spaCy/spacy/tests/pipeline/test_senter.py
Sofie Van Landeghem fcbf899b08
Feature/example only (#5707)
* remove _convert_examples

* fix test_gold, raise TypeError if tuples are used instead of Example's

* throwing proper errors when the wrong type of objects are passed

* fix deprectated format in tests

* fix deprectated format in parser tests

* fix tests for NEL, morph, senter, tagger, textcat

* update regression tests with new Example format

* use make_doc

* more fixes to nlp.update calls

* few more small fixes for rehearse and evaluate

* only import ml_datasets if really necessary
2020-07-06 13:02:36 +02:00

64 lines
1.7 KiB
Python

import pytest
from spacy import util
from spacy.gold import Example
from spacy.lang.en import English
from spacy.language import Language
from spacy.tests.util import make_tempdir
def test_label_types():
nlp = Language()
nlp.add_pipe(nlp.create_pipe("senter"))
with pytest.raises(NotImplementedError):
nlp.get_pipe("senter").add_label("A")
SENT_STARTS = [0] * 14
SENT_STARTS[0] = 1
SENT_STARTS[5] = 1
SENT_STARTS[9] = 1
TRAIN_DATA = [
(
"I like green eggs. Eat blue ham. I like purple eggs.",
{"sent_starts": SENT_STARTS},
),
(
"She likes purple eggs. They hate ham. You like yellow eggs.",
{"sent_starts": SENT_STARTS},
),
]
def test_overfitting_IO():
# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
nlp = English()
senter = nlp.create_pipe("senter")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.add_pipe(senter)
optimizer = nlp.begin_training()
for i in range(200):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
assert losses["senter"] < 0.001
# test the trained model
test_text = TRAIN_DATA[0][0]
doc = nlp(test_text)
gold_sent_starts = [0] * 14
gold_sent_starts[0] = 1
gold_sent_starts[5] = 1
gold_sent_starts[9] = 1
assert [int(t.is_sent_start) for t in doc] == gold_sent_starts
# 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 [int(t.is_sent_start) for t in doc2] == gold_sent_starts