mirror of https://github.com/explosion/spaCy.git
413 lines
15 KiB
Python
413 lines
15 KiB
Python
import pytest
|
|
from numpy.testing import assert_equal
|
|
from spacy.attrs import DEP
|
|
|
|
from spacy.lang.en import English
|
|
from spacy.training import Example
|
|
from spacy.tokens import Doc
|
|
from spacy import util
|
|
|
|
from ..util import apply_transition_sequence, make_tempdir
|
|
|
|
|
|
TRAIN_DATA = [
|
|
(
|
|
"They trade mortgage-backed securities.",
|
|
{
|
|
"heads": [1, 1, 4, 4, 5, 1, 1],
|
|
"deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"],
|
|
},
|
|
),
|
|
(
|
|
"I like London and Berlin.",
|
|
{
|
|
"heads": [1, 1, 1, 2, 2, 1],
|
|
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
|
|
},
|
|
),
|
|
]
|
|
|
|
|
|
CONFLICTING_DATA = [
|
|
(
|
|
"I like London and Berlin.",
|
|
{
|
|
"heads": [1, 1, 1, 2, 2, 1],
|
|
"deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"],
|
|
},
|
|
),
|
|
(
|
|
"I like London and Berlin.",
|
|
{
|
|
"heads": [0, 0, 0, 0, 0, 0],
|
|
"deps": ["ROOT", "nsubj", "nsubj", "cc", "conj", "punct"],
|
|
},
|
|
),
|
|
]
|
|
|
|
PARTIAL_DATA = [
|
|
(
|
|
"I like London.",
|
|
{
|
|
"heads": [1, 1, 1, None],
|
|
"deps": ["nsubj", "ROOT", "dobj", None],
|
|
},
|
|
),
|
|
]
|
|
|
|
eps = 0.1
|
|
|
|
|
|
def test_parser_root(en_vocab):
|
|
words = ["i", "do", "n't", "have", "other", "assistance"]
|
|
heads = [3, 3, 3, 3, 5, 3]
|
|
deps = ["nsubj", "aux", "neg", "ROOT", "amod", "dobj"]
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
|
for t in doc:
|
|
assert t.dep != 0, t.text
|
|
|
|
|
|
@pytest.mark.skip(
|
|
reason="The step_through API was removed (but should be brought back)"
|
|
)
|
|
@pytest.mark.parametrize("words", [["Hello"]])
|
|
def test_parser_parse_one_word_sentence(en_vocab, en_parser, words):
|
|
doc = Doc(en_vocab, words=words, heads=[0], deps=["ROOT"])
|
|
assert len(doc) == 1
|
|
with en_parser.step_through(doc) as _: # noqa: F841
|
|
pass
|
|
assert doc[0].dep != 0
|
|
|
|
|
|
@pytest.mark.skip(
|
|
reason="The step_through API was removed (but should be brought back)"
|
|
)
|
|
def test_parser_initial(en_vocab, en_parser):
|
|
words = ["I", "ate", "the", "pizza", "with", "anchovies", "."]
