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spaCy/spacy/tests/parser/test_ner.py
Ines Montani f90482d077 Tidy up and auto-format
2021-07-18 15:44:56 +10:00

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import pytest
from numpy.testing import assert_equal
from spacy.attrs import ENT_IOB
from spacy import util
from spacy.lang.en import English
from spacy.language import Language
from spacy.lookups import Lookups
from spacy.pipeline._parser_internals.ner import BiluoPushDown
from spacy.training import Example
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab, registry
import logging
from ..util import make_tempdir
from ...pipeline import EntityRecognizer
from ...pipeline.ner import DEFAULT_NER_MODEL
TRAIN_DATA = [
("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
("I like London and Berlin.", {"entities": [(7, 13, "LOC"), (18, 24, "LOC")]}),
]
@pytest.fixture
def neg_key():
return "non_entities"
@pytest.fixture
def vocab():
return Vocab()
@pytest.fixture
def doc(vocab):
return Doc(vocab, words=["Casey", "went", "to", "New", "York", "."])
@pytest.fixture
def entity_annots(doc):
casey = doc[0:1]
ny = doc[3:5]
return [
(casey.start_char, casey.end_char, "PERSON"),
(ny.start_char, ny.end_char, "GPE"),
]
@pytest.fixture
def entity_types(entity_annots):
return sorted(set([label for (s, e, label) in entity_annots]))
@pytest.fixture
def tsys(vocab, entity_types):
actions = BiluoPushDown.get_actions(entity_types=entity_types)
return BiluoPushDown(vocab.strings, actions)
def test_get_oracle_moves(tsys, doc, entity_annots):
example = Example.from_dict(doc, {"entities": entity_annots})
act_classes = tsys.get_oracle_sequence(example, _debug=False)
names = [tsys.get_class_name(act) for act in act_classes]
assert names == ["U-PERSON", "O", "O", "B-GPE", "L-GPE", "O"]
def test_negative_samples_two_word_input(tsys, vocab, neg_key):
"""Test that we don't get stuck in a two word input when we have a negative
span. This could happen if we don't have the right check on the B action.
"""
tsys.cfg["neg_key"] = neg_key
doc = Doc(vocab, words=["A", "B"])
entity_annots = [None, None]
example = Example.from_dict(doc, {"entities": entity_annots})
# These mean that the oracle sequence shouldn't have O for the first
# word, and it shouldn't analyse it as B-PERSON, L-PERSON
example.y.spans[neg_key] = [
Span(example.y, 0, 1, label="O"),
Span(example.y, 0, 2, label="PERSON"),
]
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
assert names[0] != "O"
assert names[0] != "B-PERSON"
assert names[1] != "L-PERSON"
def test_negative_samples_three_word_input(tsys, vocab, neg_key):
"""Test that we exclude a 2-word entity correctly using a negative example."""
tsys.cfg["neg_key"] = neg_key
doc = Doc(vocab, words=["A", "B", "C"])
entity_annots = [None, None, None]
example = Example.from_dict(doc, {"entities": entity_annots})
# These mean that the oracle sequence shouldn't have O for the first
# word, and it shouldn't analyse it as B-PERSON, L-PERSON
example.y.spans[neg_key] = [
Span(example.y, 0, 1, label="O"),
Span(example.y, 0, 2, label="PERSON"),
]
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
assert names[0] != "O"
assert names[1] != "B-PERSON"
def test_negative_samples_U_entity(tsys, vocab, neg_key):
"""Test that we exclude a 2-word entity correctly using a negative example."""
tsys.cfg["neg_key"] = neg_key
doc = Doc(vocab, words=["A"])
entity_annots = [None]
example = Example.from_dict(doc, {"entities": entity_annots})
# These mean that the oracle sequence shouldn't have O for the first
# word, and it shouldn't analyse it as B-PERSON, L-PERSON
example.y.spans[neg_key] = [
Span(example.y, 0, 1, label="O"),
Span(example.y, 0, 1, label="PERSON"),
]
act_classes = tsys.get_oracle_sequence(example)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
assert names[0] != "O"
assert names[0] != "U-PERSON"
def test_negative_sample_key_is_in_config(vocab, entity_types):
actions = BiluoPushDown.get_actions(entity_types=entity_types)
tsys = BiluoPushDown(vocab.strings, actions, incorrect_spans_key="non_entities")
assert tsys.cfg["neg_key"] == "non_entities"
