def test_minimal_pattern_validation(en_vocab, pattern, n_errors, n_min_errors):
matcher = Matcher(en_vocab)
if n_min_errors > 0:
with pytest.raises(ValueError):
matcher.add("TEST", [pattern])
elif n_errors == 0:
matcher.add("TEST", [pattern])
def test_matcher_match_zero_plus(en_vocab):
words = 'He said , " some words " ...'.split()
pattern = [{"ORTH": '"'}, {"OP": "*", "IS_PUNCT": False}, {"ORTH": '"'}]
matcher = Matcher(en_vocab)
matcher.add("Quote", [pattern])
doc = Doc(en_vocab, words=words)
assert len(matcher(doc)) == 1
def test_pattern_errors(en_vocab):
matcher = Matcher(en_vocab)
# normalize "regex" to upper like "text"
matcher.add("TEST1", [[{"text": {"regex": "regex"}}]])
# error if subpattern attribute isn't recognized and processed
with pytest.raises(MatchPatternError):
matcher.add("TEST2", [[{"TEXT": {"XX": "xx"}}]])
def test_matcher_schema_token_attributes(en_vocab, pattern, text):
matcher = Matcher(en_vocab)
doc = Doc(en_vocab, words=text.split(" "))
matcher.add("Rule", [pattern])
assert len(matcher) == 1
matches = matcher(doc)
assert len(matches) == 1
def test_operator_combos(en_vocab):
cases = [
("aaab", "a a a b", True),
("aaab", "a+ b", True),
("aaab", "a+ a+ b", True),
("aaab", "a+ a+ a b", True),
("aaab", "a+ a+ a+ b", True),
("aaab", "a+ a a b", True),
("aaab", "a+ a a", True),
("aaab", "a+", True),
("aaa", "a+ b", False),
("aaa", "a+ a+ b", False),
("aaa", "a+ a+ a+ b", False),
("aaa", "a+ a b", False),
("aaa", "a+ a a b", False),
("aaab", "a+ a a", True),
("aaab", "a+", True),
("aaab", "a+ a b", True),
]
for string, pattern_str, result in cases:
matcher = Matcher(en_vocab)
doc = Doc(en_vocab, words=list(string))
pattern = []
for part in pattern_str.split():
if part.endswith("+"):
pattern.append({"ORTH": part[0], "OP": "+"})
else:
pattern.append({"ORTH": part})
matcher.add("PATTERN", [pattern])
matches = matcher(doc)
if result:
assert matches, (string, pattern_str)
else:
assert not matches, (string, pattern_str)
def test_matcher_regex(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"REGEX": r"\bUS\d+\b"}]
matcher.add("REGEX", [pattern])
text = "This is a test for a regex, US12345."
doc = Doc(en_vocab, words=text.split())
matches = matcher(doc)
assert matches == [(14188318820720882904, 7, 8)]
def test_matcher_callback(en_vocab):
mock = Mock()
matcher = Matcher(en_vocab)
pattern = [{"ORTH": "test"}]
matcher.add("Rule", [pattern], on_match=mock)
doc = Doc(en_vocab, words=["This", "is", "a", "test", "."])
matches = matcher(doc)
mock.assert_called_once_with(matcher, doc, 0, matches)
def test_matcher_any_token_operator(en_vocab):
"""Test that patterns with "any token" {} work with operators."""
matcher = Matcher(en_vocab)
matcher.add("TEST", [[{"ORTH": "test"}, {"OP": "*"}]])
doc = Doc(en_vocab, words=["test", "hello", "world"])
matches = [doc[start:end].text for _, start, end in matcher(doc)]
assert len(matches) == 1
assert matches[0] == "test hello world"
def test_match_consuming(doc, text, pattern, re_pattern):
"""Test that matcher.__call__ consumes tokens on a match similar to
re.findall."""
