spaCy/spacy/tests/spans/test_span.py

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# coding: utf-8
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from __future__ import unicode_literals
from ..util import get_doc
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import pytest
@pytest.fixture
def doc(en_tokenizer):
text = "This is a sentence. This is another sentence. And a third."
heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3, 0, 1, -2, -1]
deps = ['nsubj', 'ROOT', 'det', 'attr', 'punct', 'nsubj', 'ROOT', 'det',
'attr', 'punct', 'ROOT', 'det', 'npadvmod', 'punct']
tokens = en_tokenizer(text)
return get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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def test_spans_sent_spans(doc):
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sents = list(doc.sents)
assert sents[0].start == 0
assert sents[0].end == 5
assert len(sents) == 3
assert sum(len(sent) for sent in sents) == len(doc)
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def test_spans_root(doc):
span = doc[2:4]
assert len(span) == 2
assert span.text == 'a sentence'
assert span.root.text == 'sentence'
assert span.root.head.text == 'is'
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def test_spans_string_fn(doc):
span = doc[0:4]
assert len(span) == 4
assert span.text == 'This is a sentence'
assert span.upper_ == 'THIS IS A SENTENCE'
assert span.lower_ == 'this is a sentence'
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def test_spans_root2(en_tokenizer):
text = "through North and South Carolina"
heads = [0, 3, -1, -2, -4]
tokens = en_tokenizer(text)
doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads)
assert doc[-2:].root.text == 'Carolina'
def test_spans_span_sent(doc):
"""Test span.sent property"""
assert len(list(doc.sents))
assert doc[:2].sent.root.text == 'is'
assert doc[:2].sent.text == 'This is a sentence .'
assert doc[6:7].sent.root.left_edge.text == 'This'
def test_spans_default_sentiment(en_tokenizer):
"""Test span.sentiment property's default averaging behaviour"""
text = "good stuff bad stuff"
tokens = en_tokenizer(text)
tokens.vocab[tokens[0].text].sentiment = 3.0
tokens.vocab[tokens[2].text].sentiment = -2.0
doc = get_doc(tokens.vocab, [t.text for t in tokens])
assert doc[:2].sentiment == 3.0 / 2
assert doc[-2:].sentiment == -2. / 2
assert doc[:-1].sentiment == (3.+-2) / 3.
def test_spans_override_sentiment(en_tokenizer):
"""Test span.sentiment property's default averaging behaviour"""
text = "good stuff bad stuff"
tokens = en_tokenizer(text)
tokens.vocab[tokens[0].text].sentiment = 3.0
tokens.vocab[tokens[2].text].sentiment = -2.0
doc = get_doc(tokens.vocab, [t.text for t in tokens])
doc.user_span_hooks['sentiment'] = lambda span: 10.0
assert doc[:2].sentiment == 10.0
assert doc[-2:].sentiment == 10.0
assert doc[:-1].sentiment == 10.0
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def test_spans_are_hashable(en_tokenizer):
"""Test spans can be hashed."""
text = "good stuff bad stuff"
tokens = en_tokenizer(text)
span1 = tokens[:2]
span2 = tokens[2:4]
assert hash(span1) != hash(span2)
span3 = tokens[0:2]
assert hash(span3) == hash(span1)