mirror of https://github.com/explosion/spaCy.git
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ..util import get_doc
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import pytest
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@pytest.fixture
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def doc(en_tokenizer):
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text = "This is a sentence. This is another sentence. And a third."
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heads = [1, 0, 1, -2, -3, 1, 0, 1, -2, -3, 0, 1, -2, -1]
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deps = ['nsubj', 'ROOT', 'det', 'attr', 'punct', 'nsubj', 'ROOT', 'det',
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'attr', 'punct', 'ROOT', 'det', 'npadvmod', 'punct']
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tokens = en_tokenizer(text)
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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)
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assert sents[0].start == 0
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assert sents[0].end == 5
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assert len(sents) == 3
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assert sum(len(sent) for sent in sents) == len(doc)
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def test_spans_root(doc):
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span = doc[2:4]
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assert len(span) == 2
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assert span.text == 'a sentence'
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assert span.root.text == 'sentence'
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assert span.root.head.text == 'is'
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def test_spans_root2(en_tokenizer):
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text = "through North and South Carolina"
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heads = [0, 3, -1, -2, -4]
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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)
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assert doc[-2:].root.text == 'Carolina'
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def test_spans_span_sent(doc):
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"""Test span.sent property"""
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assert len(list(doc.sents))
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assert doc[:2].sent.root.text == 'is'
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assert doc[:2].sent.text == 'This is a sentence .'
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assert doc[6:7].sent.root.left_edge.text == 'This'
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def test_spans_default_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = get_doc(tokens.vocab, [t.text for t in tokens])
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assert doc[:2].sentiment == 3.0 / 2
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assert doc[-2:].sentiment == -2. / 2
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assert doc[:-1].sentiment == (3.+-2) / 3.
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def test_spans_override_sentiment(en_tokenizer):
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"""Test span.sentiment property's default averaging behaviour"""
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text = "good stuff bad stuff"
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tokens = en_tokenizer(text)
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tokens.vocab[tokens[0].text].sentiment = 3.0
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tokens.vocab[tokens[2].text].sentiment = -2.0
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doc = get_doc(tokens.vocab, [t.text for t in tokens])
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doc.user_span_hooks['sentiment'] = lambda span: 10.0
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assert doc[:2].sentiment == 10.0
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assert doc[-2:].sentiment == 10.0
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assert doc[:-1].sentiment == 10.0
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