def test_dummy():
scorer = DummyScorer()
pp = PartialParse((Time(), Interval()), ("rule1", "rule2"))
assert scorer.score("a", datetime.datetime(2019, 1, 1), pp) == 0.0
assert scorer.score_final("a", datetime.datetime(2019, 1, 1), pp,
pp.prod[0]) == 0.0
def test_random():
rng = random.Random(42)
scorer = RandomScorer(rng)
pp = PartialParse((Time(), Interval()), ("rule1", "rule2"))
assert 0.0 <= scorer.score("a", datetime.datetime(2019, 1, 1), pp) <= 1.0
assert (0.0 <= scorer.score_final("a", datetime.datetime(2019, 1, 1), pp,
pp.prod[1]) <= 1.0)
def test_partial_parse() -> None:
match_a = regex.match("(?<R1>a)", "ab")
match_b = next(regex.finditer("(?<R2>b)", "ab"))
pp = PartialParse.from_regex_matches(
(RegexMatch(1, match_a), RegexMatch(2, match_b)))
assert len(pp.prod) == 2
assert len(pp.rules) == 2
assert isinstance(pp.score, float)
def mock_rule(ts: datetime.datetime, a: Time) -> Time:
return Time()
pp2 = pp.apply_rule(datetime.datetime(day=1, month=1, year=2015),
mock_rule, 'mock_rule', (0, 1))
assert pp != pp2
with pytest.raises(ValueError):
PartialParse((), ())
def test_nbscorer():
# We only test that it runs just fine
X = [("a", "b"), ("a", ), ("b"), ("a", "b", "a", "b")]
y = [False, True, True, False]
model = train_naive_bayes(X, y)
scorer = NaiveBayesScorer(model)
pp = PartialParse((Time(), Interval()), ("rule1", "rule2"))
pp.prod[0].mstart = 0
pp.prod[1].mend = 1
pp.prod[0].mend = 1
pp.prod[1].mend = 2
assert 0.0 <= scorer.score("ab", datetime.datetime(2019, 1, 1), pp) <= 1.0
assert (0.0 <= scorer.score_final("ab", datetime.datetime(2019, 1, 1), pp,
pp.prod[1]) <= 1.0)