def test_optuna_search_convert_deprecated_distribution() -> None:
param_dist = {
"ud": distributions.UniformDistribution(low=0, high=10),
"dud": distributions.DiscreteUniformDistribution(low=0, high=10, q=2),
"lud": distributions.LogUniformDistribution(low=1, high=10),
"id": distributions.IntUniformDistribution(low=0, high=10),
"idd": distributions.IntUniformDistribution(low=0, high=10, step=2),
"ild": distributions.IntLogUniformDistribution(low=1, high=10),
}
expected_param_dist = {
"ud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=None),
"dud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=2),
"lud": distributions.FloatDistribution(low=1,
high=10,
log=True,
step=None),
"id": distributions.IntDistribution(low=0, high=10, log=False, step=1),
"idd": distributions.IntDistribution(low=0, high=10, log=False,
step=2),
"ild": distributions.IntDistribution(low=1, high=10, log=True, step=1),
}
optuna_search = integration.OptunaSearchCV(
KernelDensity(),
param_dist,
)
assert optuna_search.param_distributions == expected_param_dist
# It confirms that ask doesn't convert non-deprecated distributions.
optuna_search = integration.OptunaSearchCV(
KernelDensity(),
expected_param_dist,
)
assert optuna_search.param_distributions == expected_param_dist
def test_check_distribution_compatibility():
# type: () -> None
# test the same distribution
for key in EXAMPLE_JSONS.keys():
distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS[key], EXAMPLE_DISTRIBUTIONS[key])
# test different distribution classes
pytest.raises(
ValueError,
lambda: distributions.check_distribution_compatibility(
EXAMPLE_DISTRIBUTIONS["u"], EXAMPLE_DISTRIBUTIONS["l"]),
)
# 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["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["ilu"],
distributions.IntLogUniformDistribution(low=1, high=13),
)
def test_empty_distribution():
# type: () -> None
# Empty distributions cannot be instantiated.
with pytest.raises(ValueError):
distributions.UniformDistribution(low=0.0, high=-100.0)
with pytest.raises(ValueError):
distributions.LogUniformDistribution(low=7.3, high=7.2)
with pytest.raises(ValueError):
distributions.DiscreteUniformDistribution(low=-30, high=-40, q=3)
with pytest.raises(ValueError):
distributions.IntUniformDistribution(low=123, high=100)
with pytest.raises(ValueError):
distributions.IntUniformDistribution(low=123, high=100, step=2)
with pytest.raises(ValueError):
distributions.CategoricalDistribution(choices=())
def test_ask_distribution_conversion() -> None:
fixed_distributions = {
"ud": distributions.UniformDistribution(low=0, high=10),
"dud": distributions.DiscreteUniformDistribution(low=0, high=10, q=2),
"lud": distributions.LogUniformDistribution(low=1, high=10),
"id": distributions.IntUniformDistribution(low=0, high=10),
"idd": distributions.IntUniformDistribution(low=0, high=10, step=2),
"ild": distributions.IntLogUniformDistribution(low=1, high=10),
}
study = create_study()
with pytest.warns(
FutureWarning,
match="See https://github.com/optuna/optuna/issues/2941",
) as record:
trial = study.ask(fixed_distributions=fixed_distributions)
assert len(record) == 6
expected_distributions = {
"ud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=None),
"dud": distributions.FloatDistribution(low=0,
high=10,
log=False,
step=2),
"lud": distributions.FloatDistribution(low=1,
high=10,
log=True,
step=None),
"id": distributions.IntDistribution(low=0, high=10, log=False, step=1),
"idd": distributions.IntDistribution(low=0, high=10, log=False,
step=2),
"ild": distributions.IntDistribution(low=1, high=10, log=True, step=1),
}
assert trial.distributions == expected_distributions
def test_empty_range_contains():
# type: () -> None
u = distributions.UniformDistribution(low=1.0, high=1.0)
assert not u._contains(0.9)
assert u._contains(1.0)
assert not u._contains(1.1)
lu = distributions.LogUniformDistribution(low=1.0, high=1.0)
assert not lu._contains(0.9)
assert lu._contains(1.0)
assert not lu._contains(1.1)
du = distributions.DiscreteUniformDistribution(low=1.0, high=1.0, q=2.0)
assert not du._contains(0.9)
assert du._contains(1.0)
assert not du._contains(1.1)
iu = distributions.IntUniformDistribution(low=1, high=1)
assert not iu._contains(0)
assert iu._contains(1)
assert not iu._contains(2)
def test_suggest_discrete_uniform(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
mock = Mock()
mock.side_effect = [1., 2., 3.]
