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73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
# coding: utf-8
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import pytest
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import numpy
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@pytest.mark.models
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class TestModelSanity:
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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.
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Tests are not meant to evaluate the content of the output, only make sure
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the output is formally okay.
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"""
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@pytest.fixture(scope='class', params=['en','de'])
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def example(self, request, EN, DE):
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assert EN.entity != None
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assert DE.entity != None
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if request.param == 'en':
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doc = EN(u'There was a stranger standing at the big ' +
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u'street talking to herself.')
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elif request.param == 'de':
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doc = DE(u'An der großen Straße stand eine merkwürdige ' +
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u'Gestalt und führte Selbstgespräche.')
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return doc
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def test_tokenization(self, example):
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# tokenization should split the document into tokens
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assert len(example) > 1
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def test_tagging(self, 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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def test_parsing(self, 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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def test_ner(self, 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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def test_vectors(self, 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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def test_probs(self, 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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