Repository: hugochan/BAMnet
Branch: master
Commit: b693616a9241
Files: 34
Total size: 169.2 KB
Directory structure:
gitextract_pbgz21hk/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── src/
├── build_all_data.py
├── build_pretrained_w2v.py
├── config/
│ ├── bamnet_webq.yml
│ └── entnet_webq.yml
├── core/
│ ├── __init__.py
│ ├── bamnet/
│ │ ├── __init__.py
│ │ ├── bamnet.py
│ │ ├── ent_modules.py
│ │ ├── entnet.py
│ │ ├── modules.py
│ │ └── utils.py
│ ├── build_data/
│ │ ├── __init__.py
│ │ ├── build_all.py
│ │ ├── build_data.py
│ │ ├── freebase.py
│ │ ├── utils.py
│ │ └── webquestions.py
│ ├── config.py
│ └── utils/
│ ├── __init__.py
│ ├── freebase_utils.py
│ ├── generic_utils.py
│ ├── metrics.py
│ └── utils.py
├── joint_test.py
├── run_freebase.py
├── run_webquestions.py
├── test.py
├── test_entnet.py
├── train.py
└── train_entnet.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
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================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# BAMnet
Code & data accompanying the NAACL2019 paper ["Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases"](https://arxiv.org/abs/1903.02188)
## Get started
### Prerequisites
This code is written in python 3. You will need to install a few python packages in order to run the code.
We recommend you to use `virtualenv` to manage your python packages and environments.
Please take the following steps to create a python virtual environment.
* If you have not installed `virtualenv`, install it with ```pip install virtualenv```.
* Create a virtual environment with ```virtualenv venv```.
* Activate the virtual environment with `source venv/bin/activate`.
* Install the package requirements with `pip install -r requirements.txt`.
### Run the KBQA system
* Download the preprocessed data from [here](https://1drv.ms/u/s!AjiSpuwVTt09gSE2niFGjdIVsqA7?e=PEf6sT) and put the data folder under the root directory.
* Create a folder (e.g., `runs/WebQ/`) to save model checkpoint. You can download the pretrained models from [here](https://1drv.ms/u/s!AjiSpuwVTt09gSLcnrp0GyKtpWBg?e=DtqYt8). (Note: if you cannot access the above data and pretrained models, please download from [here](http://academic.hugochan.net/download/BAMnet-WebQ.zip).)
* Please modify the config files in the `src/config/` folder to suit your needs. Note that you can start with modifying only the data folder (e.g., `data_dir`, `model_file`, `pre_word2vec`) and vocab size (e.g., `vocab_size`, `num_ent_types`, `num_relations`), and leave other hyperparameters as they are.
* Go to the `BAMnet/src` folder, train the BAMnet model
```
python train.py -config config/bamnet_webq.yml
```
* Test the BAMnet model (with ground-truth topic entity)
```
python test.py -config config/bamnet_webq.yml
```
* Train the topic entity predictor
```
python train_entnet.py -config config/entnet_webq.yml
```
* Test the topic entity predictor
```
python test_entnet.py -config config/entnet_webq.yml
```
* Test the whole system (BAMnet + topic entity predictor)
```
python joint_test.py -bamnet_config config/bamnet_webq.yml -entnet_config config/entnet_webq.yml -raw_data ../data/WebQ
```
### Preprocess the dataset on your own
* Go to the `BAMnet/src` folder, to prepare data for the BAMnet model, run the following cmd:
```
python build_all_data.py -data_dir ../data/WebQ -fb_dir ../data/WebQ -out_dir ../data/WebQ
```
* To prepare data for the topic entity predictor model, run the following cmd:
```
python build_all_data.py -dtype ent -data_dir ../data/WebQ -fb_dir ../data/WebQ -out_dir ../data/WebQ
```
Note that in the message printed out, your will see some data statistics such as `vocab_size`, `num_ent_types `, `num_relations`. These numbers will be used later when modifying the config files.
* Download the pretrained Glove word ebeddings [glove.840B.300d.zip](http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip).
* Unzip the file and convert glove format to word2vec format using the following cmd:
```
python -m gensim.scripts.glove2word2vec --input glove.840B.300d.txt --output glove.840B.300d.w2v
```
* Fetch the pretrained Glove vectors for our vocabulary.
```
python build_pretrained_w2v.py -emb glove.840B.300d.w2v -data_dir ../data/WebQ -out ../data/WebQ/glove_pretrained_300d_w2v.npy -emb_size 300
```
## Architecture
<center><img src="images/overall_arch.png"/></center>
## Experiment results on WebQuestions
### Results on WebQuestions test set. Bold: best in-category performance.
<center><img src="images/results.png" width="300" height="500"/></center>
### Predicted answers of BAMnet w/ and w/o bidirectional attention on the WebQuestions test set
### Attention heatmap generated by the reasoning module
## Reference
If you found this code useful, please consider citing the following paper:
Yu Chen, Lingfei Wu, Mohammed J. Zaki. **"Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases."** *In Proc. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT2019). June 2019.*
@article{chen2019bidirectional,
title={Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases},
author={Chen, Yu and Wu, Lingfei and Zaki, Mohammed J},
journal={arXiv preprint arXiv:1903.02188},
year={2019}
}
================================================
FILE: requirements.txt
================================================
rapidfuzz==0.3.0
gensim==3.5.0
nltk==3.4.5
numpy==1.14.5
PyYAML==5.1
torch==0.4.1
================================================
FILE: src/build_all_data.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import argparse
from core.build_data.build_data import build_vocab, build_data, build_seed_ent_data
from core.utils.utils import *
from core.build_data import utils as build_utils
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-data_dir', '--data_dir', required=True, type=str, help='path to the data dir')
parser.add_argument('-fb_dir', '--fb_dir', required=True, type=str, help='path to the freebase dir')
parser.add_argument('-out_dir', '--out_dir', required=True, type=str, help='path to the output dir')
parser.add_argument('-dtype', '--data_type', default='qa', type=str, help='data type')
parser.add_argument('-min_freq', '--min_freq', default=1, type=int, help='min word vocab freq')
parser.add_argument('-topn', '--topn', default=15, type=int, help='top n candidates')
args = parser.parse_args()
train_data = load_ndjson(os.path.join(args.data_dir, 'raw_train.json'))
valid_data = load_ndjson(os.path.join(args.data_dir, 'raw_valid.json'))
test_data = load_ndjson(os.path.join(args.data_dir, 'raw_test.json'))
freebase = load_ndjson(os.path.join(args.fb_dir, 'freebase_full.json'), return_type='dict')
if not (os.path.exists(os.path.join(args.out_dir, 'entity2id.json')) and \
os.path.exists(os.path.join(args.out_dir, 'entityType2id.json')) and \
os.path.exists(os.path.join(args.out_dir, 'relation2id.json')) and \
os.path.exists(os.path.join(args.out_dir, 'vocab2id.json'))):
used_fbkeys = set()
for each in train_data + valid_data:
used_fbkeys.update(each['freebaseKeyCands'][:args.topn])
print('# of used_fbkeys: {}'.format(len(used_fbkeys)))
entity2id, entityType2id, relation2id, vocab2id = build_vocab(train_data + valid_data, freebase, used_fbkeys, min_freq=args.min_freq)
dump_json(entity2id, os.path.join(args.out_dir, 'entity2id.json'))
dump_json(entityType2id, os.path.join(args.out_dir, 'entityType2id.json'))
dump_json(relation2id, os.path.join(args.out_dir, 'relation2id.json'))
dump_json(vocab2id, os.path.join(args.out_dir, 'vocab2id.json'))
else:
entity2id = load_json(os.path.join(args.out_dir, 'entity2id.json'))
entityType2id = load_json(os.path.join(args.out_dir, 'entityType2id.json'))
relation2id = load_json(os.path.join(args.out_dir, 'relation2id.json'))
vocab2id = load_json(os.path.join(args.out_dir, 'vocab2id.json'))
print('Using pre-built vocabs stored in %s' % args.out_dir)
if args.data_type == 'qa':
train_vec = build_data(train_data, freebase, entity2id, entityType2id, relation2id, vocab2id)
valid_vec = build_data(valid_data, freebase, entity2id, entityType2id, relation2id, vocab2id)
test_vec = build_data(test_data, freebase, entity2id, entityType2id, relation2id, vocab2id)
dump_json(train_vec, os.path.join(args.out_dir, 'train_vec.json'))
dump_json(valid_vec, os.path.join(args.out_dir, 'valid_vec.json'))
dump_json(test_vec, os.path.join(args.out_dir, 'test_vec.json'))
print('Saved data to {}'.format(os.path.join(args.out_dir, 'train(valid, or test)_vec.json')))
else:
train_vec = build_seed_ent_data(train_data, freebase, entity2id, entityType2id, relation2id, vocab2id, args.topn, dtype='train')
valid_vec = build_seed_ent_data(valid_data, freebase, entity2id, entityType2id, relation2id, vocab2id, args.topn, dtype='valid')
test_vec = build_seed_ent_data(test_data, freebase, entity2id, entityType2id, relation2id, vocab2id, args.topn, dtype='test')
dump_json(train_vec, os.path.join(args.out_dir, 'train_ent_vec.json'))
dump_json(valid_vec, os.path.join(args.out_dir, 'valid_ent_vec.json'))
dump_json(test_vec, os.path.join(args.out_dir, 'test_ent_vec.json'))
print('Saved data to {}'.format(os.path.join(args.out_dir, 'train(valid, or test)_ent_vec.json')))
# Mark the data as built.
build_utils.mark_done(args.out_dir)
================================================
FILE: src/build_pretrained_w2v.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import argparse
import os
from core.utils.utils import load_json
from core.utils.generic_utils import dump_embeddings
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-emb', '--embed_path', required=True, type=str, help='path to the pretrained word embeddings')
parser.add_argument('-data_dir', '--data_dir', required=True, type=str, help='path to the data dir')
parser.add_argument('-out', '--out_path', required=True, type=str, help='path to the output path')
parser.add_argument('-emb_size', '--emb_size', required=True, type=int, help='embedding size')
parser.add_argument('--binary', action='store_true', help='flag: binary file')
args = parser.parse_args()
vocab_dict = load_json(os.path.join(args.data_dir, 'vocab2id.json'))
dump_embeddings(vocab_dict, args.embed_path, args.out_path, emb_size=args.emb_size, binary=True if args.binary else False)
================================================
FILE: src/config/bamnet_webq.yml
================================================
# Seed 15 Data
name: 'WebQuestions'
data_dir: '../data/WebQ/'
train_data: 'train_vec.json'
valid_data: 'valid_vec.json'
test_data: 'test_vec.json'
pre_word2vec: '../data/WebQ/glove_pretrained_300d_w2v.npy'
# Full vocab
vocab_size: 100797
num_ent_types: 1712
num_relations: 4996
num_query_words: 10
# Output
model_file: '../runs/WebQ/bamnet.md'
# Model
query_size: 32
query_markup_size: 1 # Not used
ans_bow_size: 1 # Not used
ans_path_bow_size: null
ans_ctx_entity_bow_size: 6
vocab_embed_size: 300
hidden_size: 128
o_embed_size: 128
mem_size: 96
word_emb_dropout: 0.3
que_enc_dropout: 0.3
ans_enc_dropout: 0.2
attention: 'add'
num_hops: 1
# Training
learning_rate: 0.001
batch_size: 32
num_epochs: 100
valid_patience: 10
margin: 1
# Testing
test_batch_size: 1
test_margin:
- 0.7
# Device
no_cuda: False
gpu: 0
================================================
FILE: src/config/entnet_webq.yml
================================================
# WebQuestions Data
name: 'WebQuestions'
data_dir: '../data/WebQ/'
train_data: 'train_ent_vec.json'
valid_data: 'valid_ent_vec.json'
test_data: 'test_ent_vec.json'
# Full vocab
vocab_size: 100797
num_ent_types: 1712
num_relations: 4996
pre_word2vec: '../data/WebQ/glove_pretrained_300d_w2v.npy'
# Output
model_file: '../runs/WebQ/entnet.md'
# Model
query_size: 32
max_seed_ent_name_size: null
max_seed_type_name_size: null
max_seed_rel_name_size: null
max_seed_rel_size: null
vocab_embed_size: 300
hidden_size: 128
o_embed_size: 128
word_emb_dropout: 0.3
que_enc_dropout: 0.3
ent_enc_dropout: 0.2
attention: 'simple'
seq_enc_type: 'cnn'
num_ent_hops: 1
# Training
learning_rate: 0.001
batch_size: 32
num_epochs: 100
valid_patience: 10
# Testing
test_batch_size: 1
# Device
no_cuda: False
gpu: 0
================================================
FILE: src/core/__init__.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
================================================
FILE: src/core/bamnet/__init__.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
================================================
FILE: src/core/bamnet/bamnet.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
import timeit
import numpy as np
import torch
from torch import optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.nn import MultiLabelMarginLoss
import torch.backends.cudnn as cudnn
from .modules import BAMnet
from .utils import to_cuda, next_batch
from ..utils.utils import load_ndarray
from ..utils.generic_utils import unique
from ..utils.metrics import *
from .. import config
CTX_BOW_INDEX = -5
def get_text_overlap(raw_query, query_mentions, ctx_ent_names, vocab2id, ctx_stops, query):
def longest_common_substring(s1, s2):
m = [[0] * (1 + len(s2)) for i in range(1 + len(s1))]
longest, x_longest = 0, 0
for x in range(1, 1 + len(s1)):
for y in range(1, 1 + len(s2)):
if s1[x - 1] == s2[y - 1]:
m[x][y] = m[x - 1][y - 1] + 1
if m[x][y] > longest:
longest = m[x][y]
x_longest = x
else:
m[x][y] = 0
return s1[x_longest - longest: x_longest]
sub_seq = longest_common_substring(raw_query, ctx_ent_names)
if len(set(sub_seq) - ctx_stops) == 0:
return []
men_type = None
for men, type_ in query_mentions:
if type_.lower() in config.constraint_mention_types:
if '_'.join(sub_seq) in '_'.join(men):
men_type = '__{}__'.format(type_.lower())
break
if men_type:
return [vocab2id[men_type] if men_type in vocab2id else config.RESERVED_TOKENS['UNK']]
else:
return [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in sub_seq]
class BAMnetAgent(object):
""" Bidirectional attentive memory network agent.
"""
def __init__(self, opt, ctx_stops, vocab2id):
self.ctx_stops = ctx_stops
self.vocab2id = vocab2id
opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()
if opt['cuda']:
print('[ Using CUDA ]')
torch.cuda.set_device(opt['gpu'])
# It enables benchmark mode in cudnn, which
# leads to faster runtime when the input sizes do not vary.
cudnn.benchmark = True
self.opt = opt
if self.opt['pre_word2vec']:
pre_w2v = load_ndarray(self.opt['pre_word2vec'])
else:
pre_w2v = None
self.model = BAMnet(opt['vocab_size'], opt['vocab_embed_size'], \
opt['o_embed_size'], opt['hidden_size'], \
opt['num_ent_types'], opt['num_relations'], \
opt['num_query_words'], \
word_emb_dropout=opt['word_emb_dropout'], \
que_enc_dropout=opt['que_enc_dropout'], \
ans_enc_dropout=opt['ans_enc_dropout'], \
pre_w2v=pre_w2v, \
num_hops=opt['num_hops'], \
att=opt['attention'], \
use_cuda=opt['cuda'])
if opt['cuda']:
self.model.cuda()
# MultiLabelMarginLoss
# For each sample in the mini-batch:
# loss(x, y) = sum_ij(max(0, 1 - (x[y[j]] - x[i]))) / x.size(0)
self.loss_fn = MultiLabelMarginLoss()
optim_params = [p for p in self.model.parameters() if p.requires_grad]
self.optimizers = {'bamnet': optim.Adam(optim_params, lr=opt['learning_rate'])}
self.scheduler = ReduceLROnPlateau(self.optimizers['bamnet'], mode='min', \
patience=self.opt['valid_patience'] // 3, verbose=True)
if opt.get('model_file') and os.path.isfile(opt['model_file']):
print('Loading existing model parameters from ' + opt['model_file'])
self.load(opt['model_file'])
super(BAMnetAgent, self).__init__()
def train(self, train_X, train_y, valid_X, valid_y, valid_cand_labels, valid_gold_ans_labels, seed=1234):
print('Training size: {}, Validation size: {}'.format(len(train_y), len(valid_y)))
random1 = np.random.RandomState(seed)
random2 = np.random.RandomState(seed)
random3 = np.random.RandomState(seed)
random4 = np.random.RandomState(seed)
random5 = np.random.RandomState(seed)
random6 = np.random.RandomState(seed)
random7 = np.random.RandomState(seed)
memories, queries, query_words, raw_queries, query_mentions, query_lengths = train_X
gold_ans_inds = train_y
valid_memories, valid_queries, valid_query_words, valid_raw_queries, valid_query_mentions, valid_query_lengths = valid_X
valid_gold_ans_inds = valid_y
n_incr_error = 0 # nb. of consecutive increase in error
best_loss = float("inf")
num_batches = len(queries) // self.opt['batch_size'] + (len(queries) % self.opt['batch_size'] != 0)
num_valid_batches = len(valid_queries) // self.opt['batch_size'] + (len(valid_queries) % self.opt['batch_size'] != 0)
for epoch in range(1, self.opt['num_epochs'] + 1):
start = timeit.default_timer()
n_incr_error += 1
random1.shuffle(memories)
random2.shuffle(queries)
random3.shuffle(query_words)
random4.shuffle(raw_queries)
random5.shuffle(query_mentions)
random6.shuffle(query_lengths)
random7.shuffle(gold_ans_inds)
train_gen = next_batch(memories, queries, query_words, raw_queries, query_mentions, query_lengths, gold_ans_inds, self.opt['batch_size'])
train_loss = 0
for batch_xs, batch_ys in train_gen:
train_loss += self.train_step(batch_xs, batch_ys) / num_batches
valid_gen = next_batch(valid_memories, valid_queries, valid_query_words, valid_raw_queries, valid_query_mentions, valid_query_lengths, valid_gold_ans_inds, self.opt['batch_size'])
valid_loss = 0
for batch_valid_xs, batch_valid_ys in valid_gen:
valid_loss += self.train_step(batch_valid_xs, batch_valid_ys, is_training=False) / num_valid_batches
self.scheduler.step(valid_loss)
# if False:
if epoch > 0:
pred = self.predict(valid_X, valid_cand_labels, batch_size=1, margin=self.opt['margin'], silence=True)
predictions = [unique([x[0] for x in each]) for each in pred]
valid_f1 = calc_avg_f1(valid_gold_ans_labels, predictions, verbose=False)[-1]
else:
valid_f1 = 0.
print('Epoch {}/{}: Runtime: {}s, Train loss: {:.4}, valid loss: {:.4}, valid F1: {:.4}'.format(epoch, self.opt['num_epochs'], \
int(timeit.default_timer() - start), train_loss, valid_loss, valid_f1))
if valid_loss < best_loss:
best_loss = valid_loss
n_incr_error = 0
self.save()
if n_incr_error >= self.opt['valid_patience']:
print('Early stopping occured. Optimization Finished!')
self.save(self.opt['model_file'] + '.final')
break
def predict(self, xs, cand_labels, batch_size=32, margin=1, ys=None, verbose=False, silence=False):
'''Prediction scores are returned in the verbose mode.
