def encode_ner_y(y_ner_list_train, y_ner_list_test, CLASS_COUNT_DICT):
y_ner_encoder = LabelEncoder(sample=CLASS_COUNT_DICT.keys())
y_ner_encoded_train = [[
y_ner_encoder.encode(label) for label in label_list
] for label_list in y_ner_list_train]
y_ner_encoded_train = [torch.stack(tens) for tens in y_ner_encoded_train]
y_ner_padded_train = torch.LongTensor(
pad_sequence(y_ner_encoded_train, MAX_SENTENCE_LEN + 1))
y_ner_encoded_test = [[
y_ner_encoder.encode(label) for label in label_list
] for label_list in y_ner_list_test]
y_ner_encoded_test = [torch.stack(tens) for tens in y_ner_encoded_test]
y_ner_padded_test = torch.LongTensor(
pad_sequence(y_ner_encoded_test, MAX_SENTENCE_LEN + 1))
if y_ner_padded_train.shape[1] > y_ner_padded_test.shape[1]:
y_ner_padded_test = torch.cat(
(
y_ner_padded_test,
torch.zeros(
y_ner_padded_test.shape[0],
y_ner_padded_train.shape[1] - y_ner_padded_test.shape[1],
),
),
dim=1,
).type(torch.long)
return y_ner_padded_train, y_ner_padded_test
def test_label_encoder_no_reserved():
sample = [
'people/deceased_person/place_of_death',
'symbols/name_source/namesakes'
]
label_encoder = LabelEncoder(sample,
reserved_labels=[],
unknown_index=None)
label_encoder.encode('people/deceased_person/place_of_death')
# No ``unknown_index`` defined causes ``RuntimeError`` if an unknown label is used.
with pytest.raises(TypeError):
label_encoder.encode('symbols/namesake/named_after')
def test_label_encoder_known(label_encoder):
input_ = 'symbols/namesake/named_after'
sample = [
'people/deceased_person/place_of_death',
'symbols/name_source/namesakes'
]
sample.append(input_)
label_encoder = LabelEncoder(sample)
output = label_encoder.encode(input_)
assert label_encoder.decode(output) == input_
def encode_ner_y(y_ner_list_train, y_ner_list_test, class_count_dict, max_sent_len):
"""
Tokenize y
:param y_ner_list_train:
:param y_ner_list_test:
:param class_count_dict:
:param max_sent_len:
:return:
"""
y_ner_encoder = LabelEncoder(sample=class_count_dict.keys())
y_ner_encoded_train = [
[y_ner_encoder.encode(label) for label in label_list]
for label_list in y_ner_list_train
]
y_ner_encoded_train = [torch.stack(tens) for tens in y_ner_encoded_train]
y_ner_padded_train = torch.LongTensor(
pad_sequence(y_ner_encoded_train, max_sent_len + 1)
)
y_ner_encoded_test = [
[y_ner_encoder.encode(label) for label in label_list]
for label_list in y_ner_list_test
]
y_ner_encoded_test = [torch.stack(tens) for tens in y_ner_encoded_test]
y_ner_padded_test = torch.LongTensor(
pad_sequence(y_ner_encoded_test, max_sent_len + 1)
)
if y_ner_padded_train.shape[1] > y_ner_padded_test.shape[1]:
y_ner_padded_test = torch.cat(
(
y_ner_padded_test,
torch.zeros(
y_ner_padded_test.shape[0],
y_ner_padded_train.shape[1] - y_ner_padded_test.shape[1],
),
),
dim=1,
).type(torch.long)
return y_ner_encoder, y_ner_padded_train, y_ner_padded_test
# Make Encoders
sentence_corpus = [row['premise'] for row in itertools.chain(train, dev, test)]
sentence_corpus += [
row['hypothesis'] for row in itertools.chain(train, dev, test)
]
sentence_encoder = WhitespaceEncoder(sentence_corpus)
label_corpus = [row['label'] for row in itertools.chain(train, dev, test)]
label_encoder = LabelEncoder(label_corpus)
