class KnowledgeGraphFieldTest(AllenNlpTestCase):
def setUp(self):
self.tokenizer = WordTokenizer(SpacyWordSplitter(pos_tags=True))
self.utterance = self.tokenizer.tokenize("where is mersin?")
self.token_indexers = {"tokens": SingleIdTokenIndexer("tokens")}
table_file = self.FIXTURES_ROOT / "data" / "wikitables" / "tables" / "341.tagged"
self.graph = TableQuestionContext.read_from_file(
table_file, self.utterance).get_table_knowledge_graph()
self.vocab = Vocabulary()
self.name_index = self.vocab.add_token_to_namespace("name",
namespace='tokens')
self.in_index = self.vocab.add_token_to_namespace("in",
namespace='tokens')
self.english_index = self.vocab.add_token_to_namespace(
"english", namespace='tokens')
self.location_index = self.vocab.add_token_to_namespace(
"location", namespace='tokens')
self.mersin_index = self.vocab.add_token_to_namespace(
"mersin", namespace='tokens')
self.oov_index = self.vocab.get_token_index('random OOV string',
namespace='tokens')
self.edirne_index = self.oov_index
self.field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
super().setUp()
def test_count_vocab_items(self):
namespace_token_counts = defaultdict(lambda: defaultdict(int))
self.field.count_vocab_items(namespace_token_counts)
assert namespace_token_counts["tokens"] == {
'name': 1,
'in': 2,
'english': 2,
'location': 1,
'mersin': 1,
}
def test_index_converts_field_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field._indexed_entity_texts.keys() == {'tokens'}
# Note that these are sorted by their _identifiers_, not their cell text, so the
# `fb:row.rows` show up after the `fb:cells`.
expected_array = [[self.mersin_index],
[
self.location_index, self.in_index,
self.english_index
],
[self.name_index, self.in_index, self.english_index]]
assert self.field._indexed_entity_texts['tokens'] == expected_array
def test_get_padding_lengths_raises_if_not_indexed(self):
with pytest.raises(AssertionError):
self.field.get_padding_lengths()
def test_padding_lengths_are_computed_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {
'num_entities': 3,
'num_entity_tokens': 3,
'num_utterance_tokens': 4
}
self.field._token_indexers[
'token_characters'] = TokenCharactersIndexer(min_padding_length=1)
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {
'num_entities': 3,
'num_entity_tokens': 3,
'num_utterance_tokens': 4,
'num_token_characters': 8
}
def test_as_tensor_produces_correct_output(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
padding_lengths['num_utterance_tokens'] += 1
padding_lengths['num_entities'] += 1
tensor_dict = self.field.as_tensor(padding_lengths)
assert tensor_dict.keys() == {'text', 'linking'}
expected_text_tensor = [
[self.mersin_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index], [0, 0, 0]
]
assert_almost_equal(
tensor_dict['text']['tokens'].detach().cpu().numpy(),
expected_text_tensor)
linking_tensor = tensor_dict['linking'].detach().cpu().numpy()
expected_linking_tensor = [
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # string:mersin, "where"
[0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0], # string:mersin, "is"
[0, 1, 1, 1, 1, 1, 0, 0, 1, 1], # string:mersin, "mersin"
[0, 0, 0, 0, 0, -5, 0, 0, 0, 0], # string:mersin, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # string:mersin, padding
[
[0, 0, 0, 0, 0, -2.6, 0, 0, 0,
0], # string_column:name_in_english, "where"
[0, 0, 0, 0, 0, -7.5, 0, 0, 0,
0], # string_column:name_in_english, "is"
[0, 0, 0, 0, 0, -1.8333, 1, 1, 0,
0], # string_column:..in_english, "mersin"
[0, 0, 0, 0, 0, -18, 0, 0, 0,
0], # string_column:name_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # string_column:name_in_english, padding
[
[0, 0, 0, 0, 0, -1.6, 0, 0, 0,
0], # string_..:location_in_english, "where"
[0, 0, 0, 0, 0, -5.5, 0, 0, 0,
0], # string_column:location_in_english, "is"
[0, 0, 0, 0, 0, -1, 0, 0, 0,
0], # string_column:location_in_english, "mersin"
[0, 0, 0, 0, 0, -14, 0, 0, 0,
0], # string_column:location_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # string_column:location_in_english, padding
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]
] # padding, padding
for entity_index, entity_features in enumerate(
expected_linking_tensor):
for question_index, feature_vector in enumerate(entity_features):
assert_almost_equal(linking_tensor[entity_index,
question_index],
feature_vector,
decimal=4,
err_msg=f"{entity_index} {question_index}")
def test_lemma_feature_extractor(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize("Names in English")
field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
entity = 'string_column:name_in_english'
lemma_feature = field._contains_lemma_match(
entity, field._entity_text_map[entity], utterance[0], 0, utterance)
assert lemma_feature == 1
def test_span_overlap_fraction(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize(
"what is the name in english of mersin?")
