mirror of https://github.com/explosion/spaCy.git
Modernise sentence boundary detection tests and don't depend on models (where possible)
This commit is contained in:
Ines Montani
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# coding: utf-8
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from __future__ import unicode_literals
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from __future__ import unicode_literals
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from ...tokens import Doc
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from ..util import get_doc, apply_transition_sequence
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import pytest
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import pytest
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from spacy.tokens import Doc
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from spacy.syntax.nonproj import PseudoProjectivity
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@pytest.mark.models
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@pytest.mark.parametrize('text', ["A test sentence"])
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def test_single_period(EN):
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@pytest.mark.parametrize('punct', ['.', '!', '?', ''])
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string = 'A test sentence.'
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def test_parser_sbd_single_punct(en_tokenizer, text, punct):
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words = EN(string)
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heads = [2, 1, 0, -1] if punct else [2, 1, 0]
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assert len(words) == 4
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tokens = en_tokenizer(text + punct)
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assert len(list(words.sents)) == 1
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
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assert sum(len(sent) for sent in words.sents) == len(words)
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assert len(doc) == 4 if punct else 3
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assert len(list(doc.sents)) == 1
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assert sum(len(sent) for sent in doc.sents) == len(doc)
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@pytest.mark.models
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def test_parser_sentence_breaks(en_tokenizer, en_parser):
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def test_single_no_period(EN):
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text = "This is a sentence . This is another one ."
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string = 'A test sentence'
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heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3]
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words = EN(string)
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deps = ['nsubj', 'ROOT', 'det', 'attr', 'punct', 'nsubj', 'ROOT', 'det',
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assert len(words) == 3
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'attr', 'punct']
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assert len(list(words.sents)) == 1
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transition = ['L-nsubj', 'S', 'L-det', 'R-attr', 'D', 'R-punct', 'B-ROOT',
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assert sum(len(sent) for sent in words.sents) == len(words)
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'L-nsubj', 'S', 'L-attr', 'R-attr', 'D', 'R-punct']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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apply_transition_sequence(en_parser, doc, transition)
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@pytest.mark.models
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def test_single_exclamation(EN):
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string = 'A test sentence!'
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words = EN(string)
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assert len(words) == 4
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assert len(list(words.sents)) == 1
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assert sum(len(sent) for sent in words.sents) == len(words)
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@pytest.mark.models
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def test_single_question(EN):
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string = 'A test sentence?'
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words = EN(string, tag=False, parse=True)
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assert len(words) == 4
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assert len(list(words.sents)) == 1
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assert sum(len(sent) for sent in words.sents) == len(words)
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@pytest.mark.models
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def test_sentence_breaks(EN):
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doc = EN.tokenizer.tokens_from_list(u'This is a sentence . This is another one .'.split(' '))
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EN.tagger(doc)
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with EN.parser.step_through(doc) as stepwise:
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-det')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('D')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('B-ROOT')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-attr')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('D')
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct')
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assert len(list(doc.sents)) == 2
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assert len(list(doc.sents)) == 2
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for tok in doc:
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for token in doc:
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assert tok.dep != 0 or tok.is_space
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assert token.dep != 0 or token.is_space
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assert [ tok.head.i for tok in doc ] == [1,1,3,1,1,6,6,8,6,6]
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assert [token.head.i for token in doc ] == [1, 1, 3, 1, 1, 6, 6, 8, 6, 6]
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def apply_transition_sequence(model, doc, sequence):
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# Currently, there's no way of setting the serializer data for the parser
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with model.parser.step_through(doc) as stepwise:
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# without loading the models, so we can't remove the model dependency here yet.
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for transition in sequence:
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stepwise.transition(transition)
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@pytest.mark.models
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@pytest.mark.models
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def test_sbd_serialization_projective(EN):
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def test_parser_sbd_serialization_projective(EN):
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"""
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"""Test that before and after serialization, the sentence boundaries are
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test that before and after serialization, the sentence boundaries are the same.
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the same."""
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"""
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example = EN.tokenizer.tokens_from_list(u"I bought a couch from IKEA. It was n't very comfortable .".split(' '))
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EN.tagger(example)
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apply_transition_sequence(EN, example, ['L-nsubj','S','L-det','R-dobj','D','R-prep','R-pobj','B-ROOT','L-nsubj','R-neg','D','S','L-advmod','R-acomp','D','R-punct'])
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example_serialized = Doc(EN.vocab).from_bytes(example.to_bytes())
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assert example.to_bytes() == example_serialized.to_bytes()
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assert [s.text for s in example.sents] == [s.text for s in example_serialized.sents]
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# TODO:
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# @pytest.mark.models
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# def test_sbd_serialization_nonprojective(DE):
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# """
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# test that before and after serialization, the sentence boundaries are the same in a non-projective sentence.
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# """
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# example = EN.tokenizer.tokens_from_list(u"Den Mann hat Peter nicht gesehen . Er war zu langsam .".split(' '))
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# EN.tagger(example)
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# apply_transition_sequence(EN, example, ['L-nk','L-oa||oc','R-sb','D','S','L-ng','B-ROOT','L-nsubj','R-neg','D','S','L-advmod','R-acomp','D','R-punct'])
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# print [(t.dep_,t.head.i) for t in example]
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# example_serialized = Doc(EN.vocab).from_bytes(example.to_bytes())
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# assert example.to_bytes() == example_serialized.to_bytes()
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# assert [s.text for s in example.sents] == [s.text for s in example_serialized.sents]
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text = "I bought a couch from IKEA It wasn't very comfortable."
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transition = ['L-nsubj', 'S', 'L-det', 'R-dobj', 'D', 'R-prep', 'R-pobj',
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'B-ROOT', 'L-nsubj', 'R-neg', 'D', 'S', 'L-advmod',
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'R-acomp', 'D', 'R-punct']
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doc = EN.tokenizer(text)
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apply_transition_sequence(EN.parser, doc, transition)
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doc_serialized = Doc(EN.vocab).from_bytes(doc.to_bytes())
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assert doc.is_parsed == True
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assert doc_serialized.is_parsed == True
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assert doc.to_bytes() == doc_serialized.to_bytes()
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assert [s.text for s in doc.sents] == [s.text for s in doc_serialized.sents]
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