Permalink
Branch: master
Find file Copy path
938 lines (735 sloc) 29.6 KB
from abc import abstractmethod
from typing import List, Dict, Union
import torch
import logging
from collections import Counter
from collections import defaultdict
from segtok.segmenter import split_single
from segtok.tokenizer import split_contractions
from segtok.tokenizer import word_tokenizer
log = logging.getLogger('flair')
class Dictionary:
"""
This class holds a dictionary that maps strings to IDs, used to generate one-hot encodings of strings.
"""
def __init__(self, add_unk=True):
# init dictionaries
self.item2idx: Dict[str, int] = {}
self.idx2item: List[str] = []
# in order to deal with unknown tokens, add <unk>
if add_unk:
self.add_item('<unk>')
def add_item(self, item: str) -> int:
"""
add string - if already in dictionary returns its ID. if not in dictionary, it will get a new ID.
:param item: a string for which to assign an id
:return: ID of string
"""
item = item.encode('utf-8')
if item not in self.item2idx:
self.idx2item.append(item)
self.item2idx[item] = len(self.idx2item) - 1
return self.item2idx[item]
def get_idx_for_item(self, item: str) -> int:
"""
returns the ID of the string, otherwise 0
:param item: string for which ID is requested
:return: ID of string, otherwise 0
"""
item = item.encode('utf-8')
if item in self.item2idx.keys():
return self.item2idx[item]
else:
return 0
def get_items(self) -> List[str]:
items = []
for item in self.idx2item:
items.append(item.decode('UTF-8'))
return items
def __len__(self) -> int:
return len(self.idx2item)
def get_item_for_index(self, idx):
return self.idx2item[idx].decode('UTF-8')
def save(self, savefile):
import pickle
with open(savefile, 'wb') as f:
mappings = {
'idx2item': self.idx2item,
'item2idx': self.item2idx
}
pickle.dump(mappings, f)
@classmethod
def load_from_file(cls, filename: str):
import pickle
dictionary: Dictionary = Dictionary()
with open(filename, 'rb') as f:
mappings = pickle.load(f, encoding='latin1')
idx2item = mappings['idx2item']
item2idx = mappings['item2idx']
dictionary.item2idx = item2idx
dictionary.idx2item = idx2item
return dictionary
@classmethod
def load(cls, name: str):
from flair.file_utils import cached_path
if name == 'chars' or name == 'common-chars':
base_path = 'https://s3.eu-central-1.amazonaws.com/alan-nlp/resources/models/common_characters'
char_dict = cached_path(base_path, cache_dir='datasets')
return Dictionary.load_from_file(char_dict)
return Dictionary.load_from_file(name)
class Label:
"""
This class represents a label of a sentence. Each label has a value and optionally a confidence score. The
score needs to be between 0.0 and 1.0. Default value for the score is 1.0.
"""
def __init__(self, value: str, score: float = 1.0):
self.value = value
self.score = score
super().__init__()
@property
def value(self):
return self._value
@value.setter
def value(self, value):
if not value and value != '':
raise ValueError('Incorrect label value provided. Label value needs to be set.')
else:
self._value = value
@property
def score(self):
return self._score
@score.setter
def score(self, score):
if 0.0 <= score <= 1.0:
self._score = score
else:
self._score = 1.0
def to_dict(self):
return {
'value': self.value,
'confidence': self.score
}
def __str__(self):
return "{} ({})".format(self._value, self._score)
def __repr__(self):
return "{} ({})".format(self._value, self._score)
class Token:
"""
This class represents one word in a tokenized sentence. Each token may have any number of tags. It may also point
to its head in a dependency tree.
