mirror of
https://github.com/wassname/optuna-dashboard.git
synced 2026-08-21 11:18:45 +08:00
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
from __future__ import annotations
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from collections.abc import Callable
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import sys
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import optuna
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from optuna import create_trial
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from optuna.distributions import CategoricalDistribution
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from optuna.distributions import FloatDistribution
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from optuna.distributions import IntDistribution
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from optuna.samplers import BaseSampler
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from optuna.trial import TrialState
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from optuna_dashboard.preferential import create_study
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import pytest
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if sys.version_info >= (3, 8):
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from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler
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else:
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PreferentialGPSampler = None
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parametrize_sampler = pytest.mark.parametrize(
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"sampler_class",
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[
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optuna.samplers.RandomSampler,
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pytest.param(
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PreferentialGPSampler,
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marks=pytest.mark.skipif(
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sys.version_info < (3, 8), reason="BoTorch dropped Python3.7 support"
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),
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),
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],
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)
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@parametrize_sampler
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def test_sample_float(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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for i in range(5):
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past_trial = create_trial(
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state=TrialState.RUNNING,
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params={"x": 1.0},
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distributions={"x": FloatDistribution(0, 10)},
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)
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study.add_trial(past_trial)
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study.report_preference(study.trials[:-1], study.trials[-1])
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trial = study.ask()
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trial.suggest_float("x", 0, 10)
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@parametrize_sampler
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def test_sample_int(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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for i in range(5):
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past_trial = create_trial(
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state=TrialState.RUNNING,
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params={"x": 1},
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distributions={"x": IntDistribution(0, 10)},
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)
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study.add_trial(past_trial)
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study.report_preference(study.trials[:-1], study.trials[-1])
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trial = study.ask()
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trial.suggest_int("x", 0, 10)
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@parametrize_sampler
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def test_sample_categorical(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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for i in range(5):
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past_trial = create_trial(
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state=TrialState.RUNNING,
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params={"x": "A"},
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distributions={"x": CategoricalDistribution(["A", "B", "C"])},
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)
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study.add_trial(past_trial)
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study.report_preference(study.trials[:-1], study.trials[-1])
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trial = study.ask()
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trial.suggest_categorical("x", ["A", "B", "C"])
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@parametrize_sampler
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def test_sample_mixed(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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for i in range(5):
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past_trial = create_trial(
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state=TrialState.RUNNING,
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params={"x": 1.0, "y": 1, "z": "A"},
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distributions={
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"x": FloatDistribution(0, 10),
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"y": IntDistribution(0, 10),
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"z": CategoricalDistribution(["A", "B", "C"]),
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},
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)
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study.add_trial(past_trial)
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study.report_preference(study.trials[:-1], study.trials[-1])
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trial = study.ask()
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trial.suggest_float("x", 0, 10)
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trial.suggest_int("y", 0, 10)
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trial.suggest_categorical("z", ["A", "B", "C"])
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@parametrize_sampler
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def test_sample_first_trial(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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trial = study.ask()
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trial.suggest_float("x", 0, 10)
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@parametrize_sampler
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def test_sample_dynamic_search_space(sampler_class: Callable[[], BaseSampler]) -> None:
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study = create_study(n_generate=4, sampler=sampler_class())
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for i in range(5):
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past_trial = create_trial(
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state=TrialState.RUNNING,
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params={"x": 1.0},
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distributions={"x": FloatDistribution(0, 10)},
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)
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study.add_trial(past_trial)
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study.report_preference(study.trials[:-1], study.trials[-1])
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trial = study.ask()
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trial.suggest_float("x", -100, 100)
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