None) out_res = self.propagate(self.out_index, x=x, edge_type=self.out_type, rel_embed=rel_embed, edge_norm=self.out_norm, mode='out', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=self.out_index[0]) out = self.drop(in_res) * (1 / 3) + self.drop(out_res) * (1 / 3) + loop_res * (1 / 3) if self.pna: out = self.linear(out) if self.p['STAREARGS']['BIAS']: out = out + self.bias out = self.bn(out) # Ignoring the self loop inserted, return. return self.act(out), torch.matmul(rel_embed, self.w_rel)[:-1] def rel_transform(self, ent_embed, rel_embed): if self.p['STAREARGS']['OPN'] == 'corr': trans_embed = ccorr(ent_embed, rel_embed) elif self.p['STAREARGS']['OPN'] == 'sub': trans_embed = ent_embed - rel_embed elif self.p['STAREARGS']['OPN'] == 'mult': trans_embed = ent_embed * rel_embed elif self.p['STAREARGS']['OPN'] == 'rotate': trans_embed = rotate(ent_embed, rel_embed) else: raise NotImplementedError return trans_embed def qual_transform(self, qualifier_ent, qualifier_rel): """ :return: """ if self.p['STAREARGS']['QUAL_OPN'] == 'corr': trans_embed = ccorr(qualifier_ent, qualifier_rel) elif self.p['STAREARGS']['QUAL_OPN'] == 'sub': trans_embed = qualifier_ent - qualifier_rel elif self.p['STAREARGS']['QUAL_OPN'] == 'mult': trans_embed = qualifier_ent * qualifier_rel elif self.p['STAREARGS']['QUAL_OPN'] == 'rotate': trans_embed = rotate(qualifier_ent, qualifier_rel) else: raise NotImplementedError return trans_embed def qualifier_aggregate(self, qualifier_emb, rel_part_emb, alpha=0.5, qual_index=None): """ In qualifier_aggregate method following steps are performed qualifier_emb looks like - qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) rel_part_emb : [qq,ww,ee,rr,tt, .....] (here qq, ww, ee .. are of 200 dim) Note that rel_part_emb for jf17k would be around 61k*200 Step1 : Pass the qualifier_emb to self.coalesce_quals and multiply the returned output with a weight. qualifier_emb : [aa,bb,cc,dd,ee, ...... ] (here aa, bb, cc are of 200 dim each) Note that now qualifier_emb has the same shape as rel_part_emb around 61k*200 Step2 : Combine the updated qualifier_emb (see Step1) with rel_part_emb based on defined aggregation strategy. Aggregates the qualifier matrix (3, edge_index, emb_dim) :param qualifier_emb: :param rel_part_emb: :param type: :param alpha :return: self.coalesce_quals returns : [q+a+b+d,w+c+e+g,e'+f,......] (here each element in the list is of 200 dim) """ if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'sum': qualifier_emb = torch.einsum('ij,jk -> ik', self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0]), self.w_q) return alpha * rel_part_emb + (1 - alpha) * qualifier_emb # [N_EDGES / 2 x EMB_DIM] elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'concat': qualifier_emb = self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0]) agg_rel = torch.cat((rel_part_emb, qualifier_emb), dim=1) # [N_EDGES / 2 x 2 * EMB_DIM] return torch.mm(agg_rel, self.w_q) # [N_EDGES / 2 x EMB_DIM] elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'mul': qualifier_emb = torch.mm(self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0], fill=1), self.w_q) return rel_part_emb * qualifier_emb elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': # only for sparse mode expanded_rels = torch.index_select(rel_part_emb, 0, qual_index) # Nquals x D expanded_rels = expanded_rels.view(-1, self.heads, self.attn_dim) # Nquals x heads x h_dim qualifier_emb = torch.mm(qualifier_emb, self.w_q).view(-1, self.heads, self.attn_dim) # Nquals x heads x h_dim alpha_r = torch.einsum('bij,kij -> bi', [torch.cat([expanded_rels, qualifier_emb], dim=-1), self.att_qual]) alpha_r = F.leaky_relu(alpha_r, self.negative_slope) # Nquals x heads alpha_r = softmax(alpha_r, qual_index, rel_part_emb.size(0)) # Nquals x heads alpha_r = F.dropout(alpha_r, p=self.attn_drop) # Nquals x heads expanded_rels = (expanded_rels * alpha_r.view(-1, self.heads, 1)).view(-1, self.heads * self.attn_dim) # Nquals x D single_rels = scatter_add(expanded_rels, qual_index, dim=0, dim_size=rel_part_emb.size(0)) # Nedges x D copy_mask = single_rels.sum(dim=1) != 0.0 rel_part_emb[copy_mask] = single_rels[copy_mask] # Nedges x D return rel_part_emb else: raise NotImplementedError def update_rel_emb_with_qualifier(self, ent_embed, rel_embed, qualifier_ent, qualifier_rel, edge_type, qual_index=None): """ The update_rel_emb_with_qualifier method performs following functions: Input is the secondary COO matrix (QE (qualifier entity), QR (qualifier relation), edge index (Connection to the primary COO)) Step1 : Embed all the input Step1a : Embed the qualifier entity via ent_embed (So QE shape is 33k,1 -> 33k,200) Step1b : Embed the qualifier relation via rel_embed (So QR shape is 33k,1 -> 33k,200) Step1c : Embed the main statement edge_type via rel_embed (So edge_type shape is 61k,1 -> 61k,200) Step2 : Combine qualifier entity emb and qualifier relation emb to create qualifier emb (See self.qual_transform). This is generally just summing up. But can be more any pair-wise function that returns one vector for a (qe,qr) vector Step3 : Update the edge_type embedding with qualifier information. This uses scatter_add/scatter_mean. before: qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) qual_index : [1,1,2,1,2,3,2,......] (here 1,2,3 .. are edge index of Main COO) edge_type : [q,w,e',r,t,y,u,i,o,p, .....] (here q,w,e' .. are of 200 dim each) After: edge_type : [q+(a+b+d),w+(c+e+g),e'+f,......] (here each element in the list is of 200 dim) :param ent_embed: essentially x (28k*200 in case of Jf17k) :param rel_embed: essentially relation embedding matrix For secondary COO matrix (QE, QR, edge index) :param qualifier_ent: QE :param qualifier_rel: QR edge_type: :return: index select from embedding phi operation between qual_ent, qual_rel """ # Step 1: embedding qualifier_emb_rel = rel_embed[qualifier_rel] qualifier_emb_ent = ent_embed[qualifier_ent] rel_part_emb = rel_embed[edge_type] # Step 2: pass it through qual_transform qualifier_emb = self.qual_transform(qualifier_ent=qualifier_emb_ent, qualifier_rel=qualifier_emb_rel) # Pass it through a aggregate layer return self.qualifier_aggregate(qualifier_emb, rel_part_emb, alpha=self.p['STAREARGS']['TRIPLE_QUAL_WEIGHT'], qual_index=qual_index) # return qualifier_emb def message(self, x_j, x_i, edge_type, rel_embed, edge_norm, mode, ent_embed=None, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None): """ The message method performs following functions Step1 : get updated relation representation (rel_embed) [edge_type] by aggregating qualifier information (self.update_rel_emb_with_qualifier). Step2 : Obtain edge message by transforming the node embedding with updated relation embedding (self.rel_transform). Step3 : Multiply edge embeddings (transform) by weight Step4 : Return the messages. They will be sent to subjects (1st line in the edge index COO) Over here the node embedding [the first list in COO matrix] is representing the message which will be sent on each edge More information about updating relation representation please refer to self.update_rel_emb_with_qualifier :param x_j: objects of the statements (2nd line in the COO) :param x_i: subjects of the statements (1st line in the COO) :param edge_type: relation types :param rel_embed: embedding matrix of all relations :param edge_norm: :param mode: in (direct) / out (inverse) / loop :param ent_embed: embedding matrix of all entities :param qualifier_ent: :param qualifier_rel: :param qual_index: :param source_index: :return: """ weight = getattr(self, 'w_{}'.format(mode)) if self.p['STATEMENT_LEN'] != 3: # add code here if mode != 'loop': rel_emb = self.update_rel_emb_with_qualifier(ent_embed, rel_embed, qualifier_ent, qualifier_rel, edge_type, qual_index) else: rel_emb = torch.index_select(rel_embed, 0, edge_type) else: rel_emb = torch.index_select(rel_embed, 0, edge_type) xj_rel = self.rel_transform(x_j, rel_emb) out = torch.einsum('ij,jk->ik', xj_rel, weight) if self.p['STAREARGS']['ATTENTION'] and mode != 'loop': out = out.view(-1, self.heads, self.attn_dim) x_i = x_i.view(-1, self.heads, self.attn_dim) alpha = torch.einsum('bij,kij -> bi', [torch.cat([x_i, out], dim=-1), self.att]) alpha = F.leaky_relu(alpha, self.negative_slope) alpha = softmax(alpha, source_index, ent_embed.size(0)) alpha = F.dropout(alpha, p=self.attn_drop) return (out * alpha.view(-1, self.heads, 1)).view(-1, self.heads * self.attn_dim) else: return out if edge_norm is None else out * edge_norm.view(-1, 1) def update(self, aggr_out, mode): if self.p['STAREARGS']['ATTENTION'] and mode != 'loop': if not self.pna: aggr_out = aggr_out.view(-1, self.heads * self.attn_dim) return aggr_out def aggregate(self, inputs: Tensor, index: Tensor, ptr: Optional[Tensor] = None, dim_size: Optional[int] = None) -> Tensor: if not self.pna: return super(StarEConvLayer, self).aggregate(inputs=inputs, index=index, ptr=ptr, dim_size=dim_size) else: out_sum = scatter(inputs, index, 0, None, dim_size, reduce='sum') out_mean = scatter(inputs, index, 0, None, dim_size, reduce='mean') out_min = scatter(inputs, index, 0, None, dim_size, reduce='min') out_max = scatter(inputs, index, 0, None, dim_size, reduce='max') mean_squares = scatter(inputs*inputs, index, 0, None, dim_size, reduce='mean') out_var = mean_squares - out_mean*out_mean out_std = torch.sqrt(torch.relu(out_var) + 1e-5) outs = torch.cat([out_sum, out_mean, out_min, out_max, out_var, out_std], dim=-1) deg = degree(index, dim_size, dtype=inputs.dtype) deg_log = (deg+1).log().mean().item() deg_lin = deg.mean().item() deg = deg.clamp_(1).view(-1, 1) #, 1) out_id = outs out_amp = outs * (torch.log(deg + 1) / deg_log) out_attun = outs * (deg_log / torch.log(deg + 1)) out_lin = outs * (deg / deg_lin) out_inv_lin = outs * (deg_lin / deg) outs = torch.cat([out_id, out_amp, out_attun, out_lin, out_inv_lin], dim=-1) return outs @staticmethod def compute_norm(edge_index, num_ent): """ Re-normalization trick used by GCN-based architectures without attention. Yet another torch scatter functionality. See coalesce_quals for a rough idea. row : [1,1,2,3,3,4,4,4,4, .....] (about 61k for Jf17k) edge_weight : [1,1,1,1,1,1,1,1,1, ....] (same as row. So about 61k for Jf17k) deg : [2,1,2,4,.....] (same as num_ent about 28k in case of Jf17k) :param edge_index: :param num_ent: :return: """ row, col = edge_index edge_weight = torch.ones_like(row).float() # Identity matrix where we know all entities are there deg = scatter_add(edge_weight, row, dim=0, dim_size=num_ent) # Summing number of weights of # the edges, D = A + I deg_inv = deg.pow(-0.5) # D^{-0.5} deg_inv[deg_inv == float('inf')] = 0 # for numerical stability norm = deg_inv[row] * edge_weight * deg_inv[col] # Norm parameter D^{-0.5} * return norm def coalesce_quals(self, qual_embeddings, qual_index, num_edges, fill=0): """ before: qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) qual_index : [1,1,2,1,2,3,2,......] (here 1,2,3 .. are edge index of Main COO) edge_type : [0,0,0,0,0,0,0, .....] (empty array of size num_edges) After: edge_type : [a+b+d,c+e+g,f ......] (here each element in the list is of 200 dim) :param qual_embeddings: shape of [1, N_QUALS] :param qual_index: shape of [1, N_QUALS] which states which quals belong to which main relation from the index, that is, all qual_embeddings that have the same index have to be summed up :param num_edges: num_edges to return the appropriate tensor :param fill: fill value for the output matrix - should be 0 for sum/concat and 1 for mul qual aggregation strat :return: [1, N_EDGES] """ if self.p['STAREARGS']['QUAL_N'] == 'sum': output = scatter_add(qual_embeddings, qual_index, dim=0, dim_size=num_edges) elif self.p['STAREARGS']['QUAL_N'] == 'mean': output = scatter_mean(qual_embeddings, qual_index, dim=0, dim_size=num_edges) if fill != 0: # by default scatter_ functions assign zeros to the output, so we assign them 1's for correct mult mask = output.sum(dim=-1) == 0 output[mask] = fill return output def __repr__(self): return '{}({}, {}, num_rels={})'.format( self.__class__.__name__, self.in_channels, self.out_channels, self.num_rels) ================================================ FILE: inductive_lp/model/lrga_model.py ================================================ # taken from https://github.com/omri1348/LRGA/blob/master/ogb/examples/linkproppred/collab/graph_global_attention_layer.py import torch import torch.nn as nn def joint_normalize2(U, V_T): # U and V_T are in block diagonal form tmp_ones = torch.ones((V_T.shape[1], 1)) if torch.cuda.is_available(): tmp_ones = tmp_ones.to(torch.device('cuda')) norm_factor = torch.mm(U, torch.mm(V_T, tmp_ones)) norm_factor = (torch.sum(norm_factor) / U.shape[0]) + 1e-6 return 1/norm_factor def weight_init(layer): if type(layer) == nn.Linear: nn.init.xavier_normal_(layer.weight.data) if layer.bias is not None: nn.init.constant(layer.bias.data, 0) return class LowRankAttention(nn.Module): def __init__(self, k, d, dropout): super().__init__() self.w = nn.Sequential(nn.Linear(d, 4*k), nn.ReLU()) self.activation = nn.ReLU() self.apply(weight_init) self.k = k self.dropout = nn.Dropout(p=dropout) def forward(self, x): tmp = self.w(x) # B x D U = tmp[:, :self.k] # B x K V = tmp[:, self.k:2*self.k] # B x K Z = tmp[:, 2*self.k:3*self.k] # B x K T = tmp[:, 3*self.k:] # B x K V_T = torch.t(V) # K x B # normalization D = joint_normalize2(U, V_T) # scalar res = torch.mm(U, torch.mm(V_T, Z)) # (B x K) mm (K x K) -> B x K res = torch.cat((res*D, T), dim=1) # B x 2K return self.dropout(res) ================================================ FILE: inductive_lp/model/nodepiece_rotate.py ================================================ import torch import random import numpy as np from torch.nn import functional from torch.nn import TransformerEncoder, TransformerEncoderLayer from typing import Optional from collections import Counter, defaultdict from torch import nn from pykeen.losses import Loss from pykeen.triples import TriplesFactory from pykeen.models import Model from tqdm import tqdm from torch_geometric.data import Data from .comp_gcn import StarE_PyG_Encoder class NodePieceRotate(Model): def __init__(self, tokenizer = None, triples: TriplesFactory = None, inference_triples: TriplesFactory = None, device: torch.device = None, loss: Loss = None, max_paths: int = None, # max anchors per node subbatch: int = 32, max_seq_len: int = None, # tied with anchor distances embedding_dim: int = 100, hid_dim: int = 200, # hidden dim for the hash encoder num_heads: int = 4, # for Trf num_layers: int = 2, # for Trf pooler: str = "cat", # "cat" or "trf" drop_prob: float = 0.1, # dropout use_distances: bool = True, rel_policy: str = "sum", random_hashes: int = 0, # for ablations nearest: bool = True, # use only K nearest anchors per node sample_rels: int = 0, # size of the relational context ablate_anchors: bool = False, # for ablations - node hashes will be constructed only from the relational context tkn_mode: str = "path", # default NodePiece vocabularization strategy gnn: bool = False, gnn_att: bool = False, gcn_att_heads: int = 2, use_lrga: bool = False, gnn_layers: int = 2, gnn_att_drop: float = 0.1, pna: bool = False, residual: bool = False, jk: bool = False, ): super().__init__( triples_factory=inference_triples, loss=loss, predict_with_sigmoid=False, automatic_memory_optimization=False, preferred_device=device, ) self.pooler = pooler self.policy = rel_policy self.nearest = nearest self.sample_rels = sample_rels self.ablate_anchors = ablate_anchors self.tkn_mode = tkn_mode self.gnn = gnn # cat pooler - concat all anchors+relations in one big vector, pass through a 2-layer MLP if pooler == "cat": self.set_enc = nn.Sequential( nn.Linear(embedding_dim * (max_paths + sample_rels), embedding_dim * 2), nn.Dropout(drop_prob), nn.ReLU(), nn.Linear(embedding_dim * 2, embedding_dim) ) if not self.ablate_anchors else nn.Sequential( nn.Linear(embedding_dim * sample_rels, embedding_dim * 2), nn.Dropout(drop_prob), nn.ReLU(), nn.Linear(embedding_dim * 2, embedding_dim) ) # trf pooler - vanilla transformer encoder with mean pooling on top elif pooler == "trf": encoder_layer = TransformerEncoderLayer( d_model=embedding_dim, nhead=num_heads, dim_feedforward=hid_dim, dropout=drop_prob, ) self.set_enc = TransformerEncoder(encoder_layer=encoder_layer, num_layers=num_layers) self.device = device self.loss = loss self.transductive_triples_factory = triples self.inductive_inference = inference_triples #self.tokenizer = tokenizer self.random_hashes = random_hashes self.subbatch = subbatch self.embedding_dim = embedding_dim self.real_embedding_dim = embedding_dim // 2 # RotatE interaction assumes vectors conists of two parts: real and imaginary self.max_seq_len = max_seq_len self.sample_paths = max_paths self.use_distances = use_distances # pykeen stuff self.automatic_memory_optimization = False # self.tokenizer.token2id[self.tokenizer.NOTHING_TOKEN] = len(tokenizer.token2id) - 1 # TODO this is a bugfix as PathTrfEncoder puts its own index here # self.anchor_embeddings = nn.Embedding(len(tokenizer.token2id), embedding_dim=embedding_dim, padding_idx=self.tokenizer.token2id[tokenizer.PADDING_TOKEN]) self.relation_embeddings = nn.Embedding(self.transductive_triples_factory.num_relations + 1, embedding_dim=embedding_dim, padding_idx=self.transductive_triples_factory.num_relations) # self.dist_emb = nn.Embedding(self.max_seq_len, embedding_dim=embedding_dim) self.entity_embeddings = None # now fix anchors per node for each node in a graph, either deterministically or randomly # we do it mostly for speed reasons, although this process can happen during the forward pass either if not self.ablate_anchors: if self.random_hashes == 0: if self.tkn_mode != "bfs": # DETERMINISTIC strategy if not self.nearest: # subsample paths, need to align them with distances sampled_paths = { entity: random.sample(paths, k=min(self.sample_paths, len(paths))) for entity, paths in self.tokenizer.vocab.items() } elif self.nearest: # sort paths by length first and take K of them sampled_paths = { entity: sorted(paths, key=lambda x: len(x))[:min(self.sample_paths, len(paths))] for entity, paths in self.tokenizer.vocab.items() } self.max_seq_len = max(len(path) for k,v in sampled_paths.items() for path in v) print(f"Changed max seq len from {max_seq_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") hashes = [ [self.tokenizer.token2id[path[0]] for path in paths] + [self.tokenizer.token2id[tokenizer.PADDING_TOKEN]]*(self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] distances = [ [len(path)-1 for path in paths] + [0] *(self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] total_paths = [ [len(paths)] for entity, paths in sampled_paths.items() ] else: # only nearest neighbors mode if not self.nearest: raise NotImplementedError("bfs mode works only with -nn True") hashes = [ [self.tokenizer.token2id[token] for token in vals['ancs'][:min(self.sample_paths, len(vals['ancs']))]] + [self.tokenizer.token2id[tokenizer.PADDING_TOKEN]] * (self.sample_paths - len(vals['ancs'])) for entity, vals in self.tokenizer.vocab.items() ] distances = [ [d for d in vals['dists'][:min(self.sample_paths, len(vals['dists']))]] + [0] * (self.sample_paths - len(vals['dists'])) for entity, vals in self.tokenizer.vocab.items() ] total_paths = distances self.max_seq_len = max([d for row in distances for d in row]) print(f"Changed max seq len from {max_seq_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.tensor(distances, dtype=torch.long, device=self.device) self.total_paths = torch.tensor(total_paths, dtype=torch.long, device=self.device) else: # RANDOM strategy # in this case, we bypass distances and won't use relations in the encoder self.anchor_embeddings = nn.Embedding(self.random_hashes, embedding_dim=embedding_dim) hashes = [ random.sample(list(range(random_hashes)), self.sample_paths) for i in range(triples.num_entities) ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.zeros((triples.num_entities, self.sample_paths), dtype=torch.long, device=self.device) self.total_paths = torch.zeros((triples.num_entities, 1), dtype=torch.long, device=self.device) # creating the relational context of M unique outgoing relation types for each node if self.sample_rels > 0: pad_idx = self.transductive_triples_factory.num_relations self.pad_idx = pad_idx e2r = defaultdict(set) ind_e2r = defaultdict(set) for row in self.transductive_triples_factory.mapped_triples: e2r[row[0].item()].add(row[1].item()) for row in self.inductive_inference.mapped_triples: ind_e2r[row[0].item()].add(row[1].item()) len_stats = [len(v) for k,v in e2r.items()] len_ind_stats = [len(v) for k,v in ind_e2r.items()] print(f"Transductive: Unique relations per node - min: {min(len_stats)}, avg: {np.mean(len_stats)}, 66th perc: {np.percentile(len_stats, 66)}, max: {max(len_stats)} ") print(f"Inductive: Unique relations per node - min: {min(len_ind_stats)}, avg: {np.mean(len_ind_stats)}, 66th perc: {np.percentile(len_ind_stats, 66)}, max: {max(len_ind_stats)} ") unique_1hop_relations = [ random.sample(e2r[i], k=min(self.sample_rels, len(e2r[i]))) + [pad_idx] * (self.sample_rels-min(len(e2r[i]), self.sample_rels)) for i in range(self.transductive_triples_factory.num_entities) ] self.unique_1hop_relations = torch.tensor(unique_1hop_relations, dtype=torch.long, device=self.device) self.inference_1hop_relations = torch.tensor( [ random.sample(ind_e2r[i], k=min(self.sample_rels, len(ind_e2r[i]))) + [pad_idx] * ( self.sample_rels - min(len(ind_e2r[i]), self.sample_rels)) for i in range(self.inductive_inference.num_entities) ], dtype=torch.long, device=self.device ) if self.gnn: self.gnn_encoder = StarE_PyG_Encoder( emb_dim=embedding_dim, device=device, num_rel=len(self.relation_embeddings.weight), use_lrga=use_lrga, num_layers=gnn_layers, drop1=drop_prob, residual=residual, jk=jk, layer_config={ 'STATEMENT_LEN': 3, 'EMBEDDING_DIM': embedding_dim, 'STAREARGS': { 'GCN_DROP': drop_prob, 'ATTENTION': gnn_att, 'GCN_DIM': embedding_dim, 'ATTENTION_HEADS': gcn_att_heads, 'ATTENTION_SLOPE': 0.1, 'ATTENTION_DROP': gnn_att_drop, 'BIAS': False, 'OPN': 'rotate', 'QUAL_AGGREGATE': 'sum', 'PNA': pna, }, }, ) def _reset_parameters_(self): if self.pooler != "avg": for module in self.set_enc.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if self.pooler == "mlp": for module in self.set_dec.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if not self.ablate_anchors: torch.nn.init.xavier_uniform_(self.anchor_embeddings.weight) torch.nn.init.xavier_uniform_(self.dist_emb.weight) if self.random_hashes == 0: with torch.no_grad(): self.anchor_embeddings.weight[self.tokenizer.token2id[self.tokenizer.PADDING_TOKEN]] = torch.zeros(self.embedding_dim) self.dist_emb.weight[0] = torch.zeros(self.embedding_dim) # for RotatE: phases randomly between 0 and 2 pi phases = 2 * np.pi * torch.rand(self.transductive_triples_factory.num_relations, self.real_embedding_dim, device=self.device) relations = torch.stack([torch.cos(phases), torch.sin(phases)], dim=-1).detach() assert torch.allclose(torch.norm(relations, p=2, dim=-1), phases.new_ones(size=(1, 1))) self.relation_embeddings.weight.data[:-1] = relations.view(self.transductive_triples_factory.num_relations, self.embedding_dim) self.relation_embeddings.weight.data[-1] = torch.zeros(self.embedding_dim) if self.gnn: self.gnn_encoder.reset_parameters() def post_parameter_update(self): # noqa: D102 # Make sure to call super first super().post_parameter_update() # Normalize relation embeddings rel = self.relation_embeddings.weight.data.view(self.transductive_triples_factory.num_relations+1, self.real_embedding_dim, 2) rel = functional.normalize(rel, p=2, dim=-1) self.relation_embeddings.weight.data = rel.view(self.transductive_triples_factory.num_relations+1, self.embedding_dim) self.entity_embeddings = None def pool_anchors(self, anc_embs: torch.FloatTensor, mask: Optional[torch.BoolTensor] = None) -> torch.FloatTensor: """ input shape: (bs, num_anchors + relational_context, emb_dim) output shape: (bs, emb_dim) """ if self.pooler == "cat": anc_embs = anc_embs.view(anc_embs.shape[0], -1) pooled = self.set_enc(anc_embs) if self.sample_paths != 1 else anc_embs elif self.pooler == "trf": pooled = self.set_enc(anc_embs.transpose(1, 0), src_key_padding_mask=mask) # output shape: (seq_len, bs, dim) pooled = pooled.mean(dim=0) # output shape: (bs, dim) return pooled def encode_by_index(self, entities: torch.LongTensor) -> torch.FloatTensor: # take a node index and find its NodePiece hash # hashes, dists, ids = self.hashes[entities], self.distances[entities], self.total_paths[entities] # # anc_embs = self.anchor_embeddings(hashes) # mask = None # # if self.use_distances: # dist_embs = self.dist_emb(dists) # anc_embs += dist_embs # # # for ablations: drop anchors entirely # if self.ablate_anchors: anc_embs = torch.tensor([], device=self.device) # add relational context (if its size > 0 ) if self.sample_rels > 0: if self.training: rels = self.unique_1hop_relations[entities] # (bs, rel_sample_size) else: rels = self.inference_1hop_relations[entities] mask = rels == self.pad_idx rels = self.relation_embeddings(rels) # (bs, rel_sample_size, dim) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask.to(anc_embs.device)) # (bs, dim) return anc_embs def get_all_representations(self): # materialize embeddings for all nodes in a graph for scoring if self.training: num_ents = len(self.unique_1hop_relations) else: num_ents = len(self.inference_1hop_relations) temp_embs = torch.zeros((num_ents, self.embedding_dim), dtype=torch.float, device=self.device) vocab_keys = list(range(num_ents)) for i in range(0, num_ents, self.subbatch): entities = torch.tensor(vocab_keys[i: i+self.subbatch], dtype=torch.long, device=self.device) embs = self.encode_by_index(entities) temp_embs[i: i + self.subbatch, :] = embs return temp_embs def encode_gnn(self): data = Data() data['x'] = self.get_all_representations() data['rels'] = self.relation_embeddings.weight if self.training: graph = self.transductive_triples_factory.mapped_triples else: graph = self.inductive_inference.mapped_triples direct_edges = graph[graph[:, 1] % 2 == 0][:, [0,2]].T inverse_edges = graph[graph[:, 1] % 2 == 1][:, [0,2]].T direct_types = graph[graph[:, 1] % 2 == 0, 1] inverse_types = graph[graph[:, 1] % 2 == 1, 1] data['edge_index'] = torch.cat([direct_edges, inverse_edges], dim=1) data['edge_type'] = torch.cat([direct_types, inverse_types], dim=0) x, r = self.gnn_encoder.forward_base(data.to(self.device)) return x, r def encode_gnn_entities(self, heads, rels, tails): x, r = self.encode_gnn() return x[heads] if heads is not None else x, r[rels], x[tails] if tails is not None else x @staticmethod def pairwise_interaction_function( h: torch.FloatTensor, r: torch.FloatTensor, ) -> torch.FloatTensor: # Decompose into real and imaginary part h_re = h[..., 0] h_im = h[..., 1] r_re = r[..., 0] r_im = r[..., 1] # Rotate (=Hadamard product in complex space). rot_h = torch.cat( [ h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re, ], dim=-1, ) return rot_h @staticmethod def interaction_function( h: torch.FloatTensor, r: torch.FloatTensor, t: torch.FloatTensor, ) -> torch.FloatTensor: # Decompose into real and imaginary part h_re = h[..., 0] h_im = h[..., 1] r_re = r[..., 0] r_im = r[..., 1] # Rotate (=Hadamard product in complex space). rot_h = torch.stack( [ h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re, ], dim=-1, ) # Workaround until https://github.com/pytorch/pytorch/issues/30704 is fixed diff = rot_h - t scores = -torch.norm(diff.view(diff.shape[:-2] + (-1,)), dim=-1) return scores def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 # when training with large # of neg samples the hrt_batch size can be too big to fit into memory, so chunk it if hrt_batch.shape[0] <= self.subbatch or self.subbatch == 0: # Get embeddings if not self.gnn: h = self.encode_by_index(hrt_batch[:, 0]).view(-1, self.real_embedding_dim, 2) r = self.relation_embeddings(hrt_batch[:, 1]).view(-1, self.real_embedding_dim, 2) t = self.encode_by_index(hrt_batch[:, 2]).view(-1, self.real_embedding_dim, 2) else: h, r, t = self.encode_gnn_entities(hrt_batch[:, 0], hrt_batch[:, 1], hrt_batch[:, 2]) h, r, t = h.view(-1, self.real_embedding_dim, 2), r.view(-1, self.real_embedding_dim, 2), t.view(-1, self.real_embedding_dim, 2) # Compute scores scores = self.interaction_function(h=h, r=r, t=t).view(-1, 1) else: scores = torch.zeros((hrt_batch.shape[0], 1), dtype=torch.float, device=hrt_batch.device) for i in range(0, hrt_batch.shape[0], self.subbatch): if not self.gnn: h = self.encode_by_index(hrt_batch[i: i+self.subbatch, 0]).view(-1, self.real_embedding_dim, 2) r = self.relation_embeddings(hrt_batch[i: i+self.subbatch, 1]).view(-1, self.real_embedding_dim, 2) t = self.encode_by_index(hrt_batch[i: i+self.subbatch, 2]).view(-1, self.real_embedding_dim, 2) else: h, r, t = self.encode_gnn_entities(hrt_batch[i: i+self.subbatch, 0], hrt_batch[i: i+self.subbatch, 1], hrt_batch[i: i+self.subbatch, 2]) h, r, t = h.view(-1, self.real_embedding_dim, 2), r.view(-1, self.real_embedding_dim, 2), t.view(-1, self.real_embedding_dim, 2) # Compute scores scores[i: i+self.subbatch] = self.interaction_function(h=h, r=r, t=t).view(-1, 1) return scores def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 if not self.gnn: # Get embeddings h = self.encode_by_index(hr_batch[:, 0]).view(-1, 1, self.real_embedding_dim, 2) r = self.relation_embeddings(hr_batch[:, 1]).view(-1, 1, self.real_embedding_dim, 2) # Rank against all entities, don't use hard negs, EXPENSIVE t = self.get_all_representations().view(1, -1, self.real_embedding_dim, 2) else: h, r, t = self.encode_gnn_entities(hr_batch[:, 0], hr_batch[:, 1], None) h, r, t = h.view(-1, 1, self.real_embedding_dim, 2), r.view(-1, 1, self.real_embedding_dim, 2), t.view(1, -1, self.real_embedding_dim, 2) # Compute scores if self.subbatch == 0: scores = self.interaction_function(h=h, r=r, t=t) else: scores = torch.zeros((hr_batch.shape[0], t.shape[1]), dtype=torch.float, device=hr_batch.device) for i in tqdm(range(0, t.shape[1], self.subbatch)): temp_scores = self.interaction_function(h=h, r=r, t=t[:, i:i+self.subbatch, :, :]) scores[:, i:i+self.subbatch] = temp_scores return scores def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 # Get embeddings if not self.gnn: r = self.relation_embeddings(rt_batch[:, 0]).view(-1, 1, self.real_embedding_dim, 2) t = self.encode_by_index(rt_batch[:, 1]).view(-1, 1, self.real_embedding_dim, 2) r_inv = torch.stack([r[:, :, :, 0], -r[:, :, :, 1]], dim=-1) # Rank against all entities h = self.get_all_representations().view(1, -1, self.real_embedding_dim, 2) else: h, r, t = self.encode_gnn_entities(None, rt_batch[:, 0], rt_batch[:, 1]) h, r, t = h.view(1, -1, self.real_embedding_dim, 2), r.view(-1, 1, self.real_embedding_dim, 2), t.view(-1, 1, self.real_embedding_dim, 2) r_inv = torch.stack([r[:, :, :, 0], -r[:, :, :, 1]], dim=-1) # Compute scores if self.subbatch == 0: scores = self.interaction_function(h=t, r=r_inv, t=h) else: scores = torch.zeros((rt_batch.shape[0], t.shape[1]), dtype=torch.float, device=rt_batch.device) for i in tqdm(range(0, t.shape[1], self.subbatch)): temp_scores = self.interaction_function(h=t, r=r_inv, t=h[:, i:i+self.subbatch, :, :]) scores[:, i:i+self.subbatch] = temp_scores return scores ================================================ FILE: inductive_lp/nodepiece_tokenizer.py ================================================ import torch import numpy as np import pickle from igraph import Graph from pykeen.triples import TriplesFactory from typing import List, Dict from pathlib import Path from tqdm import tqdm import random class NodePiece_Tokenizer: """ Tokenizer for KGs: will select topK anchor nodes according to certain strategies; and encode other nodes as 'words' as hashes comprised of relations and anchor nodes """ def __init__(self, triples: TriplesFactory, dataset_name: str, num_anchors: int, anchor_strategy: Dict[str, float], limit_shortest: int = 0, limit_random: int = 0, add_identity: bool = True, mode: str = "path", ) -> None: super().__init__() self.triples_factory = triples # original triples of the training graph, assuming inverses are added self.dataset_name = dataset_name self.num_anchors = num_anchors # total N anchor nodes self.anchor_strategy = anchor_strategy # ratios of strategies for sampling anchor nodes self.num_paths = num_anchors # aux variable self.sp_limit = limit_shortest # keep only K nearest anchor nodes per entity self.rand_limit = limit_random # keep only K random anchor nodes per entity if self.sp_limit * self.rand_limit != 0: raise Exception("-sp_limit and -rand_limit are mutually exclusive") self.add_identity = add_identity # anchor nodes will have their own indices with distance 0 in their hashes self.tkn_mode = mode # only "path" mode is implemented atm # auxiliary tokens for the vocabulary self.NOTHING_TOKEN = -99 # means the node is not reachable from any of anchor nodes self.CLS_TOKEN = -1 self.MASK_TOKEN = -10 self.PADDING_TOKEN = -100 self.SEP_TOKEN = -2 # well, betwenness is disabled self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) # load or create the vocabulary self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() # numerical indices for entities and relations self.token2id = {t: i for i, t in enumerate(self.top_entities)} self.rel2token = {t: i + len(self.top_entities) for i, t in enumerate(list(self.triples_factory.relation_to_id.values()))} self.vocab_size = len(self.token2id) + len(self.rel2token) # although we don't use paths, we count their lengths for anchor distances self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) if self.add_identity: # add identity for anchor nodes as the first / closest node if self.tkn_mode != "bfs": for anchor in self.top_entities[:-4]: # last 4 are always service tokens self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] else: for anchor in self.top_entities[:-4]: self.vocab[anchor]['ancs'] = [anchor] + self.vocab[anchor]['ancs'][:-1] self.vocab[anchor]['dists'][0] = 0 def tokenize_kg(self): # creating a filename strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" if self.sp_limit > 0: filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths if self.rand_limit > 0: filename += f"_{self.rand_limit}rand" if self.tkn_mode == "bfs": filename += "_bfs" filename += ".pkl" self.model_name = filename.split('.pkl')[0] path = Path(filename) if path.is_file(): anchors, non_anchors, vocab = pickle.load(open(path, "rb")) return anchors, non_anchors, vocab if type(self.triples_factory.mapped_triples) == torch.Tensor: src, tgt, rels = self.triples_factory.mapped_triples[:, 0].numpy(), self.triples_factory.mapped_triples[:, 2].numpy(), self.triples_factory.mapped_triples[:, 1].numpy() else: print("Input triples are expected to be in the torch.Tensor format") raise NotImplementedError # create an input object for iGraph - edge list with relation types, and then create a graph edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] graph = Graph(n=self.triples_factory.num_entities, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) # sampling anchor nodes anchors = [] for strategy, ratio in self.anchor_strategy.items(): if ratio <= 0.0: continue topK = int(np.ceil(ratio * self.num_anchors)) print(f"Computing the {strategy} nodes") if strategy == "degree": top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) elif strategy == "betweenness": # This is O(V^3) - disabled raise NotImplementedError("Betweenness is disabled due to computational costs") elif strategy == "pagerank": top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) elif strategy == "random": top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.triples_factory.num_entities))] selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] anchors.extend(selected_nodes) print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") # now mine the anchors per node vocab = self.create_all_paths(graph, anchors) top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] non_core_entities = [i for i in range(self.triples_factory.num_entities) if i not in anchors] pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) print("Vocabularized and saved!") return top_entities, non_core_entities, vocab def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: vocab = {} if self.rand_limit == 0: print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} shortest paths per node") else: print(f"Computing the entity vocabulary - paths, retaining {self.rand_limit} random paths per node") if self.tkn_mode: anc_set = set(top_entities) # single-threaded mining is found to be as fast as multi-processing + igraph for some reason, so let's use a dummy for-loop for i in tqdm(range(self.triples_factory.num_entities)): if self.tkn_mode == "path": paths = graph.get_shortest_paths(v=i, to=top_entities, output="epath", mode='in') if len(paths[0]) > 0: relation_paths = [[graph.es[path[-1]].source] + [graph.es[k]['relation'] for k in path[::-1]] for path in paths if len(path) > 0] else: # if NO anchor can be reached from the node - encode with a special NOTHING_TOKEN relation_paths = [[self.NOTHING_TOKEN] for _ in range(self.num_paths)] if self.sp_limit > 0: relation_paths = sorted(relation_paths, key=lambda x: len(x))[:self.sp_limit] if self.rand_limit > 0: random.shuffle(relation_paths) relation_paths = relation_paths[:self.rand_limit] vocab[i] = relation_paths elif self.tkn_mode == "bfs": # overall limit of anchors per node limit = self.sp_limit if self.sp_limit != 0 else (self.rand_limit if self.rand_limit != 0 else self.num_paths) nearest_ancs, anc_dists = [], [] hop = 1 while len(nearest_ancs) < limit: neigbs = graph.neighborhood(vertices=i, order=hop, mode="in", mindist=hop) # get k-hop neighbors ancs = list(set(neigbs).intersection(anc_set).difference(set(nearest_ancs))) # find anchors in this neighborhood nearest_ancs.extend(ancs) # update the list of anchors anc_dists.extend([hop for _ in range(len(ancs))]) # update the list of anchor distances hop += 1 if hop >= 50: # hardcoded constant for a disconnected node nearest_ancs.extend([self.NOTHING_TOKEN for _ in range(limit - len(nearest_ancs))]) anc_dists.extend([0 for _ in range(limit - len(anc_dists))]) break vocab[i] = {'ancs': nearest_ancs[:limit], 'dists': anc_dists[:limit]} # update the vocabulary else: raise NotImplementedError return vocab ================================================ FILE: inductive_lp/run_ilp.py ================================================ from pykeen.datasets import FB15k237, WN18RR, YAGO310 from pykeen.utils import resolve_device from pykeen.losses import BCEWithLogitsLoss, SoftplusLoss, MarginRankingLoss, NSSALoss from pykeen.training import SLCWATrainingLoop, LCWATrainingLoop from pykeen.trackers import WANDBResultTracker from pykeen.evaluation import RankBasedEvaluator from pykeen.sampling import BasicNegativeSampler from pykeen.stoppers import EarlyStopper from pykeen.models import RotatE from torch.optim import Adam from model.nodepiece_rotate import NodePieceRotate from nodepiece_tokenizer import NodePiece_Tokenizer from loops.inductive_slcwa import InductiveSLCWATrainingLoop from loops.relation_rank_evaluator import RelationPredictionRankBasedEvaluator from loops.ilp_evaluator import ILPRankBasedEvaluator from utils.sample_negatives import sample_negatives from datasets.ind_dataset import Ind_FB15k237, Ind_WN18RR, Ind_NELL import torch import click import numpy as np import wandb import random torch.manual_seed(42) np.random.seed(42) random.seed(42) @click.command() @click.option('-embedding', '--embedding-dimension', type=int, default=100) # embedding dim for anchors and relations @click.option('-loss', '--loss_fc', type=str, default='nssal') @click.option('-loop', '--loop', type=str, default='slcwa') # slcwa - negative sampling, lcwa - 1-N scoring @click.option('-trf_hidden', '--transformer-hidden-dim', type=int, default=200) @click.option('-trf_heads', '--transformer-num-heads', type=int, default=4) @click.option('-trf_layers', '--transformer-layers', type=int, default=2) @click.option('-trf_drop', '--transformer-dropout', type=float, default=0.1) @click.option('-b', '--batch-size', type=int, default=512) @click.option('-eval_bs', '--eval_bs', type=int, default=512) @click.option('-epochs', '--num-epochs', type=int, default=1000) @click.option('-lr', '--learning-rate', type=float, default=0.0005) @click.option('-wandb', '--enable_wandb', type=bool, default=False) @click.option('-data', '--dataset_name', type=str, default='wn18rr') @click.option('-eval_every', '--eval_every', type=int, default=1) @click.option('-ft_maxp', '--ft_max_paths', type=int, default=100) # max anchor per node, should be <= total N of anchors @click.option('-anc_dist', '--use_anchor_distances', type=bool, default=False) # whether to add anchor distances @click.option('-margin', '--margin', type=float, default=15) @click.option('-max_seq_len', '--max_seq_len', type=int, default=0) @click.option('-pool', '--pooling', type=str, default="cat") # available encoders: "cat" or "trf" @click.option('-subbatch', '--trf_subbatch', type=int, default=3000) @click.option('-negs', '--num_negatives_ent', type=int, default=1) # number of negative samples when training LP in sLCWA @click.option('-smoothing', '--lbl_smoothing', type=float, default=0.0) # label smoothing in the 1-N setup @click.option('-relpol', '--rel_policy', type=str, default="sum") @click.option('-rand_hashes', '--random_hashing', type=int, default=0) # for ablations: use only random numbers as hashes @click.option('-nn', '--nearest_neighbors', type=bool, default=True) # use only nearest anchors per node @click.option('-sample_rels', '--sample_rels', type=int, default=4) # size of the relational context M @click.option('-tkn_mode', '--tkn_mode', type=str, default="bfs") # mining paths in iGRAPH, @click.option('-no_anc', '--ablate_anchors', type=bool, default=True) # don't use any anchors in hashes, keep only the relational context @click.option('-ind_v', '--ind_v', type=int, default=1) # 1 / 2 / 3 / 4 - which version of GraIL splits to use @click.option('-rp', '--rp', type=bool, default=False) # turn on for the relation prediction task @click.option('-gnn', '--gnn', type=bool, default=False) # whether to use a GNN encoder on top of NodePiece features @click.option('-gnn_att', '--gnn_att', type=bool, default=False) # GNN with attentional aggregation @click.option('-gnn_lrga', '--gnn_lrga', type=bool, default=False) # Low-Rank Global Attention @click.option('-gnn_layers', '--gnn_layers', type=int, default=2) @click.option('-gnn_att_drop', '--gnn_att_drop', type=float, default=0.1) @click.option('-ilp_eval', '--ilp_eval', type=bool, default=True) # True stands for evaluation of 50 random samples to replicate the Teru et al setup, otherwise eval against all entities @click.option('-pna', '--pna', type=bool, default=False) # whether to use Principal Neighborhood Aggregation @click.option('-residual', '--residual', type=bool, default=False) # whether to use residual connections for deep GNNs @click.option('-jk', '--jk', type=bool, default=False) # JK connections for GNN def main( embedding_dimension, loss_fc, loop, transformer_hidden_dim: int, transformer_num_heads: int, transformer_layers: int, transformer_dropout: float, batch_size: int, eval_bs: int, num_epochs: int, learning_rate: float, enable_wandb: bool, dataset_name: str, eval_every: int, ft_max_paths: int, use_anchor_distances: bool, margin: float, max_seq_len: int, pooling: str, trf_subbatch: int, num_negatives_ent: int, lbl_smoothing: float, rel_policy: str, random_hashing: int, nearest_neighbors: bool, sample_rels: int, tkn_mode: str, ablate_anchors: bool, ind_v: int, rp: bool, gnn: bool, gnn_att: bool, gnn_lrga: bool, gnn_layers: int, gnn_att_drop: float, ilp_eval: bool, pna: bool, residual: bool, jk: bool, ): # Standard dataset loading procedures, inverses are necessary for reachability of nodes if dataset_name == 'fb15k237': dataset = Ind_FB15k237(create_inverse_triples=True, version=ind_v) elif dataset_name == 'wn18rr': dataset = Ind_WN18RR(create_inverse_triples=True, version=ind_v) elif dataset_name == "nell": dataset = Ind_NELL(create_inverse_triples=True, version=ind_v) negative_sampler_cls = BasicNegativeSampler negative_sampler_kwargs = dict(num_negs_per_pos=num_negatives_ent) loop_type = InductiveSLCWATrainingLoop training_triples_factory = dataset.transductive_part inference_triples_factory = dataset.inductive_inference # No need for anchor tokenization in inductive datasets # We will do relation-only tokenization inside the model kg_tokenizer = None device = resolve_device() # selecting the loss function if loss_fc == "softplus": ft_loss = SoftplusLoss() elif loss_fc == "bce": ft_loss = BCEWithLogitsLoss() elif loss_fc == "mrl": ft_loss = MarginRankingLoss(margin=margin) elif loss_fc == "nssal": ft_loss = NSSALoss(margin=margin) # Create the model finetuning_model = NodePieceRotate(embedding_dim=embedding_dimension, device=device, loss=ft_loss, triples=training_triples_factory, inference_triples=inference_triples_factory,max_paths=ft_max_paths, subbatch=trf_subbatch, max_seq_len=max_seq_len, tokenizer=kg_tokenizer, pooler=pooling, hid_dim=transformer_hidden_dim, num_heads=transformer_num_heads, use_distances=use_anchor_distances, num_layers=transformer_layers, drop_prob=transformer_dropout, rel_policy=rel_policy, random_hashes=random_hashing, nearest=nearest_neighbors, sample_rels=sample_rels, ablate_anchors=ablate_anchors, tkn_mode=tkn_mode, gnn=gnn, gnn_att=gnn_att, use_lrga=gnn_lrga, gnn_layers=gnn_layers, gnn_att_drop=gnn_att_drop, pna=pna, residual=residual, jk=jk) optimizer = Adam(params=finetuning_model.parameters(), lr=learning_rate) print(f"Number of params: {sum(p.numel() for p in finetuning_model.parameters())}") if loop == "slcwa": ft_loop = loop_type(model=finetuning_model, optimizer=optimizer, negative_sampler_cls=negative_sampler_cls, negative_sampler_kwargs=negative_sampler_kwargs) else: ft_loop = LCWATrainingLoop(model=finetuning_model, optimizer=optimizer) # add the results tracker if requested if enable_wandb: project_name = "NodePiece_ILP" tracker = WANDBResultTracker(project=project_name, group=None, settings=wandb.Settings(start_method='fork')) tracker.wandb.config.update(click.get_current_context().params) else: tracker = None if rp: valid_evaluator = RelationPredictionRankBasedEvaluator() else: if not ilp_eval: valid_evaluator = RankBasedEvaluator else: # GraIL-style evaluation against 50 random negatives head_samples, tail_samples = sample_negatives(valid_triples=dataset.inductive_val, all_pos=dataset.inductive_inference) valid_evaluator = ILPRankBasedEvaluator(head_samples=head_samples, tail_samples=tail_samples) valid_evaluator.batch_size = eval_bs # we don't actually use the early stopper here by setting the patience to 100000 early_stopper = EarlyStopper( model=finetuning_model, relative_delta=0.0005, evaluation_triples_factory=dataset.inductive_val, frequency=eval_every, patience=100000, result_tracker=tracker, evaluation_batch_size=eval_bs, evaluator=valid_evaluator, ) # Train LP / RP if loop == "lcwa": ft_loop.train(num_epochs=num_epochs, batch_size=batch_size, result_tracker=tracker, stopper=early_stopper, label_smoothing=lbl_smoothing) else: ft_loop.train(num_epochs=num_epochs, batch_size=batch_size, result_tracker=tracker, stopper=early_stopper) # run the final test eval if rp: test_evaluator = RelationPredictionRankBasedEvaluator() else: if not ilp_eval: test_evaluator = RankBasedEvaluator else: # test evaluator head_samples, tail_samples = sample_negatives(valid_triples=dataset.inductive_test, all_pos=dataset.inductive_inference) test_evaluator = ILPRankBasedEvaluator(head_samples=head_samples, tail_samples=tail_samples) test_evaluator.batch_size = eval_bs # test_evaluator metric_results = test_evaluator.evaluate( model=finetuning_model, mapped_triples=dataset.inductive_test.mapped_triples, additional_filter_triples=[dataset.inductive_inference.mapped_triples, dataset.inductive_val.mapped_triples], use_tqdm=True, batch_size=eval_bs, ) # log final results if enable_wandb: tracker.log_metrics( metrics=metric_results.to_flat_dict(), step=num_epochs + 1, prefix='test', ) print("Test results") print(metric_results) if __name__ == '__main__': main() ================================================ FILE: inductive_lp/utils/sample_negatives.py ================================================ import torch import numpy as np from pykeen.datasets import TriplesFactory from collections import defaultdict def sample_negatives(valid_triples: TriplesFactory, all_pos: TriplesFactory, num_samples: int = 50): val_triples = valid_triples.mapped_triples all_pos_triples = all_pos.mapped_triples num_entities = all_pos.num_entities head_samples, tail_samples = [[] for _ in range(len(val_triples))], [[] for _ in range(len(val_triples))] head_index = defaultdict(list) tail_index = defaultdict(list) for triple in all_pos_triples: h,r,t = triple[0].item(), triple[1].item(), triple[2].item() tail_index[(h, r)].append(t) head_index[(r, t)].append(h) for i, row in enumerate(val_triples): head, rel, tail = row[0].item(), row[1].item(), row[2].item() head_samples[i].append(head) while len(head_samples[i]) < num_samples: neg_head = np.random.choice(num_entities) if neg_head != head and neg_head not in head_index[(rel, tail)]: head_samples[i].append(neg_head) tail_samples[i].append(tail) while len(tail_samples[i]) < num_samples: neg_tail = np.random.choice(num_entities) if neg_tail != tail and neg_tail not in tail_index[(head, rel)]: tail_samples[i].append(neg_tail) head_samples = torch.tensor(head_samples, dtype=torch.long) tail_samples = torch.tensor(tail_samples, dtype=torch.long) return head_samples, tail_samples ================================================ FILE: inductive_lp/utils/utils_gcn.py ================================================ import inspect import numpy as np # PyTorch related imports import torch import torch_scatter from torch.nn import Parameter from torch.nn.init import xavier_normal_ from torch_scatter import scatter_add, scatter_max np.set_printoptions(precision=4) class MessagePassing(torch.nn.Module): r"""Base class for creating message passing layers .. math:: \mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i, \square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}} \left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{i,j}\right) \right), where :math:`\square` denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}` and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as MLPs. See `here `__ for the accompanying tutorial. """ def __init__(self, aggr='add'): super(MessagePassing, self).