field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
entity = 'string_column:name_in_english'
entity_text = field._entity_text_map[entity]
feature_values = [
field._span_overlap_fraction(entity, entity_text, token, i,
utterance)
for i, token in enumerate(utterance)
]
assert feature_values == [0, 0, 0, 1, 1, 1, 0, 0, 0]
def test_batch_tensors(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
tensor_dict1 = self.field.as_tensor(padding_lengths)
tensor_dict2 = self.field.as_tensor(padding_lengths)
batched_tensor_dict = self.field.batch_tensors(
[tensor_dict1, tensor_dict2])
assert batched_tensor_dict.keys() == {'text', 'linking'}
expected_single_tensor = [
[self.mersin_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index]
]
expected_batched_tensor = [
expected_single_tensor, expected_single_tensor
]
assert_almost_equal(
batched_tensor_dict['text']['tokens'].detach().cpu().numpy(),
expected_batched_tensor)
expected_linking_tensor = torch.stack(
[tensor_dict1['linking'], tensor_dict2['linking']])
assert_almost_equal(
batched_tensor_dict['linking'].detach().cpu().numpy(),
expected_linking_tensor.detach().cpu().numpy())
def test_field_initialized_with_empty_constructor(self):
try:
self.field.empty_field()
except AssertionError as e:
pytest.fail(str(e), pytrace=True)
class KnowledgeGraphFieldTest(AllenNlpTestCase):
def setUp(self):
self.tokenizer = WordTokenizer(SpacyWordSplitter(pos_tags=True))
self.utterance = self.tokenizer.tokenize("where is mersin?")
self.token_indexers = {"tokens": SingleIdTokenIndexer("tokens")}
json = {
'question': self.utterance,
'columns': ['Name in English', 'Location in English'],
'cells': [['Paradeniz', 'Mersin'], ['Lake Gala', 'Edirne']]
}
self.graph = TableQuestionKnowledgeGraph.read_from_json(json)
self.vocab = Vocabulary()
self.name_index = self.vocab.add_token_to_namespace("name",
namespace='tokens')
self.in_index = self.vocab.add_token_to_namespace("in",
namespace='tokens')
self.english_index = self.vocab.add_token_to_namespace(
"english", namespace='tokens')
self.location_index = self.vocab.add_token_to_namespace(
"location", namespace='tokens')
self.paradeniz_index = self.vocab.add_token_to_namespace(
"paradeniz", namespace='tokens')
self.mersin_index = self.vocab.add_token_to_namespace(
"mersin", namespace='tokens')
self.lake_index = self.vocab.add_token_to_namespace("lake",
namespace='tokens')
self.gala_index = self.vocab.add_token_to_namespace("gala",
namespace='tokens')
self.negative_one_index = self.vocab.add_token_to_namespace(
"-1", namespace='tokens')
self.zero_index = self.vocab.add_token_to_namespace("0",
namespace='tokens')
self.one_index = self.vocab.add_token_to_namespace("1",
namespace='tokens')
self.oov_index = self.vocab.get_token_index('random OOV string',
namespace='tokens')
self.edirne_index = self.oov_index
self.field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
super(KnowledgeGraphFieldTest, self).setUp()
def test_count_vocab_items(self):
namespace_token_counts = defaultdict(lambda: defaultdict(int))
self.field.count_vocab_items(namespace_token_counts)
assert namespace_token_counts["tokens"] == {
'-1': 1,
'0': 1,
'1': 1,
'name': 1,
'in': 2,
'english': 2,
'location': 1,
'paradeniz': 1,
'mersin': 1,
'lake': 1,
'gala': 1,
'edirne': 1,
}
def test_index_converts_field_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field._indexed_entity_texts.keys() == {'tokens'}