matcher = Matcher(doc.vocab)
matcher.add(re_pattern, [pattern])
matches = matcher(doc)
re_matches = [m.span() for m in re.finditer(re_pattern, text)]
assert len(matches) == len(re_matches)
def test_matcher_operator_shadow(en_vocab):
matcher = Matcher(en_vocab)
doc = Doc(en_vocab, words=["a", "b", "c"])
pattern = [{"ORTH": "a"}, {"IS_ALPHA": True, "OP": "+"}, {"ORTH": "c"}]
matcher.add("A.C", [pattern])
matches = matcher(doc)
assert len(matches) == 1
assert matches[0][1:] == (0, 3)
def test_greedy_matching(doc, text, pattern, re_pattern):
"""Test that the greedy matching behavior of the * op is consistant with
other re implementations."""
matcher = Matcher(doc.vocab)
matcher.add(re_pattern, [pattern])
matches = matcher(doc)
re_matches = [m.span() for m in re.finditer(re_pattern, text)]
for match, re_match in zip(matches, re_matches):
assert match[1:] == re_match
def matcher(en_vocab):
rules = {
"JS": [[{"ORTH": "JavaScript"}]],
"GoogleNow": [[{"ORTH": "Google"}, {"ORTH": "Now"}]],
"Java": [[{"LOWER": "java"}]],
}
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns)
return matcher
def test_matcher_compare_length(en_vocab, cmp, bad):
matcher = Matcher(en_vocab)
pattern = [{"LENGTH": {cmp: 2}}]
matcher.add("LENGTH_COMPARE", [pattern])
doc = Doc(en_vocab, words=["a", "aa", "aaa"])
matches = matcher(doc)
assert len(matches) == len(doc) - len(bad)
doc = Doc(en_vocab, words=bad)
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_set_value(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": {"IN": ["an", "a"]}}]
matcher.add("A_OR_AN", [pattern])
doc = Doc(en_vocab, words=["an", "a", "apple"])
matches = matcher(doc)
assert len(matches) == 2
doc = Doc(en_vocab, words=["aardvark"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_orth_regex(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": {"REGEX": r"(?:a|an)"}}]
matcher.add("A_OR_AN", [pattern])
doc = Doc(en_vocab, words=["an", "a", "hi"])
matches = matcher(doc)
assert len(matches) == 2
doc = Doc(en_vocab, words=["bye"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_set_value_operator(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": {"IN": ["a", "the"]}, "OP": "?"}, {"ORTH": "house"}]
matcher.add("DET_HOUSE", [pattern])
doc = Doc(en_vocab, words=["In", "a", "house"])
matches = matcher(doc)
assert len(matches) == 1
doc = Doc(en_vocab, words=["my", "house"])
matches = matcher(doc)
assert len(matches) == 1
def test_matcher_regex_shape(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"SHAPE": {"REGEX": r"^[^x]+$"}}]
matcher.add("NON_ALPHA", [pattern])
doc = Doc(en_vocab, words=["99", "problems", "!"])
matches = matcher(doc)
assert len(matches) == 2
doc = Doc(en_vocab, words=["bye"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_empty_dict(en_vocab):
"""Test matcher allows empty token specs, meaning match on any token."""
matcher = Matcher(en_vocab)
doc = Doc(en_vocab, words=["a", "b", "c"])
matcher.add("A.C", [[{"ORTH": "a"}, {}, {"ORTH": "c"}]])
matches = matcher(doc)
assert len(matches) == 1
assert matches[0][1:] == (0, 3)
matcher = Matcher(en_vocab)
matcher.add("A.", [[{"ORTH": "a"}, {}]])
matches = matcher(doc)
assert matches[0][1:] == (0, 2)
def test_matcher_extension_attribute(en_vocab):
matcher = Matcher(en_vocab)
get_is_fruit = lambda token: token.text in ("apple", "banana")
Token.set_extension("is_fruit", getter=get_is_fruit, force=True)
pattern = [{"ORTH": "an"}, {"_": {"is_fruit": True}}]
matcher.add("HAVING_FRUIT", [pattern])
doc = Doc(en_vocab, words=["an", "apple"])
matches = matcher(doc)
assert len(matches) == 1
doc = Doc(en_vocab, words=["an", "aardvark"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_from_api_docs(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": "test"}]
assert len(matcher) == 0
matcher.add("Rule", [pattern])
assert len(matcher) == 1
matcher.remove("Rule")
assert "Rule" not in matcher
matcher.add("Rule", [pattern])
assert "Rule" in matcher
on_match, patterns = matcher.get("Rule")
