Apply yanase's comment

basic_and_faq_usages/simple_artifact_store.py

@@ -12,6 +12,13 @@
 
 dataset_path = "demo-dataset.csv"
 fig_name = "result-trial.png"
+# NOTE: The uploaded artifacts can be viewed in Optuna Dashboard with the following command:
+# $ optuna-dashboard sqlite:///artifact-demo.db --artifact-dir ./save-artifact-here
+base_path = os.path.join("./save-artifact-here")
+# Make the directory used for artifact store.
+os.makedirs(base_path, exist_ok=True)
+# Instantiate an artifact store.
+artifact_store = FileSystemArtifactStore(base_path=base_path)
 
 
 def create_dataset(dataset_path):
@@ -33,13 +40,14 @@ def plot_predictions(a, b, trial):
     x = np.linspace(0, 1, 100)
     ax.scatter(dataset["X"], dataset["Y"], label="Dataset", color="blue")
     ax.plot(x, a * x + b, label="Prediction", color="darkred")
+    ax.set_title(f"a={a:.2f}, b={b:.2f}")
     ax.grid()
     ax.legend()
     plt.savefig(fig_name)
     plt.close()
 
 
-def objective(trial, artifact_store):
+def objective(trial):
     a = trial.suggest_float("a", -5, 5)
     b = trial.suggest_float("b", -5, 5)
     plot_predictions(a, b, trial)
@@ -70,18 +78,11 @@ def show_best_result(study, artifact_store):
 
 
 if __name__ == "__main__":
-    # NOTE: The uploaded artifacts can be viewed in Optuna Dashboard with the following command:
-    # $ optuna-dashboard sqlite:///artifact-demo.db --artifact-dir ./save-artifact-here
     # Create a study with a SQLite storage.
     study = optuna.create_study(
         study_name="demo", storage="sqlite:///simple-artifact-store-demo.db", load_if_exists=True
     )
-    base_path = os.path.join("./save-artifact-here")
-    # Make the directory used for artifact store.
-    os.makedirs(base_path, exist_ok=True)
-    # Instantiate an artifact store.
-    artifact_store = FileSystemArtifactStore(base_path=base_path)
     # Upload the dataset to use by artifact store API.
     upload_artifact(artifact_store=artifact_store, file_path=dataset_path, study_or_trial=study)
-    study.optimize(lambda trial: objective(trial, artifact_store), n_trials=30)
+    study.optimize(objective, n_trials=30)
     show_best_result(study, artifact_store)