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121 lines
3.9 KiB
Python
121 lines
3.9 KiB
Python
from typing import Dict, Iterable, Callable
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import pytest
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from thinc.api import Config, fix_random_seed
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from spacy import Language
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from spacy.util import load_model_from_config, registry, resolve_dot_names
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from spacy.schemas import ConfigSchemaTraining
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from spacy.training import Example
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def test_readers():
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config_string = """
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[training]
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[corpora]
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@readers = "myreader.v1"
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "textcat"]
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.textcat]
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factory = "textcat"
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"""
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@registry.readers("myreader.v1")
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def myreader() -> Dict[str, Callable[[Language], Iterable[Example]]]:
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annots = {"cats": {"POS": 1.0, "NEG": 0.0}}
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def reader(nlp: Language):
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doc = nlp.make_doc(f"This is an example")
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return [Example.from_dict(doc, annots)]
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return {"train": reader, "dev": reader, "extra": reader, "something": reader}
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config = Config().from_str(config_string)
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nlp = load_model_from_config(config, auto_fill=True)
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T = registry.resolve(
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nlp.config.interpolate()["training"], schema=ConfigSchemaTraining
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)
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dot_names = [T["train_corpus"], T["dev_corpus"]]
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train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
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assert isinstance(train_corpus, Callable)
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optimizer = T["optimizer"]
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# simulate a training loop
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nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
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for example in train_corpus(nlp):
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nlp.update([example], sgd=optimizer)
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scores = nlp.evaluate(list(dev_corpus(nlp)))
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assert scores["cats_macro_auc"] == 0.0
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# ensure the pipeline runs
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doc = nlp("Quick test")
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assert doc.cats
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corpora = {"corpora": nlp.config.interpolate()["corpora"]}
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extra_corpus = registry.resolve(corpora)["corpora"]["extra"]
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assert isinstance(extra_corpus, Callable)
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@pytest.mark.slow
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@pytest.mark.parametrize(
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"reader,additional_config",
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[
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("ml_datasets.imdb_sentiment.v1", {"train_limit": 10, "dev_limit": 10}),
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("ml_datasets.dbpedia.v1", {"train_limit": 10, "dev_limit": 10}),
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("ml_datasets.cmu_movies.v1", {"limit": 10, "freq_cutoff": 200, "split": 0.8}),
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],
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)
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def test_cat_readers(reader, additional_config):
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nlp_config_string = """
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[training]
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seed = 0
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[training.score_weights]
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cats_macro_auc = 1.0
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[corpora]
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@readers = "PLACEHOLDER"
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "textcat_multilabel"]
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.textcat_multilabel]
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factory = "textcat_multilabel"
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"""
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config = Config().from_str(nlp_config_string)
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fix_random_seed(config["training"]["seed"])
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config["corpora"]["@readers"] = reader
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config["corpora"].update(additional_config)
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nlp = load_model_from_config(config, auto_fill=True)
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T = registry.resolve(nlp.config["training"], schema=ConfigSchemaTraining)
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dot_names = [T["train_corpus"], T["dev_corpus"]]
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train_corpus, dev_corpus = resolve_dot_names(nlp.config, dot_names)
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optimizer = T["optimizer"]
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# simulate a training loop
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nlp.initialize(lambda: train_corpus(nlp), sgd=optimizer)
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for example in train_corpus(nlp):
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assert example.y.cats
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# this shouldn't fail if each training example has at least one positive label
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assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
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nlp.update([example], sgd=optimizer)
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# simulate performance benchmark on dev corpus
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dev_examples = list(dev_corpus(nlp))
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for example in dev_examples:
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# this shouldn't fail if each dev example has at least one positive label
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assert sorted(list(set(example.y.cats.values()))) == [0.0, 1.0]
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scores = nlp.evaluate(dev_examples)
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assert scores["cats_score"]
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# ensure the pipeline runs
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doc = nlp("Quick test")
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assert doc.cats
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