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Repository: fanyangxyz/Neural-LP
Branch: master
Commit: 239af3bc907c
Files: 42
Total size: 29.1 MB
Directory structure:
gitextract_9c3p2_6k/
├── .gitignore
├── LICENSE
├── README.md
├── datasets/
│ ├── README.md
│ ├── family/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── fb15k-237/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── kinship/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── umls/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ └── wn-18/
│ ├── entities.txt
│ ├── facts.txt
│ ├── relations.txt
│ ├── test.txt
│ ├── train.txt
│ └── valid.txt
├── eval/
│ ├── collect_all_facts.sh
│ ├── evaluate.py
│ └── get_truths.py
└── src/
├── data.py
├── experiment.py
├── main.py
├── model.py
└── utils.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.pyc
*.pckl
datasets/*/all.txt
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2017 Fan Yang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Neural LP
This is the implementation of Neural Logic Programming, proposed in the following paper:
[Differentiable Learning of Logical Rules for Knowledge Base Reasoning](https://arxiv.org/abs/1702.08367).
Fan Yang, Zhilin Yang, William W. Cohen.
NIPS 2017.
## Requirements
- Python 2.7
- Numpy
- Tensorflow 1.0.1
## Quick start
The following command starts training a dataset about family relations, and stores the experiment results in the folder `exps/demo/`.
```
python src/main.py --datadir=datasets/family --exps_dir=exps/ --exp_name=demo
```
Wait for around 8 minutes, navigate to `exps/demo/`, there is `rules.txt` that contains learned logical rules.
## Evaluation
To evaluate the prediction results, follow the steps below. The first two steps is preparation so that we can compute _filtered_ ranks (see [TransE](https://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf) for details).
We use the experiment from Quick Start as an example. Change the folder names (datasets/family, exps/dev) for other experiments.
```
. eval/collect_all_facts.sh datasets/family
python eval/get_truths.py datasets/family
python eval/evaluate.py --preds=exps/demo/test_predictions.txt --truths=datasets/family/truths.pckl
```
================================================
FILE: datasets/README.md
================================================
All data are preprocessed and ready to use for Neural-LP. The major step in preprocessing is partitioning the original train file into facts.txt and train.txt. More details can be found in the paper.
================================================
FILE: datasets/family/entities.txt
================================================
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================================================
FILE: datasets/family/facts.txt
================================================
7 aunt 72
331 aunt 337
733 aunt 2115
2780 aunt 2811
467 aunt 2693
2861 aunt 1630
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1000 aunt 828
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45 aunt 224
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12 aunt 71
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58 brother 61
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418 brother 414
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1400 brother 1403
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2629 brother 2631
1547 brother 1512
1265 brother 1271
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2083 brother 2086
1563 brother 1565
834 brother 2867
1757 brother 1755
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565 brother 2655
889 brother 888
522 brother 523
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1108 brother 1107
698 brother 702
337 brother 334
10 brother 5
283 brother 284
328 brother 329
521 brother 520
2870 brother 2869
1148 brother 1149
1005 brother 999
2889 brother 2887
1187 brother 1190
1696 brother 2975
83 brother 39
133 brother 212
652 brother 653
412 brother 414
423 brother 426
1235 brother 1241
2665 brother 2666
710 brother 711
2608 brother 2607
568 brother 1167
1944 brother 1942
445 brother 449
50 brother 49
1664 brother 1665
2465 brother 127
178 brother 177
2029 brother 2031
843 brother 842
767 brother 766
543 brother 541
655 brother 1691
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399 daughter 779
709 daughter 707
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433 daughter 409
999 daughter 991
1398 daughter 1516
433 daughter 27
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2996 daughter 2999
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2544 daughter 2540
1273 daughter 1261
2837 daughter 2832
1163 daughter 343
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699 daughter 692
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183 daughter 364
93 daughter 368
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292 daughter 2982
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138 daughter 2614
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830 daughter 774
154 daughter 40
1263 daughter 1261
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79 daughter 22
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714 daughter 707
657 daughter 655
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2742 daughter 449
1434 daughter 2101
716 daughter 715
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595 daughter 484
1295 daughter 2689
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332 daughter 2143
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2834 daughter 2833
1386 daughter 1380
1987 daughter 1964
111 daughter 981
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326 daughter 321
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224 daughter 347
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2100 daughter 841
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2769 daughter 2737
733 daughter 725
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812 daughter 2946
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393 daughter 795
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538 daughter 521
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333 daughter 323
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2714 daughter 2713
39 daughter 22
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428 daughter 412
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726 daughter 738
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734 daughter 726
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136 daughter 132
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241 daughter 239
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2941 daughter 2942
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270 daughter 264
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98 daughter 94
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2752 daughter 2749
174 daughter 146
228 daughter 353
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305 daughter 303
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1283 daughter 1281
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118 daughter 117
233 daughter 76
106 daughter 66
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2763 daughter 2747
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628 daughter 627
