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code/analyze.py

@@ -18,7 +18,14 @@ def load(p):
 
 CV, LG = load(f"{RES}/chatvec.jsonl"), load(f"{RES}/ledger.jsonl")
 diag = {r["fork"]: r["diag"] for r in CV if r.get("kind") == "diag"}
-refs = {r["arm"]: r for r in CV if r.get("kind") == "reference"}
+# Reference rows were written by more than one worker, and a later worker that only knew about
+# one target language would otherwise clobber the languages the first one measured. Merge the
+# accuracy dicts across every reference record instead of taking the last write.
+refs = {}
+for r in CV:
+    if r.get("kind") == "reference":
+        cur = refs.setdefault(r["arm"], {"arm": r["arm"], "acc": {}, "model": r.get("model")})
+        cur["acc"].update(r.get("acc") or {})
 byfork = {}
 for r in CV:
     if r.get("kind") in ("fork", "merge", "control"): byfork.setdefault(r["fork"], []).append(r)
@@ -66,20 +73,21 @@ for fk, d in sorted(diag.items()):
 
 if rows:
     ks = sorted({k for r in rows for k in r})
-    ks = ["fork", "lang", "lam", "is_control", "frac_layers_permuted", "coord_share"] + \
-         [k for k in ks if k not in ("fork", "lang", "lam", "is_control", "frac_layers_permuted", "coord_share")]
+    head = ["fork", "lang", "lam", "is_control", "g_is_effectively_identity",
+            "frac_layers_permuted", "coord_share", "predicted_align_helps"]
+    ks = head + [k for k in ks if k not in head]
     with open(f"{RES}/chatvec_summary.csv", "w") as f:
         f.write(",".join(ks) + "\n")
-        for r in rows: f.write(",".join(str(r.get(k, "")) for k in ks) + "\n")
+        for r in rows:
+            f.write(",".join(str(r.get(k, "")) for k in ks) + "\n")
 
-# per-model raw accuracy table
 with open(f"{RES}/all_model_accuracies.csv", "w") as f:
     allm = sorted({m for r in CV if r.get("acc") for m in r["acc"]})
     f.write("key,kind,fork,arm,lam," + ",".join(allm) + "\n")
     for r in sorted(CV, key=lambda z: z["key"]):
         if not r.get("acc"): continue
         f.write(f'{r["key"]},{r.get("kind")},{r.get("fork")},{r.get("arm")},{r.get("lam")},'
-                + ",".join(f'{r["acc"].get(m,""):.4f}' if isinstance(r["acc"].get(m), float) else ""
+                + ",".join(f'{r["acc"][m]:.4f}' if isinstance(r["acc"].get(m), float) else ""
                            for m in allm) + "\n")
 
 with open(f"{RES}/diagnostics.csv", "w") as f:
@@ -94,41 +102,85 @@ print(f"{len(rows)} summary rows, {len(diag)} diagnostics")
 
 # ================================================================== FIGURES
 plt.rcParams.update({"figure.dpi": 150, "font.size": 9, "axes.grid": True, "grid.alpha": 0.25,
-                     "axes.spines.top": False, "axes.spines.right": False})
+                     "axes.spines.top": False, "axes.spines.right": False,
+                     "axes.edgecolor": "#94a3b8", "text.color": "#1e293b",
+                     "axes.labelcolor": "#334155", "xtick.color": "#475569",
+                     "ytick.color": "#475569"})
 CR, CC, CG = "#2563eb", "#dc2626", "#059669"
 THRESH = 0.01
 
 def lab(fk):
-    return fk.replace("_PERM", " perm ").replace("swallow_ja", "Swallow").replace(
-        "typhoon2_th", "Typhoon2").replace("sealion_id", "SEA-LION")
+    return (fk.replace("_PERM", " perm ").replace("swallow_ja", "Swallow")
+              .replace("typhoon2_th", "Typhoon2").replace("sealion_id", "SEA-LION"))
 
-AXES = [("ifeval_prompt", "IFEval strict prompt accuracy\n(instruction following — what the chat vector is FOR)"),
+AXES = [("ifeval_prompt", "IFEval strict prompt accuracy\n(instruction following -- what the chat vector is FOR)"),
         ("tgt", "Belebele, target language\n(language capability)"),
         ("belebele_eng_Latn", "Belebele English\n(retention)")]
 