'''
if not silence:
print('Testing size: {}'.format(len(cand_labels)))
memories, queries, query_words, raw_queries, query_mentions, query_lengths = xs
gen = next_batch(memories, queries, query_words, raw_queries, query_mentions, query_lengths, cand_labels, batch_size)
predictions = []
for batch_xs, batch_cands in gen:
batch_pred = self.predict_step(batch_xs, batch_cands, margin, verbose=verbose)
predictions.extend(batch_pred)
return predictions
def train_step(self, xs, ys, is_training=True):
# Sets the module in training mode.
# This has any effect only on modules such as Dropout or BatchNorm.
self.model.train(mode=is_training)
with torch.set_grad_enabled(is_training):
# Organize inputs for network
selected_memories, new_ys, ctx_mask = self.dynamic_ctx_negative_sampling(xs[0], ys, self.opt['mem_size'], \
self.opt['ans_ctx_entity_bow_size'], xs[3], xs[4], xs[1])
selected_memories = [to_cuda(torch.LongTensor(np.array(x)), self.opt['cuda']) for x in zip(*selected_memories)]
ctx_mask = to_cuda(ctx_mask, self.opt['cuda'])
queries = to_cuda(torch.LongTensor(xs[1]), self.opt['cuda'])
query_words = to_cuda(torch.LongTensor(xs[2]), self.opt['cuda'])
query_lengths = to_cuda(torch.LongTensor(xs[5]), self.opt['cuda'])
mem_hop_scores = self.model(selected_memories, queries, query_lengths, query_words, ctx_mask=None)
# Set margin
new_ys, mask_ys = self.pack_gold_ans(new_ys, mem_hop_scores[-1].size(1), placeholder=-1)
loss = 0
for _, s in enumerate(mem_hop_scores):
s = self.set_loss_margin(s, mask_ys, self.opt['margin'])
loss += self.loss_fn(s, new_ys)
loss /= len(mem_hop_scores)
if is_training:
for o in self.optimizers.values():
o.zero_grad()
loss.backward()
for o in self.optimizers.values():
o.step()
return loss.item()
def predict_step(self, xs, cand_labels, margin, verbose=False):
self.model.train(mode=False)
with torch.set_grad_enabled(False):
# Organize inputs for network
memories, ctx_mask = self.pad_ctx_memory(xs[0], self.opt['ans_ctx_entity_bow_size'], xs[3], xs[4], xs[1])
memories = [to_cuda(torch.LongTensor(np.array(x)), self.opt['cuda']) for x in zip(*memories)]
ctx_mask = to_cuda(ctx_mask, self.opt['cuda'])
queries = to_cuda(torch.LongTensor(xs[1]), self.opt['cuda'])
query_words = to_cuda(torch.LongTensor(xs[2]), self.opt['cuda'])
query_lengths = to_cuda(torch.LongTensor(xs[5]), self.opt['cuda'])
mem_hop_scores = self.model(memories, queries, query_lengths, query_words, ctx_mask=None)
predictions = self.ranked_predictions(cand_labels, mem_hop_scores[-1].data, margin)
return predictions
def dynamic_ctx_negative_sampling(self, memories, ys, mem_size, ctx_bow_size, raw_queries, query_mentions, queries):
# Randomly select negative samples from the candidiate answer set
ctx_bow_size = max(min(max(map(len, (a for x in list(zip(*memories))[CTX_BOW_INDEX] for y in x for a in y)), default=0), ctx_bow_size), 1)
selected_memories = []
new_ys = []
ctx_mask = []
for i in range(len(ys)):
n = len(memories[i][0]) - 1 # The last element is a dummy candidate
num_gold = len(ys[i]) if mem_size > len(ys[i]) else \
(mem_size - min(mem_size // 2, n - len(ys[i]))) # Max possible (pos, neg) pairs
selected_gold_inds = np.random.choice(ys[i], num_gold, replace=False).tolist() if len(ys[i]) > 0 else []
if n > len(ys[i]):
p = np.ones(n)
p[ys[i]] = 0
p = p / np.sum(p)
selected_inds = np.random.choice(n, min(mem_size, n) - num_gold, replace=False, p=p).tolist()
else:
selected_inds = []
augmented_selected_inds = selected_gold_inds + selected_inds + [-1] * max(mem_size - n, 0)
xx = [min(mem_size, n)] + [np.array(x)[augmented_selected_inds] for x in memories[i][:CTX_BOW_INDEX]]
ctx_bow = []
ctx_bow_len = []
ctx_num = []
tmp_ctx_mask = np.zeros(mem_size)
for _, idx in enumerate(augmented_selected_inds):
tmp_ctx = []
tmp_ctx_len = []
for ctx_ent_names in memories[i][CTX_BOW_INDEX][idx]:
sub_seq = get_text_overlap(raw_queries[i], query_mentions[i], ctx_ent_names, self.vocab2id, self.ctx_stops, queries[i])
if len(sub_seq) > 0:
tmp_ctx_mask[_] = 1
tmp_ctx.append(sub_seq[:ctx_bow_size] + [config.RESERVED_TOKENS['PAD']] * max(0, ctx_bow_size - len(sub_seq)))
tmp_ctx_len.append(max(min(ctx_bow_size, len(sub_seq)), 1))
ctx_bow.append(tmp_ctx)
ctx_bow_len.append(tmp_ctx_len)
ctx_num.append(len(tmp_ctx))
xx += [ctx_bow, ctx_bow_len, ctx_num]
xx += [np.array(x)[augmented_selected_inds] for x in memories[i][CTX_BOW_INDEX+1:]]
selected_memories.append(xx)
new_ys.append(list(range(num_gold)))
ctx_mask.append(tmp_ctx_mask)
max_ctx_num = max(max([y for x in selected_memories for y in x[CTX_BOW_INDEX]]), 1)
for i in range(len(selected_memories)): # Example
for j in range(len(selected_memories[i][-1])): # Cand
count = selected_memories[i][CTX_BOW_INDEX][j]
if count < max_ctx_num:
selected_memories[i][CTX_BOW_INDEX - 2][j] += [[config.RESERVED_TOKENS['PAD']] * ctx_bow_size] * (max_ctx_num - count)
selected_memories[i][CTX_BOW_INDEX - 1][j] += [1] * (max_ctx_num - count)
return selected_memories, new_ys, torch.Tensor(np.array(ctx_mask))
def pad_ctx_memory(self, memories, ctx_bow_size, raw_queries, query_mentions, queries):
cand_ans_size = max(max(map(len, list(zip(*memories))[0]), default=0) - 1, 1) # The last element is a dummy candidate
ctx_bow_size = max(min(max(map(len, (a for x in list(zip(*memories))[CTX_BOW_INDEX] for y in x for a in y)), default=0), ctx_bow_size), 1)
pad_memories = []
ctx_mask = []
for i in range(len(memories)):
n = len(memories[i][0]) - 1 # The last element is a dummy candidate
augmented_inds = list(range(n)) + [-1] * (cand_ans_size - n)
xx = [n] + [np.array(x)[augmented_inds] for x in memories[i][:CTX_BOW_INDEX]]
ctx_bow = []
ctx_bow_len = []
ctx_num = []
tmp_ctx_mask = np.zeros(cand_ans_size)
for _, idx in enumerate(augmented_inds):
tmp_ctx = []
tmp_ctx_len = []
for ctx_ent_names in memories[i][CTX_BOW_INDEX][idx]:
sub_seq = get_text_overlap(raw_queries[i], query_mentions[i], ctx_ent_names, self.vocab2id, self.ctx_stops, queries[i])
if len(sub_seq) > 0:
tmp_ctx_mask[_] = 1
tmp_ctx.append(sub_seq[:ctx_bow_size] + [config.RESERVED_TOKENS['PAD']] * max(0, ctx_bow_size - len(sub_seq)))
tmp_ctx_len.append(max(min(ctx_bow_size, len(sub_seq)), 1))
ctx_bow.append(tmp_ctx)
ctx_bow_len.append(tmp_ctx_len)
ctx_num.append(len(tmp_ctx))
xx += [ctx_bow, ctx_bow_len, ctx_num]
xx += [np.array(x)[augmented_inds] for x in memories[i][CTX_BOW_INDEX+1:]]
pad_memories.append(xx)
ctx_mask.append(tmp_ctx_mask)
max_ctx_num = max(max([y for x in pad_memories for y in x[CTX_BOW_INDEX]]), 1)
for i in range(len(pad_memories)): # Example
for j in range(len(pad_memories[i][-1])): # Cand
count = pad_memories[i][CTX_BOW_INDEX][j]
if count < max_ctx_num:
pad_memories[i][CTX_BOW_INDEX - 2][j] += [[config.RESERVED_TOKENS['PAD']] * ctx_bow_size] * (max_ctx_num - count)
pad_memories[i][CTX_BOW_INDEX - 1][j] += [1] * (max_ctx_num - count)
return pad_memories, torch.Tensor(np.array(ctx_mask))
def pack_gold_ans(self, x, N, placeholder=-1):
y = np.ones((len(x), N), dtype='int64') * placeholder
mask = np.zeros((len(x), N))
for i in range(len(x)):
y[i, :len(x[i])] = x[i]
mask[i, :len(x[i])] = 1
return to_cuda(torch.LongTensor(y), self.opt['cuda']), to_cuda(torch.Tensor(mask), self.opt['cuda'])
def set_loss_margin(self, scores, gold_mask, margin):
"""Since the pytorch built-in MultiLabelMarginLoss fixes the margin as 1.
We simply work around this annoying feature by *modifying* the golden scores.
E.g., if we want margin as 3, we decrease each golden score by 3 - 1 before
feeding it to the built-in loss.
"""
new_scores = scores - (margin - 1) * gold_mask
return new_scores
def ranked_predictions(self, cand_labels, scores, margin):
_, sorted_inds = scores.sort(descending=True, dim=1)
return [[(cand_labels[i][j], scores[i][j]) for j in r if scores[i][j] + margin >= scores[i][r[0]] \
and cand_labels[i][j] != 'UNK'] \
if len(cand_labels[i]) > 0 and scores[i][r[0]] > -1e4 else [] \
for i, r in enumerate(sorted_inds)] # Very large negative ones are dummy candidates
def save(self, path=None):
path = self.opt.get('model_file', None) if path is None else path
if path:
checkpoint = {}
checkpoint['bamnet'] = self.model.state_dict()
checkpoint['bamnet_optim'] = self.optimizers['bamnet'].state_dict()
with open(path, 'wb') as write:
torch.save(checkpoint, write)
print('Saved model to {}'.format(path))
def load(self, path):
with open(path, 'rb') as read:
checkpoint = torch.load(read, map_location=lambda storage, loc: storage)
self.model.load_state_dict(checkpoint['bamnet'])
self.optimizers['bamnet'].load_state_dict(checkpoint['bamnet_optim'])
================================================
FILE: src/core/bamnet/ent_modules.py
================================================
'''
Created on Sep, 2018
@author: hugo
'''
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
import torch.nn.functional as F
from .modules import SeqEncoder, SelfAttention_CoAtt, Attention
from .utils import to_cuda
INF = 1e20
VERY_SMALL_NUMBER = 1e-10
class Entnet(nn.Module):
def __init__(self, vocab_size, vocab_embed_size, o_embed_size, \
hidden_size, num_ent_types, num_relations, \
seq_enc_type='cnn', \
word_emb_dropout=None, \
que_enc_dropout=None,\
ent_enc_dropout=None, \
pre_w2v=None, \
num_hops=1, \
att='add', \
use_cuda=True):
super(Entnet, self).__init__()
self.use_cuda = use_cuda
self.seq_enc_type = seq_enc_type
self.que_enc_dropout = que_enc_dropout
self.ent_enc_dropout = ent_enc_dropout
self.num_hops = num_hops
self.hidden_size = hidden_size
self.que_enc = SeqEncoder(vocab_size, vocab_embed_size, hidden_size, \
seq_enc_type=seq_enc_type, \
word_emb_dropout=word_emb_dropout, \
bidirectional=True, \
cnn_kernel_size=[2, 3], \
init_word_embed=pre_w2v, \
use_cuda=use_cuda).que_enc
self.ent_enc = EntEncoder(o_embed_size, hidden_size, \
num_ent_types, num_relations, \
vocab_size=vocab_size, \
vocab_embed_size=vocab_embed_size, \
shared_embed=self.que_enc.embed, \
seq_enc_type=seq_enc_type, \
word_emb_dropout=word_emb_dropout, \
ent_enc_dropout=ent_enc_dropout, \
use_cuda=use_cuda)
self.batchnorm = nn.BatchNorm1d(hidden_size)
if seq_enc_type in ('lstm', 'gru'):
self.self_atten = SelfAttention_CoAtt(hidden_size)
print('[ Using self-attention on question encoder ]')
self.ent_memory_hop = EntRomHop(hidden_size, hidden_size, hidden_size, atten_type=att)
print('[ Using {}-hop entity memory update ]'.format(num_hops))
def forward(self, memories, queries, query_lengths):
x_ent_names, x_ent_name_len, x_type_names, x_types, x_type_name_len, x_rel_names, x_rels, x_rel_name_len, x_rel_mask = memories
x_rel_mask = self.create_mask_3D(x_rel_mask, x_rels.size(-1), use_cuda=self.use_cuda)
# Question encoder
if self.seq_enc_type in ('lstm', 'gru'):
Q_r = self.que_enc(queries, query_lengths)[0]
if self.que_enc_dropout:
Q_r = F.dropout(Q_r, p=self.que_enc_dropout, training=self.training)
query_mask = self.create_mask(query_lengths, Q_r.size(1), self.use_cuda)
q_r = self.self_atten(Q_r, query_lengths, query_mask)
else:
q_r = self.que_enc(queries, query_lengths)[1]
if self.que_enc_dropout:
q_r = F.dropout(q_r, p=self.que_enc_dropout, training=self.training)
# Entity encoder
ent_val, ent_key = self.ent_enc(x_ent_names, x_ent_name_len, x_type_names, x_types, x_type_name_len, x_rel_names, x_rels, x_rel_name_len, x_rel_mask)
ent_val = torch.cat([each.unsqueeze(2) for each in ent_val], 2)
ent_key = torch.cat([each.unsqueeze(2) for each in ent_key], 2)
ent_val = torch.sum(ent_val, 2)
ent_key = torch.sum(ent_key, 2)
mem_hop_scores = []
mid_score = self.clf_score(q_r, ent_key)
mem_hop_scores.append(mid_score)
for _ in range(self.num_hops):
q_r = q_r + self.ent_memory_hop(q_r, ent_key, ent_val)
q_r = self.batchnorm(q_r)
mid_score = self.clf_score(q_r, ent_key)
mem_hop_scores.append(mid_score)
return mem_hop_scores
def clf_score(self, q_r, ent_key):
return torch.matmul(ent_key, q_r.unsqueeze(-1)).squeeze(-1)
def create_mask(self, x, N, use_cuda=True):
x = x.data
mask = np.zeros((x.size(0), N))
for i in range(x.size(0)):
mask[i, :x[i]] = 1
return to_cuda(torch.Tensor(mask), use_cuda)
def create_mask_3D(self, x, N, use_cuda=True):
x = x.data
mask = np.zeros((x.size(0), x.size(1), N))
for i in range(x.size(0)):
for j in range(x.size(1)):
mask[i, j, :x[i, j]] = 1
return to_cuda(torch.Tensor(mask), use_cuda)
class EntEncoder(nn.Module):
"""Entity Encoder"""
def __init__(self, o_embed_size, hidden_size, num_ent_types, num_relations, vocab_size=None, \
vocab_embed_size=None, shared_embed=None, seq_enc_type='lstm', word_emb_dropout=None, \
ent_enc_dropout=None, use_cuda=True):
super(EntEncoder, self).__init__()
# Cannot have embed and vocab_size set as None at the same time.
self.ent_enc_dropout = ent_enc_dropout
self.hidden_size = hidden_size
self.relation_embed = nn.Embedding(num_relations, o_embed_size, padding_idx=0)
self.embed = shared_embed if shared_embed is not None else nn.Embedding(vocab_size, vocab_embed_size, padding_idx=0)
self.vocab_embed_size = self.embed.weight.data.size(1)
self.linear_node_name_key = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_node_type_key = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_rels_key = nn.Linear(hidden_size + o_embed_size, hidden_size, bias=False)
self.linear_node_name_val = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_node_type_val = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_rels_val = nn.Linear(hidden_size + o_embed_size, hidden_size, bias=False)
self.kg_enc_ent = SeqEncoder(vocab_size, \
self.vocab_embed_size, \
hidden_size, \
seq_enc_type=seq_enc_type, \
word_emb_dropout=word_emb_dropout, \
bidirectional=True, \
cnn_kernel_size=[3], \
shared_embed=shared_embed, \
use_cuda=use_cuda).que_enc # entity name
self.kg_enc_type = SeqEncoder(vocab_size, \
self.vocab_embed_size, \
hidden_size, \
seq_enc_type=seq_enc_type, \
word_emb_dropout=word_emb_dropout, \
bidirectional=True, \
cnn_kernel_size=[3], \
shared_embed=shared_embed, \
use_cuda=use_cuda).que_enc # entity type name
self.kg_enc_rel = SeqEncoder(vocab_size, \
self.vocab_embed_size, \
hidden_size, \
seq_enc_type=seq_enc_type, \
word_emb_dropout=word_emb_dropout, \
bidirectional=True, \
cnn_kernel_size=[3], \
shared_embed=shared_embed, \
use_cuda=use_cuda).que_enc # relation name
def forward(self, x_ent_names, x_ent_name_len, x_type_names, x_types, x_type_name_len, x_rel_names, x_rels, x_rel_name_len, x_rel_mask):
node_ent_names, node_type_names, node_types, edge_rel_names, edge_rels = self.enc_kg_features(x_ent_names, x_ent_name_len, x_type_names, x_types, x_type_name_len, x_rel_names, x_rels, x_rel_name_len, x_rel_mask)
node_name_key = self.linear_node_name_key(node_ent_names)
node_type_key = self.linear_node_type_key(node_type_names)
rel_key = self.linear_rels_key(torch.cat([edge_rel_names, edge_rels], -1))
node_name_val = self.linear_node_name_val(node_ent_names)
node_type_val = self.linear_node_type_val(node_type_names)
rel_val = self.linear_rels_val(torch.cat([edge_rel_names, edge_rels], -1))
ent_comp_val = [node_name_val, node_type_val, rel_val]
ent_comp_key = [node_name_key, node_type_key, rel_key]
return ent_comp_val, ent_comp_key
def enc_kg_features(self, x_ent_names, x_ent_name_len, x_type_names, x_types, x_type_name_len, x_rel_names, x_rels, x_rel_name_len, x_rel_mask):
node_ent_names = (self.kg_enc_ent(x_ent_names.view(-1, x_ent_names.size(-1)), x_ent_name_len.view(-1))[1]).view(x_ent_names.size(0), x_ent_names.size(1), -1)
node_type_names = (self.kg_enc_type(x_type_names.view(-1, x_type_names.size(-1)), x_type_name_len.view(-1))[1]).view(x_type_names.size(0), x_type_names.size(1), -1)
node_types = None
edge_rel_names = torch.mean((self.kg_enc_rel(x_rel_names.view(-1, x_rel_names.size(-1)), x_rel_name_len.view(-1))[1]).view(x_rel_names.size(0), x_rel_names.size(1), x_rel_names.size(2), -1), 2)
edge_rels = torch.mean(self.relation_embed(x_rels.view(-1, x_rels.size(-1))), 1).view(x_rels.size(0), x_rels.size(1), -1)
if self.ent_enc_dropout:
node_ent_names = F.dropout(node_ent_names, p=self.ent_enc_dropout, training=self.training)
node_type_names = F.dropout(node_type_names, p=self.ent_enc_dropout, training=self.training)
# node_types = F.dropout(node_types, p=self.ent_enc_dropout, training=self.training)
edge_rel_names = F.dropout(edge_rel_names, p=self.ent_enc_dropout, training=self.training)
edge_rels = F.dropout(edge_rels, p=self.ent_enc_dropout, training=self.training)
return node_ent_names, node_type_names, node_types, edge_rel_names, edge_rels
class EntRomHop(nn.Module):
def __init__(self, query_embed_size, in_memory_embed_size, hidden_size, atten_type='add'):
super(EntRomHop, self).__init__()
self.atten = Attention(hidden_size, query_embed_size, in_memory_embed_size, atten_type=atten_type)
self.gru_step = GRUStep(hidden_size, in_memory_embed_size)
def forward(self, h_state, key_memory_embed, val_memory_embed, atten_mask=None):
attention = self.atten(h_state, key_memory_embed, atten_mask=atten_mask)
probs = torch.softmax(attention, dim=-1)
memory_output = torch.bmm(probs.unsqueeze(1), val_memory_embed).squeeze(1)
h_state = self.gru_step(h_state, memory_output)
return h_state
class GRUStep(nn.Module):
def __init__(self, hidden_size, input_size):
super(GRUStep, self).__init__()
'''GRU module'''
self.linear_z = nn.Linear(hidden_size + input_size, hidden_size, bias=False)
self.linear_r = nn.Linear(hidden_size + input_size, hidden_size, bias=False)
self.linear_t = nn.Linear(hidden_size + input_size, hidden_size, bias=False)
def forward(self, h_state, input_):
z = torch.sigmoid(self.linear_z(torch.cat([h_state, input_], -1)))
r = torch.sigmoid(self.linear_r(torch.cat([h_state, input_], -1)))
t = torch.tanh(self.linear_t(torch.cat([r * h_state, input_], -1)))
h_state = (1 - z) * h_state + z * t
return h_state
================================================
FILE: src/core/bamnet/entnet.py
================================================
'''
Created on Sep, 2018
@author: hugo
'''
import os
import timeit
import numpy as np
import torch
from torch import optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.nn import CrossEntropyLoss, MultiLabelMarginLoss
import torch.backends.cudnn as cudnn
from .ent_modules import Entnet
from .utils import to_cuda, next_ent_batch
from ..utils.utils import load_ndarray
from ..utils.generic_utils import unique
from ..utils.metrics import *
class EntnetAgent(object):
def __init__(self, opt):
opt['cuda'] = not opt['no_cuda'] and torch.cuda.is_available()
if opt['cuda']:
print('[ Using CUDA ]')
torch.cuda.set_device(opt['gpu'])
