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analyze.py

@@ -156,6 +156,29 @@ def claim34(paths):
             "final_se2": float(2 * np.nanstd(v[:, -1]) / np.sqrt(len(good))),
         }
     n_policy_chosen = n_dep - 1  # the first drifter is placed uniformly at random
+
+    # The paper defines iso-performance as "averaged over each iteration's
+    # results" (Sec. 5.2) and reports the saving as a single number ("save about
+    # 3 drifters ~16%"). We therefore headline the mean over deployment
+    # iterations, which matches the paper's Claim-3 number almost exactly (3.4 vs
+    # 3). The final-iteration value ("drifters needed to match uniform's *final*
+    # accuracy") is a different, larger statistic and is kept as a secondary read.
+    iso_avg = {p: float(np.nanmean(iso[p]["mean"])) for p in pols}
+    iso_avg_se2 = {
+        p: float(2 * np.nanstd([np.nanmean(
+            [iso_performance(E[p][i], E["unif"][i])]) for i in range(len(good))])
+            / np.sqrt(len(good)))
+        for p in pols
+    }
+    # per-run averaged-over-iterations, for a correct standard error
+    iso_avg_runs = {
+        p: np.array([np.nanmean(iso_performance(E[p][i], E["unif"][i]))
+                     for i in range(len(good))])
+        for p in pols
+    }
+    iso_avg = {p: float(np.nanmean(iso_avg_runs[p])) for p in pols}
+    iso_avg_se2 = {p: float(2 * np.nanstd(iso_avg_runs[p]) / np.sqrt(len(good)))
+                   for p in pols}
     return {
         "n_runs": len(good),
         "policies": pols,
@@ -165,9 +188,10 @@ def claim34(paths):
         "err_mean": {p: E[p].mean(0).tolist() for p in pols},
         "err_se2": {p: (2 * E[p].std(0) / np.sqrt(len(good))).tolist() for p in pols},
         "iso": iso,
-        "savings_pct": {
-            p: 100 * iso[p]["final"] / n_policy_chosen for p in pols
-        },
+        "iso_avg": iso_avg,  # paper's metric: averaged over iterations
+        "iso_avg_se2": iso_avg_se2,
+        "savings_pct_avg": {p: 100 * iso_avg[p] / n_policy_chosen for p in pols},
+        "savings_pct_final": {p: 100 * iso[p]["final"] / n_policy_chosen for p in pols},
         "n_obs_mean": {
             p: float(np.mean([r["n_obs"] for r in res if r["policy"] == p])) for p in pols
         },