sampler = samplers.RandomSampler()
with patch.object(sampler, 'sample', mock) as mock_object:
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study.storage.create_new_trial_id(study.study_id))
distribution = distributions.DiscreteUniformDistribution(low=0.,
high=3.,
q=1.)
assert trial._suggest('x',
distribution) == 1. # Test suggesting a param.
assert trial._suggest(
'x', distribution) == 1. # Test suggesting the same param.
assert trial._suggest(
'y', distribution) == 3. # Test suggesting a different param.
assert trial.params == {'x': 1., 'y': 3.}
assert mock_object.call_count == 3
def suggest_discrete_uniform(self, name, low, high, q):
# type: (str, float, float, float) -> float
"""Suggest a value for the discrete parameter.
The value is sampled from the range ``[low, high]``, and the step of discretization is
``q``.
Example:
Suggest a fraction of samples used for fitting the individual learners of
`GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/
sklearn.ensemble.GradientBoostingClassifier.html>`_.
.. code::
>>> def objective(trial):
>>> ...
>>> subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1)
>>> clf = sklearn.ensemble.GradientBoostingClassifier(subsample=subsample)
>>> ...
Args:
name:
A parameter name.
low:
Lower endpoint of the range of suggested values. ``low`` is included in the range.
high:
Upper endpoint of the range of suggested values. ``high`` is included in the range.
q:
A step of discretization.
Returns:
A suggested float value.
"""
discrete = distributions.DiscreteUniformDistribution(low=low,
high=high,
q=q)
return self._suggest(name, discrete)
def test_suggest_discrete_uniform(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
mock = Mock()
mock.side_effect = [1.0, 2.0, 3.0]
sampler = samplers.RandomSampler()
with patch.object(sampler, "sample_independent", mock) as mock_object:
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
distribution = distributions.DiscreteUniformDistribution(low=0.0,
high=3.0,
q=1.0)
assert trial._suggest("x",
distribution) == 1.0 # Test suggesting a param.
assert trial._suggest(
"x", distribution) == 1.0 # Test suggesting the same param.
assert trial._suggest(
"y", distribution) == 3.0 # Test suggesting a different param.
assert trial.params == {"x": 1.0, "y": 3.0}
assert mock_object.call_count == 3
def test_contains():
# type: () -> None
u = distributions.UniformDistribution(low=1., high=2.)
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)
du = distributions.DiscreteUniformDistribution(low=1., high=10., q=2.)
assert not du._contains(0.9)
assert du._contains(1.0)
assert du._contains(3.5)
assert du._contains(6)
assert du._contains(10)
assert not du._contains(10.1)
iu = distributions.IntUniformDistribution(low=1, high=10)
assert not iu._contains(0.9)
assert iu._contains(1)
assert iu._contains(3.5)
assert iu._contains(6)
assert iu._contains(10)
assert iu._contains(10.1)
assert not iu._contains(11)
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)
def test_infer_relative_search_space() -> None:
sampler = TPESampler()
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.IntUniformDistribution(0, 100, step=2),
"f": distributions.IntLogUniformDistribution(1, 100),
"g": distributions.CategoricalDistribution(["x", "y", "z"]),
}
def obj(t: Trial) -> float:
t.suggest_uniform("a", 1.0, 100.0)
t.suggest_loguniform("b", 1.0, 100.0)
t.suggest_discrete_uniform("c", 1.0, 100.0, 3.0)
t.suggest_int("d", 1, 100)
t.suggest_int("e", 0, 100, step=2)
t.suggest_int("f", 1, 100, log=True)
t.suggest_categorical("g", ["x", "y", "z"])
return 0.0
# Study and frozen-trial are not supposed to be accessed.
study1 = Mock(spec=[])
frozen_trial = Mock(spec=[])
assert sampler.infer_relative_search_space(study1, frozen_trial) == {}
study2 = optuna.create_study(sampler=sampler)
study2.optimize(obj, n_trials=1)
assert sampler.infer_relative_search_space(study2, study2.best_trial) == {}
with warnings.catch_warnings():
warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
sampler = TPESampler(multivariate=True)
study3 = optuna.create_study(sampler=sampler)
study3.optimize(obj, n_trials=1)
assert sampler.infer_relative_search_space(
study3, study3.best_trial) == search_space
def suggest_float(self, name, low, high, *, log=False, step=None):
# type: (str, float, float, bool, Optional[float]) -> float
if step is not None:
if log:
raise NotImplementedError(
"The parameter `step` is not supported when `log` is True."