# Note that these are sorted by their _identifiers_, not their cell text, so the
# `fb:row.rows` show up after the `fb:cells`.
expected_array = [[self.negative_one_index], [self.zero_index],
[self.one_index], [self.edirne_index],
[self.lake_index, self.gala_index],
[self.mersin_index], [self.paradeniz_index],
[
self.location_index, self.in_index,
self.english_index
],
[self.name_index, self.in_index, self.english_index]]
assert self.field._indexed_entity_texts['tokens'] == expected_array
def test_get_padding_lengths_raises_if_not_indexed(self):
with pytest.raises(AssertionError):
self.field.get_padding_lengths()
def test_padding_lengths_are_computed_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {
'num_entities': 9,
'num_entity_tokens': 3,
'num_utterance_tokens': 4
}
self.field._token_indexers[
'token_characters'] = TokenCharactersIndexer(min_padding_length=1)
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {
'num_entities': 9,
'num_entity_tokens': 3,
'num_utterance_tokens': 4,
'num_token_characters': 9
}
def test_as_tensor_produces_correct_output(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
padding_lengths['num_utterance_tokens'] += 1
padding_lengths['num_entities'] += 1
tensor_dict = self.field.as_tensor(padding_lengths)
assert tensor_dict.keys() == {'text', 'linking'}
expected_text_tensor = [
[self.negative_one_index, 0, 0], [self.zero_index, 0, 0],
[self.one_index, 0, 0], [self.edirne_index, 0, 0],
[self.lake_index, self.gala_index, 0], [self.mersin_index, 0, 0],
[self.paradeniz_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index], [0, 0, 0]
]
assert_almost_equal(
tensor_dict['text']['tokens'].detach().cpu().numpy(),
expected_text_tensor)
linking_tensor = tensor_dict['linking'].detach().cpu().numpy()
expected_linking_tensor = [
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "mersin"
[0, 0, 0, 0, 0, -1, 0, 0, 0, 0]
], # -1, "?"
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # 0, "?"
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # 1, "?"
[
[0, 0, 0, 0, 0, .2, 0, 0, 0, 0], # fb:cell.edirne, "where"
[0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0], # fb:cell.edirne, "is"
[0, 0, 0, 0, 0, .1666, 0, 0, 0, 0], # fb:cell.edirne, "mersin"
[0, 0, 0, 0, 0, -5, 0, 0, 0, 0], # fb:cell.edirne, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:cell.edirne, padding
[
[0, 0, 0, 0, 0, -.6, 0, 0, 0, 0], # fb:cell.lake_gala, "where"
[0, 0, 0, 0, 0, -3.5, 0, 0, 0, 0], # fb:cell.lake_gala, "is"
[0, 0, 0, 0, 0, -.3333, 0, 0, 0,
0], # fb:cell.lake_gala, "mersin"
[0, 0, 0, 0, 0, -8, 0, 0, 0, 0], # fb:cell.lake_gala, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:cell.lake_gala, padding
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # fb:cell.mersin, "where"
[0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0], # fb:cell.mersin, "is"
[0, 1, 1, 1, 1, 1, 0, 0, 1, 1], # fb:cell.mersin, "mersin"
[0, 0, 0, 0, 0, -5, 0, 0, 0, 0], # fb:cell.mersin, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:cell.mersin, padding
[
[0, 0, 0, 0, 0, -.6, 0, 0, 0, 0], # fb:cell.paradeniz, "where"
[0, 0, 0, 0, 0, -3, 0, 0, 0, 0], # fb:cell.paradeniz, "is"
[0, 0, 0, 0, 0, -.1666, 0, 0, 0,
0], # fb:cell.paradeniz, "mersin"
