import json
import pytest
from optuna import distributions
from optuna import types
if types.TYPE_CHECKING:
from typing import Any # NOQA
from typing import Dict # NOQA
EXAMPLE_DISTRIBUTIONS = {
'u': distributions.UniformDistribution(low=1., high=2.),
'l': distributions.LogUniformDistribution(low=0.001, high=100),
'du': distributions.DiscreteUniformDistribution(low=1., high=10., q=2.),
'iu': distributions.IntUniformDistribution(low=1, high=10),
'c1': distributions.CategoricalDistribution(choices=(2.71, -float('inf'))),
'c2': distributions.CategoricalDistribution(choices=('Roppongi', 'Azabu'))
} # type: Dict[str, Any]
EXAMPLE_JSONS = {
'u':
'{"name": "UniformDistribution", "attributes": {"low": 1.0, "high": 2.0}}',
'l':
'{"name": "LogUniformDistribution", "attributes": {"low": 0.001, "high": 100}}',
'du':
'{"name": "DiscreteUniformDistribution",'
'"attributes": {"low": 1.0, "high": 10.0, "q": 2.0}}',
'iu':
'{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 10}}',
'c1':
'{"name": "CategoricalDistribution", "attributes": {"choices": [2.71, -Infinity]}}',
import copy
import json
from typing import Any
from typing import Dict
from typing import List
import warnings
import pytest
from optuna import distributions
EXAMPLE_DISTRIBUTIONS: Dict[str, Any] = {
"u": distributions.UniformDistribution(low=1.0, high=2.0),
"l": distributions.LogUniformDistribution(low=0.001, high=100),
"du": distributions.DiscreteUniformDistribution(low=1.0, high=9.0, q=2.0),
"iu": distributions.IntUniformDistribution(low=1, high=9, step=2),
"c1": distributions.CategoricalDistribution(choices=(2.71, -float("inf"))),
"c2": distributions.CategoricalDistribution(choices=("Roppongi", "Azabu")),
"c3": distributions.CategoricalDistribution(choices=["Roppongi", "Azabu"]),
"ilu": distributions.IntLogUniformDistribution(low=2, high=12, step=2),
}
EXAMPLE_JSONS = {
"u": '{"name": "UniformDistribution", "attributes": {"low": 1.0, "high": 2.0}}',
"l": '{"name": "LogUniformDistribution", "attributes": {"low": 0.001, "high": 100}}',
"du": '{"name": "DiscreteUniformDistribution",'
'"attributes": {"low": 1.0, "high": 9.0, "q": 2.0}}',
"iu": '{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 9, "step": 2}}',
"c1": '{"name": "CategoricalDistribution", "attributes": {"choices": [2.71, -Infinity]}}',
"c2": '{"name": "CategoricalDistribution", "attributes": {"choices": ["Roppongi", "Azabu"]}}',
def test_contains() -> None:
u = distributions.UniformDistribution(low=1.0, high=2.0)
assert not u._contains(0.9)
assert u._contains(1)
assert u._contains(1.5)
assert not u._contains(2)
lu = distributions.LogUniformDistribution(low=0.001, high=100)
assert not lu._contains(0.0)
assert lu._contains(0.001)
assert lu._contains(12.3)
assert not lu._contains(100)
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by 2.
# The range will be replaced with [1.0, 9.0].
warnings.simplefilter("ignore", category=UserWarning)
du = distributions.DiscreteUniformDistribution(low=1.0,
high=10.0,
q=2.0)
assert not du._contains(0.9)
assert du._contains(1.0)
assert du._contains(3.5)
assert du._contains(6)
assert du._contains(9)
assert not du._contains(9.1)
assert not du._contains(10)
iu = distributions.IntUniformDistribution(low=1, high=10)
assert not iu._contains(0.9)
assert iu._contains(1)
assert iu._contains(4)
assert iu._contains(6)
assert iu._contains(10)
assert not iu._contains(10.1)
assert not iu._contains(11)
# IntUniformDistribution with a 'step' parameter.
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by 2.
