Upload code/analyze.py with huggingface_hub

code/analyze.py

@@ -228,6 +228,30 @@ if pred_rows:
         r["bh_q"] = float(qq) if np.isfinite(qq) else ""
     to_csv(pred_rows, f"{R}/predictor_auroc.csv")
 
+# ---------------- P0-2 CONFIRMATORY family: the five predictors the audit brief itself names,
+# on the one outcome it asks about. Fixed from the brief, not chosen after seeing the table, and
+# BH-corrected within this small family only. Everything else in predictor_auroc.csv is exploratory.
+CONFIRMATORY = [("p_weight_cosine", "weight cosine"),
+                ("p_coord_share_bnd_perm", "coordinate share (block-normalised / permutation)"),
+                ("p_qmd_act_perm", "QMD (quotient_residual / permutation)"),
+                ("p_cka_mean", "CKA (mean over layers / unaligned)"),
+                ("p_task_vector_cosine", "task-vector cosine")]
+conf = []
+for r in pred_rows:
+    if r["outcome"] != "rescue_frac":
+        continue
+    for pk, lbl in CONFIRMATORY:
+        if r["predictor"] == pk[2:]:
+            conf.append({"substrate": r["substrate"], "predictor": lbl, "n_pairs": r["n_pairs"],
+                         "spearman": r["spearman_rescue"], "auroc_heldout_by_seed": r["auroc_heldout_by_seed"],
+                         "perm_null_mean": r["perm_null_mean"], "perm_p": r["perm_null_p"],
+                         "n_null_draws": r["n_null_draws"]})
+if conf:
+    qq = bh([c["perm_p"] for c in conf])
+    for c, q in zip(conf, qq):
+        c["bh_q_within_confirmatory_family"] = float(q) if np.isfinite(q) else ""
+    to_csv(conf, f"{R}/predictor_confirmatory.csv")
+
 # ---------------- P0-2b: does the predictor transfer ACROSS substrates (leave-one-size-out)?
 xfer = []
 szs_all = sorted({r["size"] for r in rows1 if len([q for q in rows1 if q["size"] == r["size"]]) >= 8})
@@ -319,7 +343,7 @@ if rows1:
 # 2. rescue vs coordinate share
 if rows1:
     fig, axes = plt.subplots(1, 2, figsize=(8.4, 3.6))
-    for ax, pk, lab in ((axes[0], "p_coord_share_bnd_perm", "coordinate share (block-normalised, permutation)"),
+    for ax, pk, lab in ((axes[0], "p_coord_share_bnd_perm", "coordinate share (block-normalised / permutation)"),
                         (axes[1], "p_cka_mean", "unaligned CKA (mean over layers)")):
         for sz in sorted({r["size"] for r in rows1}, key=lambda s: int(s[:-1])):
             sub = [r for r in rows1 if r["size"] == sz]