mirror of
https://github.com/explosion/spaCy.git
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369 lines
12 KiB
Python
369 lines
12 KiB
Python
import pytest
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from spacy import util
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.lookups import Lookups
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from spacy.syntax.ner import BiluoPushDown
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from spacy.gold import Example
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from spacy.tokens import Doc
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from spacy.vocab import Vocab
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from ..util import make_tempdir
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TRAIN_DATA = [
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("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
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("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
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]
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def doc(vocab):
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return Doc(vocab, words=["Casey", "went", "to", "New", "York", "."])
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@pytest.fixture
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def entity_annots(doc):
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casey = doc[0:1]
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ny = doc[3:5]
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return [
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(casey.start_char, casey.end_char, "PERSON"),
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(ny.start_char, ny.end_char, "GPE"),
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]
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@pytest.fixture
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def entity_types(entity_annots):
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return sorted(set([label for (s, e, label) in entity_annots]))
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@pytest.fixture
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def tsys(vocab, entity_types):
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actions = BiluoPushDown.get_actions(entity_types=entity_types)
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return BiluoPushDown(vocab.strings, actions)
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def test_get_oracle_moves(tsys, doc, entity_annots):
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example = Example.from_dict(doc, {"entities": entity_annots})
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names == ["U-PERSON", "O", "O", "B-GPE", "L-GPE", "O"]
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def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
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entity_annots = [(s, e, "!" + label) for s, e, label in entity_annots]
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example = Example.from_dict(doc, {"entities": entity_annots})
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ex_dict = example.to_dict()
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for i, tag in enumerate(ex_dict["doc_annotation"]["entities"]):
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if tag == "L-!GPE":
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ex_dict["doc_annotation"]["entities"][i] = "-"
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example = Example.from_dict(doc, ex_dict)
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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def test_get_oracle_moves_negative_entities2(tsys, vocab):
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doc = Doc(vocab, words=["A", "B", "C", "D"])
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entity_annots = ["B-!PERSON", "L-!PERSON", "B-!PERSON", "L-!PERSON"]
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example = Example.from_dict(doc, {"entities": entity_annots})
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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@pytest.mark.skip(reason="Maybe outdated? Unsure")
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def test_get_oracle_moves_negative_O(tsys, vocab):
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doc = Doc(vocab, words=["A", "B", "C", "D"])
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entity_annots = ["O", "!O", "O", "!O"]
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example = Example.from_dict(doc, {"entities": entity_annots})
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act_classes = tsys.get_oracle_sequence(example)
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names = [tsys.get_class_name(act) for act in act_classes]
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assert names
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# We can't easily represent this on a Doc object. Not sure what the best solution
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# would be, but I don't think it's an important use case?
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@pytest.mark.skip(reason="No longer supported")
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def test_oracle_moves_missing_B(en_vocab):
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words = ["B", "52", "Bomber"]
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biluo_tags = [None, None, "L-PRODUCT"]
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doc = Doc(en_vocab, words=words)
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example = Example.from_dict(doc, {"words": words, "entities": biluo_tags})
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moves = BiluoPushDown(en_vocab.strings)
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move_types = ("M", "B", "I", "L", "U", "O")
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for tag in biluo_tags:
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if tag is None:
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continue
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elif tag == "O":
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moves.add_action(move_types.index("O"), "")
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else:
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action, label = tag.split("-")
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moves.add_action(move_types.index("B"), label)
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moves.add_action(move_types.index("I"), label)
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moves.add_action(move_types.index("L"), label)
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moves.add_action(move_types.index("U"), label)
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moves.get_oracle_sequence(example)
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# We can't easily represent this on a Doc object. Not sure what the best solution
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# would be, but I don't think it's an important use case?
