fix order classification bug

ai_diplomacy/game_history.py

@@ -291,10 +291,15 @@ class GameHistory:
 
         def _order_type(order: str) -> str:
             o = order.upper()
-            if o == "WAIVE":
+
+            if o.strip() == "WAIVE":
                 return "waive"
-            if " H" in o or " HOLD" in o:
+
+            # hold: ends with “ H” or “ HOLD”
+            import re
+            if re.search(r"\sH\s*$", o) or re.search(r"\sHOLD\s*$", o):
                 return "hold"
+
             if " S " in o:
                 return "support"
             if " C " in o:
@@ -303,12 +308,13 @@ class GameHistory:
                 return "retreat"
             if " - " in o:
                 return "move"
-            if " BUILD" in o or o.endswith(" B") or " B " in o:
+            if re.search(r"\sBUILD\s*$", o) or o.endswith(" B") or " B " in o:
                 return "build"
-            if " DISBAND" in o or o.endswith(" D") or " D " in o:
+            if re.search(r"\sDISBAND\s*$", o) or o.endswith(" D") or " D " in o:
                 return "disband"
             return "other"
 
+
         # engine fallback
         engine_phases = {ph.name: ph for ph in getattr(game, "get_phase_history", lambda: [])()}
         eng2code = {

experiment_runner.py

@@ -116,6 +116,15 @@ def _add_experiment_flags(p: argparse.ArgumentParser) -> None:
         default=0.05,
         help="α for hypothesis tests in comparison mode (default 0.05).",
     )
+    p.add_argument(
+        "--showall",
+        action="store_true",
+        help=(
+            "When used together with --compare_to, prints every metric in the "
+            "console output, not just significant results (confidence intervals still use --sig_level)."
+        ),
+    )
+
 
 
 def _add_lm_game_flags(p: argparse.ArgumentParser) -> None:
@@ -469,7 +478,13 @@ def main() -> None:
     if exp_args.compare_to is not None:
         from experiment_runner.analysis import compare_stats   # local import
 
-        compare_stats.run(exp_dir, exp_args.compare_to, alpha=exp_args.sig_level)
+        compare_stats.run(
+            exp_dir,
+            exp_args.compare_to,
+            alpha=exp_args.sig_level,
+            show_all=exp_args.showall,
+        )
+
         log.info("comparison complete; artefacts in %s/analysis/comparison", exp_dir)
         return
 

experiment_runner/analysis/compare_stats.py

@@ -336,7 +336,7 @@ def _plot_phase_overlay(exp_a: Path, exp_b: Path, out_dir: Path) -> None:
 
 
 # ───────────────────────── public API ─────────────────────────
-def run(exp_a: Path, exp_b: Path, alpha: float = 0.05) -> None:
+def run(exp_a: Path, exp_b: Path, alpha: float = 0.05, show_all: bool = False) -> None:
     df_a = _load_games(exp_a)
     df_b = _load_games(exp_b)
 
@@ -373,16 +373,18 @@ def run(exp_a: Path, exp_b: Path, alpha: float = 0.05) -> None:
     print(f"Comparing {tag_a} to {tag_b}: All comparisons are [{tag_b}] – [{tag_a}].")
 
     sig_agg = _significant(agg_df, alpha)
-    if sig_agg.empty:
+    rows_to_print = agg_df if show_all else _significant(agg_df, alpha)
+    if rows_to_print.empty:
         _print_hdr(f"Aggregated Across Powers – no metric significant at {ci_pct}% CI")
     else:
         n_a, n_b = int(sig_agg.iloc[0]["n_A"]), int(sig_agg.iloc[0]["n_B"])
         _print_hdr(
-            f"Aggregated Across Powers – significant at {ci_pct}% "
+            f"Aggregated Across Powers – "
+            f"{'all metrics' if show_all else f'significant at {ci_pct}%'} "
             f"(n({tag_a})={n_a}, n({tag_b})={n_b})"
         )
         width = max(len(m) for m in sig_agg["Metric"]) + 2
-        for _, r in sig_agg.iterrows():
+        for _, r in rows_to_print.iterrows():
             print(_fmt_row(r["Metric"], r, width, ci_label))
 
     # ── section 2: per-power breakdown ────────────────────────
@@ -410,15 +412,20 @@ def run(exp_a: Path, exp_b: Path, alpha: float = 0.05) -> None:
     pow_df.to_csv(pow_csv, index=False)
 
     sig_pow = _significant(pow_df, alpha)
-    if sig_pow.empty:
+    
+    rows_to_print_pow = pow_df if show_all else _significant(pow_df, alpha)
+    if rows_to_print_pow.empty:
         _print_hdr(f"Per-Power Breakdown – no metric significant at {ci_pct}% CI")
     else:
         _print_hdr(
-            f"Per-Power Breakdown – metrics significant at {ci_pct}% CI (α={alpha})"
+                f"Per-Power Breakdown – "
+                f"{'all metrics' if show_all else f'metrics significant at {ci_pct}% CI'} "
+                f"(α={alpha})"
+
         )
         width = max(len(m) for m in sig_pow["Metric"]) + 2
         for power in powers:
-            sub = sig_pow[sig_pow["Power"] == power]
+            sub = rows_to_print_pow[rows_to_print_pow["Power"] == power]
             if sub.empty:
                 continue
             n_a, n_b = int(sub.iloc[0]["n_A"]), int(sub.iloc[0]["n_B"])