[0, 0, 0, 0, 0, -8, 0, 0, 0, 0], # fb:cell.paradeniz, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:cell.paradeniz, padding
[
[0, 0, 0, 0, 0, -2.6, 0, 0, 0,
0], # fb:row.row.name_in_english, "where"
[0, 0, 0, 0, 0, -7.5, 0, 0, 0,
0], # fb:row.row.name_in_english, "is"
[0, 0, 0, 0, 0, -1.8333, 1, 1, 0,
0], # fb:row.row.name_in_english, "mersin"
[0, 0, 0, 0, 0, -18, 0, 0, 0,
0], # fb:row.row.name_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:row.row.name_in_english, padding
[
[0, 0, 0, 0, 0, -1.6, 0, 0, 0,
0], # fb:row.row.location_in_english, "where"
[0, 0, 0, 0, 0, -5.5, 0, 0, 0,
0], # fb:row.row.location_in_english, "is"
[0, 0, 0, 0, 0, -1, 0, 0, 0,
0], # fb:row.row.location_in_english, "mersin"
[0, 0, 0, 0, 0, -14, 0, 0, 0,
0], # fb:row.row.location_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
], # fb:row.row.location_in_english, padding
[
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]
] # padding, padding
for entity_index, entity_features in enumerate(
expected_linking_tensor):
for question_index, feature_vector in enumerate(entity_features):
assert_almost_equal(linking_tensor[entity_index,
question_index],
feature_vector,
decimal=4,
err_msg=f"{entity_index} {question_index}")
def test_lemma_feature_extractor(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize("Names in English")
field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
entity = 'fb:row.row.name_in_english'
lemma_feature = field._contains_lemma_match(
entity, field._entity_text_map[entity], utterance[0], 0, utterance)
assert lemma_feature == 1
def test_span_overlap_fraction(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize(
"what is the name in english of mersin?")
field = KnowledgeGraphField(self.graph, self.utterance,
self.token_indexers, self.tokenizer)
entity = 'fb:row.row.name_in_english'
entity_text = field._entity_text_map[entity]
feature_values = [
field._span_overlap_fraction(entity, entity_text, token, i,
utterance)
for i, token in enumerate(utterance)
]
assert feature_values == [0, 0, 0, 1, 1, 1, 0, 0, 0]
def test_batch_tensors(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
tensor_dict1 = self.field.as_tensor(padding_lengths)
tensor_dict2 = self.field.as_tensor(padding_lengths)
batched_tensor_dict = self.field.batch_tensors(
[tensor_dict1, tensor_dict2])
assert batched_tensor_dict.keys() == {'text', 'linking'}
expected_single_tensor = [
[self.negative_one_index, 0, 0], [self.zero_index, 0, 0],
[self.one_index, 0, 0], [self.edirne_index, 0, 0],
[self.lake_index, self.gala_index, 0], [self.mersin_index, 0, 0],
[self.paradeniz_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index]
]
expected_batched_tensor = [
expected_single_tensor, expected_single_tensor
]
assert_almost_equal(
batched_tensor_dict['text']['tokens'].detach().cpu().numpy(),
expected_batched_tensor)
expected_linking_tensor = torch.stack(
[tensor_dict1['linking'], tensor_dict2['linking']])
assert_almost_equal(
batched_tensor_dict['linking'].detach().cpu().numpy(),
expected_linking_tensor.detach().cpu().numpy())
class KnowledgeGraphFieldTest(AllenNlpTestCase):
def setUp(self):
self.tokenizer = WordTokenizer(SpacyWordSplitter(pos_tags=True))
self.utterance = self.tokenizer.tokenize("where is mersin?")
