format

examples/preferential-optimization/evaluator.py

@@ -9,10 +9,10 @@ from typing import NoReturn
 import uuid
 
 from optuna_dashboard.artifact.file_system import FileSystemBackend
-import streamlit as st
-
 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")

examples/preferential-optimization/generator.py

@@ -2,20 +2,16 @@ from __future__ import annotations
 
 import os
 import tempfile
+import textwrap
 import time
-from time import sleep
 from typing import NoReturn
 
-import optuna
+from optuna_dashboard import save_note
 from optuna_dashboard.artifact import upload_artifact
 from optuna_dashboard.artifact.file_system import FileSystemBackend
-from PIL import Image
-
 from optuna_dashboard.preferential import create_study
 from optuna_dashboard.preferential.samplers._gp import PreferentialGPSampler
-
-from optuna_dashboard import save_note
-import textwrap
+from PIL import Image
 
 
 STORAGE_URL = "sqlite:///st-example.db"
@@ -58,7 +54,6 @@ def main() -> NoReturn:
             trial.set_user_attr("image_caption", f"(R, G, B) = ({r}, {g}, {b})")
             print("RGB:", (r, g, b))
 
-
             # 4. Save Note
             note = textwrap.dedent(
                 f"""\

optuna_dashboard/preferential/samplers/_gp.py

@@ -260,7 +260,10 @@ class _PreferentialGP(GPyTorchModel, ExactGP):
 
 
 def _set_params(
-    module: gpytorch.Module, params_dict: dict[str, torch.Tensor], memo: set | None=None, prefix: str=""
+    module: gpytorch.Module,
+    params_dict: dict[str, torch.Tensor],
+    memo: set | None = None,
+    prefix: str = "",
 ) -> None:
     if memo is None:
         memo = set()