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code/analyze.py

@@ -432,3 +432,57 @@ if rows1:
     ax.set_title("Both the obstruction AND alignment's purchase\nshrink with scale", fontsize=9)
     fig.tight_layout(); fig.savefig(f"{F}/set1_scale_trend.png", bbox_inches="tight"); plt.close(fig)
 print("extra figures written")
+
+# ------------------------------------------------------------------ 7. B-GPT ceiling
+bgc = load("bgpt_ceiling.jsonl")
+if bgc:
+    arms = list(bgc[0]["arms"])
+    nice = {"bgpt_joint_bilingual": "B-GPT\njoint bilingual", "goldfish_eng_parent": "Goldfish\neng parent",
+            "goldfish_partner_parent": "Goldfish\npartner parent", "merge_M0_naive": "merge\nM0 naive",
+            "merge_M1a_vocab": "merge\nM1a vocab"}
+    fig, axes = plt.subplots(1, 2, figsize=(10, 3.9))
+    langs = [r["lang"].split("_")[0] for r in bgc]
+    w = 0.8 / len(arms)
+    for i, a in enumerate(arms):
+        axes[0].bar(np.arange(len(bgc)) + i * w, [0.5 * (r["arms"][a]["nats_per_byte_eng"] +
+                                                          r["arms"][a]["nats_per_byte_x"]) for r in bgc],
+                    width=w, label=nice.get(a, a).replace("\n", " "))
+        axes[1].bar(np.arange(len(bgc)) + i * w, [0.5 * (r["arms"][a]["multiblimp_eng"] +
+                                                          r["arms"][a]["multiblimp_x"]) for r in bgc],
+                    width=w, label=nice.get(a, a).replace("\n", " "))
+    for ax, yl, ttl in ((axes[0], "nats / UTF-8 byte (lower better)", "Likelihood"),
+                        (axes[1], "MultiBLiMP accuracy (higher better)", "Accuracy")):
+        ax.set_xticks(np.arange(len(bgc)) + 0.4 - w / 2)
+        ax.set_xticklabels([f"eng–{l}" for l in langs])
+        ax.set_ylabel(yl, fontsize=8); ax.set_title(ttl, fontsize=9)
+    axes[1].axhline(0.5, color="k", ls="--", lw=.8)
+    axes[1].set_ylim(0.0, 1.02)
+    axes[0].legend(fontsize=6.5, frameon=False, ncol=2)
+    fig.suptitle("SET 4 · what success looks like: a jointly-trained bilingual model vs the merges\n"
+                 "(all arms re-scored at a matched 128-token context)", fontsize=9)
+    fig.tight_layout(); fig.savefig(f"{F}/set4_joint_ceiling.png", bbox_inches="tight"); plt.close(fig)
+
+# ------------------------------------------------------------------ 8. SET 4 likelihood vs accuracy
+mbr = load("set4_multiblimp.jsonl")
+if mbr and rows4:
+    by_lang = {r["lang"]: r for r in rows4}
+    fig, ax = plt.subplots(figsize=(5.2, 4))
+    for r in mbr:
+        s4 = by_lang.get(r["lang"])
+        if not s4: continue
+        for k in r["rungs"]:
+            key = f"delta_floor_eng_{k}"
+            if key not in s4: continue
+            ax.scatter(s4[key], r["rungs"][k]["mb_eng"], s=28, alpha=.8,
+                       label=r["lang"].split("_")[0] if k == "M0_naive_avg" else None)
+    ax.scatter([0], [mbr[0]["parents"]["eng_on_mb_eng"]], marker="*", s=200, color="k",
+               label="English parent", zorder=5)
+    ax.axhline(0.5, color="grey", ls="--", lw=.8)
+    ax.text(0.02, 0.505, "chance", fontsize=7, transform=ax.get_yaxis_transform())
+    ax.set_xlabel("Δfloor on English text  (nats/byte, LIKELIHOOD)")
+    ax.set_ylabel("MultiBLiMP-English  (ACCURACY)")
+    ax.set_title("SET 4 · a merge can be destroyed by likelihood\nand still score well above chance",
+                 fontsize=9)
+    ax.legend(fontsize=7, frameon=False)
+    fig.tight_layout(); fig.savefig(f"{F}/set4_likelihood_vs_accuracy.png", bbox_inches="tight"); plt.close(fig)
+print("ceiling figures written")