import pytest import numpy import os import spacy from spacy.matcher import Matcher from spacy.attrs import ORTH, LOWER, ENT_IOB, ENT_TYPE from spacy.attrs import ORTH, TAG, LOWER, IS_ALPHA, FLAG63 from spacy.symbols import DATE def test_overlap_issue118(EN): '''Test a bug that arose from having overlapping matches''' doc = EN.tokenizer(u'how many points did lebron james score against the boston celtics last night') ORG = doc.vocab.strings['ORG'] matcher = Matcher(EN.vocab, {'BostonCeltics': ('ORG', {}, [ [{LOWER: 'celtics'}], [{LOWER: 'boston'}, {LOWER: 'celtics'}], ] ) } ) assert len(list(doc.ents)) == 0 matches = matcher(doc) assert matches == [(ORG, 9, 11), (ORG, 10, 11)] ents = list(doc.ents) assert len(ents) == 1 assert ents[0].label == ORG assert ents[0].start == 9 assert ents[0].end == 11 def test_overlap_issue242(): '''Test overlapping multi-word phrases.''' patterns = [ [{LOWER: 'food'}, {LOWER: 'safety'}], [{LOWER: 'safety'}, {LOWER: 'standards'}], ] if os.environ.get('SPACY_DATA'): data_dir = os.environ.get('SPACY_DATA') else: data_dir = None nlp = spacy.en.English(data_dir=data_dir, tagger=False, parser=False, entity=False) nlp.matcher.add('FOOD', 'FOOD', {}, patterns) doc = nlp.tokenizer(u'There are different food safety standards in different countries.') food_safety, safety_standards = nlp.matcher(doc) assert food_safety[1] == 3 assert food_safety[2] == 5 assert safety_standards[1] == 4 assert safety_standards[2] == 6 def test_overlap_reorder(EN): '''Test order dependence''' doc = EN.tokenizer(u'how many points did lebron james score against the boston celtics last night') ORG = doc.vocab.strings['ORG'] matcher = Matcher(EN.vocab, {'BostonCeltics': ('ORG', {}, [ [{LOWER: 'boston'}, {LOWER: 'celtics'}], [{LOWER: 'celtics'}], ] ) } ) assert len(list(doc.ents)) == 0 matches = matcher(doc) assert matches == [(ORG, 9, 11), (ORG, 10, 11)] ents = list(doc.ents) assert len(ents) == 1 assert ents[0].label == ORG assert ents[0].start == 9 assert ents[0].end == 11 def test_overlap_prefix(EN): '''Test order dependence''' doc = EN.tokenizer(u'how many points did lebron james score against the boston celtics last night') ORG = doc.vocab.strings['ORG'] matcher = Matcher(EN.vocab, {'BostonCeltics': ('ORG', {}, [ [{LOWER: 'boston'}], [{LOWER: 'boston'}, {LOWER: 'celtics'}], ] ) } ) assert len(list(doc.ents)) == 0 matches = matcher(doc) assert matches == [(ORG, 9, 10), (ORG, 9, 11)] ents = list(doc.ents) assert len(ents) == 1 assert ents[0].label == ORG assert ents[0].start == 9 assert ents[0].end == 11 def test_overlap_prefix_reorder(EN): '''Test order dependence''' doc = EN.tokenizer(u'how many points did lebron james score against the boston celtics last night') ORG = doc.vocab.strings['ORG'] matcher = Matcher(EN.vocab, {'BostonCeltics': ('ORG', {}, [ [{LOWER: 'boston'}, {LOWER: 'celtics'}], [{LOWER: 'boston'}], ] ) } ) assert len(list(doc.ents)) == 0 matches = matcher(doc) assert matches == [(ORG, 9, 10), (ORG, 9, 11)] ents = list(doc.ents) assert len(ents) == 1 assert ents[0].label == ORG assert ents[0].start == 9 assert ents[0].end == 11 @pytest.mark.models def test_ner_interaction(EN): EN.matcher.add('LAX_Airport', 'AIRPORT', {}, [[{ORTH: 'LAX'}]]) EN.matcher.add('SFO_Airport', 'AIRPORT', {}, [[{ORTH: 'SFO'}]]) doc = EN(u'get me a flight from SFO to LAX leaving 20 December and arriving on January 5th') ents = [(ent.label_, ent.text) for ent in doc.ents] assert ents[0] == ('AIRPORT', 'SFO') assert ents[1] == ('AIRPORT', 'LAX') assert ents[2] == ('DATE', '20 December') assert ents[3] == ('DATE', 'January 5th')