# Encode
for row in itertools.chain(train, dev, test):
row['premise'] = sentence_encoder.encode(row['premise'])
row['hypothesis'] = sentence_encoder.encode(row['hypothesis'])
row['label'] = label_encoder.encode(row['label'])
config = args
config.n_embed = sentence_encoder.vocab_size
config.d_out = label_encoder.vocab_size
config.n_cells = config.n_layers
# double the number of cells for bidirectional networks
if config.birnn:
config.n_cells *= 2
if args.resume_snapshot:
model = torch.load(
args.resume_snapshot,
map_location=lambda storage, location: storage.cuda(args.gpu))
else:
class ActionSequenceEncoder:
def __init__(self, samples: Samples, token_threshold: int):
reserved_labels: List[Union[Unknown,
CloseVariadicFieldRule]] = [Unknown()]
reserved_labels.append(CloseVariadicFieldRule())
self._rule_encoder = LabelEncoder(samples.rules,
reserved_labels=reserved_labels,
unknown_index=0)
self._node_type_encoder = LabelEncoder(samples.node_types)
reserved_labels = [Unknown()]
self._token_encoder = LabelEncoder(samples.tokens,
min_occurrences=token_threshold,
reserved_labels=reserved_labels,
unknown_index=0)
self.value_to_idx: Dict[str, List[int]] = {}
for kind, value in self._token_encoder.vocab[len(reserved_labels):]:
idx = self._token_encoder.encode((kind, value))
if value not in self.value_to_idx:
self.value_to_idx[value] = []
self.value_to_idx[value].append(idx)
def decode(self, tensor: torch.LongTensor, reference: List[Token]) \
-> Optional[ActionSequence]:
"""
Return the action sequence corresponding to the tensor
Parameters
----------
tensor: torch.LongTensor
The encoded tensor with the shape of
(len(action_sequence), 3). Each action will be encoded by the tuple
of (ID of the applied rule, ID of the inserted token,
the index of the word copied from the reference).
The padding value should be -1.
reference
Returns
-------
Optional[action_sequence]
The action sequence corresponding to the tensor
None if the action sequence cannot be generated.
"""
retval = ActionSequence()
for i in range(tensor.shape[0]):
if tensor[i, 0] > 0:
# ApplyRule
rule = self._rule_encoder.decode(tensor[i, 0])
retval.eval(ApplyRule(rule))
elif tensor[i, 1] > 0:
# GenerateToken
kind, value = self._token_encoder.decode(tensor[i, 1])
retval.eval(GenerateToken(kind, value))
elif tensor[i, 2] >= 0:
# GenerateToken (Copy)
index = int(tensor[i, 2].numpy())
if index >= len(reference):
logger.debug("reference index is out-of-bounds")
return None
token = reference[index]
retval.eval(GenerateToken(token.kind, token.raw_value))
else:
logger.debug("invalid actions")
return None
return retval
def encode_action(self,
action_sequence: ActionSequence,
reference: List[Token]) \
-> Optional[torch.Tensor]:
"""
Return the tensor encoded the action sequence
Parameters
----------
action_sequence: action_sequence
The action_sequence containing action sequence to be encoded
reference
Returns
-------
Optional[torch.Tensor]
The encoded tensor. The shape of tensor is
(len(action_sequence) + 1, 4). Each action will be encoded by
the tuple of (ID of the node types, ID of the applied rule,
ID of the inserted token, the index of the word copied from
the reference. The padding value should be -1.
None if the action sequence cannot be encoded.
