spaCy/spacy/tests/parser/test_ner.py
2020-02-28 11:57:41 +01:00

324 lines
11 KiB
Python

import pytest
from spacy import util
from spacy.lang.en import English
from spacy.ml.models.defaults import default_ner
from spacy.pipeline import EntityRecognizer, EntityRuler
from spacy.vocab import Vocab
from spacy.syntax.ner import BiluoPushDown
from spacy.gold import GoldParse
from spacy.tests.util import make_tempdir
from spacy.tokens import Doc
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 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):
gold = GoldParse(doc, entities=entity_annots)
tsys.preprocess_gold(gold)
act_classes = tsys.get_oracle_sequence(doc, gold)
names = [tsys.get_class_name(act) for act in act_classes]
assert names == ["U-PERSON", "O", "O", "B-GPE", "L-GPE", "O"]
def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
entity_annots = [(s, e, "!" + label) for s, e, label in entity_annots]
gold = GoldParse(doc, entities=entity_annots)
for i, tag in enumerate(gold.ner):
if tag == "L-!GPE":
gold.ner[i] = "-"
tsys.preprocess_gold(gold)
act_classes = tsys.get_oracle_sequence(doc, gold)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
def test_get_oracle_moves_negative_entities2(tsys, vocab):
doc = Doc(vocab, words=["A", "B", "C", "D"])
gold = GoldParse(doc, entities=[])
gold.ner = ["B-!PERSON", "L-!PERSON", "B-!PERSON", "L-!PERSON"]
tsys.preprocess_gold(gold)
act_classes = tsys.get_oracle_sequence(doc, gold)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
def test_get_oracle_moves_negative_O(tsys, vocab):
doc = Doc(vocab, words=["A", "B", "C", "D"])
gold = GoldParse(doc, entities=[])
gold.ner = ["O", "!O", "O", "!O"]
tsys.preprocess_gold(gold)
act_classes = tsys.get_oracle_sequence(doc, gold)
names = [tsys.get_class_name(act) for act in act_classes]
assert names
def test_oracle_moves_missing_B(en_vocab):
words = ["B", "52", "Bomber"]
biluo_tags = [None, None, "L-PRODUCT"]
doc = Doc(en_vocab, words=words)
gold = GoldParse(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.preprocess_gold(gold)
moves.get_oracle_sequence(doc, gold)
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)
gold = GoldParse(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(action), label)
moves.preprocess_gold(gold)
moves.get_oracle_sequence(doc, gold)
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")
ner1 = EntityRecognizer(doc1.vocab, default_ner())
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")
ner2 = EntityRecognizer(doc2.vocab, default_ner())
# set "New York" to a blocked entity
doc2.ents = [(0, 3, 5)]
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_overwrite_token():
nlp = English()
ner1 = nlp.create_pipe("ner")
nlp.add_pipe(ner1, name="ner")
nlp.begin_training()
# 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
ner2 = EntityRecognizer(doc.vocab, default_ner())
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.create_pipe("ner")
ner.add_label("MY_LABEL")
nlp.add_pipe(ner)
nlp.begin_training()
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
ruler = EntityRuler(nlp)
patterns = [{"label": "THING", "pattern": "This"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
# 2: untrained NER - should set everything else to O
untrained_ner = nlp.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner)
nlp.begin_training()
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_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.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner, name="uner")
nlp.begin_training()
# 2 : Entity Ruler - should set "this" to B and keep everything else O
ruler = EntityRuler(nlp)
patterns = [{"label": "THING", "pattern": "This"}]
ruler.add_patterns(patterns)
nlp.add_pipe(ruler)
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(BlockerComponent1(2, 5))
untrained_ner = nlp.create_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.add_pipe(untrained_ner, name="uner")
nlp.begin_training()
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
def test_overfitting_IO():
# Simple test to try and quickly overfit the NER component - ensuring the ML models work correctly
nlp = English()
ner = nlp.create_pipe("ner")
for _, annotations in TRAIN_DATA:
for ent in annotations.get("entities"):
ner.add_label(ent[2])
nlp.add_pipe(ner)
optimizer = nlp.begin_training()
for i in range(50):
losses = {}
nlp.update(TRAIN_DATA, 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"
class BlockerComponent1(object):
name = "my_blocker"
def __init__(self, start, end):
self.start = start
self.end = end
def __call__(self, doc):
doc.ents = [(0, self.start, self.end)]
return doc