mirror of
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
545 lines
20 KiB
Python
545 lines
20 KiB
Python
import pytest
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from numpy.testing import assert_equal
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from thinc.api import Adam
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from spacy import registry, util
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from spacy.attrs import DEP, NORM
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from spacy.lang.en import English
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from spacy.pipeline import DependencyParser
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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from spacy.tokens import Doc
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from spacy.training import Example
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from spacy.vocab import Vocab
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from ..util import apply_transition_sequence, make_tempdir
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TRAIN_DATA = [
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(
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"They trade mortgage-backed securities.",
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{
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"heads": [1, 1, 4, 4, 5, 1, 1],
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"deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"],
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},
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),
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(
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"I like London and Berlin.",
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{
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"heads": [1, 1, 1, 2, 2, 1],
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"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
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},
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),
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]
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CONFLICTING_DATA = [
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(
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"I like London and Berlin.",
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{
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"heads": [1, 1, 1, 2, 2, 1],
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"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
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},
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),
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(
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"I like London and Berlin.",
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{
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"heads": [0, 0, 0, 0, 0, 0],
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"deps": ["ROOT", "nsubj", "nsubj", "cc", "conj", "punct"],
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},
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),
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]
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PARTIAL_DATA = [
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(
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"I like London.",
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{
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"heads": [1, 1, 1, None],
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"deps": ["nsubj", "ROOT", "dobj", None],
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},
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),
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]
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eps = 0.1
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@pytest.fixture
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def vocab():
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return Vocab(lex_attr_getters={NORM: lambda s: s})
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@pytest.fixture
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def parser(vocab):
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vocab.strings.add("ROOT")
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = DependencyParser(vocab, model)
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parser.cfg["token_vector_width"] = 4
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parser.cfg["hidden_width"] = 32
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# parser.add_label('right')
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parser.add_label("left")
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parser.initialize(lambda: [_parser_example(parser)])
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sgd = Adam(0.001)
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for i in range(10):
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losses = {}
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doc = Doc(vocab, words=["a", "b", "c", "d"])
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example = Example.from_dict(
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doc, {"heads": [1, 1, 3, 3], "deps": ["left", "ROOT", "left", "ROOT"]}
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)
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parser.update([example], sgd=sgd, losses=losses)
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return parser
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def _parser_example(parser):
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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gold = {"heads": [1, 1, 3, 3], "deps": ["right", "ROOT", "left", "ROOT"]}
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return Example.from_dict(doc, gold)
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@pytest.mark.issue(2772)
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def test_issue2772(en_vocab):
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"""Test that deprojectivization doesn't mess up sentence boundaries."""
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# fmt: off
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words = ["When", "we", "write", "or", "communicate", "virtually", ",", "we", "can", "hide", "our", "true", "feelings", "."]
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# fmt: on
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# A tree with a non-projective (i.e. crossing) arc
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# The arcs (0, 4) and (2, 9) cross.
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heads = [4, 2, 9, 2, 2, 4, 9, 9, 9, 9, 12, 12, 9, 9]
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deps = ["dep"] * len(heads)
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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assert doc[1].is_sent_start is False
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@pytest.mark.issue(3830)
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def test_issue3830_no_subtok():
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"""Test that the parser doesn't have subtok label if not learn_tokens"""
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config = {
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"learn_tokens": False,
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}
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model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
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parser = DependencyParser(Vocab(), model, **config)
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parser.add_label("nsubj")
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assert "subtok" not in parser.labels
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parser.initialize(lambda: [_parser_example(parser)])
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assert "subtok" not in parser.labels
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@pytest.mark.issue(3830)
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def test_issue3830_with_subtok():
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"""Test that the parser does have subtok label if learn_tokens=True."""
