def setUp(self):
self.vocab = Vocabulary()
self.vocab.add_token_to_namespace("this", "words")
self.vocab.add_token_to_namespace("is", "words")
self.vocab.add_token_to_namespace("a", "words")
self.vocab.add_token_to_namespace("sentence", 'words')
self.vocab.add_token_to_namespace("s", 'characters')
self.vocab.add_token_to_namespace("e", 'characters')
self.vocab.add_token_to_namespace("n", 'characters')
self.vocab.add_token_to_namespace("t", 'characters')
self.vocab.add_token_to_namespace("c", 'characters')
for label in ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']:
self.vocab.add_token_to_namespace(label, 'labels')
self.word_indexer = {"words": SingleIdTokenIndexer("words")}
self.words_and_characters_indexers = {"words": SingleIdTokenIndexer("words"),
"characters": TokenCharactersIndexer("characters")}
self.field1 = TextField([Token(t) for t in ["this", "is", "a", "sentence"]],
self.word_indexer)
self.field2 = TextField([Token(t) for t in ["this", "is", "a", "different", "sentence"]],
self.word_indexer)
self.field3 = TextField([Token(t) for t in ["this", "is", "another", "sentence"]],
self.word_indexer)
self.empty_text_field = self.field1.empty_field()
self.index_field = IndexField(1, self.field1)
self.empty_index_field = self.index_field.empty_field()
self.sequence_label_field = SequenceLabelField([1, 1, 0, 1], self.field1)
self.empty_sequence_label_field = self.sequence_label_field.empty_field()
super(TestListField, self).setUp()
class TestListField(AllenNlpTestCase):
def setUp(self):
self.vocab = Vocabulary()
self.vocab.add_token_to_namespace("this", "words")
self.vocab.add_token_to_namespace("is", "words")
self.vocab.add_token_to_namespace("a", "words")
self.vocab.add_token_to_namespace("sentence", 'words')
self.vocab.add_token_to_namespace("s", 'characters')
self.vocab.add_token_to_namespace("e", 'characters')
self.vocab.add_token_to_namespace("n", 'characters')
self.vocab.add_token_to_namespace("t", 'characters')
self.vocab.add_token_to_namespace("c", 'characters')
for label in ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']:
self.vocab.add_token_to_namespace(label, 'labels')
self.word_indexer = {"words": SingleIdTokenIndexer("words")}
self.words_and_characters_indexers = {"words": SingleIdTokenIndexer("words"),
"characters": TokenCharactersIndexer("characters")}
self.field1 = TextField([Token(t) for t in ["this", "is", "a", "sentence"]],
self.word_indexer)
self.field2 = TextField([Token(t) for t in ["this", "is", "a", "different", "sentence"]],
self.word_indexer)
self.field3 = TextField([Token(t) for t in ["this", "is", "another", "sentence"]],
self.word_indexer)
self.empty_text_field = self.field1.empty_field()
self.index_field = IndexField(1, self.field1)
self.empty_index_field = self.index_field.empty_field()
self.sequence_label_field = SequenceLabelField([1, 1, 0, 1], self.field1)
self.empty_sequence_label_field = self.sequence_label_field.empty_field()
super(TestListField, self).setUp()
def test_get_padding_lengths(self):
list_field = ListField([self.field1, self.field2, self.field3])
list_field.index(self.vocab)
lengths = list_field.get_padding_lengths()
assert lengths == {"num_fields": 3, "list_words_length": 5, "list_num_tokens": 5}
def test_list_field_can_handle_empty_text_fields(self):
list_field = ListField([self.field1, self.field2, self.empty_text_field])
list_field.index(self.vocab)
tensor_dict = list_field.as_tensor(list_field.get_padding_lengths())
numpy.testing.assert_array_equal(tensor_dict["words"].detach().cpu().numpy(),
numpy.array([[2, 3, 4, 5, 0],
[2, 3, 4, 1, 5],
[0, 0, 0, 0, 0]]))
def test_list_field_can_handle_empty_index_fields(self):
list_field = ListField([self.index_field, self.index_field, self.empty_index_field])
list_field.index(self.vocab)
tensor = list_field.as_tensor(list_field.get_padding_lengths())
numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(), numpy.array([[1], [1], [-1]]))
def test_list_field_can_handle_empty_sequence_label_fields(self):
list_field = ListField([self.sequence_label_field,
self.sequence_label_field,
self.empty_sequence_label_field])
list_field.index(self.vocab)
tensor = list_field.as_tensor(list_field.get_padding_lengths())
numpy.testing.assert_array_equal(tensor.detach().cpu().numpy(),
numpy.array([[1, 1, 0, 1],
[1, 1, 0, 1],
[0, 0, 0, 0]]))
def test_all_fields_padded_to_max_length(self):
list_field = ListField([self.field1, self.field2, self.field3])
list_field.index(self.vocab)
tensor_dict = list_field.as_tensor(list_field.get_padding_lengths())
