mirror of https://github.com/explosion/spaCy.git
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import numpy
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import pytest
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@pytest.fixture
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def example(EN):
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"""
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This is to make sure the model works as expected. The tests make sure that
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values are properly set. Tests are not meant to evaluate the content of the
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output, only make sure the output is formally okay.
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"""
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assert EN.entity != None
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return EN('There was a stranger standing at the big street talking to herself.')
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@pytest.mark.models('en')
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def test_en_models_tokenization(example):
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# tokenization should split the document into tokens
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assert len(example) > 1
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@pytest.mark.models('en')
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def test_en_models_tagging(example):
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# if tagging was done properly, pos tags shouldn't be empty
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assert example.is_tagged
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assert all(t.pos != 0 for t in example)
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assert all(t.tag != 0 for t in example)
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@pytest.mark.models('en')
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def test_en_models_parsing(example):
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# if parsing was done properly
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# - dependency labels shouldn't be empty
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# - the head of some tokens should not be root
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assert example.is_parsed
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assert all(t.dep != 0 for t in example)
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assert any(t.dep != i for i,t in enumerate(example))
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@pytest.mark.models('en')
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def test_en_models_ner(example):
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# if ner was done properly, ent_iob shouldn't be empty
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assert all([t.ent_iob != 0 for t in example])
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@pytest.mark.models('en')
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def test_en_models_vectors(example):
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# if vectors are available, they should differ on different words
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# this isn't a perfect test since this could in principle fail
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# in a sane model as well,
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# but that's very unlikely and a good indicator if something is wrong
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vector0 = example[0].vector
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vector1 = example[1].vector
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vector2 = example[2].vector
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assert not numpy.array_equal(vector0,vector1)
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assert not numpy.array_equal(vector0,vector2)
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assert not numpy.array_equal(vector1,vector2)
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@pytest.mark.xfail
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@pytest.mark.models('en')
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def test_en_models_probs(example):
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# if frequencies/probabilities are okay, they should differ for
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# different words
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# this isn't a perfect test since this could in principle fail
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# in a sane model as well,
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# but that's very unlikely and a good indicator if something is wrong
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prob0 = example[0].prob
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prob1 = example[1].prob
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prob2 = example[2].prob
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assert not prob0 == prob1
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assert not prob0 == prob2
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assert not prob1 == prob2
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