Remove duplicated management

basic_and_faq_usages/simple_artifact_store.py

@@ -11,6 +11,7 @@
 
 
 dataset_path = "demo-dataset.csv"
+fig_name = "result-trial.png"
 
 
 def create_dataset(dataset_path):
@@ -28,26 +29,23 @@ def create_dataset(dataset_path):
 
 def plot_predictions(a, b, trial):
     # Create an artifact, which is figure in this example, to upload.
-    os.makedirs("figs/", exist_ok=True)
     _, ax = plt.subplots()
-    fig_path = f"figs/result-trial{trial.number}.png"
     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.grid()
     ax.legend()
-    plt.savefig(fig_path)
+    plt.savefig(fig_name)
     plt.close()
-    return fig_path
 
 
 def objective(trial, artifact_store):
     a = trial.suggest_float("a", -5, 5)
     b = trial.suggest_float("b", -5, 5)
-    fig_path = plot_predictions(a, b, trial)
+    plot_predictions(a, b, trial)
 
     # Link the plotted figure with trial using artifact store API.
-    upload_artifact(artifact_store=artifact_store, file_path=fig_path, study_or_trial=trial)
+    upload_artifact(artifact_store=artifact_store, file_path=fig_name, study_or_trial=trial)
 
     return np.mean((a * dataset["X"] + b - dataset["Y"]) ** 2)
 
@@ -56,7 +54,7 @@ def show_best_result(study, artifact_store):
     best_trial = study.best_trial
     # Get all the artifact information linked to best_trial. (Here we have only one.)
     artifact_meta = get_all_artifact_meta(best_trial, storage=study._storage)
-    fig_path = "figs/result-best-trial.png"
+    fig_path = "./result-best-trial.png"
     # Download the figure from the artifact store to fig_path.
     download_artifact(
         artifact_store=artifact_store,