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https://github.com/explosion/spaCy.git
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e2b70df012
* Use isort with Black profile * isort all the things * Fix import cycles as a result of import sorting * Add DOCBIN_ALL_ATTRS type definition * Add isort to requirements * Remove isort from build dependencies check * Typo
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
import pytest
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from numpy.testing import assert_equal
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from spacy import util
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from spacy.attrs import SENT_START
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.tests.util import make_tempdir
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from spacy.training import Example
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def test_label_types():
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nlp = Language()
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senter = nlp.add_pipe("senter")
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with pytest.raises(NotImplementedError):
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senter.add_label("A")
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SENT_STARTS = [0] * 14
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SENT_STARTS[0] = 1
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SENT_STARTS[5] = 1
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SENT_STARTS[9] = 1
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TRAIN_DATA = [
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(
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"I like green eggs. Eat blue ham. I like purple eggs.",
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{"sent_starts": SENT_STARTS},
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),
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(
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"She likes purple eggs. They hate ham. You like yellow eggs.",
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{"sent_starts": SENT_STARTS},
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),
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]
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def test_initialize_examples():
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nlp = Language()
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nlp.add_pipe("senter")
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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# you shouldn't really call this more than once, but for testing it should be fine
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nlp.initialize()
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nlp.initialize(get_examples=lambda: train_examples)
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with pytest.raises(TypeError):
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nlp.initialize(get_examples=lambda: None)
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with pytest.raises(TypeError):
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nlp.initialize(get_examples=train_examples)
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the senter - ensuring the ML models work correctly
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nlp = English()
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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# add some cases where SENT_START == -1
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train_examples[0].reference[10].is_sent_start = False
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train_examples[1].reference[1].is_sent_start = False
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train_examples[1].reference[11].is_sent_start = False
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nlp.add_pipe("senter")
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optimizer = nlp.initialize()
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for i in range(200):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["senter"] < 0.001
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# test the trained model
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test_text = TRAIN_DATA[0][0]
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doc = nlp(test_text)
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gold_sent_starts = [0] * 14
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gold_sent_starts[0] = 1
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gold_sent_starts[5] = 1
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gold_sent_starts[9] = 1
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assert [int(t.is_sent_start) for t in doc] == gold_sent_starts
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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doc2 = nlp2(test_text)
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assert [int(t.is_sent_start) for t in doc2] == gold_sent_starts
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# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
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texts = [
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"Just a sentence.",
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"Then one more sentence about London.",
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"Here is another one.",
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"I like London.",
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]
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batch_deps_1 = [doc.to_array([SENT_START]) for doc in nlp.pipe(texts)]
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batch_deps_2 = [doc.to_array([SENT_START]) for doc in nlp.pipe(texts)]
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no_batch_deps = [
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doc.to_array([SENT_START]) for doc in [nlp(text) for text in texts]
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]
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assert_equal(batch_deps_1, batch_deps_2)
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assert_equal(batch_deps_1, no_batch_deps)
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# test internal pipe labels vs. Language.pipe_labels with hidden labels
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assert nlp.get_pipe("senter").labels == ("I", "S")
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assert "senter" not in nlp.pipe_labels
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