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config = {
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"learn_tokens": True,
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}
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model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
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parser = DependencyParser(Vocab(), model, **config)
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parser.add_label("nsubj")
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assert "subtok" not in parser.labels
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parser.initialize(lambda: [_parser_example(parser)])
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assert "subtok" in parser.labels
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@pytest.mark.issue(7716)
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@pytest.mark.xfail(reason="Not fixed yet")
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def test_partial_annotation(parser):
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doc = Doc(parser.vocab, words=["a", "b", "c", "d"])
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doc[2].is_sent_start = False
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# Note that if the following line is used, then doc[2].is_sent_start == False
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# doc[3].is_sent_start = False
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doc = parser(doc)
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assert doc[2].is_sent_start == False
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def test_parser_root(en_vocab):
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words = ["i", "do", "n't", "have", "other", "assistance"]
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heads = [3, 3, 3, 3, 5, 3]
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deps = ["nsubj", "aux", "neg", "ROOT", "amod", "dobj"]
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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for t in doc:
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assert t.dep != 0, t.text
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@pytest.mark.skip(
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reason="The step_through API was removed (but should be brought back)"
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)
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@pytest.mark.parametrize("words", [["Hello"]])
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def test_parser_parse_one_word_sentence(en_vocab, en_parser, words):
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doc = Doc(en_vocab, words=words, heads=[0], deps=["ROOT"])
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assert len(doc) == 1
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with en_parser.step_through(doc) as _: # noqa: F841
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pass
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assert doc[0].dep != 0
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@pytest.mark.skip(
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reason="The step_through API was removed (but should be brought back)"
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)
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def test_parser_initial(en_vocab, en_parser):
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words = ["I", "ate", "the", "pizza", "with", "anchovies", "."]
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transition = ["L-nsubj", "S", "L-det"]
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doc = Doc(en_vocab, words=words)
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apply_transition_sequence(en_parser, doc, transition)
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assert doc[0].head.i == 1
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assert doc[1].head.i == 1
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assert doc[2].head.i == 3
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assert doc[3].head.i == 3
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def test_parser_parse_subtrees(en_vocab, en_parser):
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words = ["The", "four", "wheels", "on", "the", "bus", "turned", "quickly"]
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heads = [2, 2, 6, 2, 5, 3, 6, 6]
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deps = ["dep"] * len(heads)
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doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
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assert len(list(doc[2].lefts)) == 2
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assert len(list(doc[2].rights)) == 1
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assert len(list(doc[2].children)) == 3
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assert len(list(doc[5].lefts)) == 1
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assert len(list(doc[5].rights)) == 0
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assert len(list(doc[5].children)) == 1
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assert len(list(doc[2].subtree)) == 6
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def test_parser_merge_pp(en_vocab):
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words = ["A", "phrase", "with", "another", "phrase", "occurs"]
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heads = [1, 5, 1, 4, 2, 5]
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deps = ["det", "nsubj", "prep", "det", "pobj", "ROOT"]
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pos = ["DET", "NOUN", "ADP", "DET", "NOUN", "VERB"]
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doc = Doc(en_vocab, words=words, deps=deps, heads=heads, pos=pos)
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with doc.retokenize() as retokenizer:
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for np in doc.noun_chunks:
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retokenizer.merge(np, attrs={"lemma": np.lemma_})
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assert doc[0].text == "A phrase"
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assert doc[1].text == "with"
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assert doc[2].text == "another phrase"
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assert doc[3].text == "occurs"
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@pytest.mark.skip(
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reason="The step_through API was removed (but should be brought back)"
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)
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def test_parser_arc_eager_finalize_state(en_vocab, en_parser):
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words = ["a", "b", "c", "d", "e"]
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# right branching
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transition = ["R-nsubj", "D", "R-nsubj", "R-nsubj", "D", "R-ROOT"]
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tokens = Doc(en_vocab, words=words)
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apply_transition_sequence(en_parser, tokens, transition)
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assert tokens[0].n_lefts == 0
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assert tokens[0].n_rights == 2
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assert tokens[0].left_edge.i == 0
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assert tokens[0].right_edge.i == 4
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assert tokens[0].head.i == 0
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assert tokens[1].n_lefts == 0