# It enables benchmark mode in cudnn, which
# leads to faster runtime when the input sizes do not vary.
cudnn.benchmark = True
self.opt = opt
if self.opt['pre_word2vec']:
pre_w2v = load_ndarray(self.opt['pre_word2vec'])
else:
pre_w2v = None
self.ent_model = Entnet(opt['vocab_size'], opt['vocab_embed_size'], \
opt['o_embed_size'], opt['hidden_size'], \
opt['num_ent_types'], opt['num_relations'], \
seq_enc_type=opt['seq_enc_type'], \
word_emb_dropout=opt['word_emb_dropout'], \
que_enc_dropout=opt['que_enc_dropout'], \
ent_enc_dropout=opt['ent_enc_dropout'], \
pre_w2v=pre_w2v, \
num_hops=opt['num_ent_hops'], \
att=opt['attention'], \
use_cuda=opt['cuda'])
if opt['cuda']:
self.ent_model.cuda()
self.loss_fn = MultiLabelMarginLoss()
optim_params = [p for p in self.ent_model.parameters() if p.requires_grad]
self.optimizers = {'entnet': optim.Adam(optim_params, lr=opt['learning_rate'])}
self.scheduler = ReduceLROnPlateau(self.optimizers['entnet'], mode='min', \
patience=self.opt['valid_patience'] // 3, verbose=True)
if opt.get('model_file') and os.path.isfile(opt['model_file']):
print('Loading existing ent_model parameters from ' + opt['model_file'])
self.load(opt['model_file'])
else:
self.save()
self.load(opt['model_file'])
super(EntnetAgent, self).__init__()
def train(self, train_X, train_y, valid_X, valid_y, seed=1234):
print('Training size: {}, Validation size: {}'.format(len(train_y), len(valid_y)))
random1 = np.random.RandomState(seed)
random2 = np.random.RandomState(seed)
random3 = np.random.RandomState(seed)
random4 = np.random.RandomState(seed)
memories, queries, query_lengths = train_X
ent_inds = train_y
valid_memories, valid_queries, valid_query_lengths = valid_X
valid_ent_inds = valid_y
n_incr_error = 0 # nb. of consecutive increase in error
best_loss = float("inf")
best_acc = 0
num_batches = len(queries) // self.opt['batch_size'] + (len(queries) % self.opt['batch_size'] != 0)
num_valid_batches = len(valid_queries) // self.opt['batch_size'] + (len(valid_queries) % self.opt['batch_size'] != 0)
for epoch in range(1, self.opt['num_epochs'] + 1):
start = timeit.default_timer()
n_incr_error += 1
random1.shuffle(memories)
random2.shuffle(queries)
random3.shuffle(query_lengths)
random4.shuffle(ent_inds)
train_gen = next_ent_batch(memories, queries, query_lengths, ent_inds, self.opt['batch_size'])
train_loss = 0
for batch_xs, batch_ys in train_gen:
train_loss += self.train_step(batch_xs, batch_ys) / num_batches
valid_gen = next_ent_batch(valid_memories, valid_queries, valid_query_lengths, valid_ent_inds, self.opt['batch_size'])
valid_loss = 0
for batch_valid_xs, batch_valid_ys in valid_gen:
valid_loss += self.train_step(batch_valid_xs, batch_valid_ys, is_training=False) / num_valid_batches
self.scheduler.step(valid_loss)
if epoch > 0:
valid_acc = self.evaluate(valid_X, valid_ent_inds, batch_size=1, silence=True)
# valid_acc = 0.
print('Epoch {}/{}: Runtime: {}s, Training loss: {:.4}, validation loss: {:.4}, validation ACC: {:.4}'.format(epoch, self.opt['num_epochs'], \
int(timeit.default_timer() - start), train_loss, valid_loss, valid_acc))
# self.scheduler.step(valid_acc)
# if valid_acc > best_acc:
# best_acc = valid_acc
# n_incr_error = 0
# self.save()
if valid_loss < best_loss:
best_loss = valid_loss
n_incr_error = 0
self.save()
if n_incr_error >= self.opt['valid_patience']:
print('Early stopping occured. Optimization Finished!')
self.save(self.opt['model_file'] + '.final')
break
def evaluate(self, xs, ys, batch_size=1, silence=False):
'''Prediction scores are returned in the verbose mode.
'''
if not silence:
print('Data size: {}'.format(len(xs[0])))
memories, queries, query_lengths = xs
gen = next_ent_batch(memories, queries, query_lengths, ys, batch_size)
correct = 0
num_samples = 0
for batch_xs, batch_ys in gen:
correct += self.evaluate_step(batch_xs, batch_ys)
num_samples += len(batch_ys)
acc = 100 * correct / num_samples
return acc
def predict(self, xs, cand_labels, batch_size=1, silence=False):
if not silence:
print('Data size: {}'.format(len(xs[0])))
memories, queries, query_lengths = xs
gen = next_ent_batch(memories, queries, query_lengths, cand_labels, batch_size)
predictions = []
for batch_xs, batch_cands in gen:
batch_pred = self.predict_step(batch_xs, batch_cands)
predictions.extend(batch_pred)
return predictions
def train_step(self, xs, ys, is_training=True):
# Sets the module in training mode.
# This has any effect only on modules such as Dropout or BatchNorm.
self.ent_model.train(mode=is_training)
with torch.set_grad_enabled(is_training):
# Organize inputs for network
memories = [to_cuda(torch.LongTensor(np.array(x)), self.opt['cuda']) for x in zip(*xs[0])]
queries = to_cuda(torch.LongTensor(xs[1]), self.opt['cuda'])
query_lengths = to_cuda(torch.LongTensor(xs[2]), self.opt['cuda'])
mem_hop_scores = self.ent_model(memories, queries, query_lengths)
# ys = to_cuda(torch.LongTensor(ys), self.opt['cuda']).squeeze(-1)
# Set margin
ys, mask_ys = self.pack_gold_ans(ys, mem_hop_scores[-1].size(1), placeholder=-1)
loss = 0
for _, s in enumerate(mem_hop_scores):
loss += self.loss_fn(s, ys)
loss /= len(mem_hop_scores)
if is_training:
for o in self.optimizers.values():
o.zero_grad()
loss.backward()
for o in self.optimizers.values():
o.step()
return loss.item()
def evaluate_step(self, xs, ys):
self.ent_model.train(mode=False)
with torch.set_grad_enabled(False):
# Organize inputs for network
memories = [to_cuda(torch.LongTensor(np.array(x)), self.opt['cuda']) for x in zip(*xs[0])]
queries = to_cuda(torch.LongTensor(xs[1]), self.opt['cuda'])
query_lengths = to_cuda(torch.LongTensor(xs[2]), self.opt['cuda'])
scores = self.ent_model(memories, queries, query_lengths)[-1]
ys = to_cuda(torch.LongTensor(ys), self.opt['cuda']).squeeze(1)
predictions = scores.max(1)[1].type_as(ys)
correct = predictions.eq(ys).sum()
return correct.item()
def predict_step(self, xs, cand_labels):
self.ent_model.train(mode=False)
with torch.set_grad_enabled(False):
# Organize inputs for network
memories = [to_cuda(torch.LongTensor(np.array(x)), self.opt['cuda']) for x in zip(*xs[0])]
queries = to_cuda(torch.LongTensor(xs[1]), self.opt['cuda'])
query_lengths = to_cuda(torch.LongTensor(xs[2]), self.opt['cuda'])
scores = self.ent_model(memories, queries, query_lengths)[-1]
predictions = self.ranked_predictions(cand_labels, scores)
return predictions
def pack_gold_ans(self, x, N, placeholder=-1):
y = np.ones((len(x), N), dtype='int64') * placeholder
mask = np.zeros((len(x), N))
for i in range(len(x)):
y[i, :len(x[i])] = x[i]
mask[i, :len(x[i])] = 1
return to_cuda(torch.LongTensor(y), self.opt['cuda']), to_cuda(torch.Tensor(mask), self.opt['cuda'])
def ranked_predictions(self, cand_labels, scores):
_, sorted_inds = scores.sort(descending=True, dim=1)
return [cand_labels[i][r[0]] if len(cand_labels[i]) > 0 else '' \
for i, r in enumerate(sorted_inds)]
def save(self, path=None):
path = self.opt.get('model_file', None) if path is None else path
if path:
checkpoint = {}
checkpoint['entnet'] = self.ent_model.state_dict()
checkpoint['entnet_optim'] = self.optimizers['entnet'].state_dict()
with open(path, 'wb') as write:
torch.save(checkpoint, write)
print('Saved ent_model to {}'.format(path))
def load(self, path):
with open(path, 'rb') as read:
checkpoint = torch.load(read, map_location=lambda storage, loc: storage)
self.ent_model.load_state_dict(checkpoint['entnet'])
self.optimizers['entnet'].load_state_dict(checkpoint['entnet_optim'])
================================================
FILE: src/core/bamnet/modules.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
import torch.nn.functional as F
from .utils import to_cuda
INF = 1e20
VERY_SMALL_NUMBER = 1e-10
class BAMnet(nn.Module):
def __init__(self, vocab_size, vocab_embed_size, o_embed_size, \
hidden_size, num_ent_types, num_relations, num_query_words, \
word_emb_dropout=None,\
que_enc_dropout=None,\
ans_enc_dropout=None, \
pre_w2v=None, \
num_hops=1, \
att='add', \
use_cuda=True):
super(BAMnet, self).__init__()
self.use_cuda = use_cuda
self.word_emb_dropout = word_emb_dropout
self.que_enc_dropout = que_enc_dropout
self.ans_enc_dropout = ans_enc_dropout
self.num_hops = num_hops
self.hidden_size = hidden_size
self.que_enc = SeqEncoder(vocab_size, vocab_embed_size, hidden_size, \
seq_enc_type='lstm', \
word_emb_dropout=word_emb_dropout, bidirectional=True, \
init_word_embed=pre_w2v, use_cuda=use_cuda).que_enc
self.ans_enc = AnsEncoder(o_embed_size, hidden_size, \
num_ent_types, num_relations, \
vocab_size=vocab_size, \
vocab_embed_size=vocab_embed_size, \
shared_embed=self.que_enc.embed, \
word_emb_dropout=word_emb_dropout, \
ans_enc_dropout=ans_enc_dropout, \
use_cuda=use_cuda)
self.qw_embed = nn.Embedding(num_query_words, o_embed_size // 8, padding_idx=0)
self.batchnorm = nn.BatchNorm1d(hidden_size)
self.init_atten = Attention(hidden_size, hidden_size, hidden_size, atten_type=att)
self.self_atten = SelfAttention_CoAtt(hidden_size)
print('[ Using self-attention on question encoder ]')
self.memory_hop = RomHop(hidden_size, hidden_size, hidden_size, atten_type=att)
print('[ Using {}-hop memory update ]'.format(self.num_hops))
def kb_aware_query_enc(self, memories, queries, query_lengths, ans_mask, ctx_mask=None):
# Question encoder
Q_r = self.que_enc(queries, query_lengths)[0]
if self.que_enc_dropout:
Q_r = F.dropout(Q_r, p=self.que_enc_dropout, training=self.training)
query_mask = create_mask(query_lengths, Q_r.size(1), self.use_cuda)
q_r_init = self.self_atten(Q_r, query_lengths, query_mask)
# Answer encoder
_, _, _, x_type_bow, x_types, x_type_bow_len, x_path_bow, x_paths, x_path_bow_len, x_ctx_ent, x_ctx_ent_len, x_ctx_ent_num, _, _, _, _ = memories
ans_comp_val, ans_comp_key = self.ans_enc(x_type_bow, x_types, x_type_bow_len, x_path_bow, x_paths, x_path_bow_len, x_ctx_ent, x_ctx_ent_len, x_ctx_ent_num)
if self.ans_enc_dropout:
for _ in range(len(ans_comp_key)):
ans_comp_key[_] = F.dropout(ans_comp_key[_], p=self.ans_enc_dropout, training=self.training)
# KB memory summary
ans_comp_atts = [self.init_atten(q_r_init, each, atten_mask=ans_mask) for each in ans_comp_key]
if ctx_mask is not None:
ans_comp_atts[-1] = ctx_mask * ans_comp_atts[-1] - (1 - ctx_mask) * INF
ans_comp_probs = [torch.softmax(each, dim=-1) for each in ans_comp_atts]
memory_summary = []
for i, probs in enumerate(ans_comp_probs):
memory_summary.append(torch.bmm(probs.unsqueeze(1), ans_comp_val[i]))
memory_summary = torch.cat(memory_summary, 1)
# Co-attention
CoAtt = torch.bmm(Q_r, memory_summary.transpose(1, 2)) # co-attention matrix
CoAtt = query_mask.unsqueeze(-1) * CoAtt - (1 - query_mask).unsqueeze(-1) * INF
if ctx_mask is not None:
# mask over empty ctx elements
ctx_mask_global = (ctx_mask.sum(-1, keepdim=True) > 0).float()
CoAtt[:, :, -1] = ctx_mask_global * CoAtt[:, :, -1].clone() - (1 - ctx_mask_global) * INF
q_att = F.max_pool1d(CoAtt, kernel_size=CoAtt.size(-1)).squeeze(-1)
q_att = torch.softmax(q_att, dim=-1)
return (ans_comp_val, ans_comp_key), (q_att, Q_r), query_mask
def forward(self, memories, queries, query_lengths, query_words, ctx_mask=None):
ctx_mask = None
mem_hop_scores = []
ans_mask = create_mask(memories[0], memories[2].size(1), self.use_cuda)
# Multi-task learning on answer type matching
# question word vec
self.qw_vec = torch.mean(self.qw_embed(query_words), 1)
# answer type vec
x_types = memories[4]
ans_types = torch.mean(self.ans_enc.ent_type_embed(x_types.view(-1, x_types.size(-1))), 1).view(x_types.size(0), x_types.size(1), -1)
qw_anstype_loss = torch.bmm(ans_types, self.qw_vec.unsqueeze(2)).squeeze(2)
if ans_mask is not None:
qw_anstype_loss = ans_mask * qw_anstype_loss - (1 - ans_mask) * INF # Make dummy candidates have large negative scores
mem_hop_scores.append(qw_anstype_loss)
# Kb-aware question attention module
(ans_val, ans_key), (q_att, Q_r), query_mask = self.kb_aware_query_enc(memories, queries, query_lengths, ans_mask, ctx_mask=ctx_mask)
ans_val = torch.cat([each.unsqueeze(2) for each in ans_val], 2)
ans_key = torch.cat([each.unsqueeze(2) for each in ans_key], 2)
q_r = torch.bmm(q_att.unsqueeze(1), Q_r).squeeze(1)
mid_score = self.scoring(ans_key.sum(2), q_r, mask=ans_mask)
mem_hop_scores.append(mid_score)
Q_r, ans_key, ans_val = self.memory_hop(Q_r, ans_key, ans_val, q_att, atten_mask=ans_mask, ctx_mask=ctx_mask, query_mask=query_mask)
q_r = torch.bmm(q_att.unsqueeze(1), Q_r).squeeze(1)
mid_score = self.scoring(ans_key, q_r, mask=ans_mask)
mem_hop_scores.append(mid_score)
# Generalization module
for _ in range(self.num_hops):
q_r_tmp = self.memory_hop.gru_step(q_r, ans_key, ans_val, atten_mask=ans_mask)
q_r = self.batchnorm(q_r + q_r_tmp)
mid_score = self.scoring(ans_key, q_r, mask=ans_mask)
mem_hop_scores.append(mid_score)
return mem_hop_scores
def premature_score(self, memories, queries, query_lengths, ctx_mask=None):
ctx_mask = None
ans_mask = create_mask(memories[0], memories[2].size(1), self.use_cuda)
# Kb-aware question attention module
(ans_val, ans_key), (q_att, Q_r), query_mask = self.kb_aware_query_enc(memories, queries, query_lengths, ans_mask, ctx_mask=ctx_mask)
ans_key = torch.cat([each.unsqueeze(2) for each in ans_key], 2)
mem_hop_scores = []
q_r = torch.bmm(q_att.unsqueeze(1), Q_r).squeeze(1)
score = self.scoring(ans_key.sum(2), q_r, mask=ans_mask)
return score
def scoring(self, ans_r, q_r, mask=None):
score = torch.bmm(ans_r, q_r.unsqueeze(2)).squeeze(2)
if mask is not None:
score = mask * score - (1 - mask) * INF # Make dummy candidates have large negative scores
return score
class RomHop(nn.Module):
def __init__(self, query_embed_size, in_memory_embed_size, hidden_size, atten_type='add'):
super(RomHop, self).__init__()
self.hidden_size = hidden_size
self.gru_linear_z = nn.Linear(2 * hidden_size, hidden_size, bias=False)
self.gru_linear_r = nn.Linear(2 * hidden_size, hidden_size, bias=False)
self.gru_linear_t = nn.Linear(2 * hidden_size, hidden_size, bias=False)
self.gru_atten = Attention(hidden_size, query_embed_size, in_memory_embed_size, atten_type=atten_type)
def forward(self, query_embed, in_memory_embed, out_memory_embed, query_att, \
atten_mask=None, ctx_mask=None, query_mask=None):
output = self.update_coatt_cat_maxpool(query_embed, in_memory_embed, out_memory_embed, query_att, \
atten_mask=atten_mask, ctx_mask=ctx_mask, query_mask=query_mask)
return output
def gru_step(self, h_state, in_memory_embed, out_memory_embed, atten_mask=None):
attention = self.gru_atten(h_state, in_memory_embed, atten_mask=atten_mask)
probs = torch.softmax(attention, dim=-1)
memory_output = torch.bmm(probs.unsqueeze(1), out_memory_embed).squeeze(1)
# GRU-like memory update
z = torch.sigmoid(self.gru_linear_z(torch.cat([h_state, memory_output], -1)))
r = torch.sigmoid(self.gru_linear_r(torch.cat([h_state, memory_output], -1)))
t = torch.tanh(self.gru_linear_t(torch.cat([r * h_state, memory_output], -1)))
output = (1 - z) * h_state + z * t
return output
def update_coatt_cat_maxpool(self, query_embed, in_memory_embed, out_memory_embed, query_att, atten_mask=None, ctx_mask=None, query_mask=None):
attention = torch.bmm(query_embed, in_memory_embed.view(in_memory_embed.size(0), -1, in_memory_embed.size(-1))\
.transpose(1, 2)).view(query_embed.size(0), query_embed.size(1), in_memory_embed.size(1), -1) # bs * N * M * k
if ctx_mask is not None:
attention[:, :, :, -1] = ctx_mask.unsqueeze(1) * attention[:, :, :, -1].clone() - (1 - ctx_mask).unsqueeze(1) * INF
if atten_mask is not None:
attention = atten_mask.unsqueeze(1).unsqueeze(-1) * attention - (1 - atten_mask).unsqueeze(1).unsqueeze(-1) * INF
if query_mask is not None:
attention = query_mask.unsqueeze(2).unsqueeze(-1) * attention - (1 - query_mask).unsqueeze(2).unsqueeze(-1) * INF
# Importance module
kb_feature_att = F.max_pool1d(attention.view(attention.size(0), attention.size(1), -1).transpose(1, 2), kernel_size=attention.size(1)).squeeze(-1).view(attention.size(0), -1, attention.size(-1))
kb_feature_att = torch.softmax(kb_feature_att, dim=-1).view(-1, kb_feature_att.size(-1)).unsqueeze(1)
in_memory_embed = torch.bmm(kb_feature_att, in_memory_embed.view(-1, in_memory_embed.size(2), in_memory_embed.size(-1))).squeeze(1).view(in_memory_embed.size(0), in_memory_embed.size(1), -1)
out_memory_embed = out_memory_embed.sum(2)
# Enhanced module
attention = F.max_pool1d(attention.view(attention.size(0), -1, attention.size(-1)), kernel_size=attention.size(-1)).squeeze(-1).view(attention.size(0), attention.size(1), attention.size(2))
probs = torch.softmax(attention, dim=-1)
new_query_embed = query_embed + query_att.unsqueeze(2) * torch.bmm(probs, out_memory_embed)
probs2 = torch.softmax(attention, dim=1)
kb_att = torch.bmm(query_att.unsqueeze(1), probs).squeeze(1)
in_memory_embed = in_memory_embed + kb_att.unsqueeze(2) * torch.bmm(probs2.transpose(1, 2), new_query_embed)
return new_query_embed, in_memory_embed, out_memory_embed
class AnsEncoder(nn.Module):
"""Answer Encoder"""
def __init__(self, o_embed_size, hidden_size, num_ent_types, num_relations, vocab_size=None, \
vocab_embed_size=None, shared_embed=None, word_emb_dropout=None, \
ans_enc_dropout=None, use_cuda=True):
super(AnsEncoder, self).__init__()