assert len(patterns[0])
def test_matcher_extension_set_membership(en_vocab):
matcher = Matcher(en_vocab)
get_reversed = lambda token: "".join(reversed(token.text))
Token.set_extension("reversed", getter=get_reversed, force=True)
pattern = [{"_": {"reversed": {"IN": ["eyb", "ih"]}}}]
matcher.add("REVERSED", [pattern])
doc = Doc(en_vocab, words=["hi", "bye", "hello"])
matches = matcher(doc)
assert len(matches) == 2
doc = Doc(en_vocab, words=["aardvark"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_match_one_plus(matcher, en_vocab):
control = Matcher(en_vocab)
control.add("BasicPhilippe", [[{"ORTH": "Philippe"}]])
doc = Doc(en_vocab, words=["Philippe", "Philippe"])
m = control(doc)
assert len(m) == 2
pattern = [
{"ORTH": "Philippe", "OP": "1"},
{"ORTH": "Philippe", "OP": "+"},
]
matcher.add("KleenePhilippe", [pattern])
m = matcher(doc)
assert len(m) == 1
def test_matcher_from_usage_docs(en_vocab):
text = "Wow 😀 This is really cool! 😂 😂"
doc = Doc(en_vocab, words=text.split(" "))
pos_emoji = ["😀", "😃", "😂", "🤣", "😊", "😍"]
pos_patterns = [[{"ORTH": emoji}] for emoji in pos_emoji]
def label_sentiment(matcher, doc, i, matches):
match_id, start, end = matches[i]
if match_id == 2686646543460454932:
doc.sentiment += 0.1
span = doc[start:end]
with doc.retokenize() as retokenizer:
retokenizer.merge(span)
token = doc[start]
token.vocab[token.text].norm_ = "happy emoji"
matcher = Matcher(en_vocab)
matcher.add("HAPPY", pos_patterns, on_match=label_sentiment)
matcher(doc)
assert doc.sentiment != 0
assert doc[1].norm_ == "happy emoji"
def _find_matches_for(filtered: Iterable[Tuple[Span, Span]],
doc: Doc) -> Iterable[Tuple[Span, Set[Span]]]:
form2other = {}
matches = []
global_matcher = Matcher(doc.vocab)
for (long_candidate, short_candidate) in filtered:
abbr = find_abbreviation(long_candidate, short_candidate)
# We look for abbreviations, so...
if abbr is None:
continue
long_form, short_form = abbr
# Look for each new abbreviation globally to find lone ones
for form, other in ((long_form, short_form), (short_form, long_form)):
form2other.setdefault(form, other)
pattern = [{"TEXT": t.text} for t in form]
global_matcher.add(form.text, [pattern])
seen = set()
# Search for lone abbreviations globally
for key, start, end in global_matcher(doc):
other = None
text = doc.vocab.strings[key]
for f, o in form2other.items():
if f.text != text or f.start > start:
continue
other = o
if f.start == start:
break
if other is None:
continue
form = doc[start:end]
# Short form should be the shortest
match = (other, form) if len(form) < len(other) else (form, other)
# Don't add duplicates
key = "/".join([str(el.start) for el in match])
if key in seen:
continue
seen.add(key)
matches.append(match)
yield from sorted(matches, key=lambda x: x[0].start)
def test_attr_pipeline_checks(en_vocab):
doc1 = Doc(en_vocab, words=["Test"])
doc1[0].dep_ = "ROOT"
doc2 = Doc(en_vocab, words=["Test"])
doc2[0].tag_ = "TAG"
doc2[0].pos_ = "X"
if spacy_version >= 3:
doc2[0].set_morph("Feat=Val")
else:
doc1.is_parsed = True
doc2[0].lemma_ = "LEMMA"
doc3 = Doc(en_vocab, words=["Test"])
# DEP requires DEP
matcher = Matcher(en_vocab)
matcher.add("TEST", [[{"DEP": "a"}]])
matcher(doc1)
with pytest.raises(ValueError):
matcher(doc2)
with pytest.raises(ValueError):
matcher(doc3)
# errors can be suppressed if desired
matcher(doc2, allow_missing=True)
matcher(doc3, allow_missing=True)
# TAG, POS, LEMMA require those values
for attr in ("TAG", "POS", "LEMMA"):
matcher = Matcher(en_vocab)
matcher.add("TEST", [[{attr: "a"}]])
if spacy_version < 3:
doc2.is_tagged = True
matcher(doc2)
with pytest.raises(ValueError):
matcher(doc1)
with pytest.raises(ValueError):
matcher(doc3)
# TEXT/ORTH only require tokens
matcher = Matcher(en_vocab)
matcher.add("TEST", [[{"ORTH": "a"}]])
matcher(doc1)
matcher(doc2)
matcher(doc3)
matcher = Matcher(en_vocab)
matcher.add("TEST", [[{"TEXT": "a"}]])
matcher(doc1)
matcher(doc2)
matcher(doc3)
class AbbrX:
"""
*Strongly based on scispacy's AbbreviationDetector*.