# We can't easily represent this on a Doc object. Not sure what the best solution
# would be, but I don't think it's an important use case?
@pytest.mark.skip(reason="No longer supported")
def test_oracle_moves_missing_B(en_vocab):
words = ["B", "52", "Bomber"]
biluo_tags = [None, None, "L-PRODUCT"]
doc = Doc(en_vocab, words=words)
example = Example.from_dict(doc, {"words": words, "entities": biluo_tags})
moves = BiluoPushDown(en_vocab.strings)
move_types = ("M", "B", "I", "L", "U", "O")
for tag in biluo_tags:
if tag is None:
continue
elif tag == "O":
moves.add_action(move_types.index("O"), "")
else:
action, label = tag.split("-")
moves.add_action(move_types.index("B"), label)
moves.add_action(move_types.index("I"), label)
moves.add_action(move_types.index("L"), label)
moves.add_action(move_types.index("U"), label)
moves.get_oracle_sequence(example)
# We can't easily represent this on a Doc object. Not sure what the best solution
# would be, but I don't think it's an important use case?
@pytest.mark.skip(reason="No longer supported")
def test_oracle_moves_whitespace(en_vocab):
words = ["production", "\n", "of", "Northrop", "\n", "Corp.", "\n", "'s", "radar"]
biluo_tags = ["O", "O", "O", "B-ORG", None, "I-ORG", "L-ORG", "O", "O"]
doc = Doc(en_vocab, words=words)
example = Example.from_dict(doc, {"entities": biluo_tags})
moves = BiluoPushDown(en_vocab.strings)
move_types = ("M", "B", "I", "L", "U", "O")
for tag in biluo_tags:
if tag is None:
continue
elif tag == "O":
moves.add_action(move_types.index("O"), "")
else:
action, label = tag.split("-")
moves.add_action(move_types.index(action), label)
moves.get_oracle_sequence(example)
def test_accept_blocked_token():
"""Test succesful blocking of tokens to be in an entity."""
# 1. test normal behaviour
nlp1 = English()
doc1 = nlp1("I live in New York")
config = {}
ner1 = nlp1.create_pipe("ner", config=config)
assert [token.ent_iob_ for token in doc1] == ["", "", "", "", ""]
assert [token.ent_type_ for token in doc1] == ["", "", "", "", ""]
# Add the OUT action
ner1.moves.add_action(5, "")
ner1.add_label("GPE")
# Get into the state just before "New"
state1 = ner1.moves.init_batch([doc1])[0]
ner1.moves.apply_transition(state1, "O")
ner1.moves.apply_transition(state1, "O")
ner1.moves.apply_transition(state1, "O")
# Check that B-GPE is valid.
assert ner1.moves.is_valid(state1, "B-GPE")
# 2. test blocking behaviour
nlp2 = English()
doc2 = nlp2("I live in New York")
config = {}
ner2 = nlp2.create_pipe("ner", config=config)
# set "New York" to a blocked entity
doc2.set_ents([], blocked=[doc2[3:5]], default="unmodified")
assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
# Check that B-GPE is now invalid.
ner2.moves.add_action(4, "")
ner2.moves.add_action(5, "")
ner2.add_label("GPE")
state2 = ner2.moves.init_batch([doc2])[0]
ner2.moves.apply_transition(state2, "O")
ner2.moves.apply_transition(state2, "O")
ner2.moves.apply_transition(state2, "O")
# we can only use U- for "New"
assert not ner2.moves.is_valid(state2, "B-GPE")
assert ner2.moves.is_valid(state2, "U-")
ner2.moves.apply_transition(state2, "U-")
# we can only use U- for "York"
assert not ner2.moves.is_valid(state2, "B-GPE")
assert ner2.moves.is_valid(state2, "U-")
def test_train_empty():
"""Test that training an empty text does not throw errors."""
train_data = [
("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
("", {"entities": []}),
]
nlp = English()
train_examples = []
for t in train_data:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
ner = nlp.add_pipe("ner", last=True)
ner.add_label("PERSON")
nlp.initialize()
for itn in range(2):
losses = {}
batches = util.minibatch(train_examples, size=8)
for batch in batches:
nlp.update(batch, losses=losses)
def test_train_negative_deprecated():
"""Test that the deprecated negative entity format raises a custom error."""
