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