"""
reference_value = [token.raw_value for token in reference]
action = \
torch.ones(len(action_sequence.action_sequence) + 1, 4).long() \
* -1
for i in range(len(action_sequence.action_sequence)):
a = action_sequence.action_sequence[i]
parent = action_sequence.parent(i)
if parent is not None:
parent_action = \
cast(ApplyRule,
action_sequence.action_sequence[parent.action])
parent_rule = cast(ExpandTreeRule, parent_action.rule)
action[i, 0] = self._node_type_encoder.encode(
parent_rule.children[parent.field][1])
if isinstance(a, ApplyRule):
rule = a.rule
action[i, 1] = self._rule_encoder.encode(rule)
else:
encoded_token = \
int(self._token_encoder.encode((a.kind, a.value)).numpy())
if encoded_token != 0:
action[i, 2] = encoded_token
# Unknown token
if a.value in reference_value:
# TODO use kind in reference
action[i, 3] = \
reference_value.index(cast(str, a.value))
if encoded_token == 0 and \
a.value not in reference_value:
logger.debug("cannot encode token")
return None
head = action_sequence.head
length = len(action_sequence.action_sequence)
if head is not None:
head_action = \
cast(ApplyRule,
action_sequence.action_sequence[head.action])
head_rule = cast(ExpandTreeRule, head_action.rule)
action[length, 0] = self._node_type_encoder.encode(
head_rule.children[head.field][1])
return action
def encode_raw_value(self, text: str) -> List[int]:
if text in self.value_to_idx:
return self.value_to_idx[text]
else:
return [self._token_encoder.encode(Unknown()).item()]
def batch_encode_raw_value(self, texts: List[str]) -> List[List[int]]:
return [self.encode_raw_value(text) for text in texts]
def encode_parent(self, action_sequence) -> torch.Tensor:
"""
Return the tensor encoded the action sequence
Parameters
----------
action_sequence: action_sequence
The action_sequence containing action sequence to be encoded
Returns
-------
torch.Tensor
The encoded tensor. The shape of `action` tensor is
(len(action_sequence) + 1, 4). Each action will be encoded by
the tuple of (ID of the parent node types, ID of the
parent-action's rule, the index of the parent action,
the index of the field).
The padding value should be -1.
"""
parent_tensor = \
torch.ones(len(action_sequence.action_sequence) + 1, 4).long() \
* -1
for i in range(len(action_sequence.action_sequence)):
parent = action_sequence.parent(i)
if parent is not None:
parent_action = \
cast(ApplyRule,
action_sequence.action_sequence[parent.action])
parent_rule = cast(ExpandTreeRule, parent_action.rule)
parent_tensor[i, 0] = \
self._node_type_encoder.encode(parent_rule.parent)
parent_tensor[i, 1] = self._rule_encoder.encode(parent_rule)
parent_tensor[i, 2] = parent.action
parent_tensor[i, 3] = parent.field
head = action_sequence.head
length = len(action_sequence.action_sequence)
if head is not None:
head_action = \
cast(ApplyRule,
action_sequence.action_sequence[head.action])
head_rule = cast(ExpandTreeRule, head_action.rule)
parent_tensor[length, 0] = \
self._node_type_encoder.encode(head_rule.parent)
parent_tensor[length, 1] = self._rule_encoder.encode(head_rule)
parent_tensor[length, 2] = head.action
parent_tensor[length, 3] = head.field
return parent_tensor
def encode_tree(self, action_sequence: ActionSequence) \
-> Union[torch.Tensor, torch.Tensor]:
"""
Return the tensor adjacency matrix of the action sequence
Parameters
----------
action_sequence: action_sequence
The action_sequence containing action sequence to be encoded
Returns
-------
depth: torch.Tensor
The depth of each action. The shape is (len(action_sequence),).
adjacency_matrix: torch.Tensor
The encoded tensor. The shape of tensor is
(len(action_sequence), len(action_sequence)). If i th action is
a parent of j th action, (i, j) element will be 1. the element
will be 0 otherwise.
