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51 lines
1.3 KiB
Python
51 lines
1.3 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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import pytest
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from spacy.language import Language
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from spacy.tokens import Span
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from ..util import get_doc
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@pytest.fixture
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def doc(en_tokenizer):
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text = "I like New York in Autumn."
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heads = [1, 0, 1, -2, -3, -1, -5]
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tags = ["PRP", "IN", "NNP", "NNP", "IN", "NNP", "."]
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pos = ["PRON", "VERB", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT"]
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deps = ["ROOT", "prep", "compound", "pobj", "prep", "pobj", "punct"]
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tokens = en_tokenizer(text)
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doc = get_doc(
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tokens.vocab,
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words=[t.text for t in tokens],
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heads=heads,
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tags=tags,
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pos=pos,
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deps=deps,
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)
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doc.ents = [Span(doc, 2, 4, doc.vocab.strings["GPE"])]
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doc.is_parsed = True
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doc.is_tagged = True
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return doc
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def test_factories_merge_noun_chunks(doc):
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assert len(doc) == 7
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nlp = Language()
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merge_noun_chunks = nlp.create_pipe("merge_noun_chunks")
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merge_noun_chunks(doc)
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assert len(doc) == 6
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assert doc[2].text == "New York"
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def test_factories_merge_ents(doc):
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assert len(doc) == 7
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assert len(list(doc.ents)) == 1
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nlp = Language()
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merge_entities = nlp.create_pipe("merge_entities")
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merge_entities(doc)
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assert len(doc) == 6
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assert len(list(doc.ents)) == 1
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assert doc[2].text == "New York"
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