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assert tokens[1].n_rights == 0
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assert tokens[1].left_edge.i == 1
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assert tokens[1].right_edge.i == 1
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assert tokens[1].head.i == 0
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assert tokens[2].n_lefts == 0
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assert tokens[2].n_rights == 2
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assert tokens[2].left_edge.i == 2
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assert tokens[2].right_edge.i == 4
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assert tokens[2].head.i == 0
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assert tokens[3].n_lefts == 0
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assert tokens[3].n_rights == 0
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assert tokens[3].left_edge.i == 3
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assert tokens[3].right_edge.i == 3
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assert tokens[3].head.i == 2
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assert tokens[4].n_lefts == 0
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assert tokens[4].n_rights == 0
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assert tokens[4].left_edge.i == 4
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assert tokens[4].right_edge.i == 4
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assert tokens[4].head.i == 2
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# left branching
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transition = ["S", "S", "S", "L-nsubj", "L-nsubj", "L-nsubj", "L-nsubj"]
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tokens = Doc(en_vocab, words=words)
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apply_transition_sequence(en_parser, tokens, transition)
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assert tokens[0].n_lefts == 0
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assert tokens[0].n_rights == 0
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assert tokens[0].left_edge.i == 0
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assert tokens[0].right_edge.i == 0
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assert tokens[0].head.i == 4
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assert tokens[1].n_lefts == 0
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assert tokens[1].n_rights == 0
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assert tokens[1].left_edge.i == 1
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assert tokens[1].right_edge.i == 1
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assert tokens[1].head.i == 4
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assert tokens[2].n_lefts == 0
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assert tokens[2].n_rights == 0
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assert tokens[2].left_edge.i == 2
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assert tokens[2].right_edge.i == 2
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assert tokens[2].head.i == 4
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assert tokens[3].n_lefts == 0
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assert tokens[3].n_rights == 0
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assert tokens[3].left_edge.i == 3
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assert tokens[3].right_edge.i == 3
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assert tokens[3].head.i == 4
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assert tokens[4].n_lefts == 4
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assert tokens[4].n_rights == 0
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assert tokens[4].left_edge.i == 0
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assert tokens[4].right_edge.i == 4
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assert tokens[4].head.i == 4
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def test_parser_set_sent_starts(en_vocab):
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# fmt: off
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words = ['Ein', 'Satz', '.', 'Außerdem', 'ist', 'Zimmer', 'davon', 'überzeugt', ',', 'dass', 'auch', 'epige-', '\n', 'netische', 'Mechanismen', 'eine', 'Rolle', 'spielen', ',', 'also', 'Vorgänge', ',', 'die', '\n', 'sich', 'darauf', 'auswirken', ',', 'welche', 'Gene', 'abgelesen', 'werden', 'und', '\n', 'welche', 'nicht', '.', '\n']
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heads = [1, 1, 1, 30, 4, 4, 7, 4, 7, 17, 14, 14, 11, 14, 17, 16, 17, 6, 17, 20, 11, 20, 26, 22, 26, 26, 20, 26, 29, 31, 31, 25, 31, 32, 17, 4, 4, 36]
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deps = ['nk', 'ROOT', 'punct', 'mo', 'ROOT', 'sb', 'op', 'pd', 'punct', 'cp', 'mo', 'nk', '', 'nk', 'sb', 'nk', 'oa', 're', 'punct', 'mo', 'app', 'punct', 'sb', '', 'oa', 'op', 'rc', 'punct', 'nk', 'sb', 'oc', 're', 'cd', '', 'oa', 'ng', 'punct', '']
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# fmt: on
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doc = Doc(en_vocab, words=words, deps=deps, heads=heads)
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for i in range(len(words)):
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if i == 0 or i == 3:
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assert doc[i].is_sent_start is True
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else:
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assert doc[i].is_sent_start is False
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for sent in doc.sents:
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for token in sent:
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assert token.head in sent
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def test_parser_constructor(en_vocab):
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config = {
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"learn_tokens": False,
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"min_action_freq": 30,
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"update_with_oracle_cut_size": 100,
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}
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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DependencyParser(en_vocab, model, **config)
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DependencyParser(en_vocab, model)
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@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
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def test_incomplete_data(pipe_name):
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# Test that the parser works with incomplete information
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nlp = English()
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parser = nlp.add_pipe(pipe_name)
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train_examples = []
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for text, annotations in PARTIAL_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for dep in annotations.get("deps", []):
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if dep is not None:
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parser.add_label(dep)
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(150):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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assert losses[pipe_name] < 0.0001
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# test the trained model
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test_text = "I like securities."
