mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
8e7557656f
* version bump to 3.0.0a16 * rename "gold" folder to "training" * rename 'annotation_setter' to 'set_extra_annotations' * formatting
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import pytest
|
|
|
|
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
|
|
|
|
|
|
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.begin_training()
|
|
|
|
|
|
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.begin_training(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.begin_training()
|
|
# 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_begin_training_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.begin_training()
|
|
nlp.begin_training(get_examples=lambda: train_examples)
|
|
with pytest.raises(TypeError):
|
|
nlp.begin_training(get_examples=lambda: None)
|
|
with pytest.raises(ValueError):
|
|
nlp.begin_training(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.begin_training(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 [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 [t.morph_ for t in doc2] == gold_morphs
|
|
assert [t.pos_ for t in doc2] == gold_pos_tags
|