spaCy/spacy/tests/pipeline/test_morphologizer.py
Adriane Boyd 9328dd5625
Handle unset token.morph in Morphologizer (#6704)
* Handle unset token.morph in Morphologizer

Handle unset `token.morph` in `Morphologizer.initialize` and
`Morphologizer.get_loss`. If both `token.morph` and `token.pos` are
unset, treat the annotation as missing rather than empty.

* Add token.has_morph()
2021-01-15 17:20:10 +01:00

164 lines
5.5 KiB
Python

import pytest
from numpy.testing import assert_equal
from spacy import util
from spacy.training import Example
from spacy.lang.en import English
from spacy.language import Language
from spacy.tests.util import make_tempdir
from spacy.morphology import Morphology
from spacy.attrs import MORPH
def test_label_types():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("Feat=A")
with pytest.raises(ValueError):
morphologizer.add_label(9)
TRAIN_DATA = [
(
"I like green eggs",
{
"morphs": ["Feat=N", "Feat=V", "Feat=J", "Feat=N"],
"pos": ["NOUN", "VERB", "ADJ", "NOUN"],
},
),
# test combinations of morph+POS
("Eat blue ham", {"morphs": ["Feat=V", "", ""], "pos": ["", "ADJ", ""]}),
]
def test_no_label():
nlp = Language()
nlp.add_pipe("morphologizer")
with pytest.raises(ValueError):
nlp.initialize()
def test_implicit_label():
nlp = Language()
nlp.add_pipe("morphologizer")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.initialize(get_examples=lambda: train_examples)
def test_no_resize():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "VERB")
nlp.initialize()
# this throws an error because the morphologizer can't be resized after initialization
with pytest.raises(ValueError):
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "ADJ")
def test_initialize_examples():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.initialize()
nlp.initialize(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.initialize(get_examples=lambda: None)
with pytest.raises(TypeError):
nlp.initialize(get_examples=train_examples)
def test_overfitting_IO():
# Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly
nlp = English()
nlp.add_pipe("morphologizer")
train_examples = []
for inst in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["morphologizer"] < 0.00001
# test the trained model
test_text = "I like blue ham"
doc = nlp(test_text)
gold_morphs = ["Feat=N", "Feat=V", "", ""]
gold_pos_tags = ["NOUN", "VERB", "ADJ", ""]
assert [str(t.morph) for t in doc] == gold_morphs
assert [t.pos_ for t in doc] == gold_pos_tags
# 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)
assert [str(t.morph) for t in doc2] == gold_morphs
assert [t.pos_ for t in doc2] == gold_pos_tags
# 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([MORPH]) for doc in nlp.pipe(texts)]
batch_deps_2 = [doc.to_array([MORPH]) for doc in nlp.pipe(texts)]
no_batch_deps = [doc.to_array([MORPH]) 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 without POS
nlp.remove_pipe("morphologizer")
nlp.add_pipe("morphologizer")
for example in train_examples:
for token in example.reference:
token.pos_ = ""
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["morphologizer"] < 0.00001
# Test the trained model
test_text = "I like blue ham"
doc = nlp(test_text)
gold_morphs = ["Feat=N", "Feat=V", "", ""]
gold_pos_tags = ["", "", "", ""]
assert [str(t.morph) for t in doc] == gold_morphs
assert [t.pos_ for t in doc] == gold_pos_tags
# Test with unset morph and partial POS
nlp.remove_pipe("morphologizer")
nlp.add_pipe("morphologizer")
for example in train_examples:
for token in example.reference:
if token.text == "ham":
token.pos_ = "NOUN"
else:
token.pos_ = ""
token.set_morph(None)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
print(nlp.get_pipe("morphologizer").labels)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["morphologizer"] < 0.00001
# Test the trained model
test_text = "I like blue ham"
doc = nlp(test_text)
gold_morphs = ["", "", "", ""]
gold_pos_tags = ["NOUN", "NOUN", "NOUN", "NOUN"]
assert [str(t.morph) for t in doc] == gold_morphs
assert [t.pos_ for t in doc] == gold_pos_tags