"""
def __init__(self,
text: str,
idx: int = None,
head_id: int = None,
whitespace_after: bool = True,
start_position: int = None
):
self.text: str = text
self.idx: int = idx
self.head_id: int = head_id
self.whitespace_after: bool = whitespace_after
self.start_pos = start_position
self.end_pos = start_position + len(text) if start_position is not None else None
self.sentence: Sentence = None
self._embeddings: Dict = {}
self.tags: Dict[str, Label] = {}
def add_tag_label(self, tag_type: str, tag: Label):
self.tags[tag_type] = tag
def add_tag(self, tag_type: str, tag_value: str, confidence=1.0):
tag = Label(tag_value, confidence)
self.tags[tag_type] = tag
def get_tag(self, tag_type: str) -> Label:
if tag_type in self.tags: return self.tags[tag_type]
return Label('')
def get_head(self):
return self.sentence.get_token(self.head_id)
def set_embedding(self, name: str, vector: torch.autograd.Variable):
self._embeddings[name] = vector.cpu()
def clear_embeddings(self):
self._embeddings: Dict = {}
def get_embedding(self) -> torch.tensor:
embeddings = [self._embeddings[embed] for embed in sorted(self._embeddings.keys())]
if embeddings:
return torch.cat(embeddings, dim=0)
return torch.Tensor()
@property
def start_position(self) -> int:
return self.start_pos
@property
def end_position(self) -> int:
return self.end_pos
@property
def embedding(self):
return self.get_embedding()
def __str__(self) -> str:
return 'Token: {} {}'.format(self.idx, self.text) if self.idx is not None else 'Token: {}'.format(self.text)
def __repr__(self) -> str:
return 'Token: {} {}'.format(self.idx, self.text) if self.idx is not None else 'Token: {}'.format(self.text)
class Span:
"""
This class represents one textual span consisting of Tokens. A span may have a tag.
"""
def __init__(self, tokens: List[Token], tag: str = None, score=1.):
self.tokens = tokens
self.tag = tag
self.score = score
self.start_pos = None
self.end_pos = None
if tokens:
self.start_pos = tokens[0].start_position
self.end_pos = tokens[len(tokens) - 1].end_position
@property
def text(self) -> str:
return ' '.join([t.text for t in self.tokens])
def to_original_text(self) -> str:
str = ''
pos = self.tokens[0].start_pos
for t in self.tokens:
while t.start_pos != pos:
str += ' '
pos += 1
str += t.text
pos += len(t.text)
return str
def to_dict(self):
return {
'text': self.to_original_text(),
'start_pos': self.start_pos,
'end_pos': self.end_pos,
'type': self.tag,
'confidence': self.score
}
def __str__(self) -> str:
ids = ','.join([str(t.idx) for t in self.tokens])
return '{}-span [{}]: "{}"'.format(self.tag, ids, self.text) \
if self.tag is not None else 'span [{}]: "{}"'.format(ids, self.text)
def __repr__(self) -> str:
ids = ','.join([str(t.idx) for t in self.tokens])
return '<{}-span ({}): "{}">'.format(self.tag, ids, self.text) \
if self.tag is not None else '<span ({}): "{}">'.format(ids, self.text)
class Sentence:
"""
A Sentence is a list of Tokens and is used to represent a sentence or text fragment.
"""
def __init__(self, text: str = None, use_tokenizer: bool = False, labels: Union[List[Label], List[str]] = None):
super(Sentence, self).__init__()
self.tokens: List[Token] = []
self.labels: List[Label] = []
if labels is not None: self.add_labels(labels)
self._embeddings: Dict = {}
# if text is passed, instantiate sentence with tokens (words)
if text is not None:
# tokenize the text first if option selected
if use_tokenizer:
# use segtok for tokenization
tokens = []
sentences = split_single(text)
for sentence in sentences:
contractions = split_contractions(word_tokenizer(sentence))
tokens.extend(contractions)
# determine offsets for whitespace_after field
index = text.index
running_offset = 0
last_word_offset = -1
last_token = None
for word in tokens:
try:
word_offset = index(word, running_offset)
start_position = word_offset
except:
word_offset = last_word_offset + 1
start_position = running_offset + 1 if running_offset > 0 else running_offset
token = Token(word, start_position=start_position)
self.add_token(token)
if word_offset - 1 == last_word_offset and last_token is not None:
last_token.whitespace_after = False
word_len = len(word)
running_offset = word_offset + word_len
last_word_offset = running_offset - 1
last_token = token
# otherwise assumes whitespace tokenized text
else:
# add each word in tokenized string as Token object to Sentence
word = ''
for index, char in enumerate(text):
if char == ' ':
if len(word) > 0:
token = Token(word, start_position=index-len(word))
self.add_token(token)
word = ''
else:
word += char
# increment for last token in sentence if not followed by whtespace
index += 1
if len(word) > 0:
token = Token(word, start_position=index-len(word))
self.add_token(token)
def get_token(self, token_id: int) -> Token:
for token in self.tokens:
if token.idx == token_id:
return token
def add_token(self, token: Token):
self.tokens.append(token)
# set token idx if not set
token.sentence = self
if token.idx is None:
token.idx = len(self.tokens)
def get_spans(self, tag_type: str, min_score=-1) -> List[Span]:
spans: List[Span] = []
current_span = []
tags = defaultdict(lambda: 0.0)
previous_tag_value: str = 'O'
for token in self:
tag: Label = token.get_tag(tag_type)
tag_value = tag.value
# non-set tags are OUT tags
if tag_value == '' or tag_value == 'O':
tag_value = 'O-'
# anything that is not a BIOES tag is a SINGLE tag
if tag_value[0:2] not in ['B-', 'I-', 'O-', 'E-', 'S-']:
tag_value = 'S-' + tag_value
# anything that is not OUT is IN
in_span = False
if tag_value[0:2] not in ['O-']:
in_span = True
# single and begin tags start a new span
starts_new_span = False
if tag_value[0:2] in ['B-', 'S-']:
starts_new_span = True
if previous_tag_value[0:2] in ['S-'] and previous_tag_value[2:] != tag_value[2:] and in_span:
starts_new_span = True
if (starts_new_span or not in_span) and len(current_span) > 0:
scores = [t.get_tag(tag_type).score for t in current_span]
span_score = sum(scores) / len(scores)
if span_score > min_score:
spans.append(Span(
current_span,
tag=sorted(tags.items(), key=lambda k_v: k_v[1], reverse=True)[0][0],
score=span_score)
)
current_span = []
tags = defaultdict(lambda: 0.0)
if in_span:
current_span.append(token)
weight = 1.1 if starts_new_span else 1.0
tags[tag_value[2:]] += weight
# remember previous tag
previous_tag_value = tag_value
if len(current_span) > 0:
scores = [t.get_tag(tag_type).score for t in current_span]
span_score = sum(scores) / len(scores)
if span_score > min_score:
spans.append(Span(
current_span,
tag=sorted(tags.items(), key=lambda k_v: k_v[1], reverse=True)[0][0],
score=span_score)
)
return spans
def add_label(self, label: Union[Label, str]):
if type(label) is Label:
self.labels.append(label)
elif type(label) is str:
self.labels.append(Label(label))
def add_labels(self, labels: Union[List[Label], List[str]]):
for label in labels:
self.add_label(label)
def get_label_names(self) -> List[str]:
return [label.value for label in self.labels]
@property
def embedding(self):
return self.get_embedding()
def set_embedding(self, name: str, vector):
self._embeddings[name] = vector.cpu()
def get_embedding(self) -> torch.tensor:
embeddings = []
for embed in sorted(self._embeddings.keys()):
embedding = self._embeddings[embed]
embeddings.append(embedding)
if embeddings:
return torch.cat(embeddings, dim=0)
return torch.Tensor()
def clear_embeddings(self, also_clear_word_embeddings: bool = True):
self._embeddings: Dict = {}
if also_clear_word_embeddings:
for token in self:
token.clear_embeddings()
def cpu_embeddings(self):
for name, vector in self._embeddings.items():
self._embeddings[name] = vector.cpu()
def to_tagged_string(self, main_tag=None) -> str:
list = []
for token in self.tokens:
list.append(token.text)
tags: List[str] = []
for tag_type in token.tags.keys():
if main_tag is not None and main_tag != tag_type: continue
if token.get_tag(tag_type).value == '' or token.get_tag(tag_type).value == 'O': continue
tags.append(token.get_tag(tag_type).value)
all_tags = '<' + '/'.join(tags) + '>'
if all_tags != '<>':
list.append(all_tags)
return ' '.join(list)
def to_tokenized_string(self) -> str:
return ' '.join([t.text for t in self.tokens])
def to_plain_string(self):
plain = ''
for token in self.tokens:
plain += token.text
if token.whitespace_after: plain += ' '
return plain.rstrip()
def convert_tag_scheme(self, tag_type: str = 'ner', target_scheme: str = 'iob'):
tags: List[Label] = []
for token in self.tokens:
token: Token = token
tags.append(token.get_tag(tag_type))
if target_scheme == 'iob':
iob2(tags)
if target_scheme == 'iobes':
iob2(tags)
tags = iob_iobes(tags)
for index, tag in enumerate(tags):
self.tokens[index].add_tag(tag_type, tag)
def infer_space_after(self):
"""
Heuristics in case you wish to infer whitespace_after values for tokenized text. This is useful for some old NLP
tasks (such as CoNLL-03 and CoNLL-2000) that provide only tokenized data with no info of original whitespacing.