__init__() # In the defined message function: get the list of arguments as list of string| # For eg. in r-gcn this will be ['x_j', 'edge_type', 'edge_norm'] (args of message fn) self.message_args = inspect.getargspec(self.message)[0][1:] # Same for update function starting from 3rd argument | first=self, second=out self.update_args = inspect.getargspec(self.update)[0][2:] def propagate(self, aggr, edge_index, **kwargs): r"""The initial call to start propagating messages. Takes in an aggregation scheme (:obj:`"add"`, :obj:`"mean"` or :obj:`"max"`), the edge indices, and all additional data which is needed to construct messages and to update node embeddings.""" assert aggr in ['add', 'mean', 'max'] kwargs['edge_index'] = edge_index size = None message_args = [] for arg in self.message_args: if arg[-2:] == '_i': # If arguments ends with _i then include indic tmp = kwargs[ arg[:-2]] # Take the front part of the variable | Mostly it will be 'x', size = tmp.size(0) message_args.append(tmp[edge_index[0]]) # Lookup for head entities in edges elif arg[-2:] == '_j': tmp = kwargs[arg[:-2]] # tmp = kwargs['x'] size = tmp.size(0) message_args.append(tmp[edge_index[1]]) # Lookup for tail entities in edges else: message_args.append(kwargs[arg]) # Take things from kwargs update_args = [kwargs[arg] for arg in self.update_args] # Take update args from kwargs out = self.message(*message_args) out = scatter_(aggr, out, edge_index[0], dim_size=size) # Aggregated neighbors for each vertex out = self.update(out, *update_args) return out def message(self, x_j): # pragma: no cover r"""Constructs messages in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in :math:`(i,j) \in \mathcal{E}`. Can take any argument which was initially passed to :meth:`propagate`. In addition, features can be lifted to the source node :math:`i` and target node :math:`j` by appending :obj:`_i` or :obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.""" return x_j def update(self, aggr_out): # pragma: no cover r"""Updates node embeddings in analogy to :math:`\gamma_{\mathbf{\Theta}}` for each node :math:`i \in \mathcal{V}`. Takes in the output of aggregation as first argument and any argument which was initially passed to :meth:`propagate`.""" return aggr_out def maybe_num_nodes(index, num_nodes=None): return index.max().item() + 1 if num_nodes is None else num_nodes def softmax(src, index, num_nodes=None): r"""Computes a sparsely evaluated softmax. Given a value tensor :attr:`src`, this function first groups the values along the first dimension based on the indices specified in :attr:`index`, and then proceeds to compute the softmax individually for each group. Args: src (Tensor): The source tensor. index (LongTensor): The indices of elements for applying the softmax. num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`index`. (default: :obj:`None`) :rtype: :class:`Tensor` """ num_nodes = maybe_num_nodes(index, num_nodes) out = src - scatter_max(src, index, dim=0, dim_size=num_nodes)[0][index] out = out.exp() out = out / ( scatter_add(out, index, dim=0, dim_size=num_nodes)[index] + 1e-16) return out def get_param(shape): param = Parameter(torch.Tensor(*shape)) xavier_normal_(param.data) return param def weight_init(layer): if type(layer) == torch.nn.Linear: torch.nn.init.xavier_normal_(layer.weight.data) if layer.bias is not None: torch.nn.init.constant_(layer.bias.data, 0) return def com_mult(a, b): r1, i1 = a[..., 0], a[..., 1] r2, i2 = b[..., 0], b[..., 1] return torch.stack([r1 * r2 - i1 * i2, r1 * i2 + i1 * r2], dim=-1) def conj(a): a[..., 1] = -a[..., 1] return a def cconv(a, b): return torch.irfft(com_mult(torch.rfft(a, 1), torch.rfft(b, 1)), 1, signal_sizes=(a.shape[-1],)) def ccorr(a, b): return torch.irfft(com_mult(conj(torch.rfft(a, 1)), torch.rfft(b, 1)), 1, signal_sizes=(a.shape[-1],)) def rotate(h, r): # re: first half, im: second half # assume embedding dim is the last dimension d = h.shape[-1] h_re, h_im = torch.split(h, d // 2, -1) r_re, r_im = torch.split(r, d // 2, -1) return torch.cat([h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re], dim=-1) def scatter_(name, src, index, dim_size=None): r"""Aggregates all values from the :attr:`src` tensor at the indices specified in the :attr:`index` tensor along the first dimension. If multiple indices reference the same location, their contributions are aggregated according to :attr:`name` (either :obj:`"add"`, :obj:`"mean"` or :obj:`"max"`). Args: name (string): The aggregation to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). src (Tensor): The source tensor. index (LongTensor): The indices of elements to scatter. dim_size (int, optional): Automatically create output tensor with size :attr:`dim_size` in the first dimension. If set to :attr:`None`, a minimal sized output tensor is returned. (default: :obj:`None`) :rtype: :class:`Tensor` """ assert name in ['add', 'mean', 'max'] op = getattr(torch_scatter, 'scatter_{}'.format(name)) fill_value = -1e38 if name == 'max' else 0 out = op(src, index, 0, None, dim_size, fill_value) if isinstance(out, tuple): out = out[0] if name == 'max': out[out == fill_value] = 0 return out ================================================ FILE: lp_rp/datasets/codex.py ================================================ # -*- coding: utf-8 -*- """The **Co**mpletion **D**atasets **Ex**tracted from Wikidata and Wikipedia (CoDEx) datasets from [safavi2020]_. - GitHub Repository: https://github.com/tsafavi/codex - Paper: https://arxiv.org/abs/2009.07810 """ #from docdata import parse_docdata import logging import os import requests import shutil import pathlib import pystow from pathlib import Path from pykeen.datasets.base import PathDataSet from typing import Optional, Mapping, Any, Union from pystow.utils import name_from_url from urllib.request import urlretrieve logger = logging.getLogger(__name__) BASE_URL = 'https://raw.githubusercontent.com/tsafavi/codex/master/data/triples/' SMALL_VALID_URL = f'{BASE_URL}/codex-s/valid.txt' SMALL_TEST_URL = f'{BASE_URL}/codex-s/test.txt' SMALL_TRAIN_URL = f'{BASE_URL}/codex-s/train.txt' MEDIUM_VALID_URL = f'{BASE_URL}/codex-m/valid.txt' MEDIUM_TEST_URL = f'{BASE_URL}/codex-m/test.txt' MEDIUM_TRAIN_URL = f'{BASE_URL}/codex-m/train.txt' LARGE_VALID_URL = f'{BASE_URL}/codex-l/valid.txt' LARGE_TEST_URL = f'{BASE_URL}/codex-l/test.txt' LARGE_TRAIN_URL = f'{BASE_URL}/codex-l/train.txt' PYKEEN_MODULE: pystow.Module = pystow.module('pykeen') #: A path representing the PyKEEN data folder #PYKEEN_HOME: Path = PYKEEN_MODULE.base #: A subdirectory of the PyKEEN data folder for datasets, defaults to ``~/.data/pykeen/datasets`` PYKEEN_DATASETS: Path = PYKEEN_MODULE.get('datasets') # If GitHub ever gets upset from too many downloads, we can switch to # the data posted at https://github.com/pykeen/pykeen/pull/154#issuecomment-730462039 def _urlretrieve(url: str, path: str, clean_on_failure: bool = True, stream: bool = True) -> None: """Download a file from a given URL. :param url: URL to download :param path: Path to download the file to :param clean_on_failure: If true, will delete the file on any exception raised during download :param stream: If true, use :func:`requests.get`. By default, use ``urlretrieve``. :raises Exception: If there's a problem wih downloading via :func:`requests.get` or copying the data with :func:`shutil.copyfileobj` :raises KeyboardInterrupt: If the user quits during download """ if not stream: logger.info('downloading from %s to %s', url, path) urlretrieve(url, path) # noqa:S310 else: # see https://requests.readthedocs.io/en/master/user/quickstart/#raw-response-content # pattern from https://stackoverflow.com/a/39217788/5775947 try: with requests.get(url, stream=True) as response, open(path, 'wb') as file: logger.info('downloading (streaming) from %s to %s', url, path) shutil.copyfileobj(response.raw, file) except (Exception, KeyboardInterrupt): if clean_on_failure: os.remove(path) raise class UnpackedRemoteDataset(PathDataSet): """A dataset with all three of train, test, and validation sets as URLs.""" def __init__( self, training_url: str, testing_url: str, validation_url: str, cache_root: Optional[str] = None, stream: bool = True, force: bool = False, eager: bool = False, create_inverse_triples: bool = False, load_triples_kwargs: Optional[Mapping[str, Any]] = None, ): """Initialize dataset. :param training_url: The URL of the training file :param testing_url: The URL of the testing file :param validation_url: The URL of the validation file :param cache_root: An optional directory to store the extracted files. Is none is given, the default PyKEEN directory is used. This is defined either by the environment variable ``PYKEEN_HOME`` or defaults to ``~/.pykeen``. :param stream: Use :mod:`requests` be used for download if true otherwise use :mod:`urllib` :param force: If true, redownload any cached files :param eager: Should the data be loaded eagerly? Defaults to false. :param create_inverse_triples: Should inverse triples be created? Defaults to false. :param load_triples_kwargs: Arguments to pass through to :func:`TriplesFactory.from_path` and ultimately through to :func:`pykeen.triples.utils.load_triples`. """ self.cache_root = self._help_cache(cache_root) self.training_url = training_url self.testing_url = testing_url self.validation_url = validation_url training_path = os.path.join(self.cache_root, name_from_url(self.training_url)) testing_path = os.path.join(self.cache_root, name_from_url(self.testing_url)) validation_path = os.path.join(self.cache_root, name_from_url(self.validation_url)) for url, path in [ (self.training_url, training_path), (self.testing_url, testing_path), (self.validation_url, validation_path), ]: if os.path.exists(path) and not force: continue _urlretrieve(url, path, stream=stream) super().__init__( training_path=training_path, testing_path=testing_path, validation_path=validation_path, eager=eager, create_inverse_triples=create_inverse_triples, #load_triples_kwargs=load_triples_kwargs, ) def _help_cache(self, cache_root: Union[None, str, pathlib.Path]) -> pathlib.Path: """Get the appropriate cache root directory. :param cache_root: If none is passed, defaults to a subfolder of the PyKEEN home directory defined in :data:`pykeen.constants.PYKEEN_HOME`. The subfolder is named based on the class inheriting from :class:`pykeen.datasets.base.Dataset`. :returns: A path object for the calculated cache root directory """ if cache_root is None: cache_root = PYKEEN_DATASETS cache_root = pathlib.Path(cache_root) / self.__class__.__name__.lower() cache_root.mkdir(parents=True, exist_ok=True) logger.debug('using cache root at %s', cache_root) return cache_root #docs]@parse_docdata class CoDExSmall(UnpackedRemoteDataset): """The CoDEx small dataset. --- name: CoDEx (small) citation: author: Safavi year: 2020 link: https://arxiv.org/abs/2009.07810 github: tsafavi/codex statistics: entities: 2034 relations: 42 training: 32888 testing: 1828 validation: 1827 triples: 36543 """ def __init__(self, create_inverse_triples: bool = False, **kwargs): """Initialize the `CoDEx `_ small dataset from [safavi2020]_. :param create_inverse_triples: Should inverse triples be created? Defaults to false. :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.UnpackedRemoteDataset`. """ # GitHub's raw.githubusercontent.com service rejects requests that are streamable. This is # normally the default for all of PyKEEN's remote datasets, so just switch the default here. kwargs.setdefault('stream', False) super().__init__( training_url=SMALL_TRAIN_URL, testing_url=SMALL_TEST_URL, validation_url=SMALL_VALID_URL, create_inverse_triples=create_inverse_triples, **kwargs, ) #[docs]@parse_docdata class CoDExMedium(UnpackedRemoteDataset): """The CoDEx medium dataset. --- name: CoDEx (medium) citation: author: Safavi year: 2020 link: https://arxiv.org/abs/2009.07810 github: tsafavi/codex statistics: entities: 17050 relations: 51 training: 185584 testing: 10311 validation: 10310 triples: 206205 """ def __init__(self, create_inverse_triples: bool = False, **kwargs): """Initialize the `CoDEx `_ medium dataset from [safavi2020]_. :param create_inverse_triples: Should inverse triples be created? Defaults to false. :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.UnpackedRemoteDataset`. """ kwargs.setdefault('stream', False) # See comment in CoDExSmall super().__init__( training_url=MEDIUM_TRAIN_URL, testing_url=MEDIUM_TEST_URL, validation_url=MEDIUM_VALID_URL, create_inverse_triples=create_inverse_triples, **kwargs, ) #[docs]@parse_docdata class CoDExLarge(UnpackedRemoteDataset): """The CoDEx large dataset. --- name: CoDEx (large) citation: author: Safavi year: 2020 link: https://arxiv.org/abs/2009.07810 github: tsafavi/codex statistics: entities: 77951 relations: 69 training: 551193 testing: 30622 validation: 30622 triples: 612437 """ def __init__(self, create_inverse_triples: bool = False, **kwargs): """Initialize the `CoDEx `_ large dataset from [safavi2020]_. :param create_inverse_triples: Should inverse triples be created? Defaults to false. :param kwargs: keyword arguments passed to :class:`pykeen.datasets.base.UnpackedRemoteDataset`. """ kwargs.setdefault('stream', False) # See comment in CoDExSmall super().__init__( training_url=LARGE_TRAIN_URL, testing_url=LARGE_TEST_URL, validation_url=LARGE_VALID_URL, create_inverse_triples=create_inverse_triples, **kwargs, ) def _main(): for cls in [CoDExSmall, CoDExMedium, CoDExLarge]: d = cls() d.summarize() if __name__ == '__main__': _main() ================================================ FILE: lp_rp/loops/filtered_sampling_loop.py ================================================ from pykeen.training import SLCWATrainingLoop # from pykeen105.negative_sampler import SelfAdversarialNegativeSampler, RelationalNegativeSampler import pdb class FilteredSLCWATrainingLoop(SLCWATrainingLoop): @property def num_negs_per_pos_rel(self) -> int: """Return number of negatives per positive from the sampler. Property for API compatibility """ return self.negative_sampler.num_negs_per_pos_rel def _mr_loss_helper(self, positive_scores, negative_scores, _label_smoothing=None): if self.num_negs_per_pos + self.num_negs_per_pos_rel > 1: positive_scores = positive_scores.repeat(self.num_negs_per_pos + self.num_negs_per_pos_rel, 1) return self.model.compute_mr_loss( positive_scores=positive_scores, negative_scores=negative_scores, ) ================================================ FILE: lp_rp/nodepiece_tokenizer.py ================================================ import torch import numpy as np import pickle from igraph import Graph from pykeen.triples import TriplesFactory from typing import List, Dict from pathlib import Path from tqdm import tqdm import random class NodePiece_Tokenizer: """ Tokenizer for KGs: will select topK anchor nodes according to certain strategies; and encode other nodes as 'words' as hashes comprised of relations and anchor nodes """ def __init__(self, triples: TriplesFactory, dataset_name: str, num_anchors: int, anchor_strategy: Dict[str, float], limit_shortest: int = 0, limit_random: int = 0, add_identity: bool = True, mode: str = "path", ) -> None: super().__init__() self.triples_factory = triples # original triples of the training graph, assuming inverses are added self.dataset_name = dataset_name self.num_anchors = num_anchors # total N anchor nodes self.anchor_strategy = anchor_strategy # ratios of strategies for sampling anchor nodes self.num_paths = num_anchors # aux variable self.sp_limit = limit_shortest # keep only K nearest anchor nodes per entity self.rand_limit = limit_random # keep only K random anchor nodes per entity if self.sp_limit * self.rand_limit != 0: raise Exception("-sp_limit and -rand_limit are mutually exclusive") self.add_identity = add_identity # anchor nodes will have their own indices with distance 0 in their hashes self.tkn_mode = mode # only "path" mode is implemented atm # auxiliary tokens for the vocabulary self.NOTHING_TOKEN = -99 # means the node is not reachable from any of anchor nodes self.CLS_TOKEN = -1 self.MASK_TOKEN = -10 self.PADDING_TOKEN = -100 self.SEP_TOKEN = -2 # well, betwenness is disabled self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) # load or create the vocabulary self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() # numerical indices for entities and relations self.token2id = {t: i for i, t in enumerate(self.top_entities)} self.rel2token = {t: i + len(self.top_entities) for i, t in enumerate(list(self.triples_factory.relation_to_id.values()))} self.vocab_size = len(self.token2id) + len(self.rel2token) # although we don't use paths, we count their lengths for anchor distances self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) if self.add_identity: # add identity for anchor nodes as the first / closest node if self.tkn_mode != "bfs": for anchor in self.top_entities[:-4]: # last 4 are always service tokens self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] else: for anchor in self.top_entities[:-4]: self.vocab[anchor]['ancs'] = [anchor] + self.vocab[anchor]['ancs'][:-1] self.vocab[anchor]['dists'][0] = 0 def tokenize_kg(self): # creating a filename strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" if self.sp_limit > 0: filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths if self.rand_limit > 0: filename += f"_{self.rand_limit}rand" if self.tkn_mode == "bfs": filename += "_bfs" filename += ".pkl" self.model_name = filename.split('.pkl')[0] path = Path(filename) if path.is_file(): anchors, non_anchors, vocab = pickle.load(open(path, "rb")) return anchors, non_anchors, vocab if type(self.triples_factory.mapped_triples) == torch.Tensor: src, tgt, rels = self.triples_factory.mapped_triples[:, 0].numpy(), self.triples_factory.mapped_triples[:, 2].numpy(), self.triples_factory.mapped_triples[:, 1].numpy() else: print("Input triples are expected to be in the torch.Tensor format") raise NotImplementedError # create an input object for iGraph - edge list with relation types, and then create a graph edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] graph = Graph(n=self.triples_factory.num_entities, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) # sampling anchor nodes anchors = [] for strategy, ratio in self.anchor_strategy.items(): if ratio <= 0.0: continue topK = int(np.ceil(ratio * self.num_anchors)) print(f"Computing the {strategy} nodes") if strategy == "degree": top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) elif strategy == "betweenness": # This is O(V^3) - disabled raise NotImplementedError("Betweenness is disabled due to computational costs") elif strategy == "pagerank": top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) elif strategy == "random": top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.triples_factory.num_entities))] selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] anchors.extend(selected_nodes) print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") # now mine the anchors per node vocab = self.create_all_paths(graph, anchors) top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] non_core_entities = [i for i in range(self.triples_factory.num_entities) if i not in anchors] pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) print("Vocabularized and saved!") return top_entities, non_core_entities, vocab def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: vocab = {} if self.rand_limit == 0: print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} shortest paths per node") else: print(f"Computing the entity vocabulary - paths, retaining {self.rand_limit} random paths per node") if self.tkn_mode: anc_set = set(top_entities) # single-threaded mining is found to be as fast as multi-processing + igraph for some reason, so let's use a dummy for-loop for i in tqdm(range(self.triples_factory.num_entities)): if self.tkn_mode == "path": paths = graph.get_shortest_paths(v=i, to=top_entities, output="epath", mode='in') if len(paths[0]) > 0: relation_paths = [[graph.es[path[-1]].source] + [graph.es[k]['relation'] for k in path[::-1]] for path in paths if len(path) > 0] else: # if NO anchor can be reached from the node - encode with a special NOTHING_TOKEN relation_paths = [[self.NOTHING_TOKEN] for _ in range(self.num_paths)] if self.sp_limit > 0: relation_paths = sorted(relation_paths, key=lambda x: len(x))[:self.sp_limit] if self.rand_limit > 0: random.shuffle(relation_paths) relation_paths = relation_paths[:self.rand_limit] vocab[i] = relation_paths elif self.tkn_mode == "bfs": # overall limit of anchors per node limit = self.sp_limit if self.sp_limit != 0 else (self.rand_limit if self.rand_limit != 0 else self.num_paths) nearest_ancs, anc_dists = [], [] hop = 1 while len(nearest_ancs) < limit: neigbs = graph.neighborhood(vertices=i, order=hop, mode="in", mindist=hop) # get k-hop neighbors ancs = list(set(neigbs).intersection(anc_set).difference(set(nearest_ancs))) # find anchors in this neighborhood nearest_ancs.extend(ancs) # update the list of anchors anc_dists.extend([hop for _ in range(len(ancs))]) # update the list of anchor distances hop += 1 if hop >= 50: # hardcoded constant for a disconnected node nearest_ancs.extend([self.NOTHING_TOKEN for _ in range(limit - len(nearest_ancs))]) anc_dists.extend([0 for _ in range(limit - len(anc_dists))]) break vocab[i] = {'ancs': nearest_ancs[:limit], 'dists': anc_dists[:limit]} # update the vocabulary else: raise NotImplementedError return vocab ================================================ FILE: lp_rp/patch/early_stopping.py ================================================ # -*- coding: utf-8 -*- """Implementation of early stopping.""" import dataclasses import logging from dataclasses import dataclass from typing import Any, Callable, List, Mapping, Optional, Union from .stopper import Stopper from ..evaluation import Evaluator from ..models.base import Model from ..trackers import ResultTracker from ..triples import TriplesFactory from ..utils import fix_dataclass_init_docs __all__ = [ 'is_improvement', 'EarlyStopper', 'StopperCallback', ] logger = logging.getLogger(__name__) StopperCallback = Callable[[Stopper, Union[int, float], int], None] def is_improvement( best_value: float, current_value: float, larger_is_better: bool, relative_delta: float = 0.0, ) -> bool: """ Decide whether the current value is an improvement over the best value. :param best_value: The best value so far. :param current_value: The current value. :param larger_is_better: Whether a larger value is better. :param relative_delta: A minimum relative improvement until it is considered as an improvement. :return: Whether the current value is better. """ if larger_is_better: return current_value > (1.0 + relative_delta) * best_value # now: smaller is better return current_value < (1.0 - relative_delta) * best_value @fix_dataclass_init_docs @dataclass class EarlyStopper(Stopper): """A harness for early stopping.""" #: The model model: Model = dataclasses.field(repr=False) #: The evaluator evaluator: Evaluator #: The triples to use for evaluation evaluation_triples_factory: Optional[TriplesFactory] #: Size of the evaluation batches evaluation_batch_size: Optional[int] = None #: Slice size of the evaluation batches evaluation_slice_size: Optional[int] = None #: The number of epochs after which the model is evaluated on validation set frequency: int = 10 #: The number of iterations (one iteration can correspond to various epochs) #: with no improvement after which training will be stopped. patience: int = 2 #: The name of the metric to use metric: str = 'hits_at_k' #: The minimum relative improvement necessary to consider it an improved result relative_delta: float = 0.01 #: The best result so far best_metric: Optional[float] = None #: The epoch at which the best result occurred best_epoch: Optional[int] = None #: The remaining patience remaining_patience: int = dataclasses.field(init=False) #: The metric results from all evaluations results: List[float] = dataclasses.field(default_factory=list, repr=False) #: Whether a larger value is better, or a smaller larger_is_better: bool = True #: The result tracker result_tracker: Optional[ResultTracker] = None #: Callbacks when after results are calculated result_callbacks: List[StopperCallback] = dataclasses.field(default_factory=list, repr=False) #: Callbacks when training gets continued continue_callbacks: List[StopperCallback] = dataclasses.field(default_factory=list, repr=False) #: Callbacks when training is stopped early stopped_callbacks: List[StopperCallback] = dataclasses.field(default_factory=list, repr=False) #: Did the stopper ever decide to stop? stopped: bool = False def __post_init__(self): """Run after initialization and check the metric is valid.""" # TODO: Fix this # if all(f.name != self.metric for f in dataclasses.fields(self.evaluator.__class__)): # raise ValueError(f'Invalid metric name: {self.metric}') if self.evaluation_triples_factory is None: raise ValueError('Must specify a validation_triples_factory or a dataset for using early stopping.') self.remaining_patience = self.patience # Dummy result tracker if self.result_tracker is None: self.result_tracker = ResultTracker() def should_evaluate(self, epoch: int) -> bool: """Decide if evaluation should be done based on the current epoch and the internal frequency.""" return epoch > 0 and epoch % self.frequency == 0 @property def number_results(self) -> int: """Count the number of results stored in the early stopper.""" return len(self.results) def should_stop(self, epoch: int) -> bool: """Evaluate on a metric and compare to past evaluations to decide if training should stop.""" # Evaluate metric_results = self.evaluator.evaluate( model=self.model, mapped_triples=self.evaluation_triples_factory.mapped_triples, additional_filter_triples=self.model.triples_factory.mapped_triples, use_tqdm=False, batch_size=self.evaluation_batch_size, slice_size=self.evaluation_slice_size, # Only perform time consuming checks for the first call. do_time_consuming_checks=self.evaluation_batch_size is None, ) # After the first evaluation pass the optimal batch and slice size is obtained and saved for re-use self.evaluation_batch_size = self.evaluator.batch_size self.evaluation_slice_size = self.evaluator.slice_size self.result_tracker.log_metrics( metrics=metric_results.to_flat_dict(), step=epoch, prefix='validation', ) result = metric_results.get_metric(self.metric) # Append to history self.results.append(result) for result_callback in self.result_callbacks: result_callback(self, result, epoch) # check for improvement if self.best_metric is None or is_improvement( best_value=self.best_metric, current_value=result, larger_is_better=self.larger_is_better, relative_delta=self.relative_delta, ): self.best_epoch = epoch self.best_metric = result self.remaining_patience = self.patience else: self.remaining_patience -= 1 # Stop if the result did not improve more than delta for patience evaluations if self.remaining_patience <= 0: logger.info( f'Stopping early after {self.number_results} evaluations at epoch {epoch}. The best result ' f'{self.metric}={self.best_metric} occurred at epoch {self.best_epoch}.', ) for stopped_callback in self.stopped_callbacks: stopped_callback(self, result, epoch) self.stopped = True return True for continue_callback in self.continue_callbacks: continue_callback(self, result, epoch) return False def get_summary_dict(self) -> Mapping[str, Any]: """Get a summary dict.""" return dict( frequency=self.frequency, patience=self.patience, relative_delta=self.relative_delta, metric=self.metric, larger_is_better=self.larger_is_better, results=self.results, stopped=self.stopped, best_epoch=self.best_epoch, best_metric=self.best_metric, ) ================================================ FILE: lp_rp/patch/evaluator.py ================================================ # -*- coding: utf-8 -*- """Basic structure of a evaluator.""" import gc import logging import timeit from abc import ABC, abstractmethod from contextlib import contextmanager from dataclasses import dataclass from math import ceil from typing import Any, Collection, List, Mapping, Optional, Tuple, Union import torch from dataclasses_json import dataclass_json from ..models.base import Model from ..tqdmw import tqdm from ..triples.triples_factory import get_unique_entity_ids_from_triples_tensor from ..typing import MappedTriples from ..utils import is_cuda_oom_error, is_cudnn_error, normalize_string, split_list_in_batches_iter __all__ = [ 'Evaluator', 'MetricResults', 'filter_scores_', 'evaluate', ] logger = logging.getLogger(__name__) @contextmanager def optional_context_manager(condition, context_manager): if condition: with context_manager: yield context_manager else: yield @dataclass_json @dataclass class MetricResults: """Results from computing metrics.""" def get_metric(self, name: str) -> float: """Get the given metric from the results.""" raise NotImplementedError def to_flat_dict(self) -> Mapping[str, Any]: """Get the results as a flattened dictionary.""" return self.to_dict() class Evaluator(ABC): """An abstract evaluator for KGE models. The evaluator encapsulates the computation of evaluation metrics based on head and tail scores. To this end, it offers two methods to process a batch of triples together with the scores produced by some model. It maintains intermediate results in its state, and offers a method to obtain the final results once finished. """ def __init__( self, filtered: bool = False, requires_positive_mask: bool = False, batch_size: int = None, slice_size: int = None, ): self.filtered = filtered self.requires_positive_mask = requires_positive_mask self.batch_size = batch_size self.slice_size = slice_size @classmethod def get_normalized_name(cls) -> str: """Get the normalized name of the evaluator.""" return normalize_string(cls.__name__, suffix=Evaluator.__name__) @abstractmethod def process_tail_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: """Process a batch of triples with their computed tail scores for all entities. :param hrt_batch: shape: (batch_size, 3) :param true_scores: shape: (batch_size) :param scores: shape: (batch_size, num_entities) :param dense_positive_mask: shape: (batch_size, num_entities) An optional binary (0/1) tensor indicating other true entities. """ raise NotImplementedError @abstractmethod def process_head_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: """Process a batch of triples with their computed head scores for all entities. :param hrt_batch: shape: (batch_size, 3) :param true_scores: shape: (batch_size) :param scores: shape: (batch_size, num_entities) :param dense_positive_mask: shape: (batch_size, num_entities) An optional binary (0/1) tensor indicating other true entities. """ raise NotImplementedError @abstractmethod def finalize(self) -> MetricResults: """Compute the final results, and clear buffers.""" raise NotImplementedError def evaluate( self, model: Model, mapped_triples: Optional[MappedTriples] = None, additional_filter_triples: Union[None, MappedTriples, List[MappedTriples]] = None, batch_size: Optional[int] = None, slice_size: Optional[int] = None, device: Optional[torch.device] = None, use_tqdm: bool = True, tqdm_kwargs: Optional[Mapping[str, str]] = None, restrict_entities_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> MetricResults: """Run :func:`pykeen.evaluation.evaluate` with this evaluator.""" if mapped_triples is None: mapped_triples = model.triples_factory.mapped_triples if batch_size is None and model.automatic_memory_optimization: batch_size, slice_size = self.batch_and_slice( model=model, mapped_triples=mapped_triples, additional_filter_triples=additional_filter_triples, batch_size=batch_size, device=device, use_tqdm=False, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) # The batch_size and slice_size should be accessible to outside objects for re-use, e.g. early stoppers. self.batch_size = batch_size self.slice_size = slice_size # Clear the ranks from the current evaluator self.finalize() return evaluate( model=model, mapped_triples=mapped_triples, additional_filtered_triples=additional_filter_triples, evaluators=self, batch_size=batch_size, slice_size=slice_size, device=device, squeeze=True, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) def batch_and_slice( self, model: Model, mapped_triples: MappedTriples, additional_filter_triples: Union[None, MappedTriples, List[MappedTriples]] = None, batch_size: Optional[int] = None, device: Optional[torch.device] = None, use_tqdm: bool = False, restrict_entities_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> Tuple[int, Optional[int]]: """Find the maximum possible batch_size and slice_size for evaluation with the current setting. The speed of evaluation can be greatly increased when the batch_size is increased, therefore this function estimates the maximal possible batch_size for the evaluation by starting with the batch_size given as argument and increasing it until the hardware runs out-of-memory(OOM). In some cases, i.e. with very large models or very large datasets, even the batch_size 1 is too big for the hardware at hand. In these cases, this function will check if the model at hand allows slicing (this needs to be implemented for the affected scoring functions) and, if possible, will search the maximum possible slice_size that would still allow to calculate the model with the given parameters on the hardware at hand. :param model: The model to evaluate. :param mapped_triples: The triples on which to evaluate. :param batch_size: The initial batch size to start with. None defaults to number_of_triples. :param device: The device on which the evaluation shall be run. If None is given, use the model's device. :param use_tqdm: Should a progress bar be displayed? :param restrict_entities_to: Whether to restrict the evaluation to certain entities of interest. :return: Maximum possible batch size and, if necessary, the slice_size, which defaults to None. :raises MemoryError: If it is not possible to evaluate the model on the hardware at hand with the given parameters. """ batch_size, evaluated_once = self._param_size_search( key='batch_size', start_value=batch_size, model=model, mapped_triples=mapped_triples, additional_filter_triples=additional_filter_triples, device=device, use_tqdm=use_tqdm, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) if evaluated_once: # slice_size = None return batch_size, None # We need to try slicing, if the evaluation for the batch_size search never succeeded slice_size, evaluated_once = self._param_size_search( key='slice_size', # Since the batch_size search with size 1, i.e. one tuple ((h, r) or (r, t)) scored on all entities, # must have failed to start slice_size search, we start with trying half the entities. start_value=ceil(model.num_entities / 2), model=model, mapped_triples=mapped_triples, additional_filter_triples=additional_filter_triples, device=device, use_tqdm=use_tqdm, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=False, ) if not evaluated_once: raise MemoryError("The current model can't be trained on this hardware with these parameters.") return batch_size, slice_size def _param_size_search( self, key: str, start_value: int, model: Model, mapped_triples: MappedTriples, additional_filter_triples: Union[None, MappedTriples, List[MappedTriples]] = None, device: Optional[torch.device] = None, use_tqdm: bool = False, restrict_entities_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> Tuple[int, bool]: values_dict = {} maximum_triples = mapped_triples.shape[0] if key == 'batch_size': if start_value is None: start_value = 256 if start_value > maximum_triples: start_value = maximum_triples values_dict[key] = start_value values_dict['slice_size'] = None elif key == 'slice_size': self._check_slicing_availability(model, batch_size=1) values_dict[key] = start_value values_dict['batch_size'] = 1 else: raise AttributeError(f'The parameter {key} is unknown.') reached_max = False evaluated_once = False logger.info(f'Starting {key} search for evaluation now...') while True: logger.debug(f'Trying {key}={values_dict[key]}') try: # The cache of the previous run has to be freed to allow accurate memory availability estimates gc.collect() torch.cuda.empty_cache() evaluate( **values_dict, model=model, mapped_triples=mapped_triples, additional_filtered_triples=additional_filter_triples, evaluators=self, only_size_probing=True, device=device, squeeze=True, use_tqdm=use_tqdm, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) evaluated_once = True except RuntimeError as runtime_error: # Due to the caused OOM Runtime Error, the failed model has to be cleared to avoid memory leakage for p in model.parameters(): if p.grad is not None: del p.grad # free some memory # The cache of the previous run has to be freed to allow accurate memory availability estimates gc.collect() torch.cuda.empty_cache() if not is_cudnn_error(runtime_error) and not is_cuda_oom_error(runtime_error): raise runtime_error if values_dict[key] == 1: logger.debug( f"Even {key} {values_dict[key]} does not fit into your memory with these parameters.", ) break values_dict[key] //= 2 reached_max = True if evaluated_once: logger.info(f'Concluded {key} search with batch_size={values_dict[key]}.') break else: logger.debug(f'The {key} {values_dict[key]} was too big, trying less now') else: # The cache of the previous run has to be freed to allow accurate memory availability estimates gc.collect() torch.cuda.empty_cache() if not reached_max and values_dict['batch_size'] < maximum_triples: values_dict[key] *= 2 else: logger.info(f'Concluded {key} search with batch_size={values_dict[key]}.') break return values_dict[key], evaluated_once @staticmethod def _check_slicing_availability(model: Model, batch_size: int) -> None: # Test if slicing is implemented for the required functions of this model if model.triples_factory.create_inverse_triples: if not model.can_slice_t: raise MemoryError(f"The current model can't be evaluated on this hardware with these parameters, as " f"evaluation batch_size={batch_size} is too big and slicing is not implemented for " f"this model yet.") elif not model.can_slice_t or not model.can_slice_h: raise MemoryError(f"The current model can't be evaluated on this hardware with these parameters, as " f"evaluation batch_size={batch_size} is too big and slicing is not implemented for this " f"model yet.") def create_sparse_positive_filter_( hrt_batch: MappedTriples, all_pos_triples: torch.LongTensor, relation_filter: torch.BoolTensor = None, filter_col: int = 0, ) -> Tuple[torch.LongTensor, torch.BoolTensor]: """Compute indices of all positives. For simplicity, only the head-side is described, i.e. filter_col=0. The tail-side is processed alike. For each (h, r, t) triple in the batch, the entity identifiers are computed such that (h', r, t) exists in all positive triples. :param hrt_batch: shape: (batch_size, 3) A batch of triples. :param all_pos_triples: shape: (num_positive_triples, 3) All positive triples to base the filtering on. :param relation_filter: shape: (batch_size, num_positive_triples) A boolean mask R[i, j] which is True iff the j-th positive triple contains the same relation as the i-th triple in the batch. :param filter_col: The column along which to filter. Allowed are {0, 2}, where 0 corresponds to filtering head-based and 2 corresponds to filtering tail-based. :return: - positives, shape: (2, m) The indices of positives in format [(batch_index, entity_id)]. - the relation filter for re-usage. """ if filter_col not in {0, 2}: raise NotImplementedError( 'This code has only been written for updating head (filter_col=0) or ' f'tail (filter_col=2) mask, but filter_col={filter_col} was given.', ) if relation_filter is None: relations = hrt_batch[:, 1:2] relation_filter = (all_pos_triples[:, 1:2]).view(1, -1) == relations # Split batch other_col = 2 - filter_col entities = hrt_batch[:, other_col:other_col + 1] entity_filter_test = (all_pos_triples[:, other_col:other_col + 1]).view(1, -1) == entities filter_batch = (entity_filter_test & relation_filter).nonzero(as_tuple=False) filter_batch[:, 1] = all_pos_triples[:, filter_col:filter_col + 1].view(1, -1)[:, filter_batch[:, 1]] return filter_batch, relation_filter def create_dense_positive_mask_( zero_tensor: torch.FloatTensor, filter_batch: torch.LongTensor, ) -> torch.FloatTensor: """Construct dense positive mask. :param zero_tensor: shape: (batch_size, num_entities) A tensor of zeros of suitable shape. :param filter_batch: shape: (m, 2) The indices of all positives in format (batch_index, entity_id) :return: The dense positive mask with x[b, i] = 1 iff (b, i) in filter_batch. """ zero_tensor[filter_batch[:, 0], filter_batch[:, 1]] = 1 return zero_tensor def filter_scores_( scores: torch.FloatTensor, filter_batch: torch.LongTensor, ) -> torch.FloatTensor: """Filter scores by setting true scores to NaN. :param scores: shape: (batch_size, num_entities) The scores for all corrupted triples (including the currently considered true triple). Are modified *in-place*. :param filter_batch: (m, 2) The indices of all positives. :return: A reference to the scores, which have been updated in-place. """ # Bind shape batch_size, num_entities = scores.shape # Set all filtered triples to NaN to ensure their exclusion in subsequent calculations scores[filter_batch[:, 0], filter_batch[:, 1]] = float('nan') # Warn if all entities will be filtered # (scores != scores) yields true for all NaN instances (IEEE 754), thus allowing to count the filtered triples. if ((scores != scores).sum(dim=1) == num_entities).any(): logger.warning( "User selected filtered metric computation, but all corrupted triples exists also as positive " "triples", ) return scores def evaluate( model: Model, mapped_triples: MappedTriples, evaluators: Union[Evaluator, Collection[Evaluator]], additional_filtered_triples: Union[None, MappedTriples, List[MappedTriples]] = None, only_size_probing: bool = False, batch_size: Optional[int] = None, slice_size: Optional[int] = None, device: Optional[torch.device] = None, squeeze: bool = True, use_tqdm: bool = True, tqdm_kwargs: Optional[Mapping[str, str]] = None, restrict_entities_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> Union[MetricResults, List[MetricResults]]: """Evaluate metrics for model on mapped triples. The model is used to predict scores for all tails and all heads for each triple. Subsequently, each abstract evaluator is applied to the scores, also receiving the batch itself (e.g. to compute entity-specific metrics). Thereby, the (potentially) expensive score computation against all entities is done only once. The metric evaluators are expected to maintain their own internal buffers. They are returned after running the evaluation, and should offer a possibility to extract some final metrics. :param model: The model to evaluate. :param mapped_triples: The triples on which to evaluate. :param evaluators: An evaluator or a list of evaluators working on batches of triples and corresponding scores. :param only_size_probing: The evaluation is only performed for two batches to test the memory footprint, especially on GPUs. :param batch_size: >0 A positive integer used as batch size. Generally chosen as large as possible. Defaults to 1 if None. :param slice_size: >0 The divisor for the scoring function when using slicing. :param device: The device on which the evaluation shall be run. If None is given, use the model's device. :param squeeze: Return a single instance of :class:`MetricResults` if only one evaluator was given. :param use_tqdm: Should a progress bar be displayed? :param restrict_entities_to: Optionally restrict the evaluation to the given entity IDs. This may be useful if one is only interested in a part of the entities, e.g. due to type constraints, but wants to train