def test_categorical_distribution_different_sequence_types(): # type: () -> None c1 = distributions.CategoricalDistribution(choices=("Roppongi", "Azabu")) c2 = distributions.CategoricalDistribution(choices=["Roppongi", "Azabu"]) assert c1 == c2
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_single(): # type: () -> None 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", )), ] # 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")), ] # type: List[distributions.BaseDistribution] for distribution in nonsingle_distributions: assert not distribution.single()
def test_single(): # type: () -> None 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.IntUniformDistribution(low=-123, high=-123), distributions.CategoricalDistribution(choices=('foo', )) ] # type: List[distributions.BaseDistribution] for distribution in single_distributions: assert distribution.single() nonsingle_distributions = [ distributions.UniformDistribution(low=0.0, high=-100.0), distributions.UniformDistribution(low=1.0, high=1.001), distributions.LogUniformDistribution(low=7.3, high=7.2), distributions.LogUniformDistribution(low=7.3, high=10), distributions.DiscreteUniformDistribution(low=-30, high=-40, q=3), distributions.DiscreteUniformDistribution(low=-30, high=-20, q=2), distributions.IntUniformDistribution(low=123, high=100), distributions.IntUniformDistribution(low=-123, high=0), distributions.CategoricalDistribution(choices=()), distributions.CategoricalDistribution(choices=('foo', 'bar')) ] # type: List[distributions.BaseDistribution] for distribution in nonsingle_distributions: assert not distribution.single()
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_convert_old_distribution_to_new_distribution_noop() -> None: # No conversion happens for CategoricalDistribution. cd = distributions.CategoricalDistribution(choices=["a", "b", "c"]) assert distributions._convert_old_distribution_to_new_distribution( cd) == cd # No conversion happens for new distributions. fd = distributions.FloatDistribution(low=0, high=10, log=False, step=None) assert distributions._convert_old_distribution_to_new_distribution( fd) == fd dfd = distributions.FloatDistribution(low=0, high=10, log=False, step=2) assert distributions._convert_old_distribution_to_new_distribution( dfd) == dfd lfd = distributions.FloatDistribution(low=1, high=10, log=True, step=None) assert distributions._convert_old_distribution_to_new_distribution( lfd) == lfd id = distributions.IntDistribution(low=0, high=10) assert distributions._convert_old_distribution_to_new_distribution( id) == id idd = distributions.IntDistribution(low=0, high=10, step=2) assert distributions._convert_old_distribution_to_new_distribution( idd) == idd ild = distributions.IntDistribution(low=1, high=10, log=True) assert distributions._convert_old_distribution_to_new_distribution( ild) == ild
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=10.0, q=3.)) distributions.check_distribution_compatibility( EXAMPLE_DISTRIBUTIONS['iu'], distributions.IntUniformDistribution(low=-1, high=1))
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_relative_sampling(storage_mode: str, comm: CommunicatorBase) -> None: relative_search_space = { "x": distributions.UniformDistribution(low=-10, high=10), "y": distributions.LogUniformDistribution(low=20, high=30), "z": distributions.CategoricalDistribution(choices=(-1.0, 1.0)), } relative_params = {"x": 1.0, "y": 25.0, "z": -1.0} sampler = DeterministicRelativeSampler( relative_search_space, relative_params # type: ignore ) with MultiNodeStorageSupplier(storage_mode, comm) as storage: study = TestChainerMNStudy._create_shared_study(storage, comm, sampler=sampler) mn_study = ChainerMNStudy(study, comm) # Invoke optimize. n_trials = 20 func = Func() mn_study.optimize(func, n_trials=n_trials) # Assert trial counts. assert len(mn_study.trials) == n_trials # Assert the parameters in `relative_params` have been suggested among all nodes. for trial in mn_study.trials: assert trial.params == relative_params
def suggest_categorical(self, name, choices): # type: (str, Sequence[T]) -> T """Suggest a value for the categorical parameter. The value is sampled from ``choices``. Example: Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/ sklearn.svm.SVC.html>`_. .. code:: >>> def objective(trial): >>> ... >>> kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf']) >>> clf = sklearn.svm.SVC(kernel=kernel) >>> ... Args: name: A parameter name. choices: Candidates of parameter values. Returns: A suggested value. """ choices = tuple(choices) return self._suggest(name, distributions.CategoricalDistribution(choices=choices))
def test_ask_distribution_conversion_noop() -> None: fixed_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), "cd": distributions.CategoricalDistribution(choices=["a", "b", "c"]), } study = create_study() trial = study.ask(fixed_distributions=fixed_distributions) # Check fixed_distributions doesn't change. assert trial.distributions == fixed_distributions
def test_invalid_distribution(): # type: () -> None with pytest.warns(UserWarning): distributions.CategoricalDistribution(choices=({ "foo": "bar" }, )) # type: ignore
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_categorical_contains() -> None: 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_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.IntDistribution(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.IntDistribution(low=0, high=10), "z": distributions.FloatDistribution(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.FloatDistribution(low=1e-2, high=1e2, log=True), "v": distributions.CategoricalDistribution(choices=["A", "B", "C"]), "y": distributions.IntDistribution(low=0, high=10), "z": distributions.FloatDistribution(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.FloatDistribution(low=1e-2, high=1e2, log=True) } 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.IntDistribution(low=0, high=10), "w": distributions.IntDistribution(low=2, high=8, log=True), } 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.IntDistribution(low=0, high=10)}