"""
L = len(action_sequence.action_sequence)
depth = torch.zeros(L)
m = torch.zeros(L, L)
for i in range(L):
p = action_sequence.parent(i)
if p is not None:
depth[i] = depth[p.action] + 1
m[p.action, i] = 1
return depth, m
def encode_each_action(self,
action_sequence: ActionSequence,
reference: List[Token],
max_arity: int) \
-> torch.Tensor:
"""
Return the tensor encoding the each action
Parameters
----------
action_sequence: action_sequence
The action_sequence containing action sequence to be encoded
reference
max_arity: int
Returns
-------
torch.Tensor
The encoded tensor. The shape of tensor is
(len(action_sequence), max_arity + 1, 3).
[:, 0, 0] encodes the parent node type. [:, i, 0] encodes
the node type of (i - 1)-th child node. [:, i, 1] encodes
the token of (i - 1)-th child node. [:, i, 2] encodes the reference
index of (i - 1)-th child node.
The padding value is -1.
"""
L = len(action_sequence.action_sequence)
reference_value = [token.raw_value for token in reference]
retval = torch.ones(L, max_arity + 1, 3).long() * -1
for i, action in enumerate(action_sequence.action_sequence):
if isinstance(action, ApplyRule):
if isinstance(action.rule, ExpandTreeRule):
# Encode parent
retval[i, 0, 0] = \
self._node_type_encoder.encode(action.rule.parent)
# Encode children
for j, (_, child) in enumerate(
action.rule.children[:max_arity]):
retval[i, j + 1, 0] = \
self._node_type_encoder.encode(child)
else:
gentoken: GenerateToken = action
kind = gentoken.kind
value = gentoken.value
encoded_token = \
int(self._token_encoder.encode((kind, value)).numpy())
if encoded_token != 0:
retval[i, 1, 1] = encoded_token
if value in reference_value:
# TODO use kind in reference
retval[i, 1, 2] = \
reference_value.index(cast(str, value))
return retval
def encode_path(self, action_sequence: ActionSequence, max_depth: int) \
-> torch.Tensor:
"""
Return the tensor encoding the each action
Parameters
----------
action_sequence: action_sequence
The action_sequence containing action sequence to be encoded
max_depth: int
Returns
-------
torch.Tensor
The encoded tensor. The shape of tensor is
(len(action_sequence), max_depth).
[i, :] encodes the path from the root node to i-th node.
Each node represented by the rule id.
The padding value is -1.
"""
L = len(action_sequence.action_sequence)
retval = torch.ones(L, max_depth).long() * -1
for i in range(L):
parent_opt = action_sequence.parent(i)
if parent_opt is not None:
p = action_sequence.action_sequence[parent_opt.action]
if isinstance(p, ApplyRule):
retval[i, 0] = self._rule_encoder.encode(p.rule)
retval[i, 1:] = retval[parent_opt.action, :max_depth - 1]
return retval
class WikiDataset(Dataset):
'''
A custom dataset object that encodes a tokenized text and its labels according to the corresponding encoders
'''
def __init__(self,
json,
text_encoder=None,
label_encoder=None,
vocab=None,
mode='train'):
'''
Initialization
Arguments:
json: Json file containing the data.