on all available data. For ranking the entities, we still compute all scores for all possible replacement entities to avoid irregular access patterns which might decrease performance, but the scores with afterwards be filtered to only keep those of interest. If provided, we assume that the triples are already filtered, such that it only contains the entities of interest. :param do_time_consuming_checks: Whether to perform some time consuming checks on the provided arguments. Currently, this encompasses: - If restrict_entities_to is not None, check whether the triples have been filtered. Disabling this option can accelerate the method. """ if isinstance(evaluators, Evaluator): # upgrade a single evaluator to a list evaluators = [evaluators] start = timeit.default_timer() # verify that the triples have been filtered if restrict_entities_to is not None and do_time_consuming_checks: present_entity_ids = set(get_unique_entity_ids_from_triples_tensor(mapped_triples=mapped_triples).tolist()) unwanted = present_entity_ids.difference(restrict_entities_to.tolist()) if len(unwanted) > 0: raise ValueError(f'mapped_triples contains IDs of entities which are not contained in restrict_entities_to:' f'{unwanted}. This will invalidate the evaluation results.') # Send to device if device is not None: model = model.to(device) device = model.device # Ensure evaluation mode model.eval() # Split evaluators into those which need unfiltered results, and those which require filtered ones filtered_evaluators = list(filter(lambda e: e.filtered, evaluators)) unfiltered_evaluators = list(filter(lambda e: not e.filtered, evaluators)) # Check whether we need to be prepared for filtering filtering_necessary = len(filtered_evaluators) > 0 # Check whether an evaluator needs access to the masks # This can only be an unfiltered evaluator. positive_masks_required = any(e.requires_positive_mask for e in unfiltered_evaluators) # Prepare for result filtering if filtering_necessary or positive_masks_required: #all_pos_triples = torch.cat([model.triples_factory.mapped_triples, mapped_triples], dim=0) if additional_filtered_triples is None: logger.warning("The filtered setting was enabled, but there were no `additional_filtered_triples" "given. This means you probably forgot to pass (at least) the training triples. Try:" "additional_filtered_triples=[dataset.training.mapped_triples]" "Or if you want to use the Bordes et al. (2013) approach to filtering, do:" "additional_filtered_triples=[dataset.training.mapped_triples,dataset.validation.mapped_triples,]") all_pos_triples = mapped_triples elif isinstance(additional_filtered_triples, (list, tuple)): all_pos_triples = torch.cat([*additional_filtered_triples, mapped_triples], dim=0) else: all_pos_triples = torch.cat([additional_filtered_triples, mapped_triples], dim=0) all_pos_triples = all_pos_triples.to(device=device) else: all_pos_triples = None # Send tensors to device mapped_triples = mapped_triples.to(device=device) # Prepare batches if batch_size is None: batch_size = 1 batches = split_list_in_batches_iter(input_list=mapped_triples, batch_size=batch_size) # Show progressbar num_triples = mapped_triples.shape[0] # Flag to check when to quit the size probing evaluated_once = False # Disable gradient tracking _tqdm_kwargs = dict( desc=f'Evaluating on {model.device}', total=num_triples, unit='triple', unit_scale=True, # Choosing no progress bar (use_tqdm=False) would still show the initial progress bar without disable=True disable=not use_tqdm, ) if tqdm_kwargs: _tqdm_kwargs.update(tqdm_kwargs) with optional_context_manager(use_tqdm, tqdm(**_tqdm_kwargs)) as progress_bar, torch.no_grad(): # batch-wise processing for batch in batches: batch_size = batch.shape[0] relation_filter = None for column in (0, 2): relation_filter = _evaluate_batch( batch=batch, model=model, column=column, filtered_evaluators=filtered_evaluators, unfiltered_evaluators=unfiltered_evaluators, slice_size=slice_size, all_pos_triples=all_pos_triples, relation_filter=relation_filter, restrict_entities_to=restrict_entities_to, positive_masks_required=positive_masks_required, filtering_necessary=filtering_necessary, ) # If we only probe sizes we do not need more than one batch if only_size_probing and evaluated_once: break evaluated_once = True if use_tqdm: progress_bar.update(batch_size) # Finalize results = [evaluator.finalize() for evaluator in evaluators] stop = timeit.default_timer() if only_size_probing: logger.debug("Evaluation took %.2fs seconds", stop - start) else: logger.info("Evaluation took %.2fs seconds", stop - start) if squeeze and len(results) == 1: return results[0] return results def _evaluate_batch( batch: MappedTriples, model: Model, column: int, filtered_evaluators: Collection[Evaluator], unfiltered_evaluators: Collection[Evaluator], slice_size: Optional[int], all_pos_triples: Optional[MappedTriples], relation_filter: Optional[torch.BoolTensor], restrict_entities_to: Optional[torch.LongTensor], positive_masks_required: bool, filtering_necessary: bool, ) -> torch.BoolTensor: """ Evaluate batch for all head predictions(column=0), or all tail predictions (column=2). :param batch: shape: (batch_size, 3) The batch of currently evaluated triples. :param model: The model to evaluate. :param column: The column which to evaluate. Either 0 for head prediction, or 2 for tail prediction. :param filtered_evaluators: The evaluators which work on filtered scores. :param unfiltered_evaluators: The evaluators which work on unfiltered scores. :param slice_size: An optional slice size for computing the scores. :param all_pos_triples: All positive triples (required if filtering is necessary). :param relation_filter: The relation filter. Can be re-used. :param restrict_entities_to: Restriction to evaluate only for these entities. :param positive_masks_required: Whether dense positive masks are required (by any unfiltered evaluator). :param filtering_necessary: Whether filtering is necessary. :return: The relation filter, which can be re-used for the same batch. """ if column not in {0, 2}: raise ValueError(f'column must be either 0 or 2, but is column={column}') # Predict scores once if column == 2: # tail scores batch_scores_of_corrupted = model.predict_scores_all_tails(batch[:, 0:2], slice_size=slice_size) else: batch_scores_of_corrupted = model.predict_scores_all_heads(batch[:, 1:3], slice_size=slice_size) # Select scores of true batch_scores_of_true = batch_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, column], ] # Create positive filter for all corrupted if filtering_necessary or positive_masks_required: # Needs all positive triples if all_pos_triples is None: raise ValueError('If filtering_necessary of positive_masks_required is True, all_pos_triples has to be ' 'provided, but is None.') # Create filter positive_filter, relation_filter = create_sparse_positive_filter_( hrt_batch=batch, all_pos_triples=all_pos_triples, relation_filter=relation_filter, filter_col=column, ) # Create a positive mask with the size of the scores from the positive filter if positive_masks_required: positive_mask = create_dense_positive_mask_( zero_tensor=torch.zeros_like(batch_scores_of_corrupted), filter_batch=positive_filter, ) else: positive_mask = None # Restrict to entities of interest if restrict_entities_to is not None: batch_scores_of_corrupted_ = batch_scores_of_corrupted[:, restrict_entities_to] positive_mask = positive_mask[:, restrict_entities_to] else: batch_scores_of_corrupted_ = batch_scores_of_corrupted # Evaluate metrics on these *unfiltered* scores for unfiltered_evaluator in unfiltered_evaluators: if column == 2: # tail scores process = unfiltered_evaluator.process_tail_scores_ else: process = unfiltered_evaluator.process_head_scores_ process( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_scores_of_corrupted_, dense_positive_mask=positive_mask, ) # Filter if filtering_necessary: batch_filtered_scores_of_corrupted = filter_scores_( scores=batch_scores_of_corrupted, filter_batch=positive_filter, ) # The scores for the true triples have to be rewritten to the scores tensor batch_filtered_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, column], ] = batch_scores_of_true # Restrict to entities of interest if restrict_entities_to is not None: batch_filtered_scores_of_corrupted = batch_filtered_scores_of_corrupted[:, restrict_entities_to] # Evaluate metrics on these *filtered* scores for filtered_evaluator in filtered_evaluators: if column == 2: # tail scores process = filtered_evaluator.process_tail_scores_ else: process = filtered_evaluator.process_head_scores_ process( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_filtered_scores_of_corrupted, ) return relation_filter ================================================ FILE: lp_rp/pykeen105/negative_sampler.py ================================================ from pykeen.sampling import NegativeSampler import pdb import os import torch import numpy as np from collections import defaultdict import pickle def get_true_subject_and_object_per_graph(triples): true_head = defaultdict(lambda: defaultdict(list)) true_tail = defaultdict(lambda: defaultdict(list)) true_relations = defaultdict(lambda: defaultdict(list)) for head, relation, tail in triples: head, relation, tail = head.item(), relation.item(), tail.item() true_tail[head][relation].append(tail) true_head[tail][relation].append(head) true_relations[head][tail].append(relation) # this is correct for head in true_tail: for relation in true_tail[head]: true_tail[head][relation] = np.array(true_tail[head][relation]) for tail in true_head: for relation in true_head[tail]: true_head[tail][relation] = np.array(true_head[tail][relation]) for head in true_relations: for tail in true_relations[head]: true_relations[head][tail] = np.array(true_relations[head][tail]) return dict(true_head), dict(true_tail), dict(true_relations) class FilteredNegativeSampler(NegativeSampler): def __init__(self, triples_factory, num_negs_per_pos=None, dataset_name='fb15k237'): super(FilteredNegativeSampler, self).__init__(triples_factory, num_negs_per_pos) true_head_path = os.path.join("cached_input", dataset_name, "true_heads.pt") true_tail_path = os.path.join("cached_input", dataset_name, "true_tails.pt") true_relations_path = os.path.join("cached_input", dataset_name, "true_relations.pt") if os.path.exists(true_head_path) and os.path.exists(true_tail_path) and os.path.exists(true_relations_path): with open(true_head_path, 'rb') as handle: self.true_head = pickle.load(handle) with open(true_tail_path, 'rb') as handle: self.true_tail = pickle.load(handle) with open(true_relations_path, 'rb') as handle: self.true_relations = pickle.load(handle) else: os.makedirs(os.path.join("cached_input", dataset_name)) self.true_head, self.true_tail, self.true_relations = \ get_true_subject_and_object_per_graph(triples_factory.mapped_triples) with open(true_head_path, 'wb') as handle: pickle.dump(self.true_head, handle) with open(true_tail_path, 'wb') as handle: pickle.dump(self.true_tail, handle) with open(true_relations_path, 'wb') as handle: pickle.dump(self.true_relations, handle) def sample_entities(self, h, r, t, corrput_head=True): true_entities = self.true_head[t][r] if corrput_head else self.true_tail[h][r] negative_sample_list = [] negative_sample_size = 0 while negative_sample_size < self.num_negs_per_pos: negative_sample = np.random.choice(self.num_entities - 1, size=self.num_negs_per_pos) mask = np.in1d( negative_sample, true_entities, assume_unique=True, invert=True ) negative_sample = negative_sample[mask] negative_sample_list.append(negative_sample) negative_sample_size += negative_sample.size return np.concatenate(negative_sample_list)[:self.num_negs_per_pos] def sample(self, positive_batch: torch.LongTensor) -> torch.LongTensor: """Generate negative samples from the positive batch.""" half = positive_batch.shape[0] // 2 negative_batch_entity = positive_batch.repeat(self.num_negs_per_pos, 1).view(-1, positive_batch.shape[0], 3) # Sample random entities as replacement for i in range(len(positive_batch)): h, r, t = positive_batch[i] h, r, t = h.item(), r.item(), t.item() if i < half: negative_head = torch.from_numpy(self.sample_entities(h, r, t, corrput_head=True)) negative_batch_entity[:, i, 0] = negative_head else: negative_tail = torch.from_numpy(self.sample_entities(h, r, t, corrput_head=False)) negative_batch_entity[:, i, 2] = negative_tail # pdb.set_trace() return negative_batch_entity.view(-1, 3) class RelationalNegativeSampler(FilteredNegativeSampler): def __init__(self, triples_factory, num_negs_per_pos=None, num_negs_per_pos_rel=None, dataset_name=None): super(RelationalNegativeSampler, self).__init__(triples_factory, num_negs_per_pos, dataset_name) self.num_negs_per_pos_rel = num_negs_per_pos_rel if num_negs_per_pos_rel is not None else 1 @property def num_relations(self) -> int: # noqa: D401 """The number of entities to sample from.""" return self.triples_factory.num_relations def sample_relations(self, h, t): true_relations = self.true_relations[h][t] negative_sample_list = [] negative_sample_size = 0 while negative_sample_size < self.num_negs_per_pos_rel: negative_sample = np.random.choice(self.num_relations - 1, size=self.num_negs_per_pos_rel) mask = np.in1d( negative_sample, true_relations, assume_unique=True, invert=True ) negative_sample = negative_sample[mask] negative_sample_list.append(negative_sample) negative_sample_size += negative_sample.size return np.concatenate(negative_sample_list)[:self.num_negs_per_pos_rel] def sample(self, positive_batch): bsz = positive_batch.shape[0] negative_entities = super().sample(positive_batch) negative_relations = positive_batch.repeat(self.num_negs_per_pos_rel, 1).view(-1, bsz, 3) for i in range(len(positive_batch)): h, r, t = positive_batch[i] h, r, t = h.item(), r.item(), t.item() negative_relation = torch.from_numpy(self.sample_relations(h, t)) negative_relations[:, i, 1] = negative_relation return torch.cat([negative_entities, negative_relations.view(-1, 3)]) ================================================ FILE: lp_rp/pykeen105/nodepiece_rotate.py ================================================ import torch import random import numpy as np from torch.nn import functional from torch.nn import TransformerEncoder, TransformerEncoderLayer from typing import Optional from collections import Counter, defaultdict from torch import nn from pykeen.losses import Loss from pykeen.triples import TriplesFactory from pykeen.models import Model from tqdm import tqdm from nodepiece_tokenizer import NodePiece_Tokenizer class NodePieceRotate(Model): def __init__(self, tokenizer: NodePiece_Tokenizer = None, triples: TriplesFactory = None, device: torch.device = None, loss: Loss = None, max_paths: int = None, # max anchors per node subbatch: int = 32, max_seq_len: int = None, # tied with anchor distances embedding_dim: int = 100, hid_dim: int = 200, # hidden dim for the hash encoder num_heads: int = 4, # for Trf num_layers: int = 2, # for Trf pooler: str = "cat", # "cat" or "trf" drop_prob: float = 0.1, # dropout use_distances: bool = True, rel_policy: str = "sum", random_hashes: int = 0, # for ablations nearest: bool = True, # use only K nearest anchors per node sample_rels: int = 0, # size of the relational context ablate_anchors: bool = False, # for ablations - node hashes will be constructed only from the relational context tkn_mode: str = "path", # default NodePiece vocabularization strategy ): super().__init__( triples_factory=triples, loss=loss, predict_with_sigmoid=False, automatic_memory_optimization=False, preferred_device=device, ) self.pooler = pooler self.policy = rel_policy self.nearest = nearest self.sample_rels = sample_rels self.ablate_anchors = ablate_anchors self.tkn_mode = tkn_mode # cat pooler - concat all anchors+relations in one big vector, pass through a 2-layer MLP if pooler == "cat": self.set_enc = nn.Sequential( nn.Linear(embedding_dim * (max_paths + sample_rels), embedding_dim * 2), nn.Dropout(drop_prob), nn.ReLU(), nn.Linear(embedding_dim * 2, embedding_dim) ) if not self.ablate_anchors else nn.Sequential( nn.Linear(embedding_dim * sample_rels, embedding_dim * 2), nn.Dropout(drop_prob), nn.ReLU(), nn.Linear(embedding_dim * 2, embedding_dim) ) # trf pooler - vanilla transformer encoder with mean pooling on top elif pooler == "trf": encoder_layer = TransformerEncoderLayer( d_model=embedding_dim, nhead=num_heads, dim_feedforward=hid_dim, dropout=drop_prob, ) self.set_enc = TransformerEncoder(encoder_layer=encoder_layer, num_layers=num_layers) self.device = device self.loss = loss self.triples_factory = triples self.tokenizer = tokenizer self.random_hashes = random_hashes self.subbatch = subbatch self.embedding_dim = embedding_dim self.real_embedding_dim = embedding_dim // 2 # RotatE interaction assumes vectors conists of two parts: real and imaginary self.max_seq_len = max_seq_len self.sample_paths = max_paths self.use_distances = use_distances # pykeen stuff self.automatic_memory_optimization = False self.tokenizer.token2id[self.tokenizer.NOTHING_TOKEN] = len(tokenizer.token2id) - 1 # TODO this is a bugfix as PathTrfEncoder puts its own index here self.anchor_embeddings = nn.Embedding(len(tokenizer.token2id), embedding_dim=embedding_dim, padding_idx=self.tokenizer.token2id[tokenizer.PADDING_TOKEN]) self.relation_embeddings = nn.Embedding(self.triples_factory.num_relations + 1, embedding_dim=embedding_dim, padding_idx=self.triples_factory.num_relations) self.dist_emb = nn.Embedding(self.max_seq_len, embedding_dim=embedding_dim) self.entity_embeddings = None # now fix anchors per node for each node in a graph, either deterministically or randomly # we do it mostly for speed reasons, although this process can happen during the forward pass either if self.random_hashes == 0: if self.tkn_mode != "bfs": # DETERMINISTIC strategy if not self.nearest: # subsample paths, need to align them with distances sampled_paths = { entity: random.sample(paths, k=min(self.sample_paths, len(paths))) for entity, paths in self.tokenizer.vocab.items() } elif self.nearest: # sort paths by length first and take K of them sampled_paths = { entity: sorted(paths, key=lambda x: len(x))[:min(self.sample_paths, len(paths))] for entity, paths in self.tokenizer.vocab.items() } self.max_seq_len = max(len(path) for k,v in sampled_paths.items() for path in v) print(f"Changed max seq len from {max_seq_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") hashes = [ [self.tokenizer.token2id[path[0]] for path in paths] + [self.tokenizer.token2id[tokenizer.PADDING_TOKEN]]*(self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] distances = [ [len(path)-1 for path in paths] + [0] *(self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] total_paths = [ [len(paths)] for entity, paths in sampled_paths.items() ] else: # only nearest neighbors mode if not self.nearest: raise NotImplementedError("bfs mode works only with -nn True") hashes = [ [self.tokenizer.token2id[token] for token in vals['ancs'][:min(self.sample_paths, len(vals['ancs']))]] + [self.tokenizer.token2id[tokenizer.PADDING_TOKEN]] * (self.sample_paths - len(vals['ancs'])) for entity, vals in self.tokenizer.vocab.items() ] distances = [ [d for d in vals['dists'][:min(self.sample_paths, len(vals['dists']))]] + [0] * (self.sample_paths - len(vals['dists'])) for entity, vals in self.tokenizer.vocab.items() ] total_paths = distances self.max_seq_len = max([d for row in distances for d in row]) print(f"Changed max seq len from {max_seq_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.tensor(distances, dtype=torch.long, device=self.device) self.total_paths = torch.tensor(total_paths, dtype=torch.long, device=self.device) else: # RANDOM strategy # in this case, we bypass distances and won't use relations in the encoder self.anchor_embeddings = nn.Embedding(self.random_hashes, embedding_dim=embedding_dim) hashes = [ random.sample(list(range(random_hashes)), self.sample_paths) for i in range(triples.num_entities) ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.zeros((triples.num_entities, self.sample_paths), dtype=torch.long, device=self.device) self.total_paths = torch.zeros((triples.num_entities, 1), dtype=torch.long, device=self.device) # creating the relational context of M unique outgoing relation types for each node if self.sample_rels > 0: pad_idx = self.triples_factory.num_relations e2r = defaultdict(set) for row in self.triples_factory.mapped_triples: e2r[row[0].item()].add(row[1].item()) len_stats = [len(v) for k,v in e2r.items()] print(f"Unique relations per node - min: {min(len_stats)}, avg: {np.mean(len_stats)}, 66th perc: {np.percentile(len_stats, 66)}, max: {max(len_stats)} ") unique_1hop_relations = [ random.sample(e2r[i], k=min(self.sample_rels, len(e2r[i]))) + [pad_idx] * (self.sample_rels-min(len(e2r[i]), self.sample_rels)) for i in range(self.triples_factory.num_entities) ] self.unique_1hop_relations = torch.tensor(unique_1hop_relations, dtype=torch.long, device=self.device) def _reset_parameters_(self): if self.pooler != "avg": for module in self.set_enc.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if self.pooler == "mlp": for module in self.set_dec.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() torch.nn.init.xavier_uniform_(self.anchor_embeddings.weight) torch.nn.init.xavier_uniform_(self.dist_emb.weight) if self.random_hashes == 0: with torch.no_grad(): self.anchor_embeddings.weight[self.tokenizer.token2id[self.tokenizer.PADDING_TOKEN]] = torch.zeros(self.embedding_dim) self.dist_emb.weight[0] = torch.zeros(self.embedding_dim) # for RotatE: phases randomly between 0 and 2 pi phases = 2 * np.pi * torch.rand(self.num_relations, self.real_embedding_dim, device=self.device) relations = torch.stack([torch.cos(phases), torch.sin(phases)], dim=-1).detach() assert torch.allclose(torch.norm(relations, p=2, dim=-1), phases.new_ones(size=(1, 1))) self.relation_embeddings.weight.data[:-1] = relations.view(self.num_relations, self.embedding_dim) self.relation_embeddings.weight.data[-1] = torch.zeros(self.embedding_dim) def post_parameter_update(self): # noqa: D102 # Make sure to call super first super().post_parameter_update() # Normalize relation embeddings rel = self.relation_embeddings.weight.data.view(self.num_relations+1, self.real_embedding_dim, 2) rel = functional.normalize(rel, p=2, dim=-1) self.relation_embeddings.weight.data = rel.view(self.num_relations+1, self.embedding_dim) self.entity_embeddings = None def pool_anchors(self, anc_embs: torch.FloatTensor, mask: Optional[torch.BoolTensor] = None) -> torch.FloatTensor: """ input shape: (bs, num_anchors + relational_context, emb_dim) output shape: (bs, emb_dim) """ if self.pooler == "cat": anc_embs = anc_embs.view(anc_embs.shape[0], -1) pooled = self.set_enc(anc_embs) if self.sample_paths != 1 else anc_embs elif self.pooler == "trf": pooled = self.set_enc(anc_embs.transpose(1, 0)) # output shape: (seq_len, bs, dim) pooled = pooled.mean(dim=0) # output shape: (bs, dim) return pooled def encode_by_index(self, entities: torch.LongTensor) -> torch.FloatTensor: # take a node index and find its NodePiece hash hashes, dists, ids = self.hashes[entities], self.distances[entities], self.total_paths[entities] anc_embs = self.anchor_embeddings(hashes) mask = None if self.use_distances: dist_embs = self.dist_emb(dists) anc_embs += dist_embs # for ablations: drop anchors entirely if self.ablate_anchors: anc_embs = torch.tensor([], device=self.device) # add relational context (if its size > 0 ) if self.sample_rels > 0: rels = self.unique_1hop_relations[entities] # (bs, rel_sample_size) rels = self.relation_embeddings(rels) # (bs, rel_sample_size, dim) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask) # (bs, dim) return anc_embs def get_all_representations(self): # materialize embeddings for all nodes in a graph for scoring temp_embs = torch.zeros((len(self.hashes), self.embedding_dim), dtype=torch.float, device=self.device) vocab_keys = list(range(len(self.hashes))) for i in tqdm(range(0, len(self.hashes), self.subbatch)): entities = torch.tensor(vocab_keys[i: i+self.subbatch], dtype=torch.long, device=self.device) embs = self.encode_by_index(entities) temp_embs[i: i + self.subbatch, :] = embs return temp_embs @staticmethod def pairwise_interaction_function( h: torch.FloatTensor, r: torch.FloatTensor, ) -> torch.FloatTensor: # Decompose into real and imaginary part h_re = h[..., 0] h_im = h[..., 1] r_re = r[..., 0] r_im = r[..., 1] # Rotate (=Hadamard product in complex space). rot_h = torch.cat( [ h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re, ], dim=-1, ) return rot_h @staticmethod def interaction_function( h: torch.FloatTensor, r: torch.FloatTensor, t: torch.FloatTensor, ) -> torch.FloatTensor: # Decompose into real and imaginary part h_re = h[..., 0] h_im = h[..., 1] r_re = r[..., 0] r_im = r[..., 1] # Rotate (=Hadamard product in complex space). rot_h = torch.stack( [ h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re, ], dim=-1, ) # Workaround until https://github.com/pytorch/pytorch/issues/30704 is fixed diff = rot_h - t scores = -torch.norm(diff.view(diff.shape[:-2] + (-1,)), dim=-1) return scores def score_hrt(self, hrt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 # when training with large # of neg samples the hrt_batch size can be too big to fit into memory, so chunk it if hrt_batch.shape[0] <= self.subbatch or self.subbatch == 0: # Get embeddings h = self.encode_by_index(hrt_batch[:, 0]).view(-1, self.real_embedding_dim, 2) r = self.relation_embeddings(hrt_batch[:, 1]).view(-1, self.real_embedding_dim, 2) t = self.encode_by_index(hrt_batch[:, 2]).view(-1, self.real_embedding_dim, 2) # Compute scores scores = self.interaction_function(h=h, r=r, t=t).view(-1, 1) else: scores = torch.zeros((hrt_batch.shape[0], 1), dtype=torch.float, device=hrt_batch.device) for i in range(0, hrt_batch.shape[0], self.subbatch): h = self.encode_by_index(hrt_batch[i: i+self.subbatch, 0]).view(-1, self.real_embedding_dim, 2) r = self.relation_embeddings(hrt_batch[i: i+self.subbatch, 1]).view(-1, self.real_embedding_dim, 2) t = self.encode_by_index(hrt_batch[i: i+self.subbatch, 2]).view(-1, self.real_embedding_dim, 2) # Compute scores scores[i: i+self.subbatch] = self.interaction_function(h=h, r=r, t=t).view(-1, 1) return scores def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 # Get embeddings h = self.encode_by_index(hr_batch[:, 0]).view(-1, 1, self.real_embedding_dim, 2) r = self.relation_embeddings(hr_batch[:, 1]).view(-1, 1, self.real_embedding_dim, 2) # Rank against all entities, don't use hard negs, EXPENSIVE t = self.get_all_representations().view(1, -1, self.real_embedding_dim, 2) # Compute scores if self.subbatch == 0: scores = self.interaction_function(h=h, r=r, t=t) else: scores = torch.zeros((hr_batch.shape[0], t.shape[1]), dtype=torch.float, device=hr_batch.device) for i in tqdm(range(0, t.shape[1], self.subbatch)): temp_scores = self.interaction_function(h=h, r=r, t=t[:, i:i+self.subbatch, :, :]) scores[:, i:i+self.subbatch] = temp_scores return scores def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa: D102 # Get embeddings r = self.relation_embeddings(rt_batch[:, 0]).view(-1, 1, self.real_embedding_dim, 2) t = self.encode_by_index(rt_batch[:, 1]).view(-1, 1, self.real_embedding_dim, 2) r_inv = torch.stack([r[:, :, :, 0], -r[:, :, :, 1]], dim=-1) # Rank against all entities h = self.get_all_representations().view(1, -1, self.real_embedding_dim, 2) # Compute scores if self.subbatch == 0: scores = self.interaction_function(h=t, r=r_inv, t=h) else: scores = torch.zeros((rt_batch.shape[0], t.shape[1]), dtype=torch.float, device=rt_batch.device) for i in tqdm(range(0, t.shape[1], self.subbatch)): temp_scores = self.interaction_function(h=t, r=r_inv, t=h[:, i:i+self.subbatch, :, :]) scores[:, i:i+self.subbatch] = temp_scores return scores ================================================ FILE: lp_rp/pykeen105/relation_rank_evaluator.py ================================================ from pykeen.evaluation import Evaluator, MetricResults, RankBasedEvaluator from pykeen.models.base import Model, MappedTriples from pykeen.evaluation.evaluator import create_dense_positive_mask_, filter_scores_, timeit, split_list_in_batches_iter, tqdm, optional_context_manager, logger from pykeen.evaluation.rank_based_evaluator import pd, RankBasedMetricResults, dataclass, field, dataclass_json, fix_dataclass_init_docs, compute_rank_from_scores, RANK_TYPES, RANK_BEST, RANK_WORST, RANK_AVERAGE, RANK_AVERAGE_ADJUSTED from collections import defaultdict import numpy as np from typing import Any, Collection, List, Mapping, Optional, Tuple, Union, Dict import torch import pdb @fix_dataclass_init_docs @dataclass_json @dataclass class RelationPredictionRankBasedMetricResults(RankBasedMetricResults): #: The mean over all ranks: mean_i r_i. Lower is better. mean_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all ranks: mean_i r_i. Lower is better.', )) #: The mean over all reciprocal ranks: mean_i (1/r_i). Higher is better. mean_reciprocal_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all reciprocal ranks: mean_i (1/r_i). Higher is better.', )) #: The hits at k for different values of k, i.e. the relative frequency of ranks not larger than k. #: Higher is better. hits_at_k: Dict[str, Dict[int, float]] = field(metadata=dict( doc='The hits at k for different values of k, i.e. the relative frequency of ranks not larger than k.' ' Higher is better.', )) #: The mean over all chance-adjusted ranks: mean_i (2r_i / (num_entities+1)). Lower is better. #: Described by [berrendorf2020]_. adjusted_mean_rank: Dict[str, float] = field(metadata=dict( doc='The mean over all chance-adjusted ranks: mean_i (2r_i / (num_entities+1)). Lower is better.', )) def get_metric(self, name: str) -> float: # noqa: D102 dot_count = name.count('.') if 0 == dot_count: # assume average by default rank_type, metric = 'avg', name elif 1 == dot_count: rank_type, metric = name.split('.') else: raise ValueError(f'Malformed metric name: {name}') if rank_type not in RANK_AVERAGE and metric in {'adjusted_mean_rank'}: raise ValueError(f'Invalid rank type for adjusted mean rank: {rank_type}. Allowed type: {RANK_AVERAGE}') elif rank_type not in RANK_TYPES: raise ValueError(f'Invalid rank type: {rank_type}. Allowed types: {RANK_TYPES}') if metric in {'mean_rank', 'mean_reciprocal_rank'}: return getattr(self, metric)[rank_type] elif metric in {'adjusted_mean_rank'}: return getattr(self, metric) rank_type_hits_at_k = self.hits_at_k[rank_type] for prefix in ('hits_at_', 'hits@'): if not metric.startswith(prefix): continue k = metric[len(prefix):] k = 10 if k == 'k' else int(k) return rank_type_hits_at_k[k] raise ValueError(f'Invalid metric name: {name}') def to_flat_dict(self): # noqa: D102 r = {'avg.adjusted_mean_rank': self.adjusted_mean_rank} for rank_type in RANK_TYPES: r[f'{rank_type}.mean_rank'] = self.mean_rank[rank_type] r[f'{rank_type}.mean_reciprocal_rank'] = self.mean_reciprocal_rank[rank_type] for k, v in self.hits_at_k[rank_type].items(): r[f'{rank_type}.hits_at_{k}'] = v return r def to_df(self): """Output the metrics as a pandas dataframe.""" rows = [ ('avg', 'adjusted_mean_rank', self.adjusted_mean_rank) ] for rank_type in RANK_TYPES: rows.append((rank_type, 'mean_rank', self.mean_rank[rank_type])) rows.append((rank_type, 'mean_reciprocal_rank', self.mean_reciprocal_rank[rank_type])) for k, v in self.hits_at_k[rank_type].items(): rows.append((rank_type, f'hits_at_{k}', v)) return pd.DataFrame(rows, columns=['Type', 'Metric', 'Value']) class RelationPredictionRankBasedEvaluator(Evaluator): def __init__( self, ks = None, filtered: bool = True, ): super().__init__(filtered=filtered) self.ks = tuple(ks) if ks is not None else (1, 3, 5, 10) for k in self.ks: if isinstance(k, float) and not (0 < k < 1): raise ValueError( 'If k is a float, it should represent a relative rank, i.e. a value between 0 and 1 (excl.)', ) self.ranks = defaultdict(list) self.num_relations = None def process_tail_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: pass def process_head_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: pass def _update_ranks_(self, true_scores, all_scores): batch_ranks = compute_rank_from_scores( true_score=true_scores, all_scores=all_scores, ) self.num_relations = all_scores.shape[1] for k, v in batch_ranks.items(): self.ranks[k].extend(v.detach().cpu().tolist()) def evaluate( self, model, mapped_triples = None, batch_size = None, slice_size = None, device = None, use_tqdm = True, tqdm_kwargs = None, restrict_entities_to = None, do_time_consuming_checks = True, additional_filter_triples=None, ): if mapped_triples is None: mapped_triples = model.triples_factory.mapped_triples if batch_size is None and model.automatic_memory_optimization: batch_size, slice_size = self.batch_and_slice( model=model, mapped_triples=mapped_triples, batch_size=batch_size, device=device, use_tqdm=False, restrict_entities_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) # The batch_size and slice_size should be accessible to outside objects for re-use, e.g. early stoppers. self.batch_size = batch_size self.slice_size = slice_size # Clear the ranks from the current evaluator self.finalize() return evaluate( model=model, mapped_triples=mapped_triples, additional_filtered_triples=additional_filter_triples, evaluators=self, batch_size=batch_size, slice_size=slice_size, device=device, squeeze=True, use_tqdm=use_tqdm, tqdm_kwargs=tqdm_kwargs, restrict_relations_to=restrict_entities_to, do_time_consuming_checks=do_time_consuming_checks, ) def _get_ranks(self, rank_type): return np.asarray(self.ranks.get(rank_type, []), dtype=np.float64) def finalize(self) -> MetricResults: mean_rank = {} mean_reciprocal_rank = {} hits_at_k = {} adjusted_mean_rank = {} for rank_type in RANK_TYPES: ranks = self._get_ranks(rank_type=rank_type) if len(ranks) < 1: continue hits_at_k[rank_type] = { k: np.mean(ranks <= k) if isinstance(k, int) else np.mean(ranks <= int(self.num_relations * k)) for k in self.ks } mean_rank[rank_type] = np.mean(ranks) mean_reciprocal_rank[rank_type] = np.mean(np.reciprocal(ranks)) adjusted_ranks = self._get_ranks(rank_type=RANK_AVERAGE_ADJUSTED) if len(adjusted_ranks) >= 1: adjusted_mean_rank = float(np.mean(adjusted_ranks)) self.ranks.clear() return RelationPredictionRankBasedMetricResults( mean_rank=dict(mean_rank), mean_reciprocal_rank=dict(mean_reciprocal_rank), hits_at_k=dict(hits_at_k), adjusted_mean_rank=adjusted_mean_rank, ) def process_relation_scores_( self, hrt_batch: MappedTriples, true_scores: torch.FloatTensor, scores: torch.FloatTensor, dense_positive_mask: Optional[torch.FloatTensor] = None, ) -> None: # noqa: D102 self._update_ranks_(true_scores=true_scores, all_scores=scores) def get_unique_relation_ids_from_triples_tensor(mapped_triples: MappedTriples) -> torch.LongTensor: """Return the unique entity IDs used in a tensor of triples.""" return mapped_triples[:, 1].unique() def evaluate( model: Model, mapped_triples: MappedTriples, evaluators: Union[Evaluator, Collection[Evaluator]], additional_filtered_triples: Union[None, MappedTriples, List[MappedTriples]] = None, only_size_probing: bool = False, batch_size: Optional[int] = None, slice_size: Optional[int] = None, device: Optional[torch.device] = None, squeeze: bool = True, use_tqdm: bool = True, tqdm_kwargs: Optional[Mapping[str, str]] = None, restrict_relations_to: Optional[torch.LongTensor] = None, do_time_consuming_checks: bool = True, ) -> Union[MetricResults, List[MetricResults]]: """Evaluate metrics for model on mapped triples. The model is used to predict scores for all tails and all heads for each triple. Subsequently, each abstract evaluator is applied to the scores, also receiving the batch itself (e.g. to compute entity-specific metrics). Thereby, the (potentially) expensive score computation against all entities is done only once. The metric evaluators are expected to maintain their own internal buffers. They are returned after running the evaluation, and should offer a possibility to extract some final metrics. :param model: The model to evaluate. :param mapped_triples: The triples on which to evaluate. :param evaluators: An evaluator or a list of evaluators working on batches of triples and corresponding scores. :param only_size_probing: The evaluation is only performed for two batches to test the memory footprint, especially on GPUs. :param batch_size: >0 A positive integer used as batch size. Generally chosen as large as possible. Defaults to 1 if None. :param slice_size: >0 The divisor for the scoring function when using slicing. :param device: The device on which the evaluation shall be run. If None is given, use the model's device. :param squeeze: Return a single instance of :class:`MetricResults` if only one evaluator was given. :param use_tqdm: Should a progress bar be displayed? :param restrict_relations_to: Optionally restrict the evaluation to the given relations IDs. This may be useful if one is only interested in a part of the relations, e.g. due to type constraints, but wants to train on all available data. For ranking the entities, we still compute all scores for all possible replacement entities to avoid irregular access patterns which might decrease performance, but the scores with afterwards be filtered to only keep those of interest. If provided, we assume that the triples are already filtered, such that it only contains the entities of interest. :param do_time_consuming_checks: Whether to perform some time consuming checks on the provided arguments. Currently, this encompasses: - If restrict_entities_to is not None, check whether the triples have been filtered. Disabling this option can accelerate the method. """ if isinstance(evaluators, Evaluator): # upgrade a single evaluator to a list evaluators = [evaluators] start = timeit.default_timer() # verify that the triples have been filtered if restrict_relations_to is not None and do_time_consuming_checks: present_relation_ids = set(get_unique_relation_ids_from_triples_tensor(mapped_triples=mapped_triples).tolist()) unwanted = present_relation_ids.difference(restrict_relations_to.tolist()) if len(unwanted) > 0: raise ValueError(f'mapped_triples contains IDs of entities which are not contained in restrict_relations_to:' f'{unwanted}. This will invalidate the evaluation results.') # Send to device if device is not None: model = model.to(device) device = model.device # Ensure evaluation mode model.eval() # Split evaluators into those which need unfiltered results, and those which require filtered ones filtered_evaluators = list(filter(lambda e: e.filtered, evaluators)) unfiltered_evaluators = list(filter(lambda e: not e.filtered, evaluators)) # Check whether we need to be prepared for filtering filtering_necessary = len(filtered_evaluators) > 0 # Check whether an evaluator needs access to the masks # This can only be an unfiltered evaluator. positive_masks_required = any(e.requires_positive_mask for e in unfiltered_evaluators) # Prepare for result filtering if filtering_necessary or positive_masks_required: #all_pos_triples = torch.cat([model.triples_factory.mapped_triples, mapped_triples], dim=0) if additional_filtered_triples is None: logger.warning("The filtered setting was enabled, but there were no `additional_filtered_triples" "given. This means you probably forgot to pass (at least) the training triples. Try:" "additional_filtered_triples=[dataset.training.mapped_triples]" "Or if you want to use the Bordes et al. (2013) approach to filtering, do:" "additional_filtered_triples=[dataset.training.mapped_triples,dataset.validation.mapped_triples,]") all_pos_triples = mapped_triples elif isinstance(additional_filtered_triples, (list, tuple)): all_pos_triples = torch.cat([*additional_filtered_triples, mapped_triples], dim=0) else: all_pos_triples = torch.cat([additional_filtered_triples, mapped_triples], dim=0) all_pos_triples = all_pos_triples.to(device=device) else: all_pos_triples = None # Send tensors to device mapped_triples = mapped_triples.to(device=device) # Prepare batches if batch_size is None: batch_size = 1 batches = split_list_in_batches_iter(input_list=mapped_triples, batch_size=batch_size) # Show progressbar num_triples = mapped_triples.shape[0] # Flag to check when to quit the size probing evaluated_once = False # Disable gradient tracking _tqdm_kwargs = dict( desc=f'Evaluating on {model.device}', total=num_triples, unit='triple', unit_scale=True, # Choosing no progress bar (use_tqdm=False) would still show the initial progress bar without disable=True disable=not use_tqdm, ) if tqdm_kwargs: _tqdm_kwargs.update(tqdm_kwargs) with optional_context_manager(use_tqdm, tqdm(**_tqdm_kwargs)) as progress_bar, torch.no_grad(): # batch-wise processing for batch in batches: batch_size = batch.shape[0] _evaluate_batch( batch=batch, model=model, filtered_evaluators=filtered_evaluators, unfiltered_evaluators=unfiltered_evaluators, slice_size=slice_size, all_pos_triples=all_pos_triples, restrict_relations_to=restrict_relations_to, positive_masks_required=positive_masks_required, filtering_necessary=filtering_necessary, ) # If we only probe sizes we do not need more than one batch if only_size_probing and evaluated_once: break evaluated_once = True if use_tqdm: progress_bar.update(batch_size) # Finalize results = [evaluator.finalize() for evaluator in evaluators] stop = timeit.default_timer() if only_size_probing: logger.debug("Evaluation took %.2fs seconds", stop - start) else: logger.info("Evaluation took %.2fs seconds", stop - start) if squeeze and len(results) == 1: return results[0] return results def _evaluate_batch( batch, model, filtered_evaluators: Collection[Evaluator], unfiltered_evaluators: Collection[Evaluator], slice_size: Optional[int], all_pos_triples: Optional[MappedTriples], restrict_relations_to: Optional[torch.LongTensor], positive_masks_required: bool, filtering_necessary: bool, ) -> torch.BoolTensor: """ Evaluate batch for all head predictions(column=0), or all tail predictions (column=2). :param batch: shape: (batch_size, 3) The batch of currently evaluated triples. :param model: The model to evaluate. :param column: The column which to evaluate. Either 0 for head prediction, or 2 for tail prediction. :param filtered_evaluators: The evaluators which work on filtered scores. :param unfiltered_evaluators: The evaluators which work on unfiltered scores. :param slice_size: An optional slice size for computing the scores. :param all_pos_triples: All positive triples (required if filtering is necessary). :param restrict_relations_to: Restriction to evaluate only for these relations. :param positive_masks_required: Whether dense positive masks are required (by any unfiltered evaluator). :param filtering_necessary: Whether filtering is necessary. :return: The relation filter, which can be re-used for the same batch. """ # Predict scores once batch_scores_of_corrupted = model.predict_scores_all_relations(batch[:, [0, 2]], slice_size=slice_size) # Select scores of true batch_scores_of_true = batch_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, 1], ] # Create positive filter for all corrupted if filtering_necessary or positive_masks_required: # Needs all positive triples if all_pos_triples is None: raise ValueError('If filtering_necessary of positive_masks_required is True, all_pos_triples has to be ' 'provided, but is None.') # Create filter positive_filter = create_sparse_positive_filter_( hrt_batch=batch, all_pos_triples=all_pos_triples, ) # Create a positive mask with the size of the scores from the positive filter if positive_masks_required: positive_mask = create_dense_positive_mask_( zero_tensor=torch.zeros_like(batch_scores_of_corrupted), filter_batch=positive_filter, ) else: positive_mask = None # Restrict to entities of interest if restrict_relations_to is not None: batch_scores_of_corrupted_ = batch_scores_of_corrupted[:, restrict_relations_to] positive_mask = positive_mask[:, restrict_relations_to] else: batch_scores_of_corrupted_ = batch_scores_of_corrupted # Evaluate metrics on these *unfiltered* scores for unfiltered_evaluator in unfiltered_evaluators: unfiltered_evaluator.process_relation_scores_( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_scores_of_corrupted_, dense_positive_mask=positive_mask, ) # Filter if filtering_necessary: batch_filtered_scores_of_corrupted = filter_scores_( scores=batch_scores_of_corrupted, filter_batch=positive_filter, ) # The scores for the true triples have to be rewritten to the scores tensor batch_filtered_scores_of_corrupted[ torch.arange(0, batch.shape[0]), batch[:, 1], ] = batch_scores_of_true # Restrict to entities of interest if restrict_relations_to is not None: batch_filtered_scores_of_corrupted = batch_filtered_scores_of_corrupted[:, restrict_relations_to] # Evaluate metrics on these *filtered* scores for filtered_evaluator in filtered_evaluators: filtered_evaluator.process_relation_scores_( hrt_batch=batch, true_scores=batch_scores_of_true[:, None], scores=batch_filtered_scores_of_corrupted, ) def create_sparse_positive_filter_( hrt_batch: MappedTriples, all_pos_triples: torch.LongTensor ) -> Tuple[torch.LongTensor, torch.BoolTensor]: # Split batch batch_heads, batch_tails = hrt_batch[:, 0:1], hrt_batch[:, 2:3] head_filter = (all_pos_triples[:, 0]).view(1, -1) == batch_heads tail_filter = (all_pos_triples[:, 2]).view(1, -1) == batch_tails filter_batch = (head_filter & tail_filter).nonzero(as_tuple=False) filter_batch[:, 1] = all_pos_triples[:, 1].view(1, -1)[:, filter_batch[:, 1]] return filter_batch ================================================ FILE: lp_rp/run_lp.py ================================================ from pykeen.datasets import FB15k237, WN18RR, YAGO310 from pykeen.utils import resolve_device from pykeen.losses import BCEWithLogitsLoss, SoftplusLoss, MarginRankingLoss, NSSALoss from pykeen.training import SLCWATrainingLoop, LCWATrainingLoop from pykeen.trackers import WANDBResultTracker from pykeen.evaluation import RankBasedEvaluator from pykeen.sampling import BasicNegativeSampler from pykeen.stoppers import EarlyStopper from pykeen.models import RotatE from torch.optim import Adam from pykeen105.nodepiece_rotate import NodePieceRotate from loops.filtered_sampling_loop import FilteredSLCWATrainingLoop from datasets.codex import CoDExLarge from nodepiece_tokenizer import NodePiece_Tokenizer from pykeen105.relation_rank_evaluator import RelationPredictionRankBasedEvaluator from pykeen105.negative_sampler import FilteredNegativeSampler, RelationalNegativeSampler import torch import click import numpy as np import wandb import random torch.manual_seed(42) np.random.seed(42) random.seed(42) @click.command() @click.option('-embedding', '--embedding-dimension', type=int, default=100) # embedding dim for anchors and relations @click.option('-loss', '--loss_fc', type=str, default='nssal') @click.option('-loop', '--loop', type=str, default='slcwa') # slcwa - negative sampling, lcwa - 1-N scoring @click.option('-trf_hidden', '--transformer-hidden-dim', type=int, default=512) @click.option('-trf_heads', '--transformer-num-heads', type=int, default=4) @click.option('-trf_layers', '--transformer-layers', type=int, default=2) @click.option('-trf_drop', '--transformer-dropout', type=float, default=0.1) @click.option('-b', '--batch-size', type=int, default=512) @click.option('-epochs', '--num-epochs', type=int, default=1000) @click.option('-lr', '--learning-rate', type=float, default=0.0005) @click.option('-wandb', '--enable_wandb', type=bool, default=False) @click.option('-anchors', '--topk_anchors', type=int, default=500) # how many total anchors to use @click.option('-data', '--dataset_name', type=str, default='wn18rr') @click.option('-anc_deg', '--strategy_degree', type=float, default=0.4) # % of anchors selected based on top node degree @click.option('-anc_betw', '--strategy_betweenness', type=float, default=0.0) # disabled @click.option('-anc_ppr', '--strategy_pagerank', type=float, default=0.4) # % of anchors selected based on top PPR @click.option('-anc_rand', '--strategy_random', type=float, default=0.2) # % of randomly selected anchors @click.option('-sp', '--k_shortest_paths', type=int, default=0) # when mining anchors per node - keep K closest @click.option('-rp', '--k_random_paths', type=int, default=0) # when mining anchors per node - keep K random @click.option('-eval_every', '--eval_every', type=int, default=1) @click.option('-mtype', '--model_type', type=str, default="nodepiece") # or "rotate" for the baseline @click.option('-ft_maxp', '--ft_max_paths', type=int, default=100) # max anchor per node, should be <= total N of anchors @click.option('-anc_dist', '--use_anchor_distances', type=bool, default=True) # whether to add anchor distances @click.option('-margin', '--margin', type=float, default=15) @click.option('-max_seq_len', '--max_seq_len', type=int, default=0) @click.option('-pool', '--pooling', type=str, default="cat") # available encoders: "cat" or "trf" @click.option('-subbatch', '--trf_subbatch', type=int, default=3000) @click.option('-negs', '--num_negatives_ent', type=int, default=1) # number of negative samples when training LP in sLCWA @click.option('-negs-rel', '--num_negatives-rel', type=int, default=1) # Optional: number of negative relations when training RP in sLCWA @click.option('-rel-prediction', '--rel-prediction', type=bool, default=False) # swtich to the Relation Prediction task (RP) @click.option('-smoothing', '--lbl_smoothing', type=float, default=0.0) # label smoothing in the 1-N setup @click.option('-relpol', '--rel_policy', type=str, default="sum") @click.option('-filtered_sampling', '--filtered_sampling', type=bool, default=False) @click.option('-rand_hashes', '--random_hashing', type=int, default=0) # for ablations: use only random numbers as hashes @click.option('-nn', '--nearest_neighbors', type=bool, default=True) # use only nearest anchors per node @click.option('-sample_rels', '--sample_rels', type=int, default=4) # size of the relational context M @click.option('-anchor_eye', '--anchor_eye', type=bool, default=False) # anchors in their own hashes will have their index at the frist place @click.option('-tkn_mode', '--tkn_mode', type=str, default="path") # mining paths in iGRAPH @click.option('-no_anc', '--ablate_anchors', type=bool, default=False) # don't use any anchors in hashes, keep only the relational context def main( embedding_dimension, loss_fc, loop, transformer_hidden_dim: int, transformer_num_heads: int, transformer_layers: int, transformer_dropout: float, batch_size: int, num_epochs: int, learning_rate: float, enable_wandb: bool, topk_anchors: int, dataset_name: str, strategy_degree: float, strategy_betweenness: float, strategy_pagerank: float, strategy_random: float, k_shortest_paths: int, k_random_paths: int, eval_every: int, model_type: str, ft_max_paths: int, use_anchor_distances: bool, margin: float, max_seq_len: int, pooling: str, trf_subbatch: int, num_negatives_ent: int, num_negatives_rel: int, rel_prediction: bool, lbl_smoothing: float, rel_policy: str, filtered_sampling: bool, random_hashing: int, nearest_neighbors: bool, sample_rels: int, anchor_eye: bool, tkn_mode: str, ablate_anchors: bool, ): # Standard dataset loading procedures, inverses are necessary for reachability of nodes if dataset_name == 'fb15k237': dataset = FB15k237(create_inverse_triples=True) elif dataset_name == 'wn18rr': dataset = WN18RR(create_inverse_triples=True) elif dataset_name == "yago": dataset = YAGO310(create_inverse_triples=True) elif dataset_name == "codex_l": dataset = CoDExLarge(create_inverse_triples=True) # if we're in the RP task - change the evaluator if rel_prediction: evaluator_type = RelationPredictionRankBasedEvaluator else: evaluator_type = RankBasedEvaluator # sampling even harder negatives - turned off by default if filtered_sampling: negative_sampler_cls = RelationalNegativeSampler negative_sampler_kwargs = dict(num_negs_per_pos=num_negatives_ent, num_negs_per_pos_rel=num_negatives_rel, dataset_name=dataset_name) loop_type = FilteredSLCWATrainingLoop else: negative_sampler_cls = BasicNegativeSampler negative_sampler_kwargs = dict(num_negs_per_pos=num_negatives_ent) loop_type = SLCWATrainingLoop training_triples_factory = dataset.training # Now let's create a NodePiece tokenizer kg_tokenizer = NodePiece_Tokenizer(triples=training_triples_factory, anchor_strategy={ "degree": strategy_degree, "betweenness": strategy_betweenness, "pagerank": strategy_pagerank, "random": strategy_random }, num_anchors=topk_anchors, dataset_name=dataset_name, limit_shortest=k_shortest_paths, add_identity=anchor_eye, mode=tkn_mode, limit_random=k_random_paths) device = resolve_device() # cater for corner cases when user-input max seq len is incorrect if max_seq_len == 0 or max_seq_len != (kg_tokenizer.max_seq_len+3): max_seq_len = kg_tokenizer.max_seq_len + 3 # as in the PathTrfEncoder, +1 CLS, +1 PAD, +1 LP tasks print(f"Set max_seq_len to{max_seq_len}") # for stability kg_tokenizer.token2id[kg_tokenizer.NOTHING_TOKEN] = kg_tokenizer.vocab_size # selecting the loss function if loss_fc == "softplus": ft_loss = SoftplusLoss() elif loss_fc == "bce": ft_loss = BCEWithLogitsLoss() elif loss_fc == "mrl": ft_loss = MarginRankingLoss(margin=margin) elif loss_fc == "nssal": ft_loss = NSSALoss(margin=margin) train_factory = dataset.training validation_factory = dataset.validation if model_type == "baseline": finetuning_model = RotatE(embedding_dim=embedding_dimension // 2, triples_factory=train_factory, loss=ft_loss, automatic_memory_optimization=False, preferred_device=device) optimizer = Adam(params=finetuning_model.parameters(), lr=learning_rate) print(f"Vanilla rotate created, Number of params: {sum(p.numel() for p in finetuning_model.parameters())}") ft_loop = SLCWATrainingLoop(model=finetuning_model, optimizer=optimizer) else: finetuning_model = NodePieceRotate(embedding_dim=embedding_dimension, device=device, loss=ft_loss, triples=train_factory, max_paths=ft_max_paths, subbatch=trf_subbatch, max_seq_len=max_seq_len, tokenizer=kg_tokenizer, pooler=pooling, hid_dim=transformer_hidden_dim, num_heads=transformer_num_heads, use_distances=use_anchor_distances, num_layers=transformer_layers, drop_prob=transformer_dropout, rel_policy=rel_policy, random_hashes=random_hashing, nearest=nearest_neighbors, sample_rels=sample_rels, ablate_anchors=ablate_anchors, tkn_mode=tkn_mode) optimizer = Adam(params=finetuning_model.parameters(), lr=learning_rate) print(f"Number of params: {sum(p.numel() for p in finetuning_model.parameters())}") if loop == "slcwa": ft_loop = loop_type(model=finetuning_model, optimizer=optimizer, negative_sampler_cls=negative_sampler_cls, negative_sampler_kwargs=negative_sampler_kwargs) else: ft_loop = LCWATrainingLoop(model=finetuning_model, optimizer=optimizer) # add the results tracker if requested if enable_wandb: project_name = "NodePiece_LP" if rel_prediction: project_name += "_RP" tracker = WANDBResultTracker(project=project_name, group=None, settings=wandb.Settings(start_method='fork')) tracker.wandb.config.update(click.get_current_context().params) else: tracker = None valid_evaluator = evaluator_type() valid_evaluator.batch_size = 256 # we don't actually use the early stopper here by setting the patience to 1000 early_stopper = EarlyStopper( model=finetuning_model, relative_delta=0.0005, evaluation_triples_factory=validation_factory, frequency=eval_every, patience=1000, result_tracker=tracker, evaluation_batch_size=256, evaluator=valid_evaluator, ) # Train LP / RP if loop == "lcwa": ft_loop.train(num_epochs=num_epochs, batch_size=batch_size, result_tracker=tracker, stopper=early_stopper, label_smoothing=lbl_smoothing) else: ft_loop.train(num_epochs=num_epochs, batch_size=batch_size, result_tracker=tracker, stopper=early_stopper) # run the final test eval test_evaluator = evaluator_type() test_evaluator.batch_size = 256 # test_evaluator metric_results = test_evaluator.evaluate( model=finetuning_model, mapped_triples=dataset.testing.mapped_triples, additional_filter_triples=[dataset.training.mapped_triples, dataset.validation.mapped_triples], use_tqdm=True, batch_size=256, ) # log final results if enable_wandb: tracker.log_metrics( metrics=metric_results.to_flat_dict(), step=num_epochs + 1, prefix='test', ) print("Test results") print(metric_results) if __name__ == '__main__': main() ================================================ FILE: nc/data/clean/wd50k/statements/wd50k_entity_index.txt ================================================ Q1 Q100 Q1000 Q1000118 Q1000136 Q10002 Q1000222 Q1000241 Q1000428 Q1000597 Q1000642 Q1001 Q100103 Q1001448 Q100159 Q100166 Q100172 Q100188 Q1002100 Q1002165 Q1002298 Q1002490 Q10027 Q1003074 Q1003702 Q1003997 Q1004 Q100440 Q1004531 Q1004712 Q1005 Q1005887 Q1005953 Q1006 Q1006668 Q1007 Q1007418 Q1007634 Q1007911 Q1008 Q10086 Q1008912 Q1009 Q10090 Q1009235 Q1009253 Q100937 Q1009477 Q1009788 Q1010 Q1010099 Q1010228 Q1010236 Q1010240 Q1010244 Q1010349 Q1010539 Q1010556 Q1010673 Q1011 Q1011509 Q1011547 Q1011564 Q1011748 Q1011895 Q1011981 Q1012216 Q1012397 Q101254 Q10127 Q1013 Q10132 Q1013228 Q1013254 Q101333 Q1013510 Q1014 Q101401 Q1015 Q101505 Q1015079 Q1015103 Q101539 Q1015681 Q1015773 Q1015805 Q1015874 Q1016 Q1016074 Q1016159 Q101625 Q1017 Q1017286 Q101730 Q101751 Q1017583 Q10179 Q1017948 Q101797 Q1017974 Q1018274 Q1018448 Q101849 Q101859 Q1018606 Q10188 Q1019 Q101918 Q101929 Q101985 Q1020 Q1020320 Q1020678 Q102090 Q1020942 Q1020994 Q1021026 Q102108 Q102124 Q1021386 Q102139 Q102140 Q1021563 Q1021714 Q102172 Q1022 Q102217 Q102225 Q102235 Q102244 Q1022488 Q10225 Q102251 Q1022513 Q1022530 Q1022955 Q1022995 Q10230 Q102301 Q102317 Q1023185 Q1023208 Q1023235 Q102341 Q1023499 Q1023532 Q1023544 Q102385 Q1023912 Q102397 Q1024037 Q102427 Q102438 Q1024426 Q102448 Q1024543 Q102462 Q1024917 Q1025 Q1025065 Q1025105 Q1025106 Q1025277 Q1025294 Q1025401 Q1025404 Q1025414 Q1025466 Q102551 Q1025801 Q1025830 Q102585 Q1026 Q10260330 Q10260332 Q1026089 Q1026270 Q1026394 Q1026417 Q102642 Q10264444 Q1026448 Q1026520 Q1026679 Q102673 Q1026792 Q1026804 Q1026827 Q10268314 Q1026837 Q1026846 Q1026860 Q1026868 Q1026919 Q1026925 Q1026926 Q1026932 Q1026939 Q1026941 Q1027 Q1027063 Q1027105 Q102711 Q10271260 Q1027127 Q102720 Q1027200 Q1027247 Q1027475 Q102754 Q1027551 Q1027770 Q1027800 Q1027880 Q1027891 Q1027898 Q1027904 Q1027913 Q1027922 Q1028 Q1028086 Q102813 Q1028181 Q102851 Q10286 Q102865 Q1028714 Q1028715 Q1029 Q1029022 Q1029099 Q102911 Q1029135 Q1029155 Q1029178 Q102920 Q1029212 Q1029274 Q102932 Q10294 Q1029467 Q1029535 Q1029538 Q1029631 Q10297252 Q1029907 Q1029982 Q1030 Q10300 Q10301418 Q1030402 Q10305 Q10306924 Q1030713 Q10307886 Q10307893 Q10308 Q103093 Q10313 Q10313792 Q1031430 Q103148 Q10315 Q103157 Q10317241 Q1031806 Q1031821 Q10318594 Q1032 Q1032175 Q1032195 Q1032247 Q10322797 Q1032304 Q1032414 Q1032457 Q1032509 Q1032540 Q10328410 Q1032842 Q1032885 Q10329 Q1033 Q1033016 Q1033131 Q10332 Q10332942 Q10333 Q1033304 Q103343 Q1033465 Q103360 Q1033692 Q1033891 Q1033909 Q1034225 Q10346114 Q1034640 Q10346582 Q103474 Q10348402 Q10349745 Q1035 Q1035067 Q1035124 Q1035260 Q103550 Q103569 Q1035745 Q103578 Q103579 Q10359064 Q10359065 Q10359068 Q10359070 Q10359071 Q10359074 Q10359075 Q10359076 Q10359077 Q10359078 Q10359080 Q10359082 Q10359083 Q10359084 Q10359085 Q10359086 Q10359087 Q10359089 Q10359091 Q10359095 Q1035938 Q1035954 Q1036 Q103618 Q1036227 Q103646 Q1036504 Q1036763 Q1037 Q1037393 Q1037408 Q103767 Q103774 Q103784 Q1037848 Q103788 Q10379 Q103801 Q103824 Q10384029 Q1038440 Q103846 Q103854 Q1038545 Q1038555 Q1038647 Q103876 Q10387684 Q10388 Q10389 Q103892 Q103893 Q103894 Q1039 Q10390 Q103916 Q103917 Q1039291 Q10393 Q10393524 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================================================ ================================================ FILE: nc/data_loaders/clean_datasets.py ================================================ from pathlib import Path from typing import Dict from collections import defaultdict import random import pickle import numpy as np import re import json import torch from torch_geometric.data import Data, DataLoader def to_sparse_graph(edges, subtype, entoid, prtoid, maxlen): edge_index, edge_type = np.zeros((2, len(edges)), dtype='int64'), np.zeros((len(edges)), dtype='int64') qualifier_rel = [] qualifier_ent = [] qualifier_edge = [] quals = None for i, st in enumerate(edges): edge_index[:, i] = [entoid[st[0]], entoid[st[2]]] edge_type[i] = prtoid[st[1]] if subtype == 'statements': qual_rel = np.array([prtoid[r] for r in st[3::2]])[ :(maxlen - 3) // 2] # cut to the max allowed qualifiers per statement qual_ent = np.array([entoid[e] for e in st[4::2]])[ :(maxlen - 3) // 2] # cut to the max allowed qualifiers per statement for j in range(qual_ent.shape[0]): qualifier_rel.append(qual_rel[j]) qualifier_ent.append(qual_ent[j]) qualifier_edge.append(i) if subtype == 'statements': quals = np.stack((qualifier_rel, qualifier_ent, qualifier_edge), axis=0) return edge_index, edge_type, quals def load_clean_pyg(name, subtype, task, inductive="transductive", ind_v=None, maxlen=43, permute=False) -> Dict: """ :param name: dataset name wd50k/wd50k_33/wd50k_66/wd50k_100 :param subtype: triples/statements :param task: so/full predict entities at sub/obj positions (for triples/statements) or all nodes incl quals :param inductive: whether to load transductive dataset (one graph for train/val/test) or inductive :param ind_v: v1 / v2 for the inductive dataset :param maxlen: max statement length :return: train/valid/test splits for the wd50k datasets suitable for loading into TORCH GEOMETRIC dataset no reciprocal edges (as will be added in the gnn layer), create directly the edge index """ assert name in ['wd50k', 'wd50k_100', 'wd50k_33', 'wd50k_66'], "Incorrect dataset" assert subtype in ["triples", "statements"], "Incorrect subtype: triples/statements" assert inductive in ["transductive", "inductive"], "Incorrect ds type: only transductive and inductive accepted" if inductive == "inductive": assert ind_v in ["v1", "v2"], "Only v1 and v2 are allowed versions for the inductive task" if inductive == "transductive": DIRNAME = Path(f'./data/clean/{name}/{subtype}') train_edges = [line.strip("\n").split(",") for line in open(DIRNAME / 'nc_edges.txt', 'r').readlines()] print(f"Transductive: With quals: {len([t for t in train_edges if len(t)>3])} / {len(train_edges)}, Ratio: {round((len([t for t in train_edges if len(t)>3]) / len(train_edges)),2)}") else: DIRNAME = Path(f'./data/clean/{name}/inductive/nc/{subtype}/{ind_v}') train_edges = [line.strip("\n").split(",") for line in open(DIRNAME / 'nc_train_edges.txt', 'r').readlines()] val_edges = [line.strip("\n").split(",") for line in open(DIRNAME / 'nc_val_edges.txt', 'r').readlines()] test_edges = [line.strip("\n").split(",") for line in open(DIRNAME / 'nc_test_edges.txt', 'r').readlines()] print( f"Inductive train: With quals: {len([t for t in train_edges if len(t) > 3])} / {len(train_edges)}, Ratio: {round((len([t for t in train_edges if len(t) > 3]) / len(train_edges)), 2)}") print( f"Inductive val: With quals: {len([t for t in val_edges if len(t) > 3])} / {len(val_edges)}, Ratio: {round((len([t for t in val_edges if len(t) > 3]) / len(val_edges)), 2)}") print( f"Inductive test: With quals: {len([t for t in test_edges if len(t) > 3])} / {len(test_edges)}, Ratio: {round((len([t for t in test_edges if len(t) > 3]) / len(test_edges)), 2)}") statement_entities = [l.strip("\n") for l in open(DIRNAME / 'nc_entities.txt', 'r').readlines()] statement_predicates = [l.strip("\n") for l in open(DIRNAME / 'nc_rels.txt', 'r').readlines()] if subtype == "triples": task = "so" with open(DIRNAME / f'nc_train_{task}_labels.json', 'r') as f: train_labels = json.load(f) with open(DIRNAME / f'nc_val_{task}_labels.json', 'r') as f: val_labels = json.load(f) with open(DIRNAME / f'nc_test_{task}_labels.json', 'r') as f: test_labels = json.load(f) # load node features with the total index entity_index = {line.strip('\n'): i for i, line in enumerate(open(f'./data/clean/{name}/statements/{name}_entity_index.txt').readlines())} total_node_features = np.load(f'./data/clean/{name}/statements/{name}_embs.pkl', allow_pickle=True) idx = np.array([entity_index[key] for key in statement_entities], dtype='int32') node_features = total_node_features[idx] if permute: node_features = np.random.permutation(node_features) entoid = {pred: i for i, pred in enumerate(statement_entities)} prtoid = {pred: i for i, pred in enumerate(statement_predicates)} print(f"Total Entities: {len(entoid)}") print(f"Total Rels: {len(prtoid)}") train_edge_index, train_edge_type, train_quals = to_sparse_graph(train_edges, subtype, entoid, prtoid, maxlen) if inductive == "inductive": val_edge_index, val_edge_type, val_quals = to_sparse_graph(val_edges, subtype, entoid, prtoid, maxlen) test_edge_index, test_edge_type, test_quals = to_sparse_graph(test_edges, subtype, entoid, prtoid, maxlen) train_mask = [entoid[e] for e in train_labels] val_mask = [entoid[e] for e in val_labels] test_mask = [entoid[e] for e in test_labels] if inductive == "inductive": print(f"Train Ents: {len(train_labels)}, Val Ents: {len(val_labels)}, Test Ents: {len(test_labels)}") all_labels = sorted(list(set([ label for v in list(train_labels.values()) + list(val_labels.values()) + list(test_labels.values()) for label in v]))) label2id = {l: i for i, l in enumerate(all_labels)} id2label = {v: k for k, v in label2id.items()} print(f"Total labels: {len(label2id)}") train_y = {entoid[k]: [label2id[vi] for vi in v] for k, v in train_labels.items()} val_y = {entoid[k]: [label2id[vi] for vi in v] for k, v in val_labels.items()} test_y = {entoid[k]: [label2id[vi] for vi in v] for k, v in test_labels.items()} train_graph = Data(x=torch.tensor(node_features, dtype=torch.float), edge_index=torch.tensor(train_edge_index, dtype=torch.long), edge_type=torch.tensor(train_edge_type, dtype=torch.long), quals=torch.tensor(train_quals, dtype=torch.long) if train_quals is not None else None, y=train_y) # explicit fix for PyG > 2.0 as its new data creation procedure uses setattr(obj, key, value) which does not # assign None values, such that quals=None does not lead to creating the `quals` with value None # so we explicitly set it to 0 - it won't affect any code execution whatsoever if train_quals is None: train_graph.quals = 0 val_graph, test_graph = None, None if inductive == "inductive": val_graph = Data(x=torch.tensor(node_features, dtype=torch.float), edge_index=torch.tensor(val_edge_index, dtype=torch.long), edge_type=torch.tensor(val_edge_type, dtype=torch.long), quals=torch.tensor(val_quals, dtype=torch.long) if val_quals is not None else None, y=val_y) test_graph = Data(x=torch.tensor(node_features, dtype=torch.float), edge_index=torch.tensor(test_edge_index, dtype=torch.long), edge_type=torch.tensor(test_edge_type, dtype=torch.long), quals=torch.tensor(test_quals, dtype=torch.long) if test_quals is not None else None, y=test_y) return {"train_graph": train_graph, "val_graph": val_graph, "test_graph": test_graph, "train_mask": train_mask, "valid_mask": val_mask, "test_mask": test_mask, "train_y": train_y, "val_y": val_y, "test_y": test_y, "all_labels": all_labels, "label2id": label2id, "id2label": id2label, "n_entities": len(statement_entities), "n_relations": len(statement_predicates), "e2id": entoid, "r2id": prtoid} if __name__ == "__main__": data = load_clean_pyg('wd50k', 'statements', 'so', inductive="inductive", ind_v='v2', maxlen=15) print("nop") #count_stats(load_clean_wd50k("wd50k","statements",43)) ================================================ FILE: nc/loops/__init__.py ================================================ ================================================ FILE: nc/loops/evaluation.py ================================================ import numpy as np # Local from utils.utils import * from utils.utils_mytorch import Timer from sklearn.metrics import roc_auc_score, auc, precision_recall_curve, average_precision_score, accuracy_score def compute_roc_auc(y_true, y_pred): """ :param y_true: true labels, shape (n_samples, n_classes) :param y_pred: predicted values, shape (n_samples, n_classes) :return: roc_auc_score """ y_true = y_true.detach().cpu().numpy() y_pred = y_pred.detach().cpu().numpy() rocauc_list = [] for i in range(y_true.shape[1]): if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == 0) > 0: is_labeled = y_true[:, i] == y_true[:, i] rocauc_list.append(roc_auc_score(y_true[is_labeled, i], y_pred[is_labeled, i])) #score = roc_auc_score(y_true, y_pred) return sum(rocauc_list)/len(rocauc_list) if len(rocauc_list) > 0 else 0 def compute_prcauc(y_true, y_pred): y_true = y_true.detach().cpu().numpy() y_pred = y_pred.detach().cpu().numpy() prcauc_list = [] for i in range(y_true.shape[1]): if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == 0) > 0: is_labeled = y_true[:, i] == y_true[:, i] precision, recall, _ = precision_recall_curve(y_true[is_labeled, i], y_pred[is_labeled, i]) prcauc = auc(recall, precision) prcauc_list.append(prcauc) return sum(prcauc_list)/len(prcauc_list) if len(prcauc_list) > 0 else 0 def compute_average_precision(y_true, y_pred): y_true = y_true.detach().cpu().numpy() y_pred = y_pred.detach().cpu().numpy() ap_list = [] for i in range(y_true.shape[1]): if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == 0) > 0: is_labeled = y_true[:, i] == y_true[:, i] ap = average_precision_score(y_true[is_labeled, i], y_pred[is_labeled, i]) ap_list.append(ap) return sum(ap_list) / len(ap_list) if len(ap_list) > 0 else 0 def hard_accuracy(y_true, y_pred): y_true = y_true.detach().cpu().numpy() y_pred = y_pred.detach().cpu().numpy().round() return accuracy_score(y_true, y_pred) def eval_classification(y_true, y_pred): rocauc = compute_roc_auc(y_true, y_pred) prcauc = compute_prcauc(y_true, y_pred) ap = compute_average_precision(y_true, y_pred) hard_acc = hard_accuracy(y_true, y_pred) return {"rocauc": rocauc, "prcauc": prcauc, "ap": ap, "hard_acc": hard_acc} if __name__ == "__main__": print("smth") ================================================ FILE: nc/loops/loops.py ================================================ import wandb import torch import numpy as np from tqdm.autonotebook import tqdm from typing import Callable, Dict, Union from loops.sampler import NodeClSampler from utils.utils_mytorch import * from torch_geometric.data import Data def training_loop_pyg_nc(epochs: int, opt: torch.optim, model: Callable, train_graph: Data, val_graph: Data, device: torch.device = torch.device('cpu'), data_fn: Callable = NodeClSampler, eval_fn: Callable = None, eval_every: int = 1, log_wandb: bool = True, run_trn_testbench: bool = True, savedir: str = None, save_content: Dict[str, list] = None, grad_clipping: bool = True, scheduler: Callable = None, criterion: Callable = None, **kwargs) -> (list, list, list): train_loss = [] train_rocauc, train_prcauc, train_ap, train_hard_acc = [], [], [], [] valid_rocauc, valid_prcauc, valid_ap, valid_hard_acc = [], [], [], [] # Epoch level for e in tqdm(range(epochs)): # Train with Timer() as timer: # Get masks and labels train_mask, train_y, val_mask, val_y = data_fn() model.train() opt.zero_grad() train_mask_ = torch.tensor(train_mask, dtype=torch.long, device=device) train_y_ = torch.tensor(train_y, dtype=torch.float, device=device) val_mask_ = torch.tensor(val_mask, dtype=torch.long, device=device) val_y_ = torch.tensor(val_y, dtype=torch.float, device=device) pred = model(train_graph.to(device=device), train_mask_) loss = criterion(pred, train_y_) per_epoch_loss = loss.item() loss.backward() if grad_clipping: torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) opt.step() if hasattr(model, "post_parameter_update"): model.post_parameter_update() # Log this stuff print(f"[Epoch: {e} ] Loss: {per_epoch_loss}") train_loss.append(per_epoch_loss) if e % eval_every == 0 and e >= 1: with torch.no_grad(): model.eval() val_preds = torch.sigmoid(model(val_graph.to(device=device), val_mask_)) val_res = eval_fn(val_y_, val_preds) valid_rocauc.append(val_res["rocauc"]) valid_prcauc.append(val_res["prcauc"]) valid_ap.append(val_res["ap"]) valid_hard_acc.append(val_res["hard_acc"]) if run_trn_testbench: # Also run train testbench train_preds = torch.sigmoid(model(train_graph.to(device=device), train_mask_)) unsmoothed_labels = (train_y_ > 0.5).float() tr_res = eval_fn(unsmoothed_labels, train_preds) train_rocauc.append(tr_res["rocauc"]) train_prcauc.append(tr_res["prcauc"]) train_ap.append(tr_res["ap"]) train_hard_acc.append(tr_res["hard_acc"]) # Print statement here print("Epoch: %(epo)03d | Loss: %(loss).5f | Tr_rocauc: %(tr_rocauc)0.5f | " "Tr_prcauc: %(tr_prcauc)0.5f | Tr_AP: %(tr_ap)0.5f | Tr_hard_acc: %(tr_hard_acc)0.5f |" "Vl_rocauc: %(val_rocauc)0.5f | Vl_prcauc: %(val_prcauc)0.5f | Vl_AP: %(val_ap)0.5f | " "Vl_hard_acc: %(val_hard_acc)0.5f | Time_trn: %(time).3f min" % {'epo': e, 'loss': float(per_epoch_loss), 'tr_rocauc': float(tr_res["rocauc"]), 'tr_prcauc': float(tr_res["prcauc"]), 'tr_ap': float(tr_res["ap"]), 'tr_hard_acc': float(tr_res["hard_acc"]), 'val_rocauc': float(val_res["rocauc"]), 'val_prcauc': float(val_res["prcauc"]), 'val_ap': float(val_res["ap"]), 'val_hard_acc': float(val_res["hard_acc"]), 'time': timer.interval / 60.0}) if log_wandb: # Wandb stuff wandb.log({ 'epoch': e, 'loss': float(np.mean(per_epoch_loss)), 'tr_rocauc': float(tr_res["rocauc"]), 'tr_prcauc': float(tr_res["prcauc"]), 'tr_ap': float(tr_res["ap"]), 'tr_hard_acc': float(tr_res["hard_acc"]), 'val_rocauc': float(val_res["rocauc"]), 'val_prcauc': float(val_res["prcauc"]), 'val_ap': float(val_res["ap"]), 'val_hard_acc': float(val_res["hard_acc"]), }) else: # Don't benchmark over train # Print Statement here print("Epoch: %(epo)03d | Loss: %(loss).5f | " "Vl_rocauc: %(val_rocauc)0.5f | Vl_prcauc: %(val_prcauc)0.5f | Vl_AP: %(val_ap)0.5f | " "Vl_hard_acc: %(val_hard_acc)0.5f | time_trn: %(time).3f min" % {'epo': e, 'loss': float(per_epoch_loss), 'val_rocauc': float(val_res["rocauc"]), 'val_prcauc': float(val_res["prcauc"]), 'val_ap': float(val_res["ap"]), 'val_hard_acc': float(val_res["hard_acc"]), 'time': timer.interval / 60.0}) if log_wandb: # Wandb stuff wandb.log({ 'epoch': e, 'loss': float(per_epoch_loss), 'val_rocauc': float(val_res["rocauc"]), 'val_prcauc': float(val_res["prcauc"]), 'val_ap': float(val_res["ap"]), 'val_hard_acc': float(val_res["hard_acc"]), }) # We might wanna save the model, too if savedir is not None: mt_save( savedir, torch_stuff=[tosave(obj=save_content['model'].state_dict(), fname='model.torch')], pickle_stuff=[tosave(fname='traces.pkl', obj=[train_loss, valid_rocauc])], json_stuff=[tosave(obj=save_content['config'], fname='config.json')]) else: # No test benches this time around print("Epoch: %(epo)03d | Loss: %(loss).5f | " "Time_Train: %(time).3f min" % {'epo': e, 'loss': float(per_epoch_loss), # 'tracc': float(np.mean(per_epoch_tr_acc)), 'time': timer.interval / 60.0}) if log_wandb: # Wandb stuff wandb.log({ 'epoch': e, 'loss': float(per_epoch_loss), # 'trn_acc': float(np.mean(per_epoch_tr_acc)) }) if scheduler is not None: scheduler.step() return { "loss": train_loss, "train_rocauc": train_rocauc, "train_prcauc": train_prcauc, "train_ap": train_ap, "train_hard_acc": train_hard_acc, "valid_rocauc": valid_rocauc, "valid_prcauc": valid_prcauc, "valid_ap": valid_ap, "valid_hard_acc": valid_hard_acc } ================================================ FILE: nc/loops/sampler.py ================================================ import numpy as np import torch from typing import Union class NodeClSampler: """ The sampler for multi-label node classification tasks. Each node has k labels The graph is processed entirely, so there are no batches. The output is [n_entities, n_labels] """ def __init__(self, data: Union[np.array, dict], num_labels: int, label2id: dict, lbl_smooth: float = 0.0): """ :param data: data as an array of statements of STATEMENT_LEN, e.g., [0,0,0] or [0,1,0,2,4] :param n_entities: total number of entities :param lbl_smooth: whether to apply label smoothing used later in the BCE loss :param bs: batch size :param with_q: whether indexing will consider qualifiers or not, default: FALSE """ self.data = data self.lbl_smooth = lbl_smooth self.num_labels = num_labels self.label2id = label2id self.train_mask, self.train_y, self.eval_mask, self.eval_y = self.generate_labels() def generate_labels(self): train = self.data["train"] # node_id: [lab1, lab2, lab3] eval = self.data["eval"] train_y = np.zeros((len(train), self.num_labels), dtype=np.float32) eval_y = np.zeros((len(eval), self.num_labels), dtype=np.float32) train_mask = np.zeros(len(train), dtype=np.long) eval_mask = np.zeros(len(eval), dtype=np.long) for i, (node, labels) in enumerate(train.items()): lbls = np.zeros((1, self.num_labels)) for l in labels: lbls[0, l] = 1.0 train_y[i] = lbls train_mask[i] = node self.pos_weights = self.compute_weights(train_y) if self.lbl_smooth != 0.0: train_y = (1.0 - self.lbl_smooth)*train_y + (1.0 / len(train)) for i, (node, labels) in enumerate(eval.items()): lbls = np.zeros((1, self.num_labels)) for l in labels: lbls[0, l] = 1.0 eval_y[i] = lbls eval_mask[i] = node return train_mask, train_y, eval_mask, eval_y def compute_weights(self, data): class_counts = data.sum(axis=0) pos_weights = np.ones_like(class_counts) neg_counts = np.array([len(data) - pos_count for pos_count in class_counts]) # <-- HERE for cdx, (pos_count, neg_count) in enumerate(zip(class_counts, neg_counts)): pos_weights[cdx] = neg_count / (pos_count + 1e-5) return torch.as_tensor(pos_weights, dtype=torch.float) def get_data(self): return self.train_mask, self.train_y, self.eval_mask, self.eval_y ================================================ FILE: nc/models/__init__.py ================================================ ================================================ FILE: nc/models/gnn_encoder.py ================================================ import torch import numpy as np from typing import Dict from torch import nn from torch.nn import Parameter import torch.nn.functional as F from utils.utils_gcn import get_param, weight_init from .gnn_layer import StarEConvLayer from .lrga_model import LowRankAttention from utils.nodepiece_tokenizer import NodePiece_Tokenizer from utils.nodepiece_encoder import NodePieceEncoder from torch_geometric.data import Data class StarE_PyG_Encoder(nn.Module): def __init__(self, config: dict, tokenizer: NodePiece_Tokenizer = None, graph: Data = None): super(StarE_PyG_Encoder, self).__init__() self.act = torch.relu # was tanh before self.model_nm = config['MODEL_NAME'] self.config = config self.emb_dim = config['EMBEDDING_DIM'] self.num_rel = config['NUM_RELATIONS'] self.num_ent = config['NUM_ENTITIES'] self.gcn_dim = config['STAREARGS']['GCN_DIM'] self.hid_drop = config['STAREARGS']['HID_DROP'] # self.bias = config['STAREARGS']['BIAS'] self.triple_mode = config['STATEMENT_LEN'] == 3 self.qual_mode = config['STAREARGS']['QUAL_REPR'] self.device = config['DEVICE'] self.num_layers = config['STAREARGS']['LAYERS'] # self.gcn_dim = self.emb_dim if self.n_layer == 1 else self.gcn_dim """ LRGA params """ self.use_lrga = config['STAREARGS']['LRGA'] self.lrga_k = config['STAREARGS']['LRGA_K'] self.lrga_drop = config['STAREARGS']['LRGA_DROP'] # if self.model_nm.endswith('transe'): # self.init_rel = get_param((self.num_rel, self.emb_dim)) # elif config['STAREARGS']['OPN'] == 'rotate' or config['STAREARGS']['QUAL_OPN'] == 'rotate': # phases = 2 * np.pi * torch.rand(self.num_rel, self.emb_dim // 2) # self.init_rel = nn.Parameter(torch.cat([ # torch.cat([torch.cos(phases), torch.sin(phases)], dim=-1), # torch.cat([torch.cos(phases), -torch.sin(phases)], dim=-1) # ], dim=0)) # else: # self.init_rel = get_param((self.num_rel * 2, self.emb_dim)) self.init_rel = nn.Embedding(self.num_rel * 2 + 1, self.emb_dim, padding_idx=self.num_rel * 2) self.init_rel.to(self.device) self.tokenizer = tokenizer if not config['USE_FEATURES']: if self.tokenizer is None: self.entity_embeddings = get_param((self.num_ent, self.emb_dim)) else: self.embedder = NodePieceEncoder(config, tokenizer, rel_embs=self.init_rel, graph=graph) self.feature_reduction = nn.Linear(config['FEATURE_DIM'], self.emb_dim) self.convs = nn.ModuleList() if self.use_lrga: self.attention = nn.ModuleList() self.dim_reduction = nn.ModuleList() # populating manually first and last layers, otherwise in a loop self.convs.append(StarEConvLayer(self.emb_dim, self.gcn_dim, self.num_rel, act=self.act, config=config)) if self.use_lrga: self.attention.append(LowRankAttention(self.lrga_k, self.emb_dim, self.lrga_drop)) self.dim_reduction.append(nn.Sequential(nn.Linear(2 * self.lrga_k + self.gcn_dim + self.emb_dim, self.gcn_dim))) self.bns = nn.ModuleList([nn.BatchNorm1d(self.gcn_dim) for _ in range(self.num_layers - 1)]) for _ in range(self.num_layers - 1): self.convs.append(StarEConvLayer(self.gcn_dim, self.gcn_dim, self.num_rel, act=self.act, config=config)) if self.use_lrga: self.attention.append(LowRankAttention(self.lrga_k, self.gcn_dim, self.lrga_drop)) self.dim_reduction.append(nn.Sequential(nn.Linear(2 * (self.lrga_k + self.gcn_dim), self.gcn_dim))) self.register_parameter('bias', Parameter(torch.zeros(self.num_ent))) def reset_parameters(self): if self.config['STAREARGS']['OPN'] == 'rotate' or self.config['STAREARGS']['QUAL_OPN'] == 'rotate': # phases = 2 * np.pi * torch.rand(self.num_rel, self.emb_dim // 2) # self.init_rel = nn.Parameter(torch.cat([ # torch.cat([torch.cos(phases), torch.sin(phases)], dim=-1), # torch.cat([torch.cos(phases), -torch.sin(phases)], dim=-1) # ], dim=0)) phases = 2 * np.pi * torch.rand(self.num_rel * 2, self.emb_dim // 2, device=self.device) relations = torch.stack([torch.cos(phases), torch.sin(phases)], dim=-1).detach() assert torch.allclose(torch.norm(relations, p=2, dim=-1), phases.new_ones(size=(1, 1))) self.init_rel.weight.data[:-1] = relations.view(self.num_rel * 2, self.emb_dim) self.init_rel.weight.data[-1] = torch.zeros(self.emb_dim) else: torch.nn.init.xavier_normal_(self.init_rel.weight.data) self.feature_reduction.apply(weight_init) torch.nn.init.constant_(self.bias.data, 0) for conv in self.convs: conv.reset_parameters() if self.use_lrga: for att in self.attention: att.apply(weight_init) for dim_r in self.dim_reduction: dim_r.apply(weight_init) for bnorm in self.bns: bnorm.reset_parameters() if not self.config['USE_FEATURES']: if self.tokenizer is None: torch.nn.init.xavier_normal_(self.entity_embeddings.data) else: self.embedder.reset_parameters() def post_parameter_update(self): rel = self.init_rel.weight.data.view(self.num_rel * 2 + 1, self.emb_dim // 2, 2) rel = F.normalize(rel, p=2, dim=-1) self.init_rel.weight.data = rel.view(self.num_rel * 2 + 1, self.emb_dim) def forward_base(self, graph, drop1, drop2): x, edge_index, edge_type, quals = graph['x'], graph['edge_index'], graph['edge_type'], graph['quals'] # Add reverse stuff reverse_index = torch.zeros_like(edge_index) reverse_index[1, :] = edge_index[0, :] reverse_index[0, :] = edge_index[1, :] rev_edge_type = edge_type + self.num_rel edge_index = torch.cat([edge_index, reverse_index], dim=1) edge_type = torch.cat([edge_type, rev_edge_type], dim=0) if not self.triple_mode: quals = torch.cat([quals, quals], dim=1) r = self.init_rel.weight if not self.model_nm.endswith('transe') \ else torch.cat([self.init_rel.weight, -self.init_rel.weight], dim=0) if self.config['USE_FEATURES']: x = self.feature_reduction(x) # TODO find a way to perform attention without dim reduction beforehand else: if self.tokenizer is None: x = self.entity_embeddings else: x = self.embedder.get_all_representations() for i, conv in enumerate(self.convs[:-1]): x_local, r = conv(x=x, edge_index=edge_index, edge_type=edge_type, rel_embed=r, quals=quals) x_local = drop1(x_local) if self.use_lrga: x_global = self.attention[i](x) x = self.dim_reduction[i](torch.cat((x_global, x_local, x), dim=1)) x = F.relu(x) x = self.bns[i](x) else: x = x_local # last layer x_local, r = self.convs[-1](x=x, edge_index=edge_index, edge_type=edge_type, rel_embed=r, quals=quals) x_local = drop2(x_local) if self.use_lrga: x_global = self.attention[-1](x) x = self.dim_reduction[-1](torch.cat((x_global, x_local, x), dim=1)) else: x = x_local return x, r ================================================ FILE: nc/models/gnn_layer.py ================================================ import torch import numpy as np import torch.nn.functional as F from utils.utils_gcn import get_param, ccorr, rotate, softmax from torch_scatter import scatter_add, scatter_mean from torch_geometric.nn import MessagePassing from torch.nn import Parameter class StarEConvLayer(MessagePassing): """ The important stuff. """ def __init__(self, in_channels, out_channels, num_rels, act=lambda x: x, config=None): super(self.__class__, self).__init__(flow='target_to_source', aggr='add') self.p = config self.in_channels = in_channels self.out_channels = out_channels self.num_rels = num_rels self.act = act self.device = None self.w_loop = get_param((in_channels, out_channels)) # (100,200) self.w_in = get_param((in_channels, out_channels)) # (100,200) self.w_out = get_param((in_channels, out_channels)) # (100,200) self.w_rel = get_param((in_channels, out_channels)) # (100,200) if self.p['STATEMENT_LEN'] != 3: if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'sum' or self.p['STAREARGS']['QUAL_AGGREGATE'] == 'mul' or \ self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': self.w_q = get_param((in_channels, in_channels)) # new for quals setup elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'concat': self.w_q = get_param((2 * in_channels, in_channels)) # need 2x size due to the concat operation self.loop_rel = get_param((1, in_channels)) # (1,100) self.loop_ent = get_param((1, in_channels)) # new self.drop = torch.nn.Dropout(self.p['STAREARGS']['GCN_DROP']) self.bn = torch.nn.BatchNorm1d(out_channels) if self.p['STAREARGS']['ATTENTION']: assert self.p['STAREARGS']['GCN_DIM'] == self.p['EMBEDDING_DIM'], "Current attn implementation requires those tto be identical" assert self.p['EMBEDDING_DIM'] % self.p['STAREARGS']['ATTENTION_HEADS'] == 0, "should be divisible" self.heads = self.p['STAREARGS']['ATTENTION_HEADS'] self.attn_dim = self.out_channels // self.heads self.negative_slope = self.p['STAREARGS']['ATTENTION_SLOPE'] self.attn_drop = self.p['STAREARGS']['ATTENTION_DROP'] self.att = get_param((1, self.heads, 2 * self.attn_dim)) if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': assert self.p['STAREARGS']['GCN_DIM'] == self.p[ 'EMBEDDING_DIM'], "Current attn implementation requires those tto be identical" assert self.p['EMBEDDING_DIM'] % self.p['STAREARGS']['ATTENTION_HEADS'] == 0, "should be divisible" if not self.p['STAREARGS']['ATTENTION']: self.heads = self.p['STAREARGS']['ATTENTION_HEADS'] self.attn_dim = self.out_channels // self.heads self.negative_slope = self.p['STAREARGS']['ATTENTION_SLOPE'] self.attn_drop = self.p['STAREARGS']['ATTENTION_DROP'] self.att_qual = get_param((1, self.heads, 2 * self.attn_dim)) if self.p['STAREARGS']['BIAS']: self.register_parameter('bias', Parameter( torch.zeros(out_channels))) def reset_parameters(self): torch.nn.init.xavier_normal_(self.w_loop.data) torch.nn.init.xavier_normal_(self.w_in.data) torch.nn.init.xavier_normal_(self.w_out.data) torch.nn.init.xavier_normal_(self.w_rel.data) if self.p['STATEMENT_LEN'] != 3: torch.nn.init.xavier_normal_(self.w_q.data) torch.nn.init.xavier_normal_(self.loop_rel.data) torch.nn.init.xavier_normal_(self.loop_ent.data) self.bn.reset_parameters() if self.p['STAREARGS']['ATTENTION']: torch.nn.init.xavier_normal_(self.att.data) if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': torch.nn.init.xavier_normal_(self.att_qual.data) def forward(self, x, edge_index, edge_type, rel_embed, qualifier_ent=None, qualifier_rel=None, quals=None): """ See end of doc string for explaining. :param x: all entities*dim_of_entities (for jf17k -> 28646*200) :param edge_index: COO matrix (2 list each having nodes with index [1,2,3,4,5] [3,4,2,5,4] Here node 1 and node 3 are connected with edge. And the type of edge can be found using edge_type. Note that there are twice the number of edges as each edge is also reversed. ) :param edge_type: The type of edge connecting the COO matrix :param rel_embed: 2 Times Total relation * emb_dim (200 in our case and 2 Times because of inverse relations) :param qualifier_ent: :param qualifier_rel: :param quals: Another sparse matrix where quals[0] --> qualifier relations type quals[1] --> qualifier entity quals[2] --> index of the original COO matrix that states for which edge this qualifier exists () For argument sake if a knowledge graph has following statements [e1,p1,e4,qr1,qe1,qr2,qe2] [e1,p1,e2,qr1,qe1,qr2,qe3] [e1,p2,e3,qr3,qe3,qr2,qe2] [e1,p2,e5,qr1,qe1] [e2,p1,e4] [e4,p3,e3,qr4,qe1,qr2,qe4] [e1,p1,e5] (incoming) (outgoing) <----(regular)------><---(inverse)-------> Edge index would be : [e1,e1,e1,e1,e2,e4,e1,e4,e2,e3,e5,e4,e3,e5] [e4,e2,e3,e5,e4,e3,e5,e1,e1,e1,e1,e2,e4,e1] Edge Type would be : [p1,p1,p2,p2,p1,p3,p1,p1_inv,p1_inv,p2_inv,p2_inv,p1_inv,p3_inv,p1_inv] <-------on incoming-----------------><---------on outgoing--------------> quals would be : [qr1,qr2,qr1,qr2,qr3,qr2,qr1,qr4,qr2,qr1,qr2,qr1,qr2,qr3,qr2,qr1,qr4,qr2] [qe1,qe2,qe1,qe3,qe3,qe2,qe1,qe1,qe4,qe1,qe2,qe1,qe3,qe3,qe2,qe1,qe1,qe4] [0,0,1,1,2,2,3,5,5,0,0,1,1,2,2,3,5,5] <--on incoming---><--outgoing-------> Note that qr1,qr2... and qe1, qe2, ... all belong to the same space :return: """ if self.device is None: self.device = edge_index.device rel_embed = torch.cat([rel_embed, self.loop_rel], dim=0) num_edges = edge_index.size(1) // 2 num_ent = x.size(0) self.in_index, self.out_index = edge_index[:, :num_edges], edge_index[:, num_edges:] self.in_type, self.out_type = edge_type[:num_edges], edge_type[num_edges:] if self.p['STATEMENT_LEN'] != 3: num_quals = quals.size(1) // 2 self.in_index_qual_ent, self.out_index_qual_ent = quals[1, :num_quals], quals[1, num_quals:] self.in_index_qual_rel, self.out_index_qual_rel = quals[0, :num_quals], quals[0, num_quals:] self.quals_index_in, self.quals_index_out = quals[2, :num_quals], quals[2, num_quals:] ''' Adding self loop by creating a COO matrix. Thus \ loop index [1,2,3,4,5] [1,2,3,4,5] loop type [10,10,10,10,10] --> assuming there are 9 relations ''' # Self edges between all the nodes self.loop_index = torch.stack([torch.arange(num_ent), torch.arange(num_ent)]).to(self.device) self.loop_type = torch.full((num_ent,), rel_embed.size(0) - 1, dtype=torch.long).to(self.device) # if rel meb is 500, the index of the self emb is # 499 .. which is just added here self.in_norm = self.compute_norm(self.in_index, num_ent) self.out_norm = self.compute_norm(self.out_index, num_ent) if self.p['STATEMENT_LEN'] != 3: in_res = self.propagate(self.in_index, x=x, edge_type=self.in_type, rel_embed=rel_embed, edge_norm=self.in_norm, mode='in', ent_embed=x, qualifier_ent=self.in_index_qual_ent, qualifier_rel=self.in_index_qual_rel, qual_index=self.quals_index_in, source_index=self.in_index[0]) loop_res = self.propagate(self.loop_index, x=x, edge_type=self.loop_type, rel_embed=rel_embed, edge_norm=None, mode='loop', ent_embed=None, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None) out_res = self.propagate(self.out_index, x=x, edge_type=self.out_type, rel_embed=rel_embed, edge_norm=self.out_norm, mode='out', ent_embed=x, qualifier_ent=self.out_index_qual_ent, qualifier_rel=self.out_index_qual_rel, qual_index=self.quals_index_out, source_index=self.out_index[0]) else: in_res = self.propagate(self.in_index, x=x, edge_type=self.in_type, rel_embed=rel_embed, edge_norm=self.in_norm, mode='in', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=self.in_index[0]) loop_res = self.propagate(self.loop_index, x=x, edge_type=self.loop_type, rel_embed=rel_embed, edge_norm=None, mode='loop', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None) out_res = self.propagate(self.out_index, x=x, edge_type=self.out_type, rel_embed=rel_embed, edge_norm=self.out_norm, mode='out', ent_embed=x, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=self.out_index[0]) out = self.drop(in_res) * (1 / 3) + self.drop(out_res) * (1 / 3) + loop_res * (1 / 3) if self.p['STAREARGS']['BIAS']: out = out + self.bias out = self.bn(out) # Ignoring the self loop inserted, return. return self.act(out), torch.matmul(rel_embed, self.w_rel)[:-1] def rel_transform(self, ent_embed, rel_embed): if self.p['STAREARGS']['OPN'] == 'corr': trans_embed = ccorr(ent_embed, rel_embed) elif self.p['STAREARGS']['OPN'] == 'sub': trans_embed = ent_embed - rel_embed elif self.p['STAREARGS']['OPN'] == 'mult': trans_embed = ent_embed * rel_embed elif self.p['STAREARGS']['OPN'] == 'rotate': trans_embed = rotate(ent_embed, rel_embed) else: raise NotImplementedError return trans_embed def qual_transform(self, qualifier_ent, qualifier_rel): """ :return: """ if self.p['STAREARGS']['QUAL_OPN'] == 'corr': trans_embed = ccorr(qualifier_ent, qualifier_rel) elif self.p['STAREARGS']['QUAL_OPN'] == 'sub': trans_embed = qualifier_ent - qualifier_rel elif self.p['STAREARGS']['QUAL_OPN'] == 'mult': trans_embed = qualifier_ent * qualifier_rel elif self.p['STAREARGS']['QUAL_OPN'] == 'rotate': trans_embed = rotate(qualifier_ent, qualifier_rel) else: raise NotImplementedError return trans_embed def qualifier_aggregate(self, qualifier_emb, rel_part_emb, alpha=0.5, qual_index=None): """ In qualifier_aggregate method following steps are performed qualifier_emb looks like - qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) rel_part_emb : [qq,ww,ee,rr,tt, .....] (here qq, ww, ee .. are of 200 dim) Note that rel_part_emb for jf17k would be around 61k*200 Step1 : Pass the qualifier_emb to self.coalesce_quals and multiply the returned output with a weight. qualifier_emb : [aa,bb,cc,dd,ee, ...... ] (here aa, bb, cc are of 200 dim each) Note that now qualifier_emb has the same shape as rel_part_emb around 61k*200 Step2 : Combine the updated qualifier_emb (see Step1) with rel_part_emb based on defined aggregation strategy. Aggregates the qualifier matrix (3, edge_index, emb_dim) :param qualifier_emb: :param rel_part_emb: :param type: :param alpha :return: self.coalesce_quals returns : [q+a+b+d,w+c+e+g,e'+f,......] (here each element in the list is of 200 dim) """ if self.p['STAREARGS']['QUAL_AGGREGATE'] == 'sum': qualifier_emb = torch.einsum('ij,jk -> ik', self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0]), self.w_q) return alpha * rel_part_emb + (1 - alpha) * qualifier_emb # [N_EDGES / 2 x EMB_DIM] elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'concat': qualifier_emb = self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0]) agg_rel = torch.cat((rel_part_emb, qualifier_emb), dim=1) # [N_EDGES / 2 x 2 * EMB_DIM] return torch.mm(agg_rel, self.w_q) # [N_EDGES / 2 x EMB_DIM] elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'mul': qualifier_emb = torch.mm(self.coalesce_quals(qualifier_emb, qual_index, rel_part_emb.shape[0], fill=1), self.w_q) return rel_part_emb * qualifier_emb elif self.p['STAREARGS']['QUAL_AGGREGATE'] == 'attn': # only for sparse mode expanded_rels = torch.index_select(rel_part_emb, 0, qual_index) # Nquals x D expanded_rels = expanded_rels.view(-1, self.heads, self.attn_dim) # Nquals x heads x h_dim qualifier_emb = torch.mm(qualifier_emb, self.w_q).view(-1, self.heads, self.attn_dim) # Nquals x heads x h_dim alpha_r = torch.einsum('bij,kij -> bi', [torch.cat([expanded_rels, qualifier_emb], dim=-1), self.att_qual]) alpha_r = F.leaky_relu(alpha_r, self.negative_slope) # Nquals x heads alpha_r = softmax(alpha_r, qual_index, rel_part_emb.size(0)) # Nquals x heads alpha_r = F.dropout(alpha_r, p=self.attn_drop) # Nquals x heads expanded_rels = (expanded_rels * alpha_r.view(-1, self.heads, 1)).view(-1, self.heads * self.attn_dim) # Nquals x D single_rels = scatter_add(expanded_rels, qual_index, dim=0, dim_size=rel_part_emb.size(0)) # Nedges x D copy_mask = single_rels.sum(dim=1) != 0.0 rel_part_emb[copy_mask] = single_rels[copy_mask] # Nedges x D return rel_part_emb else: raise NotImplementedError def update_rel_emb_with_qualifier(self, ent_embed, rel_embed, qualifier_ent, qualifier_rel, edge_type, qual_index=None): """ The update_rel_emb_with_qualifier method performs following functions: Input is the secondary COO matrix (QE (qualifier entity), QR (qualifier relation), edge index (Connection to the primary COO)) Step1 : Embed all the input Step1a : Embed the qualifier entity via ent_embed (So QE shape is 33k,1 -> 33k,200) Step1b : Embed the qualifier relation via rel_embed (So QR shape is 33k,1 -> 33k,200) Step1c : Embed the main statement edge_type via rel_embed (So edge_type shape is 61k,1 -> 61k,200) Step2 : Combine qualifier entity emb and qualifier relation emb to create qualifier emb (See self.qual_transform). This is generally just summing up. But can be more any pair-wise function that returns one vector for a (qe,qr) vector Step3 : Update the edge_type embedding with qualifier information. This uses scatter_add/scatter_mean. before: qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) qual_index : [1,1,2,1,2,3,2,......] (here 1,2,3 .. are edge index of Main COO) edge_type : [q,w,e',r,t,y,u,i,o,p, .....] (here q,w,e' .. are of 200 dim each) After: edge_type : [q+(a+b+d),w+(c+e+g),e'+f,......] (here each element in the list is of 200 dim) :param ent_embed: essentially x (28k*200 in case of Jf17k) :param rel_embed: essentially relation embedding matrix For secondary COO matrix (QE, QR, edge index) :param qualifier_ent: QE :param qualifier_rel: QR edge_type: :return: index select from embedding phi operation between qual_ent, qual_rel """ # Step 1: embedding qualifier_emb_rel = rel_embed[qualifier_rel] qualifier_emb_ent = ent_embed[qualifier_ent] rel_part_emb = rel_embed[edge_type] # Step 2: pass it through qual_transform qualifier_emb = self.qual_transform(qualifier_ent=qualifier_emb_ent, qualifier_rel=qualifier_emb_rel) # Pass it through a aggregate layer return self.qualifier_aggregate(qualifier_emb, rel_part_emb, alpha=self.p['STAREARGS']['TRIPLE_QUAL_WEIGHT'], qual_index=qual_index) # return qualifier_emb def message(self, x_j, x_i, edge_type, rel_embed, edge_norm, mode, ent_embed=None, qualifier_ent=None, qualifier_rel=None, qual_index=None, source_index=None): """ The message method performs following functions Step1 : get updated relation representation (rel_embed) [edge_type] by aggregating qualifier information (self.update_rel_emb_with_qualifier). Step2 : Obtain edge message by transforming the node embedding with updated relation embedding (self.rel_transform). Step3 : Multiply edge embeddings (transform) by weight Step4 : Return the messages. They will be sent to subjects (1st line in the edge index COO) Over here the node embedding [the first list in COO matrix] is representing the message which will be sent on each edge More information about updating relation representation please refer to self.update_rel_emb_with_qualifier :param x_j: objects of the statements (2nd line in the COO) :param x_i: subjects of the statements (1st line in the COO) :param edge_type: relation types :param rel_embed: embedding matrix of all relations :param edge_norm: :param mode: in (direct) / out (inverse) / loop :param ent_embed: embedding matrix of all entities :param qualifier_ent: :param qualifier_rel: :param qual_index: :param source_index: :return: """ weight = getattr(self, 'w_{}'.format(mode)) if self.p['STATEMENT_LEN'] != 3: # add code here if mode != 'loop': rel_emb = self.update_rel_emb_with_qualifier(ent_embed, rel_embed, qualifier_ent, qualifier_rel, edge_type, qual_index) else: rel_emb = torch.index_select(rel_embed, 0, edge_type) else: rel_emb = torch.index_select(rel_embed, 0, edge_type) xj_rel = self.rel_transform(x_j, rel_emb) out = torch.einsum('ij,jk->ik', xj_rel, weight) if self.p['STAREARGS']['ATTENTION'] and mode != 'loop': out = out.view(-1, self.heads, self.attn_dim) x_i = x_i.view(-1, self.heads, self.attn_dim) alpha = torch.einsum('bij,kij -> bi', [torch.cat([x_i, out], dim=-1), self.att]) alpha = F.leaky_relu(alpha, self.negative_slope) alpha = softmax(alpha, source_index, ent_embed.size(0)) alpha = F.dropout(alpha, p=self.attn_drop) return (out * alpha.view(-1, self.heads, 1)).view(-1, self.heads * self.attn_dim) else: return out if edge_norm is None else out * edge_norm.view(-1, 1) def update(self, aggr_out, mode): if self.p['STAREARGS']['ATTENTION'] and mode != 'loop': aggr_out = aggr_out.view(-1, self.heads * self.attn_dim) return aggr_out @staticmethod def compute_norm(edge_index, num_ent): """ Re-normalization trick used by GCN-based architectures without attention. Yet another torch scatter functionality. See coalesce_quals for a rough idea. row : [1,1,2,3,3,4,4,4,4, .....] (about 61k for Jf17k) edge_weight : [1,1,1,1,1,1,1,1,1, ....] (same as row. So about 61k for Jf17k) deg : [2,1,2,4,.....] (same as num_ent about 28k in case of Jf17k) :param edge_index: :param num_ent: :return: """ row, col = edge_index edge_weight = torch.ones_like( row).float() # Identity matrix where we know all entities are there deg = scatter_add(edge_weight, row, dim=0, dim_size=num_ent) # Summing number of weights of # the edges, D = A + I deg_inv = deg.pow(-0.5) # D^{-0.5} deg_inv[deg_inv == float('inf')] = 0 # for numerical stability norm = deg_inv[row] * edge_weight * deg_inv[ col] # Norm parameter D^{-0.5} * return norm def coalesce_quals(self, qual_embeddings, qual_index, num_edges, fill=0): """ before: qualifier_emb : [a,b,c,d,e,f,g,......] (here a,b,c ... are of 200 dim) qual_index : [1,1,2,1,2,3,2,......] (here 1,2,3 .. are edge index of Main COO) edge_type : [0,0,0,0,0,0,0, .....] (empty array of size num_edges) After: edge_type : [a+b+d,c+e+g,f ......] (here each element in the list is of 200 dim) :param qual_embeddings: shape of [1, N_QUALS] :param qual_index: shape of [1, N_QUALS] which states which quals belong to which main relation from the index, that is, all qual_embeddings that have the same index have to be summed up :param num_edges: num_edges to return the appropriate tensor :param fill: fill value for the output matrix - should be 0 for sum/concat and 1 for mul qual aggregation strat :return: [1, N_EDGES] """ if self.p['STAREARGS']['QUAL_N'] == 'sum': output = scatter_add(qual_embeddings, qual_index, dim=0, dim_size=num_edges) elif self.p['STAREARGS']['QUAL_N'] == 'mean': output = scatter_mean(qual_embeddings, qual_index, dim=0, dim_size=num_edges) if fill != 0: # by default scatter_ functions assign zeros to the output, so we assign them 1's for correct mult mask = output.sum(dim=-1) == 0 output[mask] = fill return output def __repr__(self): return '{}({}, {}, num_rels={})'.format( self.__class__.__name__, self.in_channels, self.out_channels, self.num_rels) ================================================ FILE: nc/models/lrga_model.py ================================================ # taken from https://github.com/omri1348/LRGA/blob/master/ogb/examples/linkproppred/collab/graph_global_attention_layer.py import torch import torch.nn as nn def joint_normalize2(U, V_T): # U and V_T are in block diagonal form tmp_ones = torch.ones((V_T.shape[1], 1)) if torch.cuda.is_available(): tmp_ones = tmp_ones.to(torch.device('cuda')) norm_factor = torch.mm(U, torch.mm(V_T, tmp_ones)) norm_factor = (torch.sum(norm_factor) / U.shape[0]) + 1e-6 return 1/norm_factor def weight_init(layer): if type(layer) == nn.Linear: nn.init.xavier_normal_(layer.weight.data) if layer.bias is not None: nn.init.constant(layer.bias.data, 0) return class LowRankAttention(nn.Module): def __init__(self, k, d, dropout): super().__init__() self.w = nn.Sequential(nn.Linear(d, 4*k), nn.ReLU()) self.activation = nn.ReLU() self.apply(weight_init) self.k = k self.dropout = nn.Dropout(p=dropout) def forward(self, x): tmp = self.w(x) # B x D U = tmp[:, :self.k] # B x K V = tmp[:, self.k:2*self.k] # B x K Z = tmp[:, 2*self.k:3*self.k] # B x K T = tmp[:, 3*self.k:] # B x K V_T = torch.t(V) # K x B # normalization D = joint_normalize2(U, V_T) # scalar res = torch.mm(U, torch.mm(V_T, Z)) # (B x K) mm (K x K) -> B x K res = torch.cat((res*D, T), dim=1) # B x 2K return self.dropout(res) ================================================ FILE: nc/models/nc_baselines.py ================================================ import torch.nn as nn import torch from utils.utils_gcn import get_param, weight_init from utils.nodepiece_encoder import NodePieceEncoder class MLP(nn.Module): """ Vanilla MLP based only on node features """ def __init__(self, initial_features, config): super().__init__() self.input_dim = initial_features.shape[1] self.hidden = config['STAREARGS']['GCN_DIM'] self.num_layers = config['STAREARGS']['LAYERS'] self.num_classes = config['NUM_CLASSES'] self.dropout = config['STAREARGS']['GCN_DROP'] self.use_features = config['USE_FEATURES'] self.device = config['DEVICE'] if not config['USE_FEATURES']: self.entity_embeddings = get_param((config['NUM_ENTITIES'], config['EMBEDDING_DIM'])) else: self.node_features = torch.cat([ torch.zeros((1, initial_features.shape[1]), device=self.device), torch.tensor(initial_features, dtype=torch.float, device=self.device)], dim=0) self.layers = nn.ModuleList([ nn.Sequential(nn.Linear(self.input_dim, self.hidden), nn.ReLU(), nn.Dropout(self.dropout)), *[nn.Sequential(nn.Linear(self.hidden, self.hidden), nn.ReLU(), nn.Dropout(self.dropout)) for _ in range(self.num_layers-2)], nn.Sequential(nn.Linear(self.hidden, self.num_classes), nn.Dropout(self.dropout)) ]) def forward(self, train_mask): if not self.use_features: x = self.entity_embeddings else: x = self.node_features for layer in self.layers: x = layer(x) probs = torch.index_select(x, 0, train_mask) return probs class MLP_PyG(nn.Module): """ Vanilla MLP based only on node features - for PyG setup """ def __init__(self, config, tokenizer, graph): super().__init__() #self.input_dim = config['FEATURE_DIM'] self.hidden = config['STAREARGS']['GCN_DIM'] self.num_layers = config['STAREARGS']['LAYERS'] self.num_classes = config['NUM_CLASSES'] self.dropout = config['STAREARGS']['GCN_DROP'] self.use_features = config['USE_FEATURES'] self.device = config['DEVICE'] self.tokenizer = tokenizer if not config['USE_FEATURES']: self.input_dim = config['EMBEDDING_DIM'] if self.tokenizer is None: self.entity_embeddings = get_param((config['NUM_ENTITIES'], self.input_dim)) else: self.init_rel = nn.Embedding(config['NUM_RELATIONS'] * 2 + 1, self.input_dim) self.embedder = NodePieceEncoder(config, tokenizer, rel_embs=self.init_rel, graph=graph) else: self.input_dim = config['FEATURE_DIM'] self.layers = nn.ModuleList([ nn.Sequential(nn.Linear(self.input_dim, self.hidden), nn.ReLU(), nn.Dropout(self.dropout)), *[nn.Sequential(nn.Linear(self.hidden, self.hidden), nn.ReLU(), nn.Dropout(self.dropout)) for _ in range(self.num_layers-2)], nn.Sequential(nn.Linear(self.hidden, self.num_classes), nn.Dropout(self.dropout)) ]) def reset_parameters(self): for layer in self.layers: layer.apply(weight_init) if not self.use_features: if self.tokenizer is None: torch.nn.init.xavier_normal_(self.entity_embeddings.data) else: torch.nn.init.xavier_normal_(self.init_rel.weight.data) self.embedder.reset_parameters() def forward(self, graph, train_mask): if not self.use_features: if self.tokenizer is None: x = self.entity_embeddings else: x = self.embedder.get_all_representations() else: x = graph['x'] for layer in self.layers: x = layer(x) probs = torch.index_select(x, 0, train_mask) return probs ================================================ FILE: nc/models_nc.py ================================================ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from typing import Dict from models.gnn_encoder import StarE_PyG_Encoder from utils.utils_gcn import get_param, weight_init from utils.nodepiece_tokenizer import NodePiece_Tokenizer from torch_geometric.data import Data class StarE_PyG_NC(StarE_PyG_Encoder): """ A model that is supposed to work with pyg-like data objects and be inductive """ def __init__(self, config: dict, tokenizer: NodePiece_Tokenizer = None, graph: Data = None): super(self.__class__, self).__init__(config, tokenizer=tokenizer, graph=graph) self.model_name = 'StarE_PyG' self.hid_drop2 = config['STAREARGS']['HID_DROP2'] self.feat_drop = config['STAREARGS']['FEAT_DROP'] self.hidden_dim = config['STAREARGS']['GCN_DIM'] self.num_classes = config['NUM_CLASSES'] self.hidden_drop = torch.nn.Dropout(self.hid_drop) self.hidden_drop2 = torch.nn.Dropout(self.hid_drop2) self.feature_drop = torch.nn.Dropout(self.feat_drop) self.to_classes = nn.Linear(self.hidden_dim, self.num_classes) def reset_parameters(self): super(StarE_PyG_NC, self).reset_parameters() self.to_classes.apply(weight_init) def forward(self, graph, train_mask): ''' :param graph: pyg data object :param train_mask: nodes for classification :return: class probabilities (logits) ''' all_ent, rels = self.forward_base(graph, self.hidden_drop, self.feature_drop) nodes = torch.index_select(all_ent, 0, train_mask) nodes = self.hidden_drop2(nodes) probs = self.to_classes(nodes) return probs ================================================ FILE: nc/run_nc.py ================================================ """ The code is largely based on the original StarE codebase https://github.com/migalkin/StarE, but adapted for node classification """ import os os.environ['MKL_NUM_THREADS'] = '1' from functools import partial import random import wandb import sys import collections from pathlib import Path from utils.utils import * from utils.utils_mytorch import parse_args, BadParameters, mt_save_dir from loops.evaluation import eval_classification from models_nc import StarE_PyG_NC from models.nc_baselines import MLP, MLP_PyG from loops.sampler import NodeClSampler from loops.loops import training_loop_pyg_nc from data_loaders.clean_datasets import load_clean_pyg from utils.nodepiece_tokenizer import NodePiece_Tokenizer """ CONFIG Things """ # Clamp the randomness np.random.seed(42) random.seed(42) torch.manual_seed(132) DEFAULT_CONFIG = { 'BATCH_SIZE': 512, 'CORRUPTION_POSITIONS': [0, 2], 'DATASET': 'wd50k', 'DEVICE': 'cpu', 'EMBEDDING_DIM': 50, 'FEATURE_DIM': 1024, 'ENT_POS_FILTERED': True, 'EPOCHS': 1000, 'EVAL_EVERY': 1, 'LEARNING_RATE': 0.0002, 'MARGIN_LOSS': 5, 'MAX_QPAIRS': 3, 'MODEL_NAME': 'stare', 'NUM_FILTER': 5, 'RUN_TESTBENCH_ON_TRAIN': True, 'SAVE': False, 'SELF_ATTENTION': 0, 'STATEMENT_LEN': 3, 'USE_TEST': False, 'WANDB': False, 'LABEL_SMOOTHING': 0.0, 'OPTIMIZER': 'adam', 'CLEANED_DATASET': True, 'NUM_RUNS': 1, 'GRAD_CLIPPING': True, 'LR_SCHEDULER': False, 'CL_TASK': 'so', # so or full 'DS_TYPE': 'transductive', # transductive or inductive 'IND_V': 'v1', # v1 or v2 'SWAP': False, 'TR_RATIO': 1.0, 'VAL_RATIO': 1.0, 'RANDPERM': False, 'PYG_DATA': True, 'USE_FEATURES': False, 'WEIGHT_LOSS': False, # NodePiece args 'SUBBATCH': 5000, 'TOKENIZE': False, 'NUM_ANCHORS': 500, 'MAX_PATHS': 20, 'NEAREST': True, 'POOLER': 'cat', 'SAMPLE_RELS': 5, 'RANDOM_HASHES': 0, 'MAX_PATH_LEN': 0, 'USE_DISTANCES': True, 'T_LAYERS': 2, 'T_HEADS': 4, 'T_HIDDEN': 512, 'T_DROP': 0.1, 'NO_ANC': False, } STAREARGS = { 'LAYERS': 2, 'N_BASES': 0, 'GCN_DIM': 80, 'GCN_DROP': 0.1, 'HID_DROP': 0.3, 'HID_DROP2': 0.1, 'FEAT_DROP': 0.3, 'BIAS': False, 'OPN': 'rotate', 'TRIPLE_QUAL_WEIGHT': 0.8, 'QUAL_AGGREGATE': 'sum', # or concat or mul 'QUAL_OPN': 'rotate', 'QUAL_N': 'sum', # or mean 'SUBBATCH': 0, 'QUAL_REPR': 'sparse', # sparse or full 'ATTENTION': False, 'ATTENTION_HEADS': 4, 'ATTENTION_SLOPE': 0.2, 'ATTENTION_DROP': 0.1, # LRGA 'LRGA': False, 'LRGA_K': 50, 'LRGA_DROP': 0.2 } DEFAULT_CONFIG['STAREARGS'] = STAREARGS if __name__ == "__main__": # Get parsed arguments config = DEFAULT_CONFIG.copy() gcnconfig = STAREARGS.copy() parsed_args = parse_args(sys.argv[1:]) print(parsed_args) # Superimpose this on default config for k, v in parsed_args.items(): # If its a generic arg if k in config.keys(): default_val = config[k.upper()] if default_val is not None: needed_type = type(default_val) config[k.upper()] = needed_type(v) else: config[k.upper()] = v # If its a gcnarg elif k.lower().startswith('gcn_') and k[4:] in gcnconfig: default_val = gcnconfig[k[4:].upper()] if default_val is not None: needed_type = type(default_val) gcnconfig[k[4:].upper()] = needed_type(v) else: gcnconfig[k[4:].upper()] = v else: config[k.upper()] = v config['STAREARGS'] = gcnconfig # Load data, by default we are in the Transductive NC mode, predicting labels of subjects/objects nodes ("so" task) data = load_clean_pyg(name=config["DATASET"], subtype="triples" if config["STATEMENT_LEN"] == 3 else "statements", task=config["CL_TASK"], inductive=config['DS_TYPE'], ind_v=config['IND_V'], maxlen=config["MAX_QPAIRS"], permute=config['RANDPERM']) train_graph, val_graph, test_graph = data['train_graph'], data['val_graph'], data['test_graph'] # we don't use features, but rather train node embeddings from scratch or based on anchors if config['USE_FEATURES']: config['FEATURE_DIM'] = train_graph.x.shape[1] config['NUM_ENTITIES'] = data["n_entities"] config['NUM_RELATIONS'] = data["n_relations"] train_mask, val_mask, test_mask = data["train_mask"], data["valid_mask"], data["test_mask"] train_y, val_y, test_y = data["train_y"], data["val_y"], data["test_y"] all_labels, label2id = data["all_labels"], data["label2id"] # Apply NodePiece to create a hash vocab if config['TOKENIZE']: enriched_graph = train_graph.clone() # Add reverse stuff reverse_index = torch.zeros_like(enriched_graph.edge_index) reverse_index[1, :] = enriched_graph.edge_index[0, :] reverse_index[0, :] = enriched_graph.edge_index[1, :] rev_edge_type = enriched_graph.edge_type + config['NUM_RELATIONS'] enriched_graph.edge_index = torch.cat([enriched_graph.edge_index, reverse_index], dim=1) enriched_graph.edge_type = torch.cat([enriched_graph.edge_type, rev_edge_type], dim=0) kg_tokenizer = NodePiece_Tokenizer(triples=enriched_graph, anchor_strategy={ "degree": 0.4, "betweenness": 0.0, "pagerank": 0.4, "random": 0.2 }, num_anchors=config['NUM_ANCHORS'], dataset_name=config['DATASET'], limit_shortest=100, add_identity=False, mode="path", limit_random=0, relation2id=data['r2id']) if config['MAX_PATH_LEN'] == 0: config['MAX_PATH_LEN'] = kg_tokenizer.max_seq_len else: kg_tokenizer = None enriched_graph = None # prepare labels config['NUM_CLASSES'] = len(all_labels) # adjust the train/val size for our semi-supervised NC task if config['USE_TEST']: input_data = {"train": train_y, "eval": test_y} else: if config['SWAP']: if config['TR_RATIO'] < 1.0: tr_keys = random.sample(list(val_y), int(len(val_y)*config['TR_RATIO'])) val_y = {k: val_y[k] for k in tr_keys} if config['VAL_RATIO'] < 1.0: vl_keys = random.sample(list(train_y), int(len(train_y)*config['VAL_RATIO'])) train_y = {k: train_y[k] for k in vl_keys} input_data = {"train": val_y, "eval": train_y} else: if config['TR_RATIO'] < 1.0: tr_keys = random.sample(list(train_y), int(len(train_y)*config['TR_RATIO'])) train_y = {k: train_y[k] for k in tr_keys} if config['VAL_RATIO'] < 1.0: vl_keys = random.sample(list(val_y), int(len(val_y)*config['VAL_RATIO'])) val_y = {k: val_y[k] for k in vl_keys} input_data = {"train": train_y, "eval": val_y} print(f"Training on {len(input_data['train'])} entities") config['DEVICE'] = torch.device(config['DEVICE']) # create the model if config['MODEL_NAME'].lower() == 'stare': model = StarE_PyG_NC(config, tokenizer=kg_tokenizer, graph=enriched_graph) elif config['MODEL_NAME'].lower() == 'mlp': model = MLP_PyG(config, tokenizer=kg_tokenizer, graph=enriched_graph) else: raise BadParameters(f"Unknown Model Name {config['MODEL_NAME']}") print("Model params ", sum([param.nelement() for param in model.parameters()])) print(f"Model params {sum(p.numel() for p in model.parameters())}") if config['SAVE']: savedir = Path(f"./models/{config['DATASET']}/{config['MODEL_NAME']}") if not savedir.exists(): savedir.mkdir(parents=True) savedir = mt_save_dir(savedir, _newdir=True) save_content = {'model': model, 'config': config} else: savedir, save_content = None, None # Arguments for the training loop args = { "epochs": config['EPOCHS'], "train_fn": model, "device": config['DEVICE'], "eval_fn": eval_classification, "eval_every": config['EVAL_EVERY'], "log_wandb": config['WANDB'], "run_trn_testbench": config['RUN_TESTBENCH_ON_TRAIN'], "savedir": savedir, "save_content": save_content, "grad_clipping": config['GRAD_CLIPPING'], "scheduler": None } training_loop = training_loop_pyg_nc sampler = NodeClSampler(data=input_data, num_labels=len(all_labels), label2id=label2id, lbl_smooth=config['LABEL_SMOOTHING']) args['data_fn'] = sampler.get_data if config['WEIGHT_LOSS']: args['criterion'] = torch.nn.BCEWithLogitsLoss(pos_weight=sampler.pos_weights.to(config['DEVICE'])) else: args['criterion'] = torch.nn.BCEWithLogitsLoss() if config['PYG_DATA']: args['train_graph'] = train_graph if config['DS_TYPE'] == "transductive": args['val_graph'] = train_graph # val and test masks are still on the train graph else: args['val_graph'] = val_graph if not config['USE_TEST'] else test_graph args['model'] = model traces = [] wandb_name = None for run in range(config['NUM_RUNS']): model.reset_parameters() model.to(config['DEVICE']) optimizer = torch.optim.Adam(model.parameters(), lr=config['LEARNING_RATE']) if config['LR_SCHEDULER']: scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.95) args['scheduler'] = scheduler args['opt'] = optimizer if config['WANDB']: wandb_run = wandb.init(project="NodePiece_NC", reinit=True, settings=wandb.Settings(start_method='fork')) wandb_name = wandb.run.name if wandb_name is None else wandb_name wandb.run.name = f"{wandb_name}-{str(run)}" #wandb.run.save() for k, v in config.items(): wandb.config[k] = v trace = training_loop(**args) traces.append(trace) if config['WANDB']: wandb_run.join() print_results(traces) ================================================ FILE: nc/utils/__init__.py ================================================ ================================================ FILE: nc/utils/nodepiece_encoder.py ================================================ import torch import numpy as np import random import torch.nn.functional as F from torch import nn from utils.nodepiece_tokenizer import NodePiece_Tokenizer from torch.nn import TransformerEncoderLayer, TransformerEncoder from tqdm import tqdm from collections import defaultdict from typing import Optional from torch_geometric.data import Data class NodePieceEncoder(nn.Module): def __init__(self, config: dict, tokenizer: NodePiece_Tokenizer, rel_embs: nn.Embedding, graph: Data): super(NodePieceEncoder, self).__init__() self.tokenizer = tokenizer self.pooler = config['POOLER'] self.policy = "sum" self.use_rels = False self.nearest = config['NEAREST'] self.use_neighbor_rels = False self.sample_rels = config['SAMPLE_RELS'] self.graph = graph if not self.use_rels: self.policy = "sum" self.random_hashes = config['RANDOM_HASHES'] self.subbatch = config['SUBBATCH'] self.embedding_dim = config['EMBEDDING_DIM'] self.real_embedding_dim = self.embedding_dim // 2 self.max_seq_len = config['MAX_PATH_LEN'] self.sample_paths = config['MAX_PATHS'] self.use_distances = config['USE_DISTANCES'] self.hid_dim = config['T_HIDDEN'] self.drop_prob = config['T_DROP'] self.num_heads = config['T_HEADS'] self.num_layers = config['T_LAYERS'] self.num_entities = config['NUM_ENTITIES'] self.num_relations = config['NUM_RELATIONS'] self.device = config['DEVICE'] self.no_anc = config['NO_ANC'] if self.pooler == "mlp": self.set_enc = nn.Sequential( nn.Linear(self.embedding_dim if self.policy != "cat" else 2 * self.embedding_dim, self.hid_dim), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.hid_dim, self.hid_dim), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.hid_dim, self.hid_dim), ) self.set_dec = nn.Sequential( nn.Linear(self.hid_dim, self.hid_dim), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.hid_dim, self.hid_dim), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.hid_dim, self.embedding_dim) ) elif self.pooler == "cat": self.set_enc = nn.Sequential( nn.Linear(self.embedding_dim * (self.sample_paths + self.sample_rels), self.embedding_dim * 2), nn.Dropout(self.drop_prob), nn.ReLU(), # nn.Linear(embedding_dim * 4, embedding_dim * 2), nn.Dropout(drop_prob), nn.ReLU(), nn.Linear(self.embedding_dim * 2, self.embedding_dim) ) if not self.no_anc else nn.Sequential( nn.Linear(self.embedding_dim * self.sample_rels, self.embedding_dim * 2), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.embedding_dim * 2, self.embedding_dim)) elif self.pooler == "trf": encoder_layer = TransformerEncoderLayer( d_model=self.embedding_dim if self.policy != "cat" else 2 * self.embedding_dim, nhead=self.num_heads if self.policy != "cat" else 2 * self.num_heads, dim_feedforward=self.hid_dim, dropout=self.drop_prob, ) self.set_enc = TransformerEncoder(encoder_layer=encoder_layer, num_layers=self.num_layers) if self.policy == "cat": self.linear = nn.Linear(2 * self.embedding_dim, self.embedding_dim) self.rel_gnn = False self.tokenizer.token2id[self.tokenizer.NOTHING_TOKEN] = len(tokenizer.token2id) self.anchor_embeddings = nn.Embedding(len(tokenizer.token2id), embedding_dim=self.embedding_dim, padding_idx=self.tokenizer.token2id[tokenizer.PADDING_TOKEN]) self.relation_embeddings = rel_embs self.dist_emb = nn.Embedding(self.max_seq_len, embedding_dim=self.embedding_dim) if self.random_hashes == 0: if not self.nearest: # subsample paths, need to align them with distances sampled_paths = { entity: random.sample(paths, k=min(self.sample_paths, len(paths))) for entity, paths in self.tokenizer.vocab.items() } elif self.nearest: # sort paths by length first and take K of them prev_max_len = max(len(path) for k, v in self.tokenizer.vocab.items() for path in v) sampled_paths = { entity: sorted(paths, key=lambda x: len(x))[:min(self.sample_paths, len(paths))] for entity, paths in self.tokenizer.vocab.items() } self.max_seq_len = max(len(path) for k, v in sampled_paths.items() for path in v) print( f"Changed max seq len from {prev_max_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") hashes = [ [self.tokenizer.token2id[path[0]] for path in paths] + [ self.tokenizer.token2id[tokenizer.PADDING_TOKEN]] * (self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] distances = [ [len(path) - 1 for path in paths] + [0] * (self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.tensor(distances, dtype=torch.long, device=self.device) else: # in this case, we bypass distances and won't use relations in the encoder self.anchor_embeddings = nn.Embedding(self.random_hashes, embedding_dim=self.embedding_dim) hashes = [ random.sample(list(range(self.random_hashes)), self.sample_paths) for i in range(self.num_entities) ] # _PRIMES = [ # 31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223 # ] # self.num_buckets = self.random_hashes # self.anchor_embeddings = nn.Embedding(self.num_buckets * self.num_hashes, embedding_dim=embedding_dim // self.num_hashes) # self.hash_projector = nn.Sequential( # nn.Linear(self.embedding_dim, self.embedding_dim), # nn.ReLU(), # nn.Linear(self.embedding_dim, self.embedding_dim) # ) # primes = _PRIMES[:self.num_hashes] # hashes = [ # [(((i+1) * prime) % self.num_buckets) + k*self.num_buckets for k, prime in enumerate(primes)] # for i in range(triples.num_entities) # ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.zeros((self.num_entities, self.sample_paths), dtype=torch.long, device=self.device) if self.use_neighbor_rels: # create a feature matrix where rows are used relations in a 1-hop neighbourhood around each node unique_sp = self.triples_factory.mapped_triples[:, [0, 1]].unique(dim=0, return_counts=False) self.relation_features = torch.zeros((self.num_entities, self.num_relations * 2), dtype=torch.float, requires_grad=False, device=self.device) # features matrix self.relation_features[unique_sp[:, 0], unique_sp[:, 1]] = 1.0 # counts.float().to(self.device) # self.relation_features = torch.nn.functional.normalize(self.relation_features, p=1, dim=1) self.projection = nn.Sequential( nn.Linear(self.embedding_dim + self.num_relations, self.hid_dim), nn.ReLU(), nn.Linear(self.hid_dim, self.embedding_dim) ) if self.sample_rels > 0: pad_idx = self.num_relations * 2 e2r = defaultdict(set) edge_index = self.graph.edge_index edge_type = self.graph.edge_type for i, src_node in enumerate(edge_index[0]): e2r[src_node.item()].add(edge_type[i].item()) len_stats = [len(v) for k, v in e2r.items()] print( f"Unique relations per node - min: {min(len_stats)}, avg: {np.mean(len_stats)}, 66th perc: {np.percentile(sorted(len_stats), 66)}, max: {max(len_stats)} ") unique_1hop_relations = [ random.sample(e2r[i], k=min(self.sample_rels, len(e2r[i]))) + [pad_idx] * ( self.sample_rels - min(len(e2r[i]), self.sample_rels)) for i in range(self.num_entities) ] self.unique_1hop_relations = torch.tensor(unique_1hop_relations, dtype=torch.long, device=self.device) def reset_parameters(self): if self.pooler != "avg": for module in self.set_enc.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if self.pooler == "mlp": for module in self.set_dec.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if self.use_neighbor_rels: for module in self.projection.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() # if self.random_hashes != 0: # for module in self.hash_projector.modules(): # if module is self: # continue # if hasattr(module, "reset_parameters"): # module.reset_parameters() torch.nn.init.xavier_uniform_(self.anchor_embeddings.weight) torch.nn.init.xavier_uniform_(self.dist_emb.weight) if self.use_rels == "joint": torch.nn.init.xavier_uniform_(self.node_types.weight) if self.random_hashes == 0: with torch.no_grad(): self.anchor_embeddings.weight[self.tokenizer.token2id[self.tokenizer.PADDING_TOKEN]] = torch.zeros(self.embedding_dim) self.dist_emb.weight[0] = torch.zeros(self.embedding_dim) # if self.use_rels == "trf": # self.rel_pos.weight[0] = torch.zeros(self.embedding_dim) # phases randomly between 0 and 2 pi # phases = 2 * np.pi * torch.rand(self.num_relations, self.real_embedding_dim, device=self.device) # relations = torch.stack([torch.cos(phases), torch.sin(phases)], dim=-1).detach() # assert torch.allclose(torch.norm(relations, p=2, dim=-1), phases.new_ones(size=(1, 1))) # self.relation_embeddings.weight.data[:-1] = relations.view(self.num_relations, self.embedding_dim) # self.relation_embeddings.weight.data[-1] = torch.zeros(self.embedding_dim) def pool_anchors(self, anc_embs: torch.FloatTensor, mask: Optional[torch.BoolTensor] = None) -> torch.FloatTensor: """ input shape: (bs, num_anchors, emb_dim) output shape: (bs, emb_dim) """ if self.pooler == "set": pooled = self.set_enc(anc_embs) elif self.pooler == "cat": anc_embs = anc_embs.view(anc_embs.shape[0], -1) pooled = self.set_enc(anc_embs) if self.sample_paths != 1 else anc_embs elif self.pooler == "trf" or self.pooler == "moe": if self.use_rels != "joint": pooled = self.set_enc(anc_embs.transpose(1, 0)) # output shape: (seq_len, bs, dim) else: pooled = self.set_enc(anc_embs.transpose(1, 0), src_key_padding_mask=mask) pooled = pooled.mean(dim=0) # output shape: (bs, dim) if self.policy == "cat": pooled = self.linear(pooled) elif self.pooler == "perc": pooled = self.set_enc(anc_embs) elif self.pooler == "mlp": pooled = self.set_dec(self.set_enc(anc_embs).mean(-2)) else: pooled = anc_embs.mean(dim=1) return pooled def encode_rels(self, rel_hashes: torch.LongTensor, weights: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: # input: (bs, num_anchors, max_seq_len) bs, num_paths, seq_len = rel_hashes.shape rel_hashes = rel_hashes.view(bs * num_paths, seq_len) if weights is not None: weights = weights.view(bs * num_paths, seq_len) pad_mask = rel_hashes != self.triples_factory.num_relations rel_hashes = self.relation_embeddings(rel_hashes) # (bs*num_paths, seq_len, hid_dim) if self.use_rels == "lstm" or self.use_rels == "gru": rel_hashes, _ = self.rel_enc(rel_hashes) # (bs, seq_len, hid_dim) rel_hashes = self.rel_proj(rel_hashes[:, -1, :]) # (bs, emb_dim) elif self.use_rels == "mlp": accumulator = torch.zeros((rel_hashes.shape[0], rel_hashes.shape[-1]), dtype=torch.float, device=rel_hashes.device) enc, dec = self.rel_enc[0], self.rel_enc[1] for i in range(seq_len-1): pair = rel_hashes[:, i:i+2].view(-1, 2 * self.embedding_dim) # (bs*num_anc, 2 * 100) pair = enc(pair) accumulator += pair rel_hashes = dec(accumulator) elif self.use_rels == "avg": if weights is None: rel_hashes = (rel_hashes * pad_mask.float().unsqueeze(-1)).sum(-2) / pad_mask.float().sum(-1).clamp_min(1.0).unsqueeze(-1) else: rel_hashes = (rel_hashes * weights.unsqueeze(-1)).sum(-2) / pad_mask.float().sum(-1).clamp_min(1.0).unsqueeze(-1) elif self.use_rels == "avg+": pos = torch.arange(seq_len, dtype=torch.long, device=rel_hashes.device).repeat(bs * num_paths, 1) pos = self.rel_pos(pos) rel_hashes = torch.cat([rel_hashes, pos], dim=-1) rel_hashes = self.rel_enc(rel_hashes) rel_hashes = (rel_hashes * pad_mask.float().unsqueeze(-1)).sum(-2) / pad_mask.float().sum(-1).clamp_min(1.0).unsqueeze(-1) elif self.use_rels == "trf": temp = torch.zeros((rel_hashes.shape[1], rel_hashes.shape[0], rel_hashes.shape[2]), dtype=torch.float, device=rel_hashes.device) positions = torch.arange(0, seq_len, dtype=torch.long, device=self.device) positions = self.rel_pos(positions) nnz_paths = rel_hashes[pad_mask.sum(1) > 0] nnz_paths += positions nnz_paths = self.rel_enc(nnz_paths.transpose(1, 0), src_key_padding_mask=~pad_mask[pad_mask.sum(1) > 0]) # (seq_len, bs, dim) nnz_paths[torch.isnan(nnz_paths)] = 1.0 # for numerical stability of empty paths with NOTHING tokens nnz_paths[torch.isinf(nnz_paths)] = 1.0 # for numerical stability of empty paths with NOTHING tokens temp[:, pad_mask.sum(1) > 0, :] = nnz_paths rel_hashes = temp rel_hashes = (rel_hashes * pad_mask.t().float().unsqueeze(-1)).sum(0) / pad_mask.float().sum(1).clamp_min(1.0).unsqueeze(-1) #rel_hashes = rel_hashes.mean(0) elif self.use_rels == "int": # replace padding 0's with 1's to prevent nans in the rotate computation rel_hashes[~pad_mask] = 1.0 start = rel_hashes[:, 0, :] for i in range(1, seq_len): target = rel_hashes[:, i, :] interaction = self.pairwise_interaction_function(start.view(-1, self.real_embedding_dim, 2), target.view(-1, self.real_embedding_dim, 2)) start = interaction rel_hashes = interaction return rel_hashes.view(bs, num_paths, self.embedding_dim) def encode_by_index(self, entities: torch.LongTensor) -> torch.FloatTensor: hashes, dists = self.hashes[entities], self.distances[entities] anc_embs = self.anchor_embeddings(hashes) mask = None if self.use_distances: dist_embs = self.dist_emb(dists) anc_embs += dist_embs if self.no_anc: anc_embs = torch.tensor([], device=self.device) if self.use_rels: rel_hashes = self.rel_hash[entities] # (bs, num_relations) path_weights = self.path_weights[entities] if self.use_mc else None # if self.rel_gnn: # self.relation_embeddings.weight.data = self.gnn_encoder(self.relation_embeddings.weight, self.edge_index) if self.use_rels != "joint": path_embs = self.encode_rels(rel_hashes, path_weights) if self.policy != "cat": anc_embs += path_embs else: anc_embs = torch.cat([anc_embs, path_embs], dim=-1) else: path_embs = self.relation_embeddings(rel_hashes) anc_embs = torch.cat([anc_embs, path_embs], dim=1) node_types = torch.cat([ torch.zeros_like(hashes, dtype=torch.long, device=self.device), torch.ones_like(rel_hashes, dtype=torch.long, device=self.device) ], dim=1) node_type_embs = self.node_types(node_types) anc_embs += node_type_embs mask = rel_hashes == self.num_relations mask = torch.cat([ torch.zeros_like(hashes, dtype=torch.bool, device=self.device), mask.to(self.device) ], dim=1).to(self.device) #set_input[mask] = anc_embs.view(-1, 1) if self.sample_rels > 0: rels = self.unique_1hop_relations[entities] # (bs, rel_sample_size) rels = self.relation_embeddings(rels) # (bs, rel_sample_size, dim) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask) # (bs, dim) if self.use_neighbor_rels: rels_one_hot = self.relation_features[entities] anc_embs = torch.cat([anc_embs, rels_one_hot], dim=-1) # (dim + num_relations) anc_embs = self.projection(anc_embs) # (bs, dim) return anc_embs def get_all_representations(self): if self.subbatch != 0: temp_embs = torch.zeros((len(self.hashes), self.embedding_dim), dtype=torch.float, device=self.device) vocab_keys = list(range(len(self.hashes))) for i in tqdm(range(0, len(self.hashes), self.subbatch)): entities = torch.tensor(vocab_keys[i: i+self.subbatch], dtype=torch.long, device=self.device) embs = self.encode_by_index(entities) temp_embs[i: i + self.subbatch, :] = embs return temp_embs else: anc_embs = self.anchor_embeddings(self.hashes) mask = None if self.use_distances: dist_embs = self.dist_emb(self.distances) anc_embs += dist_embs if self.use_rels: if self.use_rels != "joint": path_embs = self.encode_rels(self.rel_hash) if self.policy != "cat": anc_embs += path_embs else: anc_embs = torch.cat([anc_embs, path_embs], dim=-1) else: path_embs = self.relation_embeddings(self.rel_hash) anc_embs = torch.cat([anc_embs, path_embs], dim=1) node_types = torch.cat([ torch.zeros_like(self.hashes, dtype=torch.long, device=self.device), torch.ones_like(self.rel_hash, dtype=torch.long, device=self.device) ], dim=1) node_type_embs = self.node_types(node_types) anc_embs += node_type_embs mask = self.rel_hash == self.num_relations mask = torch.cat([ torch.zeros_like(self.hashes, dtype=torch.bool, device=self.device), mask.to(self.device) ], dim=1).to(self.device) res = self.pool_anchors(anc_embs, mask=mask) return res ================================================ FILE: nc/utils/nodepiece_tokenizer.py ================================================ import torch import numpy as np import pickle from igraph import Graph from pykeen.triples import TriplesFactory from typing import List, Dict, Union from pathlib import Path from tqdm import tqdm import random from torch_geometric.data import Data class NodePiece_Tokenizer: """ Tokenizer for KGs: will select topK anchor nodes according to certain strategies; and encode other nodes as 'words' as hashes comprised of relations and anchor nodes """ def __init__(self, triples: Union[TriplesFactory, Data], dataset_name: str, num_anchors: int, anchor_strategy: Dict[str, float], limit_shortest: int = 0, limit_random: int = 0, add_identity: bool = True, mode: str = "path", relation2id: dict = None ) -> None: super().__init__() self.triples_factory = triples # original triples of the training graph, assuming inverses are added self.dataset_name = dataset_name self.num_anchors = num_anchors # total N anchor nodes self.anchor_strategy = anchor_strategy # ratios of strategies for sampling anchor nodes self.num_paths = num_anchors # aux variable self.sp_limit = limit_shortest # keep only K nearest anchor nodes per entity self.rand_limit = limit_random # keep only K random anchor nodes per entity if self.sp_limit * self.rand_limit != 0: raise Exception("-sp_limit and -rand_limit are mutually exclusive") self.add_identity = add_identity # anchor nodes will have their own indices with distance 0 in their hashes self.tkn_mode = mode # only "path" mode is implemented atm # auxiliary tokens for the vocabulary self.NOTHING_TOKEN = -99 # means the node is not reachable from any of anchor nodes self.CLS_TOKEN = -1 self.MASK_TOKEN = -10 self.PADDING_TOKEN = -100 self.SEP_TOKEN = -2 self.r2id = relation2id # well, betwenness is disabled self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) # load or create the vocabulary self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() # numerical indices for entities and relations self.token2id = {t: i for i, t in enumerate(self.top_entities)} self.rel2token = {t: i + len(self.top_entities) for i, t in enumerate(list(self.r2id.values()))} self.vocab_size = len(self.token2id) + len(self.rel2token) # although we don't use paths, we count their lengths for anchor distances self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) if self.add_identity: # add identity for anchor nodes as the first / closest node for anchor in self.top_entities[:-4]: # last 4 are always service tokens self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] def tokenize_kg(self): # creating a filename strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" if self.sp_limit > 0: filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths if self.rand_limit > 0: filename += f"_{self.rand_limit}rand" filename += ".pkl" self.model_name = filename.split('.pkl')[0] path = Path(filename) if path.is_file(): anchors, non_anchors, vocab = pickle.load(open(path, "rb")) return anchors, non_anchors, vocab # adjustment for a slightly different input format # create an input object for iGraph - edge list with relation types, and then create a graph src, tgt, rels = self.triples_factory.edge_index[0].numpy(), self.triples_factory.edge_index[1].numpy(), self.triples_factory.edge_type.numpy() edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] graph = Graph(n=self.triples_factory.num_nodes, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) # sampling anchor nodes anchors = [] for strategy, ratio in self.anchor_strategy.items(): if ratio <= 0.0: continue topK = int(np.ceil(ratio * self.num_anchors)) print(f"Computing the {strategy} nodes") if strategy == "degree": top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) elif strategy == "betweenness": # This is O(V^3) - disabled raise NotImplementedError("Betweenness is disabled due to computational costs") elif strategy == "pagerank": top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) elif strategy == "random": top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.triples_factory.num_nodes))] selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] anchors.extend(selected_nodes) print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") # now mine the anchors per node vocab = self.create_all_paths(graph, anchors) top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] non_core_entities = [i for i in range(self.triples_factory.num_nodes) if i not in anchors] pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) print("Vocabularized and saved!") return top_entities, non_core_entities, vocab def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: vocab = {} if self.rand_limit == 0: print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} shortest paths per node") else: print(f"Computing the entity vocabulary - paths, retaining {self.rand_limit} random paths per node") # single-threaded mining is found to be as fast as multi-processing + igraph for some reason, so let's use a dummy for-loop for i in tqdm(range(self.triples_factory.num_nodes)): if self.tkn_mode == "path": paths = graph.get_shortest_paths(v=i, to=top_entities, output="epath", mode='in') if len(paths[0]) > 0: relation_paths = [[graph.es[path[-1]].source] + [graph.es[k]['relation'] for k in path[::-1]] for path in paths if len(path) > 0] else: # if NO anchor can be reached from the node - encode with a special NOTHING_TOKEN relation_paths = [[self.NOTHING_TOKEN] for _ in range(self.num_paths)] if self.sp_limit > 0: relation_paths = sorted(relation_paths, key=lambda x: len(x))[:self.sp_limit] if self.rand_limit > 0: random.shuffle(relation_paths) relation_paths = relation_paths[:self.rand_limit] vocab[i] = relation_paths else: raise NotImplementedError return vocab ================================================ FILE: nc/utils/utils.py ================================================ """ Some things needed across the board""" import torch import numpy as np from typing import Optional, List, Union, Dict, Callable, Tuple KNOWN_DATASETS = ['wd50k'] def masked_softmax(x, m=None, dim=-1): """ Softmax with mask :param x: :param m: :param dim: :return: """ if m is not None: m = m.float() x = x * m e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0]) if m is not None: e_x = e_x * m softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-6) return softmax def combine(*args: Union[np.ndarray, list, tuple]): """ Used to semi-intelligently combine data splits Case A) args is a single element, an ndarray. Return as is. Case B) args are multiple ndarray. Numpy concat them. Case C) args is a single dict. Return as is. Case D) args is multiple dicts. Concat individual elements :param args: (see above) :return: A nd array or a dict """ # Case A, C if len(args) == 1 and type(args[0]) is not dict: return np.array(args[0]) if len(args) == 1 and type(args) is dict: return args # Case B if type(args) is tuple and (type(args[0]) is np.ndarray or type(args[0]) is list): # Expected shape will be a x n, b x n. Simple concat will do. return np.concatenate(args) # Case D if type(args) is tuple and type(args[0]) is dict: keys = args[0].keys() combined = {} for k in keys: combined[k] = np.concatenate([arg[k] for arg in args], dim=-1) return combined def print_results(traces: List): """ traces: List of Dicts of "loss": train_loss, "train_rocauc:": train_rocauc, "train_prcauc": train_prcauc, "train_ap": train_ap, "train_hard_acc": train_hard_acc, "valid_rocauc": valid_rocauc, "valid_prcauc": valid_prcauc, "valid_ap": valid_ap, "valid_hard_acc": valid_hard_acc """ for key in ["train_rocauc", "train_prcauc", "train_ap", "train_hard_acc", "valid_rocauc", "valid_prcauc", "valid_ap", "valid_hard_acc"]: result = np.array([t[key][-1] for t in traces]) print(f"Avg {key}: {np.mean(result): .3f}, std dev: {np.std(result):.3f}") ================================================ FILE: nc/utils/utils_gcn.py ================================================ import logging import logging.config import inspect import numpy as np # PyTorch related imports import torch import torch_scatter from torch.nn import Parameter from torch.nn.init import xavier_normal_ from torch_scatter import scatter_add, scatter_max np.set_printoptions(precision=4) class MessagePassing(torch.nn.Module): r"""Base class for creating message passing layers .. math:: \mathbf{x}_i^{\prime} = \gamma_{\mathbf{\Theta}} \left( \mathbf{x}_i, \square_{j \in \mathcal{N}(i)} \, \phi_{\mathbf{\Theta}} \left(\mathbf{x}_i, \mathbf{x}_j,\mathbf{e}_{i,j}\right) \right), where :math:`\square` denotes a differentiable, permutation invariant function, *e.g.