mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
221 lines
7.6 KiB
Python
221 lines
7.6 KiB
Python
import pytest
|
|
|
|
from spacy.lang.en import English
|
|
from ..util import get_doc, apply_transition_sequence, make_tempdir
|
|
from ... import util
|
|
from ...gold import Example
|
|
|
|
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"],
|
|
},
|
|
),
|
|
]
|
|
|
|
|
|
def test_parser_root(en_tokenizer):
|
|
text = "i don't have other assistance"
|
|
heads = [3, 2, 1, 0, 1, -2]
|
|
deps = ["nsubj", "aux", "neg", "ROOT", "amod", "dobj"]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps)
|
|
for t in doc:
|
|
assert t.dep != 0, t.text
|
|
|
|
|
|
@pytest.mark.xfail
|
|
# @pytest.mark.parametrize("text", ["Hello"])
|
|
def test_parser_parse_one_word_sentence(en_tokenizer, en_parser, text):
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(
|
|
tokens.vocab, words=[t.text for t in tokens], heads=[0], deps=["ROOT"]
|
|
)
|
|
|
|
assert len(doc) == 1
|
|
with en_parser.step_through(doc) as _: # noqa: F841
|
|
pass
|
|
assert doc[0].dep != 0
|
|
|
|
|
|
# We removed the step_through API a while ago. we should bring it back though
|
|
@pytest.mark.xfail(reason="Unsupported")
|
|
def test_parser_initial(en_tokenizer, en_parser):
|
|
text = "I ate the pizza with anchovies."
|
|
# heads = [1, 0, 1, -2, -3, -1, -5]
|
|
transition = ["L-nsubj", "S", "L-det"]
|
|
tokens = en_tokenizer(text)
|
|
apply_transition_sequence(en_parser, tokens, transition)
|
|
assert tokens[0].head.i == 1
|
|
assert tokens[1].head.i == 1
|
|
assert tokens[2].head.i == 3
|
|
assert tokens[3].head.i == 3
|
|
|
|
|
|
def test_parser_parse_subtrees(en_tokenizer, en_parser):
|
|
text = "The four wheels on the bus turned quickly"
|
|
heads = [2, 1, 4, -1, 1, -2, 0, -1]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads)
|
|
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_tokenizer):
|
|
text = "A phrase with another phrase occurs"
|
|
heads = [1, 4, -1, 1, -2, 0]
|
|
deps = ["det", "nsubj", "prep", "det", "pobj", "ROOT"]
|
|
tags = ["DT", "NN", "IN", "DT", "NN", "VBZ"]
|
|
tokens = en_tokenizer(text)
|
|
doc = get_doc(
|
|
tokens.vocab, words=[t.text for t in tokens], deps=deps, heads=heads, tags=tags
|
|
)
|
|
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"
|
|
|
|
|
|
# We removed the step_through API a while ago. we should bring it back though
|
|
@pytest.mark.xfail(reason="Unsupported")
|
|
def test_parser_arc_eager_finalize_state(en_tokenizer, en_parser):
|
|
text = "a b c d e"
|
|
|
|
# right branching
|
|
transition = ["R-nsubj", "D", "R-nsubj", "R-nsubj", "D", "R-ROOT"]
|
|
tokens = en_tokenizer(text)
|
|
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 = en_tokenizer(text)
|
|
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, 0, -1, 27, 0, -1, 1, -3, -1, 8, 4, 3, -1, 1, 3, 1, 1, -11, -1, 1, -9, -1, 4, -1, 2, 1, -6, -1, 1, 2, 1, -6, -1, -1, -17, -31, -32, -1]
|
|
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 = get_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 None
|
|
for sent in doc.sents:
|
|
for token in sent:
|
|
assert token.head in sent
|
|
|
|
|
|
def test_overfitting_IO():
|
|
# Simple test to try and quickly overfit the dependency parser - ensuring the ML models work correctly
|
|
nlp = English()
|
|
parser = nlp.create_pipe("parser")
|
|
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)
|
|
nlp.add_pipe(parser)
|
|
optimizer = nlp.begin_training()
|
|
|
|
for i in range(100):
|
|
losses = {}
|
|
nlp.update(train_examples, sgd=optimizer, losses=losses)
|
|
assert losses["parser"] < 0.0001
|
|
|
|
# test the trained model
|
|
test_text = "I like securities."
|
|
doc = nlp(test_text)
|
|
assert doc[0].dep_ is "nsubj"
|
|
assert doc[2].dep_ is "dobj"
|
|
assert doc[3].dep_ is "punct"
|
|
|
|
# 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_ is "nsubj"
|
|
assert doc2[2].dep_ is "dobj"
|
|
assert doc2[3].dep_ is "punct"
|