train_data = [
("Who is Shaka Khan?", {"entities": [(7, 17, "!PERSON")]}),
]
nlp = English()
train_examples = []
for t in train_data:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
ner = nlp.add_pipe("ner", last=True)
ner.add_label("PERSON")
nlp.initialize()
for itn in range(2):
losses = {}
batches = util.minibatch(train_examples, size=8)
for batch in batches:
with pytest.raises(ValueError):
nlp.update(batch, losses=losses)
def test_overwrite_token():
nlp = English()
nlp.add_pipe("ner")
nlp.initialize()
# The untrained NER will predict O for each token
doc = nlp("I live in New York")
assert [token.ent_iob_ for token in doc] == ["O", "O", "O", "O", "O"]
assert [token.ent_type_ for token in doc] == ["", "", "", "", ""]
# Check that a new ner can overwrite O
config = {}
ner2 = nlp.create_pipe("ner", config=config)
ner2.moves.add_action(5, "")
ner2.add_label("GPE")
state = ner2.moves.init_batch([doc])[0]
assert ner2.moves.is_valid(state, "B-GPE")
assert ner2.moves.is_valid(state, "U-GPE")
ner2.moves.apply_transition(state, "B-GPE")
assert ner2.moves.is_valid(state, "I-GPE")
assert ner2.moves.is_valid(state, "L-GPE")
def test_empty_ner():
nlp = English()
ner = nlp.add_pipe("ner")
ner.add_label("MY_LABEL")
nlp.initialize()
doc = nlp("John is watching the news about Croatia's elections")
# if this goes wrong, the initialization of the parser's upper layer is probably broken
result = ["O", "O", "O", "O", "O", "O", "O", "O", "O"]
assert [token.ent_iob_ for token in doc] == result
def test_ruler_before_ner():
"""Test that an NER works after an entity_ruler: the second can add annotations"""
nlp = English()
# 1 : Entity Ruler - should set "this" to B and everything else to empty
patterns = [{"label": "THING", "pattern": "This"}]
ruler = nlp.add_pipe("entity_ruler")
# 2: untrained NER - should set everything else to O
untrained_ner = nlp.add_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.initialize()
ruler.add_patterns(patterns)
doc = nlp("This is Antti Korhonen speaking in Finland")
expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
expected_types = ["THING", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
def test_ner_constructor(en_vocab):
config = {
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.resolve(cfg, validate=True)["model"]
EntityRecognizer(en_vocab, model, **config)
EntityRecognizer(en_vocab, model)
def test_ner_before_ruler():
"""Test that an entity_ruler works after an NER: the second can overwrite O annotations"""
nlp = English()
# 1: untrained NER - should set everything to O
untrained_ner = nlp.add_pipe("ner", name="uner")
untrained_ner.add_label("MY_LABEL")
nlp.initialize()
# 2 : Entity Ruler - should set "this" to B and keep everything else O
patterns = [{"label": "THING", "pattern": "This"}]
ruler = nlp.add_pipe("entity_ruler")
ruler.add_patterns(patterns)
doc = nlp("This is Antti Korhonen speaking in Finland")
expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
expected_types = ["THING", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
def test_block_ner():
"""Test functionality for blocking tokens so they can't be in a named entity"""
# block "Antti L Korhonen" from being a named entity
nlp = English()
nlp.add_pipe("blocker", config={"start": 2, "end": 5})
untrained_ner = nlp.add_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.initialize()
doc = nlp("This is Antti L Korhonen speaking in Finland")
expected_iobs = ["O", "O", "B", "B", "B", "O", "O", "O"]
expected_types = ["", "", "", "", "", "", "", ""]
assert [token.ent_iob_ for token in doc] == expected_iobs
assert [token.ent_type_ for token in doc] == expected_types
@pytest.mark.parametrize("use_upper", [True, False])
def test_overfitting_IO(use_upper):
# Simple test to try and quickly overfit the NER component
nlp = English()
ner = nlp.add_pipe("ner", config={"model": {"use_upper": use_upper}})
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.initialize()
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["ner"] < 0.00001
# test the trained model
test_text = "I like London."
