spaCy/spacy/tests/doc/test_doc_api.py
Álvaro Abella Bascarán e03e1eee92 Bugfix/get lca matrix (#3110)
This PR adds a test for an untested case of `Span.get_lca_matrix`, and fixes a bug for that scenario, which I introduced in [this PR](https://github.com/explosion/spaCy/pull/3089) (sorry!).

## Description
The previous implementation of get_lca_matrix was failing for the case `doc[j:k].get_lca_matrix()` where `j > 0`. A test has been added for this case and the bug has been fixed.

### Types of change
Bug fix

## Checklist

- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
2019-01-06 19:07:50 +01:00

323 lines
11 KiB
Python

# coding: utf-8
from __future__ import unicode_literals
from ..util import get_doc
from ...tokens import Doc
from ...vocab import Vocab
from ...attrs import LEMMA
from ...tokens import Span
import pytest
import numpy
@pytest.mark.parametrize('text', [["one", "two", "three"]])
def test_doc_api_compare_by_string_position(en_vocab, text):
doc = get_doc(en_vocab, text)
# Get the tokens in this order, so their ID ordering doesn't match the idx
token3 = doc[-1]
token2 = doc[-2]
token1 = doc[-1]
token1, token2, token3 = doc
assert token1 < token2 < token3
assert not token1 > token2
assert token2 > token1
assert token2 <= token3
assert token3 >= token1
def test_doc_api_getitem(en_tokenizer):
text = "Give it back! He pleaded."
tokens = en_tokenizer(text)
assert tokens[0].text == 'Give'
assert tokens[-1].text == '.'
with pytest.raises(IndexError):
tokens[len(tokens)]
def to_str(span):
return '/'.join(token.text for token in span)
span = tokens[1:1]
assert not to_str(span)
span = tokens[1:4]
assert to_str(span) == 'it/back/!'
span = tokens[1:4:1]
assert to_str(span) == 'it/back/!'
with pytest.raises(ValueError):
tokens[1:4:2]
with pytest.raises(ValueError):
tokens[1:4:-1]
span = tokens[-3:6]
assert to_str(span) == 'He/pleaded'
span = tokens[4:-1]
assert to_str(span) == 'He/pleaded'
span = tokens[-5:-3]
assert to_str(span) == 'back/!'
span = tokens[5:4]
assert span.start == span.end == 5 and not to_str(span)
span = tokens[4:-3]
assert span.start == span.end == 4 and not to_str(span)
span = tokens[:]
assert to_str(span) == 'Give/it/back/!/He/pleaded/.'
span = tokens[4:]
assert to_str(span) == 'He/pleaded/.'
span = tokens[:4]
assert to_str(span) == 'Give/it/back/!'
span = tokens[:-3]
assert to_str(span) == 'Give/it/back/!'
span = tokens[-3:]
assert to_str(span) == 'He/pleaded/.'
span = tokens[4:50]
assert to_str(span) == 'He/pleaded/.'
span = tokens[-50:4]
assert to_str(span) == 'Give/it/back/!'
span = tokens[-50:-40]
assert span.start == span.end == 0 and not to_str(span)
span = tokens[40:50]
assert span.start == span.end == 7 and not to_str(span)
span = tokens[1:4]
assert span[0].orth_ == 'it'
subspan = span[:]
assert to_str(subspan) == 'it/back/!'
subspan = span[:2]
assert to_str(subspan) == 'it/back'
subspan = span[1:]
assert to_str(subspan) == 'back/!'
subspan = span[:-1]
assert to_str(subspan) == 'it/back'
subspan = span[-2:]
assert to_str(subspan) == 'back/!'
subspan = span[1:2]
assert to_str(subspan) == 'back'
subspan = span[-2:-1]
assert to_str(subspan) == 'back'
subspan = span[-50:50]
assert to_str(subspan) == 'it/back/!'
subspan = span[50:-50]
assert subspan.start == subspan.end == 4 and not to_str(subspan)
@pytest.mark.parametrize('text', ["Give it back! He pleaded.",
" Give it back! He pleaded. "])
def test_doc_api_serialize(en_tokenizer, text):
tokens = en_tokenizer(text)
new_tokens = get_doc(tokens.vocab).from_bytes(tokens.to_bytes())
