mirror of
https://github.com/wassname/optuna-dashboard.git
synced 2026-09-18 12:40:09 +08:00
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
from __future__ import annotations
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import os
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import tempfile
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import textwrap
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import time
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from typing import NoReturn
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from optuna_dashboard import save_note
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from optuna_dashboard.artifact import get_artifact_path
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from optuna_dashboard.artifact import upload_artifact
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from optuna_dashboard.artifact.file_system import FileSystemBackend
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from optuna_dashboard.preferential import create_study
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from optuna_dashboard.preferential.samplers.gp import PreferentialGPSampler
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from PIL import Image
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STORAGE_URL = "sqlite:///example.db"
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artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
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artifact_backend = FileSystemBackend(base_path=artifact_path)
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os.makedirs(artifact_path, exist_ok=True)
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def main() -> NoReturn:
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study = create_study(
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n_generate=5,
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study_name="Preferential Optimization",
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storage=STORAGE_URL,
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sampler=PreferentialGPSampler(),
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load_if_exists=True,
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)
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with tempfile.TemporaryDirectory() as tmpdir:
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while True:
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# If n_comparison "best" trials (that are not reported bad) exists,
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# the generator waits for human evaluation.
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if not study.should_generate():
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time.sleep(0.1) # Avoid busy-loop
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continue
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trial = study.ask()
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# 1. Ask new parameters
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r = trial.suggest_int("r", 0, 255)
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g = trial.suggest_int("g", 0, 255)
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b = trial.suggest_int("b", 0, 255)
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# 2. Generate image
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image_path = os.path.join(tmpdir, f"sample-{trial.number}.png")
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image = Image.new("RGB", (320, 240), color=(r, g, b))
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image.save(image_path)
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# 3. Upload Artifact
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artifact_id = upload_artifact(artifact_backend, trial, image_path)
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trial.set_user_attr("artifact_id", artifact_id)
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print("RGB:", (r, g, b))
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# 4. Save Note
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note = textwrap.dedent(
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f"""\
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})
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(R, G, B) = ({r}, {g}, {b})
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"""
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)
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save_note(trial, note)
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if __name__ == "__main__":
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main()
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