doc = nlp(test_text)
ents = doc.ents
assert len(ents) == 1
assert ents[0].text == "London"
assert ents[0].label_ == "LOC"
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
ents2 = doc2.ents
assert len(ents2) == 1
assert ents2[0].text == "London"
assert ents2[0].label_ == "LOC"
# Ensure that the predictions are still the same, even after adding a new label
ner2 = nlp2.get_pipe("ner")
assert ner2.model.attrs["has_upper"] == use_upper
ner2.add_label("RANDOM_NEW_LABEL")
doc3 = nlp2(test_text)
ents3 = doc3.ents
assert len(ents3) == 1
assert ents3[0].text == "London"
assert ents3[0].label_ == "LOC"
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
texts = [
"Just a sentence.",
"Then one more sentence about London.",
"Here is another one.",
"I like London.",
]
batch_deps_1 = [doc.to_array([ENT_IOB]) for doc in nlp.pipe(texts)]
batch_deps_2 = [doc.to_array([ENT_IOB]) for doc in nlp.pipe(texts)]
no_batch_deps = [doc.to_array([ENT_IOB]) for doc in [nlp(text) for text in texts]]
assert_equal(batch_deps_1, batch_deps_2)
assert_equal(batch_deps_1, no_batch_deps)
# test that kb_id is preserved
test_text = "I like London and London."
doc = nlp.make_doc(test_text)
doc.ents = [Span(doc, 2, 3, label="LOC", kb_id=1234)]
ents = doc.ents
assert len(ents) == 1
assert ents[0].text == "London"
assert ents[0].label_ == "LOC"
assert ents[0].kb_id == 1234
doc = nlp.get_pipe("ner")(doc)
ents = doc.ents
assert len(ents) == 2
assert ents[0].text == "London"
assert ents[0].label_ == "LOC"
assert ents[0].kb_id == 1234
# ent added by ner has kb_id == 0
assert ents[1].text == "London"
assert ents[1].label_ == "LOC"
assert ents[1].kb_id == 0
def test_beam_ner_scores():
# Test that we can get confidence values out of the beam_ner pipe
beam_width = 16
beam_density = 0.0001
nlp = English()
config = {
"beam_width": beam_width,
"beam_density": beam_density,
}
ner = nlp.add_pipe("beam_ner", config=config)
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.initialize()
# update once
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
# test the scores from the beam
test_text = "I like London."
doc = nlp.make_doc(test_text)
docs = [doc]
beams = ner.predict(docs)
entity_scores = ner.scored_ents(beams)[0]
for j in range(len(doc)):
for label in ner.labels:
score = entity_scores[(j, j + 1, label)]
eps = 0.00001
assert 0 - eps <= score <= 1 + eps
def test_beam_overfitting_IO(neg_key):
# Simple test to try and quickly overfit the Beam NER component
nlp = English()
beam_width = 16
beam_density = 0.0001
config = {
"beam_width": beam_width,
"beam_density": beam_density,
"incorrect_spans_key": neg_key,
}
ner = nlp.add_pipe("beam_ner", config=config)
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.initialize()
# run overfitting
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["beam_ner"] < 0.0001
# test the scores from the beam
test_text = "I like London"
docs = [nlp.make_doc(test_text)]
beams = ner.predict(docs)
entity_scores = ner.scored_ents(beams)[0]
assert entity_scores[(2, 3, "LOC")] == 1.0
assert entity_scores[(2, 3, "PERSON")] == 0.0
assert len(nlp(test_text).ents) == 1
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
docs2 = [nlp2.make_doc(test_text)]
ner2 = nlp2.get_pipe("beam_ner")
beams2 = ner2.predict(docs2)
entity_scores2 = ner2.scored_ents(beams2)[0]
assert entity_scores2[(2, 3, "LOC")] == 1.0
assert entity_scores2[(2, 3, "PERSON")] == 0.0
# Try to unlearn the entity by using negative annotations
neg_doc = nlp.make_doc(test_text)
neg_ex = Example(neg_doc, neg_doc)
neg_ex.reference.spans[neg_key] = [Span(neg_doc, 2, 3, "LOC")]
neg_train_examples = [neg_ex]
for i in range(20):
losses = {}
nlp.update(neg_train_examples, sgd=optimizer, losses=losses)
# test the "untrained" model
assert len(nlp(test_text).ents) == 0
def test_neg_annotation(neg_key):
"""Check that the NER update works with a negative annotation that is a different label of the correct one,
or partly overlapping, etc"""
nlp = English()
beam_width = 16
beam_density = 0.0001
config = {
"beam_width": beam_width,
"beam_density": beam_density,
"incorrect_spans_key": neg_key,
}
ner = nlp.add_pipe("beam_ner", config=config)
train_text = "Who is Shaka Khan?"