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@pytest.mark.skip(reason="No longer supported")
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def test_oracle_moves_whitespace(en_vocab):
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words = ["production", "\n", "of", "Northrop", "\n", "Corp.", "\n", "'s", "radar"]
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biluo_tags = ["O", "O", "O", "B-ORG", None, "I-ORG", "L-ORG", "O", "O"]
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doc = Doc(en_vocab, words=words)
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example = Example.from_dict(doc, {"entities": biluo_tags})
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moves = BiluoPushDown(en_vocab.strings)
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move_types = ("M", "B", "I", "L", "U", "O")
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for tag in biluo_tags:
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if tag is None:
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continue
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elif tag == "O":
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moves.add_action(move_types.index("O"), "")
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else:
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action, label = tag.split("-")
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moves.add_action(move_types.index(action), label)
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moves.get_oracle_sequence(example)
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def test_accept_blocked_token():
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"""Test succesful blocking of tokens to be in an entity."""
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# 1. test normal behaviour
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nlp1 = English()
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doc1 = nlp1("I live in New York")
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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}
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ner1 = nlp1.create_pipe("ner", config=config)
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assert [token.ent_iob_ for token in doc1] == ["", "", "", "", ""]
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assert [token.ent_type_ for token in doc1] == ["", "", "", "", ""]
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# Add the OUT action
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ner1.moves.add_action(5, "")
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ner1.add_label("GPE")
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# Get into the state just before "New"
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state1 = ner1.moves.init_batch([doc1])[0]
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ner1.moves.apply_transition(state1, "O")
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ner1.moves.apply_transition(state1, "O")
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ner1.moves.apply_transition(state1, "O")
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# Check that B-GPE is valid.
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assert ner1.moves.is_valid(state1, "B-GPE")
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# 2. test blocking behaviour
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nlp2 = English()
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doc2 = nlp2("I live in New York")
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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}
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ner2 = nlp2.create_pipe("ner", config=config)
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# set "New York" to a blocked entity
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doc2.ents = [(0, 3, 5)]
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assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
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assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
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# Check that B-GPE is now invalid.
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ner2.moves.add_action(4, "")
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ner2.moves.add_action(5, "")
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ner2.add_label("GPE")
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state2 = ner2.moves.init_batch([doc2])[0]
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ner2.moves.apply_transition(state2, "O")
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ner2.moves.apply_transition(state2, "O")
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ner2.moves.apply_transition(state2, "O")
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# we can only use U- for "New"
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assert not ner2.moves.is_valid(state2, "B-GPE")
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assert ner2.moves.is_valid(state2, "U-")
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ner2.moves.apply_transition(state2, "U-")
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# we can only use U- for "York"
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assert not ner2.moves.is_valid(state2, "B-GPE")
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assert ner2.moves.is_valid(state2, "U-")
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def test_train_empty():
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"""Test that training an empty text does not throw errors."""