)
else:
return self._suggest(
name,
distributions.DiscreteUniformDistribution(low=low,
high=high,
q=step))
else:
if log:
return self._suggest(
name,
distributions.LogUniformDistribution(low=low, high=high))
else:
return self._suggest(
name, distributions.UniformDistribution(low=low,
high=high))
def test_convert_old_distribution_to_new_distribution() -> None:
ud = distributions.UniformDistribution(low=0, high=10)
assert distributions._convert_old_distribution_to_new_distribution(
ud) == distributions.FloatDistribution(low=0,
high=10,
log=False,
step=None)
dud = distributions.DiscreteUniformDistribution(low=0, high=10, q=2)
assert distributions._convert_old_distribution_to_new_distribution(
dud) == distributions.FloatDistribution(low=0,
high=10,
log=False,
step=2)
lud = distributions.LogUniformDistribution(low=1, high=10)
assert distributions._convert_old_distribution_to_new_distribution(
lud) == distributions.FloatDistribution(low=1,
high=10,
log=True,
step=None)
id = distributions.IntUniformDistribution(low=0, high=10)
assert distributions._convert_old_distribution_to_new_distribution(
id) == distributions.IntDistribution(low=0, high=10, log=False, step=1)
idd = distributions.IntUniformDistribution(low=0, high=10, step=2)
assert distributions._convert_old_distribution_to_new_distribution(
idd) == distributions.IntDistribution(low=0,
high=10,
log=False,
step=2)
ild = distributions.IntLogUniformDistribution(low=1, high=10)
assert distributions._convert_old_distribution_to_new_distribution(
ild) == distributions.IntDistribution(low=1, high=10, log=True, step=1)
def test_distributions(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
def objective(trial):
# type: (Trial) -> float
trial.suggest_uniform("a", 0, 10)
trial.suggest_loguniform("b", 0.1, 10)
trial.suggest_discrete_uniform("c", 0, 10, 1)
trial.suggest_int("d", 0, 10)
trial.suggest_categorical("e", ["foo", "bar", "baz"])
return 1.0
study = create_study(storage_init_func())
study.optimize(objective, n_trials=1)
assert study.best_trial.distributions == {
"a": distributions.UniformDistribution(low=0, high=10),
"b": distributions.LogUniformDistribution(low=0.1, high=10),
"c": distributions.DiscreteUniformDistribution(low=0, high=10, q=1),
"d": distributions.IntUniformDistribution(low=0, high=10),
"e": distributions.CategoricalDistribution(choices=("foo", "bar", "baz")),
}
def test_distributions(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
def objective(trial):
# type: (Trial) -> float
trial.suggest_uniform('a', 0, 10)
trial.suggest_loguniform('b', 0.1, 10)
trial.suggest_discrete_uniform('c', 0, 10, 1)
trial.suggest_int('d', 0, 10)
trial.suggest_categorical('e', ['foo', 'bar', 'baz'])
return 1.0
study = create_study(storage_init_func())
study.optimize(objective, n_trials=1)
assert study.best_trial.distributions == {
'a': distributions.UniformDistribution(low=0, high=10),
'b': distributions.LogUniformDistribution(low=0.1, high=10),
'c': distributions.DiscreteUniformDistribution(low=0, high=10, q=1),
'd': distributions.IntUniformDistribution(low=0, high=10),
'e': distributions.CategoricalDistribution(choices=('foo', 'bar', 'baz'))
}
def test_single():
# type: () -> None
with warnings.catch_warnings():
