def read(self, file_path):
with open(file_path, "r") as data_file:
instances = []
logger.info("Reading instances from lines in file at: %s",
file_path)
for line in tqdm.tqdm(data_file):
line = line.strip("\n")
# skip blank lines
if not line:
continue
tokens_and_tags = [
pair.rsplit(self._word_tag_delimiter, 1)
for pair in line.split(self._token_delimiter)
]
tokens = [x[0] for x in tokens_and_tags]
tags = [x[1] for x in tokens_and_tags]
sequence = TextField(tokens, self._token_indexers)
sequence_tags = TagField(tags, sequence)
instances.append(
Instance({
'tokens': sequence,
'tags': sequence_tags
}))
if not instances:
raise ConfigurationError(
"No instances were read from the given filepath {}. "
"Is the path correct?".format(file_path))
return Dataset(instances)
def _process_sentence(
self, sentence: List[str], verbal_predicates: List[int],
predicate_argument_labels: List[List[str]]) -> List[Instance]:
"""
Parameters
----------
sentence : List[str], required.
The tokenised sentence.
verbal_predicates : List[int], required.
The indexes of the verbal predicates in the
sentence which have an associated annotation.
predicate_argument_labels : List[List[str]], required.
A list of predicate argument labels, one for each verbal_predicate. The
internal lists are of length: len(sentence).
Returns
-------
A list of Instances.
"""
sentence_field = TextField(sentence, self._token_indexers)
if not verbal_predicates:
# Sentence contains no predicates.
tags = TagField(["O" for _ in sentence], sentence_field)
verb_indicator = IndexField(None, sentence_field)
instance = Instance(
fields={
"tokens": sentence_field,
"verb_indicator": verb_indicator,
"tags": tags
})
return [instance]
else:
instances = []
for verb_index, annotation in zip(verbal_predicates,
predicate_argument_labels):
tags = TagField(annotation, sentence_field)
verb_indicator = IndexField(verb_index, sentence_field)
instances.append(
Instance(
fields={
"tokens": sentence_field,
"verb_indicator": verb_indicator,
"tags": tags
}))
return instances
def test_count_vocab_items_correctly_indexes_tags(self):
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, self.text, tag_namespace="tags")
counter = defaultdict(lambda: defaultdict(int))
tag_field.count_vocab_items(counter)
assert counter["tags"]["B"] == 1
assert counter["tags"]["I"] == 1
assert counter["tags"]["O"] == 3
assert set(counter.keys()) == {"tags"}
def test_count_vocab_items_correctly_indexes_tags(self):
text = TextField(["here", "are", "some", "words", "."],
[token_indexers["single id"]("words")])
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, text, tag_namespace="tags")
counter = defaultdict(lambda: defaultdict(int))
tag_field.count_vocab_items(counter)
assert counter["tags"]["B"] == 1
assert counter["tags"]["I"] == 1
assert counter["tags"]["O"] == 3
assert set(counter.keys()) == {"tags"}
def test_index_converts_field_correctly(self):
vocab = Vocabulary()
b_index = vocab.add_token_to_namespace("B", namespace='*tags')
i_index = vocab.add_token_to_namespace("I", namespace='*tags')
o_index = vocab.add_token_to_namespace("O", namespace='*tags')
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, self.text, tag_namespace="*tags")
tag_field.index(vocab)
# pylint: disable=protected-access
assert tag_field._indexed_tags == [b_index, i_index, o_index, o_index, o_index]
assert tag_field._num_tags == 3
def test_pad_produces_one_hot_targets(self):
vocab = Vocabulary()
vocab.add_token_to_namespace("B", namespace='*tags')
vocab.add_token_to_namespace("I", namespace='*tags')
vocab.add_token_to_namespace("O", namespace='*tags')
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, self.text, tag_namespace="*tags")
tag_field.index(vocab)
padding_lengths = tag_field.get_padding_lengths()
array = tag_field.as_array(padding_lengths)
numpy.testing.assert_array_almost_equal(
array,
numpy.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0,
1]]))
def test_pad_produces_one_hot_targets(self):
vocab = Vocabulary()
vocab.add_token_to_namespace("B", namespace='*tags')
vocab.add_token_to_namespace("I", namespace='*tags')
vocab.add_token_to_namespace("O", namespace='*tags')
text = TextField(["here", "are", "some", "words", "."],
[token_indexers["single id"]("words")])
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, text, tag_namespace="*tags")
tag_field.index(vocab)
padding_lengths = tag_field.get_padding_lengths()
array = tag_field.pad(padding_lengths)
numpy.testing.assert_array_almost_equal(
array,
numpy.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1], [0, 0,
1]]))
def test_index_converts_field_correctly(self):
vocab = Vocabulary()
b_index = vocab.add_token_to_namespace("B", namespace='*tags')
i_index = vocab.add_token_to_namespace("I", namespace='*tags')
o_index = vocab.add_token_to_namespace("O", namespace='*tags')
text = TextField(["here", "are", "some", "words", "."],
[token_indexers["single id"]("words")])
tags = ["B", "I", "O", "O", "O"]
tag_field = TagField(tags, text, tag_namespace="*tags")
tag_field.index(vocab)
# pylint: disable=protected-access
assert tag_field._indexed_tags == [
b_index, i_index, o_index, o_index, o_index
]
assert tag_field._num_tags == 3
def read(self, file_path):
with open(file_path, "r") as data_file:
instances = []
for line in data_file:
tokens_and_tags = [
pair.split("###") for pair in line.strip("\n").split("\t")
]
tokens = [x[0] for x in tokens_and_tags]
tags = [x[1] for x in tokens_and_tags]
sequence = TextField(tokens, self._token_indexers)
sequence_tags = TagField(tags, sequence)
instances.append(
Instance({
'tokens': sequence,
'tags': sequence_tags
}))
return Dataset(instances)
def test_tag_length_mismatch_raises(self):
with pytest.raises(ConfigurationError):
text = TextField(["here", "are", "some", "words", "."], [])
wrong_tags = ["B", "O", "O"]
_ = TagField(wrong_tags, text)
def test_tag_length_mismatch_raises(self):
with pytest.raises(ConfigurationError):
wrong_tags = ["B", "O", "O"]
_ = TagField(wrong_tags, self.text)