self.token_indexers = {"tokens": SingleIdTokenIndexer("tokens")}
json = {
'question': self.utterance,
'columns': ['Name in English', 'Location in English'],
'cells': [['Paradeniz', 'Mersin'],
['Lake Gala', 'Edirne']]
}
self.graph = TableQuestionKnowledgeGraph.read_from_json(json)
self.vocab = Vocabulary()
self.name_index = self.vocab.add_token_to_namespace("name", namespace='tokens')
self.in_index = self.vocab.add_token_to_namespace("in", namespace='tokens')
self.english_index = self.vocab.add_token_to_namespace("english", namespace='tokens')
self.location_index = self.vocab.add_token_to_namespace("location", namespace='tokens')
self.paradeniz_index = self.vocab.add_token_to_namespace("paradeniz", namespace='tokens')
self.mersin_index = self.vocab.add_token_to_namespace("mersin", namespace='tokens')
self.lake_index = self.vocab.add_token_to_namespace("lake", namespace='tokens')
self.gala_index = self.vocab.add_token_to_namespace("gala", namespace='tokens')
self.negative_one_index = self.vocab.add_token_to_namespace("-1", namespace='tokens')
self.zero_index = self.vocab.add_token_to_namespace("0", namespace='tokens')
self.one_index = self.vocab.add_token_to_namespace("1", namespace='tokens')
self.oov_index = self.vocab.get_token_index('random OOV string', namespace='tokens')
self.edirne_index = self.oov_index
self.field = KnowledgeGraphField(self.graph, self.utterance, self.token_indexers, self.tokenizer)
super(KnowledgeGraphFieldTest, self).setUp()
def test_count_vocab_items(self):
namespace_token_counts = defaultdict(lambda: defaultdict(int))
self.field.count_vocab_items(namespace_token_counts)
assert namespace_token_counts["tokens"] == {
'-1': 1,
'0': 1,
'1': 1,
'name': 1,
'in': 2,
'english': 2,
'location': 1,
'paradeniz': 1,
'mersin': 1,
'lake': 1,
'gala': 1,
'edirne': 1,
}
def test_index_converts_field_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field._indexed_entity_texts.keys() == {'tokens'}
# Note that these are sorted by their _identifiers_, not their cell text, so the
# `fb:row.rows` show up after the `fb:cells`.
expected_array = [[self.negative_one_index],
[self.zero_index],
[self.one_index],
[self.edirne_index],
[self.lake_index, self.gala_index],
[self.mersin_index],
[self.paradeniz_index],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index]]
assert self.field._indexed_entity_texts['tokens'] == expected_array
def test_get_padding_lengths_raises_if_not_indexed(self):
with pytest.raises(AssertionError):
self.field.get_padding_lengths()
def test_padding_lengths_are_computed_correctly(self):
# pylint: disable=protected-access
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {'num_entities': 9, 'num_entity_tokens': 3,
'num_utterance_tokens': 4}
self.field._token_indexers['token_characters'] = TokenCharactersIndexer()
self.field.index(self.vocab)
assert self.field.get_padding_lengths() == {'num_entities': 9, 'num_entity_tokens': 3,
'num_utterance_tokens': 4,
'num_token_characters': 9}
def test_as_tensor_produces_correct_output(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
padding_lengths['num_utterance_tokens'] += 1
padding_lengths['num_entities'] += 1
tensor_dict = self.field.as_tensor(padding_lengths)
assert tensor_dict.keys() == {'text', 'linking'}
expected_text_tensor = [[self.negative_one_index, 0, 0],
[self.zero_index, 0, 0],
[self.one_index, 0, 0],
[self.edirne_index, 0, 0],
[self.lake_index, self.gala_index, 0],
[self.mersin_index, 0, 0],
[self.paradeniz_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index],
[0, 0, 0]]
assert_almost_equal(tensor_dict['text']['tokens'].detach().cpu().numpy(), expected_text_tensor)
linking_tensor = tensor_dict['linking'].detach().cpu().numpy()
expected_linking_tensor = [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # -1, "mersin"
[0, 0, 0, 0, 0, -1, 0, 0, 0, 0]], # -1, "?"
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 0, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # 0, "?"
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 1, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # 1, "?"