def suggest_categorical(self, name, choices): # type: (str, Sequence[CategoricalChoiceType]) -> CategoricalChoiceType """Suggest a value for the categorical parameter. The value is sampled from ``choices``. Example: Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/ sklearn.svm.SVC.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.svm import SVC def objective(trial): kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf']) clf = SVC(kernel=kernel, gamma='scale', 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. choices: Parameter value candidates. .. seealso:: :class:`~optuna.distributions.CategoricalDistribution`. Returns: A suggested value. """ choices = tuple(choices) # There is no need to call self._check_distribution because # CategoricalDistribution does not support dynamic value space. return self._suggest( name, distributions.CategoricalDistribution(choices=choices))
def test_distributions(storage_mode: str, comm: CommunicatorBase) -> None: with MultiNodeStorageSupplier(storage_mode, comm) as storage: study = TestChainerMNStudy._create_shared_study(storage, comm) mn_trial = _create_new_chainermn_trial(study, comm) mn_trial.suggest_categorical("x", [1]) assert mn_trial.distributions == { "x": distributions.CategoricalDistribution(choices=(1, )) }
def test_ask_fixed_search_space() -> None: fixed_distributions = { "x": distributions.UniformDistribution(0, 1), "y": distributions.CategoricalDistribution(["bacon", "spam"]), } study = create_study() trial = study.ask(fixed_distributions=fixed_distributions) params = trial.params assert len(trial.params) == 2 assert 0 <= params["x"] < 1 assert params["y"] in ["bacon", "spam"]
def test_distributions(storage_mode, cache_mode, comm): # type: (str, bool, CommunicatorBase) -> None with MultiNodeStorageSupplier(storage_mode, cache_mode, comm) as storage: study = TestChainerMNStudy._create_shared_study(storage, comm) trial_id = storage.create_new_trial_id(study.study_id) trial = Trial(study, trial_id) mn_trial = integration.chainermn._ChainerMNTrial(trial, comm) mn_trial.suggest_categorical('x', [1]) assert mn_trial.distributions == { 'x': distributions.CategoricalDistribution(choices=(1, )) }
def test_optuna_search_invalid_param_dist() -> None: X, y = make_blobs(n_samples=10) est = KernelDensity() param_dist = ["kernel", distributions.CategoricalDistribution(("gaussian", "linear"))] with pytest.raises(TypeError, match="param_distributions must be a dictionary."): integration.OptunaSearchCV( est, param_dist, # type: ignore cv=3, error_score="raise", random_state=0, return_train_score=True, )
def test_contains(): # type: () -> 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) 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(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(4) assert iu._contains(6) assert iu._contains(10) assert not iu._contains(10.1) assert not iu._contains(11) # IntUniformDistribution with a 'q' parameter. 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(10) assert not iuq._contains(10.1) assert not iuq._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_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_optuna_search_invalid_param_dist(): # type: () -> None X, y = make_blobs(n_samples=10) est = KernelDensity() param_dist = [ 'kernel', distributions.CategoricalDistribution(('gaussian', 'linear')) ] optuna_search = integration.OptunaSearchCV( est, param_dist, # type: ignore cv=3, error_score='raise', random_state=0, return_train_score=True) with pytest.raises(ValueError, match='param_distributions must be a dictionary.'): optuna_search.fit(X)
def suggest_categorical(self, name, choices): # type: (str, Sequence[CategoricalChoiceType]) -> CategoricalChoiceType """Suggest a value for the categorical parameter. The value is sampled from ``choices``. Example: Suggest a kernel function of `SVC <https://scikit-learn.org/stable/modules/generated/ sklearn.svm.SVC.html>`_. .. code:: >>> def objective(trial): >>> ... >>> kernel = trial.suggest_categorical('kernel', ['linear', 'poly', 'rbf']) >>> clf = sklearn.svm.SVC(kernel=kernel) >>> ... Args: name: A parameter name. choices: Parameter value candidates. .. seealso:: :class:`~optuna.distributions.CategoricalDistribution`. Returns: A suggested value. """ choices = tuple(choices) # There is no need to call self._check_distribution because # CategoricalDistribution does not support dynamic value space. return self._suggest( name, distributions.CategoricalDistribution(choices=choices))
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_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_create_trial_distribution_conversion_noop() -> None: fixed_params = { "ud": 0, "dud": 2, "lud": 1, "id": 0, "idd": 2, "ild": 1, "cd": "a", } fixed_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), "cd": distributions.CategoricalDistribution(choices=["a", "b", "c"]), } trial = create_trial(params=fixed_params, distributions=fixed_distributions, value=1) # Check fixed_distributions doesn't change. assert trial.distributions == fixed_distributions
def suggest_categorical(self, name, choices): # type: (str, Sequence[T]) -> T choices = tuple(choices) return self._suggest(name, distributions.CategoricalDistribution(choices=choices))
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 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": '{"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 9, "step": 2}}',