:return:
"""
last_token = None
quote_count: int = 0
# infer whitespace after field
for token in self.tokens:
if token.text == '"':
quote_count += 1
if quote_count % 2 != 0:
token.whitespace_after = False
elif last_token is not None:
last_token.whitespace_after = False
if last_token is not None:
if token.text in ['.', ':', ',', ';', ')', 'n\'t', '!', '?']:
last_token.whitespace_after = False
if token.text.startswith('\''):
last_token.whitespace_after = False
if token.text in ['(']:
token.whitespace_after = False
last_token = token
return self
def to_original_text(self) -> str:
str = ''
pos = 0
for t in self.tokens:
while t.start_pos != pos:
str += ' '
pos += 1
str += t.text
pos += len(t.text)
return str
def to_dict(self, tag_type: str = None):
labels = []
entities = []
if tag_type:
entities = [span.to_dict() for span in self.get_spans(tag_type)]
if self.labels:
labels = [l.to_dict() for l in self.labels]
return {
'text': self.to_original_text(),
'labels': labels,
'entities': entities
}
def __getitem__(self, idx: int) -> Token:
return self.tokens[idx]
def __iter__(self):
return iter(self.tokens)
def __repr__(self):
return 'Sentence: "{}" - {} Tokens'.format(' '.join([t.text for t in self.tokens]), len(self))
def __copy__(self):
s = Sentence()
for token in self.tokens:
nt = Token(token.text)
for tag_type in token.tags:
nt.add_tag(tag_type, token.get_tag(tag_type).value, token.get_tag(tag_type).score)
s.add_token(nt)
return s
def __str__(self) -> str:
if self.labels:
return f'Sentence: "{self.to_tokenized_string()}" - {len(self)} Tokens - Labels: {self.labels} '
else:
return f'Sentence: "{self.to_tokenized_string()}" - {len(self)} Tokens'
def __len__(self) -> int:
return len(self.tokens)
class Corpus:
@property
@abstractmethod
def train(self) -> List[Sentence]:
pass
@property
@abstractmethod
def dev(self) -> List[Sentence]:
pass
@property
@abstractmethod
def test(self) -> List[Sentence]:
pass
@abstractmethod
def downsample(self, percentage: float = 0.1, only_downsample_train=False):
"""Downsamples this corpus to a percentage of the sentences."""
pass
@abstractmethod
def get_all_sentences(self) -> List[Sentence]:
"""Gets all sentences in the corpus (train, dev and test splits together)."""
pass
@abstractmethod
def make_tag_dictionary(self, tag_type: str) -> Dictionary:
"""Produces a dictionary of token tags of tag_type."""
pass
@abstractmethod
def make_label_dictionary(self) -> Dictionary:
"""
Creates a dictionary of all labels assigned to the sentences in the corpus.
:return: dictionary of labels
"""
pass
class TaggedCorpus(Corpus):
def __init__(self, train: List[Sentence], dev: List[Sentence], test: List[Sentence], name: str = 'corpus'):
self._train: List[Sentence] = train
self._dev: List[Sentence] = dev
self._test: List[Sentence] = test
self.name: str = name
@property
def train(self) -> List[Sentence]:
return self._train
@property
def dev(self) -> List[Sentence]:
return self._dev
@property
def test(self) -> List[Sentence]:
return self._test
def downsample(self, percentage: float = 0.1, only_downsample_train=False):
self._train = self._downsample_to_proportion(self.train, percentage)
if not only_downsample_train:
self._dev = self._downsample_to_proportion(self.dev, percentage)
self._test = self._downsample_to_proportion(self.test, percentage)
return self
def get_all_sentences(self) -> List[Sentence]:
all_sentences: List[Sentence] = []
all_sentences.extend(self.train)
all_sentences.extend(self.dev)
all_sentences.extend(self.test)
return all_sentences
def make_tag_dictionary(self, tag_type: str) -> Dictionary:
# Make the tag dictionary
tag_dictionary: Dictionary = Dictionary()
tag_dictionary.add_item('O')
for sentence in self.get_all_sentences():
for token in sentence.tokens:
token: Token = token
tag_dictionary.add_item(token.get_tag(tag_type).value)
tag_dictionary.add_item('<START>')
tag_dictionary.add_item('<STOP>')
return tag_dictionary
def make_label_dictionary(self) -> Dictionary:
"""
Creates a dictionary of all labels assigned to the sentences in the corpus.