# Cannot have embed and vocab_size set as None at the same time.
self.use_cuda = use_cuda
self.ans_enc_dropout = ans_enc_dropout
self.hidden_size = hidden_size
self.ent_type_embed = nn.Embedding(num_ent_types, o_embed_size // 8, padding_idx=0)
self.relation_embed = nn.Embedding(num_relations, o_embed_size, padding_idx=0)
self.embed = shared_embed if shared_embed is not None else nn.Embedding(vocab_size, vocab_embed_size, padding_idx=0)
self.vocab_embed_size = self.embed.weight.data.size(1)
self.linear_type_bow_key = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_paths_key = nn.Linear(hidden_size + o_embed_size, hidden_size, bias=False)
self.linear_ctx_key = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_type_bow_val = nn.Linear(hidden_size, hidden_size, bias=False)
self.linear_paths_val = nn.Linear(hidden_size + o_embed_size, hidden_size, bias=False)
self.linear_ctx_val = nn.Linear(hidden_size, hidden_size, bias=False)
# lstm for ans encoder
self.lstm_enc_type = EncoderRNN(vocab_size, self.vocab_embed_size, hidden_size, \
dropout=word_emb_dropout, \
bidirectional=True, \
shared_embed=shared_embed, \
rnn_type='lstm', \
use_cuda=use_cuda)
self.lstm_enc_path = EncoderRNN(vocab_size, self.vocab_embed_size, hidden_size, \
dropout=word_emb_dropout, \
bidirectional=True, \
shared_embed=shared_embed, \
rnn_type='lstm', \
use_cuda=use_cuda)
self.lstm_enc_ctx = EncoderRNN(vocab_size, self.vocab_embed_size, hidden_size, \
dropout=word_emb_dropout, \
bidirectional=True, \
shared_embed=shared_embed, \
rnn_type='lstm', \
use_cuda=use_cuda)
def forward(self, x_type_bow, x_types, x_type_bow_len, x_path_bow, x_paths, x_path_bow_len, x_ctx_ents, x_ctx_ent_len, x_ctx_ent_num):
ans_type_bow, ans_types, ans_path_bow, ans_paths, ans_ctx_ent = self.enc_ans_features(x_type_bow, x_types, x_type_bow_len, x_path_bow, x_paths, x_path_bow_len, x_ctx_ents, x_ctx_ent_len, x_ctx_ent_num)
ans_val, ans_key = self.enc_comp_kv(ans_type_bow, ans_types, ans_path_bow, ans_paths, ans_ctx_ent)
return ans_val, ans_key
def enc_comp_kv(self, ans_type_bow, ans_types, ans_path_bow, ans_paths, ans_ctx_ent):
ans_type_bow_val = self.linear_type_bow_val(ans_type_bow)
ans_paths_val = self.linear_paths_val(torch.cat([ans_path_bow, ans_paths], -1))
ans_ctx_val = self.linear_ctx_val(ans_ctx_ent)
ans_type_bow_key = self.linear_type_bow_key(ans_type_bow)
ans_paths_key = self.linear_paths_key(torch.cat([ans_path_bow, ans_paths], -1))
ans_ctx_key = self.linear_ctx_key(ans_ctx_ent)
ans_comp_val = [ans_type_bow_val, ans_paths_val, ans_ctx_val]
ans_comp_key = [ans_type_bow_key, ans_paths_key, ans_ctx_key]
return ans_comp_val, ans_comp_key
def enc_ans_features(self, x_type_bow, x_types, x_type_bow_len, x_path_bow, x_paths, x_path_bow_len, x_ctx_ents, x_ctx_ent_len, x_ctx_ent_num):
'''
x_types: answer type
x_paths: answer path, i.e., bow of relation
x_ctx_ents: answer context, i.e., bow of entity words, (batch_size, num_cands, num_ctx, L)
'''
# ans_types = torch.mean(self.ent_type_embed(x_types.view(-1, x_types.size(-1))), 1).view(x_types.size(0), x_types.size(1), -1)
ans_type_bow = (self.lstm_enc_type(x_type_bow.view(-1, x_type_bow.size(-1)), x_type_bow_len.view(-1))[1]).view(x_type_bow.size(0), x_type_bow.size(1), -1)
ans_path_bow = (self.lstm_enc_path(x_path_bow.view(-1, x_path_bow.size(-1)), x_path_bow_len.view(-1))[1]).view(x_path_bow.size(0), x_path_bow.size(1), -1)
ans_paths = torch.mean(self.relation_embed(x_paths.view(-1, x_paths.size(-1))), 1).view(x_paths.size(0), x_paths.size(1), -1)
# Avg over ctx
ctx_num_mask = create_mask(x_ctx_ent_num.view(-1), x_ctx_ents.size(2), self.use_cuda).view(x_ctx_ent_num.shape + (-1,))
ans_ctx_ent = (self.lstm_enc_ctx(x_ctx_ents.view(-1, x_ctx_ents.size(-1)), x_ctx_ent_len.view(-1))[1]).view(x_ctx_ents.size(0), x_ctx_ents.size(1), x_ctx_ents.size(2), -1)
ans_ctx_ent = ctx_num_mask.unsqueeze(-1) * ans_ctx_ent
ans_ctx_ent = torch.sum(ans_ctx_ent, dim=2) / torch.clamp(x_ctx_ent_num.float().unsqueeze(-1), min=VERY_SMALL_NUMBER)
if self.ans_enc_dropout:
# ans_types = F.dropout(ans_types, p=self.ans_enc_dropout, training=self.training)
ans_type_bow = F.dropout(ans_type_bow, p=self.ans_enc_dropout, training=self.training)
ans_path_bow = F.dropout(ans_path_bow, p=self.ans_enc_dropout, training=self.training)
ans_paths = F.dropout(ans_paths, p=self.ans_enc_dropout, training=self.training)
ans_ctx_ent = F.dropout(ans_ctx_ent, p=self.ans_enc_dropout, training=self.training)
return ans_type_bow, None, ans_path_bow, ans_paths, ans_ctx_ent
class SeqEncoder(object):
"""Question Encoder"""
def __init__(self, vocab_size, embed_size, hidden_size, \
seq_enc_type='lstm', word_emb_dropout=None,
cnn_kernel_size=[3], bidirectional=False, \
shared_embed=None, init_word_embed=None, use_cuda=True):
if seq_enc_type in ('lstm', 'gru'):
self.que_enc = EncoderRNN(vocab_size, embed_size, hidden_size, \
dropout=word_emb_dropout, \
bidirectional=bidirectional, \
shared_embed=shared_embed, \
init_word_embed=init_word_embed, \
rnn_type=seq_enc_type, \
use_cuda=use_cuda)
elif seq_enc_type == 'cnn':
self.que_enc = EncoderCNN(vocab_size, embed_size, hidden_size, \
kernel_size=cnn_kernel_size, dropout=word_emb_dropout, \
shared_embed=shared_embed, \
init_word_embed=init_word_embed, \
use_cuda=use_cuda)
else:
raise RuntimeError('Unknown SeqEncoder type: {}'.format(seq_enc_type))
class EncoderRNN(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, dropout=None, \
bidirectional=False, shared_embed=None, init_word_embed=None, rnn_type='lstm', use_cuda=True):
super(EncoderRNN, self).__init__()
if not rnn_type in ('lstm', 'gru'):
raise RuntimeError('rnn_type is expected to be lstm or gru, got {}'.format(rnn_type))
if bidirectional:
print('[ Using bidirectional {} encoder ]'.format(rnn_type))
else:
print('[ Using {} encoder ]'.format(rnn_type))
if bidirectional and hidden_size % 2 != 0:
raise RuntimeError('hidden_size is expected to be even in the bidirectional mode!')
self.dropout = dropout
self.rnn_type = rnn_type
self.use_cuda = use_cuda
self.hidden_size = hidden_size // 2 if bidirectional else hidden_size
self.num_directions = 2 if bidirectional else 1
self.embed = shared_embed if shared_embed is not None else nn.Embedding(vocab_size, embed_size, padding_idx=0)
model = nn.LSTM if rnn_type == 'lstm' else nn.GRU
self.model = model(embed_size, self.hidden_size, 1, batch_first=True, bidirectional=bidirectional)
if shared_embed is None:
self.init_weights(init_word_embed)
def init_weights(self, init_word_embed):
if init_word_embed is not None:
print('[ Using pretrained word embeddings ]')
self.embed.weight.data.copy_(torch.from_numpy(init_word_embed))
else:
self.embed.weight.data.uniform_(-0.08, 0.08)
def forward(self, x, x_len):
"""x: [batch_size * max_length]
x_len: [batch_size]
"""
x = self.embed(x)
if self.dropout:
x = F.dropout(x, p=self.dropout, training=self.training)
sorted_x_len, indx = torch.sort(x_len, 0, descending=True)
x = pack_padded_sequence(x[indx], sorted_x_len.data.tolist(), batch_first=True)
h0 = to_cuda(torch.zeros(self.num_directions, x_len.size(0), self.hidden_size), self.use_cuda)
if self.rnn_type == 'lstm':
c0 = to_cuda(torch.zeros(self.num_directions, x_len.size(0), self.hidden_size), self.use_cuda)
packed_h, (packed_h_t, _) = self.model(x, (h0, c0))
if self.num_directions == 2:
packed_h_t = torch.cat([packed_h_t[i] for i in range(packed_h_t.size(0))], -1)
else:
packed_h, packed_h_t = self.model(x, h0)
if self.num_directions == 2:
packed_h_t = packed_h_t.transpose(0, 1).contiguous().view(query_lengths.size(0), -1)
hh, _ = pad_packed_sequence(packed_h, batch_first=True)
# restore the sorting
_, inverse_indx = torch.sort(indx, 0)
restore_hh = hh[inverse_indx]
restore_packed_h_t = packed_h_t[inverse_indx]
return restore_hh, restore_packed_h_t
class EncoderCNN(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, kernel_size=[2, 3], \
dropout=None, shared_embed=None, init_word_embed=None, use_cuda=True):
super(EncoderCNN, self).__init__()
print('[ Using CNN encoder with kernel size: {} ]'.format(kernel_size))
self.use_cuda = use_cuda
self.dropout = dropout
self.embed = shared_embed if shared_embed is not None else nn.Embedding(vocab_size, embed_size, padding_idx=0)
self.cnns = nn.ModuleList([nn.Conv1d(embed_size, hidden_size, kernel_size=k, padding=k-1) for k in kernel_size])
if len(kernel_size) > 1:
self.fc = nn.Linear(len(kernel_size) * hidden_size, hidden_size)
if shared_embed is None:
self.init_weights(init_word_embed)
def init_weights(self, init_word_embed):
if init_word_embed is not None:
print('[ Using pretrained word embeddings ]')
self.embed.weight.data.copy_(torch.from_numpy(init_word_embed))
else:
self.embed.weight.data.uniform_(-0.08, 0.08)
def forward(self, x, x_len=None):
"""x: [batch_size * max_length]
x_len: reserved
"""
x = self.embed(x)
if self.dropout:
x = F.dropout(x, p=self.dropout, training=self.training)
# Trun(batch_size, seq_len, embed_size) to (batch_size, embed_size, seq_len) for cnn1d
x = x.transpose(1, 2)
z = [conv(x) for conv in self.cnns]
output = [F.max_pool1d(i, kernel_size=i.size(-1)).squeeze(-1) for i in z]
if len(output) > 1:
output = self.fc(torch.cat(output, -1))
else:
output = output[0]
return None, output
class Attention(nn.Module):
def __init__(self, hidden_size, h_state_embed_size=None, in_memory_embed_size=None, atten_type='simple'):
super(Attention, self).__init__()
self.atten_type = atten_type
if not h_state_embed_size:
h_state_embed_size = hidden_size
if not in_memory_embed_size:
in_memory_embed_size = hidden_size
if atten_type in ('mul', 'add'):
self.W = torch.Tensor(h_state_embed_size, hidden_size)
self.W = nn.Parameter(nn.init.xavier_uniform_(self.W))
if atten_type == 'add':
self.W2 = torch.Tensor(in_memory_embed_size, hidden_size)
self.W2 = nn.Parameter(nn.init.xavier_uniform_(self.W2))
self.W3 = torch.Tensor(hidden_size, 1)
self.W3 = nn.Parameter(nn.init.xavier_uniform_(self.W3))
elif atten_type == 'simple':
pass
else:
raise RuntimeError('Unknown atten_type: {}'.format(self.atten_type))
def forward(self, query_embed, in_memory_embed, atten_mask=None):
if self.atten_type == 'simple': # simple attention
attention = torch.bmm(in_memory_embed, query_embed.unsqueeze(2)).squeeze(2)
elif self.atten_type == 'mul': # multiplicative attention
attention = torch.bmm(in_memory_embed, torch.mm(query_embed, self.W).unsqueeze(2)).squeeze(2)
elif self.atten_type == 'add': # additive attention
attention = torch.tanh(torch.mm(in_memory_embed.view(-1, in_memory_embed.size(-1)), self.W2)\
.view(in_memory_embed.size(0), -1, self.W2.size(-1)) \
+ torch.mm(query_embed, self.W).unsqueeze(1))
attention = torch.mm(attention.view(-1, attention.size(-1)), self.W3).view(attention.size(0), -1)
else:
raise RuntimeError('Unknown atten_type: {}'.format(self.atten_type))
if atten_mask is not None:
# Exclude masked elements from the softmax
attention = atten_mask * attention - (1 - atten_mask) * INF
return attention
class SelfAttention_CoAtt(nn.Module):
def __init__(self, hidden_size, use_cuda=True):
super(SelfAttention_CoAtt, self).__init__()
self.use_cuda = use_cuda
self.hidden_size = hidden_size
self.model = nn.LSTM(2 * hidden_size, hidden_size // 2, batch_first=True, bidirectional=True)
def forward(self, x, x_len, atten_mask):
CoAtt = torch.bmm(x, x.transpose(1, 2))
CoAtt = atten_mask.unsqueeze(1) * CoAtt - (1 - atten_mask).unsqueeze(1) * INF
CoAtt = torch.softmax(CoAtt, dim=-1)
new_x = torch.cat([torch.bmm(CoAtt, x), x], -1)
sorted_x_len, indx = torch.sort(x_len, 0, descending=True)
new_x = pack_padded_sequence(new_x[indx], sorted_x_len.data.tolist(), batch_first=True)
h0 = to_cuda(torch.zeros(2, x_len.size(0), self.hidden_size // 2), self.use_cuda)
c0 = to_cuda(torch.zeros(2, x_len.size(0), self.hidden_size // 2), self.use_cuda)
packed_h, (packed_h_t, _) = self.model(new_x, (h0, c0))
# restore the sorting
_, inverse_indx = torch.sort(indx, 0)
packed_h_t = torch.cat([packed_h_t[i] for i in range(packed_h_t.size(0))], -1)
restore_packed_h_t = packed_h_t[inverse_indx]
output = restore_packed_h_t
return output
def create_mask(x, N, use_cuda=True):
x = x.data
mask = np.zeros((x.size(0), N))
for i in range(x.size(0)):
mask[i, :x[i]] = 1
return to_cuda(torch.Tensor(mask), use_cuda)
================================================
FILE: src/core/bamnet/utils.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import torch
from torch.autograd import Variable
import numpy as np
def to_cuda(x, use_cuda=True):
if use_cuda and torch.cuda.is_available():
x = x.cuda()
return x
# One pass over the dataset
def next_batch(memories, queries, query_words, raw_queries, query_mentions, query_lengths, gold_ans_inds, batch_size):
for i in range(0, len(memories), batch_size):
yield (memories[i: i + batch_size], queries[i: i + batch_size], query_words[i: i + batch_size], raw_queries[i: i + batch_size], query_mentions[i: i + batch_size], query_lengths[i: i + batch_size]), gold_ans_inds[i: i + batch_size]
# One pass over the dataset
def next_ent_batch(memories, queries, query_lengths, gold_inds, batch_size):
for i in range(0, len(memories), batch_size):
yield (memories[i: i + batch_size], queries[i: i + batch_size], query_lengths[i: i + batch_size]), gold_inds[i: i + batch_size]
================================================
FILE: src/core/build_data/__init__.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
================================================
FILE: src/core/build_data/build_all.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import os
from . import utils as build_utils
from ..utils.utils import *
from .build_data import build_vocab, build_data
def build(dpath, version=None, out_dir=None):
if not build_utils.built(dpath, version_string=version):
raise RuntimeError("Please build/preprocess the data by running the build_all_data.py script!")
================================================
FILE: src/core/build_data/build_data.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
import math
import argparse
from itertools import count
from rapidfuzz import fuzz, process
from collections import defaultdict
from ..utils.utils import *
from ..utils.generic_utils import normalize_answer, unique
from ..utils.freebase_utils import if_filterout
from .. import config
IGNORE_DUMMY = True
ENT_TYPE_HOP = 1
# Entity mention types: 'NP', 'ORGANIZATION', 'DATE', 'NUMBER', 'MISC', 'ORDINAL', 'DURATION', 'PERSON', 'TIME', 'LOCATION'
def build_kb_data(kb, used_fbkeys=None):
entities = defaultdict(int)
entity_types = defaultdict(int)
relations = defaultdict(int)
vocabs = defaultdict(int)
if not used_fbkeys:
used_fbkeys = kb.keys()
for k in used_fbkeys:
if not k in kb:
continue
v = kb[k]
entities[v['id']] += 1
# We prefer notable_types than type since they are more representative.
# If notable_types are not available, we use only the first available type.
# We found the type field contains much noise.
selected_types = (v['notable_types'] + v['type'])[:ENT_TYPE_HOP]
for ent_type in selected_types:
entity_types[ent_type] += 1
for token in [y for x in selected_types for y in x.lower().split('/')[-1].split('_')]:
vocabs[token] += 1
# Add entity vocabs
selected_names = v['name'][:1] + v['alias'] # We need all topic entity alias
for token in [y for x in selected_names for y in tokenize(x.lower())]:
vocabs[token] += 1
if not 'neighbors' in v:
continue
for kk, vv in v['neighbors'].items(): # 1st hop
if if_filterout(kk):
continue
relations[kk] += 1
# Add relation vocabs
for token in [x for x in kk.lower().split('/')[-1].split('_')]:
vocabs[token] += 1
for nbr in vv:
if isinstance(nbr, str):
for token in [y for y in tokenize(nbr.lower())]:
vocabs[token] += 1
continue
elif isinstance(nbr, bool):
continue
elif isinstance(nbr, float):
continue
# vocabs.update([y for y in tokenize(str(nbr).lower())])
elif isinstance(nbr, dict):
nbr_k = list(nbr.keys())[0]
nbr_v = nbr[nbr_k]
entities[nbr_k] += 1
selected_types = (nbr_v['notable_types'] + nbr_v['type'])[:ENT_TYPE_HOP]
for ent_type in selected_types:
entity_types[ent_type] += 1
selected_names = (nbr_v['name'] + nbr_v['alias'])[:1]
for token in [y for x in selected_names for y in tokenize(x.lower())] + \
[y for x in selected_types for y in x.lower().split('/')[-1].split('_')]:
vocabs[token] += 1
if not 'neighbors' in nbr_v:
continue
for kkk, vvv in nbr_v['neighbors'].items(): # 2nd hop
if if_filterout(kkk):
continue
relations[kkk] += 1
# Add relation vocabs
for token in [x for x in kkk.lower().split('/')[-1].split('_')]:
vocabs[token] += 1
for nbr_nbr in vvv:
if isinstance(nbr_nbr, str):
for token in [y for y in tokenize(nbr_nbr.lower())]:
vocabs[token] += 1
continue
elif isinstance(nbr_nbr, bool):
continue
elif isinstance(nbr_nbr, float):
# vocabs.update([y for y in tokenize(str(nbr_nbr).lower())])
continue
elif isinstance(nbr_nbr, dict):
nbr_nbr_k = list(nbr_nbr.keys())[0]
nbr_nbr_v = nbr_nbr[nbr_nbr_k]
entities[nbr_nbr_k] += 1
selected_types = (nbr_nbr_v['notable_types'] + nbr_nbr_v['type'])[:ENT_TYPE_HOP]
for ent_type in selected_types:
entity_types[ent_type] += 1
selected_names = (nbr_nbr_v['name'] + nbr_nbr_v['alias'])[:1]
for token in [y for x in selected_names for y in tokenize(x.lower())] + \
[y for x in selected_types for y in x.lower().split('/')[-1].split('_')]:
vocabs[token] += 1
else:
raise RuntimeError('Unknown type: %s' % type(nbr_nbr))
else:
raise RuntimeError('Unknown type: %s' % type(nbr))
return (entities, entity_types, relations, vocabs)
def build_qa_vocab(qa):
vocabs = defaultdict(int)
for each in qa:
for token in tokenize(each['qText'].lower()):
vocabs[token] += 1
return vocabs
def delex_query_topic_ent(query, topic_ent, ent_types):
query = tokenize(query.lower())
if topic_ent == '':
return query, None
ent_type_dict = {}
for ent, type_ in ent_types:
if ent not in ent_type_dict:
ent_type_dict[ent] = type_
else:
if ent_type_dict[ent] == 'NP':
ent_type_dict[ent] = type_
ret = process.extract(topic_ent.replace('_', ' '), set(list(zip(*ent_types))[0]), scorer=fuzz.token_sort_ratio)
if len(ret) == 0:
return query, None
# We prefer Non-NP entity mentions
# e.g., we prefer `uk` than `people in the uk` when matching `united_kingdom`
topic_men = None
topic_score = None
for token, score in ret:
if ent_type_dict[token].lower() in config.topic_mention_types:
topic_men = token
topic_score = score
break
if topic_men is None:
return query, None
topic_ent_type = ent_type_dict[topic_men].lower()
topic_tokens = tokenize(topic_men.lower())
indices = [i for i, x in enumerate(query) if x == topic_tokens[0]]
for i in indices:
if query[i: i + len(topic_tokens)] == topic_tokens:
start_idx = i
end_idx = i + len(topic_tokens)
break
query_template = query[:start_idx] + [topic_ent_type] + query[end_idx:]
return query_template, topic_men
def delex_query(query, ent_mens, mention_types):
for men, type_ in ent_mens:
type_ = type_.lower()
if type_ in mention_types:
men = tokenize(men.lower())
indices = [i for i, x in enumerate(query) if x == men[0]]
start_idx = None
for i in indices:
if query[i: i + len(men)] == men:
start_idx = i
end_idx = i + len(men)
break
if start_idx is not None:
query = query[:start_idx] + ['__{}__'.format(type_)] + query[end_idx:]
return query
def build_data(qa, kb, entity2id, entityType2id, relation2id, vocab2id, pred_seed_ents=None):
queries = []
raw_queries = []
query_mentions = []
memories = []
cand_labels = [] # Candidate answer labels (i.e., names)
gold_ans_labels = [] # True gold answer labels
gold_ans_inds = [] # The "gold" answer indices corresponding to the cand list
for qid, each in enumerate(qa):
freebase_key = each['freebaseKey'] if not pred_seed_ents else pred_seed_ents[qid]
if isinstance(freebase_key, list):
freebase_key = freebase_key[0] if len(freebase_key) > 0 else ''
# Convert query to query template
query, topic_men = delex_query_topic_ent(each['qText'], freebase_key, each['entities'])
query2 = delex_query(query, each['entities'], config.delex_mention_types)
q = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in query2]
queries.append(q)
raw_queries.append(query)
query_mentions.append([(tokenize(x[0].lower()), x[1].lower()) for x in each['entities'] if topic_men != x[0]])
gold_ans_labels.append(each['answers'])
if not freebase_key in kb:
gold_ans_inds.append([])
memories.append([[]] * 8)
cand_labels.append([])
continue
ans_cands = build_ans_cands(kb[freebase_key], entity2id, entityType2id, relation2id, vocab2id)
memories.append(ans_cands[:-1])
cand_labels.append(ans_cands[-1])
if len(ans_cands[0]) == 0:
gold_ans_inds.append([])
continue
norm_cand_labels = [normalize_answer(x) for x in ans_cands[-1]]
tmp_cand_inds = []
for a in each['answers']:
a = normalize_answer(a)
