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# built-in
from math import isclose
# external
import hypothesis
import pytest
# project
import textdistance
ALGS = (
textdistance.arith_ncd,
textdistance.bwtrle_ncd,
textdistance.bz2_ncd,
# too slow, makes CI flaky
# textdistance.lzma_ncd,
textdistance.rle_ncd,
textdistance.zlib_ncd,
textdistance.sqrt_ncd,
textdistance.entropy_ncd,
)
@pytest.mark.parametrize('alg', ALGS)
def test_monotonicity(alg):
same = alg('test', 'test')
similar = alg('test', 'text')
diffirent = alg('test', 'nani')
assert same <= similar <= diffirent
@pytest.mark.parametrize('alg', ALGS)
@hypothesis.given(
left=hypothesis.strategies.text(),
right=hypothesis.strategies.text(),
)
def test_simmetry(left, right, alg):
assert alg.similarity(left, right) == alg.similarity(right, left)
assert alg.distance(left, right) == alg.distance(right, left)
assert alg.normalized_similarity(left, right) == alg.normalized_similarity(right, left)
assert alg.normalized_distance(left, right) == alg.normalized_distance(right, left)
@pytest.mark.parametrize('alg', ALGS)
@hypothesis.given(
left=hypothesis.strategies.text(),
right=hypothesis.strategies.text(),
)
def test_is_normalized(left, right, alg):
a = alg(left, right)
d = alg.distance(left, right)
nd = alg.normalized_distance(left, right)
assert a == d == nd
@pytest.mark.parametrize('alg', ALGS)
@hypothesis.given(
left=hypothesis.strategies.text(),
right=hypothesis.strategies.text(),
)
def test_normalized_by_one(left, right, alg):
s = alg.normalized_similarity(left, right)
d = alg.normalized_distance(left, right)
assert isclose(s + d, 1)
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