numpy.testing.assert_array_almost_equal(tensor_dict["words"][0].detach().cpu().numpy(),
numpy.array([2, 3, 4, 5, 0]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][1].detach().cpu().numpy(),
numpy.array([2, 3, 4, 1, 5]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][2].detach().cpu().numpy(),
numpy.array([2, 3, 1, 5, 0]))
def test_nested_list_fields_are_padded_correctly(self):
nested_field1 = ListField([LabelField(c) for c in ['a', 'b', 'c', 'd', 'e']])
nested_field2 = ListField([LabelField(c) for c in ['f', 'g', 'h', 'i', 'j', 'k']])
list_field = ListField([nested_field1.empty_field(), nested_field1, nested_field2])
list_field.index(self.vocab)
padding_lengths = list_field.get_padding_lengths()
assert padding_lengths == {'num_fields': 3, 'list_num_fields': 6}
tensor = list_field.as_tensor(padding_lengths).detach().cpu().numpy()
numpy.testing.assert_almost_equal(tensor, [[-1, -1, -1, -1, -1, -1],
[0, 1, 2, 3, 4, -1],
[5, 6, 7, 8, 9, 10]])
def test_fields_can_pad_to_greater_than_max_length(self):
list_field = ListField([self.field1, self.field2, self.field3])
list_field.index(self.vocab)
padding_lengths = list_field.get_padding_lengths()
padding_lengths["list_words_length"] = 7
padding_lengths["num_fields"] = 5
tensor_dict = list_field.as_tensor(padding_lengths)
numpy.testing.assert_array_almost_equal(tensor_dict["words"][0].detach().cpu().numpy(),
numpy.array([2, 3, 4, 5, 0, 0, 0]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][1].detach().cpu().numpy(),
numpy.array([2, 3, 4, 1, 5, 0, 0]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][2].detach().cpu().numpy(),
numpy.array([2, 3, 1, 5, 0, 0, 0]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][3].detach().cpu().numpy(),
numpy.array([0, 0, 0, 0, 0, 0, 0]))
numpy.testing.assert_array_almost_equal(tensor_dict["words"][4].detach().cpu().numpy(),
numpy.array([0, 0, 0, 0, 0, 0, 0]))
def test_as_tensor_can_handle_multiple_token_indexers(self):
# pylint: disable=protected-access
self.field1._token_indexers = self.words_and_characters_indexers
self.field2._token_indexers = self.words_and_characters_indexers
self.field3._token_indexers = self.words_and_characters_indexers
list_field = ListField([self.field1, self.field2, self.field3])
list_field.index(self.vocab)
padding_lengths = list_field.get_padding_lengths()
tensor_dict = list_field.as_tensor(padding_lengths)
words = tensor_dict["words"].detach().cpu().numpy()
characters = tensor_dict["characters"].detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(words, numpy.array([[2, 3, 4, 5, 0],
[2, 3, 4, 1, 5],
[2, 3, 1, 5, 0]]))
numpy.testing.assert_array_almost_equal(characters[0], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 3, 4, 5, 3, 4, 6, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]))
numpy.testing.assert_array_almost_equal(characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 3, 1, 3, 4, 5],
[2, 3, 4, 5, 3, 4, 6, 3, 0]]))
numpy.testing.assert_array_almost_equal(characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[1, 4, 1, 5, 1, 3, 1, 0, 0],
[2, 3, 4, 5, 3, 4, 6, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]))
def test_as_tensor_can_handle_multiple_token_indexers_and_empty_fields(self):
# pylint: disable=protected-access
self.field1._token_indexers = self.words_and_characters_indexers
self.field2._token_indexers = self.words_and_characters_indexers
self.field3._token_indexers = self.words_and_characters_indexers
list_field = ListField([self.field1.empty_field(), self.field1, self.field2])
list_field.index(self.vocab)
padding_lengths = list_field.get_padding_lengths()
tensor_dict = list_field.as_tensor(padding_lengths)
words = tensor_dict["words"].detach().cpu().numpy()
characters = tensor_dict["characters"].detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(words, numpy.array([[0, 0, 0, 0, 0],
[2, 3, 4, 5, 0],
[2, 3, 4, 1, 5]]))
numpy.testing.assert_array_almost_equal(characters[0], numpy.zeros([5, 9]))
numpy.testing.assert_array_almost_equal(characters[1], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 3, 4, 5, 3, 4, 6, 3, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]))
numpy.testing.assert_array_almost_equal(characters[2], numpy.array([[5, 1, 1, 2, 0, 0, 0, 0, 0],
[1, 2, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 3, 1, 3, 4, 5],
[2, 3, 4, 5, 3, 4, 6, 3, 0]]))
def test_printing_doesnt_crash(self):
list_field = ListField([self.field1, self.field2])
print(list_field)
def test_sequence_methods(self):
list_field = ListField([self.field1, self.field2, self.field3])