# Find all the candidiate answers which match the gold answer.
inds = [i for i, j in zip(count(), norm_cand_labels) if j == a]
tmp_cand_inds.extend(inds)
# Note that tmp_cand_inds can be empty in which case
# the question can *NOT* be answered by this KB entity.
gold_ans_inds.append(tmp_cand_inds)
return (queries, raw_queries, query_mentions, memories, cand_labels, gold_ans_inds, gold_ans_labels)
def build_vocab(data, freebase, used_fbkeys=None, min_freq=1):
entities, entity_types, relations, kb_vocabs = build_kb_data(freebase, used_fbkeys)
# Entity
all_entities = set({ent for ent in entities if entities[ent] >= min_freq})
entity2id = dict(zip(all_entities, range(len(config.RESERVED_ENTS), len(all_entities) + len(config.RESERVED_ENTS))))
for ent, idx in config.RESERVED_ENTS.items():
entity2id.update({ent: idx})
# Entity type
all_ent_types = set({ent_type for ent_type in entity_types if entity_types[ent_type] >= min_freq})
all_ent_types.update(config.extra_ent_types)
entityType2id = dict(zip(all_ent_types, range(len(config.RESERVED_ENT_TYPES), len(all_ent_types) + len(config.RESERVED_ENT_TYPES))))
for ent_type, idx in config.RESERVED_ENT_TYPES.items():
entityType2id.update({ent_type: idx})
# Relation
all_relations = set({rel for rel in relations if relations[rel] >= min_freq})
all_relations.update(config.extra_rels)
relation2id = dict(zip(all_relations, range(len(config.RESERVED_RELS), len(all_relations) + len(config.RESERVED_RELS))))
for rel, idx in config.RESERVED_RELS.items():
relation2id.update({rel: idx})
# Vocab
vocabs = build_qa_vocab(data)
for token, count in kb_vocabs.items():
vocabs[token] += count
# sorted_vocabs = sorted(vocabs.items(), key=lambda d:d[1], reverse=True)
all_tokens = set({token for token in vocabs if vocabs[token] >= min_freq})
all_tokens.update(config.extra_vocab_tokens)
vocab2id = dict(zip(all_tokens, range(len(config.RESERVED_TOKENS), len(all_tokens) + len(config.RESERVED_TOKENS))))
for token, idx in config.RESERVED_TOKENS.items():
vocab2id.update({token: idx})
print('Num of entities: %s' % len(entity2id))
print('Num of entity_types: %s' % len(entityType2id))
print('Num of relations: %s' % len(relation2id))
print('Num of vocabs: %s' % len(vocab2id))
return entity2id, entityType2id, relation2id, vocab2id
def build_ans_cands(graph, entity2id, entityType2id, relation2id, vocab2id):
cand_ans_bows = [] # bow of answer entity
cand_ans_entities = [] # answer entity
cand_ans_types = [] # type of answer entity
cand_ans_type_bows = [] # bow of answer entity type
cand_ans_paths = [] # relation path from topic entity to answer entity
cand_ans_path_bows = []
cand_ans_ctx = [] # context (i.e., 1-hop entity bows and relation bows) connects to the answer path
cand_ans_topic_key_type = [] # topic key entity type
cand_labels = [] # candidiate answers
selected_types = (graph['notable_types'] + graph['type'])[:ENT_TYPE_HOP]
topic_key_ent_type_bows = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for y in selected_types for x in y.lower().split('/')[-1].split('_')]
topic_key_ent_type = [entityType2id[x] if x in entityType2id else config.RESERVED_ENT_TYPES['UNK'] for x in selected_types]
# We only consider the alias relations of topic entityies
for each in graph['alias']:
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
ent_bow = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for y in tokenize(each.lower())]
cand_ans_bows.append(ent_bow)
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([])
cand_ans_type_bows.append([])
cand_ans_paths.append([relation2id['alias'] if 'alias' in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append([vocab2id['alias']])
# We do not count the topic_entity as context since it is trivial
cand_ans_ctx.append([[], []])
cand_labels.append(each)
if len(cand_labels) == 0 and (not 'neighbors' in graph or len(graph['neighbors']) == 0):
return ([], [], [], [], [], [], [], [], [])
for k, v in graph['neighbors'].items():
if if_filterout(k):
continue
k_bow = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in k.lower().split('/')[-1].split('_')]
for nbr in v:
if isinstance(nbr, str):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
ent_bow = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for y in tokenize(nbr.lower())]
cand_ans_bows.append(ent_bow)
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([])
cand_ans_type_bows.append([])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(k_bow)
cand_ans_ctx.append([[], []])
cand_labels.append(nbr)
continue
elif isinstance(nbr, bool):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
cand_ans_bows.append([vocab2id['true' if nbr else 'false']])
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([entityType2id['bool']])
cand_ans_type_bows.append([vocab2id['bool']])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(k_bow)
cand_ans_ctx.append([[], []])
cand_labels.append('true' if nbr else 'false')
continue
elif isinstance(nbr, float):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
cand_ans_bows.append([vocab2id[str(nbr)] if str(nbr) in vocab2id else config.RESERVED_TOKENS['UNK']])
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([entityType2id['num']])
cand_ans_type_bows.append([vocab2id['num']])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(k_bow)
cand_ans_ctx.append([[], []])
cand_labels.append(str(nbr))
continue
elif isinstance(nbr, dict):
nbr_k = list(nbr.keys())[0]
nbr_v = nbr[nbr_k]
selected_names = (nbr_v['name'] + nbr_v['alias'])[:1]
is_dummy = True
if not IGNORE_DUMMY or len(selected_names) > 0: # Otherwise, it is an intermediate (dummpy) node
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
nbr_k_bow = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for x in selected_names for y in tokenize(x.lower())]
cand_ans_bows.append(nbr_k_bow)
cand_ans_entities.append(entity2id[nbr_k] if nbr_k in entity2id else config.RESERVED_ENTS['UNK'])
selected_types = (nbr_v['notable_types'] + nbr_v['type'])[:ENT_TYPE_HOP]
cand_ans_types.append([entityType2id[x] if x in entityType2id else config.RESERVED_ENT_TYPES['UNK'] for x in selected_types])
cand_ans_type_bows.append([vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for y in selected_types for x in y.lower().split('/')[-1].split('_')])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(k_bow)
cand_labels.append(selected_names[0] if len(selected_names) > 0 else 'UNK')
is_dummy = False
if not 'neighbors' in nbr_v:
if not is_dummy:
cand_ans_ctx.append([[], []])
continue
rels = []
labels = []
all_ctx = [set(), set()]
for kk, vv in nbr_v['neighbors'].items(): # 2nd hop
if if_filterout(kk):
continue
kk_bow = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in kk.lower().split('/')[-1].split('_')]
all_ctx[1].add(kk)
for nbr_nbr in vv:
if isinstance(nbr_nbr, str):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
ent_bow = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for y in tokenize(nbr_nbr.lower())]
cand_ans_bows.append(ent_bow)
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([])
cand_ans_type_bows.append([])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK'], relation2id[kk] if kk in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(kk_bow + k_bow)
labels.append(nbr_nbr)
all_ctx[0].add(nbr_nbr)
rels.append(kk)
continue
elif isinstance(nbr_nbr, bool):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
cand_ans_bows.append([vocab2id['true' if nbr_nbr else 'false']])
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([entityType2id['bool']])
cand_ans_type_bows.append([vocab2id['bool']])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK'], relation2id[kk] if kk in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(kk_bow + k_bow)
labels.append('true' if nbr_nbr else 'false')
all_ctx[0].add('true' if nbr_nbr else 'false')
rels.append(kk)
continue
elif isinstance(nbr_nbr, float):
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
cand_ans_bows.append([vocab2id[str(nbr_nbr)] if str(nbr_nbr) in vocab2id else config.RESERVED_TOKENS['UNK']])
cand_ans_entities.append(config.RESERVED_ENTS['PAD'])
cand_ans_types.append([entityType2id['num']])
cand_ans_type_bows.append([vocab2id['num']])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK'], relation2id[kk] if kk in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(kk_bow + k_bow)
labels.append(str(nbr_nbr))
all_ctx[0].add(str(nbr_nbr))
rels.append(kk)
continue
elif isinstance(nbr_nbr, dict):
nbr_nbr_k = list(nbr_nbr.keys())[0]
nbr_nbr_v = nbr_nbr[nbr_nbr_k]
selected_names = (nbr_nbr_v['name'] + nbr_nbr_v['alias'])[:1]
if not IGNORE_DUMMY or len(selected_names) > 0:
cand_ans_topic_key_type.append([topic_key_ent_type_bows, topic_key_ent_type])
ent_bow = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for x in selected_names for y in tokenize(x.lower())]
cand_ans_bows.append(ent_bow)
cand_ans_entities.append(entity2id[nbr_nbr_k] if nbr_nbr_k in entity2id else config.RESERVED_ENTS['UNK'])
selected_types = (nbr_nbr_v['notable_types'] + nbr_nbr_v['type'])[:ENT_TYPE_HOP]
cand_ans_types.append([entityType2id[x] if x in entityType2id else config.RESERVED_ENT_TYPES['UNK'] for x in selected_types])
cand_ans_type_bows.append([vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for y in selected_types for x in y.lower().split('/')[-1].split('_')])
cand_ans_paths.append([relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK'], relation2id[kk] if kk in relation2id else config.RESERVED_RELS['UNK']])
cand_ans_path_bows.append(kk_bow + k_bow)
labels.append(selected_names[0] if len(selected_names) > 0 else 'UNK')
if len(selected_names) > 0:
all_ctx[0].add(selected_names[0])
rels.append(kk)
else:
raise RuntimeError('Unknown type: %s' % type(nbr_nbr))
assert len(labels) == len(rels)
if not is_dummy:
ctx_ent_bow = [tokenize(x.lower()) for x in all_ctx[0]]
# ctx_rel_bow = list(set([vocab2id[y] for x in all_ctx[1] for y in x.lower().split('/')[-1].split('_') if y in vocab2id]))
ctx_rel_bow = []
cand_ans_ctx.append([ctx_ent_bow, ctx_rel_bow])
for i in range(len(labels)):
tmp_ent_names = all_ctx[0] - set([labels[i]])
# tmp_rel_names = all_ctx[1] - set([rels[i]])
ctx_ent_bow = [tokenize(x.lower()) for x in tmp_ent_names]
# ctx_rel_bow = list(set([vocab2id[y] for x in tmp_rel_names for y in x.lower().split('/')[-1].split('_') if y in vocab2id]))
ctx_rel_bow = []
cand_ans_ctx.append([ctx_ent_bow, ctx_rel_bow])
cand_labels.extend(labels)
else:
raise RuntimeError('Unknown type: %s' % type(nbr))
assert len(cand_ans_bows) == len(cand_ans_entities) == len(cand_ans_types) == len(cand_ans_type_bows) == len(cand_ans_paths) \
== len(cand_ans_ctx) == len(cand_labels) == len(cand_ans_topic_key_type) == len(cand_ans_path_bows)
return (cand_ans_bows, cand_ans_entities, cand_ans_type_bows, cand_ans_types, cand_ans_path_bows, cand_ans_paths, cand_ans_ctx, cand_ans_topic_key_type, cand_labels)
# Build seed entity candidates for topic entity classification
def build_seed_ent_data(qa, kb, entity2id, entityType2id, relation2id, vocab2id, topn, dtype):
queries = []
seed_ent_features = []
seed_ent_labels = []
seed_ent_inds = []
for each in qa:
query = tokenize(each['qText'].lower())
q = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in query]
queries.append(q)
tmp_features = []
tmp_labels = []
tmp_inds = []
for i, freebase_key in enumerate(each['freebaseKeyCands'][:topn]):
tmp_labels.append(freebase_key)
if freebase_key == each['freebaseKey']:
tmp_inds.append(i)
if freebase_key in kb:
features = build_seed_entity_feature(freebase_key, kb[freebase_key], entity2id, entityType2id, relation2id, vocab2id)
tmp_features.append(features)
else:
tmp_features.append([[]] * 5)
if dtype == 'test':
if len(tmp_inds) == 0: # No answer
tmp_inds.append(-1)
else:
assert len(tmp_labels) == topn
assert len(tmp_inds) == 1
seed_ent_features.append(list(zip(*tmp_features)))
seed_ent_labels.append(tmp_labels)
seed_ent_inds.append(tmp_inds)
return (queries, seed_ent_features, seed_ent_labels, seed_ent_inds)
def build_seed_entity_feature(seed_ent, graph, entity2id, entityType2id, relation2id, vocab2id):
# candidate seed entity features:
# entity name
# entity type
# entity neighboring relations
selected_names = (graph['name'] + graph['alias'])[:1]
seed_ent_name = [vocab2id[y] if y in vocab2id else config.RESERVED_TOKENS['UNK'] for x in selected_names for y in tokenize(x.lower())]
selected_types = (graph['notable_types'] + graph['type'])[:ENT_TYPE_HOP]
seed_ent_type_name = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for y in selected_types for x in y.lower().split('/')[-1].split('_')]
seed_ent_type = [entityType2id[x] if x in entityType2id else config.RESERVED_ENT_TYPES['UNK'] for x in selected_types]
seed_rel_names = []
seed_rels = []
for k in graph['neighbors']:
if if_filterout(k):
continue
k_bow = [vocab2id[x] if x in vocab2id else config.RESERVED_TOKENS['UNK'] for x in k.lower().split('/')[-1].split('_')]
seed_rel_names.append(k_bow)
seed_rels.append(relation2id[k] if k in relation2id else config.RESERVED_RELS['UNK'])
return (seed_ent_name, seed_ent_type_name, seed_ent_type, seed_rel_names, seed_rels)
================================================
FILE: src/core/build_data/freebase.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
from ..utils.utils import *
def fetch_meta(path):
try:
data = load_gzip_json(path)
except:
return {}
content = {}
properties = data['property']
if '/type/object/name' in properties:
content['name'] = [x['value'] for x in properties['/type/object/name']['values']]
else:
content['name'] = []
if '/common/topic/alias' in properties:
content['alias'] = [x['value'] for x in properties['/common/topic/alias']['values']]
else:
content['alias'] = []
if '/common/topic/notable_types' in properties:
content['notable_types'] = [x['id'] for x in properties['/common/topic/notable_types']['values']]
else:
content['notable_types'] = []
if '/type/object/type' in properties:
content['type'] = [x['id'] for x in properties['/type/object/type']['values']]
else:
content['type'] = []
return content
def fetch(data, data_dir):
if not 'id' in data:
return data['value']
mid = data['id']
# meta data might not be in the subgraph, get it from target files
meta = fetch_meta(os.path.join(data_dir, '{}.json.gz'.format(mid.strip('/').replace('/', '.'))))
if meta == {}:
if not 'property' in data:
if 'text' in data:
return data['text']
else:
import pdb;pdb.set_trace()
properties = data['property']
if '/type/object/name' in properties:
meta['name'] = [x['value'] for x in properties['/type/object/name']['values']]
else:
meta['name'] = []
if '/common/topic/alias' in properties:
meta['alias'] = [x['value'] for x in properties['/common/topic/alias']['values']]
else:
meta['alias'] = []
if '/common/topic/notable_types' in properties:
meta['notable_types'] = [x['id'] for x in properties['/common/topic/notable_types']['values']]
else:
meta['notable_types'] = []
if '/type/object/type' in properties:
meta['type'] = [x['id'] for x in properties['/type/object/type']['values']]
else:
meta['type'] = []
graph = {mid: meta}
if not 'property' in data: # we stop at the 2nd hop
return graph
properties = data['property']
neighbors = {}
for k, v in properties.items():
if k.startswith('/common') or k.startswith('/type') \
or k.startswith('/freebase') or k.startswith('/user') \
or k.startswith('/imdb'):
continue
if len(v['values']) > 0:
neighbors[k] = []
for nbr in v['values']:
nbr_graph = fetch(nbr, data_dir)
neighbors[k].append(nbr_graph)
graph[mid]['neighbors'] = neighbors
return graph
================================================
FILE: src/core/build_data/utils.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
import datetime
import shutil
from collections import defaultdict
import numpy as np
from scipy.sparse import *
RESERVED_TOKENS = {'PAD': 0, 'UNK': 1}
def built(path, version_string=None):
"""Checks if 'built.log' flag has been set for that task.
If a version_string is provided, this has to match, or the version
is regarded as not built.
"""
if version_string:
fname = os.path.join(path, 'built.log')
if not os.path.isfile(fname):
return False
else:
with open(fname, 'r') as read:
text = read.read().split('\n')
return (len(text) > 1 and text[1] == version_string)
else:
return os.path.isfile(os.path.join(path, 'built.log'))
def mark_done(path, version_string=None):
"""Marks the path as done by adding a 'built.log' file with the current
timestamp plus a version description string if specified.