:return: dictionary of labels
"""
labels = set(self._get_all_label_names())
label_dictionary: Dictionary = Dictionary(add_unk=False)
for label in labels:
label_dictionary.add_item(label)
return label_dictionary
def make_vocab_dictionary(self, max_tokens=-1, min_freq=1) -> Dictionary:
"""
Creates a dictionary of all tokens contained in the corpus.
By defining `max_tokens` you can set the maximum number of tokens that should be contained in the dictionary.
If there are more than `max_tokens` tokens in the corpus, the most frequent tokens are added first.
If `min_freq` is set the a value greater than 1 only tokens occurring more than `min_freq` times are considered
to be added to the dictionary.
:param max_tokens: the maximum number of tokens that should be added to the dictionary (-1 = take all tokens)
:param min_freq: a token needs to occur at least `min_freq` times to be added to the dictionary (-1 = there is no limitation)
:return: dictionary of tokens
"""
tokens = self._get_most_common_tokens(max_tokens, min_freq)
vocab_dictionary: Dictionary = Dictionary()
for token in tokens:
vocab_dictionary.add_item(token)
return vocab_dictionary
def _get_most_common_tokens(self, max_tokens, min_freq) -> List[str]:
tokens_and_frequencies = Counter(self._get_all_tokens())
tokens_and_frequencies = tokens_and_frequencies.most_common()
tokens = []
for token, freq in tokens_and_frequencies:
if (min_freq != -1 and freq < min_freq) or (max_tokens != -1 and len(tokens) == max_tokens):
break
tokens.append(token)
return tokens
def _get_all_label_names(self) -> List[str]:
return [label.value for sent in self.train for label in sent.labels]
def _get_all_tokens(self) -> List[str]:
tokens = list(map((lambda s: s.tokens), self.train))
tokens = [token for sublist in tokens for token in sublist]
return list(map((lambda t: t.text), tokens))
def _downsample_to_proportion(self, list: List, proportion: float):
counter = 0.0
last_counter = None
downsampled: List = []
for item in list:
counter += proportion
if int(counter) != last_counter:
downsampled.append(item)
last_counter = int(counter)
return downsampled
def obtain_statistics(self, tag_type: str = None, pretty_print: bool = True) -> dict:
"""
Print statistics about the class distribution (only labels of sentences are taken into account) and sentence
sizes.
