from __future__ import annotations import os import tempfile import textwrap import time from typing import NoReturn from optuna.artifacts import FileSystemArtifactStore from optuna.artifacts import upload_artifact from optuna_dashboard import save_note from optuna_dashboard.artifact import get_artifact_path from optuna_dashboard.preferential import create_study from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler from PIL import Image STORAGE_URL = "sqlite:///example.db" artifact_path = os.path.join(os.path.dirname(__file__), "artifact") artifact_store = FileSystemArtifactStore(base_path=artifact_path) os.makedirs(artifact_path, exist_ok=True) def main() -> NoReturn: study = create_study( n_generate=5, study_name="Preferential Optimization", storage=STORAGE_URL, sampler=PreferentialGPSampler(), load_if_exists=True, ) with tempfile.TemporaryDirectory() as tmpdir: while True: # If study.should_generate() returns False, # the generator waits for human evaluation. if not study.should_generate(): time.sleep(0.1) # Avoid busy-loop continue trial = study.ask() # 1. Ask new parameters r = trial.suggest_int("r", 0, 255) g = trial.suggest_int("g", 0, 255) b = trial.suggest_int("b", 0, 255) # 2. Generate image image_path = os.path.join(tmpdir, f"sample-{trial.number}.png") image = Image.new("RGB", (320, 240), color=(r, g, b)) image.save(image_path) # 3. Upload Artifact artifact_id = upload_artifact(trial, image_path, artifact_store) trial.set_user_attr("artifact_id", artifact_id) print("RGB:", (r, g, b)) # 4. Save Note note = textwrap.dedent( f"""\ ![generated-image]({get_artifact_path(trial, artifact_id)}) (R, G, B) = ({r}, {g}, {b}) """ ) save_note(trial, note) if __name__ == "__main__": main()