"""
with open(os.path.join(path, 'built.log'), 'w') as write:
write.write(str(datetime.datetime.today()))
if version_string:
write.write('\n' + version_string)
def make_dir(path):
"""Makes the directory and any nonexistent parent directories."""
os.makedirs(path, exist_ok=True)
def remove_dir(path):
"""Removes the given directory, if it exists."""
shutil.rmtree(path, ignore_errors=True)
def vectorize_data(queries, query_mentions, memories, max_query_size=None, max_query_markup_size=None, max_mem_size=None, \
max_ans_bow_size=None, max_ans_type_bow_size=None, max_ans_path_bow_size=None, max_ans_path_size=None, \
max_ans_ctx_entity_bows_size=None, max_ans_ctx_relation_bows_size=1, \
verbose=True, fixed_size=False, vocab2id=None):
cand_ans_bows, cand_ans_entities, cand_ans_type_bows, cand_ans_types, cand_ans_path_bows, cand_ans_paths, cand_ans_ctx, cand_ans_topic_key = zip(*memories)
cand_ans_size = min(max(map(len, (x for x in cand_ans_entities)), default=0), max_mem_size if max_mem_size else float('inf'))
if fixed_size:
query_size = max_query_size
# query_markup_size = max_query_markup_size
cand_ans_bows_size = max_ans_bow_size
cand_ans_type_bows_size = max_ans_type_bow_size
cand_ans_path_bows_size = max_ans_path_bow_size
cand_ans_paths_size = max_ans_path_size
else:
query_size = max(min(max(map(len, queries), default=0), max_query_size if max_query_size else float('inf')), 1)
# query_markup_size = max(min(max(map(len, query_mentions), default=0), max_query_markup_size if max_query_markup_size else float('inf')), 1)
cand_ans_bows_size = max(min(max(map(len, (y for x in cand_ans_bows for y in x)), default=0), max_ans_bow_size if max_ans_bow_size else float('inf')), 1)
cand_ans_type_bows_size = max(min(max(map(len, (y for x in cand_ans_type_bows for y in x)), default=0), max_ans_type_bow_size if max_ans_type_bow_size else float('inf')), 1)
cand_ans_path_bows_size = max(min(max(map(len, (y for x in cand_ans_path_bows for y in x)), default=0), max_ans_path_bow_size if max_ans_path_bow_size else float('inf')), 1)
cand_ans_paths_size = max(min(max(map(len, (y for x in cand_ans_paths for y in x)), default=0), max_ans_path_size if max_ans_path_size else float('inf')), 1)
cand_ans_types_size = max(max(map(len, (y for x in cand_ans_types for y in x)), default=0), 1)
cand_ans_ctx_entity_bows_size = max(min(max(map(len, (z for x in cand_ans_ctx for y in x for z in y[0])), default=0), max_ans_ctx_entity_bows_size if max_ans_ctx_entity_bows_size else float('inf')), 1)
cand_ans_ctx_relation_bows_size = max(min(max(map(len, (y[1] for x in cand_ans_ctx for y in x)), default=0), max_ans_ctx_relation_bows_size if max_ans_ctx_relation_bows_size else float('inf')), 1)
cand_ans_topic_key_ent_type_bows_size = max(max(map(len, (y[0] for x in cand_ans_topic_key for y in x)), default=0), 1)
cand_ans_topic_key_ent_types_size = max(max(map(len, (y[1] for x in cand_ans_topic_key for y in x)), default=0), 1)
if verbose:
print('\nquery_size: {}, cand_ans_size: {}, cand_ans_bows_size: {}, '
'cand_ans_type_bows_size: {}, cand_ans_types_size: {}, cand_ans_path_bows_size: {}, cand_ans_paths_size: {}, '
'cand_ans_ctx_entity_bows_size: {}, cand_ans_topic_key_ent_types_size: {}'\
.format(query_size, cand_ans_size, cand_ans_bows_size, cand_ans_type_bows_size, \
cand_ans_types_size, cand_ans_path_bows_size, cand_ans_paths_size, cand_ans_ctx_entity_bows_size, \
cand_ans_topic_key_ent_types_size))
# Question word
qw_tokens = ["which", "what", "who", "whose", "whom", "where", "when", "how", "why", "whether"]
qw_vids = [vocab2id[each] for each in qw_tokens if each in vocab2id]
qw_vid2id = dict(zip(qw_vids, range(len(qw_vids))))
Q = []
QW = []
Q_len = []
for i, q in enumerate(queries):
Q_len.append(min(query_size, len(q)))
lq = max(0, query_size - len(q))
q_vec = q[-query_size:] + [0] * lq
Q.append(q_vec)
tmp = [qw_vid2id[each] for each in q if each in qw_vid2id]
tmp = tmp[-query_size:] + [0] * max(0, query_size - len(tmp))
QW.append(tmp)
cand_ans_bows_vec = []
for x in cand_ans_bows:
tmp = []
for y in x:
l = max(0, cand_ans_bows_size - len(y))
tmp1 = y[:cand_ans_bows_size] + [0] * l
tmp.append(tmp1)
tmp += [[0] * cand_ans_bows_size] # Add a dummy candidate after the true sequence
cand_ans_bows_vec.append(tmp)
cand_ans_entities_vec = []
for x in cand_ans_entities:
cand_ans_entities_vec.append(x + [0]) # Add a dummy candidate after the true sequence
cand_ans_types_vec = []
for x in cand_ans_types:
tmp = []
for y in x:
l = max(0, cand_ans_types_size - len(y))
tmp1 = y[:cand_ans_types_size] + [0] * l
tmp.append(tmp1)
tmp += [[0] * cand_ans_types_size] # Add a dummy candidate after the true sequence
cand_ans_types_vec.append(tmp)
cand_ans_type_bows_vec = []
cand_ans_type_bows_len = []
for x in cand_ans_type_bows:
tmp = []
tmp_len = []
for y in x:
l = max(0, cand_ans_type_bows_size - len(y))
tmp1 = y[:cand_ans_type_bows_size] + [0] * l
tmp.append(tmp1)
tmp_len.append(max(min(cand_ans_type_bows_size, len(y)), 1))
tmp += [[0] * cand_ans_type_bows_size] # Add a dummy candidate after the true sequence
tmp_len += [1]
cand_ans_type_bows_vec.append(tmp)
cand_ans_type_bows_len.append(tmp_len)
cand_ans_paths_vec = []
for x in cand_ans_paths:
tmp = []
for y in x:
l = max(0, cand_ans_paths_size - len(y))
tmp1 = y[:cand_ans_paths_size] + [0] * l
tmp.append(tmp1)
tmp += [[0] * cand_ans_paths_size] # Add a dummy candidate after the true sequence
cand_ans_paths_vec.append(tmp)
cand_ans_path_bows_vec = []
cand_ans_path_bows_len = []
for x in cand_ans_path_bows:
tmp = []
tmp_len = []
for y in x:
l = max(0, cand_ans_path_bows_size - len(y))
tmp1 = y[:cand_ans_path_bows_size] + [0] * l
tmp.append(tmp1)
tmp_len.append(max(min(cand_ans_path_bows_size, len(y)), 1))
tmp += [[0] * cand_ans_path_bows_size] # Add a dummy candidate after the true sequence
tmp_len += [1]
cand_ans_path_bows_vec.append(tmp)
cand_ans_path_bows_len.append(tmp_len)
cand_ans_ctx_entity_vec = []
cand_ans_ctx_relation_vec = []
for x in cand_ans_ctx:
tmp_ent = []
tmp_rel = []
for y in x:
tmp_ent.append(y[0]) # y[0] is a list of lists
l_rel = max(0, cand_ans_ctx_relation_bows_size - len(y[1]))
tmp_rel.append(y[1][:cand_ans_ctx_relation_bows_size] + [0] * l_rel)
tmp_ent += [[]] # Add a dummy candidate after the true sequence
tmp_rel += [[0] * cand_ans_ctx_relation_bows_size]
cand_ans_ctx_entity_vec.append(tmp_ent)
cand_ans_ctx_relation_vec.append(tmp_rel)
cand_ans_topic_key_ent_type_bows_vec = []
cand_ans_topic_key_ent_type_vec = []
cand_ans_topic_key_ent_type_bows_len = []
for x in cand_ans_topic_key:
tmp_ent_type_bows = []
tmp_ent_type = []
tmp_ent_type_bow_len = []
for y in x:
tmp_ent_type_bows.append(y[0][:cand_ans_topic_key_ent_type_bows_size] + [0] * max(0, cand_ans_topic_key_ent_type_bows_size - len(y[0])))
tmp_ent_type.append(y[1][:cand_ans_topic_key_ent_types_size] + [0] * max(0, cand_ans_topic_key_ent_types_size - len(y[1])))
tmp_ent_type_bow_len.append(max(min(cand_ans_topic_key_ent_type_bows_size, len(y[0])), 1))
tmp_ent_type_bows += [[0] * cand_ans_topic_key_ent_type_bows_size] # Add a dummy candidate after the true sequence
tmp_ent_type += [[0] * cand_ans_topic_key_ent_types_size]
tmp_ent_type_bow_len += [1]
cand_ans_topic_key_ent_type_bows_vec.append(tmp_ent_type_bows)
cand_ans_topic_key_ent_type_vec.append(tmp_ent_type)
cand_ans_topic_key_ent_type_bows_len.append(tmp_ent_type_bow_len)
return Q, QW, Q_len, list(zip(cand_ans_bows_vec, cand_ans_entities_vec, cand_ans_type_bows_vec, cand_ans_types_vec, cand_ans_type_bows_len, cand_ans_path_bows_vec, cand_ans_paths_vec, cand_ans_path_bows_len, cand_ans_ctx_entity_vec, cand_ans_ctx_relation_vec, cand_ans_topic_key_ent_type_bows_vec, cand_ans_topic_key_ent_type_vec, cand_ans_topic_key_ent_type_bows_len))
def vectorize_ent_data(queries, ent_memories, max_query_size=None, \
max_seed_ent_name_size=None, max_seed_type_name_size=None, \
max_seed_rel_name_size=None, max_seed_rel_size=None, verbose=True):
seed_ent_name, seed_ent_type_name, seed_ent_type, seed_rel_names, seed_rels = zip(*ent_memories)
max_query_size = max(min(max(map(len, queries), default=0), max_query_size if max_query_size else float('inf')), 1)
cand_seed_ent_name_size = max(min(max(map(len, (y for x in seed_ent_name for y in x)), default=0), max_seed_ent_name_size if max_seed_ent_name_size else float('inf')), 1)
cand_seed_type_name_size = max(min(max(map(len, (y for x in seed_ent_type_name for y in x)), default=0), max_seed_type_name_size if max_seed_type_name_size else float('inf')), 1)
cand_seed_types_size = max(max(map(len, (y for x in seed_ent_type for y in x)), default=0), 1)
cand_seed_rel_name_size = max(min(max(map(len, (z for x in seed_rel_names for y in x for z in y)), default=0), max_seed_rel_name_size if max_seed_rel_name_size else float('inf')), 1)
cand_seed_rel_size = max(min(max(map(len, (y for x in seed_rels for y in x)), default=0), max_seed_rel_size if max_seed_rel_size else float('inf')), 1)
if verbose:
print('\nmax_query_size: {}, cand_seed_ent_name_size: {}, cand_seed_type_name_size: {}, '
'cand_seed_types_size: {}, cand_seed_rel_name_size: {}, cand_seed_rel_size: {}'.format(max_query_size, \
cand_seed_ent_name_size, cand_seed_type_name_size, cand_seed_types_size, \
cand_seed_rel_name_size, cand_seed_rel_size))
# Query vectorization
Q = []
Q_len = []
for q in queries:
Q_len.append(min(max_query_size, len(q)))
lq = max(0, max_query_size - len(q))
q_vec = q[-max_query_size:] + [0] * lq
Q.append(q_vec)
# Entity vectorization
cand_seed_ent_name_vec = []
cand_seed_ent_name_len = []
for x in seed_ent_name:
tmp = []
tmp_len = []
for y in x:
l = max(0, cand_seed_ent_name_size - len(y))
tmp1 = y[:cand_seed_ent_name_size] + [0] * l
tmp.append(tmp1)
tmp_len.append(max(min(cand_seed_ent_name_size, len(y)), 1))
cand_seed_ent_name_vec.append(tmp)
cand_seed_ent_name_len.append(tmp_len)
cand_seed_type_vec = []
for x in seed_ent_type:
tmp = []
for y in x:
l = max(0, cand_seed_types_size - len(y))
tmp1 = y[:cand_seed_types_size] + [0] * l
tmp.append(tmp1)
cand_seed_type_vec.append(tmp)
cand_seed_type_name_vec = []
cand_seed_type_name_len = []
for x in seed_ent_type_name:
tmp = []
tmp_len = []
for y in x:
l = max(0, cand_seed_type_name_size - len(y))
tmp1 = y[:cand_seed_type_name_size] + [0] * l
tmp.append(tmp1)
tmp_len.append(max(min(cand_seed_type_name_size, len(y)), 1))
cand_seed_type_name_vec.append(tmp)
cand_seed_type_name_len.append(tmp_len)
cand_seed_rel_vec = []
cand_seed_rel_mask = []
for x in seed_rels: # example
x_tmp = []
x_mask = []
for y in x: # seed entity
l = max(0, cand_seed_rel_size - len(y))
y_tmp = y[:cand_seed_rel_size] + [0] * l
x_tmp.append(y_tmp)
x_mask.append(min(len(y), cand_seed_rel_size))
cand_seed_rel_vec.append(x_tmp)
cand_seed_rel_mask.append(x_mask)
cand_seed_rel_name_vec = []
cand_seed_rel_name_len = []
for x in seed_rel_names: # example
x_tmp = []
x_tmp_len = []
for y in x: # seed entity
y_tmp = []
y_tmp_len = []
for z in y: # relation
z_l = max(0, cand_seed_rel_name_size - len(z))
z_tmp = z[:cand_seed_rel_name_size] + [0] * z_l
y_tmp.append(z_tmp)
y_tmp_len.append(max(min(cand_seed_rel_name_size, len(z)), 1))
y_l = max(0, cand_seed_rel_size - len(y))
y_tmp += [[0] * cand_seed_rel_name_size] * y_l
y_tmp_len += [1] * y_l
x_tmp.append(y_tmp)
x_tmp_len.append(y_tmp_len)
cand_seed_rel_name_vec.append(x_tmp)
cand_seed_rel_name_len.append(x_tmp_len)
return Q, Q_len, list(zip(cand_seed_ent_name_vec, cand_seed_ent_name_len, cand_seed_type_name_vec, cand_seed_type_vec, cand_seed_type_name_len, cand_seed_rel_name_vec, cand_seed_rel_vec, cand_seed_rel_name_len, cand_seed_rel_mask))
================================================
FILE: src/core/build_data/webquestions.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
# import re
import argparse
from nltk.parse.stanford import StanfordDependencyParser
from ..utils.utils import *
from ..utils.freebase_utils import if_filterout
from ..utils.generic_utils import *
def get_used_fbkeys(data_dir, out_dir):
# Fetch freebase keys used in training and validation sets.
fbkeys = set()
split = ['factoid_webqa/train.json', 'factoid_webqa/valid.json']
files = [os.path.join(data_dir, x) for x in split]
for f in files:
data = load_json(f)
for qa in data:
fbkeys.add(qa['freebaseKey'])
dump_json(list(fbkeys), os.path.join(out_dir, 'fbkeys_train_valid.json'), indent=1)
def get_all_fbkeys(data_dir, out_dir):
# Fetch all freebase keys possibily useful to answer questions.
fbkeys = set()
split = ['factoid_webqa/train.json', 'factoid_webqa/valid.json', 'factoid_webqa/test.json']
files = [os.path.join(data_dir, x) for x in split]
for f in files:
data = load_json(f)
for qa in data:
fbkeys.add(qa['freebaseKey'])
retrieved_test_path = os.path.join(data_dir, 'factoid_webqa/webquestions.examples.test.retrieved.json')
if os.path.exists(retrieved_test_path):
data = load_json(retrieved_test_path)
for qa in data:
if not 'retrievedList' in qa:
continue
for x in qa['retrievedList'].split():
fbkeys.add(x.split(':')[0])
dump_json(list(fbkeys), os.path.join(out_dir, 'fbkeys_train_valid_test_retrieved.json'), indent=1)
def main(fb_path, mid2key_path, data_dir, out_dir):
HAS_DEP = False
if HAS_DEP:
dep_parser = StanfordDependencyParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz") # Set CLASSPATH and STANFORD_MODELS environment variables beforehand
kb = load_ndjson(fb_path, return_type='dict')
mid2key = load_json(mid2key_path)
all_split_questions = []
split = ['factoid_webqa/train.json', 'factoid_webqa/valid.json', 'factoid_webqa/test.json']
files = [os.path.join(data_dir, x) for x in split]
missing_mid2key = []
for f in files:
data_type = os.path.basename(f).split('.')[0]
num_unanswerable = 0
all_questions = []
data = load_json(f)
for q in data:
questions = {}
questions['answers'] = q['answers']
questions['entities'] = q['entities']
questions['qText'] = q['qText']
questions['qId'] = q['qId']
questions['freebaseKey'] = q['freebaseKey']
questions['freebaseKeyCands'] = [q['freebaseKey']]
for x in q['freebaseMids']:
if x['mid'] in mid2key:
fbkey = mid2key[x['mid']]
if fbkey != q['freebaseKey']:
questions['freebaseKeyCands'].append(fbkey)
else:
missing_mid2key.append(x['mid'])
qtext = tokenize(q['qText'])
if HAS_DEP:
qw = list(set(qtext).intersection(question_word_list))
question_word = qw[0] if len(qw) > 0 else ''
topic_ent = q['freebaseKey']
dep_path = extract_dep_feature(dep_parser, ' '.join(qtext), topic_ent, question_word)
else:
dep_path = []
questions['dep_path'] = dep_path
all_questions.append(questions)
if not q['freebaseKey'] in kb:
num_unanswerable += 1
continue
cand_ans = fetch_ans_cands(kb[q['freebaseKey']])
norm_cand_ans = set([normalize_answer(x) for x in cand_ans])
norm_gold_ans = [normalize_answer(x) for x in q['answers']]
# Check if we can find the gold answer from the candidiate answers.
if len(norm_cand_ans.intersection(norm_gold_ans)) == 0:
num_unanswerable += 1
continue
all_split_questions.append(all_questions)
print('{} set: Num of unanswerable questions: {}'.format(data_type, num_unanswerable))
for i, each in enumerate(all_split_questions):
dump_ndjson(each, os.path.join(out_dir, split[i].split('/')[-1]))
def fetch_ans_cands(graph):
cand_ans = set() # candidiate answers
# We only consider the alias relations of topic entityies
cand_ans.update(graph['alias'])
for k, v in graph['neighbors'].items():
if if_filterout(k):
continue
for nbr in v:
if isinstance(nbr, str):
cand_ans.add(nbr)
continue
elif isinstance(nbr, bool):
cand_ans.add('true' if nbr else 'false')
continue
elif isinstance(nbr, float):
cand_ans.add(str(nbr))
continue
elif isinstance(nbr, dict):
nbr_k = list(nbr.keys())[0]
nbr_v = nbr[nbr_k]
selected_names = nbr_v['name'] if 'name' in nbr_v and len(nbr_v['name']) > 0 else (nbr_v['alias'][:1] if 'alias' in nbr_v else [])
cand_ans.add(selected_names[0] if len(selected_names) > 0 else 'UNK')
if not 'neighbors' in nbr_v:
continue
for kk, vv in nbr_v['neighbors'].items(): # 2nd hop
if if_filterout(kk):
continue
for nbr_nbr in vv:
if isinstance(nbr_nbr, str):
cand_ans.add(nbr_nbr)
continue
elif isinstance(nbr_nbr, bool):
cand_ans.add('true' if nbr_nbr else 'false')
continue
elif isinstance(nbr_nbr, float):
cand_ans.add(str(nbr_nbr))
continue
elif isinstance(nbr_nbr, dict):
nbr_nbr_k = list(nbr_nbr.keys())[0]
nbr_nbr_v = nbr_nbr[nbr_nbr_k]
selected_names = nbr_nbr_v['name'] if 'name' in nbr_nbr_v and len(nbr_nbr_v['name']) > 0 else (nbr_nbr_v['alias'][:1] if 'alias' in nbr_nbr_v else [])
cand_ans.add(selected_names[0] if len(selected_names) > 0 else 'UNK')
else:
raise RuntimeError('Unknown type: %s' % type(nbr_nbr))
else:
raise RuntimeError('Unknown type: %s' % type(nbr))
return list(cand_ans)
================================================
FILE: src/core/config.py
================================================
# Vocabulary
RESERVED_TOKENS = {'PAD': 0, 'UNK': 1}
RESERVED_ENTS = {'PAD': 0, 'UNK': 1}
RESERVED_ENT_TYPES = {'PAD': 0, 'UNK': 1}
RESERVED_RELS = {'PAD': 0, 'UNK': 1}
extra_vocab_tokens = ['alias', 'true', 'false', 'num', 'bool'] + \
['np', 'organization', 'date', 'number', 'misc', 'ordinal', 'duration', 'person', 'time', 'location'] + \
['__np__', '__organization__', '__date__', '__number__', '__misc__', '__ordinal__', '__duration__', '__person__', '__time__', '__location__']
extra_rels = ['alias']
extra_ent_types = ['num', 'bool']
# BAMnet entity mention types
topic_mention_types = {'person', 'organization', 'location', 'misc'}
# delex_mention_types = {'date', 'time', 'ordinal', 'number'}
delex_mention_types = {'date', 'ordinal', 'number'}
constraint_mention_types = delex_mention_types
================================================
FILE: src/core/utils/__init__.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
================================================
FILE: src/core/utils/freebase_utils.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
from rapidfuzz import fuzz, process
def if_filterout(s):
if s.endswith('has_sentences') or \
s.endswith('exceptions') or s.endswith('sww_base/source') or \
s.endswith('kwtopic/assessment'):
return True
else:
return False
def query_kb(kb, ent_name, fuzz_threshold=90):
results = []
for k, v in kb.items():
ret = process.extractOne(ent_name, v['name'] + v['alias'], scorer=fuzz.token_sort_ratio)
if ret[1] > fuzz_threshold:
results.append((k, ret[0], ret[1]))
results = sorted(results, key=lambda d:d[-1], reverse=True)
return list(zip(*results))[0] if len(results) > 0 else []
================================================
FILE: src/core/utils/generic_utils.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import re, string
import numpy as np
from rapidfuzz import fuzz, process
from nltk.corpus import stopwords
from .utils import dump_ndarray, tokenize
question_word_list = 'who, when, what, where, how, which, why, whom, whose'.split(', ')
stop_words = set(stopwords.words("english"))
def find_parent(x, tree, conn='<-'):
root = tree[0][0]
path = []
for parent, indicator, child in tree:
if x == child[0]:
path.extend([conn, '__{}__'.format(indicator), '-', parent[0]])
if not parent == root:
p = find_parent(parent[0], tree, conn)
path.extend(p)
return path
return path
def extract_dep_feature(dep_parser, text, topic_ent, question_word):
dep = dep_parser.raw_parse(text).__next__()
tree = list(dep.triples())
topic_ent = list(set(tokenize(topic_ent)) - stop_words)
text = text.split()
path_len = 1e5
topic_ent_to_root = []
for each in topic_ent:
ret = process.extractOne(each, text, scorer=fuzz.token_sort_ratio)
if ret[1] < 85:
continue
tmp = find_parent(ret[0], tree, '->')
if len(tmp) > 0 and len(tmp) < path_len:
topic_ent_to_root = tmp
path_len = len(tmp)
question_word_to_root = find_parent(question_word, tree)
# if len(question_word_to_root) == 0 or len(topic_ent_to_root) == 0:
# import pdb;pdb.set_trace()
return question_word_to_root + list(reversed(topic_ent_to_root[:-1]))
def unique(seq):
seen = set()
seen_add = seen.add
return [x for x in seq if not (x in seen or seen_add(x))]
re_art = re.compile(r'\b(a|an|the)\b')
re_punc = re.compile(r'[%s]' % re.escape(string.punctuation))
def normalize_answer(s):
"""Lower text and remove extra whitespace."""