*, sum, mean or max, and :math:`\gamma_{\mathbf{\Theta}}` and :math:`\phi_{\mathbf{\Theta}}` denote differentiable functions such as MLPs. See `here `__ for the accompanying tutorial. """ def __init__(self, aggr='add'): super(MessagePassing, self).__init__() # In the defined message function: get the list of arguments as list of string| # For eg. in r-gcn this will be ['x_j', 'edge_type', 'edge_norm'] (args of message fn) self.message_args = inspect.getargspec(self.message)[0][1:] # Same for update function starting from 3rd argument | first=self, second=out self.update_args = inspect.getargspec(self.update)[0][2:] def propagate(self, aggr, edge_index, **kwargs): r"""The initial call to start propagating messages. Takes in an aggregation scheme (:obj:`"add"`, :obj:`"mean"` or :obj:`"max"`), the edge indices, and all additional data which is needed to construct messages and to update node embeddings.""" assert aggr in ['add', 'mean', 'max'] kwargs['edge_index'] = edge_index size = None message_args = [] for arg in self.message_args: if arg[-2:] == '_i': # If arguments ends with _i then include indic tmp = kwargs[ arg[:-2]] # Take the front part of the variable | Mostly it will be 'x', size = tmp.size(0) message_args.append(tmp[edge_index[0]]) # Lookup for head entities in edges elif arg[-2:] == '_j': tmp = kwargs[arg[:-2]] # tmp = kwargs['x'] size = tmp.size(0) message_args.append(tmp[edge_index[1]]) # Lookup for tail entities in edges else: message_args.append(kwargs[arg]) # Take things from kwargs update_args = [kwargs[arg] for arg in self.update_args] # Take update args from kwargs out = self.message(*message_args) out = scatter_(aggr, out, edge_index[0], dim_size=size) # Aggregated neighbors for each vertex out = self.update(out, *update_args) return out def message(self, x_j): # pragma: no cover r"""Constructs messages in analogy to :math:`\phi_{\mathbf{\Theta}}` for each edge in :math:`(i,j) \in \mathcal{E}`. Can take any argument which was initially passed to :meth:`propagate`. In addition, features can be lifted to the source node :math:`i` and target node :math:`j` by appending :obj:`_i` or :obj:`_j` to the variable name, *.e.g.* :obj:`x_i` and :obj:`x_j`.""" return x_j def update(self, aggr_out): # pragma: no cover r"""Updates node embeddings in analogy to :math:`\gamma_{\mathbf{\Theta}}` for each node :math:`i \in \mathcal{V}`. Takes in the output of aggregation as first argument and any argument which was initially passed to :meth:`propagate`.""" return aggr_out def maybe_num_nodes(index, num_nodes=None): return index.max().item() + 1 if num_nodes is None else num_nodes def softmax(src, index, num_nodes=None): r"""Computes a sparsely evaluated softmax. Given a value tensor :attr:`src`, this function first groups the values along the first dimension based on the indices specified in :attr:`index`, and then proceeds to compute the softmax individually for each group. Args: src (Tensor): The source tensor. index (LongTensor): The indices of elements for applying the softmax. num_nodes (int, optional): The number of nodes, *i.e.* :obj:`max_val + 1` of :attr:`index`. (default: :obj:`None`) :rtype: :class:`Tensor` """ num_nodes = maybe_num_nodes(index, num_nodes) out = src - scatter_max(src, index, dim=0, dim_size=num_nodes)[0][index] out = out.exp() out = out / ( scatter_add(out, index, dim=0, dim_size=num_nodes)[index] + 1e-16) return out def get_param(shape): param = Parameter(torch.Tensor(*shape)) xavier_normal_(param.data) return param def weight_init(layer): if type(layer) == torch.nn.Linear: torch.nn.init.xavier_normal_(layer.weight.data) if layer.bias is not None: torch.nn.init.constant_(layer.bias.data, 0) return def com_mult(a, b): r1, i1 = a[..., 0], a[..., 1] r2, i2 = b[..., 0], b[..., 1] return torch.stack([r1 * r2 - i1 * i2, r1 * i2 + i1 * r2], dim=-1) def conj(a): a[..., 1] = -a[..., 1] return a def cconv(a, b): return torch.irfft(com_mult(torch.rfft(a, 1), torch.rfft(b, 1)), 1, signal_sizes=(a.shape[-1],)) def ccorr(a, b): return torch.irfft(com_mult(conj(torch.rfft(a, 1)), torch.rfft(b, 1)), 1, signal_sizes=(a.shape[-1],)) def rotate(h, r): # re: first half, im: second half # assume embedding dim is the last dimension d = h.shape[-1] h_re, h_im = torch.split(h, d // 2, -1) r_re, r_im = torch.split(r, d // 2, -1) return torch.cat([h_re * r_re - h_im * r_im, h_re * r_im + h_im * r_re], dim=-1) def scatter_(name, src, index, dim_size=None): r"""Aggregates all values from the :attr:`src` tensor at the indices specified in the :attr:`index` tensor along the first dimension. If multiple indices reference the same location, their contributions are aggregated according to :attr:`name` (either :obj:`"add"`, :obj:`"mean"` or :obj:`"max"`). Args: name (string): The aggregation to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). src (Tensor): The source tensor. index (LongTensor): The indices of elements to scatter. dim_size (int, optional): Automatically create output tensor with size :attr:`dim_size` in the first dimension. If set to :attr:`None`, a minimal sized output tensor is returned. (default: :obj:`None`) :rtype: :class:`Tensor` """ assert name in ['add', 'mean', 'max'] op = getattr(torch_scatter, 'scatter_{}'.format(name)) fill_value = -1e38 if name == 'max' else 0 out = op(src, index, 0, None, dim_size, fill_value) if isinstance(out, tuple): out = out[0] if name == 'max': out[out == fill_value] = 0 return out ================================================ FILE: nc/utils/utils_mytorch.py ================================================ from typing import List, Dict, Union from pathlib import Path from collections import namedtuple import warnings import os import time import json import torch import numpy as np import pickle import traceback class ImproperCMDArguments(Exception): pass class MismatchedDataError(Exception): pass class BadParameters(Exception): def __init___(self, dErrorArguments): Exception.__init__(self, "Unexpected value of parameter {0}".format(dErrorArguments)) self.dErrorArguments = dErrorArguments tosave = namedtuple('ObjectsToSave','fname obj') class FancyDict(dict): def __init__(self, *args, **kwargs): super().__init__(**kwargs) self.__dict__ = self class Timer: """ Simple block which can be called as a context, to know the time of a block. """ def __enter__(self): self.start = time.perf_counter() return self def __exit__(self, *args): self.end = time.perf_counter() self.interval = self.end - self.start def default_eval(y_pred, y_true): """ Expects a batch of input :param y_pred: tensor of shape (b, nc) :param y_true: tensor of shape (b, 1) """ return torch.mean((torch.argmax(y_pred, dim=1) == y_true).float()) def compute_mask(t: Union[torch.Tensor, np.array], padding_idx=0): """ compute mask on given tensor t :param t: either a tensor or a nparry :param padding_idx: the ID used to represented padded data :return: a mask of the same shape as t """ if type(t) is np.ndarray: mask = np.not_equal(t, padding_idx)*1.0 else: mask = torch.ne(t, padding_idx).float() return mask # Transparent, and simple argument parsing FTW! def convert_nicely(arg, possible_types=(bool, float, int, str)): """ Try and see what sticks. Possible types can be changed. """ for data_type in possible_types: try: if data_type is bool: # Hard code this shit if arg in ['T', 'True', 'true']: return True if arg in ['F', 'False', 'false']: return False raise ValueError else: proper_arg = data_type(arg) return proper_arg except ValueError: continue # Here, i.e. no data type really stuck warnings.warn(f"None of the possible datatypes matched for {arg}. Returning as-is") return arg def parse_args(raw_args: List[str], compulsory: List[str] = (), compulsory_msg: str = "", types: Dict[str, type] = None, discard_unspecified: bool = False): """ I don't like argparse. Don't like specifying a complex two liner for each every config flag/macro. If you maintain a dict of default arguments, and want to just overwrite it based on command args, call this function, specify some stuff like :param raw_args: unparsed sys.argv[1:] :param compulsory: if some flags must be there :param compulsory_msg: what if some compulsory flags weren't there :param types: a dict of confignm: type(configvl) :param discard_unspecified: flag so that if something doesn't appear in config it is not returned. :return: """ # parsed_args = _parse_args_(raw_args, compulsory=compulsory, compulsory_msg=compulsory_msg) # # # Change the "type" of arg, anyway parsed = {} while True: try: # Get next value nm = raw_args.pop(0) except IndexError: # We emptied the list break # Get value try: vl = raw_args.pop(0) except IndexError: raise ImproperCMDArguments(f"A value was expected for {nm} parameter. Not found.") # Get type of value if types: try: parsed[nm] = types[nm](vl) except ValueError: raise ImproperCMDArguments(f"The value for {nm}: {vl} can not take the type {types[nm]}! ") except KeyError: # This name was not included in the types dict if not discard_unspecified: # Add it nonetheless parsed[nm] = convert_nicely(vl) else: # Discard it. continue else: parsed[nm] = convert_nicely(vl) # Check if all the compulsory things are in here. for key in compulsory: try: assert key in parsed except AssertionError: raise ImproperCMDArguments(compulsory_msg + f"Found keys include {[k for k in parsed.keys()]}") # Finally check if something unwanted persists here return parsed def mt_save_dir(parentdir: Path, _newdir: bool = False): """ Function which returns the last filled/or newest unfilled folder in a particular dict. Eg.1 parentdir is empty dir -> mkdir 0 -> cd 0 -> return parentdir/0 Eg.2 ls savedir -> 0, 1, 2, ... 9, 10, 11 -> mkdir 12 && cd 12 (if newdir is True) else 11 -> return parentdir/11 (or 12) ** Usage ** Get a path using this function like so: parentdir = Path('runs') savedir = save_dir(parentdir, _newdir=True) :param parentdir: pathlib.Path object of the parent directory :param _newdir: bool flag to save in the last dir or make a new one :return: None """ # Check if the dir exits try: assert parentdir.exists(), f'{parentdir} does not exist. Making it' except AssertionError: parentdir.mkdir(parents=False) # List all folders within, and convert them to ints existing = sorted([int(x) for x in os.listdir(parentdir) if x.isdigit()], reverse=True) if not existing: # If no subfolder exists parentdir = parentdir / '0' parentdir.mkdir() elif _newdir: # If there are subfolders and we want to make a new dir parentdir = parentdir / str(existing[0] + 1) parentdir.mkdir() else: # There are other folders and we dont wanna make a new folder parentdir = parentdir / str(existing[0]) return parentdir def mt_save(savedir: Path, message: str = None, message_fname: str = None, torch_stuff: list = None, pickle_stuff: list = None, numpy_stuff: list = None, json_stuff: list = None): """ Saves bunch of diff stuff in a particular dict. NOTE: all the stuff to save should also have an accompanying filename, and so we use tosave named tuple defined above as tosave = namedtuple('ObjectsToSave','fname obj') ** Usage ** # say `encoder` is torch module, and `traces` is a python obj (dont care what) parentdir = Path('runs') savedir = save_dir(parentdir, _newdir=True) save( savedir, torch_stuff = [tosave(fname='model.torch', obj=encoder)], pickle_stuff = [tosave('traces.pkl', traces)] ) :param savedir: pathlib.Path object of the parent directory :param message: a message to be saved in the folder alongwith (as text) :param torch_stuff: list of tosave tuples to be saved with torch.save functions :param pickle_stuff: list of tosave tuples to be saved with pickle.dump :param numpy_stuff: list of tosave tuples to be saved with numpy.save :param json_stuff: list of tosave tuples to be saved with json.dump :return: None """ assert savedir.is_dir(), f'{savedir} is not a directory!' # Commence saving shit! if message: with open(savedir / 'message.txt' if message_fname is None else savedir / message_fname, 'w+') as f: f.write(message) for data in torch_stuff or (): try: torch.save(data.obj, savedir / data.fname) except: traceback.print_exc() for data in pickle_stuff or (): try: pickle.dump(data.obj, open(savedir / data.fname, 'wb+')) except: traceback.print_exc() for data in numpy_stuff or (): try: np.save(savedir / data.fname, data.obj) except: traceback.print_exc() for data in json_stuff or (): # Filter out: only things belonging to specific types should be saved. saving_data = {k: v for k, v in data.obj.items() if type(v) in [str, int, float, np.int, np.float, np.long, bool]} try: json.dump(saving_data, open(savedir / data.fname, 'w+')) except: traceback.print_exc() class SimplestSampler: """ Given X and Y matrices (or lists of lists), it returns a batch worth of stuff upon __next__ :return: """ def __init__(self, data, bs: int = 64): try: assert len(data["x"]) == len(data["y"]) except AssertionError: raise MismatchedDataError(f"Length of x is {len(data['x'])} while of y is {len(data['y'])}") self.x = data["x"] self.y = data["y"] self.n = len(self.x) self.bs = bs # Batch Size def __len__(self): return self.n // self.bs - (1 if self.n % self.bs else 0) def __iter__(self): self.i, self.iter = 0, 0 return self def __next__(self): if self.i + self.bs >= self.n: raise StopIteration _x, _y = self.x[self.i:self.i + self.bs], self.y[self.i:self.i + self.bs] self.i += self.bs return _x, _y ================================================ FILE: ogb/download.sh ================================================ wget -P ./data/ https://nodepiece-data.s3.ca-central-1.amazonaws.com/ogb-wikikg2/ogbl-wikikg2_20000_anchors_20000_paths_d0.4_b0.0_p0.4_r0.2_pykeen_50sp_bfs.pkl ================================================ FILE: ogb/ogb_tokenizer.py ================================================ import torch import numpy as np import pickle import multiprocessing as mp import networkx as nx from functools import partial from igraph import Graph from torch_geometric.data.cluster import ClusterData from torch_geometric.data import Data from torch import nn from pykeen.triples import TriplesFactory from typing import List, Dict from pathlib import Path from tqdm import tqdm from tqdm.contrib.concurrent import process_map, thread_map import random class NodePiece_OGB: """ NodePiece for OGB: uses a parallel batch mode for anchor mining """ def __init__(self, triples: TriplesFactory, dataset_name: str, num_anchors: int, anchor_strategy: Dict[str, float], num_paths: int, limit_shortest: int = 0, limit_random: int = 0, add_identity: bool = False, mode: str = "bfs", tkn_batch: int = 100, inv: bool = False, dir: str = "in", partition: int = 1, cpus: int = 1, ) -> None: super().__init__() self.triples_factory = triples self.dataset_name = dataset_name self.num_anchors = num_anchors self.anchor_strategy = anchor_strategy self.num_paths = num_paths self.sp_limit = limit_shortest self.rand_limit = limit_random self.partition = partition self.cpus = cpus if self.sp_limit * self.rand_limit != 0: raise Exception("-sp_limit and -rand_limit are mutually exclusive") self.add_identity = add_identity self.tkn_mode = mode self.NOTHING_TOKEN = -99 self.CLS_TOKEN = -1 self.MASK_TOKEN = -10 self.PADDING_TOKEN = -100 self.SEP_TOKEN = -2 self.batch_size = tkn_batch self.use_inv = inv self.dir = dir self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() self.token2id = {t: i for i, t in enumerate(self.top_entities)} self.rel2token = {t: i + len(self.top_entities) for i, t in enumerate(list(self.triples_factory.relation_to_id.values()))} self.vocab_size = len(self.token2id) + len(self.rel2token) self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) if self.add_identity: # add identity for anchor nodes as the first / closest node if self.tkn_mode != "bfs": for anchor in self.top_entities[:-4]: # last 4 are always service tokens self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] else: for anchor in self.top_entities[:-4]: self.vocab[anchor]['ancs'] = [anchor] + self.vocab[anchor]['ancs'][:-1] self.vocab[anchor]['dists'][0] = 0 def tokenize_kg(self): strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" if self.sp_limit > 0: filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths if self.rand_limit > 0: filename += f"_{self.rand_limit}rand" if self.tkn_mode == "bfs": filename += "_bfs" if self.partition > 1: filename += f"_metis{self.partition}" filename += ".pkl" self.model_name = filename.split('.pkl')[0] path = Path(filename) if path.is_file(): anchors, non_anchors, vocab = pickle.load(open(path, "rb")) return anchors, non_anchors, vocab if type(self.triples_factory.mapped_triples) == torch.Tensor: src, tgt, rels = self.triples_factory.mapped_triples[:, 0].numpy(), self.triples_factory.mapped_triples[:, 2].numpy(), self.triples_factory.mapped_triples[:, 1].numpy() else: # dummy triple factory for OGB src, tgt, rels = self.triples_factory.mapped_triples['head'], self.triples_factory.mapped_triples['tail'], self.triples_factory.mapped_triples['relation'] edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] graph = Graph(n=self.triples_factory.num_entities, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) anchors = [] for strategy, ratio in self.anchor_strategy.items(): if ratio <= 0.0: continue topK = int(np.ceil(ratio * self.num_anchors)) print(f"Computing the {strategy} nodes") if strategy == "degree": # top_nodes = sorted(graph.degree(), key=lambda x: x[1], reverse=True) # OLD NetworkX top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) elif strategy == "betweenness": raise NotImplementedError("Betweenness is disabled due to computational costs") elif strategy == "pagerank": #top_nodes = sorted(nx.pagerank(nx.DiGraph(graph)).items(), key=lambda x: x[1], reverse=True) top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) elif strategy == "random": top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.triples_factory.num_entities))] # slow version # selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] # faster version tops = {k: v for k, v in top_nodes} # remove ancs for a in anchors: tops.pop(a, None) selected_nodes = [k for k, v in tops.items()][:topK] # dict is ordered so the sorted order is preserved anchors.extend(selected_nodes) print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") vocab = self.create_all_paths(graph, anchors) if self.partition == 1 else self.mine_parallel(anchors) # self.mine_partitions(anchors) top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] non_core_entities = [i for i in range(self.triples_factory.num_entities) if i not in anchors] pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) print("Vocabularized and saved!") return top_entities, non_core_entities, vocab def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: vocab = {} if self.rand_limit == 0: print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} shortest paths per node") else: print(f"Computing the entity vocabulary - paths, retaining {self.rand_limit} random paths per node") if self.tkn_mode: top_np = np.array(top_entities) anc_set = set(top_entities) # igraph_mode = "in" if self.use_inv else "all" for i in tqdm(range(0, self.triples_factory.num_entities, self.batch_size)): batch_verts = list(range(0, self.triples_factory.num_entities))[i: i+self.batch_size] limit = self.sp_limit if self.sp_limit != 0 else (self.rand_limit if self.rand_limit != 0 else self.num_paths) nearest_ancs, anc_dists = [[] for _ in range(len(batch_verts))], [[] for _ in range(len(batch_verts))] hop = 1 while any([len(lst) < limit for lst in nearest_ancs]): tgt_idx = [k for k in range(len(batch_verts)) if len(nearest_ancs[k]) < limit] verts = [batch_verts[idx] for idx in tgt_idx] neigbs_list = graph.neighborhood(vertices=verts, order=hop, mode=self.dir, mindist=hop) # list of lists ancs = [list(set(neigbs).intersection(anc_set).difference(set(nearest_ancs[id]))) for id, neigbs in enumerate(neigbs_list)] # updating anchor lists for local_idx, global_idx in enumerate(tgt_idx): nearest_ancs[global_idx].extend(ancs[local_idx]) anc_dists[global_idx].extend([hop for _ in range(len(ancs[local_idx]))]) # nearest_ancs.extend(ancs) # anc_dists.extend([hop for _ in range(len(ancs))]) hop += 1 if hop >= 50: # hardcoded constant for a disconnected node for idx in tgt_idx: nearest_ancs[idx].extend([self.NOTHING_TOKEN for _ in range(limit - len(nearest_ancs[idx]))]) anc_dists[idx].extend([0 for _ in range(limit - len(anc_dists[idx]))]) break # update the vocab for idx, v in enumerate(batch_verts): vocab[v] = {'ancs': nearest_ancs[idx][:limit], 'dists': anc_dists[idx][:limit]} return vocab def mine_partitions(self, anchors) -> Dict[int, List]: # let's try splitting the graph into connected components with METIS and run mining on them src, tgt = self.triples_factory.mapped_triples['head'], self.triples_factory.mapped_triples['tail'] edgelist = [[s, t] for s, t in zip(src, tgt)] pyg_graph = Data(edge_index=torch.tensor(edgelist).T, num_nodes=self.triples_factory.num_entities) print(f"Using METIS to partition the graph into {self.partition} partitions") clusters = ClusterData(pyg_graph, num_parts=self.partition) vocab = {} # now find anchors in each cluster and tokenize clusters one by one for cluster_id in range(len(clusters)): print(f"Processing cluster {cluster_id}") start = int(clusters.partptr[cluster_id]) end = int(clusters.partptr[cluster_id + 1]) cluster_nodes = clusters.perm[start: end] cluster_anchors = list(set(cluster_nodes.cpu().numpy()).intersection(set(anchors))) # map global anchor IDs to local ids [they start from 0 to num_nodes in cluster] local_anchor_ids = [] for anc_idx, anc in enumerate(cluster_anchors): local_id = (cluster_nodes == anc).nonzero(as_tuple=False) local_anchor_ids.append(local_id.item()) node_mapping = {i: n.item() for i, n in enumerate(cluster_nodes)} anchor_mapping = {loc_id: glob_id for loc_id, glob_id in zip(local_anchor_ids, cluster_anchors)} anchor_mapping[-99] = -99 cluster = clusters[cluster_id] cl_vocab = self.bfs_cluster(cluster, local_anchor_ids) # re-map back to global ids cl_vocab = {node_mapping[k]: { 'ancs': [anchor_mapping[a] for a in v['ancs']], 'dists': v['dists'] } for k, v in cl_vocab.items()} vocab.update(cl_vocab) # sort vocab by key - need it to be in the ascending order 0 - n vocab = dict(sorted(vocab.items())) return vocab def bfs_cluster(self, cluster: Data, anchors: List, tqdm_pos=None) -> Dict[int, List]: num_nodes = cluster.edge_index.max() + 1 edge_index = cluster.edge_index anc_set = set(anchors) vocab = {} edgelist = [[s.item(), t.item()] for s, t in zip(edge_index[0], edge_index[1])] graph = Graph(n=num_nodes, edges=edgelist, directed=False) # for i in tqdm(range(num_nodes)): # limit = self.sp_limit if self.sp_limit != 0 else (self.rand_limit if self.rand_limit != 0 else self.num_paths) # nearest_ancs, anc_dists = [], [] # hop = 1 # while len(nearest_ancs) < limit: # neigbs = graph.neighborhood(vertices=i, order=hop, mode="all", mindist=hop) # ancs = list(set(neigbs).intersection(anc_set).difference(set(nearest_ancs))) # nearest_ancs.extend(ancs) # anc_dists.extend([hop for _ in range(len(ancs))]) # hop += 1 # if hop >= 50: # hardcoded constant for a disconnected node # nearest_ancs.extend([self.NOTHING_TOKEN for _ in range(limit - len(nearest_ancs))]) # anc_dists.extend([0 for _ in range(limit - len(anc_dists))]) # break # vocab[i] = {'ancs': nearest_ancs[:limit], 'dists': anc_dists[:limit]} ## BATCH version for i in tqdm(range(0, num_nodes, self.batch_size), position=tqdm_pos): batch_verts = list(range(0, num_nodes))[i: i+self.batch_size] limit = self.sp_limit if self.sp_limit != 0 else (self.rand_limit if self.rand_limit != 0 else self.num_paths) nearest_ancs, anc_dists = [[] for _ in range(len(batch_verts))], [[] for _ in range(len(batch_verts))] hop = 1 while any([len(lst) < limit for lst in nearest_ancs]): tgt_idx = [k for k in range(len(batch_verts)) if len(nearest_ancs[k]) < limit] verts = [batch_verts[idx] for idx in tgt_idx] neigbs_list = graph.neighborhood(vertices=verts, order=hop, mode=self.dir, mindist=hop) # list of lists ancs = [list(set(neigbs).intersection(anc_set).difference(set(nearest_ancs[id]))) for id, neigbs in enumerate(neigbs_list)] # updating anchor lists for local_idx, global_idx in enumerate(tgt_idx): nearest_ancs[global_idx].extend(ancs[local_idx]) anc_dists[global_idx].extend([hop for _ in range(len(ancs[local_idx]))]) hop += 1 if hop >= 50: # hardcoded constant for a disconnected node for idx in tgt_idx: nearest_ancs[idx].extend([self.NOTHING_TOKEN for _ in range(limit - len(nearest_ancs[idx]))]) anc_dists[idx].extend([0 for _ in range(limit - len(anc_dists[idx]))]) break # update the vocab for idx, v in enumerate(batch_verts): vocab[v] = {'ancs': nearest_ancs[idx][:limit], 'dists': anc_dists[idx][:limit]} return vocab def mine_parallel(self, anchors): from tqdm.contrib.concurrent import process_map # let's try splitting the graph into connected components with METIS and run mining on them src, tgt = self.triples_factory.mapped_triples['head'], self.triples_factory.mapped_triples['tail'] edgelist = [[s, t] for s, t in zip(src, tgt)] pyg_graph = Data(edge_index=torch.tensor(edgelist).T, num_nodes=self.triples_factory.num_entities) print(f"Using METIS to partition the graph into {self.partition} partitions") clusters = ClusterData(pyg_graph, num_parts=self.partition) vocab = {} data_points = [] # now find anchors in each cluster and tokenize clusters one by one for cluster_id in range(len(clusters)): start = int(clusters.partptr[cluster_id]) end = int(clusters.partptr[cluster_id + 1]) cluster_nodes = clusters.perm[start: end] cluster_anchors = list(set(cluster_nodes.cpu().numpy()).intersection(set(anchors))) # map global anchor IDs to local ids [they start from 0 to num_nodes in cluster] local_anchor_ids = [] for anc_idx, anc in enumerate(cluster_anchors): local_id = (cluster_nodes == anc).nonzero(as_tuple=False) local_anchor_ids.append(local_id.item()) node_mapping = {i: n.item() for i, n in enumerate(cluster_nodes)} anchor_mapping = {loc_id: glob_id for loc_id, glob_id in zip(local_anchor_ids, cluster_anchors)} anchor_mapping[-99] = -99 #cluster = clusters[cluster_id] data_points.append({ 'clusters': clusters, 'node_mapping': node_mapping, 'anchor_mapping': anchor_mapping, 'local_anchor_id': local_anchor_ids, 'tqdm_pos': cluster_id + 1, # we have an outer loop that starts with 0 }) all_batches = process_map(self.mining_subp, data_points, max_workers=self.cpus) for d in all_batches: vocab.update(d) # sort vocab by key - need it to be in the ascending order 0 - n vocab = dict(sorted(vocab.items())) return vocab def mining_subp(self, data_point): clusters, node_mapping, anchor_mapping, local_anchor_ids, tqdm_pos = data_point.values() cluster = clusters[tqdm_pos - 1] cl_vocab = self.bfs_cluster(cluster, local_anchor_ids, tqdm_pos) # re-map back to global ids cl_vocab = {node_mapping[k]: { 'ancs': [anchor_mapping[a] for a in v['ancs']], 'dists': v['dists'] } for k, v in cl_vocab.items()} return cl_vocab ================================================ FILE: ogb/ogb_wikikg2/dataloader.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import torch from torch.utils.data import Dataset class TrainDataset(Dataset): def __init__(self, triples, nentity, nrelation, negative_sample_size, mode, count, true_head, true_tail): self.len = len(triples['head']) self.triples = triples self.nentity = nentity self.nrelation = nrelation self.negative_sample_size = negative_sample_size self.mode = mode self.count = count self.true_head = true_head self.true_tail = true_tail def __len__(self): return self.len def __getitem__(self, idx): head, relation, tail = self.triples['head'][idx], self.triples['relation'][idx], self.triples['tail'][idx] positive_sample = [head, relation, tail] subsampling_weight = self.count[(head, relation)] + self.count[(tail, -relation - 1)] subsampling_weight = torch.sqrt(1 / torch.Tensor([subsampling_weight])) # negative_sample_list = [] # negative_sample_size = 0 # while negative_sample_size < self.negative_sample_size: # negative_sample = np.random.randint(self.nentity, size=self.negative_sample_size*2) # if self.mode == 'head-batch': # mask = np.in1d( # negative_sample, # self.true_head[(relation, tail)], # assume_unique=True, # invert=True # ) # elif self.mode == 'tail-batch': # mask = np.in1d( # negative_sample, # self.true_tail[(head, relation)], # assume_unique=True, # invert=True # ) # else: # raise ValueError('Training batch mode %s not supported' % self.mode) # negative_sample = negative_sample[mask] # negative_sample_list.append(negative_sample) # negative_sample_size += negative_sample.size # negative_sample = np.concatenate(negative_sample_list)[:self.negative_sample_size] # negative_sample = torch.from_numpy(negative_sample) # negative_sample = torch.from_numpy(np.random.randint(self.nentity, size=self.negative_sample_size)) negative_sample = torch.randint(0, self.nentity, (self.negative_sample_size,)) positive_sample = torch.LongTensor(positive_sample) return positive_sample, negative_sample, subsampling_weight, self.mode @staticmethod def collate_fn(data): positive_sample = torch.stack([_[0] for _ in data], dim=0) negative_sample = torch.stack([_[1] for _ in data], dim=0) subsample_weight = torch.cat([_[2] for _ in data], dim=0) mode = data[0][3] return positive_sample, negative_sample, subsample_weight, mode class TestDataset(Dataset): def __init__(self, triples, args, mode, random_sampling): self.len = len(triples['head']) self.triples = triples self.nentity = args.nentity self.nrelation = args.nrelation self.mode = mode self.random_sampling = random_sampling if random_sampling: self.neg_size = args.neg_size_eval_train def __len__(self): return self.len def __getitem__(self, idx): head, relation, tail = self.triples['head'][idx], self.triples['relation'][idx], self.triples['tail'][idx] positive_sample = torch.LongTensor((head, relation, tail)) if self.mode == 'head-batch': if not self.random_sampling: negative_sample = torch.cat([torch.LongTensor([head]), torch.from_numpy(self.triples['head_neg'][idx])]) else: negative_sample = torch.cat( [torch.LongTensor([head]), torch.randint(0, self.nentity, size=(self.neg_size,))]) elif self.mode == 'tail-batch': if not self.random_sampling: negative_sample = torch.cat([torch.LongTensor([tail]), torch.from_numpy(self.triples['tail_neg'][idx])]) else: negative_sample = torch.cat( [torch.LongTensor([tail]), torch.randint(0, self.nentity, size=(self.neg_size,))]) return positive_sample, negative_sample, self.mode @staticmethod def collate_fn(data): positive_sample = torch.stack([_[0] for _ in data], dim=0) negative_sample = torch.stack([_[1] for _ in data], dim=0) mode = data[0][2] return positive_sample, negative_sample, mode class BidirectionalOneShotIterator(object): def __init__(self, dataloader_head, dataloader_tail): self.iterator_head = self.one_shot_iterator(dataloader_head) self.iterator_tail = self.one_shot_iterator(dataloader_tail) self.step = 0 def __next__(self): self.step += 1 if self.step % 2 == 0: data = next(self.iterator_head) else: data = next(self.iterator_tail) return data @staticmethod def one_shot_iterator(dataloader): ''' Transform a PyTorch Dataloader into python iterator ''' while True: for data in dataloader: yield data ================================================ FILE: ogb/ogb_wikikg2/dummy_factory.py ================================================ class DummyTripleFactory: ''' This is to mimic pykeen's triple factory which is a default input for the KG Tokenizer ''' def __init__(self, triples, ne, nr): self.mapped_triples = triples self.num_entities = ne self.num_relations = nr self.relation_to_id = {i: i for i in range(nr)} ================================================ FILE: ogb/ogb_wikikg2/model.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import logging import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import random from torch.utils.data import DataLoader from ogb_wikikg2.dataloader import TestDataset from collections import defaultdict from typing import Optional from tqdm import tqdm from torch.nn import TransformerEncoderLayer, TransformerEncoder, GRU from ogb.linkproppred import Evaluator class KGEModel(nn.Module): def __init__(self, model_name, nentity, nrelation, hidden_dim, gamma, evaluator, tokenizer, pooler, use_rels, rel_policy, sample_paths, trf_layers, trf_heads, trf_hidden, drop, use_distances, max_seq_len, sample_rels, triples, ablate_anchors, device, double_entity_embedding=False, double_relation_embedding=False): super(KGEModel, self).__init__() self.model_name = model_name self.nentity = nentity self.nrelation = nrelation self.hidden_dim = hidden_dim self.epsilon = 2.0 self.pooler = pooler self.use_rels = use_rels self.policy = rel_policy self.sample_paths = sample_paths self.use_distances = use_distances self.max_seq_len = max_seq_len self.sample_rels = sample_rels self.drop = drop self.triples = triples self.ablate_anchors = ablate_anchors self.device = device self.gamma = nn.Parameter( torch.Tensor([gamma]), requires_grad=False ) self.embedding_range = nn.Parameter( torch.Tensor([(self.gamma.item() + self.epsilon) / hidden_dim]), requires_grad=False ) self.entity_dim = hidden_dim * 2 if double_entity_embedding else hidden_dim self.relation_dim = hidden_dim * 2 if double_relation_embedding else hidden_dim # anchors hashing mechanism self.set_enc = nn.Sequential( nn.Linear(self.entity_dim * (self.sample_paths + self.sample_rels), self.entity_dim * 2), nn.Dropout(drop), nn.ReLU(), nn.Linear(self.entity_dim * 2, self.entity_dim) ) if not self.ablate_anchors else nn.Sequential( nn.Linear(self.entity_dim * sample_rels, self.entity_dim * 2), nn.Dropout(drop), nn.ReLU(), nn.Linear(self.entity_dim * 2, self.entity_dim) ) # init for module in self.set_enc.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() self.anchor_embeddings = nn.Embedding(num_embeddings=len(tokenizer.token2id)+1, embedding_dim=self.entity_dim) nn.init.uniform_( tensor=self.anchor_embeddings.weight, # .weight for Embedding a=-self.embedding_range.item(), b=self.embedding_range.item() ) # back to normal relation embs, +1 for the padding relation self.relation_embedding = nn.Embedding(num_embeddings=nrelation, embedding_dim=self.relation_dim) nn.init.uniform_( tensor=self.relation_embedding.weight, a=-self.embedding_range.item(), b=self.embedding_range.item() ) # Do not forget to modify this line when you add a new model in the "forward" function if model_name not in ['TransE', 'DistMult', 'ComplEx', 'RotatE', 'AutoSF']: raise ValueError('model %s not supported' % model_name) if model_name == 'RotatE' and (not double_entity_embedding or double_relation_embedding): raise ValueError('RotatE should use --double_entity_embedding') if model_name == 'ComplEx' and (not double_entity_embedding or not double_relation_embedding): raise ValueError('ComplEx should use --double_entity_embedding and --double_relation_embedding') if model_name == 'PairRE' and (not double_relation_embedding): raise ValueError('PairRE should use --double_relation_embedding') self.evaluator = evaluator print("Creating hashes") hashes = [ [tokenizer.token2id[token] for token in vals['ancs'][:min(self.sample_paths, len(vals['ancs']))]] + [ tokenizer.token2id[tokenizer.PADDING_TOKEN]] * (self.sample_paths - len(vals['ancs'])) for entity, vals in tokenizer.vocab.items() ] distances = [ [d for d in vals['dists'][:min(self.sample_paths, len(vals['dists']))]] + [0] * ( self.sample_paths - len(vals['dists'])) for entity, vals in tokenizer.vocab.items() ] self.max_seq_len = max([d for row in distances for d in row]) print( f"Changed max seq len from {max_seq_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.tensor(distances, dtype=torch.long, device=self.device) print("Creating relational context") if self.sample_rels > 0: pad_idx = nrelation - 1 e2r = defaultdict(set) for i in tqdm(range(len(self.triples['head']))): e2r[self.triples['head'][i]].add(self.triples['relation'][i]) len_stats = [len(v) for k,v in e2r.items()] print(f"Unique relations per node - min: {min(len_stats)}, avg: {np.mean(len_stats)}, 66th perc: {np.percentile(len_stats, 66)}, max: {max(len_stats)} ") unique_1hop_relations = [ random.sample(e2r[i], k=min(self.sample_rels, len(e2r[i]))) + [pad_idx] * (self.sample_rels-min(len(e2r[i]), self.sample_rels)) for i in range(self.nentity) ] self.unique_1hop_relations = torch.tensor(unique_1hop_relations, dtype=torch.long, device=self.device) # distance integer to denote path lengths self.dist_emb = nn.Embedding(self.max_seq_len + 1, embedding_dim=self.entity_dim) torch.nn.init.xavier_uniform_(self.dist_emb.weight) with torch.no_grad(): self.anchor_embeddings.weight[tokenizer.token2id[tokenizer.PADDING_TOKEN]] = torch.zeros(self.entity_dim) self.relation_embedding.weight.data[-1] = torch.zeros(self.relation_dim) self.dist_emb.weight[0] = torch.zeros(self.entity_dim) def pool_anchors(self, anc_embs: torch.FloatTensor, mask: Optional[torch.BoolTensor] = None) -> torch.FloatTensor: """ input shape: (bs, num_anchors, emb_dim) output shape: (bs, emb_dim) """ if self.pooler == "set": pooled = self.set_enc(anc_embs) elif self.pooler == "cat": anc_embs = anc_embs.view(anc_embs.shape[0], -1) pooled = self.set_enc(anc_embs) if self.sample_paths != 1 else anc_embs elif self.pooler == "trf" or self.pooler == "moe": pooled = self.set_enc(anc_embs.transpose(1, 0)) # output shape: (seq_len, bs, dim) pooled = pooled.mean(dim=0) # output shape: (bs, dim) if self.policy == "cat": pooled = self.linear(pooled) return pooled def encode_by_index(self, entities: torch.LongTensor) -> torch.FloatTensor: hashes, dists = self.hashes[entities], self.distances[entities] #anc_embs = torch.index_select(self.anchor_embeddings, dim=0, index=hashes) anc_embs = self.anchor_embeddings(hashes) mask = None if self.use_distances: dist_embs = self.dist_emb(dists) anc_embs += dist_embs if self.sample_rels > 0: rels = self.unique_1hop_relations[entities] # (bs, rel_sample_size) #rels = torch.index_select(self.relation_embedding, dim=0, index=rels) # (bs, rel_sample_size, dim) rels = self.relation_embedding(rels) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask) return anc_embs def forward(self, sample, mode='single'): ''' Forward function that calculate the score of a batch of triples. In the 'single' mode, sample is a batch of triple. In the 'head-batch' or 'tail-batch' mode, sample consists two part. The first part is usually the positive sample. And the second part is the entities in the negative samples. Because negative samples and positive samples usually share two elements in their triple ((head, relation) or (relation, tail)). ''' if mode == 'single': batch_size, negative_sample_size = sample.size(0), 1 head = self.encode_by_index(sample[:, 0]).unsqueeze(1) relation = self.relation_embedding(sample[:, 1]).unsqueeze(1) # relation = torch.index_select( # self.relation_embedding, # dim=0, # index=sample[:, 1] # ).unsqueeze(1) tail = self.encode_by_index(sample[:, 2]).unsqueeze(1) elif mode == 'head-batch': tail_part, head_part = sample batch_size, negative_sample_size = head_part.size(0), head_part.size(1) head = self.encode_by_index(head_part.view(-1)).view(batch_size, negative_sample_size, -1) relation = self.relation_embedding(tail_part[:, 1]).unsqueeze(1) # relation = torch.index_select( # self.relation_embedding, # dim=0, # index=tail_part[:, 1] # ).unsqueeze(1) tail = self.encode_by_index(tail_part[:, 2]).unsqueeze(1) elif mode == 'tail-batch': head_part, tail_part = sample batch_size, negative_sample_size = tail_part.size(0), tail_part.size(1) head = self.encode_by_index(head_part[:, 0]).unsqueeze(1) relation = self.relation_embedding(head_part[:, 1]).unsqueeze(1) # relation = torch.index_select( # self.relation_embedding, # dim=0, # index=head_part[:, 1] # ).unsqueeze(1) tail = self.encode_by_index(tail_part.view(-1)).view(batch_size, negative_sample_size, -1) else: raise ValueError('mode %s not supported' % mode) model_func = { 'TransE': self.TransE, 'DistMult': self.DistMult, 'ComplEx': self.ComplEx, 'RotatE': self.RotatE, 'AutoSF': self.AutoSF, 'PairRE': self.PairRE, } if self.model_name in model_func: score = model_func[self.model_name](head, relation, tail, mode) else: raise ValueError('model %s not supported' % self.model_name) return score def AutoSF(self, head, relation, tail, mode): if mode == 'head-batch': rs = torch.chunk(relation, 4, dim=-1) ts = torch.chunk(tail, 4, dim=-1) rt0 = rs[0] * ts[0] rt1 = rs[1] * ts[1] + rs[2] * ts[3] rt2 = rs[0] * ts[2] + rs[2] * ts[3] rt3 = -rs[1] * ts[1] + rs[3] * ts[2] rts = torch.cat([rt0, rt1, rt2, rt3], dim=-1) score = torch.sum(head * rts, dim=-1) else: hs = torch.chunk(head, 4, dim=-1) rs = torch.chunk(relation, 4, dim=-1) hr0 = hs[0] * rs[0] hr1 = hs[1] * rs[1] - hs[3] * rs[1] hr2 = hs[2] * rs[0] + hs[3] * rs[3] hr3 = hs[1] * rs[2] + hs[2] * rs[2] hrs = torch.cat([hr0, hr1, hr2, hr3], dim=-1) score = torch.sum(hrs * tail, dim=-1) return score def PairRE(self, head, relation, tail, mode): re_head, re_tail = torch.chunk(relation, 2, dim=2) head = F.normalize(head, 2, -1) tail = F.normalize(tail, 2, -1) score = head * re_head - tail * re_tail score = self.gamma.item() - torch.norm(score, p=1, dim=2) return score def TransE(self, head, relation, tail, mode): if mode == 'head-batch': score = head + (relation - tail) else: score = (head + relation) - tail score = self.gamma.item() - torch.norm(score, p=1, dim=2) return score def DistMult(self, head, relation, tail, mode): if mode == 'head-batch': score = head * (relation * tail) else: score = (head * relation) * tail score = score.sum(dim=2) return score def ComplEx(self, head, relation, tail, mode): re_head, im_head = torch.chunk(head, 2, dim=2) re_relation, im_relation = torch.chunk(relation, 2, dim=2) re_tail, im_tail = torch.chunk(tail, 2, dim=2) if mode == 'head-batch': re_score = re_relation * re_tail + im_relation * im_tail im_score = re_relation * im_tail - im_relation * re_tail score = re_head * re_score + im_head * im_score else: re_score = re_head * re_relation - im_head * im_relation im_score = re_head * im_relation + im_head * re_relation score = re_score * re_tail + im_score * im_tail score = score.sum(dim=2) return score def RotatE(self, head, relation, tail, mode): pi = 3.14159265358979323846 re_head, im_head = torch.chunk(head, 2, dim=2) re_tail, im_tail = torch.chunk(tail, 2, dim=2) # Make phases of relations uniformly distributed in [-pi, pi] phase_relation = relation / (self.embedding_range.item() / pi) re_relation = torch.cos(phase_relation) im_relation = torch.sin(phase_relation) if mode == 'head-batch': re_score = re_relation * re_tail + im_relation * im_tail im_score = re_relation * im_tail - im_relation * re_tail re_score = re_score - re_head im_score = im_score - im_head else: re_score = re_head * re_relation - im_head * im_relation im_score = re_head * im_relation + im_head * re_relation re_score = re_score - re_tail im_score = im_score - im_tail score = torch.stack([re_score, im_score], dim=0) score = score.norm(dim=0) score = self.gamma.item() - score.sum(dim=2) return score @staticmethod def train_step(model, optimizer, train_iterator, args): ''' A single train step. Apply back-propation and return the loss ''' model.train() optimizer.zero_grad() positive_sample, negative_sample, subsampling_weight, mode = next(train_iterator) if args.cuda: positive_sample = positive_sample.cuda() negative_sample = negative_sample.cuda() subsampling_weight = subsampling_weight.cuda() negative_score = model((positive_sample, negative_sample), mode=mode) if args.negative_adversarial_sampling: # In self-adversarial sampling, we do not apply back-propagation on the sampling weight negative_score = (F.softmax(negative_score * args.adversarial_temperature, dim=1).detach() * F.logsigmoid(-negative_score)).sum(dim=1) else: negative_score = F.logsigmoid(-negative_score).mean(dim=1) positive_score = model(positive_sample) positive_score = F.logsigmoid(positive_score).squeeze(dim=1) if args.uni_weight: positive_sample_loss = - positive_score.mean() negative_sample_loss = - negative_score.mean() else: positive_sample_loss = - (subsampling_weight * positive_score).sum() / subsampling_weight.sum() negative_sample_loss = - (subsampling_weight * negative_score).sum() / subsampling_weight.sum() loss = (positive_sample_loss + negative_sample_loss) / 2 if args.regularization != 0.0: # Use L3 regularization for ComplEx and DistMult regularization = args.regularization * ( model.entity_embedding.norm(p=3) ** 3 + model.relation_embedding.norm(p=3).norm(p=3) ** 3 ) loss = loss + regularization regularization_log = {'regularization': regularization.item()} else: regularization_log = {} loss.backward() optimizer.step() log = { **regularization_log, 'positive_sample_loss': positive_sample_loss.item(), 'negative_sample_loss': negative_sample_loss.item(), 'loss': loss.item() } return log @staticmethod def test_step(model, test_triples, args, random_sampling=False): ''' Evaluate the model on test or valid datasets ''' model.eval() # Prepare dataloader for evaluation test_dataloader_head = DataLoader( TestDataset( test_triples, args, 'head-batch', random_sampling ), batch_size=args.test_batch_size, num_workers=max(1, args.cpu_num // 2), collate_fn=TestDataset.collate_fn ) test_dataloader_tail = DataLoader( TestDataset( test_triples, args, 'tail-batch', random_sampling ), batch_size=args.test_batch_size, num_workers=max(1, args.cpu_num // 2), collate_fn=TestDataset.collate_fn ) test_dataset_list = [test_dataloader_head, test_dataloader_tail] test_logs = defaultdict(list) step = 0 total_steps = sum([len(dataset) for dataset in test_dataset_list]) with torch.no_grad(): for test_dataset in test_dataset_list: for positive_sample, negative_sample, mode in test_dataset: if args.cuda: positive_sample = positive_sample.cuda() negative_sample = negative_sample.cuda() batch_size = positive_sample.size(0) score = model((positive_sample, negative_sample), mode) batch_results = model.evaluator.eval({'y_pred_pos': score[:, 0], 'y_pred_neg': score[:, 1:]}) for metric in batch_results: test_logs[metric].append(batch_results[metric]) if step % args.test_log_steps == 0: logging.info('Evaluating the model... (%d/%d)' % (step, total_steps)) step += 1 metrics = {} for metric in test_logs: metrics[metric] = torch.cat(test_logs[metric]).mean().item() return metrics ================================================ FILE: ogb/readme.md ================================================ ## NodePiece for OGB WikiKG 2 The code to run NodePiece on the OGB WikiKG 2 dataset. * OGB repo: [github](https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/wikikg2) * Decoder models taken from the AutoSF repo: [github](https://github.com/AutoML-4Paradigm/AutoSF/tree/AutoSF-OGB/wikikg2) * Experiments executed on: Tesla V100 32GB, 64GB RAM NodePiece dramatically reduces the entity embeddings matrix and can be used with standard decoders from the OGB Leaderboard. Paired with `AutoSF` scoring function, NodePiece yields the following performance being 70-180x smaller in #parameters (cf. [OGB WikiKG2 Leaderboard](https://ogb.stanford.edu/docs/leader_linkprop/)): | Model | Vocabulary Size | Parameters | Validation MRR | Test MRR | | ----- | ------ | ----- | ----- | ----- | | `NodePiece + AutoSF` | 20k | 6,860,602 | 0.5806 ± 0.0047 | 0.5703 ± 0.0035 | | `AutoSF` | 2.5M | 500,227,800 | 0.5510 ± 0.0063 | 0.5458 ± 0.0052 | | `TransE (500d)` | 2.5M | 1,250,569,500 | 0.4272 ± 0.0030 | 0.4256 ± 0.0030 | ### Running the experiment 1. We have pre-computed a vocabulary of 20k anchor nodes (~910 MB). Download it using the `download.sh` script: ```bash sh download.sh ``` 2. Install the requirements from the `requirements.txt` 3. Run the code with the best hyperparameters using the main script ```bash sh run_ogb.sh ``` ### Vocabulary construction (Tokenization) Alternatively, you can create a new vocab and run the tokenization with any number of anchors. For that, we improved the anchor mining procedure to be parallelizable and work in a batch fashion: * We first run METIS partitioning available in [PyTorch-Geometric](https://github.com/rusty1s/pytorch_geometric) so that anchor mining will be done independently for each partition. The number of partitions `--part` should ideally be equal or twice larger that the `--cpu_num` parameter. * Then we tokenize `--tkn_batch` nodes in each iteration (eg, 500K nodes will create 5000 batches) * Note that this process might be RAM-hungry. Approximate requirements would be a server with 8 CPUs and 64 GB RAM. * Tokenization time depends on several parameters (cf. the [main codebase](https://github.com/migalkin/NodePiece)). For the above server configuration, mining the vocab of 20k anchors might take 2-8 hours in the multiprocessing mode. ================================================ FILE: ogb/requirements.txt ================================================ torch==1.7.1 pykeen==1.0.5 click torch-geometric==1.6.3 torch_sparse==0.6.8 torch-scatter==2.0.5 torch-cluster==1.5.8 torch-spline-conv==1.2.0 pystow==0.1.0 einops python-igraph tqdm wandb ogb==1.3.1 tensorboardx ================================================ FILE: ogb/run_ogb.