mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
from typing import Dict, Iterable, Callable
|
|
import pytest
|
|
from thinc.api import Config
|
|
from spacy import Language
|
|
from spacy.util import load_model_from_config, registry, dot_to_object
|
|
from spacy.training import Example
|
|
|
|
|
|
def test_readers():
|
|
config_string = """
|
|
[training]
|
|
|
|
[corpora]
|
|
@readers = "myreader.v1"
|
|
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["tok2vec", "textcat"]
|
|
|
|
[components]
|
|
|
|
[components.tok2vec]
|
|
factory = "tok2vec"
|
|
|
|
[components.textcat]
|
|
factory = "textcat"
|
|
"""
|
|
|
|
@registry.readers.register("myreader.v1")
|
|
def myreader() -> Dict[str, Callable[[Language, str], Iterable[Example]]]:
|
|
annots = {"cats": {"POS": 1.0, "NEG": 0.0}}
|
|
|
|
def reader(nlp: Language):
|
|
doc = nlp.make_doc(f"This is an example")
|
|
return [Example.from_dict(doc, annots)]
|
|
|
|
return {"train": reader, "dev": reader, "extra": reader, "something": reader}
|
|
|
|
config = Config().from_str(config_string)
|
|
nlp, resolved = load_model_from_config(config, auto_fill=True)
|
|
|
|
train_corpus = dot_to_object(resolved, resolved["training"]["train_corpus"])
|
|
assert isinstance(train_corpus, Callable)
|
|
optimizer = resolved["training"]["optimizer"]
|
|
# simulate a training loop
|
|
nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
|
|
for example in train_corpus(nlp):
|
|
nlp.update([example], sgd=optimizer)
|
|
dev_corpus = dot_to_object(resolved, resolved["training"]["dev_corpus"])
|
|
scores = nlp.evaluate(list(dev_corpus(nlp)))
|
|
assert scores["cats_score"]
|
|
# ensure the pipeline runs
|
|
doc = nlp("Quick test")
|
|
assert doc.cats
|
|
extra_corpus = resolved["corpora"]["extra"]
|
|
assert isinstance(extra_corpus, Callable)
|
|
|
|
|
|
@pytest.mark.slow
|
|
@pytest.mark.parametrize(
|
|
"reader,additional_config",
|
|
[
|
|
("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 2}),
|
|
("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 2}),
|
|
("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
|
|
],
|
|
)
|
|
def test_cat_readers(reader, additional_config):
|
|
nlp_config_string = """
|
|
[training]
|
|
|
|
[corpora]
|
|
@readers = "PLACEHOLDER"
|
|
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["tok2vec", "textcat"]
|
|
|
|
[components]
|
|
|
|
[components.tok2vec]
|
|
factory = "tok2vec"
|
|
|
|
[components.textcat]
|
|
factory = "textcat"
|
|
"""
|
|
config = Config().from_str(nlp_config_string)
|
|
config["corpora"]["@readers"] = reader
|
|
config["corpora"].update(additional_config)
|
|
nlp, resolved = load_model_from_config(config, auto_fill=True)
|
|
|
|
train_corpus = dot_to_object(resolved, resolved["training"]["train_corpus"])
|
|
optimizer = resolved["training"]["optimizer"]
|
|
# simulate a training loop
|
|
nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
|
|
for example in train_corpus(nlp):
|
|
assert example.y.cats
|
|
# this shouldn't fail if each training example has at least one positive label
|
|
assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
|
|
nlp.update([example], sgd=optimizer)
|
|
# simulate performance benchmark on dev corpus
|
|
dev_corpus = dot_to_object(resolved, resolved["training"]["dev_corpus"])
|
|
dev_examples = list(dev_corpus(nlp))
|
|
for example in dev_examples:
|
|
# this shouldn't fail if each dev example has at least one positive label
|
|
assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
|
|
scores = nlp.evaluate(dev_examples)
|
|
assert scores["cats_score"]
|
|
# ensure the pipeline runs
|
|
doc = nlp("Quick test")
|
|
assert doc.cats
|