mirror of
https://github.com/explosion/spaCy.git
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177 lines
7.5 KiB
Python
177 lines
7.5 KiB
Python
from __future__ import unicode_literals
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import pytest
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from spacy.tokens import Doc
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@pytest.mark.models
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def test_single_period(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_no_period(EN):
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string = 'A test sentence'
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words = EN(string)
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assert len(words) == 3
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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_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=False)
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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_no_space(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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# stack empty, automatic Shift (This)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj') # attach This
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# stack empty, automatic Shift (is)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S') # shift a
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-det') # attach a
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr') # attach sentence
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stepwise.transition('D') # remove sentence
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct') # attach .
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('B-ROOT') # set sentence start on This
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# automatic reduction of the stack, automatic Shift to start second sentence
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj') # attach This
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# stack empty, automatic Shift (is)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S') # shift another
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-attr') # attach another
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr') # attach one
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('D') # remove one
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct') # attach .
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# buffer empty, automatic cleanup
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assert len(list(doc.sents)) == 2
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for tok in doc:
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assert tok.dep != 0 or tok.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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@pytest.mark.models
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def test_sentence_breaks_with_space(EN):
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doc = EN.tokenizer.tokens_from_list(u'\t This is \n a sentence \n \n . \n \t \n This is another \t 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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# stack empty, automatic Shift (This)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj') # attach This
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# stack empty, automatic Shift (is)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S') # shift a
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-det') # attach a
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr') # attach sentence
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stepwise.transition('D') # remove sentence
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct') # attach .
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('B-ROOT') # set sentence start on This
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# automatic reduction of the stack, automatic Shift to start second sentence
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-nsubj') # attach This
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# stack empty, automatic Shift (is)
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('S') # shift another
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('L-attr') # attach another
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-attr') # attach one
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assert EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('D') # remove one
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assert not EN.parser.moves.is_valid(stepwise.stcls,'B-ROOT')
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stepwise.transition('R-punct') # attach .
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# buffer empty, automatic cleanup
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assert len(list(doc.sents)) == 2
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for tok in doc:
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assert tok.dep != 0 or tok.is_space
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assert [ tok.head.i for tok in doc ] == [1,2,2,2,5,2,5,5,2,8,8,8,13,13,16,14,13,13]
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def apply_transition_sequence(model, doc, sequence):
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with model.parser.step_through(doc) as stepwise:
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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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def test_sbd_for_root_label_dependents(EN):
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"""
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make sure that the parser properly introduces a sentence boundary without
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the break transition by checking for dependents with the root label
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"""
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example = EN.tokenizer.tokens_from_list(u"I saw a firefly It glowed".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','S','L-nsubj','R-ROOT'])
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print ['%s/%s' % (t.dep_,t.head.i) for t in example]
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assert example[1].head.i == 1
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assert example[5].head.i == 5
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sents = list(example.sents)
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assert len(sents) == 2
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assert sents[1][0].orth_ == u'It'
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@pytest.mark.models
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def test_sbd_serialization(EN):
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"""
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test that before and after serialization, the sentence boundaries are the same even
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if the parser predicted two roots for the sentence that were made into two sentences
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after parsing by arc_eager.finalize()
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This is actually an interaction between the sentence boundary prediction and doc.from_array
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The process is the following: if the parser doesn't predict a sentence boundary but attaches
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a word with the ROOT label, the second root node is made root of its own sentence after parsing.
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During serialization, sentence boundary information is lost and reintroduced when the code
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is deserialized by introducing sentence starts at every left-edge of every root node.
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BUG that is tested here: So far, the parser wasn't introducing a sentence start when
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it introduced the second root node.
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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','D','D','S','L-nsubj','R-ROOT','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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