+# cross-group point: coordinate share vs the accuracy change alignment produced (alpha = 0.5)
+xg = None
+try:
+    _d = next(r["diag"] for r in LG if r.get("arm") == "diag")
+    _cs = _d.get("coord_fraction_bn_perm", 0.0)
+    _n = next(r for r in LG if r.get("arm") == "naive" and r.get("alpha") == 0.5)
+    _a = next(r for r in LG if r.get("arm") == "aligned" and r.get("alpha") == 0.5)
+    xg = (_cs, _a["acc"]["mean"] - _n["acc"]["mean"])
+except Exception:
+    xg = None
+
+# Categorical palette validated with the dataviz six-checks (worst adjacent CVD dE 8.6 deutan,
+# normal-vision 32.0, contrast all >= 3:1). Marker SHAPE carries identity too, which is the
+# secondary encoding the 6-8 dE band requires.
+RESOLUTION = 2.0 / 300.0     # +/- 2 items on a 300-item benchmark: what "no change" means here
+
+def _group_annotate(ax, pts, dx=9, dy=5):
+    """Coincident points get ONE label, not three stacked on top of each other."""
+    b = {}
+    for x, y, name in pts:
+        b.setdefault((round(x, 6), round(y, 5)), []).append(name)
+    for (x, y), names in b.items():
+        ax.annotate(" / ".join(names), (x, y), textcoords="offset points",
+                    xytext=(dx, dy), fontsize=7, color="#334155")
+
 if rows:
-    fig, axs = plt.subplots(1, 3, figsize=(13.2, 4.2))
+    fig, axs = plt.subplots(1, 3, figsize=(13.4, 4.4))
     for ax, (m, ttl) in zip(axs, AXES):
-        pts = [(r["coord_share"], r.get(f"{m}__delta"), r) for r in rows if r.get(f"{m}__delta") is not None]
+        pts = [(r["coord_share"], r.get(f"{m}__delta"), r) for r in rows
+               if r.get(f"{m}__delta") is not None]
+        ys = [y for _, y, _ in pts] + ([xg[1]] if (m == "tgt" and xg) else []) + [0.0]
+        lim = max(0.05, max(abs(v) for v in ys) * 1.35)
+        ax.axhspan(-RESOLUTION, RESOLUTION, color="#94a3b8", alpha=0.18, lw=0, zorder=0)
+        ax.axhline(0, color="#334155", lw=1.0, zorder=1)
+        ax.axvline(THRESH, color="#b45309", lw=1.1, ls=":", zorder=1)
         for x, y, r in pts:
-            ax.scatter(x, y, s=40 + 90 * r["frac_layers_permuted"],
+            ax.scatter(x, y, s=46 + 90 * r["frac_layers_permuted"],
                        c=CC if r["is_control"] else CR, marker="D" if r["is_control"] else "o",
-                       zorder=3, edgecolors="white", linewidths=0.9)
-            ax.annotate(lab(r["fork"]), (x, y), textcoords="offset points", xytext=(7, 4), fontsize=6.5)
-        ax.axhline(0, color="#111", lw=0.9)
-        ax.axvline(THRESH, color="#f59e0b", lw=1.1, ls=":",
-                   label=f"decision threshold {THRESH}")
-        ax.set_xlabel("pre-merge coordinate share  (diagnostic, computed BEFORE any merge)")
-        ax.set_ylabel("accuracy gain from aligning the chat vector")
-        ax.set_title(ttl, fontsize=8.5, loc="left")
-        ax.set_xscale("symlog", linthresh=1e-3)
+                       zorder=3, edgecolors="white", linewidths=1.2)
+        _group_annotate(ax, [(x, y, lab(r["fork"])) for x, y, r in pts])
+        if m == "tgt" and xg is not None:
+            ax.scatter(xg[0], xg[1], s=80, c=CG, marker="^", zorder=3,
+                       edgecolors="white", linewidths=1.2)
+            ax.annotate("pythia x Zh-Pythia", (xg[0], xg[1]), textcoords="offset points",
+                        xytext=(9, -13), fontsize=7, color="#334155")
+        ax.set_ylim(-lim, lim)
+        ax.set_xscale("symlog", linthresh=1e-3); ax.set_xlim(-2e-4, 1.8)
+        if ax is axs[0]:
+            ax.set_ylabel("accuracy gain from aligning")
+        ax.set_title(ttl, fontsize=8.5, loc="left", color="#334155")
+        ax.grid(alpha=0.22)
     axs[0].scatter([], [], c=CR, s=60, label="real community CPT fork")
     axs[0].scatter([], [], c=CC, marker="D", s=60, label="permutation control (ground truth)")
-    axs[0].legend(fontsize=7, frameon=False, loc="best")
+    axs[0].scatter([], [], c=CG, marker="^", s=60, label="cross-group direct merge")
+    axs[0].plot([], [], color="#94a3b8", lw=6, alpha=0.35, label="+/- 2 items: measurement floor")
+    axs[0].plot([], [], ls=":", color="#b45309", label=f"decision threshold {THRESH}")
+    axs[0].legend(fontsize=7, frameon=False, loc="upper left")
+    fig.supxlabel("pre-merge coordinate share  (the diagnostic, computed BEFORE any merge)",
+                  fontsize=9.5, y=0.015)
     fig.suptitle("Does the pre-merge diagnostic predict whether the chat vector needs aligning?",
-                 fontsize=11.5, x=0.01, ha="left")
-    fig.tight_layout(rect=[0, 0, 1, 0.93])
-    fig.savefig(f"{FIG}/headline_diagnostic_vs_gain.png", bbox_inches="tight"); plt.close(fig)
+                 fontsize=12, x=0.006, ha="left")
+    fig.tight_layout(rect=[0, 0.05, 1, 0.93])
+    fig.savefig(f"{FIG}/headline_diagnostic_vs_gain.png", bbox_inches="tight")
+    plt.close(fig)
 
     # ---- secondary: naive vs aligned, y = x -------------------------------------------------
     fig, axs = plt.subplots(1, 2, figsize=(9.6, 4.6))