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doc = nlp(test_text)
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assert doc[0].dep_ == "nsubj"
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assert doc[2].dep_ == "dobj"
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assert doc[0].head.i == 1
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assert doc[2].head.i == 1
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@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
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def test_overfitting_IO(pipe_name):
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# Simple test to try and quickly overfit the dependency parser (normal or beam)
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nlp = English()
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parser = nlp.add_pipe(pipe_name)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for dep in annotations.get("deps", []):
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parser.add_label(dep)
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optimizer = nlp.initialize()
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# run overfitting
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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[pipe_name] < 0.0001
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# test the trained model
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test_text = "I like securities."
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doc = nlp(test_text)
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assert doc[0].dep_ == "nsubj"
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assert doc[2].dep_ == "dobj"
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assert doc[3].dep_ == "punct"
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assert doc[0].head.i == 1
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assert doc[2].head.i == 1
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assert doc[3].head.i == 1
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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 doc2[0].dep_ == "nsubj"
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assert doc2[2].dep_ == "dobj"
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assert doc2[3].dep_ == "punct"
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assert doc2[0].head.i == 1
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assert doc2[2].head.i == 1
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assert doc2[3].head.i == 1
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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([DEP]) for doc in nlp.pipe(texts)]
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batch_deps_2 = [doc.to_array([DEP]) for doc in nlp.pipe(texts)]
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no_batch_deps = [doc.to_array([DEP]) 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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# fmt: off
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@pytest.mark.slow
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@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
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@pytest.mark.parametrize(
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"parser_config",
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[
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# TransitionBasedParser V1
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({"@architectures": "spacy.TransitionBasedParser.v1", "tok2vec": DEFAULT_TOK2VEC_MODEL, "state_type": "parser", "extra_state_tokens": False, "hidden_width": 64, "maxout_pieces": 2, "use_upper": True}),
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# TransitionBasedParser V2
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({"@architectures": "spacy.TransitionBasedParser.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "state_type": "parser", "extra_state_tokens": False, "hidden_width": 64, "maxout_pieces": 2, "use_upper": True}),
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],
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)
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# fmt: on
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def test_parser_configs(pipe_name, parser_config):
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pipe_config = {"model": parser_config}
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nlp = English()
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parser = nlp.add_pipe(pipe_name, config=pipe_config)
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train_examples = []
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for text, annotations in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
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for dep in annotations.get("deps", []):
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parser.add_label(dep)
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optimizer = nlp.initialize()
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for i in range(5):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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def test_beam_parser_scores():
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# Test that we can get confidence values out of the beam_parser pipe
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beam_width = 16
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beam_density = 0.0001
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nlp = English()
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config = {
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"beam_width": beam_width,
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"beam_density": beam_density,
|
|
}
|
|
parser = nlp.add_pipe("beam_parser", config=config)
|
|
train_examples = []
|
|
for text, annotations in CONFLICTING_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
for dep in annotations.get("deps", []):
|
|
parser.add_label(dep)
|
|
optimizer = nlp.initialize()
|
|
|
|
# update a bit with conflicting data
|
|
for i in range(10):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
|
|
# test the scores from the beam
|
|
test_text = "I like securities."