assert len(list_field) == 3
assert list_field[1] == self.field2
assert [f for f in list_field] == [self.field1, self.field2, self.field3]
def test_as_tensor_converts_field_correctly(self):
index_field = IndexField(4, self.text)
tensor = index_field.as_tensor(index_field.get_padding_lengths()).data.cpu().numpy()
numpy.testing.assert_array_equal(tensor, numpy.array([4]))
def test_index_field_empty_field_works(self):
index_field = IndexField(4, self.text)
empty_index = index_field.empty_field()
assert empty_index.sequence_index == -1
def _read(self, file_path: str):
file_path = cached_path(file_path)
logger.info("Reading file at %s", file_path)
with open(file_path) as dataset_file:
dataset = json.load(dataset_file)
# if self._span_file_path is not None:
span_file = open(self._span_file_path)
span_file = json.load(span_file)
#archive = load_archive(self._extraction_model_path)
#model = archive.model
model = None
p1_dataset_reader = DatasetReader.from_params(
archive.config["dataset_reader"])
p1_token_indexers = p1_dataset_reader._token_indexers
logger.info("Reading the dataset")
for data, best_span in zip(dataset, span_file):
answer = data['answers'][0]
question = data['query']
well_formed_answer = data['wellFormedAnswers'][0]
passages_json = data['passages']
passages = [
passages_json[i]['passage_text']
for i in range(len(passages_json))
]
# passages_length = [len(p) for p in passages]
passages_is_selected = [
passages_json[i]['is_selected']
for i in range(len(passages_json))
]
# concatenated_passage = ' '.join(passages)
tokenized_passages_list = [
self._tokenizer.tokenize(util.normalize_text(p))
for p in passages
]
passages_length = [len(p) for p in tokenized_passages_list]
cumulative_passages_length = np.cumsum(passages_length)
normalized_answer = None
if answer != None:
normalized_answer = util.normalize_text(answer)
normalized_question = util.normalize_text(question)
tokenized_answer = self._tokenizer.tokenize(normalized_answer)
tokenized_question = self._tokenizer.tokenize(normalized_question)
question_field = TextField(tokenized_question,
self._token_indexers)
fields = {'question': question_field}
start_idx, end_idx, rouge_score, passage_idx = None, None, None, None
tokenized_answer.insert(0, Token(START_SYMBOL))
tokenized_answer.append(Token(END_SYMBOL))
tokenized_passage = [
token for sublist in tokenized_passages_list
for token in sublist
]
passage_field = TextField(tokenized_passage, self._token_indexers)
fields['passage'] = passage_field
p1_question_field = TextField(tokenized_question,
p1_token_indexers)
p1_passage_field = TextField(tokenized_passage, p1_token_indexers)
p1_fields = {
'question': p1_question_field,
'passage': p1_passage_field
}
p1_instance = Instance(p1_fields)
outputs = model.forward_on_instance(p1_instance, -1)
start_idx = outputs['span_start_idx']
end_idx = outputs['span_end_idx']
for idx in range(len(cumulative_passages_length)):
if start_idx < cumulative_passages_length[idx]:
break
if idx != 0:
start_idx = start_idx - cumulative_passages_length[idx - 1]
end_idx = end_idx - cumulative_passages_length[idx - 1]
assert start_idx <= end_idx, "Span prediction does not make sense!!!"
# yield instance from predicted span
span_start_field = IndexField(int(start_idx), passage_field)
span_end_field = IndexField(int(end_idx), passage_field)
answer_field = TextField(tokenized_answer, self._token_indexers)
fields['passage'] = passage_field
fields['span_start'] = span_start_field
fields['span_end'] = span_end_field
fields['answer'] = answer_field
evidence = self.get_evidence(tokenized_passage, int(start_idx),
int(end_idx))
fields['metadata'] = MetadataField({
'evidence': evidence,
'question_text': normalized_question,
'answer_text': normalized_answer
})
yield Instance(fields)
# yield instances from gold spans
for item in best_span:
if item['score'] > 0.5:
passage_field = TextField(
tokenized_passages_list[item['passage']],
self._token_indexers)
span_start_field = IndexField(item['start'], passage_field)
span_end_field = IndexField(item['end'], passage_field)
answer_field = TextField(tokenized_answer,
self._token_indexers)
fields['passage'] = passage_field
fields['span_start'] = span_start_field
fields['span_end'] = span_end_field
fields['answer'] = answer_field
evidence = self.get_evidence(
tokenized_passages_list[item['passage']],
int(start_idx), int(end_idx))
fields['metadata'] = MetadataField({
'evidence':
evidence,
'question_text':
normalized_question,
'answer_text':
normalized_answer
})
yield Instance(fields)