from optuna import distributions
from optuna import samplers
from optuna import storages
from optuna.study import create_study
from optuna.testing.sampler import DeterministicRelativeSampler
from optuna.trial import FixedTrial
from optuna.trial import Trial
from optuna import type_checking
if type_checking.TYPE_CHECKING:
import typing # NOQA
parametrize_storage = pytest.mark.parametrize(
'storage_init_func',
[storages.InMemoryStorage, lambda: storages.RDBStorage('sqlite:///:memory:')])
@parametrize_storage
def test_suggest_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_independent', 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.UniformDistribution(low=0., high=3.)
from optuna.study import create_study
from optuna.testing.integration import DeterministicPruner
from optuna.testing.sampler import DeterministicRelativeSampler
from optuna.trial import FixedTrial
from optuna.trial import Trial
from optuna import type_checking
if type_checking.TYPE_CHECKING:
from datetime import datetime # NOQA
import typing # NOQA
parametrize_storage = pytest.mark.parametrize(
"storage_init_func",
[
storages.InMemoryStorage,
lambda: storages.RDBStorage("sqlite:///:memory:")
],
)
@parametrize_storage
def test_check_distribution_suggest_float(storage_init_func):
# type: (typing.Callable[[], storages.BaseStorage]) -> None
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
x1 = trial.suggest_float("x1", 10, 20)
x2 = trial.suggest_uniform("x1", 10, 20)
from optuna import samplers
from optuna import storages
from optuna.study import create_study
from optuna.testing.integration import DeterministicPruner
from optuna.testing.sampler import DeterministicRelativeSampler
from optuna.trial._frozen import create_trial
from optuna.trial import BaseTrial
from optuna.trial import FixedTrial
from optuna.trial import FrozenTrial
from optuna.trial import Trial
from optuna.trial import TrialState
parametrize_storage = pytest.mark.parametrize(
"storage_init_func",
[storages.InMemoryStorage, lambda: storages.RDBStorage("sqlite:///:memory:")],
)
@parametrize_storage
def test_check_distribution_suggest_float(
storage_init_func: Callable[[], storages.BaseStorage]
) -> None:
sampler = samplers.RandomSampler()
study = create_study(storage_init_func(), sampler=sampler)
trial = Trial(study, study._storage.create_new_trial(study._study_id))
x1 = trial.suggest_float("x1", 10, 20)
x2 = trial.suggest_uniform("x1", 10, 20)
- Removes old training-related tables (e.g., losses)
- Sets up fresh Optuna tables ready for new studies
"""
training_tables = [
"studies", "version_info", "study_user_attributes",
"study_system_attributes", "trials", "trial_user_attributes",
"trial_system_attributes", "trial_params", "trial_values",
"alembic_version", "predictions", "training_summaries", "best_trials",
"evaluation_metrics", "shap_values"
]
DBNAME = snakemake.params["dbname"]
DBUSER = snakemake.params["dbuser"]
DBSCHEMA = snakemake.params["dbschema"]
psql_url = f"postgresql://{DBUSER}@dbserver/{DBNAME}?options=-c%20search_path={DBSCHEMA}"
engine = create_engine(psql_url)
with engine.connect() as connection:
q = " ".join(f"DROP TABLE IF EXISTS {DBSCHEMA}.{table} CASCADE;"
for table in training_tables)
connection.execute(text("BEGIN; " + q +
"COMMIT;")) # remove training-related tables
storages.RDBStorage(url=psql_url,
engine_kwargs={"pool_size":
0}) # initiate empty Optuna tables
with open(snakemake.output[0], "w") as f:
f.write(str(datetime.now()))