# The range will be replaced with [1, 9].
warnings.simplefilter("ignore", category=UserWarning)
iuq = distributions.IntUniformDistribution(low=1, high=10, step=2)
assert not iuq._contains(0.9)
assert iuq._contains(1)
assert iuq._contains(4)
assert iuq._contains(6)
assert iuq._contains(9)
assert not iuq._contains(9.1)
assert not iuq._contains(10)
c = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu"))
assert not c._contains(-1)
assert c._contains(0)
assert c._contains(1)
assert c._contains(1.5)
assert not c._contains(3)
ilu = distributions.IntUniformDistribution(low=2, high=12)
assert not ilu._contains(0.9)
assert ilu._contains(2)
assert ilu._contains(4)
assert ilu._contains(6)
assert ilu._contains(12)
assert not ilu._contains(12.1)
assert not ilu._contains(13)
iluq = distributions.IntLogUniformDistribution(low=2, high=7)
assert not iluq._contains(0.9)
assert iluq._contains(2)
assert iluq._contains(4)
assert iluq._contains(5)
assert iluq._contains(6)
assert not iluq._contains(7.1)
assert not iluq._contains(8)
def suggest_uniform(self, name, low, high):
# type: (str, float, float) -> float
return self._suggest(
name, distributions.UniformDistribution(low=low, high=high))
def test_group() -> None:
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = TPESampler(multivariate=True, group=True)
study = optuna.create_study(sampler=sampler)
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=2)
assert mock.call_count == 1
assert study.trials[-1].distributions == {
"x": distributions.IntUniformDistribution(low=0, high=10)
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10) + t.suggest_float("z", -3, 3), n_trials=1
)
assert mock.call_count == 1
assert study.trials[-1].distributions == {
"y": distributions.IntUniformDistribution(low=0, high=10),
"z": distributions.UniformDistribution(low=-3, high=3),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10)
+ t.suggest_float("z", -3, 3)
+ t.suggest_float("u", 1e-2, 1e2, log=True)
+ bool(t.suggest_categorical("v", ["A", "B", "C"])),
n_trials=1,
)
assert mock.call_count == 2
assert study.trials[-1].distributions == {
"u": distributions.LogUniformDistribution(low=1e-2, high=1e2),
"v": distributions.CategoricalDistribution(choices=["A", "B", "C"]),
"y": distributions.IntUniformDistribution(low=0, high=10),
"z": distributions.UniformDistribution(low=-3, high=3),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_float("u", 1e-2, 1e2, log=True), n_trials=1)
assert mock.call_count == 3
assert study.trials[-1].distributions == {
"u": distributions.LogUniformDistribution(low=1e-2, high=1e2)
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(
lambda t: t.suggest_int("y", 0, 10) + t.suggest_int("w", 2, 8, log=True), n_trials=1
)
assert mock.call_count == 4
assert study.trials[-1].distributions == {
"y": distributions.IntUniformDistribution(low=0, high=10),
"w": distributions.IntLogUniformDistribution(low=2, high=8),
}
with patch.object(sampler, "_sample_relative", wraps=sampler._sample_relative) as mock:
study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=1)
assert mock.call_count == 6
assert study.trials[-1].distributions == {
"x": distributions.IntUniformDistribution(low=0, high=10)
}
from typing import Dict
from typing import List
from unittest.mock import patch
import numpy as np
import pytest
from optuna import distributions
from optuna.samplers._tpe.parzen_estimator import _ParzenEstimator
from optuna.samplers._tpe.parzen_estimator import _ParzenEstimatorParameters
from optuna.samplers._tpe.sampler import default_weights
SEARCH_SPACE = {
"a": distributions.UniformDistribution(1.0, 100.0),
"b": distributions.LogUniformDistribution(1.0, 100.0),
"c": distributions.DiscreteUniformDistribution(1.0, 100.0, 3.0),
"d": distributions.IntUniformDistribution(1, 100),
"e": distributions.IntLogUniformDistribution(1, 100),
"f": distributions.CategoricalDistribution(["x", "y", "z"]),
}
MULTIVARIATE_SAMPLES = {
"a": np.array([1.0]),
"b": np.array([1.0]),
"c": np.array([1.0]),
"d": np.array([1]),
"e": np.array([1]),
"f": np.array([1]),
}
_PRECOMPUTE_SIGMAS0 = "optuna.samplers._tpe.parzen_estimator._ParzenEstimator._precompute_sigmas0"
def test_check_distribution_compatibility() -> None:
# test the same distribution
for key in EXAMPLE_JSONS:
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS[key], EXAMPLE_DISTRIBUTIONS[key])
# test different distribution classes
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["i"], EXAMPLE_DISTRIBUTIONS["fl"]),
)
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["u"], EXAMPLE_DISTRIBUTIONS["l"]),
)
# test compatibility between IntDistributions.
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["i"],
EXAMPLE_DISTRIBUTIONS["il"])
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["il"],
EXAMPLE_DISTRIBUTIONS["id"])
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["id"],
EXAMPLE_DISTRIBUTIONS["i"])
# test compatibility between FloatDistributions.
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["f"],
EXAMPLE_DISTRIBUTIONS["fl"])
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["fl"],
EXAMPLE_DISTRIBUTIONS["fd"])
distributions.check_distribution_compatibility(EXAMPLE_DISTRIBUTIONS["fd"],
EXAMPLE_DISTRIBUTIONS["f"])
# test dynamic value range (CategoricalDistribution)
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["c2"],
distributions.CategoricalDistribution(choices=("Roppongi",
"Akasaka")),
),
)
# test dynamic value range (except CategoricalDistribution)
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["i"],
distributions.IntDistribution(low=-3, high=2))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["il"],
distributions.IntDistribution(low=1, high=13, log=True))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["id"],
distributions.IntDistribution(low=-3, high=2, step=2))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["f"],
distributions.FloatDistribution(low=-3.0, high=-2.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["fl"],
distributions.FloatDistribution(low=0.1, high=1.0, log=True))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["fd"],
distributions.FloatDistribution(low=-1.0, high=11.0, step=0.5))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["u"],
distributions.UniformDistribution(low=-3.0, high=-2.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["l"],
distributions.LogUniformDistribution(low=0.1, high=1.0))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["du"],
distributions.DiscreteUniformDistribution(low=-1.0, high=11.0, q=3.0),
)
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["iu"],
distributions.IntUniformDistribution(low=-1, high=1))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["iuq"],
distributions.IntUniformDistribution(low=-1, high=1))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["ilu"],
distributions.IntLogUniformDistribution(low=1, high=13))
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["iluq"],
distributions.IntLogUniformDistribution(low=1, high=13))