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131 daughter 2147
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528 daughter 97
113 daughter 111
228 daughter 354
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127 daughter 2486
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571 daughter 568
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2792 daughter 2785
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1059 daughter 121
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342 daughter 2141
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1921 daughter 1923
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389 daughter 791
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539 daughter 521
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338 daughter 332
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2932 daughter 2929
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1358 daughter 1356
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373 daughter 375
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2554 daughter 417
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354 daughter 355
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74 daughter 20
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2152 daughter 1707
1156 daughter 637
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1628 daughter 2549
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1220 daughter 2075
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728 daughter 726
1318 daughter 1316
938 daughter 936
2780 daughter 2779
2753 daughter 2745
2587 daughter 2586
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473 daughter 1065
317 daughter 89
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215 daughter 131
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454 daughter 453
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2662 daughter 2664
2616 daughter 2615
428 daughter 427
331 daughter 322
1539 daughter 1774
2337 daughter 2342
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2421 daughter 2131
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2901 daughter 1698
1000 daughter 991
45 daughter 349
157 daughter 2682
502 daughter 155
144 daughter 141
118 daughter 7
2788 daughter 2780
2319 daughter 2393
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153 nephew 227
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353 nephew 1029
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gitextract_9c3p2_6k/
├── .gitignore
├── LICENSE
├── README.md
├── datasets/
│ ├── README.md
│ ├── family/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── fb15k-237/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── kinship/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ ├── umls/
│ │ ├── entities.txt
│ │ ├── facts.txt
│ │ ├── relations.txt
│ │ ├── test.txt
│ │ ├── train.txt
│ │ └── valid.txt
│ └── wn-18/
│ ├── entities.txt
│ ├── facts.txt
│ ├── relations.txt
│ ├── test.txt
│ ├── train.txt
│ └── valid.txt
├── eval/
│ ├── collect_all_facts.sh
│ ├── evaluate.py
│ └── get_truths.py
└── src/
├── data.py
├── experiment.py
├── main.py
├── model.py
└── utils.py
SYMBOL INDEX (59 symbols across 6 files)
FILE: eval/evaluate.py
function evaluate (line 10) | def evaluate():
FILE: src/data.py
function resplit (line 7) | def resplit(train, facts, no_link_percent):
class Data (line 46) | class Data(object):
method __init__ (line 47) | def __init__(self, folder, seed, type_check, domain_size, no_extra_fac...
method _create_parser (line 120) | def _create_parser(self):
method _parse_domains_file (line 143) | def _parse_domains_file(self, file_name):
method _numerical_encode (line 171) | def _numerical_encode(self):
method _parse_triplets (line 187) | def _parse_triplets(self, file):
method _split_valid_from_train (line 199) | def _split_valid_from_train(self):
method _db_to_matrix_db (line 211) | def _db_to_matrix_db(self, db):
method _combine_two_mdbs (line 223) | def _combine_two_mdbs(self, mdbA, mdbB):
method _count_batch (line 236) | def _count_batch(self, samples, batch_size):
method reset (line 242) | def reset(self, batch_size):
method train_resplit (line 256) | def train_resplit(self, no_link_percent):
method _subset_of_matrix_db (line 263) | def _subset_of_matrix_db(self, matrix_db, domain):
method _augment_with_reverse (line 269) | def _augment_with_reverse(self, triplets):
method _next_batch (line 277) | def _next_batch(self, start, size, samples):
method _triplet_to_feed (line 296) | def _triplet_to_feed(self, triplets):
method next_test (line 300) | def next_test(self):
method next_valid (line 315) | def next_valid(self):
method next_train (line 330) | def next_train(self):
class DataPlus (line 350) | class DataPlus(Data):
method __init__ (line 351) | def __init__(self, folder, seed):
method _numerical_encode (line 394) | def _numerical_encode(self, file_name):
method _parse_examples (line 399) | def _parse_examples(self, file_name):
method _parse_facts (line 407) | def _parse_facts(self, file_name):
method _create_parser (line 415) | def _create_parser(self):
method is_true (line 432) | def is_true(self, q, h, t):
FILE: src/experiment.py
class Experiment (line 10) | class Experiment():
method __init__ (line 28) | def __init__(self, sess, saver, option, learner, data):
method one_epoch (line 51) | def one_epoch(self, mode, num_batch, next_fn):
method one_epoch_train (line 79) | def one_epoch_train(self):
method one_epoch_valid (line 88) | def one_epoch_valid(self):
method one_epoch_test (line 96) | def one_epoch_test(self):
method early_stop (line 102) | def early_stop(self):
method train (line 113) | def train(self):
method get_predictions (line 138) | def get_predictions(self):
method get_attentions (line 204) | def get_attentions(self):
method get_rules (line 245) | def get_rules(self):
method get_vocab_embedding (line 275) | def get_vocab_embedding(self):
method close_log_file (line 287) | def close_log_file(self):
FILE: src/main.py
class Option (line 11) | class Option(object):
method __init__ (line 12) | def __init__(self, d):
method save (line 14) | def save(self):
function main (line 20) | def main():
FILE: src/model.py
class Learner (line 5) | class Learner(object):
method __init__ (line 15) | def __init__(self, option):
method _random_uniform_unit (line 44) | def _random_uniform_unit(self, r, c):
method _clip_if_not_None (line 51) | def _clip_if_not_None(self, g, v, low, high):
method _build_input (line 59) | def _build_input(self):
method _build_graph (line 86) | def _build_graph(self):
method _run_graph (line 210) | def _run_graph(self, sess, qq, hh, tt, mdb, to_fetch):
method update (line 228) | def update(self, sess, qq, hh, tt, mdb):
method predict (line 233) | def predict(self, sess, qq, hh, tt, mdb):
method get_predictions_given_queries (line 238) | def get_predictions_given_queries(self, sess, qq, hh, tt, mdb):
method get_attentions_given_queries (line 243) | def get_attentions_given_queries(self, sess, queries):
method get_vocab_embedding (line 253) | def get_vocab_embedding(self, sess):
FILE: src/utils.py
function list_rules (line 6) | def list_rules(attn_ops, attn_mems, the):
function print_rules (line 45) | def print_rules(q_id, rules, parser, query_is_language):
Copy disabled (too large)
Download .json
Condensed preview — 42 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (15,538K chars).