# UserWarning will be raised since the range is not divisible by step.
warnings.simplefilter("ignore", category=UserWarning)
single_distributions = [
distributions.UniformDistribution(low=1.0, high=1.0),
distributions.LogUniformDistribution(low=7.3, high=7.3),
distributions.DiscreteUniformDistribution(low=2.22,
high=2.22,
q=0.1),
distributions.DiscreteUniformDistribution(low=2.22,
high=2.24,
q=0.3),
distributions.IntUniformDistribution(low=-123, high=-123),
distributions.IntUniformDistribution(low=-123, high=-120, step=4),
distributions.CategoricalDistribution(choices=("foo", )),
distributions.IntLogUniformDistribution(low=2, high=2),
distributions.IntLogUniformDistribution(low=2, high=2, step=2),
] # type: List[distributions.BaseDistribution]
for distribution in single_distributions:
assert distribution.single()
nonsingle_distributions = [
distributions.UniformDistribution(low=1.0, high=1.001),
distributions.LogUniformDistribution(low=7.3, high=10),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2),
distributions.DiscreteUniformDistribution(low=-30, high=-20, q=10),
# In Python, "0.3 - 0.2 != 0.1" is True.
distributions.DiscreteUniformDistribution(low=0.2, high=0.3, q=0.1),
distributions.DiscreteUniformDistribution(low=0.7, high=0.8, q=0.1),
distributions.IntUniformDistribution(low=-123, high=0),
distributions.IntUniformDistribution(low=-123, high=0, step=123),
distributions.CategoricalDistribution(choices=("foo", "bar")),
distributions.IntLogUniformDistribution(low=2, high=4),
distributions.IntLogUniformDistribution(low=2, high=4, step=2),
] # type: List[distributions.BaseDistribution]
for distribution in nonsingle_distributions:
assert not distribution.single()
def suggest_discrete_uniform(self, name, low, high, q):
# type: (str, float, float, float) -> float
high = _adjust_discrete_uniform_high(name, low, high, q)
discrete = distributions.DiscreteUniformDistribution(low=low, high=high, q=q)
return self._suggest(name, discrete)
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)
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 = {
"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),
} # 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": 9.0, "q": 2.0}}',
"iu":
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))
def suggest_discrete_uniform(self, name: str, low: float, high: float, q: float) -> float:
discrete = distributions.DiscreteUniformDistribution(low=low, high=high, q=q)
return self._suggest(name, discrete)
import json
import pytest
from optuna import distributions
from optuna import type_checking
if type_checking.TYPE_CHECKING:
from typing import Any # NOQA
from typing import Dict # NOQA
from typing import List # 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]}}',
'c2': '{"name": "CategoricalDistribution", "attributes": {"choices": ["Roppongi", "Azabu"]}}'
}
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 suggest_discrete_uniform(self, name, low, high, q):
# type: (str, float, float, float) -> float
"""Suggest a value for the discrete parameter.
The value is sampled from the range :math:`[\\mathsf{low}, \\mathsf{high}]`,
and the step of discretization is :math:`q`. More specifically,
this method returns one of the values in the sequence
:math:`\\mathsf{low}, \\mathsf{low} + q, \\mathsf{low} + 2 q, \\dots,
\\mathsf{low} + k q \\le \\mathsf{high}`,
where :math:`k` denotes an integer. Note that :math:`high` may be changed due to round-off
errors if :math:`q` is not an integer. Please check warning messages to find the changed
values.
Example:
Suggest a fraction of samples used for fitting the individual learners of
`GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/
sklearn.ensemble.GradientBoostingClassifier.html>`_.
.. testsetup::
import numpy as np
from sklearn.model_selection import train_test_split
np.random.seed(seed=0)
X = np.random.randn(50).reshape(-1, 1)
y = np.random.randint(0, 2, 50)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)
.. testcode::
import optuna
from sklearn.ensemble import GradientBoostingClassifier
def objective(trial):
subsample = trial.suggest_discrete_uniform('subsample', 0.1, 1.0, 0.1)
clf = GradientBoostingClassifier(subsample=subsample, random_state=0)
clf.fit(X_train, y_train)
return clf.score(X_valid, y_valid)
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=3)
Args:
name:
A parameter name.
low:
Lower endpoint of the range of suggested values. ``low`` is included in the range.
high:
Upper endpoint of the range of suggested values. ``high`` is included in the range.
q:
A step of discretization.
Returns:
A suggested float value.
"""
high = _adjust_discrete_uniform_high(name, low, high, q)
distribution = distributions.DiscreteUniformDistribution(low=low,
high=high,
q=q)
self._check_distribution(name, distribution)
if low == high:
return self._set_new_param_or_get_existing(name, low, distribution)
return self._suggest(name, distribution)