[[0, 0, 0, 0, 0, .2, 0, 0, 0, 0], # fb:cell.edirne, "where"
[0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0], # fb:cell.edirne, "is"
[0, 0, 0, 0, 0, .1666, 0, 0, 0, 0], # fb:cell.edirne, "mersin"
[0, 0, 0, 0, 0, -5, 0, 0, 0, 0], # fb:cell.edirne, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:cell.edirne, padding
[[0, 0, 0, 0, 0, -.6, 0, 0, 0, 0], # fb:cell.lake_gala, "where"
[0, 0, 0, 0, 0, -3.5, 0, 0, 0, 0], # fb:cell.lake_gala, "is"
[0, 0, 0, 0, 0, -.3333, 0, 0, 0, 0], # fb:cell.lake_gala, "mersin"
[0, 0, 0, 0, 0, -8, 0, 0, 0, 0], # fb:cell.lake_gala, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:cell.lake_gala, padding
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # fb:cell.mersin, "where"
[0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0], # fb:cell.mersin, "is"
[0, 1, 1, 1, 1, 1, 0, 0, 1, 1], # fb:cell.mersin, "mersin"
[0, 0, 0, 0, 0, -5, 0, 0, 0, 0], # fb:cell.mersin, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:cell.mersin, padding
[[0, 0, 0, 0, 0, -.6, 0, 0, 0, 0], # fb:cell.paradeniz, "where"
[0, 0, 0, 0, 0, -3, 0, 0, 0, 0], # fb:cell.paradeniz, "is"
[0, 0, 0, 0, 0, -.1666, 0, 0, 0, 0], # fb:cell.paradeniz, "mersin"
[0, 0, 0, 0, 0, -8, 0, 0, 0, 0], # fb:cell.paradeniz, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:cell.paradeniz, padding
[[0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0], # fb:row.row.name_in_english, "where"
[0, 0, 0, 0, 0, -7.5, 0, 0, 0, 0], # fb:row.row.name_in_english, "is"
[0, 0, 0, 0, 0, -1.8333, 1, 1, 0, 0], # fb:row.row.name_in_english, "mersin"
[0, 0, 0, 0, 0, -18, 0, 0, 0, 0], # fb:row.row.name_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:row.row.name_in_english, padding
[[0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0], # fb:row.row.location_in_english, "where"
[0, 0, 0, 0, 0, -5.5, 0, 0, 0, 0], # fb:row.row.location_in_english, "is"
[0, 0, 0, 0, 0, -1, 0, 0, 0, 0], # fb:row.row.location_in_english, "mersin"
[0, 0, 0, 0, 0, -14, 0, 0, 0, 0], # fb:row.row.location_in_english, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], # fb:row.row.location_in_english, padding
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "where"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "is"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "mersin"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # padding, "?"
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]] # padding, padding
for entity_index, entity_features in enumerate(expected_linking_tensor):
for question_index, feature_vector in enumerate(entity_features):
assert_almost_equal(linking_tensor[entity_index, question_index],
feature_vector,
decimal=4,
err_msg=f"{entity_index} {question_index}")
def test_lemma_feature_extractor(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize("Names in English")
field = KnowledgeGraphField(self.graph, self.utterance, self.token_indexers, self.tokenizer)
entity = 'fb:row.row.name_in_english'
lemma_feature = field._contains_lemma_match(entity,
field._entity_text_map[entity],
utterance[0],
0,
utterance)
assert lemma_feature == 1
def test_span_overlap_fraction(self):
# pylint: disable=protected-access
utterance = self.tokenizer.tokenize("what is the name in english of mersin?")
field = KnowledgeGraphField(self.graph, self.utterance, self.token_indexers, self.tokenizer)
entity = 'fb:row.row.name_in_english'
entity_text = field._entity_text_map[entity]
feature_values = [field._span_overlap_fraction(entity, entity_text, token, i, utterance)
for i, token in enumerate(utterance)]
assert feature_values == [0, 0, 0, 1, 1, 1, 0, 0, 0]
def test_batch_tensors(self):
self.field.index(self.vocab)
padding_lengths = self.field.get_padding_lengths()
tensor_dict1 = self.field.as_tensor(padding_lengths)
tensor_dict2 = self.field.as_tensor(padding_lengths)
batched_tensor_dict = self.field.batch_tensors([tensor_dict1, tensor_dict2])
assert batched_tensor_dict.keys() == {'text', 'linking'}
expected_single_tensor = [[self.negative_one_index, 0, 0],
[self.zero_index, 0, 0],
[self.one_index, 0, 0],
[self.edirne_index, 0, 0],
[self.lake_index, self.gala_index, 0],
[self.mersin_index, 0, 0],
[self.paradeniz_index, 0, 0],
[self.location_index, self.in_index, self.english_index],
[self.name_index, self.in_index, self.english_index]]
expected_batched_tensor = [expected_single_tensor, expected_single_tensor]
assert_almost_equal(batched_tensor_dict['text']['tokens'].detach().cpu().numpy(),
expected_batched_tensor)
expected_linking_tensor = torch.stack([tensor_dict1['linking'], tensor_dict2['linking']])
assert_almost_equal(batched_tensor_dict['linking'].detach().cpu().numpy(),
expected_linking_tensor.detach().cpu().numpy())