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import json import logging import os import random import numpy as np import torch from torch.utils.data import DataLoader from ogb_wikikg2.model import KGEModel from ogb_wikikg2.dataloader import TrainDataset from ogb_wikikg2.dataloader import BidirectionalOneShotIterator from ogb.linkproppred import LinkPropPredDataset, Evaluator from collections import defaultdict from tqdm import tqdm import time from tensorboardX import SummaryWriter from ogb_tokenizer import NodePiece_OGB from ogb_wikikg2.dummy_factory import DummyTripleFactory def parse_args(args=None): parser = argparse.ArgumentParser( description='Training and Testing Knowledge Graph Embedding Models', usage='train.py [] [-h | --help]' ) parser.add_argument('--cuda', action='store_true', help='use GPU') parser.add_argument('--do_train', action='store_true') parser.add_argument('--do_valid', action='store_true') parser.add_argument('--do_test', action='store_true') parser.add_argument('--evaluate_train', action='store_true', help='Evaluate on training data') parser.add_argument('--dataset', type=str, default='ogbl-wikikg2', help='dataset name, default to wikikg2') parser.add_argument('--model', default='RotatE', type=str) parser.add_argument('-de', '--double_entity_embedding', action='store_true') parser.add_argument('-dr', '--double_relation_embedding', action='store_true') parser.add_argument('-n', '--negative_sample_size', default=1, type=int) parser.add_argument('-d', '--hidden_dim', default=100, type=int) parser.add_argument('-g', '--gamma', default=12.0, type=float) parser.add_argument('-adv', '--negative_adversarial_sampling', action='store_true') parser.add_argument('-a', '--adversarial_temperature', default=1.0, type=float) parser.add_argument('-b', '--batch_size', default=512, type=int) parser.add_argument('-r', '--regularization', default=0.0, type=float) parser.add_argument('--test_batch_size', default=4, type=int, help='valid/test batch size') parser.add_argument('--uni_weight', action='store_true', help='Otherwise use subsampling weighting like in word2vec') parser.add_argument('-lr', '--learning_rate', default=0.0005, type=float) parser.add_argument('-cpu', '--cpu_num', default=2, type=int) parser.add_argument('-randomSeed', default=0, type=int) parser.add_argument('-init', '--init_checkpoint', default=None, type=str) parser.add_argument('-save', '--save_path', default=None, type=str) parser.add_argument('--max_steps', default=10000, type=int) parser.add_argument('--warm_up_steps', default=None, type=int) parser.add_argument('--save_checkpoint_steps', default=1000, type=int) parser.add_argument('--valid_steps', default=1, type=int) parser.add_argument('--log_steps', default=1, type=int, help='train log every xx steps') parser.add_argument('--test_log_steps', default=1, type=int, help='valid/test log every xx steps') parser.add_argument('--nentity', type=int, default=0, help='DO NOT MANUALLY SET') parser.add_argument('--nrelation', type=int, default=0, help='DO NOT MANUALLY SET') parser.add_argument('--print_on_screen', action='store_true', help='log on screen or not') parser.add_argument('--ntriples_eval_train', type=int, default=200000, help='number of training triples to evaluate eventually') parser.add_argument('--neg_size_eval_train', type=int, default=500, help='number of negative samples when evaluating training triples') parser.add_argument('--anchors', type=int, default=10000, help='Number of anchors to mine and use') parser.add_argument('--ancs_sp', type=int, default=50, help='Limit to topK of shortest paths per entity') parser.add_argument('--st_deg', type=float, default=0.4, help='Anchors: ratio of top degree nodes') parser.add_argument('--st_ppr', type=float, default=0.4, help='Anchors: ratio of top ppr nodes') parser.add_argument('--st_rand', type=float, default=0.2, help='Anchors: ratio of randomly selected nodes') parser.add_argument('--tkn_batch', type=int, default=100, help='Batch size for iGraph anchor mining') parser.add_argument('--inverses', action='store_true', help='whether to add inverse edges') parser.add_argument('--val_inverses', action='store_true', help='whether to add inverse edges to the validation set') parser.add_argument('--tkn_dir', type=str, default="all", help='neighbors direction for igraph') parser.add_argument('--part', type=int, default=1, help='tokenization on METIS graph partitions (for large graphs)') parser.add_argument('--pooler', type=str, default="cat", help='Set encoder') parser.add_argument('--rel_hash', type=str, default=None, help='Path encoder: avg or gru') parser.add_argument('--policy', type=str, default="sum", help='Sum or cat anchors and aggregated paths') parser.add_argument('--max_paths', type=int, default=10, help='How many paths per anchor to retain') parser.add_argument('--trf_layers', type=int, default=4, help='Num of transformer layers') parser.add_argument('--trf_heads', type=int, default=8, help='Num of transformer heads') parser.add_argument('--trf_hidden', type=int, default=512, help='Transformer FC size and REL encoder size') parser.add_argument('--drop', type=float, default=0.1, help='Dropout in layers') parser.add_argument('--use_dists', action='store_true', default=True, help='use path lengths as pos enc') parser.add_argument('--sample_rels', type=int, default=5, help='number of relations in the relational context') parser.add_argument('--noanc', action='store_true', help='ablation: no anchors') return parser.parse_args(args) def override_config(args): ''' Override model and data configuration ''' with open(os.path.join(args.init_checkpoint, 'config.json'), 'r') as fjson: argparse_dict = json.load(fjson) args.dataset = argparse_dict['dataset'] args.model = argparse_dict['model'] args.double_entity_embedding = argparse_dict['double_entity_embedding'] args.double_relation_embedding = argparse_dict['double_relation_embedding'] args.hidden_dim = argparse_dict['hidden_dim'] args.test_batch_size = argparse_dict['test_batch_size'] def save_model(model, optimizer, save_variable_list, args): ''' Save the parameters of the model and the optimizer, as well as some other variables such as step and learning_rate ''' argparse_dict = vars(args) with open(os.path.join(args.save_path, 'config.json'), 'w') as fjson: json.dump(argparse_dict, fjson) torch.save({ **save_variable_list, 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict()}, os.path.join(args.save_path, 'checkpoint') ) entity_embedding = model.anchor_embeddings.detach().cpu().numpy() np.save( os.path.join(args.save_path, 'anchor_embedding'), entity_embedding ) relation_embedding = model.relation_embedding.detach().cpu().numpy() np.save( os.path.join(args.save_path, 'relation_embedding'), relation_embedding ) def set_logger(args): ''' Write logs to checkpoint and console ''' if args.do_train: log_file = os.path.join(args.save_path or args.init_checkpoint, 'train.log') else: log_file = os.path.join(args.save_path or args.init_checkpoint, 'test.log') logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S', filename=log_file, filemode='w' ) if args.print_on_screen: console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s') console.setFormatter(formatter) logging.getLogger('').addHandler(console) def log_metrics(mode, step, metrics, writer): ''' Print the evaluation logs ''' for metric in metrics: logging.info('%s %s at step %d: %f' % (mode, metric, step, metrics[metric])) writer.add_scalar("_".join([mode, metric]), metrics[metric], step) def main(args): if (not args.do_train) and (not args.do_valid) and (not args.do_test) and (not args.evaluate_train): raise ValueError('one of train/val/test mode must be choosed.') if args.init_checkpoint: override_config(args) args.save_path = 'log/%s/%s/%s-%s/%s' % ( args.dataset, args.model, args.hidden_dim, args.gamma, time.time()) if args.save_path == None else args.save_path writer = SummaryWriter(args.save_path) # Write logs to checkpoint and console set_logger(args) logging.info('Random seed: {}'.format(args.randomSeed)) torch.manual_seed(args.randomSeed) np.random.seed(args.randomSeed) dataset = LinkPropPredDataset(name=args.dataset) split_dict = dataset.get_edge_split() nentity = dataset.graph['num_nodes'] nrelation = int(max(dataset.graph['edge_reltype'])[0]) + 1 evaluator = Evaluator(name=args.dataset) args.nentity = nentity args.nrelation = nrelation logging.info('Model: %s' % args.model) logging.info('Dataset: %s' % args.dataset) logging.info('#entity: %d' % nentity) logging.info('#relation: %d' % nrelation) train_triples = split_dict['train'] logging.info('#train: %d' % len(train_triples['head'])) valid_triples = split_dict['valid'] logging.info('#valid: %d' % len(valid_triples['head'])) test_triples = split_dict['test'] logging.info('#test: %d' % len(test_triples['head'])) if args.inverses: # add inverse triples print("Adding inverse edges") orig_head, orig_tail = train_triples['head'], train_triples['tail'] train_triples['head'] = np.concatenate([orig_head, orig_tail]) train_triples['tail'] = np.concatenate([orig_tail, orig_head]) train_triples['relation'] = np.concatenate([train_triples['relation'], train_triples['relation'] + nrelation]) # let's add inverses to the validation if args.val_inverses: logging.info("Adding inverses to the validation set") orig_head, orig_tail = valid_triples['head'], valid_triples['tail'] orig_head_negs, orig_tail_negs = valid_triples['head_neg'], valid_triples['tail_neg'] valid_triples['head'] = np.concatenate([orig_head, orig_tail]) valid_triples['tail'] = np.concatenate([orig_tail, orig_head]) valid_triples['relation'] = np.concatenate([valid_triples['relation'], valid_triples['relation'] + nrelation]) valid_triples['head_neg'] = np.concatenate([orig_head_negs, orig_tail_negs], axis=0) valid_triples['tail_neg'] = np.concatenate([orig_tail_negs, orig_head_negs], axis=0) logging.info("Adding inverses to the test set") orig_head, orig_tail = test_triples['head'], test_triples['tail'] orig_head_negs, orig_tail_negs = test_triples['head_neg'], test_triples['tail_neg'] test_triples['head'] = np.concatenate([orig_head, orig_tail]) test_triples['tail'] = np.concatenate([orig_tail, orig_head]) test_triples['relation'] = np.concatenate([test_triples['relation'], test_triples['relation'] + nrelation]) test_triples['head_neg'] = np.concatenate([orig_head_negs, orig_tail_negs], axis=0) test_triples['tail_neg'] = np.concatenate([orig_tail_negs, orig_head_negs], axis=0) del orig_head, orig_tail, orig_head_negs, orig_tail_negs nrelation = nrelation * 2 + 1 else: print("No inverse edges") nrelation += 1 print(f"Total num relations: {nrelation}") # create a tokenizer based on train triples tokenizer = NodePiece_OGB( triples=DummyTripleFactory(train_triples, ne=nentity, nr=nrelation), anchor_strategy={ "degree": args.st_deg, "betweenness": 0.0, "pagerank": args.st_ppr, "random": args.st_rand }, num_anchors=args.anchors, num_paths=args.anchors, dataset_name=args.dataset, limit_shortest=args.ancs_sp, add_identity=False, mode="bfs", tkn_batch=args.tkn_batch, inv=args.inverses, dir=args.tkn_dir, partition=args.part, cpus=args.cpu_num ) #if args.max_seq_len == 0 or args.max_seq_len != (tokenizer.max_seq_len + 3): max_seq_len = tokenizer.max_seq_len + 3 # as in the PathTrfEncoder, +1 CLS, +1 PAD, +1 LP tasks print(f"Set max_seq_len to {max_seq_len}") tokenizer.token2id[tokenizer.NOTHING_TOKEN] = len(tokenizer.token2id) train_count, train_true_head, train_true_tail = defaultdict(lambda: 4), defaultdict(list), defaultdict(list) for i in tqdm(range(len(train_triples['head']))): head, relation, tail = train_triples['head'][i], train_triples['relation'][i], train_triples['tail'][i] train_count[(head, relation)] += 1 if not args.inverses: train_count[(tail, -relation - 1)] += 1 train_true_head[(relation, tail)].append(head) train_true_tail[(head, relation)].append(tail) kge_model = KGEModel( model_name=args.model, nentity=nentity, nrelation=nrelation, hidden_dim=args.hidden_dim, gamma=args.gamma, double_entity_embedding=args.double_entity_embedding, double_relation_embedding=args.double_relation_embedding, evaluator=evaluator, tokenizer=tokenizer, pooler=args.pooler, use_rels=args.rel_hash, rel_policy=args.policy, sample_paths=args.max_paths, trf_layers=args.trf_layers, trf_heads=args.trf_heads, trf_hidden=args.trf_hidden, drop=args.drop, use_distances=args.use_dists, max_seq_len=max_seq_len, sample_rels=args.sample_rels, triples=train_triples, ablate_anchors=args.noanc, device=torch.device('cuda') if args.cuda else torch.device('cpu'), ) logging.info('Model Parameter Configuration:') for name, param in kge_model.named_parameters(): logging.info('Parameter %s: %s, require_grad = %s' % (name, str(param.size()), str(param.requires_grad))) logging.info(f"Total number of params: {sum(p.numel() for p in kge_model.parameters())}") if args.cuda: kge_model = kge_model.cuda() if args.do_train: # Set training dataloader iterator train_dataloader_head = DataLoader( TrainDataset(train_triples, nentity, nrelation, args.negative_sample_size, 'head-batch', train_count, train_true_head, train_true_tail), batch_size=args.batch_size, shuffle=True, num_workers=max(1, args.cpu_num // 2), collate_fn=TrainDataset.collate_fn ) train_dataloader_tail = DataLoader( TrainDataset(train_triples, nentity, nrelation, args.negative_sample_size, 'tail-batch', train_count, train_true_head, train_true_tail), batch_size=args.batch_size, shuffle=True, num_workers=max(1, args.cpu_num // 2), collate_fn=TrainDataset.collate_fn ) train_iterator = BidirectionalOneShotIterator(train_dataloader_head, train_dataloader_tail) # Set training configuration current_learning_rate = args.learning_rate optimizer = torch.optim.Adam( filter(lambda p: p.requires_grad, kge_model.parameters()), lr=current_learning_rate ) if args.warm_up_steps: warm_up_steps = args.warm_up_steps else: warm_up_steps = args.max_steps // 2 if args.init_checkpoint: # Restore model from checkpoint directory logging.info('Loading checkpoint %s...' % args.init_checkpoint) checkpoint = torch.load(os.path.join(args.init_checkpoint, 'checkpoint')) init_step = checkpoint['step'] kge_model.load_state_dict(checkpoint['model_state_dict']) if args.do_train: current_learning_rate = checkpoint['current_learning_rate'] warm_up_steps = checkpoint['warm_up_steps'] optimizer.load_state_dict(checkpoint['optimizer_state_dict']) else: logging.info('Ramdomly Initializing %s Model...' % args.model) init_step = 0 step = init_step logging.info('Start Training...') logging.info('init_step = %d' % init_step) logging.info('batch_size = %d' % args.batch_size) logging.info('negative_adversarial_sampling = %d' % args.negative_adversarial_sampling) logging.info('hidden_dim = %d' % args.hidden_dim) logging.info('gamma = %f' % args.gamma) logging.info('negative_adversarial_sampling = %s' % str(args.negative_adversarial_sampling)) if args.negative_adversarial_sampling: logging.info('adversarial_temperature = %f' % args.adversarial_temperature) # Set valid dataloader as it would be evaluated during training if args.do_train: logging.info('learning_rate = %d' % current_learning_rate) training_logs = [] max_val_mrr = 0 best_val_metrics = None best_test_metrics = None best_metrics_step = 0 # Training Loop for step in tqdm(range(init_step, args.max_steps)): log = kge_model.train_step(kge_model, optimizer, train_iterator, args) training_logs.append(log) if step >= warm_up_steps: current_learning_rate = current_learning_rate / 10 logging.info('Change learning_rate to %f at step %d' % (current_learning_rate, step)) optimizer = torch.optim.Adam( filter(lambda p: p.requires_grad, kge_model.parameters()), lr=current_learning_rate ) warm_up_steps = warm_up_steps * 3 if step % args.save_checkpoint_steps == 0 and step > 0: # ~ 41 seconds/saving save_variable_list = { 'step': step, 'current_learning_rate': current_learning_rate, 'warm_up_steps': warm_up_steps } save_model(kge_model, optimizer, save_variable_list, args) if step % args.log_steps == 0: metrics = {} for metric in training_logs[0].keys(): metrics[metric] = sum([log[metric] for log in training_logs]) / len(training_logs) log_metrics('Train', step, metrics, writer) training_logs = [] if args.do_valid and step % args.valid_steps == 0 and step > 0: logging.info('Evaluating on Valid Dataset...') metrics = kge_model.test_step(kge_model, valid_triples, args) log_metrics('Valid', step, metrics, writer) val_mrr = metrics['mrr_list'] # evaluate on test set if val_mrr > max_val_mrr: max_val_mrr = val_mrr best_val_metrics = metrics best_metrics_step = step if args.do_test: logging.info('Evaluating on Test Dataset...') metrics = kge_model.test_step(kge_model, test_triples, args) log_metrics('Test', step, metrics, writer) best_test_metrics = metrics # record best metrics on validate and test set if args.do_valid and best_val_metrics != None: log_metrics('Best Val Metrics', best_metrics_step, best_val_metrics, writer) if args.do_test and best_test_metrics != None: log_metrics('Best Test Metrics', best_metrics_step, best_test_metrics, writer) save_variable_list = { 'step': step, 'current_learning_rate': current_learning_rate, 'warm_up_steps': warm_up_steps } save_model(kge_model, optimizer, save_variable_list, args) if args.do_valid: logging.info('Evaluating on Valid Dataset...') metrics = kge_model.test_step(kge_model, valid_triples, args) log_metrics('Valid', step, metrics, writer) if args.do_test: logging.info('Evaluating on Test Dataset...') metrics = kge_model.test_step(kge_model, test_triples, args) log_metrics('Test', step, metrics, writer) if args.evaluate_train: logging.info('Evaluating on Training Dataset...') small_train_triples = {} indices = np.random.choice(len(train_triples['head']), args.ntriples_eval_train, replace=False) for i in train_triples: small_train_triples[i] = train_triples[i][indices] metrics = kge_model.test_step(kge_model, small_train_triples, args, random_sampling=True) log_metrics('Train', step, metrics, writer) if __name__ == '__main__': main(parse_args()) ================================================ FILE: ogb/run_ogb.sh ================================================ python run_ogb.py --anchors 20000 --max_paths 20 --do_train --double_entity_embedding --double_relation_embedding -n 128 -adv --use_dists --model AutoSF --print_on_screen --cuda -lr 0.0001 --valid_steps 50000 --log_steps 10000 --max_steps 250000 --save_checkpoint_steps 1000000 --do_valid --inverses --test_log_steps 20000 --gamma 50 --sample_rels 12 -a 3.0 --val_inverses --do_test ================================================ FILE: oos_lp/src/common/dataset.py ================================================ # Copyright (c) 2019-present, Royal Bank of Canada. # Copyright (c) 2019-present, Bahare Fatemi. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. ##################################################################################### # Code is from https://github.com/baharefatemi/SimplE by Bahare Fatemi #################################################################################### import json import math import random import numpy as np import torch from utils import random_new_ent_mask class Dataset: def __init__(self, dataset_name, cons_masking=False, mask_prob=0.5, tokenize=False): self.dataset_name = dataset_name self.data_path = "../datasets/" + dataset_name + "/processed/" self.ent2id = {} self.rel2id = {} self.train_data = self.read_text(self.data_path + "train.txt") self.init_num_ent = self.num_ent() if not tokenize: self.adj_list, self.ent_freq, self.rel_freq = self.dataset_stat(self.train_data) self.max_degree = max(map(len, self.adj_list)) self.val_test_data = { "valid": self.read_json(self.data_path + "valid.json"), "test": self.read_json(self.data_path + "test.json"), } self.batch_index = 0 self.cons_masking = cons_masking self.mask_prob = mask_prob def read_text(self, file_path): with open(file_path, "r") as f: lines = f.readlines() triples = np.zeros((len(lines), 3)) for i, line in enumerate(lines): triples[i] = np.array(self.triple2ids(line.strip().split("\t"))) return triples def dataset_stat(self, triples): adj_list = [[] for _ in range(self.num_ent())] ent_freq = torch.zeros(self.num_ent()) rel_freq = torch.zeros(self.num_rel()) for t in triples: t = t.astype(int) adj_list[t[0]].append([t[2], t[1], 0]) adj_list[t[2]].append([t[0], t[1], 1]) ent_freq[t[0]] += 1 ent_freq[t[2]] += 1 rel_freq[t[1]] += 1 max_degree = max(map(len, adj_list)) for item in adj_list: item.extend( [[self.num_ent(), self.num_rel(), 0]] * (max_degree - len(item)) ) return ( torch.tensor(adj_list), ent_freq / triples.shape[0], rel_freq / triples.shape[0], ) def read_json(self, file_path): with open(file_path) as json_file: data = json.load(json_file) for k, v in data.items(): triples = [] for t in v: if t[0] == k: triples_ids = [t[0], self.get_rel_id(t[1]), self.get_ent_id(t[2])] else: triples_ids = [self.get_ent_id(t[0]), self.get_rel_id(t[1]), t[2]] triples.append(triples_ids) data[k] = triples return data def triple2ids(self, triple): return [ self.get_ent_id(triple[0]), self.get_rel_id(triple[1]), self.get_ent_id(triple[2]), ] def get_ent_id(self, ent, add_ent=True): if not ent in self.ent2id: if add_ent: self.ent2id[ent] = len(self.ent2id) else: return self.init_num_ent return self.ent2id[ent] def get_rel_id(self, rel): if not rel in self.rel2id: self.rel2id[rel] = len(self.rel2id) return self.rel2id[rel] def num_ent(self): return len(self.ent2id) def num_rel(self): return len(self.rel2id) def rand_ent_except(self, ent): rand_ent = random.randint(0, self.init_num_ent - 1) while rand_ent == ent: rand_ent = random.randint(0, self.init_num_ent - 1) return rand_ent def next_pos_batch(self, batch_size): if self.batch_index + batch_size < len(self.train_data): batch = self.train_data[self.batch_index: self.batch_index + batch_size] self.batch_index += batch_size else: batch = self.train_data[self.batch_index:] self.batch_index = 0 return np.append(batch, np.ones((len(batch), 1)), axis=1).astype( "int" ) # appending the +1 label def generate_neg(self, pos_batch, neg_ratio): neg_batch = np.repeat(np.copy(pos_batch), neg_ratio, axis=0) for i in range(len(neg_batch)): if random.random() < 0.5: neg_batch[i][0] = self.rand_ent_except(neg_batch[i][0]) # flipping head else: neg_batch[i][2] = self.rand_ent_except(neg_batch[i][2]) # flipping tail neg_batch[:, -1] = -1 return neg_batch def next_batch(self, batch_size, neg_ratio, device): pos_batch = self.next_pos_batch(batch_size) neg_batch = self.generate_neg(pos_batch, neg_ratio) batch = np.append(pos_batch, neg_batch, axis=0) labels = torch.tensor(batch[:, 3], dtype=torch.float, device=device) triples = batch[:, 0:3] if self.cons_masking: pos_new_ent_mask = random_new_ent_mask(pos_batch[:, 0:3], self.mask_prob) neg_new_ent_mask = np.repeat(np.copy(pos_new_ent_mask), neg_ratio, axis=0) new_ent_mask = np.append(pos_new_ent_mask, neg_new_ent_mask, axis=0) new_ent_mask = torch.tensor(new_ent_mask, dtype=torch.long).to(device) else: new_ent_mask = torch.tensor( random_new_ent_mask(triples, self.mask_prob), dtype=torch.long ).to(device) return ( torch.tensor(triples, dtype=torch.long, device=device), labels, new_ent_mask, ) def was_last_batch(self): return self.batch_index == 0 def num_batch(self, batch_size): return int(math.ceil(float(len(self.train_data)) / batch_size)) def num_batch_simulated(self, simulate_batch_size): return int(math.ceil(float(len(self.train_data)) / simulate_batch_size)) def generate_neg_obs(self, obs_triples, new_ent, neg_ratio): neg_triples = np.repeat(np.copy(obs_triples), neg_ratio, axis=0) for i in range(len(neg_triples)): if neg_triples[i, 0] == new_ent: # new entity is in head -> flip tail neg_triples[i, 2] = self.rand_ent_except(neg_triples[i, 2]) else: # new entity is in tail -> flip head neg_triples[i, 0] = self.rand_ent_except(neg_triples[i, 0]) return neg_triples ================================================ FILE: oos_lp/src/common/measure.py ================================================ # Copyright (C) 2019 Bahare Fatemi # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # # https://github.com/baharefatemi/SimplE # class Measure: def __init__(self): self.hit1 = 0.0 self.hit3 = 0.0 self.hit10 = 0.0 self.mrr = 0.0 self.mr = 0.0 self.num_facts = 0.0 def update(self, rank): if rank == 1: self.hit1 += 1.0 if rank <= 3: self.hit3 += 1.0 if rank <= 10: self.hit10 += 1.0 self.mr += rank self.mrr += 1.0 / rank self.num_facts += 1.0 def normalize(self): if self.hit1 > 0.0: self.hit1 /= self.num_facts if self.hit3 > 0.0: self.hit3 /= self.num_facts if self.hit10 > 0.0: self.hit10 /= self.num_facts self.mr /= self.num_facts self.mrr /= self.num_facts def print_(self): print("\tHit@1 =", self.hit1) print("\tHit@3 =", self.hit3) print("\tHit@10 =", self.hit10) print("\tMR =", self.mr) print("\tMRR =", self.mrr) print("") ================================================ FILE: oos_lp/src/main.py ================================================ """ The code is largely based on the original repo https://github.com/BorealisAI/OOS-KGE """ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import argparse import time import torch from common.dataset import Dataset from tester import OutKGTester from trainer import OutKGTrainer from vocab.nodepiece_tokenizer import NodePiece_Tokenizer import numpy as np import random # Clamp the randomness np.random.seed(42) random.seed(42) torch.manual_seed(42) def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def get_parameters(): parser = argparse.ArgumentParser() parser.add_argument("-dataset", default="WN18RR", type=str, help="dataset name") parser.add_argument( "-model_name", default="DisMult", type=str, help="initial embedding model" ) parser.add_argument( "-opt", default="adagrad", type=str, help="optimizer. Currenty only adagrad and adam are supported", ) parser.add_argument( "-emb_method", default="ERAverage", type=str, help="method to find new enitity's embedding", ) parser.add_argument("-emb_dim", default=200, type=int, help="embedding dimension") parser.add_argument( "-neg_ratio", default=1, type=int, help="number of negative examples per positive example", ) parser.add_argument("-batch_size", default=1000, type=int, help="batch size") parser.add_argument( "-simulated_batch_size", default=1000, type=int, help="batch size to be simulated", ) parser.add_argument( "-save_each", default=5, type=int, help="validate every k epochs" ) parser.add_argument("-ne", default=1000, type=int, help="number of epochs") parser.add_argument("-lr", default=0.1, type=float, help="learning rate") parser.add_argument( "-reg_lambda", default=0.01, type=float, help="l2 regularization parameter" ) parser.add_argument( "-reg_ls", default=0.01, type=float, help="l2 regularization parameter (for Least Squares)", ) parser.add_argument( "-val", default=False, type=bool, help="start validation after training" ) parser.add_argument( "-use_custom_reg", default=False, type=str2bool, help="use custom regularisation" ) parser.add_argument("-use_acc", default=False, type=bool, help="use_acc flag") parser.add_argument( "-cons_mask", default=False, type=bool, help="Use consistent masking" ) parser.add_argument( "-mask_prob", default=0.5, type=float, help="The probability of observed entities", ) parser.add_argument("-tokenize", default=False, type=bool, help="Use tokenizer") parser.add_argument("-anchors", default=500, type=int, help="Num anchors") parser.add_argument("-sample_size", default=20, type=int, help="Num anchors per node") parser.add_argument("-pooler", default="cat", type=str, help="Pooler for anchors") parser.add_argument("-sample_rels", default=5, type=int, help="Num unique relations from 1-hop neighborhood") parser.add_argument("-random_hashes", default=0, type=int, help="Number of random hashes as a baseline") parser.add_argument("-t_hidden", default=512, type=int, help="Hidden size") parser.add_argument("-t_drop", default=0.1, type=float, help="Default dropout") parser.add_argument("-t_heads", default=4, type=int, help="Num attn heads for trf") parser.add_argument("-t_layers", default=2, type=int, help="Num layers for trf") parser.add_argument("-subbatch", default=5000, type=int, help="Subbatch for 1-N scoring") parser.add_argument("-max_path_len", default=0, type=int, help="automatic inf of max path len") parser.add_argument("-anc_dist", default=True, type=bool, help="use anchor distances") parser.add_argument("-no_anc", default=False, type=bool, help="Turn off any anchors") parser.add_argument("-nearest_ancs", default=True, type=bool, help="Use closest or random anchors") parser.add_argument("-loss_fc", default="spl", type=str, help="loss function - spl or nssal") parser.add_argument("-margin", default=15, type=int, help="margin for nssal loss") parser.add_argument("-wandb", default=False, type=str2bool, help="whether to use wandb for logging") parser.add_argument("-eval_every", default=1, type=int, help="how often to eval, -1 to turn off") args = parser.parse_args() return args if __name__ == "__main__": args = get_parameters() for arg in vars(args): print("{}: {}".format(arg, getattr(args, arg))) dataset = Dataset(args.dataset, args.cons_mask, args.mask_prob, args.tokenize) if args.tokenize: # add inverses num_default_rels = len(dataset.rel2id) inverse_triples = np.stack([dataset.train_data[:, 2], dataset.train_data[:, 1] + num_default_rels, dataset.train_data[:, 0]], axis=1) dataset.train_data = np.vstack([dataset.train_data, inverse_triples]) dataset.rel2id.update({ f"{k}_inverse": v + num_default_rels for k, v in dataset.rel2id.items() }) tokenizer = NodePiece_Tokenizer(dataset.train_data, anchor_strategy={ "degree": 0.4, "betweenness": 0.0, "pagerank": 0.4, "random": 0.2 }, num_anchors=args.anchors, dataset_name=args.dataset, limit_shortest=100, relation2id=dataset.rel2id, num_nodes=dataset.init_num_ent ) if args.max_path_len == 0: args.max_path_len = tokenizer.max_seq_len else: tokenizer = None outKG_trainer = OutKGTrainer(dataset, args, tokenizer) print("~~~~ Training ~~~~") outKG_trainer.train() if args.val or True: with torch.no_grad(): # print("~~~~ Select best epoch on validation set ~~~~") # epochs2test = [ # str(int(args.save_each * i)) # for i in range(1, args.ne // args.save_each) # ] # best_mrr = -1.0 # best_epoch = "0" # valid_performance = None # for epoch in epochs2test: # start = time.time() # print("epoch: ", epoch) # outKG_tester = OutKGTester(dataset) # model_path = ( # "logs/" # + args.model_name # + "/" # + args.emb_method # + "/" # + args.dataset # + "/" # + epoch # + "_" # + str(args.lr) # + "_" # + str(args.reg_lambda) # + "_" # + str(args.neg_ratio) # + "_" # + str(args.emb_dim) # + ".chkpnt" # ) # mrr = outKG_tester.test(model_path, "valid") # if mrr > best_mrr: # best_mrr = mrr # best_epoch = epoch # valid_performance = [ # best_epoch, # outKG_tester.measure.hit1, # outKG_tester.measure.hit3, # outKG_tester.measure.hit10, # outKG_tester.measure.mr, # outKG_tester.measure.mrr, # ] # print("validation time: ", time.time() - start) # # print("Best epoch: " + best_epoch) # if valid_performance: # print("\nValidation Performance : ") # print("\tBest epoch : ", valid_performance[0]) # print("\tHit@1 : ", valid_performance[1]) # print("\tHit@3 : ", valid_performance[2]) # print("\tHit@10 : ", valid_performance[3]) # print("\tMR : ", valid_performance[4]) # print("\tMRR : ", valid_performance[5]) print("~~~~ Testing on the last epoch ~~~~") # best_model_path = ( # "logs/" # + args.model_name # + "/" # + args.emb_method # + "/" # + args.dataset # + "/" # + best_epoch # + "_" # + str(args.lr) # + "_" # + str(args.reg_lambda) # + "_" # + str(args.neg_ratio) # + "_" # + str(args.emb_dim) # + ".chkpnt" # ) best_model_path = outKG_trainer.model outKG_tester = OutKGTester(dataset) start = time.time() outKG_tester.test(best_model_path, "test") print("Inference time: ", time.time() - start) print("Done :) ") ================================================ FILE: oos_lp/src/model/BaseOutKG.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import torch from torch import nn class BaseOutKG(nn.Module): def __init__(self, dataset, args, device): super(BaseOutKG, self).__init__() self.dataset = dataset self.args = args self.emb_method = self.args.emb_method self.emb_dim = self.args.emb_dim self.device = device self.build_model() def build_model(self): raise NotImplementedError def forward(self, triples, mask): new_ent_embs = torch.zeros(mask.shape[0], self.emb_dim).to(self.device) obs_ent_embs = torch.zeros(mask.shape[0], self.emb_dim).to(self.device) # get embeddings for unobserved entities (entity mask = 0 or 2) deep_mask = mask[mask != 1] if len(deep_mask) > 0: obs_ents_mask = torch.add(torch.mul(deep_mask, -1), 2) self.obs_ents_id = triples[mask != 1, obs_ents_mask] obs_ent_embs[mask != 1] = self.get_ent_embs(self.obs_ents_id) new_ent_embs[mask != 1] = self.get_new_ent_embs( triples[mask != 1], deep_mask ) # get embeddings for observed entities (entity mask = 1) obs_ent_embs[mask == 1] = self.get_ent_embs(triples[mask == 1, 0]) new_ent_embs[mask == 1] = self.get_ent_embs(triples[mask == 1, 2]) rel_embs = self.get_rel_embs(triples[:, 1]) scores = self.cal_score(obs_ent_embs, new_ent_embs, rel_embs) return scores, new_ent_embs def cal_score(self, obs_ents, new_ents, rels): scores = (obs_ents * new_ents) * rels scores = torch.sum(scores, dim=1) return scores def get_ent_embs(self, ent_id): raise NotImplementedError def get_new_ent_embs(self, triples, mask): raise NotImplementedError def get_rel_embs(self, rel_id): raise NotImplementedError def l2_loss(self): raise NotImplementedError def find_embedding(self, new_ent, obs_triples): raise NotImplementedError ================================================ FILE: oos_lp/src/model/DisMultOutKG.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import numpy as np import torch from torch import nn from model.BaseOutKG import BaseOutKG class DisMultOutKG(BaseOutKG): def build_model(self): self.num_ent = self.dataset.init_num_ent self.num_rel = self.dataset.num_rel() self.ent_embs = nn.Embedding( self.num_ent + 1, self.emb_dim, padding_idx=self.num_ent ).to(self.device) self.rel_embs = nn.Embedding( self.num_rel + 1, self.emb_dim, padding_idx=self.num_rel ).to(self.device) nn.init.xavier_uniform_(self.ent_embs.weight[:-1]) nn.init.xavier_uniform_(self.rel_embs.weight[:-1]) def get_ent_embs(self, ent_id): # lookup the embedding return self.ent_embs(ent_id) def get_rel_embs(self, rel_id): return self.rel_embs(rel_id) def get_new_ent_embs(self, triples, mask): # find the embedding for the unobserved entities new_ents_id = triples[torch.arange(triples.shape[0], dtype=torch.long), mask] ent_neighbors = self.dataset.adj_list[new_ents_id].to(self.device) neighbors_ent_embs = self.get_ent_embs(ent_neighbors[:, :, 0].view(-1)).view( ent_neighbors.shape[0], ent_neighbors.shape[1], self.emb_dim ) neighbors_rel_embs = self.get_rel_embs(ent_neighbors[:, :, 1].view(-1)).view( ent_neighbors.shape[0], ent_neighbors.shape[1], self.emb_dim ) ent_mask = (ent_neighbors[:, :, 0] != self.obs_ents_id.unsqueeze(1)) * ( ent_neighbors[:, :, 0] != self.num_ent ) neighbors_ent_embs = neighbors_ent_embs * ent_mask.unsqueeze(2).expand( neighbors_ent_embs.shape ).to(torch.float) neighbors_rel_embs = neighbors_rel_embs * ent_mask.unsqueeze(2).expand( neighbors_rel_embs.shape ).to(torch.float) return self.infer_emb(neighbors_ent_embs, neighbors_rel_embs, mask=ent_mask) def l2_loss(self): emb_reg = (torch.norm(self.ent_embs.weight, p=2) ** 2) + ( torch.norm(self.rel_embs.weight, p=2) ** 2 ) return emb_reg / 2 def get_ent_exc_ids(self, obs_triples, new_ent): heads = np.where(obs_triples[:, 0] != new_ent)[0] h1 = np.zeros((len(heads), 1)) tails = np.where(obs_triples[:, 2] != new_ent)[0] t1 = np.zeros((len(tails), 1)) t1.fill(2) heads = np.expand_dims(heads, axis=1) heads = np.concatenate((heads, h1), axis=1) tails = np.expand_dims(tails, axis=1) tails = np.concatenate((tails, t1), axis=1) obs_ent = np.concatenate((heads, tails)) obs_ent = obs_ent[np.argsort(obs_ent[:, 0])].astype(int) obs_ent_ids = obs_triples[obs_ent[:, 0], obs_ent[:, 1]] return obs_ent_ids def find_embedding(self, new_ent, obs_triples): obs_ent_ids = self.get_ent_exc_ids(obs_triples, new_ent) obs_rel_ids = obs_triples[:, 1] obs_ent_embs = self.get_ent_embs( torch.tensor(obs_ent_ids, dtype=torch.long).to(self.device) ).unsqueeze(0) obs_rel_embs = self.get_rel_embs( torch.tensor(obs_rel_ids, dtype=torch.long).to(self.device) ).unsqueeze(0) return self.infer_emb(obs_ent_embs, obs_rel_embs).squeeze(0) def infer_emb(self, ent_emb, rel_emb, mask=None): # infers embedding of unobserved entities based on the emb_method (aggregation function) if self.emb_method == "ERAverage": return self.find_neighbors_avg(ent_emb, rel_emb=rel_emb, mask=mask) elif self.emb_method == "Average": return self.find_neighbors_avg(ent_emb, mask=mask) elif self.emb_method == "LS": labels = torch.ones(ent_emb.shape[0], ent_emb.shape[1]).to(self.device) if mask is not None: labels = labels * mask.to(torch.float) A = nn.functional.normalize(ent_emb * rel_emb, dim=2) A_t = A.permute(0, 2, 1) c = torch.inverse( torch.bmm(A_t, A) + self.args.reg_ls * ( torch.eye(A.shape[2]) .unsqueeze(0) .expand(A.shape[0], A.shape[2], A.shape[2]) ).to(self.device) ) return torch.bmm(torch.bmm(c, A_t), labels.unsqueeze(2)).squeeze() elif self.emb_method == "LS_unnorm": labels = torch.ones(ent_emb.shape[0], ent_emb.shape[1]).to(self.device) if mask is not None: labels = labels * mask.to(torch.float) A = ent_emb * rel_emb A_t = A.permute(0, 2, 1) c = torch.inverse( torch.bmm(A_t, A) + self.args.reg_ls * ( torch.eye(A.shape[2]) .unsqueeze(0) .expand(A.shape[0], A.shape[2], A.shape[2]) ).to(self.device) ) return torch.bmm(torch.bmm(c, A_t), labels.unsqueeze(2)).squeeze() def find_neighbors_avg(self, ent_emb, rel_emb=None, mask=None): if mask is None: if rel_emb is None: return torch.mean(ent_emb, dim=1) else: return torch.mean(ent_emb * rel_emb, dim=1) else: mask_sum = torch.sum(mask, dim=1) mask_sum[mask_sum == 0] = 1.0 if rel_emb is None: return torch.sum(ent_emb, dim=1) / mask_sum.unsqueeze(1).to(torch.float) else: return torch.sum(ent_emb * rel_emb, dim=1) / mask_sum.unsqueeze(1).to( torch.float ) ================================================ FILE: oos_lp/src/model/dm_tokenized.py ================================================ import torch import numpy as np from torch import nn from vocab.nodepiece_tokenizer import NodePiece_Tokenizer from vocab.nodepiece_encoder import NodePieceEncoder from common.dataset import Dataset class TokenizedDistMult(nn.Module): def __init__(self, args: dict, device: torch.device, dataset: Dataset, tokenizer: NodePiece_Tokenizer): super().__init__() self.rel_embs = nn.Embedding(len(dataset.rel2id) + 1, args.emb_dim, padding_idx=len(dataset.rel2id)) torch.nn.init.xavier_uniform_(self.rel_embs.weight[:-1]) self.embedder = NodePieceEncoder(args, tokenizer, rel_embs=self.rel_embs, graph=dataset, device=device) self.embedder.reset_parameters() self.device = device self.num_direct_rels = len(dataset.rel2id) // 2 def forward(self, triples, mask): # don't do anythign with the mask, just compute embs and scoring function subs = self.embedder.encode_by_index(triples[:, 0]) rels = self.rel_embs(triples[:, 1]) objs = self.embedder.encode_by_index(triples[: , 2]) score = torch.sum(subs * rels * objs, dim=-1) return score, None # for backporting with the eval code def get_rel_embs(self, rels: torch.LongTensor): return self.rel_embs(rels) def get_ent_embs(self, entities: torch.LongTensor): if self.all is None: self.all = self.embedder.get_all_representations() return self.all[entities] def reset(self): self.all = None def cal_score(self, obs_ents, new_ents, rels): scores = (obs_ents * new_ents) * rels scores = torch.sum(scores, dim=1) return scores def get_ent_exc_ids(self, obs_triples, new_ent): heads = np.where(obs_triples[:, 0] != new_ent)[0] h1 = np.zeros((len(heads), 1)) tails = np.where(obs_triples[:, 2] != new_ent)[0] t1 = np.zeros((len(tails), 1)) t1.fill(2) heads = np.expand_dims(heads, axis=1) heads = np.concatenate((heads, h1), axis=1) tails = np.expand_dims(tails, axis=1) tails = np.concatenate((tails, t1), axis=1) obs_ent = np.concatenate((heads, tails)) obs_ent = obs_ent[np.argsort(obs_ent[:, 0])].astype(int) obs_ent_ids = obs_triples[obs_ent[:, 0], obs_ent[:, 1]] return obs_ent_ids def prune_tokens(self, temp_hashes: torch.LongTensor, temp_dist: torch.LongTensor): # this function selects self.sample_paths number of UNIQUE and NEAREST anchors from the list of anchors and their distances nothing_token = self.embedder.tokenizer.token2id[self.embedder.tokenizer.NOTHING_TOKEN] unique_ancs, unique_dists = [], [] for anchor, dist in zip(temp_hashes, temp_dist): if anchor.item() not in unique_ancs and anchor.item() != nothing_token: unique_ancs.append(anchor.item()) unique_dists.append(dist.item()) else: continue # in case we stuck with the disconnected node w/o anchors, add only NOTHING tokens if len(unique_ancs) < self.embedder.sample_paths: unique_ancs += [nothing_token] * (self.embedder.sample_paths - len(unique_ancs)) unique_dists += [0] * (self.embedder.sample_paths - len(unique_dists)) return unique_ancs, unique_dists def find_embedding(self, ent_id: int, observed_triples: np.ndarray): # tokenizing unseen nodes using the nodepiece vocab and relational context all_relations = np.unique(observed_triples[:, 1]) # build a relational context - get only outgoing edges and inverses of incoming outgoing_rels = observed_triples[observed_triples[:, 0] == ent_id][:, 1] incoming_rels = np.unique(np.array(list(set(list(all_relations)) - set(list(outgoing_rels))))) outgoing_rels = np.unique(np.array(outgoing_rels)) if len(incoming_rels) > 0: incoming_inv = incoming_rels + self.num_direct_rels relational_context = np.concatenate([outgoing_rels, incoming_inv]) else: relational_context = outgoing_rels # unique for sorting as we do in pre-processing in training # first, get hashes (and anchor distances) of seen nodes in the neighborhood seen_nodes = torch.tensor(self.get_ent_exc_ids(observed_triples, ent_id), dtype=torch.long, device=self.device) hashes = self.embedder.hashes[seen_nodes].flatten() distances = self.embedder.distances[seen_nodes].flatten() # get topK closest anchors from the given hashes topk_idx = torch.argsort(distances, descending=False) temp_hashes = hashes[topk_idx] temp_dist = distances[topk_idx] unique_ancs, unique_dists = self.prune_tokens(temp_hashes, temp_dist) # build a full hash of the unseen node using nodepiece anchors, distances, and relational context top_hashes = torch.tensor(unique_ancs[:self.embedder.sample_paths], dtype=torch.long, device=self.device) top_distances = torch.tensor(unique_dists[:self.embedder.sample_paths], dtype=torch.long, device=self.device) top_distances = top_distances + 1 # as we are 1 hop away # return the encoded hash return self.embedder.encode_by_hash(top_hashes, top_distances, relational_context) ================================================ FILE: oos_lp/src/preprocess/dataset_prep.