|
|
transition = ["L-nsubj", "S", "L-det"]
|
|
doc = Doc(en_vocab, words=words)
|
|
apply_transition_sequence(en_parser, doc, transition)
|
|
assert doc[0].head.i == 1
|
|
assert doc[1].head.i == 1
|
|
assert doc[2].head.i == 3
|
|
assert doc[3].head.i == 3
|
|
|
|
|
|
def test_parser_parse_subtrees(en_vocab, en_parser):
|
|
words = ["The", "four", "wheels", "on", "the", "bus", "turned", "quickly"]
|
|
heads = [2, 2, 6, 2, 5, 3, 6, 6]
|
|
deps = ["dep"] * len(heads)
|
|
doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
|
|
assert len(list(doc[2].lefts)) == 2
|
|
assert len(list(doc[2].rights)) == 1
|
|
assert len(list(doc[2].children)) == 3
|
|
assert len(list(doc[5].lefts)) == 1
|
|
assert len(list(doc[5].rights)) == 0
|
|
assert len(list(doc[5].children)) == 1
|
|
assert len(list(doc[2].subtree)) == 6
|
|
|
|
|
|
def test_parser_merge_pp(en_vocab):
|
|
words = ["A", "phrase", "with", "another", "phrase", "occurs"]
|
|
heads = [1, 5, 1, 4, 2, 5]
|
|
deps = ["det", "nsubj", "prep", "det", "pobj", "ROOT"]
|
|
pos = ["DET", "NOUN", "ADP", "DET", "NOUN", "VERB"]
|
|
doc = Doc(en_vocab, words=words, deps=deps, heads=heads, pos=pos)
|
|
with doc.retokenize() as retokenizer:
|
|
for np in doc.noun_chunks:
|
|
retokenizer.merge(np, attrs={"lemma": np.lemma_})
|
|
assert doc[0].text == "A phrase"
|
|
assert doc[1].text == "with"
|
|
assert doc[2].text == "another phrase"
|
|
assert doc[3].text == "occurs"
|
|
|
|
|
|
@pytest.mark.skip(
|
|
reason="The step_through API was removed (but should be brought back)"
|
|
)
|
|
def test_parser_arc_eager_finalize_state(en_vocab, en_parser):
|
|
words = ["a", "b", "c", "d", "e"]
|
|
# right branching
|
|
transition = ["R-nsubj", "D", "R-nsubj", "R-nsubj", "D", "R-ROOT"]
|
|
tokens = Doc(en_vocab, words=words)
|
|
apply_transition_sequence(en_parser, tokens, transition)
|
|
|
|
assert tokens[0].n_lefts == 0
|
|
assert tokens[0].n_rights == 2
|
|
assert tokens[0].left_edge.i == 0
|
|
assert tokens[0].right_edge.i == 4
|
|
assert tokens[0].head.i == 0
|
|
|
|
assert tokens[1].n_lefts == 0
|
|
assert tokens[1].n_rights == 0
|
|
assert tokens[1].left_edge.i == 1
|
|
assert tokens[1].right_edge.i == 1
|
|
assert tokens[1].head.i == 0
|
|
|
|
assert tokens[2].n_lefts == 0
|
|
assert tokens[2].n_rights == 2
|
|
assert tokens[2].left_edge.i == 2
|
|
assert tokens[2].right_edge.i == 4
|
|
assert tokens[2].head.i == 0
|
|
|
|
assert tokens[3].n_lefts == 0
|
|
assert tokens[3].n_rights == 0
|
|
assert tokens[3].left_edge.i == 3
|
|
assert tokens[3].right_edge.i == 3
|
|
assert tokens[3].head.i == 2
|
|
|
|
assert tokens[4].n_lefts == 0
|
|
assert tokens[4].n_rights == 0
|
|
assert tokens[4].left_edge.i == 4
|
|
assert tokens[4].right_edge.i == 4
|
|
assert tokens[4].head.i == 2
|
|
|
|
# left branching
|
|
transition = ["S", "S", "S", "L-nsubj", "L-nsubj", "L-nsubj", "L-nsubj"]
|
|
tokens = Doc(en_vocab, words=words)
|
|
apply_transition_sequence(en_parser, tokens, transition)
|
|
|
|
assert tokens[0].n_lefts == 0
|
|
assert tokens[0].n_rights == 0
|
|
assert tokens[0].left_edge.i == 0
|
|
assert tokens[0].right_edge.i == 0
|
|
assert tokens[0].head.i == 4
|
|
|
|
assert tokens[1].n_lefts == 0
|
|
assert tokens[1].n_rights == 0
|
|
assert tokens[1].left_edge.i == 1
|
|
assert tokens[1].right_edge.i == 1
|
|
assert tokens[1].head.i == 4
|
|
|
|
assert tokens[2].n_lefts == 0
|
|
assert tokens[2].n_rights == 0
|
|
assert tokens[2].left_edge.i == 2
|
|
assert tokens[2].right_edge.i == 2
|
|
assert tokens[2].head.i == 4
|