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"path": "LICENSE",
"chars": 1065,
"preview": "MIT License\n\nCopyright (c) 2017 Fan Yang\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\no"
},
{
"path": "README.md",
"chars": 1269,
"preview": "# Neural LP\n\nThis is the implementation of Neural Logic Programming, proposed in the following paper:\n\n[Differentiable L"
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"path": "datasets/family/valid.txt",
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"path": "datasets/fb15k-237/relations.txt",
"chars": 14684,
"preview": "/american_football/football_team/current_roster./sports/sports_team_roster/position\n/award/award_category/category_of\n/a"
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"chars": 1519499,
"preview": "/m/08966\t/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month\t/m/05lf_\r\n/m/01hww_\t/music/"
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"chars": 4983701,
"preview": "/m/0g56t9t\t/film/film/other_crew./film/film_crew_gig/film_crew_role\t/m/02r96rf\n/m/025vw4t\t/people/person/nationality\t/m/"
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"path": "datasets/umls/entities.txt",
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},
{
"path": "datasets/umls/facts.txt",
"chars": 199310,
"preview": "Body_Location_or_Region\tAdjacent_to\tBody_Space_or_Junction\nBody_Part_Organ_or_Organ_Component\tAdjacent_to\tCell\nTissue\tAd"
},
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"path": "datasets/umls/relations.txt",
"chars": 531,
"preview": "Manifestation_of\nEvaluation_of\nPerforms\nIngredient_of\nLocation_of\nAffects\nContains\nExhibits\nAssociated_with\nCo-occurs_wi"
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"path": "datasets/umls/test.txt",
"chars": 31572,
"preview": "Health_Care_Related_Organization\tProduces\tIntellectual_Product\nVitamin\tComplicates\tMental_or_Behavioral_Dysfunction\nBody"
},
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"path": "datasets/umls/train.txt",
"chars": 66720,
"preview": "Injury_or_Poisoning\tIssue_in\tBiomedical_Occupation_or_Discipline\nResearch_Activity\tMeasures\tClinical_Attribute\nSocial_Be"
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"path": "eval/collect_all_facts.sh",
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"preview": "folder=$1\n\npushd $folder\ncat train.txt facts.txt valid.txt test.txt > all.txt\npopd\n"
},
{
"path": "eval/evaluate.py",
"chars": 2673,
"preview": "import numpy as np\nimport os\nimport sys\nimport pickle\nimport argparse\nimport time\nfrom collections import Counter, defau"
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{
"path": "eval/get_truths.py",
"chars": 713,
"preview": "import sys\nimport os\nimport pickle\nfrom collections import defaultdict\n\nfolder_name = sys.argv[1]\nall_file = os.path.joi"
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{
"path": "src/data.py",
"chars": 18565,
"preview": "import numpy as np \nimport os\nimport copy\nfrom math import ceil\nfrom collections import Counter\n "
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{
"path": "src/experiment.py",
"chars": 12628,
"preview": "import sys\nimport os\nimport time\nimport pickle\nfrom collections import Counter\nimport numpy as np\nfrom utils import list"
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"chars": 5826,
"preview": "import os\nimport argparse\nimport time\nimport tensorflow as tf\nimport numpy as np\nfrom model import Learner\nfrom data imp"
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{
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"chars": 12132,
"preview": "import numpy as np \nimport tensorflow as tf\n\n\nclass Learner(object):\n \"\"\"\n This class builds a computation graph t"
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"chars": 3108,
"preview": "import numpy as np\nimport sys\nimport os\n\n\ndef list_rules(attn_ops, attn_mems, the):\n \"\"\"\n Given attentions over op"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the fanyangxyz/Neural-LP GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 42 files (29.1 MB), approximately 3.6M tokens, and a symbol index with 59 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.