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import argparse import json import os import random import numpy as np """ Code for dataset preparation. """ class DatasetPreprocess: def __init__(self, dataset_name, smpl_ratio=0.2, spl_ratio=0.5): self.data_path = "datasets/" + dataset_name + "/" self.ent2id = {} self.rel2id = {} self.all_triples = self.read_all() self.smpl_ratio = smpl_ratio self.spl_ratio = spl_ratio self.old_ent = [] self.new_ent = [] self.test_triples = [] def read_all(self): all_lines = [] for spl in ["train", "valid", "test"]: file_path = self.data_path + spl + ".txt" with open(file_path, "r") as f: lines = f.readlines() for line in lines: all_lines.append(line) triples = np.zeros((len(all_lines), 3), dtype=int) for i, line in enumerate(all_lines): triples[i] = np.array(self.triple2ids(line.strip().split("\t"))) return triples def make_dataset(self): self.single_triple_ent() self.split_entities() self.separate_triples() self.find_dangling_ent() self.find_dangling_rel() self.explore_split_dataset() self.constraint_check() self.save_dataset() def single_triple_ent(self): """ find those entities that are participated in only one triple add them to self.old_ent """ ent_triple_count = np.zeros(self.num_ent()) for i in range(self.num_ent()): ent_triple_count[i] = np.sum(self.all_triples[:, 0] == i) + np.sum( self.all_triples[:, 2] == i ) single_triple_ent = np.where(ent_triple_count == 1)[0] self.old_ent.extend(list(single_triple_ent)) def split_entities(self): all_ent = set(range(self.num_ent())) all_ent = all_ent - set(self.old_ent) self.new_ent = random.sample(list(all_ent), int(len(all_ent) * self.smpl_ratio)) self.old_ent.extend(list(all_ent - set(self.new_ent))) def save_dataset(self): # save train new_dir = self.data_path + "processed/" if not os.path.isdir(new_dir): os.makedirs(new_dir) print("Saving old triples") old_triples_seq = [self.ids2triple(t) for t in self.old_triples] out_f = open(new_dir + "train.txt", "w") out_f.writelines(old_triples_seq) out_f.close() # split new triples to [val, test] and save print("Saving new triples") with open(new_dir + "valid.json", "w") as json_file: json.dump(self.valid_dict, json_file) with open(new_dir + "test.json", "w") as json_file: json.dump(self.test_dict, json_file) def constraint_check(self): old_ent = np.union1d(self.old_triples[:, 0], self.old_triples[:, 2]) new_ent = set(self.test_dict.keys()).union(set(self.valid_dict.keys())) all_ent = len(old_ent) + len(new_ent) removed_ent = self.num_ent() - all_ent print("New entity ratio: ", len(new_ent) / self.num_ent()) print( "Number of deleted entities: {}, ratio: {}".format( removed_ent, removed_ent / self.num_ent() ) ) total_triples = len(self.old_triples) + len(self.test_triples) removed_triples = len(self.all_triples) - total_triples print( "Number of deleted triples: {}, ratio: {}".format( removed_triples, removed_triples / len(self.all_triples) ) ) print( "[Train] #entities: {}, #triples: {}".format( len(self.old_ent), len(self.old_triples) ) ) print( "[Valid/Test] #entities: {}, #triples: {}".format( len(new_ent), len(self.test_triples) ) ) def separate_triples(self): self.new_triples, new_ids = self.get_ent_triples( self.new_ent, self.all_triples, return_ids=True ) mask = np.ones(len(self.all_triples), dtype=bool) mask[new_ids] = False self.old_triples = self.all_triples[mask] def find_dangling_ent(self): old_triples_ent = np.union1d(self.old_triples[:, 0], self.old_triples[:, 2]) self.dang_ent = list(set(self.old_ent) - set(old_triples_ent)) def find_dangling_rel(self): old_triples_rel = set(self.old_triples[:, 1]) rel_ids = list(self.rel2id.values()) self.dang_rel = list(set(rel_ids) - old_triples_rel) def explore_split_dataset(self): new_ent_dict = {} for new_e in self.new_ent: ent_triples = self.get_ent_triples([new_e], self.new_triples) # remove those triples that contain dangle entity _, ids = self.get_ent_triples(self.dang_ent, ent_triples, return_ids=True) mask = np.ones(len(ent_triples), dtype=bool) mask[ids] = False ent_triples = ent_triples[mask] # remove those triples that contain dangle relations ids = np.nonzero(np.in1d(ent_triples[:, 1], self.dang_rel)) mask = np.ones(len(ent_triples), dtype=bool) mask[ids] = False ent_triples = ent_triples[mask] # remove those triples that contain other new_ent other_new_ent = list(set(self.new_ent) - set([new_e])) _, ids = self.get_ent_triples(other_new_ent, ent_triples, return_ids=True) mask = np.ones(len(ent_triples), dtype=bool) mask[ids] = False ent_triples = ent_triples[mask] # remove reflexive triples ref_ids = np.where(ent_triples[:, 0] == ent_triples[:, 2]) mask = np.ones(len(ent_triples), dtype=bool) mask[ref_ids] = False ent_triples = ent_triples[mask] if len(ent_triples) >= 2: new_ent_dict[self.get_ent_str(new_e)] = [ [ self.get_ent_str(t[0]), self.get_rel_str(t[1]), self.get_ent_str(t[2]), ] for t in ent_triples ] self.test_triples.extend(ent_triples.tolist()) new_keys = list(new_ent_dict.keys()) valid_ent = random.sample(new_keys, int(len(new_keys) * self.spl_ratio)) test_ent = list(set(new_keys) - set(valid_ent)) self.valid_dict = {k: new_ent_dict[k] for k in valid_ent} self.test_dict = {k: new_ent_dict[k] for k in test_ent} def get_ent_triples(self, e_ids, triples, return_ids=False): h_ids = np.nonzero(np.in1d(triples[:, 0], e_ids)) t_ids = np.nonzero(np.in1d(triples[:, 2], e_ids)) triple_ids = np.union1d(h_ids, t_ids) if return_ids: return triples[triple_ids], triple_ids return triples[triple_ids] def smpl_new_ent(self): all_keys = self.ent2id.keys() new_keys = random.sample(all_keys, int(self.num_ent() * self.smpl_ratio)) old_keys = set(all_keys) - set(new_keys) self.new_ent2id = {k: self.ent2id[k] for k in new_keys} self.old_ent2id = {k: self.ent2id[k] for k in old_keys} self.new_ent = list(self.new_ent2id.values()) self.old_ent = list(self.old_ent2id.values()) def num_ent(self): return len(self.ent2id) def num_rel(self): return len(self.rel2id) def triple2ids(self, triple): return [ self.get_ent_id(triple[0]), self.get_rel_id(triple[1]), self.get_ent_id(triple[2]), ] def ids2triple(self, ids): return "{0}\t{1}\t{2}\n".format( self.get_ent_str(ids[0]), self.get_rel_str(ids[1]), self.get_ent_str(ids[2]) ) def get_ent_id(self, ent): if not ent in self.ent2id: self.ent2id[ent] = len(self.ent2id) return self.ent2id[ent] def get_rel_id(self, rel): if not rel in self.rel2id: self.rel2id[rel] = len(self.rel2id) return self.rel2id[rel] def get_ent_str(self, e_id): for key, value in self.ent2id.items(): if value == e_id: return key def get_rel_str(self, r_id): for key, value in self.rel2id.items(): if value == r_id: return key if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "-dataset", default="YAGO3-10", type=str, help="wordnet dataset" ) parser.add_argument( "-smpl_ratio", default=0.2, type=float, help="new entities ratio" ) parser.add_argument( "-spl_ratio", default=0.5, type=float, help="new dataset split ratio" ) args = parser.parse_args() print("sample ratio: ", args.smpl_ratio) dataset_prep = DatasetPreprocess( args.dataset, smpl_ratio=args.smpl_ratio, spl_ratio=args.spl_ratio ) print("saving datasets...", args.dataset) dataset_prep.make_dataset() print("done!") ================================================ FILE: oos_lp/src/tester.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import numpy as np import torch from tqdm import tqdm from common.measure import Measure class OutKGTester: def __init__(self, dataset): self.dataset = dataset self.measure = Measure() self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.unseen_ent = self.get_unseen_entities() self.new_node_init = None def test(self, model_path, valid_or_test): if type(model_path) == "tuple" or type(model_path) == "str": self.model = torch.load(model_path) # removed module else: self.model = model_path # removed module self.model.to(self.device) # for bypassing torch DP self.model.eval() if hasattr(self.model, "reset"): self.model.reset() for i, new_ent in enumerate(tqdm(self.dataset.val_test_data[valid_or_test].keys())): self.new_node_init = None ent_triples = self.get_ent_triples(new_ent, valid_or_test) for j in range(len(ent_triples)): obs_triples = np.delete(ent_triples, [j], axis=0) target_triple = ent_triples[j] new_ent_id = self.dataset.init_num_ent self.predict(target_triple, new_ent_id, obs_triples) self.measure.normalize() self.measure.print_() return self.measure.mrr def predict(self, target_triple, new_ent, obs_triples): head_or_tail = "tail" if target_triple[0] == new_ent else "head" queries_ent, rel_id = self.create_queries( target_triple, head_or_tail, obs_triples ) rel_emb = self.model.get_rel_embs( torch.tensor(rel_id, dtype=torch.long).to(self.device) ) new_ent_emb = self.model.find_embedding(new_ent, obs_triples) queries_ent_emb = self.model.get_ent_embs( torch.tensor(queries_ent, dtype=torch.long).to(self.device) ) scores = self.model.cal_score(queries_ent_emb, new_ent_emb, rel_emb) rank = self.get_rank(scores) self.measure.update(rank) def filter_entities(self, obs_triples, rel_id, head_or_tail): idx = np.where(obs_triples[:, 1] == rel_id)[0] if head_or_tail == "head": return obs_triples[idx, 0] else: return obs_triples[idx, 2] def create_queries(self, target_triple, head_or_tail, obs_triples): head_id, rel_id, tail_id = target_triple ent_list = list(range(self.dataset.init_num_ent)) filtered_ent_list = self.filter_entities( obs_triples, rel_id, head_or_tail ).tolist() ent_list = set(ent_list) - set(filtered_ent_list) if head_or_tail == "head": ent_list = list(ent_list - set([head_id])) return [head_id] + ent_list, rel_id elif head_or_tail == "tail": ent_list = list(ent_list - set([tail_id])) return [tail_id] + ent_list, rel_id def get_rank(self, sim_scores): # assuming the test fact is the first one equals = ((sim_scores == sim_scores[0]).sum() - 1) // 2 higher = (sim_scores > sim_scores[0]).sum() rank = (equals + higher + 1.0).to(torch.float) return rank def get_unseen_entities(self): valid_ent = set(self.dataset.val_test_data["valid"].keys()) test_ent = set(self.dataset.val_test_data["test"].keys()) return valid_ent.union(test_ent) def get_ent_triples(self, ent, valid_or_test): triples = np.asarray(self.dataset.val_test_data[valid_or_test][ent]) h = np.where(triples[:, 0] == ent) triples[h, 0] = self.dataset.init_num_ent t = np.where(triples[:, 2] == ent) triples[t, 2] = self.dataset.init_num_ent return triples.astype(int) ================================================ FILE: oos_lp/src/trainer.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import torch from torch import nn from model.DisMultOutKG import DisMultOutKG from model.dm_tokenized import TokenizedDistMult from utils import save_model from tqdm import tqdm from tester import OutKGTester from pykeen.losses import NSSALoss import wandb class OutKGTrainer: def __init__(self, dataset, args, tokenizer): self.dataset = dataset self.args = args self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if self.args.tokenize: self.model = TokenizedDistMult(self.args, self.device, dataset, tokenizer) else: self.model = DisMultOutKG(self.dataset, self.args, self.device) #self.model = nn.DataParallel(self.model) if self.args.loss_fc == "spl": self.predict_loss = nn.Softplus() else: self.predict_loss = NSSALoss(margin=args.margin) if self.args.wandb: wandb.init(project="oog_token", entity='lilbert', reinit=True, settings=wandb.Settings(start_method='fork')) wandb.config.update(vars(self.args)) #self.tester = OutKGTester(dataset) def l2_loss(self): return self.model.l2_loss() # removed model.module def train(self, save=True): self.model.to(self.device) # for bypassing torch DataParallel self.model.train() if self.args.use_acc: initial_accumulator_value = 0.1 else: initial_accumulator_value = 0.0 if self.args.use_custom_reg: weight_decay = 0.0 else: weight_decay = self.args.reg_lambda if self.args.opt == "adagrad": print("using adagrad") optimizer = torch.optim.Adagrad( self.model.parameters(), lr=self.args.lr, weight_decay=weight_decay, initial_accumulator_value=initial_accumulator_value, # this is added because of the consistency to the original tensorflow code ) else: print("using adam") optimizer = torch.optim.Adam( self.model.parameters(), lr=self.args.lr, weight_decay=self.args.reg_lambda, ) print(f"Number of params: {sum(p.numel() for p in self.model.parameters())}") iters_per_update = self.args.simulated_batch_size // self.args.batch_size if iters_per_update < 1: raise ("Actual batch size smaller than batch size to be simulated.") else: print("iterations before the gradient step : ", iters_per_update) for epoch in tqdm(range(self.args.ne)): optimizer.zero_grad() last_batch = False total_loss = 0.0 pbar = tqdm(total=self.dataset.num_batch(self.args.batch_size)) num_iters = 1 while not last_batch: triples, l, new_ent_mask = self.dataset.next_batch( self.args.batch_size, neg_ratio=self.args.neg_ratio, device=self.device, ) last_batch = self.dataset.was_last_batch() if self.args.loss_fc == "spl": scores, predicted_emb = self.model(triples, new_ent_mask) predict_loss = torch.sum(self.predict_loss(-l * scores)) else: pos_batch = triples[:self.args.batch_size, ...] neg_batch = triples[self.args.batch_size:, ...] positive_scores, _ = self.model(pos_batch, None) negative_scores, _ = self.model(neg_batch, None) if self.args.neg_ratio > 1: positive_scores = positive_scores.repeat_interleave(self.args.neg_ratio) predict_loss = self.predict_loss(positive_scores, negative_scores) if self.args.use_custom_reg: if num_iters % iters_per_update == 0 or last_batch == True: l2_loss = ( self.args.reg_lambda * self.l2_loss() / ( self.dataset.num_batch_simulated( self.args.simulated_batch_size ) ) ) loss = predict_loss + l2_loss else: loss = predict_loss else: loss = predict_loss loss.backward() if num_iters % iters_per_update == 0 or last_batch == True: if last_batch: print("last batch triggered gradient update.") print( "remaining iters for gradient update :", num_iters % iters_per_update, ) optimizer.step() optimizer.zero_grad() total_loss += loss #print(num_iters) num_iters += 1 pbar.update(1) print( "Loss in iteration " + str(epoch) + ": " + str(total_loss.item() / num_iters) + "(" + self.dataset.dataset_name + ")" ) if self.args.eval_every > 0 and epoch % self.args.eval_every == 0: tester = OutKGTester(self.dataset) with torch.no_grad(): mrr = tester.test(self.model, valid_or_test="valid") mrr, mr, hits_1, hits_3, hits_10 = tester.measure.mrr, tester.measure.mr, tester.measure.hit1, tester.measure.hit3, tester.measure.hit10 wandb_log_dict = { 'step': epoch, 'loss': float(total_loss.item() / num_iters), 'val.mrr': mrr.item(), 'val.mr': mr.item(), 'val.hits_1': hits_1, 'val.hits_3': hits_3, 'val.hits_10': hits_10 } else: wandb_log_dict = {'step': epoch, 'loss': float(total_loss.item() / num_iters)} # log to wandb if self.args.wandb: wandb.log(wandb_log_dict) if epoch > 0 and epoch % self.args.save_each == 0 and save: print('save model...') save_model( self.model, self.args.model_name, self.args.emb_method, self.dataset.dataset_name, epoch, self.args.lr, self.args.reg_lambda, self.args.neg_ratio, self.args.emb_dim, ) print("===== TEST ======") test_tester = OutKGTester(self.dataset) with torch.no_grad(): mrr = test_tester.test(self.model, valid_or_test="test") mrr, mr, hits_1, hits_3, hits_10 = test_tester.measure.mrr, test_tester.measure.mr, test_tester.measure.hit1, test_tester.measure.hit3, test_tester.measure.hit10 if self.args.wandb: wandb_log_dict = { 'loss': float(total_loss.item() / num_iters), 'test.mrr': mrr.item(), 'test.mr': mr.item(), 'test.hits_1': hits_1, 'test.hits_3': hits_3, 'test.hits_10': hits_10 } wandb.log(wandb_log_dict) ================================================ FILE: oos_lp/src/utils.py ================================================ # Copyright (c) 2020-present, Royal Bank of Canada. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import os import numpy as np import torch def save_model( model, model_name, emb_method, dataset_name, chkpnt, lr, reg_lambda, neg_ratio, emb_dim, ): print("Saving the model") directory = "logs/" + model_name + "/" + emb_method + "/" + dataset_name + "/" os.makedirs(directory, exist_ok=True) torch.save( model, directory + str(chkpnt) + "_" + str(lr) + "_" + str(reg_lambda) + "_" + str(neg_ratio) + "_" + str(emb_dim) + ".chkpnt", ) def random_new_ent_mask(triples, mask_prob): """ Assign a random mask value to each triplet based on the given probability. mask_prob here corresponds to (1-psi) in Algorithm 2 (https://arxiv.org/pdf/2004.13230.pdf) if mask = 0, the head of the triplet is considered as an unobserved entity if mask = 2, the tail of the triplet is considered as an unobserved entity if mask = 1, both head and tail are observed """ probs = [(1 - mask_prob) / 2, mask_prob, (1 - mask_prob) / 2] return np.random.choice([0, 1, 2], triples.shape[0], p=probs) ================================================ FILE: oos_lp/src/vocab/nodepiece_encoder.py ================================================ import torch import numpy as np import random import torch.nn.functional as F from torch import nn from vocab.nodepiece_tokenizer import NodePiece_Tokenizer from torch.nn import TransformerEncoderLayer, TransformerEncoder from tqdm import tqdm from collections import defaultdict from typing import Optional from common.dataset import Dataset from argparse import Namespace class NodePieceEncoder(nn.Module): """ Pretty much the same thing as in link prediction / nodepiece_rotate """ def __init__(self, config: Namespace, tokenizer: NodePiece_Tokenizer, rel_embs: nn.Embedding, graph: Dataset, device: torch.device): super(NodePieceEncoder, self).__init__() self.tokenizer = tokenizer self.pooler = config.pooler self.policy = "sum" self.nearest = config.nearest_ancs self.sample_rels = config.sample_rels self.random_hashes = config.random_hashes self.subbatch = config.subbatch self.embedding_dim = config.emb_dim self.real_embedding_dim = self.embedding_dim // 2 self.max_seq_len = config.max_path_len self.sample_paths = config.sample_size self.use_distances = config.anc_dist self.hid_dim = config.t_hidden self.drop_prob = config.t_drop self.num_heads = config.t_heads self.num_layers = config.t_layers self.num_entities = graph.init_num_ent self.num_relations = len(graph.rel2id) self.device = device self.no_anc = config.no_anc if self.pooler == "cat": self.set_enc = nn.Sequential( nn.Linear(self.embedding_dim * (self.sample_paths + self.sample_rels), self.embedding_dim * 2), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.embedding_dim * 2, self.embedding_dim) ) if not self.ablate_anchors else nn.Sequential( nn.Linear(self.embedding_dim * self.sample_rels, self.embedding_dim * 2), nn.Dropout(self.drop_prob), nn.ReLU(), nn.Linear(self.embedding_dim * 2, self.embedding_dim) ) elif self.pooler == "trf": encoder_layer = TransformerEncoderLayer( d_model=self.embedding_dim, nhead=self.num_heads, dim_feedforward=self.hid_dim, dropout=self.drop_prob, ) self.set_enc = TransformerEncoder(encoder_layer=encoder_layer, num_layers=self.num_layers) self.tokenizer.token2id[self.tokenizer.NOTHING_TOKEN] = len(tokenizer.token2id) self.anchor_embeddings = nn.Embedding(len(tokenizer.token2id), embedding_dim=self.embedding_dim, padding_idx=self.tokenizer.token2id[tokenizer.PADDING_TOKEN]) self.relation_embeddings = rel_embs self.dist_emb = nn.Embedding(self.max_seq_len, embedding_dim=self.embedding_dim) if self.random_hashes == 0: if not self.nearest: # subsample paths, need to align them with distances sampled_paths = { entity: random.sample(paths, k=min(self.sample_paths, len(paths))) for entity, paths in self.tokenizer.vocab.items() } elif self.nearest: # sort paths by length first and take K of them prev_max_len = max(len(path) for k, v in self.tokenizer.vocab.items() for path in v) sampled_paths = { entity: sorted(paths, key=lambda x: len(x))[:min(self.sample_paths, len(paths))] for entity, paths in self.tokenizer.vocab.items() } self.max_seq_len = max(len(path) for k, v in sampled_paths.items() for path in v) print( f"Changed max seq len from {prev_max_len} to {self.max_seq_len} after keeping {self.sample_paths} shortest paths") hashes = [ [self.tokenizer.token2id[path[0]] for path in paths] + [ self.tokenizer.token2id[tokenizer.PADDING_TOKEN]] * (self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] distances = [ [len(path) - 1 for path in paths] + [0] * (self.sample_paths - len(paths)) for entity, paths in sampled_paths.items() ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.tensor(distances, dtype=torch.long, device=self.device) else: # in this case, we bypass distances and won't use relations in the encoder self.anchor_embeddings = nn.Embedding(self.random_hashes, embedding_dim=self.embedding_dim) hashes = [ random.sample(list(range(self.random_hashes)), self.sample_paths) for i in range(self.num_entities) ] self.hashes = torch.tensor(hashes, dtype=torch.long, device=self.device) self.distances = torch.zeros((self.num_entities, self.sample_paths), dtype=torch.long, device=self.device) if self.sample_rels > 0: pad_idx = self.num_relations e2r = defaultdict(set) for row in graph.train_data: e2r[int(row[0])].add(int(row[1])) len_stats = [len(v) for k, v in e2r.items()] print( f"Unique relations per node - min: {min(len_stats)}, avg: {np.mean(len_stats)}, 66th perc: {np.percentile(sorted(len_stats), 66)}, max: {max(len_stats)} ") unique_1hop_relations = [ random.sample(e2r[i], k=min(self.sample_rels, len(e2r[i]))) + [pad_idx] * ( self.sample_rels - min(len(e2r[i]), self.sample_rels)) for i in range(self.num_entities) ] self.unique_1hop_relations = torch.tensor(unique_1hop_relations, dtype=torch.long, device=self.device) def reset_parameters(self): if self.pooler != "avg": for module in self.set_enc.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() if self.pooler == "mlp": for module in self.set_dec.modules(): if module is self: continue if hasattr(module, "reset_parameters"): module.reset_parameters() torch.nn.init.xavier_uniform_(self.anchor_embeddings.weight) torch.nn.init.xavier_uniform_(self.dist_emb.weight) if self.random_hashes == 0: with torch.no_grad(): self.anchor_embeddings.weight[self.tokenizer.token2id[self.tokenizer.PADDING_TOKEN]] = torch.zeros(self.embedding_dim) self.dist_emb.weight[0] = torch.zeros(self.embedding_dim) def pool_anchors(self, anc_embs: torch.FloatTensor, mask: Optional[torch.BoolTensor] = None) -> torch.FloatTensor: """ input shape: (bs, num_anchors, emb_dim) output shape: (bs, emb_dim) """ if self.pooler == "cat": anc_embs = anc_embs.view(anc_embs.shape[0], -1) pooled = self.set_enc(anc_embs) if self.sample_paths != 1 else anc_embs elif self.pooler == "trf": pooled = self.set_enc(anc_embs.transpose(1, 0)) # output shape: (seq_len, bs, dim) pooled = pooled.mean(dim=0) # output shape: (bs, dim) return pooled def encode_by_index(self, entities: torch.LongTensor) -> torch.FloatTensor: hashes, dists = self.hashes[entities], self.distances[entities] anc_embs = self.anchor_embeddings(hashes) mask = None if self.use_distances: dist_embs = self.dist_emb(dists) anc_embs += dist_embs if self.no_anc: anc_embs = torch.tensor([], device=self.device) if self.sample_rels > 0: rels = self.unique_1hop_relations[entities] # (bs, rel_sample_size) rels = self.relation_embeddings(rels) # (bs, rel_sample_size, dim) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask) # (bs, dim) return anc_embs def get_all_representations(self): temp_embs = torch.zeros((len(self.hashes), self.embedding_dim), dtype=torch.float, device=self.device) vocab_keys = list(range(len(self.hashes))) for i in tqdm(range(0, len(self.hashes), self.subbatch)): entities = torch.tensor(vocab_keys[i: i+self.subbatch], dtype=torch.long, device=self.device) embs = self.encode_by_index(entities) temp_embs[i: i + self.subbatch, :] = embs return temp_embs def encode_by_hash(self, hashes: torch.LongTensor, distances: torch.LongTensor, rels: np.ndarray): anc_embs = self.anchor_embeddings(hashes.unsqueeze(0)) mask = None pad_idx = self.num_relations if self.use_distances: dist_embs = self.dist_emb(distances) anc_embs += dist_embs if self.sample_rels > 0: if len(rels) < self.sample_rels: rels = torch.cat( [torch.tensor(rels, dtype=torch.long, device=self.device), torch.tensor([pad_idx]*(self.sample_rels-len(rels)), dtype=torch.long, device=self.device) ]) elif len(rels) > self.sample_rels: rels = torch.tensor(np.random.permutation(rels)[:self.sample_rels], dtype=torch.long, device=self.device) else: rels = torch.tensor(rels, dtype=torch.long, device=self.device) rels = self.relation_embeddings(rels.unsqueeze(0)) # (bs, rel_sample_size, dim) anc_embs = torch.cat([anc_embs, rels], dim=1) # (bs, ancs+rel_sample_size, dim) anc_embs = self.pool_anchors(anc_embs, mask=mask) # (bs, dim) return anc_embs ================================================ FILE: oos_lp/src/vocab/nodepiece_tokenizer.py ================================================ # import torch # import numpy as np # import pickle # import multiprocessing as mp # import networkx as nx # from functools import partial # from igraph import Graph # # from torch import nn # from pykeen.triples import TriplesFactory # from typing import List, Dict, Union # from pathlib import Path # from tqdm import tqdm # from tqdm.contrib.concurrent import process_map, thread_map # from collections import Counter # #from utils.bpe import KG_BPE # import random # from common.dataset import Dataset # # #from tokenizers import Tokenizer, models, normalizers, pre_tokenizers, decoders, trainers, AddedToken # #from tokenizers.models import BPE, WordLevel, WordPiece # # # class NodePiece_Tokenizer: # # """ # Tokenizer for KGs: will select topK anchor nodes according to certain strategies; # and encode other nodes as 'words' as paths comprised of relations and anchor nodes # """ # # def __init__(self, # triples: Union[TriplesFactory, Dataset], # dataset_name: str, # num_anchors: int, # anchor_strategy: Dict[str, float], # num_paths: int, # betw_ratio: float = 0.01, # prune_freq: int = 0, # prune_dist_lim: int = 0, # prune_dist_op: str = 'lt', # bpe: bool = True, # bpe_merges: int = 100, # single_path: bool = False, # limit_shortest: int = 0, # relation2id: dict = None, # num_nodes: int = None, # ) -> None: # super().__init__() # # self.triples_factory = triples # self.dataset_name = dataset_name # self.num_anchors = num_anchors # self.anchor_strategy = anchor_strategy # self.num_paths = num_paths # self.betw_ratio = betw_ratio # self.sp_limit = limit_shortest # # self.NOTHING_TOKEN = -99 # self.CLS_TOKEN = -1 # self.MASK_TOKEN = -10 # self.PADDING_TOKEN = -100 # self.SEP_TOKEN = -2 # # self.prune_freq = prune_freq != 0 # self.common_threshold = prune_freq # # self.prune_dist_lim = prune_dist_lim # self.prune_dist_op = prune_dist_op # # self.apply_bpe = bpe # self.bpe_merges = bpe_merges # self.cache = {} # # self.single_path = single_path # Need that to finalize pruning # self.r2id = relation2id # self.num_nodes = num_nodes # # # # add inverses # # num_default_rels = len(relation2id) # # inverse_triples = torch.stack([triples[:, 2], triples[:, 1] + num_default_rels, triples[:, 0]]) # # self.triples_factory = torch.cat([self.triples_factory, inverse_triples], dim=0) # # self.r2id.update({ # # f"{k}_inverse": v+num_default_rels for k, v in self.r2id.items() # # }) # # # self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) # # assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" # assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) # # self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() # # self.token2id = {t: i for i, t in enumerate(self.top_entities)} # self.rel2token = {t: i + len(self.top_entities) for i, t in # enumerate(list(self.r2id.values()))} # self.vocab_size = len(self.token2id) + len(self.rel2token) # # # self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) # # # add identity for anchor nodes as the first / closest node # for anchor in self.top_entities[:-4]: # last 4 are always service tokens # self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] # # # # def tokenize_kg(self): # # strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" # # filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" # if self.sp_limit > 0: # filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths # filename += ".pkl" # self.model_name = filename.split('.pkl')[0] # path = Path(filename) # if path.is_file(): # anchors, non_anchors, vocab = pickle.load(open(path, "rb")) # return anchors, non_anchors, vocab # # ## TODO: find smth more scalable than networkx for larger graphs. UPD: let's use igraph # # src, tgt, rels = self.triples_factory[:, 0].astype(int), self.triples_factory[:, 2].astype(int), self.triples_factory[:, 1].astype(int) # edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] # graph = Graph(n=self.num_nodes, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) # # graph = nx.MultiDiGraph() # # for triple in self.triples_factory.mapped_triples: # # graph.add_edge(triple[0].item(), triple[2].item(), relation=triple[1].item()) # # # make sure inverse relations are already there, otherwise, uncomment: # # # graph.add_edge(triple[2].item(), triple[0].item(), relation=triple[1].item()+1) # # anchors = [] # for strategy, ratio in self.anchor_strategy.items(): # if ratio <= 0.0: # continue # topK = int(np.ceil(ratio * self.num_anchors)) # print(f"Computing the {strategy} nodes") # if strategy == "degree": # # top_nodes = sorted(graph.degree(), key=lambda x: x[1], reverse=True) # OLD NetworkX # top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) # elif strategy == "betweenness": # # This is O(V^3) - you don't want to compute that forever, so let's take 10% nodes approximation # # top_nodes = sorted(nx.betweenness_centrality(nx.Graph(graph), k=int(np.ceil(self.betw_ratio * self.triples_factory.num_entities))).items(), # # key=lambda x: x[1], # # reverse=True) # raise NotImplementedError("Betweenness is disabled due to computational costs") # elif strategy == "pagerank": # #top_nodes = sorted(nx.pagerank(nx.DiGraph(graph)).items(), key=lambda x: x[1], reverse=True) # top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) # elif strategy == "random": # top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.num_nodes))] # # selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] # # anchors.extend(selected_nodes) # print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") # # vocab = self.create_all_paths(graph, anchors) # top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] # non_core_entities = [i for i in range(self.num_nodes) if i not in anchors] # # pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) # print("Vocabularized and saved!") # # return top_entities, non_core_entities, vocab # # # def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: # # vocab = {} # print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} paths per node") # # # with mp.Pool() as pool: # # nodes = list(np.arange(self.triples_factory.num_entities)) # # f = partial(self.get_paths, top_entities=top_entities, graph=graph, nothing_token=self.NOTHING_TOKEN, max_paths=self.num_paths) # # #all_batches = tqdm(pool.imap(f, nodes)) # # all_batches = thread_map(f, nodes, chunksize=1000) # # vocab = {k: v for batch in all_batches for (k, v) in batch.items()} # # # TODO seems like a direct loop is faster than mp.Pool - consider graph-tool + OpenMP to be even faster # for i in tqdm(range(self.num_nodes)): # paths = graph.get_shortest_paths(v=i, to=top_entities, output="epath", mode='in') # if len(paths[0]) > 0: # relation_paths = [[graph.es[path[-1]].source] + [graph.es[k]['relation'] for k in path[::-1]] for path in paths if len(path) > 0] # else: # relation_paths = [[self.NOTHING_TOKEN] for _ in range(self.num_paths)] # if self.sp_limit > 0: # relation_paths = sorted(relation_paths, key=lambda x: len(x))[:self.sp_limit] # vocab[i] = relation_paths # # nodes = list(range(self.triples_factory.num_entities)) # # f = partial(self.igraph_path_mining, graph=Graph, anchors=top_entities, nothing_token=self.NOTHING_TOKEN, max_paths=self.num_paths) # # all_batches = thread_map(f, nodes, chunksize=1000) # # vocab = {k: v for batch in all_batches for (k, v) in batch.items()} # # return vocab # import torch import numpy as np import pickle from igraph import Graph from pykeen.triples import TriplesFactory from typing import List, Dict from pathlib import Path from tqdm import tqdm import random class NodePiece_Tokenizer: """ Tokenizer for KGs: will select topK anchor nodes according to certain strategies; and encode other nodes as 'words' as hashes comprised of relations and anchor nodes """ def __init__(self, triples: TriplesFactory, dataset_name: str, num_anchors: int, anchor_strategy: Dict[str, float], limit_shortest: int = 0, limit_random: int = 0, add_identity: bool = True, mode: str = "path", relation2id: dict = None, num_nodes: int = None, ) -> None: super().__init__() self.triples_factory = triples # original triples of the training graph, assuming inverses are added self.dataset_name = dataset_name self.num_anchors = num_anchors # total N anchor nodes self.anchor_strategy = anchor_strategy # ratios of strategies for sampling anchor nodes self.num_paths = num_anchors # aux variable self.sp_limit = limit_shortest # keep only K nearest anchor nodes per entity self.rand_limit = limit_random # keep only K random anchor nodes per entity if self.sp_limit * self.rand_limit != 0: raise Exception("-sp_limit and -rand_limit are mutually exclusive") self.add_identity = add_identity # anchor nodes will have their own indices with distance 0 in their hashes self.tkn_mode = mode # only "path" mode is implemented atm # auxiliary tokens for the vocabulary self.NOTHING_TOKEN = -99 # means the node is not reachable from any of anchor nodes self.CLS_TOKEN = -1 self.MASK_TOKEN = -10 self.PADDING_TOKEN = -100 self.SEP_TOKEN = -2 self.r2id = relation2id self.num_nodes = num_nodes # well, betwenness is disabled self.AVAILABLE_STRATEGIES = set(["degree", "betweenness", "pagerank", "random"]) assert sum(self.anchor_strategy.values()) == 1.0, "Ratios of strategies should sum up to one" assert set(self.anchor_strategy.keys()).issubset(self.AVAILABLE_STRATEGIES) # load or create the vocabulary self.top_entities, self.other_entities, self.vocab = self.tokenize_kg() # numerical indices for entities and relations self.token2id = {t: i for i, t in enumerate(self.top_entities)} self.rel2token = {t: i + len(self.top_entities) for i, t in enumerate(list(self.r2id.values()))} self.vocab_size = len(self.token2id) + len(self.rel2token) # although we don't use paths, we count their lengths for anchor distances self.max_seq_len = max([len(path) for k, v in self.vocab.items() for path in v]) if self.add_identity: # add identity for anchor nodes as the first / closest node for anchor in self.top_entities[:-4]: # last 4 are always service tokens self.vocab[anchor] = [[anchor]] + self.vocab[anchor][:-1] def tokenize_kg(self): # creating a filename strategy_encoding = f"d{self.anchor_strategy['degree']}_b{self.anchor_strategy['betweenness']}_p{self.anchor_strategy['pagerank']}_r{self.anchor_strategy['random']}" filename = f"data/{self.dataset_name}_{self.num_anchors}_anchors_{self.num_paths}_paths_{strategy_encoding}_pykeen" if self.sp_limit > 0: filename += f"_{self.sp_limit}sp" # for separating vocabs with limited mined shortest paths if self.rand_limit > 0: filename += f"_{self.rand_limit}rand" filename += ".pkl" self.model_name = filename.split('.pkl')[0] path = Path(filename) if path.is_file(): anchors, non_anchors, vocab = pickle.load(open(path, "rb")) return anchors, non_anchors, vocab # create an input object for iGraph - edge list with relation types, and then create a graph src, tgt, rels = self.triples_factory[:, 0].astype(int), self.triples_factory[:, 2].astype(int), self.triples_factory[:, 1].astype(int) edgelist = [[s, t] for s, t, r in zip(src, tgt, rels)] graph = Graph(n=self.num_nodes, edges=edgelist, edge_attrs={'relation': list(rels)}, directed=True) # sampling anchor nodes anchors = [] for strategy, ratio in self.anchor_strategy.items(): if ratio <= 0.0: continue topK = int(np.ceil(ratio * self.num_anchors)) print(f"Computing the {strategy} nodes") if strategy == "degree": top_nodes = sorted([(i, n) for i, n in enumerate(graph.degree())], key=lambda x: x[1], reverse=True) elif strategy == "betweenness": # This is O(V^3) - disabled raise NotImplementedError("Betweenness is disabled due to computational costs") elif strategy == "pagerank": top_nodes = sorted([(i, n) for i, n in enumerate(graph.personalized_pagerank())], key=lambda x: x[1], reverse=True) elif strategy == "random": top_nodes = [(int(k), 1) for k in np.random.permutation(np.arange(self.triples_factory.num_entities))] selected_nodes = [node for node, d in top_nodes if node not in anchors][:topK] anchors.extend(selected_nodes) print(f"Added {len(selected_nodes)} nodes under the {strategy} strategy") # now mine the anchors per node vocab = self.create_all_paths(graph, anchors) top_entities = anchors + [self.CLS_TOKEN] + [self.MASK_TOKEN] + [self.PADDING_TOKEN] + [self.SEP_TOKEN] non_core_entities = [i for i in range(self.triples_factory.num_entities) if i not in anchors] pickle.dump((top_entities, non_core_entities, vocab), open(filename, "wb")) print("Vocabularized and saved!") return top_entities, non_core_entities, vocab def create_all_paths(self, graph: Graph, top_entities: List = None) -> Dict[int, List]: vocab = {} if self.rand_limit == 0: print(f"Computing the entity vocabulary - paths, retaining {self.sp_limit if self.sp_limit >0 else self.num_paths} shortest paths per node") else: print(f"Computing the entity vocabulary - paths, retaining {self.rand_limit} random paths per node") # single-threaded mining is found to be as fast as multi-processing + igraph for some reason, so let's use a dummy for-loop for i in tqdm(range(self.triples_factory.num_entities)): if self.tkn_mode == "path": paths = graph.get_shortest_paths(v=i, to=top_entities, output="epath", mode='in') if len(paths[0]) > 0: relation_paths = [[graph.es[path[-1]].source] + [graph.es[k]['relation'] for k in path[::-1]] for path in paths if len(path) > 0] else: # if NO anchor can be reached from the node - encode with a special NOTHING_TOKEN relation_paths = [[self.NOTHING_TOKEN] for _ in range(self.num_paths)] if self.sp_limit > 0: relation_paths = sorted(relation_paths, key=lambda x: len(x))[:self.sp_limit] if self.rand_limit > 0: random.shuffle(relation_paths) relation_paths = relation_paths[:self.rand_limit] vocab[i] = relation_paths else: raise NotImplementedError return vocab ================================================ FILE: readme.md ================================================ # NodePiece - Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

NodePiece is a "tokenizer" for reducing entity vocabulary size in knowledge graphs. Instead of shallow embedding every node to a vector, we first "tokenize" each node by K anchor nodes and M relation types in its relational context. Then, the resulting hash sequence is encoded through any injective function, e.g., MLP or Transformer. Similar to Byte-Pair Encoding and WordPiece tokenizers commonly used in NLP, NodePiece can tokenize unseen nodes attached to the seen graph using the same anchor and relation vocabulary, which allows NodePiece to work out-of-the-box in the inductive settings using all the well-known scoring functions in the classical KG completion (like TransE or RotatE). NodePiece also works with GNNs (we tested on node classification, but not limited to it, of course). ## Updates * **NodePiece is now also implemented in [PyKEEN](https://github.com/pykeen/pykeen)!** Try it with 40+ KGE models on 30+ datasets! * **01.02.2022: New Inductive Link Prediction task!** * **20.01.2022: Accepted to ICLR 2022** 🎉 * **25.12.2021: A NodePiece-powered model is Top-1 on the [OGB WikiKG 2](https://ogb.stanford.edu/docs/leader_linkprop/#ogbl-wikikg2)** * **17.07.2021: NodePiece for OGB WikiKG 2 (2.5M nodes)** ## NodePiece source code The repo contains the code and experimental setups for reproducibility studies. Each experiment resides in the respective folder: * LP_RP - link prediction and relation prediction * NC - node classification * OOS_LP - out-of-sample link prediction * ogb - NodePiece version for the OGB WikiKG 2 benchmark * inductive_lp - inductive link prediction The repo is based on Python 3.8. `wandb` is an optional requirement in case you have an existing account there and would like to track experimental results. If you have a `wandb` account, the repo assumes you've performed ``` wandb login ``` Using a GPU is recommended. First, run a script which will download all the necessary pre-processed data and datasets. It takes approximately 1 GB. ``` sh download_data.sh ``` We packed the pre-processed data for faster experimenting with the repo. Note that there are two NodePiece tokenization **modes** (`-tkn_mode [option]`): `path` and `bfs`: * `path` is an old tokenization strategy (based on finding shortest paths between each node and all anchors) under which we performed the experiments and packed the data for reproducibility; * `bfs` is a new strategy (based on iterative expansion of node's neighborhood until a desired number of anchors is reached) which is 5-50x faster and takes 4-5x less space depending on the KG. Currently, works for transductive LP/RP tasks; Pre-processing times tested on M1 MacBook Pro / 8 GB: | mode | FB15k-237 / vocab size | WN18RR / vocab size | YAGO 3-10 / vocab size | | ----- | ------ | ------ | ------ | | `path` | 2 min / 28 MB | 5 min / 140 MB | ~ 5 hours / 240 MB | | `bfs` | 8 sec / 7.5 MB | 30 sec / 20 MB | 4.5 min / 40 MB | CoDEx-Large and YAGO `path` pre-processing is better run on a server with 16-32 GB RAM and might take 2-5 hours depending on the chosen number of anchors. NB: we seek to further improve the algorithms to make the tokenization process even faster than the `bfs` strategy. Second, install the dependencies in `requirements.txt`. Note that when installing Torch-Geometric you might want to use pre-compiled binaries for a certain version of python and torch. Check the manual [here](https://github.com/rusty1s/pytorch_geometric). In the link prediction tasks, all the necessary datasets will be downloaded upon first script execution. ## Link Prediction The link prediction (LP) and relation prediction (RP) tasks use models, datasets, and evaluation protocols from [PyKEEN](https://github.com/pykeen/pykeen). Navigate to the `lp_rp` folder: `cd lp_rp`. The list of CLI params can be found in `run_lp.py`. * Run the fb15k-237 experiment ``` python run_lp.py -loop lcwa -loss bce -b 512 -data fb15k237 -anchors 1000 -sp 100 -lr 0.0005 -ft_maxp 20 -pool cat -embedding 200 -sample_rels 15 -smoothing 0.4 -epochs 401 ``` * Run the wn18rr experiment ``` python run_lp.py -loop slcwa -loss nssal -margin 15 -b 512 -data wn18rr -anchors 500 -sp 100 -lr 0.0005 -ft_maxp 50 -pool cat -embedding 200 -negs 20 -subbatch 2000 -sample_rels 4 -epochs 601 ``` * Run the codex-l experiment ``` python run_lp.py -loop lcwa -loss bce -b 256 -data codex_l -anchors 7000 -sp 100 -lr 0.0005 -ft_maxp 20 -pool cat -embedding 200 -subbatch 10000 -sample_rels 6 -smoothing 0.3 -epochs 120 ``` * Run the yago 3-10 experiment ``` python run_lp.py -loop slcwa -loss nssal -margin 50 -b 512 -data yago -anchors 10000 -sp 100 -lr 0.00025 -ft_maxp 20 -pool cat -embedding 200 -subbatch 2000 -sample_rels 5 -negs 10 -epochs 601 ``` ### Test evaluation reproducibility patch PyKEEN 1.0.5 used in this repo has been identified to have issues at the filtering stage when evaluating on the test set. In order to fully reproduce the reported test set numbers for transductive LP/RP experiments from the paper and resolve this issue, please apply the patch from the `lp_rp/patch` folder: 1. Locate pykeen in your environment installation: ``` /lib/python3./site-packages/pykeen ``` 2. Replace the `evaluation/evaluator.py` with the one from the `patch` folder ```bash cp ./lp_rp/patch/evaluator.py /lib/python3./site-packages/pykeen/evaluation/ ``` 3. Replace the `stoppers/early_stopping.py` with the one from the `patch` folder ```bash cp ./lp_rp/patch/early_stopping.py /lib/python3./site-packages/pykeen/stoppers/ ``` This won't be needed once we port the codebase to newest versions of PyKEEN (1.4.0+) where this was fixed ## Inductive Link Prediction The task is coined in the paper by Teru et al [Inductive relation prediction by subgraph reasoning](https://arxiv.org/abs/1911.06962). Training is executed on one graph, but inference link prediction is executed on a completely new graph with unseen entities (but known relations). NodePiece is inductive out-of-the-box and can be used for this task exactly in the same way as in the transductive link prediction tasks! See more details in the respective [README](/inductive_lp/README.md) to run inductive LP experiments. ## Open Graph Benchmark (OGB) NodePiece made it to the tops of the [OGB WikiKG 2 leaderboard](https://ogb.stanford.edu/docs/leader_linkprop/#ogbl-wikikg2) powering models with a dramatically reduced parameter budget (7M vs 500-1500M). Instead of learning 2.5M entity embeddings, the current configuration needs only 20K anchor nodes. See the [README](/ogb/readme.md) in the respective folder to get the precomputed vocabulary and for more details. Our results on NodePiece + AutoSF from the paper:
Model Params Node Tokenization Test MRR
NodePiece 6.9M 20 anchors + 12 relations 0.570
- w/o relations 5.9M 20 anchors 0.592
- w/o anchors 1.3M 12 relations 0.476
## Relation Prediction The setup is very similar to that of link prediction (LP) but we predict relations `(h,?,t)` now. Navigate to the `lp_rp` folder: `cd lp_rp`. The list of CLI params can be found in `run_lp.py` * Run the fb15k-237 experiment ``` python run_lp.py -loop slcwa -loss nssal -b 512 -data fb15k237 -anchors 1000 -sp 100 -lr 0.0005 -ft_maxp 20 -margin 15 -subbatch 2000 -pool cat -embedding 200 -negs 20 -sample_rels 15 -epochs 21 --rel-prediction True ``` * Run the wn18rr experiment ``` python run_lp.py -loop slcwa -loss nssal -b 512 -data wn18rr -anchors 500 -sp 100 -lr 0.0005 -ft_maxp 50 -margin 12 -subbatch 2000 -pool cat -embedding 200 -negs 20 -sample_rels 4 -epochs 151 --rel-prediction True ``` * Run the yago 3-10 experiment ``` python run_lp.py -loop slcwa -loss nssal -b 512 -data yago -anchors 10000 -sp 100 -lr 0.0005 -ft_maxp 20 -margin 25 -subbatch 2000 -pool cat -embedding 200 -negs 20 -sample_rels 5 -epochs 7 --rel-prediction True ``` ## Node Classification Navigate to the `nc` folder: `cd nc` . The list of CLI params can be found in `run_nc.py` If you have a GPU, use `DEVICE cuda` otherwise `DEVICE cpu`. The run on 5% of labeled data: ``` python run_nc.py DATASET wd50k MAX_QPAIRS 3 STATEMENT_LEN 3 LABEL_SMOOTHING 0.1 EVAL_EVERY 5 DEVICE cpu WANDB False EPOCHS 4001 GCN_HID_DROP2 0.5 GCN_HID_DROP 0.5 GCN_FEAT_DROP 0.5 EMBEDDING_DIM 100 GCN_GCN_DIM 100 LEARNING_RATE 0.001 GCN_ATTENTION True GCN_GCN_DROP 0.3 GCN_ATTENTION_DROP 0.3 GCN_LAYERS 3 DS_TYPE transductive MODEL_NAME stare TR_RATIO 0.05 USE_FEATURES False TOKENIZE True NUM_ANCHORS 50 MAX_PATHS 10 USE_TEST True ``` The run on 10% of labeled data: ``` python run_nc.py DATASET wd50k MAX_QPAIRS 3 STATEMENT_LEN 3 LABEL_SMOOTHING 0.1 EVAL_EVERY 5 DEVICE cpu WANDB False EPOCHS 4001 GCN_HID_DROP2 0.5 GCN_HID_DROP 0.5 GCN_FEAT_DROP 0.5 EMBEDDING_DIM 100 GCN_GCN_DIM 100 LEARNING_RATE 0.001 GCN_ATTENTION True GCN_GCN_DROP 0.3 GCN_ATTENTION_DROP 0.3 GCN_LAYERS 3 DS_TYPE transductive MODEL_NAME stare TR_RATIO 0.1 USE_FEATURES False TOKENIZE True NUM_ANCHORS 50 MAX_PATHS 10 USE_TEST True ``` ## Out-of-sample Link Prediction Navigate to the `oos_lp` folder: `cd oos_lp/src`. The list of CLI params can be found in `main.py`. * Run the oos fb15k-237 experiment ``` python main.py -dataset FB15k-237 -model_name DM_NP_fb -ne 41 -lr 0.0005 -emb_dim 200 -batch_size 256 -simulated_batch_size 256 -save_each 100 -tokenize True -opt adam -pool trf -use_custom_reg False -reg_lambda 0.0 -loss_fc spl -margin 15 -neg_ratio 5 -wandb False -eval_every 20 -anchors 1000 -sample_rels 15 ``` * Run the oos yago3-10 experiment ``` python main.py -dataset YAGO3-10 -model_name DM_NP_yago -ne 41 -lr 0.0005 -emb_dim 200 -batch_size 256 -simulated_batch_size 256 -save_each 100 -tokenize True -opt adam -pool trf -use_custom_reg False -reg_lambda 0.0 -loss_fc spl -margin 15 -neg_ratio 5 -wandb False -eval_every 20 -anchors 10000 -sample_rels 5 ``` ## Citation If you find this work useful, please consider citing the paper: ``` @inproceedings{ galkin2022nodepiece, title={NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs}, author={Mikhail Galkin and Etienne Denis and Jiapeng Wu and William L. Hamilton}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=xMJWUKJnFSw} } ``` ================================================ FILE: requirements.txt ================================================ torch==1.7.1 pykeen==1.0.5 click torch-geometric==1.6.3 torch_sparse==0.6.8 torch-scatter==2.0.5 torch-cluster==1.5.8 torch-spline-conv==1.2.0 pystow==0.1.0 einops python-igraph tqdm wandb