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258 lines
8.9 KiB
Python
258 lines
8.9 KiB
Python
from typing import cast
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import pytest
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from numpy.testing import assert_equal, assert_almost_equal
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from thinc.api import get_current_ops
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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.pipeline import TrainablePipe
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from spacy.attrs import MORPH
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from spacy.tokens import Doc
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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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TAGS = ["Feat=N", "Feat=V", "Feat=J"]
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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_label_smoothing():
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nlp = Language()
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morph_no_ls = nlp.add_pipe("morphologizer", "no_label_smoothing")
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morph_ls = nlp.add_pipe(
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"morphologizer", "label_smoothing", config=dict(label_smoothing=0.05)
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)
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train_examples = []
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losses = {}
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for tag in TAGS:
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morph_no_ls.add_label(tag)
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morph_ls.add_label(tag)
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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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tag_scores, bp_tag_scores = morph_ls.model.begin_update(
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[eg.predicted for eg in train_examples]
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)
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ops = get_current_ops()
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no_ls_grads = ops.to_numpy(morph_no_ls.get_loss(train_examples, tag_scores)[1][0])
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ls_grads = ops.to_numpy(morph_ls.get_loss(train_examples, tag_scores)[1][0])
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assert_almost_equal(ls_grads / no_ls_grads, 0.94285715)
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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_is_distillable():
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nlp = English()
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morphologizer = nlp.add_pipe("morphologizer")
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assert morphologizer.is_distillable
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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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# Test overwrite+extend settings
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# (note that "" is unset, "_" is set and empty)
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morphs = ["Feat=V", "Feat=N", "_"]
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doc = Doc(nlp.vocab, words=["blue", "ham", "like"], morphs=morphs)
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orig_morphs = [str(t.morph) for t in doc]
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orig_pos_tags = [t.pos_ for t in doc]
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morphologizer = nlp.get_pipe("morphologizer")
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# don't overwrite or extend
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morphologizer.cfg["overwrite"] = False
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doc = morphologizer(doc)
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assert [str(t.morph) for t in doc] == orig_morphs
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assert [t.pos_ for t in doc] == orig_pos_tags
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# overwrite and extend
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morphologizer.cfg["overwrite"] = True
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morphologizer.cfg["extend"] = True
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doc = Doc(nlp.vocab, words=["I", "like"], morphs=["Feat=A|That=A|This=A", ""])
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doc = morphologizer(doc)
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assert [str(t.morph) for t in doc] == ["Feat=N|That=A|This=A", "Feat=V"]
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# extend without overwriting
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morphologizer.cfg["overwrite"] = False
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morphologizer.cfg["extend"] = True
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doc = Doc(nlp.vocab, words=["I", "like"], morphs=["Feat=A|That=A|This=A", "That=B"])
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doc = morphologizer(doc)
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assert [str(t.morph) for t in doc] == ["Feat=A|That=A|This=A", "Feat=V|That=B"]
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# overwrite without extending
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morphologizer.cfg["overwrite"] = True
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morphologizer.cfg["extend"] = False
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doc = Doc(nlp.vocab, words=["I", "like"], morphs=["Feat=A|That=A|This=A", ""])
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doc = morphologizer(doc)
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assert [str(t.morph) for t in doc] == ["Feat=N", "Feat=V"]
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# Test with unset morph and partial 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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if token.text == "ham":
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token.pos_ = "NOUN"
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else:
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token.pos_ = ""
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token.set_morph(None)
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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assert nlp.get_pipe("morphologizer").labels is not None
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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 = ["", "", "", ""]
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gold_pos_tags = ["NOUN", "NOUN", "NOUN", "NOUN"]
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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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def test_save_activations():
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nlp = English()
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morphologizer = cast(TrainablePipe, 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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nlp.initialize(get_examples=lambda: train_examples)
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doc = nlp("This is a test.")
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assert "morphologizer" not in doc.activations
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morphologizer.save_activations = True
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doc = nlp("This is a test.")
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assert "morphologizer" in doc.activations
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assert set(doc.activations["morphologizer"].keys()) == {
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"label_ids",
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"probabilities",
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}
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assert doc.activations["morphologizer"]["probabilities"].shape == (5, 6)
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assert doc.activations["morphologizer"]["label_ids"].shape == (5,)
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