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a7e7d6c6c9
Fix bug where `Morphologizer.get_loss` treated misaligned annotation as `EMPTY_MORPH` rather than ignoring it. Remove unneeded default `EMPTY_MORPH` mappings.
139 lines
4.6 KiB
Python
139 lines
4.6 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.training import Example
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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.morphology import Morphology
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from spacy.attrs import MORPH
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def test_label_types():
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nlp = Language()
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morphologizer = nlp.add_pipe("morphologizer")
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morphologizer.add_label("Feat=A")
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with pytest.raises(ValueError):
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morphologizer.add_label(9)
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TRAIN_DATA = [
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(
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"I like green eggs",
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{
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"morphs": ["Feat=N", "Feat=V", "Feat=J", "Feat=N"],
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"pos": ["NOUN", "VERB", "ADJ", "NOUN"],
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},
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),
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# test combinations of morph+POS
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("Eat blue ham", {"morphs": ["Feat=V", "", ""], "pos": ["", "ADJ", ""]}),
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]
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def test_no_label():
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nlp = Language()
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nlp.add_pipe("morphologizer")
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with pytest.raises(ValueError):
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nlp.initialize()
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def test_implicit_label():
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nlp = Language()
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nlp.add_pipe("morphologizer")
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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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nlp.initialize(get_examples=lambda: train_examples)
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def test_no_resize():
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nlp = Language()
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morphologizer = nlp.add_pipe("morphologizer")
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morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
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morphologizer.add_label("POS" + Morphology.FIELD_SEP + "VERB")
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nlp.initialize()
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# this throws an error because the morphologizer can't be resized after initialization
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with pytest.raises(ValueError):
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morphologizer.add_label("POS" + Morphology.FIELD_SEP + "ADJ")
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def test_initialize_examples():
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nlp = Language()
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morphologizer = nlp.add_pipe("morphologizer")
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morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
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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 morphologizer - ensuring the ML models work correctly
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nlp = English()
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nlp.add_pipe("morphologizer")
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train_examples = []
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for inst in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1]))
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["morphologizer"] < 0.00001
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# test the trained model
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test_text = "I like blue ham"
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doc = nlp(test_text)
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gold_morphs = ["Feat=N", "Feat=V", "", ""]
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gold_pos_tags = ["NOUN", "VERB", "ADJ", ""]
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assert [str(t.morph) for t in doc] == gold_morphs
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assert [t.pos_ for t in doc] == gold_pos_tags
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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 [str(t.morph) for t in doc2] == gold_morphs
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assert [t.pos_ for t in doc2] == gold_pos_tags
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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([MORPH]) for doc in nlp.pipe(texts)]
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batch_deps_2 = [doc.to_array([MORPH]) for doc in nlp.pipe(texts)]
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no_batch_deps = [doc.to_array([MORPH]) for doc in [nlp(text) for text in texts]]
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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 without POS
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nlp.remove_pipe("morphologizer")
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nlp.add_pipe("morphologizer")
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for example in train_examples:
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for token in example.reference:
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token.pos_ = ""
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(50):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses["morphologizer"] < 0.00001
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# Test the trained model
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test_text = "I like blue ham"
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doc = nlp(test_text)
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gold_morphs = ["Feat=N", "Feat=V", "", ""]
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gold_pos_tags = ["", "", "", ""]
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assert [str(t.morph) for t in doc] == gold_morphs
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assert [t.pos_ for t in doc] == gold_pos_tags
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