def normalization_interval_condition_test(p, min, max, scale_min,
scale_max):
condition = IntervalCondition(p=p, min=min, max=max)
assert (condition.normalize(scale_min=scale_min,
scale_max=scale_max).denormalize(
scale_min=scale_min,
scale_max=scale_max) == condition)
def test_weights_mixture():
conditions = [
IntervalCondition(p=0.4, max=1, weight=0.01),
IntervalCondition(p=0.5, max=2, weight=100),
IntervalCondition(p=0.8, max=2.2, weight=0.01),
IntervalCondition(p=0.9, max=2.3, weight=0.01),
]
dist = LogisticMixture.from_conditions(conditions,
num_components=1,
verbose=True)
assert dist.components[0].loc == pytest.approx(2, rel=0.1)
def test_mixture_from_percentiles():
conditions = [
IntervalCondition(p=0.1, max=1),
IntervalCondition(p=0.5, max=2),
IntervalCondition(p=0.6, max=3),
]
dist = LogisticMixture.from_conditions(conditions,
num_components=3,
verbose=True)
for condition in conditions:
assert dist.cdf(condition.max) == pytest.approx(condition.p, rel=0.1)
def test_normalization_interval_condition():
def normalization_interval_condition_test(p, min, max, scale_min,
scale_max):
condition = IntervalCondition(p=p, min=min, max=max)
assert (condition.normalize(scale_min=scale_min,
scale_max=scale_max).denormalize(
scale_min=scale_min,
scale_max=scale_max) == condition)
# straightforward scenario
normalization_interval_condition_test(p=0.5,
min=10,
max=100,
scale_min=10,
scale_max=1000)
# left open
normalization_interval_condition_test(p=0.5,
min=None,
max=10000,
scale_min=10,
scale_max=1000)
# right open
normalization_interval_condition_test(p=0.5,
min=10,
max=None,
scale_min=10,
scale_max=1000)
# negative values
normalization_interval_condition_test(p=0.5,
min=-1000,
max=-100,
scale_min=-10000,
scale_max=-1000)
# p = 1
normalization_interval_condition_test(p=1,
min=10,
max=100,
scale_min=10,
scale_max=1000)
# interval bigger than scale
normalization_interval_condition_test(p=1,
min=0,
max=1000,
scale_min=10,
scale_max=100)
assert IntervalCondition(p=0.5, min=0, max=5).normalize(
scale_min=0, scale_max=10) == IntervalCondition(p=0.5, min=0, max=0.5)
def test_mixture_from_percentile():
for value in [0.01, 0.1, 1, 3]:
conditions = [IntervalCondition(p=0.5, max=value)]
dist = LogisticMixture.from_conditions(conditions,
num_components=1,
verbose=True)
loc = dist.components[0].loc
assert loc == pytest.approx(value, rel=0.1), loc
def test_interval_condition():
dist = Uniform(min=-1, max=1)
assert IntervalCondition(p=0.5, min=0, max=1).loss(dist) == 0
assert IntervalCondition(p=0.25, min=0, max=1).loss(dist) == 0.25**2
assert IntervalCondition(p=1, max=0).loss(dist) == 0.5**2
assert IntervalCondition(p=1).loss(dist) == 0
assert IntervalCondition(p=0, min=-1, max=1).loss(dist) == 1
assert IntervalCondition(p=0, min=-1, max=1, weight=10).loss(dist) == 10
assert (IntervalCondition(p=0.25, min=0,
max=1).describe_fit(dist)["loss"] == 0.25**2)
assert (IntervalCondition(
p=0, min=-1, max=0).describe_fit(dist)["p_in_interval"] == 0.5)
assert (IntervalCondition(
p=1, min=-1, max=0).describe_fit(dist)["p_in_interval"] == 0.5)
def test_most_likely_outcome_condition():
base_conditions = [IntervalCondition(p=0.4, max=0.5)]
base_dist = HistogramDist.from_conditions(base_conditions, verbose=True)
# Most likely condition should increase chance of specified outcome
outcome_conditions = base_conditions + [
MostLikelyOutcomeCondition(outcome=0.25)
]
outcome_dist = HistogramDist.from_conditions(outcome_conditions,
verbose=True)
assert outcome_dist.pdf(0.25) > base_dist.pdf(0.25)
# Highly weighted most likely condition should make specified outcome most likely
strong_condition = MostLikelyOutcomeCondition(outcome=0.25, weight=1000)
strong_outcome_conditions = base_conditions + [strong_condition]
strong_outcome_dist = HistogramDist.from_conditions(
strong_outcome_conditions, verbose=True)
assert strong_condition.loss(strong_outcome_dist) == 0
def test_percentile_roundtrip():
conditions = [
IntervalCondition(p=0.01, max=0.61081324517545),
IntervalCondition(p=0.1, max=0.8613634657212543),
IntervalCondition(p=0.25, max=1),
IntervalCondition(p=0.5, max=1.5),
IntervalCondition(p=0.75, max=2),
IntervalCondition(p=0.9, max=2.1386364698410034),
IntervalCondition(p=0.99, max=2.3891870975494385),
]
mixture = LogisticMixture.from_conditions(conditions,
num_components=3,
verbose=True)
recovered_conditions = mixture.percentiles(
percentiles=[condition.p for condition in conditions])
for (condition, recovered_condition) in zip(conditions,
recovered_conditions):
assert recovered_condition.max == pytest.approx(condition.max, rel=0.1)
def test_hist_from_percentile():
for value in [0.01, 0.1, 0.5, 0.9]:
conditions = [IntervalCondition(p=0.5, max=value)]
dist = HistogramDist.from_conditions(conditions)
assert dist.ppf(0.5) == pytest.approx(value, abs=0.1)
def test_mixed_2(histogram):
conditions = (
HistogramCondition(histogram["xs"], histogram["densities"]),
IntervalCondition(p=0.4, max=1),
IntervalCondition(p=0.45, max=1.2),
IntervalCondition(p=0.48, max=1.3),
IntervalCondition(p=0.5, max=2),
IntervalCondition(p=0.7, max=2.2),
IntervalCondition(p=0.9, max=2.3),
)
dist = LogisticMixture.from_conditions(conditions,
num_components=3,
verbose=True)
assert dist.pdf1(-5) == pytest.approx(0, abs=0.1)
assert dist.pdf1(6) == pytest.approx(0, abs=0.1)
my_cache = {}
my_cache[conditions] = 3
conditions_2 = (
HistogramCondition(histogram["xs"], histogram["densities"]),
IntervalCondition(p=0.4, max=1),
IntervalCondition(p=0.45, max=1.2),
IntervalCondition(p=0.48, max=1.3),
IntervalCondition(p=0.5, max=2),
IntervalCondition(p=0.7, max=2.2),
IntervalCondition(p=0.9, max=2.3),
)
assert hash(conditions) == hash(conditions_2)
assert my_cache[conditions_2] == 3
def single_interval_condition_cases():
for lower_bound in [None, 0.3]:
for upper_bound in [None, 0.9]:
yield IntervalCondition(p=0.5, min=lower_bound, max=upper_bound)