def remove_articles(text):
return re_art.sub(' ', text)
def remove_punc(text):
return re_punc.sub(' ', text) # convert punctuation to spaces
def white_space_fix(text):
return ' '.join(text.split())
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def dump_embeddings(vocab_dict, emb_file, out_path, emb_size=300, binary=False, seed=123):
vocab_emb = get_embeddings(emb_file, vocab_dict, binary)
vocab_size = len(vocab_dict)
np.random.seed(seed)
embeddings = np.random.uniform(-0.08, 0.08, (vocab_size, emb_size))
for w, idx in vocab_dict.items():
if w in vocab_emb:
embeddings[int(idx)] = vocab_emb[w]
embeddings[0] = 0
dump_ndarray(embeddings, out_path)
return embeddings
def get_embeddings(emb_file, vocab, binary=False):
pt = PreTrainEmbedding(emb_file, binary)
vocab_embs = {}
i = 0.
for each in vocab:
emb = pt.get_embeddings(each)
if not emb is None:
vocab_embs[each] = emb
i += 1
print('get_wordemb hit ratio: %s' % (i / len(vocab)))
return vocab_embs
class PreTrainEmbedding():
def __init__(self, file, binary=False):
import gensim
self.model = gensim.models.KeyedVectors.load_word2vec_format(file, binary=binary)
def get_embeddings(self, word):
word_list = [word, word.upper(), word.lower(), word.title(), string.capwords(word, '_')]
for w in word_list:
try:
return self.model[w]
except KeyError:
# print('Can not get embedding for ', w)
continue
return None
================================================
FILE: src/core/utils/metrics.py
================================================
'''
Created on Oct, 2017
@author: hugo
Note: Modified the official evaluation script provided by Berant et al.
(https://github.com/percyliang/sempre/blob/master/scripts/evaluation.py)
'''
from .generic_utils import normalize_answer
def calc_f1(gold_list, pred_list):
"""Return a tuple with recall, precision, and f1 for one example"""
# Assume all questions have at least one answer
if len(gold_list) == 0:
raise RuntimeError('Gold list may not be empty')
# If we return an empty list recall is zero and precision is one
if len(pred_list) == 0:
return (0, 1, 0)
# It is guaranteed now that both lists are not empty
# Normalize answers
gold_list = [normalize_answer(s) for s in gold_list]
pred_list = [normalize_answer(s) for s in pred_list]
precision = 0
for entity in pred_list:
if entity in gold_list:
precision += 1
precision = float(precision) / len(pred_list)
recall = 0
for entity in gold_list:
if entity in pred_list:
recall += 1
recall = float(recall) / len(gold_list)
f1 = 0
if precision + recall > 0:
f1 = 2 * recall * precision / (precision + recall)
return (recall, precision, f1)
def calc_avg_f1(gold_list, pred_list, verbose=True):
"""Go over all examples and compute recall, precision and F1"""
avg_recall = 0
avg_precision = 0
avg_f1 = 0
count = 0
out_f = open('error_analysis.txt', 'w')
assert len(gold_list) == len(pred_list)
for i, gold in enumerate(gold_list):
recall, precision, f1 = calc_f1(gold, pred_list[i])
avg_recall += recall
avg_precision += precision
avg_f1 += f1
count += 1
if True:
# if f1 < 0.6:
out_f.write('{}\t{}\t{}\t{}\n'.format(i, gold, pred_list[i], f1))
out_f.close()
avg_recall = float(avg_recall) / count
avg_precision = float(avg_precision) / count
avg_f1 = float(avg_f1) / count
avg_new_f1 = 0
if avg_precision + avg_recall > 0:
avg_new_f1 = 2 * avg_recall * avg_precision / (avg_precision + avg_recall)
if verbose:
print("Number of questions: " + str(count))
print("Average recall over questions: " + str(avg_recall))
print("Average precision over questions: " + str(avg_precision))
print("Average f1 over questions: " + str(avg_f1))
# print("F1 of average recall and average precision: " + str(avg_new_f1))
return count, avg_recall, avg_precision, avg_f1
================================================
FILE: src/core/utils/utils.py
================================================
'''
Created on Sep, 2017
@author: hugo
'''
import os
import re
import yaml
import gzip
import json
import string
import numpy as np
from nltk.tokenize import wordpunct_tokenize#, word_tokenize
# tokenize = lambda s: word_tokenize(re.sub(r'[%s]' % punc_wo_dot, ' ', re.sub(r'(?<!\d)[%s](?!\d)' % string.punctuation, ' ', s)))
tokenize = lambda s: wordpunct_tokenize(re.sub('[%s]' % re.escape(string.punctuation), ' ', s))
def get_config(config_path="config.yml"):
with open(config_path, "r") as setting:
config = yaml.load(setting)
return config
def print_config(config):
print("**************** MODEL CONFIGURATION ****************")
for key in sorted(config.keys()):
val = config[key]
keystr = "{}".format(key) + (" " * (24 - len(key)))
print("{} --> {}".format(keystr, val))
print("**************** MODEL CONFIGURATION ****************")
def read_lines(path_to_file):
data = []
try:
with open(path_to_file, 'r') as f:
for line in f:
tmp = [float(x) for x in line.strip().split()]
data.append(tmp)
except Exception as e:
raise e
return data
def dump_ndarray(data, path_to_file):
try:
with open(path_to_file, 'wb') as f:
np.save(f, data)
except Exception as e:
raise e
def load_ndarray(path_to_file):
try:
with open(path_to_file, 'rb') as f:
data = np.load(f)
except Exception as e:
raise e
return data
def dump_ndjson(data, file):
try:
with open(file, 'w') as f:
for each in data:
f.write(json.dumps(each) + '\n')
except Exception as e:
raise e
def load_ndjson(file, return_type='array'):
if return_type == 'array':
return load_ndjson_to_array(file)
elif return_type == 'dict':
return load_ndjson_to_dict(file)
else:
raise RuntimeError('Unknown return_type: %s' % return_type)
def load_ndjson_to_array(file):
data = []
try:
with open(file, 'r') as f:
for line in f:
data.append(json.loads(line.strip()))
except Exception as e:
raise e
return data
def load_ndjson_to_dict(file):
data = {}
try:
with open(file, 'r') as f:
for line in f:
data.update(json.loads(line.strip()))
except Exception as e:
raise e
return data
def dump_json(data, file, indent=None):
try:
with open(file, 'w') as f:
json.dump(data, f, indent=indent)
except Exception as e:
raise e
def load_json(file):
try:
with open(file, 'r') as f:
data = json.load(f)
except Exception as e:
raise e
return data
def dump_dict_ndjson(data, file):
try:
with open(file, 'w') as f:
for k, v in data.items():
line = json.dumps([k, v]) + '\n'
f.write(line)
except Exception as e:
raise e
def load_gzip_json(file):
try:
with gzip.open(file, 'r') as f:
data = json.load(f)
except Exception as e:
raise e
return data
def get_all_files(dir, recursive=False):
if recursive:
return [os.path.join(root, file) for root, dirnames, filenames in os.walk(dir) for file in filenames if os.path.isfile(os.path.join(root, file)) and not file.startswith('.')]
else:
return [os.path.join(dir, filename) for filename in os.listdir(dir) if os.path.isfile(os.path.join(dir, filename)) and not filename.startswith('.')]
# Print iterations progress
def printProgressBar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):
"""
Call in a loop to create terminal progress bar
@params:
iteration - Required : current iteration (Int)
total - Required : total iterations (Int)
prefix - Optional : prefix string (Str)
suffix - Optional : suffix string (Str)
decimals - Optional : positive number of decimals in percent complete (Int)
length - Optional : character length of bar (Int)
fill - Optional : bar fill character (Str)
"""
percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
filledLength = int(length * iteration // total)
bar = fill * filledLength + '-' * (length - filledLength)
print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\r')
================================================
FILE: src/joint_test.py
================================================
import timeit
import argparse
import numpy as np
from core.bamnet.entnet import EntnetAgent
from core.bamnet.bamnet import BAMnetAgent
from core.build_data.build_all import build
from core.build_data.utils import vectorize_ent_data, vectorize_data
from core.build_data.build_data import build_data
from core.utils.generic_utils import unique
from core.utils.utils import *
from core.utils.metrics import *
def dynamic_pred(pred, margin):
predictions = []
for i in range(len(pred)):
predictions.append(unique([x[0] for x in pred[i] if x[1] + margin >= pred[i][0][1]]))
return predictions
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-bamnet_config', '--bamnet_config', required=True, type=str, help='path to the config file')
parser.add_argument('-entnet_config', '--entnet_config', required=True, type=str, help='path to the config file')
parser.add_argument('-raw_data', '--raw_data_dir', required=True, type=str, help='raw data dir')
cfg = vars(parser.parse_args())
bamnet_opt = get_config(cfg['bamnet_config'])
entnet_opt = get_config(cfg['entnet_config'])
start = timeit.default_timer()
# Entnet
# Ensure data is built
build(entnet_opt['data_dir'])
data_vec = load_json(os.path.join(entnet_opt['data_dir'], entnet_opt['test_data']))
queries, memories, ent_labels, ent_inds = data_vec
queries, query_lengths, memories = vectorize_ent_data(queries, \
memories, max_query_size=entnet_opt['query_size'], \
max_seed_ent_name_size=entnet_opt['max_seed_ent_name_size'], \
max_seed_type_name_size=entnet_opt['max_seed_type_name_size'], \
max_seed_rel_name_size=entnet_opt['max_seed_rel_name_size'], \
max_seed_rel_size=entnet_opt['max_seed_rel_size'])
ent_model = EntnetAgent(entnet_opt)
acc = ent_model.evaluate([memories, queries, query_lengths], ent_inds, batch_size=entnet_opt['test_batch_size'])
print('acc: {}'.format(acc))
pred_seed_ents = ent_model.predict([memories, queries, query_lengths], ent_labels, batch_size=entnet_opt['test_batch_size'])
# BAMnet
# Ensure data is built
build(bamnet_opt['data_dir'])
entity2id = load_json(os.path.join(bamnet_opt['data_dir'], 'entity2id.json'))
entityType2id = load_json(os.path.join(bamnet_opt['data_dir'], 'entityType2id.json'))
relation2id = load_json(os.path.join(bamnet_opt['data_dir'], 'relation2id.json'))
vocab2id = load_json(os.path.join(bamnet_opt['data_dir'], 'vocab2id.json'))
ctx_stopwords = {'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"}
# Build data in real time
freebase = load_ndjson(os.path.join(cfg['raw_data_dir'], 'freebase_full.json'), return_type='dict')
test_data = load_ndjson(os.path.join(cfg['raw_data_dir'], 'raw_test.json'))
data_vec = build_data(test_data, freebase, entity2id, entityType2id, relation2id, vocab2id, pred_seed_ents=pred_seed_ents)
queries, raw_queries, query_mentions, memories, cand_labels, _, gold_ans_labels = data_vec
queries, query_words, query_lengths, memories_vec = vectorize_data(queries, query_mentions, memories, \
max_query_size=bamnet_opt['query_size'], \
max_query_markup_size=bamnet_opt['query_markup_size'], \
max_ans_bow_size=bamnet_opt['ans_bow_size'], \
vocab2id=vocab2id)
model = BAMnetAgent(bamnet_opt, ctx_stopwords, vocab2id)
pred = model.predict([memories_vec, queries, query_words, raw_queries, query_mentions, query_lengths], cand_labels, batch_size=bamnet_opt['test_batch_size'], margin=2)
print('\nPredictions')
for margin in bamnet_opt['test_margin']:
print('\nMargin: {}'.format(margin))
predictions = dynamic_pred(pred, margin)
calc_avg_f1(gold_ans_labels, predictions)
print('Runtime: %ss' % (timeit.default_timer() - start))
================================================
FILE: src/run_freebase.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import argparse
import os
import json
from core.build_data.freebase import *
from core.utils.utils import *
parser = argparse.ArgumentParser()
parser.add_argument('-data_dir', '--data_dir', required=True, type=str, help='path to the data dir')
parser.add_argument('-fbkeys', '--freebase_keys', required=True, type=str, help='path to the freebase key file')
parser.add_argument('-out_dir', '--out_dir', type=str, required=True, help='path to the output dir')
args = parser.parse_args()
ids = load_json(args.freebase_keys)
total = len(ids)
print('Fetching {} entities and their 2-hop neighbors.'.format(total))
print_bar_len = 50
cnt = 0
missing_ids = set()
with open(os.path.join(args.out_dir, 'freebase.json'), 'a') as out_f:
for id_ in ids:
try:
data = load_gzip_json(os.path.join(args.data_dir, '{}.json.gz'.format(id_)))
except:
missing_ids.add(id_)
continue
graph = fetch(data, args.data_dir)
graph2 = {id_: list(graph.values())[0]}
graph2[id_]['id'] = list(graph.keys())[0]
line = json.dumps(graph2) + '\n'
out_f.write(line)
cnt += 1
if cnt % int(total / print_bar_len) == 0:
printProgressBar(cnt, total, prefix='Progress:', suffix='Complete', length=print_bar_len)
printProgressBar(cnt, total, prefix='Progress:', suffix='Complete', length=print_bar_len)
print('Missed %s mids' % len(missing_ids))
dump_json(list(missing_ids), os.path.join(args.out_dir, 'missing_fbids.json'))
================================================
FILE: src/run_webquestions.py
================================================
'''
Created on Oct, 2017
@author: hugo
'''
import argparse
from core.build_data.webquestions import *
parser = argparse.ArgumentParser()
parser.add_argument('-fb', '--freebase_path', required=True, type=str, help='path to the freebase data')
parser.add_argument('-mid2key', '--mid2key_path', required=True, type=str, help='path to the freebase data')
parser.add_argument('-data_dir', '--data_dir', required=True, type=str, help='path to the data dir')
parser.add_argument('-out_dir', '--out_dir', type=str, required=True, help='path to the output dir')
args = parser.parse_args()
main(args.freebase_path, args.mid2key_path, args.data_dir, args.out_dir)
# get_used_fbkeys(args.data_dir, args.out_dir)
# get_all_fbkeys(args.data_dir, args.out_dir)
================================================
FILE: src/test.py
================================================
import timeit
import argparse
from core.bamnet.bamnet import BAMnetAgent
from core.build_data.build_all import build
from core.build_data.utils import vectorize_data
from core.utils.utils import *
from core.utils.generic_utils import unique
from core.utils.metrics import *
def dynamic_pred(pred, margin):
predictions = []
for i in range(len(pred)):
predictions.append(unique([x[0] for x in pred[i] if x[1] + margin >= pred[i][0][1]]))
return predictions
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-config', '--config', required=True, type=str, help='path to the config file')
cfg = vars(parser.parse_args())
opt = get_config(cfg['config'])
# Ensure data is built
build(opt['data_dir'])
data_vec = load_json(os.path.join(opt['data_dir'], opt['test_data']))
vocab2id = load_json(os.path.join(opt['data_dir'], 'vocab2id.json'))
ctx_stopwords = {'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"}
queries, raw_queries, query_mentions, memories, cand_labels, _, gold_ans_labels = data_vec
queries, query_words, query_lengths, memories_vec = vectorize_data(queries, query_mentions, memories, \
max_query_size=opt['query_size'], \
max_query_markup_size=opt['query_markup_size'], \
max_ans_bow_size=opt['ans_bow_size'], \
vocab2id=vocab2id)
start = timeit.default_timer()
model = BAMnetAgent(opt, ctx_stopwords, vocab2id)
pred = model.predict([memories_vec, queries, query_words, raw_queries, query_mentions, query_lengths], cand_labels, batch_size=opt['test_batch_size'], margin=2)
print('\nPredictions')
for margin in opt['test_margin']:
print('\nMargin: {}'.format(margin))
predictions = dynamic_pred(pred, margin)
calc_avg_f1(gold_ans_labels, predictions)
print('Runtime: %ss' % (timeit.default_timer() - start))
import pdb;pdb.set_trace()
================================================
FILE: src/test_entnet.py
================================================
import timeit
import argparse
import numpy as np
from core.bamnet.entnet import EntnetAgent
from core.build_data.build_all import build
from core.build_data.utils import vectorize_ent_data
from core.utils.utils import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-dt', '--datatype', default='test', type=str, help='data type: {train, valid, test}')
parser.add_argument('-config', '--config', required=True, type=str, help='path to the config file')
cfg = vars(parser.parse_args())
opt = get_config(cfg['config'])
# Ensure data is built
build(opt['data_dir'])
data_vec = load_json(os.path.join(opt['data_dir'], opt['test_data']))
queries, memories, ent_labels, ent_inds = data_vec
queries, query_lengths, memories = vectorize_ent_data(queries, \
memories, max_query_size=opt['query_size'], \
max_seed_ent_name_size=opt['max_seed_ent_name_size'], \
max_seed_type_name_size=opt['max_seed_type_name_size'], \
max_seed_rel_name_size=opt['max_seed_rel_name_size'], \
max_seed_rel_size=opt['max_seed_rel_size'])
start = timeit.default_timer()
ent_model = EntnetAgent(opt)
acc = ent_model.evaluate([memories, queries, query_lengths], ent_inds, batch_size=opt['test_batch_size'])
print('acc: {}'.format(acc))
print('Runtime: %ss' % (timeit.default_timer() - start))
================================================
FILE: src/train.py
================================================
import timeit
import argparse
import numpy as np
from core.bamnet.bamnet import BAMnetAgent
from core.build_data.build_all import build
from core.build_data.utils import vectorize_data
from core.utils.utils import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-config', '--config', required=True, type=str, help='path to the config file')
cfg = vars(parser.parse_args())
opt = get_config(cfg['config'])
print_config(opt)
# Ensure data is built
build(opt['data_dir'])
train_vec = load_json(os.path.join(opt['data_dir'], opt['train_data']))
valid_vec = load_json(os.path.join(opt['data_dir'], opt['valid_data']))
vocab2id = load_json(os.path.join(opt['data_dir'], 'vocab2id.json'))
ctx_stopwords = {'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"}
train_queries, train_raw_queries, train_query_mentions, train_memories, _, train_gold_ans_inds, _ = train_vec
train_queries, train_query_words, train_query_lengths, train_memories = vectorize_data(train_queries, train_query_mentions, \
train_memories, max_query_size=opt['query_size'], \
max_query_markup_size=opt['query_markup_size'], \
max_mem_size=opt['mem_size'], \
max_ans_bow_size=opt['ans_bow_size'], \
max_ans_path_bow_size=opt['ans_path_bow_size'], \
vocab2id=vocab2id)
valid_queries, valid_raw_queries, valid_query_mentions, valid_memories, valid_cand_labels, valid_gold_ans_inds, valid_gold_ans_labels = valid_vec
valid_queries, valid_query_words, valid_query_lengths, valid_memories = vectorize_data(valid_queries, valid_query_mentions, \
valid_memories, max_query_size=opt['query_size'], \
max_query_markup_size=opt['query_markup_size'], \
max_mem_size=opt['mem_size'], \
max_ans_bow_size=opt['ans_bow_size'], \
max_ans_path_bow_size=opt['ans_path_bow_size'], \
vocab2id=vocab2id)
start = timeit.default_timer()
model = BAMnetAgent(opt, ctx_stopwords, vocab2id)
model.train([train_memories, train_queries, train_query_words, train_raw_queries, train_query_mentions, train_query_lengths], train_gold_ans_inds, \
[valid_memories, valid_queries, valid_query_words, valid_raw_queries, valid_query_mentions, valid_query_lengths], \
valid_gold_ans_inds, valid_cand_labels, valid_gold_ans_labels)
print('Runtime: %ss' % (timeit.default_timer() - start))
================================================
FILE: src/train_entnet.py
================================================
import timeit
import argparse
import numpy as np
from core.bamnet.entnet import EntnetAgent
from core.build_data.build_all import build
from core.build_data.utils import vectorize_ent_data
from core.utils.utils import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-config', '--config', required=True, type=str, help='path to the config file')
cfg = vars(parser.parse_args())
opt = get_config(cfg['config'])
print_config(opt)
# Ensure data is built
build(opt['data_dir'])
train_vec = load_json(os.path.join(opt['data_dir'], opt['train_data']))
valid_vec = load_json(os.path.join(opt['data_dir'], opt['valid_data']))
train_queries, train_memories, _, train_ent_inds = train_vec
train_queries, train_query_lengths, train_memories = vectorize_ent_data(train_queries, \
train_memories, max_query_size=opt['query_size'], \
max_seed_ent_name_size=opt['max_seed_ent_name_size'], \
max_seed_type_name_size=opt['max_seed_type_name_size'], \
max_seed_rel_name_size=opt['max_seed_rel_name_size'], \
max_seed_rel_size=opt['max_seed_rel_size'])
valid_queries, valid_memories, _, valid_ent_inds = valid_vec
valid_queries, valid_query_lengths, valid_memories = vectorize_ent_data(valid_queries, \
valid_memories, max_query_size=opt['query_size'], \
max_seed_ent_name_size=opt['max_seed_ent_name_size'], \
max_seed_type_name_size=opt['max_seed_type_name_size'], \
max_seed_rel_name_size=opt['max_seed_rel_name_size'], \
max_seed_rel_size=opt['max_seed_rel_size'])
start = timeit.default_timer()
ent_model = EntnetAgent(opt)
ent_model.train([train_memories, train_queries, train_query_lengths], train_ent_inds, \
[valid_memories, valid_queries, valid_query_lengths], valid_ent_inds)
print('Runtime: %ss' % (timeit.default_timer() - start))
gitextract_pbgz21hk/
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt
└── src/
├── build_all_data.py
├── build_pretrained_w2v.py
├── config/
│ ├── bamnet_webq.yml
│ └── entnet_webq.yml
├── core/
│ ├── __init__.py
│ ├── bamnet/
│ │ ├── __init__.py
│ │ ├── bamnet.py
│ │ ├── ent_modules.py
│ │ ├── entnet.py
│ │ ├── modules.py
│ │ └── utils.py
│ ├── build_data/
│ │ ├── __init__.py
│ │ ├── build_all.py
│ │ ├── build_data.py
│ │ ├── freebase.py
│ │ ├── utils.py
│ │ └── webquestions.py
│ ├── config.py
│ └── utils/
│ ├── __init__.py
│ ├── freebase_utils.py
│ ├── generic_utils.py
│ ├── metrics.py
│ └── utils.py
├── joint_test.py
├── run_freebase.py
├── run_webquestions.py
├── test.py
├── test_entnet.py
├── train.py
└── train_entnet.py
SYMBOL INDEX (130 symbols across 16 files)
FILE: src/core/bamnet/bamnet.py
function get_text_overlap (line 26) | def get_text_overlap(raw_query, query_mentions, ctx_ent_names, vocab2id,...