"""
json_string = {
"TRAIN": self._obtain_statistics_for(self.train, "TRAIN", tag_type),
"TEST": self._obtain_statistics_for(self.test, "TEST", tag_type),
"DEV": self._obtain_statistics_for(self.dev, "DEV", tag_type),
}
if pretty_print:
import json
json_string = json.dumps(json_string, indent=4)
return json_string
@staticmethod
def _obtain_statistics_for(sentences, name, tag_type) -> dict:
if len(sentences) == 0:
return {}
classes_to_count = TaggedCorpus._get_class_to_count(sentences)
tags_to_count = TaggedCorpus._get_tag_to_count(sentences, tag_type)
tokens_per_sentence = TaggedCorpus._get_tokens_per_sentence(sentences)
label_size_dict = {}
for l, c in classes_to_count.items():
label_size_dict[l] = c
tag_size_dict = {}
for l, c in tags_to_count.items():
tag_size_dict[l] = c
return {
'dataset': name,
'total_number_of_documents': len(sentences),
'number_of_documents_per_class': label_size_dict,
'number_of_tokens_per_tag': tag_size_dict,
'number_of_tokens': {
'total': sum(tokens_per_sentence),
'min': min(tokens_per_sentence),
'max': max(tokens_per_sentence),
'avg': sum(tokens_per_sentence) / len(sentences)
}
}
@staticmethod
def _get_tokens_per_sentence(sentences):
return list(map(lambda x: len(x.tokens), sentences))
@staticmethod
def _get_class_to_count(sentences):
class_to_count = defaultdict(lambda: 0)
for sent in sentences:
for label in sent.labels:
class_to_count[label.value] += 1
return class_to_count
@staticmethod
def _get_tag_to_count(sentences, tag_type):
tag_to_count = defaultdict(lambda: 0)
for sent in sentences:
for word in sent.tokens:
if tag_type in word.tags:
label = word.tags[tag_type]
tag_to_count[label.value] += 1
return tag_to_count
def __str__(self) -> str:
return 'TaggedCorpus: %d train + %d dev + %d test sentences' % (len(self.train), len(self.dev), len(self.test))
def iob2(tags):
"""
Check that tags have a valid IOB format.
Tags in IOB1 format are converted to IOB2.
"""
for i, tag in enumerate(tags):
if tag.value == 'O':
continue
split = tag.value.split('-')
if len(split) != 2 or split[0] not in ['I', 'B']:
return False
if split[0] == 'B':
continue
elif i == 0 or tags[i - 1].value == 'O': # conversion IOB1 to IOB2
tags[i].value = 'B' + tag.value[1:]
elif tags[i - 1].value[1:] == tag.value[1:]:
continue
else: # conversion IOB1 to IOB2
tags[i].value = 'B' + tag.value[1:]
return True
def iob_iobes(tags):
"""
IOB -> IOBES
"""
new_tags = []
for i, tag in enumerate(tags):
if tag.value == 'O':
new_tags.append(tag.value)
elif tag.value.split('-')[0] == 'B':
if i + 1 != len(tags) and \
tags[i + 1].value.split('-')[0] == 'I':
new_tags.append(tag.value)
else:
new_tags.append(tag.value.replace('B-', 'S-'))
elif tag.value.split('-')[0] == 'I':
if i + 1 < len(tags) and \
tags[i + 1].value.split('-')[0] == 'I':
new_tags.append(tag.value)
else:
new_tags.append(tag.value.replace('I-', 'E-'))
else:
raise Exception('Invalid IOB format!')
return new_tags
class MultiCorpus(Corpus):
def __init__(self, corpora: List[TaggedCorpus]):
self.corpora: List[TaggedCorpus] = corpora
@property
def train(self) -> List[Sentence]:
train: List[Sentence] = []
for corpus in self.corpora:
train.extend(corpus.train)
return train
@property
def dev(self) -> List[Sentence]:
dev: List[Sentence] = []
for corpus in self.corpora:
dev.extend(corpus.dev)
return dev
@property
def test(self) -> List[Sentence]:
test: List[Sentence] = []
for corpus in self.corpora:
test.extend(corpus.test)
return test
def __str__(self):
return '\n'.join([str(corpus) for corpus in self.corpora])
def get_all_sentences(self) -> List[Sentence]:
sentences = []
for corpus in self.corpora:
sentences.extend(corpus.get_all_sentences())
return sentences
def downsample(self, percentage: float = 0.1, only_downsample_train=False):
for corpus in self.corpora:
corpus.downsample(percentage, only_downsample_train)
return self
def make_tag_dictionary(self, tag_type: str) -> Dictionary:
# Make the tag dictionary
tag_dictionary: Dictionary = Dictionary()
tag_dictionary.add_item('O')
for corpus in self.corpora:
for sentence in corpus.get_all_sentences():
for token in sentence.tokens:
token: Token = token
tag_dictionary.add_item(token.get_tag(tag_type).value)
tag_dictionary.add_item('<START>')
tag_dictionary.add_item('<STOP>')
return tag_dictionary
def make_label_dictionary(self) -> Dictionary:
label_dictionary: Dictionary = Dictionary(add_unk=False)
for corpus in self.corpora:
labels = set(corpus._get_all_label_names())
for label in labels:
label_dictionary.add_item(label)
return label_dictionary