assert tokens.text == new_tokens.text
assert [t.text for t in tokens] == [t.text for t in new_tokens]
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
new_tokens = get_doc(tokens.vocab).from_bytes(
tokens.to_bytes(tensor=False), tensor=False)
assert tokens.text == new_tokens.text
assert [t.text for t in tokens] == [t.text for t in new_tokens]
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
new_tokens = get_doc(tokens.vocab).from_bytes(
tokens.to_bytes(sentiment=False), sentiment=False)
assert tokens.text == new_tokens.text
assert [t.text for t in tokens] == [t.text for t in new_tokens]
assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
def test_doc_api_set_ents(en_tokenizer):
text = "I use goggle chrone to surf the web"
tokens = en_tokenizer(text)
assert len(tokens.ents) == 0
tokens.ents = [(tokens.vocab.strings['PRODUCT'], 2, 4)]
assert len(list(tokens.ents)) == 1
assert [t.ent_iob for t in tokens] == [0, 0, 3, 1, 0, 0, 0, 0]
assert tokens.ents[0].label_ == 'PRODUCT'
assert tokens.ents[0].start == 2
assert tokens.ents[0].end == 4
def test_doc_api_merge(en_tokenizer):
text = "WKRO played songs by the beach boys all night"
# merge 'The Beach Boys'
doc = en_tokenizer(text)
assert len(doc) == 9
doc.merge(doc[4].idx, doc[6].idx + len(doc[6]), tag='NAMED', lemma='LEMMA',
ent_type='TYPE')
assert len(doc) == 7
assert doc[4].text == 'the beach boys'
assert doc[4].text_with_ws == 'the beach boys '
assert doc[4].tag_ == 'NAMED'
# merge 'all night'
doc = en_tokenizer(text)
assert len(doc) == 9
doc.merge(doc[7].idx, doc[8].idx + len(doc[8]), tag='NAMED', lemma='LEMMA',
ent_type='TYPE')
assert len(doc) == 8
assert doc[7].text == 'all night'
assert doc[7].text_with_ws == 'all night'
# merge both with bulk merge
doc = en_tokenizer(text)
assert len(doc) == 9
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[4: 7], attrs={'tag':'NAMED', 'lemma':'LEMMA',
'ent_type':'TYPE'})
retokenizer.merge(doc[7: 9], attrs={'tag':'NAMED', 'lemma':'LEMMA',
'ent_type':'TYPE'})
assert len(doc) == 6
assert doc[4].text == 'the beach boys'
assert doc[4].text_with_ws == 'the beach boys '
assert doc[4].tag_ == 'NAMED'
assert doc[5].text == 'all night'
assert doc[5].text_with_ws == 'all night'
assert doc[5].tag_ == 'NAMED'
def test_doc_api_merge_children(en_tokenizer):
"""Test that attachments work correctly after merging."""
text = "WKRO played songs by the beach boys all night"
doc = en_tokenizer(text)
assert len(doc) == 9
doc.merge(doc[4].idx, doc[6].idx + len(doc[6]), tag='NAMED', lemma='LEMMA',
ent_type='TYPE')
for word in doc:
if word.i < word.head.i:
assert word in list(word.head.lefts)
elif word.i > word.head.i:
assert word in list(word.head.rights)
def test_doc_api_merge_hang(en_tokenizer):
text = "through North and South Carolina"
doc = en_tokenizer(text)
doc.merge(18, 32, tag='', lemma='', ent_type='ORG')
doc.merge(8, 32, tag='', lemma='', ent_type='ORG')
def test_doc_api_retokenizer(en_tokenizer):
doc = en_tokenizer("WKRO played songs by the beach boys all night")
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[4:7])
assert len(doc) == 7
assert doc[4].text == 'the beach boys'
def test_doc_api_retokenizer_attrs(en_tokenizer):
doc = en_tokenizer("WKRO played songs by the beach boys all night")
# test both string and integer attributes and values
attrs = {LEMMA: 'boys', 'ENT_TYPE': doc.vocab.strings['ORG']}
with doc.retokenize() as retokenizer:
retokenizer.merge(doc[4:7], attrs=attrs)
assert len(doc) == 7
assert doc[4].text == 'the beach boys'
assert doc[4].lemma_ == 'boys'