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train_data = [
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("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
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("", {"entities": []}),
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]
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nlp = English()
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train_examples = []
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for t in train_data:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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ner = nlp.add_pipe("ner", last=True)
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ner.add_label("PERSON")
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nlp.begin_training()
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for itn in range(2):
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losses = {}
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batches = util.minibatch(train_examples)
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for batch in batches:
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nlp.update(batch, losses=losses)
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def test_overwrite_token():
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nlp = English()
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nlp.add_pipe("ner")
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nlp.begin_training()
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# The untrained NER will predict O for each token
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doc = nlp("I live in New York")
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assert [token.ent_iob_ for token in doc] == ["O", "O", "O", "O", "O"]
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assert [token.ent_type_ for token in doc] == ["", "", "", "", ""]
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# Check that a new ner can overwrite O
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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}
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ner2 = nlp.create_pipe("ner", config=config)
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ner2.moves.add_action(5, "")
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ner2.add_label("GPE")
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state = ner2.moves.init_batch([doc])[0]
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assert ner2.moves.is_valid(state, "B-GPE")
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assert ner2.moves.is_valid(state, "U-GPE")
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ner2.moves.apply_transition(state, "B-GPE")
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assert ner2.moves.is_valid(state, "I-GPE")
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assert ner2.moves.is_valid(state, "L-GPE")
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def test_empty_ner():
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nlp = English()
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ner = nlp.add_pipe("ner")
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ner.add_label("MY_LABEL")
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nlp.begin_training()
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doc = nlp("John is watching the news about Croatia's elections")
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# if this goes wrong, the initialization of the parser's upper layer is probably broken
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result = ["O", "O", "O", "O", "O", "O", "O", "O", "O"]
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assert [token.ent_iob_ for token in doc] == result
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def test_ruler_before_ner():
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""" Test that an NER works after an entity_ruler: the second can add annotations """
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nlp = English()
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# 1 : Entity Ruler - should set "this" to B and everything else to empty
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patterns = [{"label": "THING", "pattern": "This"}]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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# 2: untrained NER - should set everything else to O
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untrained_ner = nlp.add_pipe("ner")
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untrained_ner.add_label("MY_LABEL")
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nlp.begin_training()
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doc = nlp("This is Antti Korhonen speaking in Finland")
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expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
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expected_types = ["THING", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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def test_ner_before_ruler():
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""" Test that an entity_ruler works after an NER: the second can overwrite O annotations """
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nlp = English()
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# 1: untrained NER - should set everything to O
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untrained_ner = nlp.add_pipe("ner", name="uner")
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untrained_ner.add_label("MY_LABEL")
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nlp.begin_training()
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# 2 : Entity Ruler - should set "this" to B and keep everything else O
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patterns = [{"label": "THING", "pattern": "This"}]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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doc = nlp("This is Antti Korhonen speaking in Finland")
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expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
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expected_types = ["THING", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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def test_block_ner():
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""" Test functionality for blocking tokens so they can't be in a named entity """
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# block "Antti L Korhonen" from being a named entity
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nlp = English()
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nlp.add_pipe("blocker", config={"start": 2, "end": 5})
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untrained_ner = nlp.add_pipe("ner")
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untrained_ner.add_label("MY_LABEL")
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nlp.begin_training()
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doc = nlp("This is Antti L Korhonen speaking in Finland")
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expected_iobs = ["O", "O", "B", "B", "B", "O", "O", "O"]
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expected_types = ["", "", "", "", "", "", "", ""]
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assert [token.ent_iob_ for token in doc] == expected_iobs
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assert [token.ent_type_ for token in doc] == expected_types
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the NER component - ensuring the ML models work correctly
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nlp = English()
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ner = nlp.add_pipe("ner")
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for ent in annotations.get("entities"):
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ner.add_label(ent[2])
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optimizer = nlp.begin_training()
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["ner"] < 0.00001
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# test the trained model
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test_text = "I like London."
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doc = nlp(test_text)
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ents = doc.ents
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assert len(ents) == 1
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assert ents[0].text == "London"
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assert ents[0].label_ == "LOC"
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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ents2 = doc2.ents
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assert len(ents2) == 1
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assert ents2[0].text == "London"
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assert ents2[0].label_ == "LOC"
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def test_ner_warns_no_lookups():
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nlp = English()
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assert nlp.lang in util.LEXEME_NORM_LANGS
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nlp.vocab.lookups = Lookups()
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assert not len(nlp.vocab.lookups)
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nlp.add_pipe("ner")
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with pytest.warns(UserWarning):
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nlp.begin_training()
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nlp.vocab.lookups.add_table("lexeme_norm")
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nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
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with pytest.warns(None) as record:
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nlp.begin_training()
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assert not record.list
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@Language.factory("blocker")
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class BlockerComponent1:
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def __init__(self, nlp, start, end, name="my_blocker"):
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self.start = start
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self.end = end
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self.name = name
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def __call__(self, doc):
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doc.ents = [(0, self.start, self.end)]
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return doc
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