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optuna-dashboard/examples/preferential-optimization/evaluator.py
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Python

from __future__ import annotations
import os
import shutil
import tempfile
import time
from typing import Callable
from typing import NoReturn
import uuid
from optuna_dashboard.artifact.file_system import FileSystemBackend
from optuna_dashboard.preferential import load_study
import streamlit as st
STORAGE_URL = "sqlite:///st-example.db"
artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
artifact_backend = FileSystemBackend(base_path=artifact_path)
os.makedirs(artifact_path, exist_ok=True)
n_comparison = 5
def get_tmp_dir() -> str:
if "tmp_dir" not in st.session_state:
tmp_dir_name = str(uuid.uuid4())
tmp_dir_path = os.path.join(tempfile.gettempdir(), tmp_dir_name)
os.makedirs(tmp_dir_path, exist_ok=True)
st.session_state.tmp_dir = tmp_dir_path
return st.session_state.tmp_dir
def main() -> NoReturn:
tmpdir = get_tmp_dir()
study = load_study(
study_name="Preferential Optimization",
storage=STORAGE_URL,
)
# 1. Get all currently best trials (i.e. trials that are not reported bad) for comparison.
comparison_trials = study.best_trials
st.text("Which is the worst?")
# 2. 各TrialのArtifact画像を並べて表示
cols = st.columns(n_comparison)
finished_dict = {t.number: t for t in comparison_trials}
col_is: dict[int, int] = st.session_state.get("col_is")
if col_is is None:
col_is = {}
col_is = {tn: col_i for (tn, col_i) in col_is.items() if tn in finished_dict}
unoccupied_col_is = [i for i in range(len(cols)) if i not in col_is.values()]
for tn, col_i in zip([tn for tn in finished_dict if tn not in col_is], unoccupied_col_is):
col_is[tn] = col_i
st.session_state["col_is"] = col_is
def on_click_factory(trial_number: int) -> Callable[[], None]:
def on_click() -> None:
better_trials = [t for t in comparison_trials if t.number != trial_number]
worse_trial = finished_dict[trial_number]
study.report_preference(better_trials, worse_trial)
return on_click
for trial_number, col_i in col_is.items():
trial = finished_dict[trial_number]
col = cols[col_i]
rgb_artifact_id = trial.user_attrs.get("rgb_artifact_id")
image_caption = trial.user_attrs.get("image_caption")
with col:
with artifact_backend.open(rgb_artifact_id) as fsrc:
tmp_img_path = os.path.join(tmpdir, rgb_artifact_id + ".png")
with open(tmp_img_path, "wb") as fdst:
shutil.copyfileobj(fsrc, fdst)
st.image(tmp_img_path, caption=image_caption)
st.button(str(trial_number), key=trial.number, on_click=on_click_factory(trial_number))
for i, col in enumerate(st.columns(n_comparison)):
if i >= len(comparison_trials):
continue
if len(comparison_trials) < n_comparison:
time.sleep(0.1)
st.experimental_rerun()
if __name__ == "__main__":
main()