mirror of https://github.com/explosion/spaCy.git
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from ....tokens.doc import Doc
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@pytest.fixture
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def en_lemmatizer(EN):
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return EN.Defaults.create_lemmatizer()
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@pytest.mark.models('en')
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def test_doc_lemmatization(EN):
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doc = Doc(EN.vocab, words=['bleed'])
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doc[0].tag_ = 'VBP'
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assert doc[0].lemma_ == 'bleed'
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@pytest.mark.models('en')
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@pytest.mark.parametrize('text,lemmas', [("aardwolves", ["aardwolf"]),
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("aardwolf", ["aardwolf"]),
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("planets", ["planet"]),
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("ring", ["ring"]),
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("axes", ["axis", "axe", "ax"])])
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def test_en_lemmatizer_noun_lemmas(en_lemmatizer, text, lemmas):
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assert en_lemmatizer.noun(text) == lemmas
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@pytest.mark.models('en')
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@pytest.mark.parametrize('text,lemmas', [("bleed", ["bleed"]),
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("feed", ["feed"]),
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("need", ["need"]),
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("ring", ["ring"])])
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def test_en_lemmatizer_noun_lemmas(en_lemmatizer, text, lemmas):
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# Cases like this are problematic -- not clear what we should do to resolve
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# ambiguity?
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# ("axes", ["ax", "axes", "axis"])])
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assert en_lemmatizer.noun(text) == lemmas
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@pytest.mark.xfail
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@pytest.mark.models('en')
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def test_en_lemmatizer_base_forms(en_lemmatizer):
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assert en_lemmatizer.noun('dive', {'number': 'sing'}) == ['dive']
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assert en_lemmatizer.noun('dive', {'number': 'plur'}) == ['diva']
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@pytest.mark.models('en')
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def test_en_lemmatizer_base_form_verb(en_lemmatizer):
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assert en_lemmatizer.verb('saw', {'verbform': 'past'}) == ['see']
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@pytest.mark.models('en')
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def test_en_lemmatizer_punct(en_lemmatizer):
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assert en_lemmatizer.punct('“') == ['"']
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assert en_lemmatizer.punct('“') == ['"']
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@pytest.mark.models('en')
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def test_en_lemmatizer_lemma_assignment(EN):
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text = "Bananas in pyjamas are geese."
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doc = EN.make_doc(text)
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EN.tagger(doc)
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assert all(t.lemma_ != '' for t in doc)
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