|
|
|
assert tokens[3].n_lefts == 0
|
|
assert tokens[3].n_rights == 0
|
|
assert tokens[3].left_edge.i == 3
|
|
assert tokens[3].right_edge.i == 3
|
|
assert tokens[3].head.i == 4
|
|
|
|
assert tokens[4].n_lefts == 4
|
|
assert tokens[4].n_rights == 0
|
|
assert tokens[4].left_edge.i == 0
|
|
assert tokens[4].right_edge.i == 4
|
|
assert tokens[4].head.i == 4
|
|
|
|
|
|
def test_parser_set_sent_starts(en_vocab):
|
|
# fmt: off
|
|
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']
|
|
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]
|
|
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', '']
|
|
# fmt: on
|
|
doc = Doc(en_vocab, words=words, deps=deps, heads=heads)
|
|
for i in range(len(words)):
|
|
if i == 0 or i == 3:
|
|
assert doc[i].is_sent_start is True
|
|
else:
|
|
assert doc[i].is_sent_start is False
|
|
for sent in doc.sents:
|
|
for token in sent:
|
|
assert token.head in sent
|
|
|
|
|
|
@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
|
|
def test_incomplete_data(pipe_name):
|
|
# Test that the parser works with incomplete information
|
|
nlp = English()
|
|
parser = nlp.add_pipe(pipe_name)
|
|
train_examples = []
|
|
for text, annotations in PARTIAL_DATA:
|
|
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
|
|
for dep in annotations.get("deps", []):
|
|
if dep is not None:
|
|
parser.add_label(dep)
|
|
optimizer = nlp.initialize(get_examples=lambda: train_examples)
|
|
for i in range(150):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses[pipe_name] < 0.0001
|
|
|
|
# test the trained model
|
|
test_text = "I like securities."
|
|
doc = nlp(test_text)
|
|
assert doc[0].dep_ == "nsubj"
|
|
assert doc[2].dep_ == "dobj"
|
|
assert doc[0].head.i == 1
|
|
assert doc[2].head.i == 1
|
|
|
|
|
|
@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
|
|
def test_overfitting_IO(pipe_name):
|
|
# Simple test to try and quickly overfit the dependency parser (normal or beam)
|
|
nlp = English()
|
|
parser = nlp.add_pipe(pipe_name)
|
|
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(200):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses[pipe_name] < 0.0001
|
|
# test the trained model
|
|
test_text = "I like securities."
|
|
doc = nlp(test_text)
|
|
assert doc[0].dep_ == "nsubj"
|
|
assert doc[2].dep_ == "dobj"
|
|
assert doc[3].dep_ == "punct"
|
|
assert doc[0].head.i == 1
|
|
assert doc[2].head.i == 1
|
|
assert doc[3].head.i == 1
|
|
# 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)
|
|
doc2 = nlp2(test_text)
|
|
assert doc2[0].dep_ == "nsubj"
|
|
assert doc2[2].dep_ == "dobj"
|
|
assert doc2[3].dep_ == "punct"
|
|
assert doc2[0].head.i == 1
|
|
assert doc2[2].head.i == 1
|
|
assert doc2[3].head.i == 1
|
|
|
|
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
|
|
texts = [
|
|
"Just a sentence.",
|
|
"Then one more sentence about London.",
|
|
"Here is another one.",
|
|
"I like London.",
|
|
]
|
|
batch_deps_1 = [doc.to_array([DEP]) for doc in nlp.pipe(texts)]
|
|
batch_deps_2 = [doc.to_array([DEP]) for doc in nlp.pipe(texts)]
|
|
no_batch_deps = [doc.to_array([DEP]) for doc in [nlp(text) for text in texts]]
|
|
assert_equal(batch_deps_1, batch_deps_2)
|
|
assert_equal(batch_deps_1, no_batch_deps)
|
|
|
|
|
|
def test_beam_parser_scores():
|
|
# Test that we can get confidence values out of the beam_parser pipe
|
|
beam_width = 16
|
|
beam_density = 0.0001
|
|
nlp = English()
|
|
config = {
|
|
"beam_width": beam_width,
|
|
"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)
|