Structure of json file:
e.g:
json: {'data' : [{'id': filename,
'title': title of page,
'toc': [list of items in table of contents section of wikipage],
'intro':introduction of wiki page,
'label':'positive'/'negative' flag}]
}
Labels-required only when mode = 'train'
text_encoder: encoder object that encodes tokens to their unique integer ids
label_encoder: encoder object that encodes labels to their unique integer ids
vocab: external vocabulary used to intialize the text encoder. If vocab = None, it would be generated based on tokens from the datasets provided
mode: 'train' or 'inference': in case of mode == 'inference', the dataset object skips the labels
'''
self.data = json
assert 'data' in self.data
# Define the mode in which the dataset object is to be used
self.mode = mode
# Define text encoder and vocabulary
if text_encoder:
self._text_encoder = text_encoder
self._vocab = self._text_encoder.vocab
elif vocab:
self._vocab = vocab
self._text_encoder = StaticTokenizerEncoder(self._vocab,
append_eos=False,
tokenize=self.split)
else:
self._vocab = self.create_vocab()
self._text_encoder = StaticTokenizerEncoder(self._vocab,
append_eos=False,
tokenize=self.split)
self._vocab_size = self._text_encoder.vocab_size
# Define label encoder
if self.mode == 'train':
if label_encoder:
self._label_encoder = label_encoder
else:
self._label_encoder = LabelEncoder(
[sample['label'] for sample in self.data['data']])
self._label_size = self._label_encoder.vocab_size
else:
self._label_encoder = None
self._label_size = None
def __len__(self):
'''
Size of dataset
'''
return len(self.data['data'])
def __getitem__(self, idx):
'''
Extract item corresponding to idx'th index in data
'''
item = self.data['data'][idx]
intro_enc = self._text_encoder.encode(item['intro'])
toc = item['toc']
if toc == []:
toc_enc = self._text_encoder.encode('.')
else:
toc = ' '.join(toc)
toc_enc = self._text_encoder.encode(toc)
title_enc = self._text_encoder.encode(item['title'])
if self.mode == 'train':
return title_enc, toc_enc, intro_enc, self._label_encoder.encode(
item['label']).view(-1)
else:
return title_enc, toc_enc, intro_enc
@property
def vocab_size(self):
return self._vocab_size
@property
def label_size(self):
return self._label_size
@property
def text_encoder(self):
return self._text_encoder
@property
def label_encoder(self):
return self._label_encoder
@property
def vocab(self):
return self._vocab
def create_vocab(self, remove_less_freq_words=True, threshold=1):
'''
Creates vocabulary from the dataset tokens
Returns:
List of unique tokens in dataset
'''
temp_vocab = []
for sample in self.data['data']:
temp_vocab.extend(sample['title'].split())
temp_vocab.extend(' '.join(sample['toc']).split())
temp_vocab.extend(sample['intro'].split())
vocab = []
if remove_less_freq_words:
count_dict = collections.Counter(temp_vocab)
for word in count_dict.keys():
if count_dict[word] > threshold:
vocab.append(word)
else:
vocab = sorted(list(set(temp_vocab)))
return vocab
def split(self, x):
'''
Splits the text into tokens
'''
return x.split()
def collate_fn(self, batch, padding=True):
"""
Collate function needs to be passed to the pytorch dataloader
Returns:
(title,title_lengths): tuple containing padded sequence tensor for title and sequence lengths
(toc,toc_lengths): tuple containing padded sequence tensor for table of contents and sequence lengths
(intro,intro_lengths): tuple containing padded sequence tensor for introduction and sequence lengths
labels: tensor containing labels for the batch
"""
if self.mode == 'train':
title, toc, intro, labels = zip(*batch)
labels = torch.cat(labels)
else:
title, toc, intro = zip(*batch)
if isinstance(intro, collections.Sequence):
if padding:
title, title_lengths = stack_and_pad_tensors(title)
toc, toc_lengths = stack_and_pad_tensors(toc)
intro, intro_lengths = stack_and_pad_tensors(intro)
if self.mode == 'train':
return (title,
title_lengths), (toc,
toc_lengths), (intro,
intro_lengths), labels
else:
return (title, title_lengths), (toc,
toc_lengths), (intro,
intro_lengths)
else:
return batch
@classmethod
def fromJsonFile(cls,
json_file,
text_encoder=None,
label_encoder=None,
vocab=None,
mode='train'):
'''
Read data from json file
Arguments:
json_file: string specifying location to json_file
'''
with open(json_file, 'r') as f:
json_data = json.load(f)
return cls(json_data, text_encoder, label_encoder, vocab, mode)
