def test_int_log_uniform_distribution_deprecation() -> None: # step != 1 is deprecated d = distributions.IntLogUniformDistribution(low=1, high=100) with pytest.warns(FutureWarning): # `step` should always be assumed to be 1 and samplers and other components should never # have to get/set the attribute. assert d.step == 1 with pytest.warns(FutureWarning): d.step = 2 with pytest.warns(FutureWarning): d = distributions.IntLogUniformDistribution(low=1, high=100, step=2) with pytest.warns(FutureWarning): assert d.step == 2 with pytest.warns(FutureWarning): d.step = 1 assert d.step == 1 with pytest.warns(FutureWarning): d.step = 2 assert d.step == 2
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=()) with pytest.raises(ValueError): distributions.IntLogUniformDistribution(low=123, high=100) with pytest.raises(ValueError): distributions.IntLogUniformDistribution(low=123, high=100, step=2)
def test_single() -> None: with warnings.catch_warnings(): # UserWarning will be raised since the range is not divisible by step. warnings.simplefilter("ignore", category=UserWarning) single_distributions: List[distributions.BaseDistribution] = [ 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), ] for distribution in single_distributions: assert distribution.single() nonsingle_distributions: List[distributions.BaseDistribution] = [ 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), ] for distribution in nonsingle_distributions: assert not distribution.single()
def test_empty_range_contains() -> None: i = distributions.IntDistribution(low=1, high=1) assert not i._contains(0) assert i._contains(1) assert not i._contains(2) f = distributions.FloatDistribution(low=1.0, high=1.0) assert not f._contains(0.9) assert f._contains(1.0) assert not f._contains(1.1) fd = distributions.FloatDistribution(low=1.0, high=1.0, step=2.0) assert not fd._contains(0.9) assert fd._contains(1.0) assert not fd._contains(1.1) 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) iuq = distributions.IntUniformDistribution(low=1, high=1, step=2) assert not iuq._contains(0) assert iuq._contains(1) assert not iuq._contains(2) ilu = distributions.IntLogUniformDistribution(low=1, high=1) assert not ilu._contains(0) assert ilu._contains(1) assert not ilu._contains(2) iluq = distributions.IntLogUniformDistribution(low=1, high=1, step=2) assert not iluq._contains(0) assert iluq._contains(1) assert not iluq._contains(2)
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 suggest_int(self, name, low, high, step=1, log=False): # type: (str, int, int, int, bool) -> int if log: sample = self._suggest( name, distributions.IntLogUniformDistribution(low=low, high=high, step=step) ) else: sample = self._suggest( name, distributions.IntUniformDistribution(low=low, high=high, step=step) ) return int(sample)
def test_create_trial_distribution_conversion() -> None: fixed_params = { "ud": 0, "dud": 2, "lud": 1, "id": 0, "idd": 2, "ild": 1, } 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), } with pytest.warns( FutureWarning, match="See https://github.com/optuna/optuna/issues/2941", ) as record: trial = create_trial(params=fixed_params, distributions=fixed_distributions, value=1) 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_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_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 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_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)
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": '{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 9, "step": 2}}', "c1": '{"name": "CategoricalDistribution", "attributes": {"choices": [2.71, -Infinity]}}', "c2":
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_int_log_uniform_distribution_deprecation(): # type: () -> None # step != 1 is deprecated with pytest.warns(FutureWarning): distributions.IntLogUniformDistribution(low=1, high=100, step=2)
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) }
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))