Anonymize the repo

Author: nabenabe0928

README.md

@@ -1,6 +1,6 @@
 # Meta-learning Tree-structured Parzen Estimator
 
-This package was used for the experiments of the paper `Multi-objective Tree-structured Parzen Estimator Meets Meta-learning`.
+This package was used for the experiments of the paper `Speeding up Multi-objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-structured Parzen Estimator`.
 
 ## Setup
 

viz/ablation_study.py

@@ -193,12 +193,12 @@ def plot_hv_for_hpolib(subplots_kwargs, legend_kwargs, hv_mode: bool) -> None:
         if PLOT_CHECK_MODE:
             plt.show()
         else:
-            plt.savefig("figs/hv2d-hpolib-ablation.png", bbox_inches='tight')
+            plt.savefig("figs/hv2d-hpolib-ablation.pdf", bbox_inches='tight')
     else:
         if PLOT_CHECK_MODE:
             plt.show()
         else:
-            plt.savefig("figs/eaf-hpolib-ablation.png", bbox_inches='tight')
+            plt.savefig("figs/eaf-hpolib-ablation.pdf", bbox_inches='tight')
 
 
 def plot_hv_for_nmt(subplots_kwargs, legend_kwargs, hv_mode: bool) -> None:
@@ -222,12 +222,12 @@ def plot_hv_for_nmt(subplots_kwargs, legend_kwargs, hv_mode: bool) -> None:
         if PLOT_CHECK_MODE:
             plt.show()
         else:
-            plt.savefig("figs/hv2d-nmt-ablation.png", bbox_inches='tight')
+            plt.savefig("figs/hv2d-nmt-ablation.pdf", bbox_inches='tight')
     else:
         if PLOT_CHECK_MODE:
             plt.show()
         else:
-            plt.savefig("figs/eaf-nmt-ablation.png", bbox_inches='tight')
+            plt.savefig("figs/eaf-nmt-ablation.pdf", bbox_inches='tight')
 
 
 if __name__ == "__main__":

viz/viz_dataset_dist.py

@@ -52,12 +52,12 @@ def plot_cum(ax: plt.Axes, nd_rank: Dict[str, np.ndarray], percentile: int, set_
             dataset_name = " to ".join([lang[s] for s in k.split("_")])
         ax.plot(np.arange(n_configs), np.cumsum(cnt), label=dataset_name, color=colors[i])
 
-    title = f"Cumulated count of Top-{percentile}% configuration"
+    title = f"Cumulative count of Top-{percentile}% configuration"
     ax.set_title(title)
     ax.set_xlabel("Config indices")
 
     if set_ylabel:
-        ax.set_ylabel("Cumulated count")
+        ax.set_ylabel("Cumulative count")
 
     ax.legend()
     ax.grid()