neg_doc = nlp.make_doc(train_text)
ner.add_label("PERSON")
ner.add_label("ORG")
example = Example.from_dict(neg_doc, {"entities": [(7, 17, "PERSON")]})
example.reference.spans[neg_key] = [
Span(neg_doc, 2, 4, "ORG"),
Span(neg_doc, 2, 3, "PERSON"),
Span(neg_doc, 1, 4, "PERSON"),
]
optimizer = nlp.initialize()
for i in range(2):
losses = {}
nlp.update([example], sgd=optimizer, losses=losses)
def test_neg_annotation_conflict(neg_key):
# Check that NER raises for a negative annotation that is THE SAME as a correct one
nlp = English()
beam_width = 16
beam_density = 0.0001
config = {
"beam_width": beam_width,
"beam_density": beam_density,
"incorrect_spans_key": neg_key,
}
ner = nlp.add_pipe("beam_ner", config=config)
train_text = "Who is Shaka Khan?"
neg_doc = nlp.make_doc(train_text)
ner.add_label("PERSON")
ner.add_label("LOC")
example = Example.from_dict(neg_doc, {"entities": [(7, 17, "PERSON")]})
example.reference.spans[neg_key] = [Span(neg_doc, 2, 4, "PERSON")]
assert len(example.reference.ents) == 1
assert example.reference.ents[0].text == "Shaka Khan"
assert example.reference.ents[0].label_ == "PERSON"
assert len(example.reference.spans[neg_key]) == 1
assert example.reference.spans[neg_key][0].text == "Shaka Khan"
assert example.reference.spans[neg_key][0].label_ == "PERSON"
optimizer = nlp.initialize()
for i in range(2):
losses = {}
with pytest.raises(ValueError):
nlp.update([example], sgd=optimizer, losses=losses)
def test_beam_valid_parse(neg_key):
"""Regression test for previously flakey behaviour"""
nlp = English()
beam_width = 16
beam_density = 0.0001
config = {
"beam_width": beam_width,
"beam_density": beam_density,
"incorrect_spans_key": neg_key,
}
nlp.add_pipe("beam_ner", config=config)
# fmt: off
tokens = ['FEDERAL', 'NATIONAL', 'MORTGAGE', 'ASSOCIATION', '(', 'Fannie', 'Mae', '):', 'Posted', 'yields', 'on', '30', 'year', 'mortgage', 'commitments', 'for', 'delivery', 'within', '30', 'days', '(', 'priced', 'at', 'par', ')', '9.75', '%', ',', 'standard', 'conventional', 'fixed', '-', 'rate', 'mortgages', ';', '8.70', '%', ',', '6/2', 'rate', 'capped', 'one', '-', 'year', 'adjustable', 'rate', 'mortgages', '.', 'Source', ':', 'Telerate', 'Systems', 'Inc.']
iob = ['B-ORG', 'I-ORG', 'I-ORG', 'L-ORG', 'O', 'B-ORG', 'L-ORG', 'O', 'O', 'O', 'O', 'B-DATE', 'L-DATE', 'O', 'O', 'O', 'O', 'O', 'B-DATE', 'L-DATE', 'O', 'O', 'O', 'O', 'O', 'B-PERCENT', 'L-PERCENT', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PERCENT', 'L-PERCENT', 'O', 'U-CARDINAL', 'O', 'O', 'B-DATE', 'I-DATE', 'L-DATE', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
# fmt: on
doc = Doc(nlp.vocab, words=tokens)
example = Example.from_dict(doc, {"ner": iob})
neg_span = Span(doc, 50, 53, "ORG")
example.reference.spans[neg_key] = [neg_span]
optimizer = nlp.initialize()
for i in range(5):
losses = {}
nlp.update([example], sgd=optimizer, losses=losses)
assert "beam_ner" in losses
def test_ner_warns_no_lookups(caplog):
nlp = English()
assert nlp.lang in util.LEXEME_NORM_LANGS
nlp.vocab.lookups = Lookups()
assert not len(nlp.vocab.lookups)
nlp.add_pipe("ner")
with caplog.at_level(logging.DEBUG):
nlp.initialize()
assert "W033" in caplog.text
caplog.clear()
nlp.vocab.lookups.add_table("lexeme_norm")
nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
with caplog.at_level(logging.DEBUG):
nlp.initialize()
assert "W033" not in caplog.text
@Language.factory("blocker")
class BlockerComponent1:
def __init__(self, nlp, start, end, name="my_blocker"):
self.start = start
self.end = end
self.name = name
def __call__(self, doc):
doc.set_ents([], blocked=[doc[self.start : self.end]], default="unmodified")
return doc
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