Detect abbreviations which are acronyms or by using the algorithm in
"A simple algorithm for identifying abbreviation definitions in biomedical
text.", (Schwartz & Hearst, 2003).
This class sets the `._.abbrs` attribute on spaCy Doc.
The abbreviations attribute is a `List[Span]` where each Span has the `Span._.long_form`
attribute set to the long form definition of the abbreviation.
Note that this class does not replace the spans, or merge them.
"""
def __init__(self, vocab) -> None:
Doc.set_extension("abbrs", default=[], force=True)
Span.set_extension("long_form", default=None, force=True)
self._matcher = Matcher(vocab)
self._matcher.add(
"abbrs",
[
# Pattern for abbreviations not enclosed in brackets
# here we limit to alpha chars only as it could
# get many exceptions
[{
"IS_ALPHA": True,
"IS_UPPER": True,
"LENGTH": {
">": 1
}
}],
# Pattern for abbreviations enclosed in brackets
# here we try to allow non alpha chars too as it is
# the more likely standard way to introduce an abbreviation
[
{
"TEXT": {
"IN": ["(", "["]
},
"OP": "+"
},
{
"OP": "+"
},
{
"TEXT": {
"IN": [")", "]"]
},
"OP": "+"
},
],
],
)
def find(self, span: Span, doc: Doc) -> Tuple[Span, Set[Span]]:
"""
Functional version of calling the matcher for a single span.
This method is helpful if you already have an abbreviation which
you want to find a definition for.
"""
dummy_matches = [(-1, int(span.start), int(span.end))]
filtered = _filter_matches(dummy_matches, doc)
abbrs = list(self.find_matches_for(filtered, doc))
if not abbrs:
return span, set()
return abbrs[0]
def __call__(self, doc: Doc) -> Doc:
matches = self._matcher(doc)
matches_no_punct = set([(
x[0],
x[1] + (1 if doc[x[1]].is_punct else 0),
x[2] - (1 if doc[x[2] - 1].is_punct else 0),
) for x in matches])
filtered = _filter_matches(matches_no_punct, doc)
occurences = _find_matches_for(filtered, doc)
for long_form, short_form in occurences:
short_form._.long_form = long_form
doc._.abbrs.append(short_form)
return doc
def test_matcher_partial_token_regex(nlp, text, count):
matcher = Matcher(nlp.vocab)
pattern = [{"LOWER": {"REGEX": r"ing\b"}}]
matcher.add("ING_FORM", [pattern])
assert len(matcher(nlp(text))) == count
def test_matcher_pattern_validation(en_vocab, pattern):
matcher = Matcher(en_vocab, validate=True)
with pytest.raises(MatchPatternError):
matcher.add("TEST", [pattern])
def test_matcher_basic_check(en_vocab):
matcher = Matcher(en_vocab)
# Potential mistake: pass in pattern instead of list of patterns
pattern = [{"TEXT": "hello"}, {"TEXT": "world"}]
with pytest.raises(ValueError):
matcher.add("TEST", pattern)
def test_matcher_valid_callback(en_vocab):
"""Test that on_match can only be None or callable."""
matcher = Matcher(en_vocab)
with pytest.raises(ValueError):
matcher.add("TEST", [[{"TEXT": "test"}]], on_match=[])
matcher(Doc(en_vocab, words=["test"]))