class BAMnetAgent (line 57) | class BAMnetAgent(object):
method __init__ (line 60) | def __init__(self, opt, ctx_stops, vocab2id):
method train (line 106) | def train(self, train_X, train_y, valid_X, valid_y, valid_cand_labels,...
method predict (line 166) | def predict(self, xs, cand_labels, batch_size=32, margin=1, ys=None, v...
method train_step (line 179) | def train_step(self, xs, ys, is_training=True):
method predict_step (line 210) | def predict_step(self, xs, cand_labels, margin, verbose=False):
method dynamic_ctx_negative_sampling (line 225) | def dynamic_ctx_negative_sampling(self, memories, ys, mem_size, ctx_bo...
method pad_ctx_memory (line 279) | def pad_ctx_memory(self, memories, ctx_bow_size, raw_queries, query_me...
method pack_gold_ans (line 321) | def pack_gold_ans(self, x, N, placeholder=-1):
method set_loss_margin (line 329) | def set_loss_margin(self, scores, gold_mask, margin):
method ranked_predictions (line 338) | def ranked_predictions(self, cand_labels, scores, margin):
method save (line 345) | def save(self, path=None):
method load (line 356) | def load(self, path):
FILE: src/core/bamnet/ent_modules.py
class Entnet (line 20) | class Entnet(nn.Module):
method __init__ (line 21) | def __init__(self, vocab_size, vocab_embed_size, o_embed_size, \
method forward (line 64) | def forward(self, memories, queries, query_lengths):
method clf_score (line 100) | def clf_score(self, q_r, ent_key):
method create_mask (line 103) | def create_mask(self, x, N, use_cuda=True):
method create_mask_3D (line 110) | def create_mask_3D(self, x, N, use_cuda=True):
class EntEncoder (line 118) | class EntEncoder(nn.Module):
method __init__ (line 120) | def __init__(self, o_embed_size, hidden_size, num_ent_types, num_relat...
method forward (line 168) | def forward(self, x_ent_names, x_ent_name_len, x_type_names, x_types, ...
method enc_kg_features (line 182) | def enc_kg_features(self, x_ent_names, x_ent_name_len, x_type_names, x...
class EntRomHop (line 198) | class EntRomHop(nn.Module):
method __init__ (line 199) | def __init__(self, query_embed_size, in_memory_embed_size, hidden_size...
method forward (line 204) | def forward(self, h_state, key_memory_embed, val_memory_embed, atten_m...
class GRUStep (line 211) | class GRUStep(nn.Module):
method __init__ (line 212) | def __init__(self, hidden_size, input_size):
method forward (line 219) | def forward(self, h_state, input_):
FILE: src/core/bamnet/entnet.py
class EntnetAgent (line 24) | class EntnetAgent(object):
method __init__ (line 25) | def __init__(self, opt):
method train (line 69) | def train(self, train_X, train_y, valid_X, valid_y, seed=1234):
method evaluate (line 126) | def evaluate(self, xs, ys, batch_size=1, silence=False):
method predict (line 141) | def predict(self, xs, cand_labels, batch_size=1, silence=False):
method train_step (line 152) | def train_step(self, xs, ys, is_training=True):
method evaluate_step (line 179) | def evaluate_step(self, xs, ys):
method predict_step (line 193) | def predict_step(self, xs, cand_labels):
method pack_gold_ans (line 205) | def pack_gold_ans(self, x, N, placeholder=-1):
method ranked_predictions (line 213) | def ranked_predictions(self, cand_labels, scores):
method save (line 218) | def save(self, path=None):
method load (line 229) | def load(self, path):
FILE: src/core/bamnet/modules.py
class BAMnet (line 19) | class BAMnet(nn.Module):
method __init__ (line 20) | def __init__(self, vocab_size, vocab_embed_size, o_embed_size, \
method kb_aware_query_enc (line 60) | def kb_aware_query_enc(self, memories, queries, query_lengths, ans_mas...
method forward (line 97) | def forward(self, memories, queries, query_lengths, query_words, ctx_m...
method premature_score (line 136) | def premature_score(self, memories, queries, query_lengths, ctx_mask=N...
method scoring (line 149) | def scoring(self, ans_r, q_r, mask=None):
class RomHop (line 155) | class RomHop(nn.Module):
method __init__ (line 156) | def __init__(self, query_embed_size, in_memory_embed_size, hidden_size...
method forward (line 164) | def forward(self, query_embed, in_memory_embed, out_memory_embed, quer...
method gru_step (line 170) | def gru_step(self, h_state, in_memory_embed, out_memory_embed, atten_m...
method update_coatt_cat_maxpool (line 182) | def update_coatt_cat_maxpool(self, query_embed, in_memory_embed, out_m...
class AnsEncoder (line 208) | class AnsEncoder(nn.Module):
method __init__ (line 210) | def __init__(self, o_embed_size, hidden_size, num_ent_types, num_relat...
method forward (line 250) | def forward(self, x_type_bow, x_types, x_type_bow_len, x_path_bow, x_p...
method enc_comp_kv (line 255) | def enc_comp_kv(self, ans_type_bow, ans_types, ans_path_bow, ans_paths...
method enc_ans_features (line 268) | def enc_ans_features(self, x_type_bow, x_types, x_type_bow_len, x_path...
class SeqEncoder (line 293) | class SeqEncoder(object):
method __init__ (line 295) | def __init__(self, vocab_size, embed_size, hidden_size, \
class EncoderRNN (line 317) | class EncoderRNN(nn.Module):
method __init__ (line 318) | def __init__(self, vocab_size, embed_size, hidden_size, dropout=None, \
method init_weights (line 340) | def init_weights(self, init_word_embed):
method forward (line 347) | def forward(self, x, x_len):
class EncoderCNN (line 378) | class EncoderCNN(nn.Module):
method __init__ (line 379) | def __init__(self, vocab_size, embed_size, hidden_size, kernel_size=[2...
method init_weights (line 393) | def init_weights(self, init_word_embed):
method forward (line 400) | def forward(self, x, x_len=None):
class Attention (line 419) | class Attention(nn.Module):
method __init__ (line 420) | def __init__(self, hidden_size, h_state_embed_size=None, in_memory_emb...
method forward (line 440) | def forward(self, query_embed, in_memory_embed, atten_mask=None):
class SelfAttention_CoAtt (line 458) | class SelfAttention_CoAtt(nn.Module):
method __init__ (line 459) | def __init__(self, hidden_size, use_cuda=True):
method forward (line 465) | def forward(self, x, x_len, atten_mask):
function create_mask (line 485) | def create_mask(x, N, use_cuda=True):
FILE: src/core/bamnet/utils.py
function to_cuda (line 12) | def to_cuda(x, use_cuda=True):
function next_batch (line 18) | def next_batch(memories, queries, query_words, raw_queries, query_mentio...
function next_ent_batch (line 23) | def next_ent_batch(memories, queries, query_lengths, gold_inds, batch_si...
FILE: src/core/build_data/build_all.py
function build (line 14) | def build(dpath, version=None, out_dir=None):
FILE: src/core/build_data/build_data.py
function build_kb_data (line 24) | def build_kb_data(kb, used_fbkeys=None):
function build_qa_vocab (line 114) | def build_qa_vocab(qa):
function delex_query_topic_ent (line 121) | def delex_query_topic_ent(query, topic_ent, ent_types):
function delex_query (line 162) | def delex_query(query, ent_mens, mention_types):
function build_data (line 178) | def build_data(qa, kb, entity2id, entityType2id, relation2id, vocab2id, ...
function build_vocab (line 225) | def build_vocab(data, freebase, used_fbkeys=None, min_freq=1):
function build_ans_cands (line 265) | def build_ans_cands(graph, entity2id, entityType2id, relation2id, vocab2...
function build_seed_ent_data (line 449) | def build_seed_ent_data(qa, kb, entity2id, entityType2id, relation2id, v...
function build_seed_entity_feature (line 484) | def build_seed_entity_feature(seed_ent, graph, entity2id, entityType2id,...
FILE: src/core/build_data/freebase.py
function fetch_meta (line 12) | def fetch_meta(path):
function fetch (line 37) | def fetch(data, data_dir):
FILE: src/core/build_data/utils.py
function built (line 17) | def built(path, version_string=None):
function mark_done (line 33) | def mark_done(path, version_string=None):
function make_dir (line 42) | def make_dir(path):
function remove_dir (line 46) | def remove_dir(path):
function vectorize_data (line 50) | def vectorize_data(queries, query_mentions, memories, max_query_size=Non...
function vectorize_ent_data (line 199) | def vectorize_ent_data(queries, ent_memories, max_query_size=None, \
FILE: src/core/build_data/webquestions.py
function get_used_fbkeys (line 17) | def get_used_fbkeys(data_dir, out_dir):
function get_all_fbkeys (line 28) | def get_all_fbkeys(data_dir, out_dir):
function main (line 48) | def main(fb_path, mid2key_path, data_dir, out_dir):
function fetch_ans_cands (line 107) | def fetch_ans_cands(graph):
FILE: src/core/utils/freebase_utils.py
function if_filterout (line 10) | def if_filterout(s):
function query_kb (line 18) | def query_kb(kb, ent_name, fuzz_threshold=90):
FILE: src/core/utils/generic_utils.py
function find_parent (line 18) | def find_parent(x, tree, conn='<-'):
function extract_dep_feature (line 30) | def extract_dep_feature(dep_parser, text, topic_ent, question_word):
function unique (line 51) | def unique(seq):
function normalize_answer (line 59) | def normalize_answer(s):
function dump_embeddings (line 75) | def dump_embeddings(vocab_dict, emb_file, out_path, emb_size=300, binary...
function get_embeddings (line 88) | def get_embeddings(emb_file, vocab, binary=False):
class PreTrainEmbedding (line 101) | class PreTrainEmbedding():
method __init__ (line 102) | def __init__(self, file, binary=False):
method get_embeddings (line 106) | def get_embeddings(self, word):
FILE: src/core/utils/metrics.py
function calc_f1 (line 12) | def calc_f1(gold_list, pred_list):
function calc_avg_f1 (line 44) | def calc_avg_f1(gold_list, pred_list, verbose=True):
FILE: src/core/utils/utils.py
function get_config (line 20) | def get_config(config_path="config.yml"):
function print_config (line 25) | def print_config(config):
function read_lines (line 33) | def read_lines(path_to_file):
function dump_ndarray (line 45) | def dump_ndarray(data, path_to_file):
function load_ndarray (line 52) | def load_ndarray(path_to_file):
function dump_ndjson (line 61) | def dump_ndjson(data, file):
function load_ndjson (line 69) | def load_ndjson(file, return_type='array'):
function load_ndjson_to_array (line 77) | def load_ndjson_to_array(file):
function load_ndjson_to_dict (line 87) | def load_ndjson_to_dict(file):
function dump_json (line 97) | def dump_json(data, file, indent=None):
function load_json (line 104) | def load_json(file):
function dump_dict_ndjson (line 112) | def dump_dict_ndjson(data, file):
function load_gzip_json (line 121) | def load_gzip_json(file):
function get_all_files (line 129) | def get_all_files(dir, recursive=False):
function printProgressBar (line 136) | def printProgressBar(iteration, total, prefix = '', suffix = '', decimal...
FILE: src/joint_test.py
function dynamic_pred (line 15) | def dynamic_pred(pred, margin):
FILE: src/test.py
function dynamic_pred (line 12) | def dynamic_pred(pred, margin):
Condensed preview — 34 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (179K chars).
[
{
"path": ".gitignore",
"chars": 1216,
"preview": "data/\nruns/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribut"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 4567,
"preview": "# BAMnet\n\n\nCode & data accompanying the NAACL2019 paper [\"Bidirectional Attentive Memory Networks for Question Answering"
},
{
"path": "requirements.txt",
"chars": 83,
"preview": "rapidfuzz==0.3.0\ngensim==3.5.0\nnltk==3.4.5\nnumpy==1.14.5\nPyYAML==5.1\ntorch==0.4.1\n\n"
},
{
"path": "src/build_all_data.py",
"chars": 4094,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport argparse\n\nfrom core.build_data.build_data import build_vocab, build_"
},
{
"path": "src/build_pretrained_w2v.py",
"chars": 974,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport argparse\nimport os\n\nfrom core.utils.utils import load_json\nfrom core"
},
{
"path": "src/config/bamnet_webq.yml",
"chars": 827,
"preview": "# Seed 15 Data\nname: 'WebQuestions'\ndata_dir: '../data/WebQ/'\ntrain_data: 'train_vec.json'\nvalid_data: 'valid_vec.json'\n"
},
{
"path": "src/config/entnet_webq.yml",
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"preview": "# WebQuestions Data\nname: 'WebQuestions'\ndata_dir: '../data/WebQ/'\ntrain_data: 'train_ent_vec.json'\nvalid_data: 'valid_e"
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"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\n"
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"chars": 17939,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\nimport timeit\nimport numpy as np\n\nimport torch\nfrom torch import "
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"chars": 11193,
"preview": "'''\nCreated on Sep, 2018\n\n@author: hugo\n\n'''\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.utils."
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"path": "src/core/bamnet/entnet.py",
"chars": 10201,
"preview": "'''\nCreated on Sep, 2018\n\n@author: hugo\n\n'''\nimport os\nimport timeit\nimport numpy as np\n\nimport torch\nfrom torch import "
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"path": "src/core/bamnet/modules.py",
"chars": 26042,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.utils."
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{
"path": "src/core/bamnet/utils.py",
"chars": 956,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport torch\nfrom torch.autograd import Variable\nimport numpy as np\n\n\ndef t"
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"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport os\n\nfrom . import utils as build_utils\nfrom ..utils.utils import *\nf"
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{
"path": "src/core/build_data/build_data.py",
"chars": 27020,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\nimport math\nimport argparse\nfrom itertools import count\nfrom rapi"
},
{
"path": "src/core/build_data/freebase.py",
"chars": 2863,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\n\nfrom ..utils.utils import *\n\n\ndef fetch_meta(path):\n try:\n "
},
{
"path": "src/core/build_data/utils.py",
"chars": 14408,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\nimport datetime\nimport shutil\nfrom collections import defaultdict"
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"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\n# import re\nimport argparse\nfrom nltk.parse.stanford import Stanf"
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"preview": "\n# Vocabulary\nRESERVED_TOKENS = {'PAD': 0, 'UNK': 1}\nRESERVED_ENTS = {'PAD': 0, 'UNK': 1}\nRESERVED_ENT_TYPES = {'PAD': 0"
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"path": "src/core/utils/__init__.py",
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"path": "src/core/utils/freebase_utils.py",
"chars": 708,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nfrom rapidfuzz import fuzz, process\n\n\ndef if_filterout(s):\n if s.endswit"
},
{
"path": "src/core/utils/generic_utils.py",
"chars": 3562,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport re, string\nimport numpy as np\nfrom rapidfuzz import fuzz, process\nfr"
},
{
"path": "src/core/utils/metrics.py",
"chars": 2533,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\nNote: Modified the official evaluation script provided by Berant et al.\n(https:"
},
{
"path": "src/core/utils/utils.py",
"chars": 4519,
"preview": "'''\nCreated on Sep, 2017\n\n@author: hugo\n\n'''\nimport os\nimport re\nimport yaml\nimport gzip\nimport json\nimport string\nimpor"
},
{
"path": "src/joint_test.py",
"chars": 5580,
"preview": "import timeit\nimport argparse\nimport numpy as np\n\nfrom core.bamnet.entnet import EntnetAgent\nfrom core.bamnet.bamnet imp"
},
{
"path": "src/run_freebase.py",
"chars": 1561,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport argparse\nimport os\nimport json\n\nfrom core.build_data.freebase import"
},
{
"path": "src/run_webquestions.py",
"chars": 750,
"preview": "'''\nCreated on Oct, 2017\n\n@author: hugo\n\n'''\nimport argparse\nfrom core.build_data.webquestions import *\n\nparser = argpar"
},
{
"path": "src/test.py",
"chars": 3484,
"preview": "import timeit\nimport argparse\n\nfrom core.bamnet.bamnet import BAMnetAgent\nfrom core.build_data.build_all import build\nfr"
},
{
"path": "src/test_entnet.py",
"chars": 1564,
"preview": "import timeit\nimport argparse\nimport numpy as np\n\nfrom core.bamnet.entnet import EntnetAgent\nfrom core.build_data.build_"
},
{
"path": "src/train.py",
"chars": 4251,
"preview": "import timeit\nimport argparse\nimport numpy as np\n\nfrom core.bamnet.bamnet import BAMnetAgent\nfrom core.build_data.build_"
},
{
"path": "src/train_entnet.py",
"chars": 2246,
"preview": "import timeit\nimport argparse\nimport numpy as np\n\nfrom core.bamnet.entnet import EntnetAgent\nfrom core.build_data.build_"
}
]
About this extraction
This page contains the full source code of the hugochan/BAMnet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 34 files (169.2 KB), approximately 43.9k tokens, and a symbol index with 130 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.