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vector.py
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/
vector.py
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#!/usr/bin/env python3
# Copyright 2018-present, HKUST-KnowComp.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Functions for putting examples into torch format."""
from collections import Counter
import torch
def vectorize(ex, model, single_answer=False):
"""Torchify a single example."""
args = model.args
word_dict = model.word_dict
char_dict = model.char_dict
feature_dict = model.feature_dict
# Index words
document = torch.LongTensor([word_dict[w] for w in ex['document']])
document_char = [torch.LongTensor([char_dict[c] for c in cs]) for cs in ex['document_char']]
question = torch.LongTensor([word_dict[w] for w in ex['question']])
question_char = [torch.LongTensor([char_dict[c] for c in cs]) for cs in ex['question_char']]
# Create extra features vector
if len(feature_dict) > 0:
c_features = torch.zeros(len(ex['document']), len(feature_dict))
q_features = torch.zeros(len(ex['question']), len(feature_dict))
else:
c_features = None
q_features = None
# f_{exact_match}
if args.use_exact_match:
q_words_cased = {w for w in ex['question']}
q_words_uncased = {w.lower() for w in ex['question']}
q_lemma = {w for w in ex['qlemma']} if args.use_lemma else None
for i in range(len(ex['document'])):
if ex['document'][i] in q_words_cased:
c_features[i][feature_dict['in_cased']] = 1.0
if ex['document'][i].lower() in q_words_uncased:
c_features[i][feature_dict['in_uncased']] = 1.0
if q_lemma and ex['clemma'][i] in q_lemma:
c_features[i][feature_dict['in_lemma']] = 1.0
c_words_cased = {w for w in ex['document']}
c_words_uncased = {w.lower() for w in ex['document']}
c_lemma = {w for w in ex['clemma']} if args.use_lemma else None
for i in range(len(ex['question'])):
if ex['question'][i] in c_words_cased:
q_features[i][feature_dict['in_cased']] = 1.0
if ex['question'][i].lower() in c_words_uncased:
q_features[i][feature_dict['in_uncased']] = 1.0
if c_lemma and ex['qlemma'][i] in c_lemma:
q_features[i][feature_dict['in_lemma']] = 1.0
# f_{token} (POS)
if args.use_pos:
for i, w in enumerate(ex['cpos']):
f = 'pos=%s' % w
if f in feature_dict:
c_features[i][feature_dict[f]] = 1.0
for i, w in enumerate(ex['qpos']):
f = 'pos=%s' % w
if f in feature_dict:
q_features[i][feature_dict[f]] = 1.0
# f_{token} (NER)
if args.use_ner:
for i, w in enumerate(ex['cner']):
f = 'ner=%s' % w
if f in feature_dict:
c_features[i][feature_dict[f]] = 1.0
for i, w in enumerate(ex['qner']):
f = 'ner=%s' % w
if f in feature_dict:
q_features[i][feature_dict[f]] = 1.0
# f_{token} (TF)
if args.use_tf:
counter = Counter([w.lower() for w in ex['document']])
l = len(ex['document'])
for i, w in enumerate(ex['document']):
c_features[i][feature_dict['tf']] = counter[w.lower()] * 1.0 / l
counter = Counter([w.lower() for w in ex['question']])
l = len(ex['question'])
for i, w in enumerate(ex['question']):
q_features[i][feature_dict['tf']] = counter[w.lower()] * 1.0 / l
# Maybe return without target
if 'answers' not in ex:
return document, document_char, c_features, question, question_char, q_features, ex['id']
# ...or with target(s) (might still be empty if answers is empty)
if single_answer:
assert(len(ex['answers']) > 0)
start = torch.LongTensor(1).fill_(ex['answers'][0][0])
end = torch.LongTensor(1).fill_(ex['answers'][0][1])
else:
start = [a[0] for a in ex['answers']]
end = [a[1] for a in ex['answers']]
return document, document_char, c_features, question, question_char, q_features, start, end, ex['id']
def batchify(batch):
"""Gather a batch of individual examples into one batch."""
NUM_INPUTS = 6
NUM_TARGETS = 2
NUM_EXTRA = 1
docs = [ex[0] for ex in batch]
doc_chars = [ex[1] for ex in batch]
c_features = [ex[2] for ex in batch]
questions = [ex[3] for ex in batch]
question_chars = [ex[4] for ex in batch]
q_features = [ex[5] for ex in batch]
ids = [ex[-1] for ex in batch]
# Batch documents and features
max_length = max([d.size(0) for d in docs])
# max_char_length = max([c.size(0) for cs in doc_chars for c in cs])
max_char_length = 13
x1 = torch.LongTensor(len(docs), max_length).zero_()
x1_c = torch.LongTensor(len(docs), max_length, max_char_length).zero_()
x1_mask = torch.ByteTensor(len(docs), max_length).fill_(1)
if c_features[0] is None:
x1_f = None
else:
x1_f = torch.zeros(len(docs), max_length, c_features[0].size(1))
for i, d in enumerate(docs):
x1[i, :d.size(0)].copy_(d)
x1_mask[i, :d.size(0)].fill_(0)
if x1_f is not None:
x1_f[i, :d.size(0)].copy_(c_features[i])
for i, cs in enumerate(doc_chars):
for j, c in enumerate(cs):
c_ = c[:max_char_length]
x1_c[i, j, :c_.size(0)].copy_(c_)
# Batch questions
max_length = max([q.size(0) for q in questions])
x2 = torch.LongTensor(len(questions), max_length).zero_()
x2_c = torch.LongTensor(len(questions), max_length, max_char_length).zero_()
x2_mask = torch.ByteTensor(len(questions), max_length).fill_(1)
if q_features[0] is None:
x2_f = None
else:
x2_f = torch.zeros(len(questions), max_length, q_features[0].size(1))
for i, d in enumerate(questions):
x2[i, :d.size(0)].copy_(d)
x2_mask[i, :d.size(0)].fill_(0)
if x2_f is not None:
x2_f[i, :d.size(0)].copy_(q_features[i])
for i, cs in enumerate(question_chars):
for j, c in enumerate(cs):
c_ = c[:max_char_length]
x2_c[i, j, :c_.size(0)].copy_(c_)
# Maybe return without targets
if len(batch[0]) == NUM_INPUTS + NUM_EXTRA:
return x1, x1_c, x1_f, x1_mask, x2, x2_c, x2_f, x2_mask, ids
elif len(batch[0]) == NUM_INPUTS + NUM_EXTRA + NUM_TARGETS:
# ...Otherwise add targets
if torch.is_tensor(batch[0][NUM_INPUTS]):
y_s = torch.cat([ex[NUM_INPUTS] for ex in batch])
y_e = torch.cat([ex[NUM_INPUTS+1] for ex in batch])
else:
y_s = [ex[NUM_INPUTS] for ex in batch]
y_e = [ex[NUM_INPUTS+1] for ex in batch]
else:
raise RuntimeError('Incorrect number of inputs per example.')
return x1, x1_c, x1_f, x1_mask, x2, x2_c, x2_f, x2_mask, y_s, y_e, ids