|
|
doc = nlp.make_doc(test_text)
|
|
docs = [doc]
|
|
beams = parser.predict(docs)
|
|
head_scores, label_scores = parser.scored_parses(beams)
|
|
|
|
for j in range(len(doc)):
|
|
for label in parser.labels:
|
|
label_score = label_scores[0][(j, label)]
|
|
assert 0 - eps <= label_score <= 1 + eps
|
|
for i in range(len(doc)):
|
|
head_score = head_scores[0][(j, i)]
|
|
assert 0 - eps <= head_score <= 1 + eps
|
|
|
|
|
|
def test_beam_overfitting_IO():
|
|
# Simple test to try and quickly overfit the Beam dependency parser
|
|
nlp = English()
|
|
beam_width = 16
|
|
beam_density = 0.0001
|
|
config = {
|
|
"beam_width": beam_width,
|
|
"beam_density": beam_density,
|
|
}
|
|
parser = nlp.add_pipe("beam_parser", config=config)
|
|
train_examples = []
|
|
for text, annotations in TRAIN_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
for dep in annotations.get("deps", []):
|
|
parser.add_label(dep)
|
|
optimizer = nlp.initialize()
|
|
# run overfitting
|
|
for i in range(150):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["beam_parser"] < 0.0001
|
|
# test the scores from the beam
|
|
test_text = "I like securities."
|
|
docs = [nlp.make_doc(test_text)]
|
|
beams = parser.predict(docs)
|
|
head_scores, label_scores = parser.scored_parses(beams)
|
|
# we only processed one document
|
|
head_scores = head_scores[0]
|
|
label_scores = label_scores[0]
|
|
# test label annotations: 0=nsubj, 2=dobj, 3=punct
|
|
assert label_scores[(0, "nsubj")] == pytest.approx(1.0, abs=eps)
|
|
assert label_scores[(0, "dobj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(0, "punct")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(2, "nsubj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(2, "dobj")] == pytest.approx(1.0, abs=eps)
|
|
assert label_scores[(2, "punct")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(3, "nsubj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(3, "dobj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores[(3, "punct")] == pytest.approx(1.0, abs=eps)
|
|
# test head annotations: the root is token at index 1
|
|
assert head_scores[(0, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores[(0, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores[(0, 2)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores[(2, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores[(2, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores[(2, 2)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores[(3, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores[(3, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores[(3, 2)] == pytest.approx(0.0, abs=eps)
|
|
|
|
# Also test the results are still the same after IO
|
|
with make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
docs2 = [nlp2.make_doc(test_text)]
|
|
parser2 = nlp2.get_pipe("beam_parser")
|
|
beams2 = parser2.predict(docs2)
|
|
head_scores2, label_scores2 = parser2.scored_parses(beams2)
|
|
# we only processed one document
|
|
head_scores2 = head_scores2[0]
|
|
label_scores2 = label_scores2[0]
|
|
# check the results again
|
|
assert label_scores2[(0, "nsubj")] == pytest.approx(1.0, abs=eps)
|
|
assert label_scores2[(0, "dobj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(0, "punct")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(2, "nsubj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(2, "dobj")] == pytest.approx(1.0, abs=eps)
|
|
assert label_scores2[(2, "punct")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(3, "nsubj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(3, "dobj")] == pytest.approx(0.0, abs=eps)
|
|
assert label_scores2[(3, "punct")] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores2[(0, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores2[(0, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores2[(0, 2)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores2[(2, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores2[(2, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores2[(2, 2)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores2[(3, 0)] == pytest.approx(0.0, abs=eps)
|
|
assert head_scores2[(3, 1)] == pytest.approx(1.0, abs=eps)
|
|
assert head_scores2[(3, 2)] == pytest.approx(0.0, abs=eps)
|