[WIP] Update

analysis/plot_results.py

@@ -90,6 +90,11 @@ def get_args():
     parser_ling_dims.add_argument("--input_path", type=Path, required=True, help="Path to the results file.")
     parser_ling_dims.add_argument("--langdata", type=Path, required=True, help="Path to the language data file.")
     parser_ling_dims.add_argument("--top_n", type=int, required=False, default=None, help="Aggregate only the scores for top-n.")
+
+    parser_translate = subparsers.add_parser("translate", help="Plot translation quality.", parents=[shared_args])
+    parser_translate.add_argument("--gtrans", type=Path, required=True, help="Path to the Google Translate results file.")
+    parser_translate.add_argument("--nllb", type=Path, required=True, help="Path to the NLLB-3.3B results file.")
+
     # fmt: on
     return parser.parse_args()
 
@@ -101,6 +106,7 @@ def main():
         "main_heatmap": plot_main_heatmap,
         "eng_drop_line": plot_eng_drop_line,
         "ling_dims": plot_ling_dims,
+        "translate": plot_translate,
     }
 
     def _filter_args(func, kwargs):
@@ -318,5 +324,75 @@ def plot_ling_dims(
     fig.savefig(output_path, bbox_inches="tight")
 
 
+def plot_translate(
+    gtrans: Path,
+    nllb: Path,
+    output_path: Path,
+    figsize: Optional[tuple[int, int]] = (18, 5),
+):
+    columns = ["Model", "Model_Type", "Avg_Multilingual"]
+    gtrans_df = pd.read_csv(gtrans)[columns].rename(columns={"Avg_Multilingual": "Avg_Gtrans"})
+    nllb_df = pd.read_csv(nllb)[columns].rename(columns={"Avg_Multilingual": "Avg_NLLB"})
+
+    combined = nllb_df.merge(gtrans_df, how="left", on="Model")
+    combined = combined[["Model", "Avg_NLLB", "Avg_Gtrans", "Model_Type_x"]].rename(
+        columns={"Model_Type_x": "Model_Type"}
+    )
+
+    print(combined.sort_values(by="Avg_NLLB", ascending=False))
+
+    colors = {
+        "Sequence Classifier": COLORS.get("green"),
+        "Generative RM": COLORS.get("purple"),
+        "DPO": COLORS.get("orange"),
+    }
+
+    labels = {
+        "Sequence Classifier": "Classifier RM",
+        "Generative RM": "Generative RM",
+        "DPO": "Implicit RM",
+    }
+
+    fig, ax = plt.subplots(figsize=figsize)
+    for _, row in combined.iterrows():
+        ax.plot(
+            [1, 2],
+            [row["Avg_NLLB"], row["Avg_Gtrans"]],
+            marker="o",
+            color=colors[row["Model_Type"]],
+            label=labels[row["Model_Type"]],
+        )
+
+    # Avoid duplicate labels in the legend
+    handles, labels = plt.gca().get_legend_handles_labels()
+    by_label = dict(zip(labels, handles))
+    ax.legend(
+        by_label.values(),
+        by_label.keys(),
+        frameon=False,
+        ncols=3,
+        loc="lower center",
+        bbox_to_anchor=(0.5, -0.2),
+    )
+
+    # ax.grid(color="gray", alpha=0.2, which="both", axis="x")
+    # ax.set_ylabel("M-RewardBench Overall Score")
+
+    ax.set_xticks([1, 2])
+    ax.set_xticklabels(["NLLB", "Google Translate"])
+    ax.yaxis.set_visible(False)
+
+    ax.spines[["top", "bottom", "left", "right"]].set_visible(False)
+    ax.vlines(
+        [1, 2],
+        ymin=combined[["Avg_NLLB", "Avg_Gtrans"]].min().min(),
+        ymax=combined[["Avg_NLLB", "Avg_Gtrans"]].max().max(),
+        colors="gray",
+    )
+
+    plt.tight_layout()
+    fig.savefig(output_path, bbox_inches="tight")
+
+
 if __name__ == "__main__":
     main()