spaCy/spacy/tests/matcher/test_phrase_matcher.py
Ines Montani ad2a514cdf Show warning if phrase pattern Doc was overprocessed (#3255)
In most cases, the PhraseMatcher will match on the verbatim token text or as of v2.1, sometimes the lowercase text. This means that we only need a tokenized Doc, without any other attributes.

If phrase patterns are created by processing large terminology lists with the full `nlp` object, this easily can make things a lot slower, because all components will be applied, even if we don't actually need the attributes they set (like part-of-speech tags, dependency labels).

The warning message also includes a suggestion to use nlp.make_doc or nlp.tokenizer.pipe for even faster processing. For now, the validation has to be enabled explicitly by setting validate=True.
2019-02-13 01:45:31 +11:00

102 lines
3.4 KiB
Python

# coding: utf-8
from __future__ import unicode_literals
import pytest
from spacy.matcher import PhraseMatcher
from spacy.tokens import Doc
from ..util import get_doc
def test_matcher_phrase_matcher(en_vocab):
doc = Doc(en_vocab, words=["Google", "Now"])
matcher = PhraseMatcher(en_vocab)
matcher.add("COMPANY", None, doc)
doc = Doc(en_vocab, words=["I", "like", "Google", "Now", "best"])
assert len(matcher(doc)) == 1
def test_phrase_matcher_length(en_vocab):
matcher = PhraseMatcher(en_vocab)
assert len(matcher) == 0
matcher.add("TEST", None, Doc(en_vocab, words=["test"]))
assert len(matcher) == 1
matcher.add("TEST2", None, Doc(en_vocab, words=["test2"]))
assert len(matcher) == 2
def test_phrase_matcher_contains(en_vocab):
matcher = PhraseMatcher(en_vocab)
matcher.add("TEST", None, Doc(en_vocab, words=["test"]))
assert "TEST" in matcher
assert "TEST2" not in matcher
def test_phrase_matcher_string_attrs(en_vocab):
words1 = ["I", "like", "cats"]
pos1 = ["PRON", "VERB", "NOUN"]
words2 = ["Yes", ",", "you", "hate", "dogs", "very", "much"]
pos2 = ["INTJ", "PUNCT", "PRON", "VERB", "NOUN", "ADV", "ADV"]
pattern = get_doc(en_vocab, words=words1, pos=pos1)
matcher = PhraseMatcher(en_vocab, attr="POS")
matcher.add("TEST", None, pattern)
doc = get_doc(en_vocab, words=words2, pos=pos2)
matches = matcher(doc)
assert len(matches) == 1
match_id, start, end = matches[0]
assert match_id == en_vocab.strings["TEST"]
assert start == 2
assert end == 5
def test_phrase_matcher_string_attrs_negative(en_vocab):
"""Test that token with the control codes as ORTH are *not* matched."""
words1 = ["I", "like", "cats"]
pos1 = ["PRON", "VERB", "NOUN"]
words2 = ["matcher:POS-PRON", "matcher:POS-VERB", "matcher:POS-NOUN"]
pos2 = ["X", "X", "X"]
pattern = get_doc(en_vocab, words=words1, pos=pos1)
matcher = PhraseMatcher(en_vocab, attr="POS")
matcher.add("TEST", None, pattern)
doc = get_doc(en_vocab, words=words2, pos=pos2)
matches = matcher(doc)
assert len(matches) == 0
def test_phrase_matcher_bool_attrs(en_vocab):
words1 = ["Hello", "world", "!"]
words2 = ["No", "problem", ",", "he", "said", "."]
pattern = Doc(en_vocab, words=words1)
matcher = PhraseMatcher(en_vocab, attr="IS_PUNCT")
matcher.add("TEST", None, pattern)
doc = Doc(en_vocab, words=words2)
matches = matcher(doc)
assert len(matches) == 2
match_id1, start1, end1 = matches[0]
match_id2, start2, end2 = matches[1]
assert match_id1 == en_vocab.strings["TEST"]
assert match_id2 == en_vocab.strings["TEST"]
assert start1 == 0
assert end1 == 3
assert start2 == 3
assert end2 == 6
def test_phrase_matcher_validation(en_vocab):
doc1 = Doc(en_vocab, words=["Test"])
doc1.is_parsed = True
doc2 = Doc(en_vocab, words=["Test"])
doc2.is_tagged = True
doc3 = Doc(en_vocab, words=["Test"])
matcher = PhraseMatcher(en_vocab, validate=True)
with pytest.warns(UserWarning):
matcher.add("TEST1", None, doc1)
with pytest.warns(UserWarning):
matcher.add("TEST2", None, doc2)
with pytest.warns(None) as record:
matcher.add("TEST3", None, doc3)
assert not record.list
matcher = PhraseMatcher(en_vocab, attr="POS", validate=True)
with pytest.warns(None) as record:
matcher.add("TEST4", None, doc2)
assert not record.list