assert doc[4].ent_type_ == 'ORG'
def test_doc_api_sents_empty_string(en_tokenizer):
doc = en_tokenizer("")
doc.is_parsed = True
sents = list(doc.sents)
assert len(sents) == 0
def test_doc_api_runtime_error(en_tokenizer):
# Example that caused run-time error while parsing Reddit
text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
deps = ['nsubj', 'prep', 'amod', 'pobj', 'ROOT', 'amod', 'attr', '',
'nummod', 'prep', 'det', 'amod', 'pobj', 'acl', 'prep', 'prep',
'pobj', '', 'nummod', 'prep', 'det', 'amod', 'pobj', 'aux', 'neg',
'ROOT', 'amod', 'dobj']
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [t.text for t in tokens], deps=deps)
nps = []
for np in doc.noun_chunks:
while len(np) > 1 and np[0].dep_ not in ('advmod', 'amod', 'compound'):
np = np[1:]
if len(np) > 1:
nps.append((np.start_char, np.end_char, np.root.tag_, np.text, np.root.ent_type_))
for np in nps:
start, end, tag, lemma, ent_type = np
doc.merge(start, end, tag=tag, lemma=lemma, ent_type=ent_type)
def test_doc_api_right_edge(en_tokenizer):
"""Test for bug occurring from Unshift action, causing incorrect right edge"""
text = "I have proposed to myself, for the sake of such as live under the government of the Romans, to translate those books into the Greek tongue."
heads = [2, 1, 0, -1, -1, -3, 15, 1, -2, -1, 1, -3, -1, -1, 1, -2, -1, 1,
-2, -7, 1, -19, 1, -2, -3, 2, 1, -3, -26]
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
assert doc[6].text == 'for'
subtree = [w.text for w in doc[6].subtree]
assert subtree == ['for', 'the', 'sake', 'of', 'such', 'as',
'live', 'under', 'the', 'government', 'of', 'the', 'Romans', ',']
assert doc[6].right_edge.text == ','
def test_doc_api_has_vector():
vocab = Vocab()
vocab.reset_vectors(width=2)
vocab.set_vector('kitten', vector=numpy.asarray([0., 2.], dtype='f'))
doc = Doc(vocab, words=['kitten'])
assert doc.has_vector
def test_doc_api_similarity_match():
doc = Doc(Vocab(), words=['a'])
assert doc.similarity(doc[0]) == 1.0
assert doc.similarity(doc.vocab['a']) == 1.0
doc2 = Doc(doc.vocab, words=['a', 'b', 'c'])
assert doc.similarity(doc2[:1]) == 1.0
assert doc.similarity(doc2) == 0.0
@pytest.mark.parametrize('sentence,heads,lca_matrix', [
('the lazy dog slept',
[2, 1, 1, 0],
numpy.array([[0, 2, 2, 3],
[2, 1, 2, 3],
[2, 2, 2, 3],
[3, 3, 3, 3]])),
('The lazy dog slept. The quick fox jumped',
[2, 1, 1, 0, -1, 2, 1, 1, 0],
numpy.array([[0, 2, 2, 3, 3, -1, -1, -1, -1],
[2, 1, 2, 3, 3, -1, -1, -1, -1],
[2, 2, 2, 3, 3, -1, -1, -1, -1],
[3, 3, 3, 3, 3, -1, -1, -1, -1],
[3, 3, 3, 3, 4, -1, -1, -1, -1],
[-1, -1, -1, -1, -1, 5, 7, 7, 8],
[-1, -1, -1, -1, -1, 7, 6, 7, 8],
[-1, -1, -1, -1, -1, 7, 7, 7, 8],
[-1, -1, -1, -1, -1, 8, 8, 8, 8]]))
])
def test_lowest_common_ancestor(en_tokenizer, sentence, heads, lca_matrix):
tokens = en_tokenizer(sentence)
doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
lca = doc.get_lca_matrix()
assert (lca == lca_matrix).all()
assert(lca[1, 1] == 1)
assert(lca[0, 1] == 2)
assert(lca[1, 2] == 2)
def test_parse_tree(en_tokenizer):
"""Tests doc.print_tree() method."""
text = 'I like New York in Autumn.'
heads = [1, 0, 1, -2, -3, -1, -5]
tags = ['PRP', 'IN', 'NNP', 'NNP', 'IN', 'NNP', '.']
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, tags=tags)
# full method parse_tree(text) is a trivial composition
trees = doc.print_tree()
assert len(trees) > 0
tree = trees[0]
assert all(k in list(tree.keys()) for k in ['word', 'lemma', 'NE', 'POS_fine', 'POS_coarse', 'arc', 'modifiers'])
assert tree['word'] == 'like' # check root is correct