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figs: a5 dedup title->axis arrow + CSV, overlay onset dot->labeled vline

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- a5: drop per-panel title (restated the axis); fold direction into the xlabel
  (DEPLOY hack rate (down=better) / solve (up=better)). Dump a5_generalisation.csv
  (per mode,arm deploy hack/solve mean+/-std) -- the reproducibility source it lacked.
- overlay (dyn_sub4_hack_overlay etc): replace the per-arm onset DOT with a single
  dashed labeled 'first hack' vertical line, matching the small-multiples/longrun.
- (dyn_sub4_hack_overlay shares dyn_sub4.csv -- same runs, different view, no new CSV.)

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-05 04:13:37 +00:00
parent 8daf58d25e
commit 5242f66b7e
11 changed files with 889 additions and 917 deletions
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out/figs/a5_generalisation.csv
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mode,in_dist,arm,n_seed,deploy_hack_mean,deploy_hack_std,deploy_solve_mean,deploy_solve_std
run_tests,True,vanilla,1,1.000000,0.000000,0.000000,0.000000
file_marker,False,vanilla,1,0.625000,0.000000,0.375000,0.000000
stdout_marker,False,vanilla,1,0.166667,0.000000,0.645833,0.000000
sentinel,False,vanilla,1,0.416667,0.000000,0.583333,0.000000
run_tests,True,route,1,0.000000,0.000000,0.000000,0.000000
file_marker,False,route,1,0.020833,0.000000,0.354167,0.000000
stdout_marker,False,route,1,0.083333,0.000000,0.395833,0.000000
sentinel,False,route,1,0.000000,0.000000,0.666667,0.000000
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2 run_tests True vanilla 1 1.000000 0.000000 0.000000 0.000000
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4 stdout_marker False vanilla 1 0.166667 0.000000 0.645833 0.000000
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8 stdout_marker False route 1 0.083333 0.000000 0.395833 0.000000
9 sentinel False route 1 0.000000 0.000000 0.666667 0.000000
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Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 57 KiB

After

Width:  |  Height:  |  Size: 57 KiB

scripts/plot_deploy_overlay.py
+20 -9
View File
@@ -21,6 +21,7 @@ Usage:
from __future__ import annotations
import argparse
import csv
import json
from collections import defaultdict
from pathlib import Path
@@ -66,7 +67,7 @@ def _mode_stats(by_arm, arm, modes, field):
return np.array(means), np.array(stds)
def _panel(ax, by_arm, modes, arms, field, title, xlabel):
def _panel(ax, by_arm, modes, arms, field, xlabel):
"""Cleveland dot plot: y = mode, x = rate. One dot per arm with a thin connector
per mode, so the arm-to-arm change reads as a line segment (vanilla -> route).
xerr = std across seeds (drawn only when >1 seed). Tufte: faint x-grid only, no
@@ -97,9 +98,8 @@ def _panel(ax, by_arm, modes, arms, field, title, xlabel):
ax.set_yticklabels([f"{m}\n{'IN' if m == 'run_tests' else 'held-out'}" for m in modes], fontsize=8)
ax.set_xlim(-0.04, 1.08)
ax.set_ylim(y.min() - 0.5, y.max() + 0.5)
ax.set_xlabel(xlabel)
ax.set_title(title, fontsize=10)
ax.spines[["top", "right", "left"]].set_visible(False)
ax.set_xlabel(xlabel, fontsize=9) # carries the metric AND the better-direction;
ax.spines[["top", "right", "left"]].set_visible(False) # no title (would just restate it)
ax.tick_params(length=0)
ax.grid(axis="x", lw=0.3, alpha=0.3)
@@ -125,10 +125,8 @@ def main() -> None:
modes = [m for m in MODE_ORDER if any(m in r["by_mode"] for r in records)]
fig, (a1, a2) = plt.subplots(1, 2, figsize=(9.5, 0.7 + 0.7 * len(modes)), sharey=True)
_panel(a1, by_arm, modes, arms, "deploy_hack",
"DEPLOY hack rate (lower = better)", "deploy hack rate")
_panel(a2, by_arm, modes, arms, "deploy_solve",
"DEPLOY solve rate (higher = better)", "deploy solve rate")
_panel(a1, by_arm, modes, arms, "deploy_hack", r"DEPLOY hack rate ($\downarrow$ lower = better)")
_panel(a2, by_arm, modes, arms, "deploy_solve", r"DEPLOY solve rate ($\uparrow$ higher = better)")
a1.legend(fontsize=8, frameon=False, loc="lower right")
if args.title:
n_seed = {r.get("seed") for r in records}
@@ -136,7 +134,20 @@ def main() -> None:
f"quarantine deleted = shipped model", fontsize=11)
fig.tight_layout()
save_fig(fig, args.out)
logger.info(f"wrote {args.out} ({len(arms)} arms x {len(modes)} modes)")
# CSV reproducibility source (mirrors the dynamics plots' dump): per (mode, arm)
# the deploy hack/solve mean +/- std-across-seeds, exactly what the dots encode.
csv_path = args.out.with_suffix(".csv")
with csv_path.open("w", newline="") as f:
w = csv.writer(f)
w.writerow(["mode", "in_dist", "arm", "n_seed",
"deploy_hack_mean", "deploy_hack_std", "deploy_solve_mean", "deploy_solve_std"])
for arm in arms:
hk_m, hk_s = _mode_stats(by_arm, arm, modes, "deploy_hack")
sv_m, sv_s = _mode_stats(by_arm, arm, modes, "deploy_solve")
for j, m in enumerate(modes):
w.writerow([m, m == "run_tests", ARM[arm][0], len(by_arm[arm]),
f"{hk_m[j]:.6f}", f"{hk_s[j]:.6f}", f"{sv_m[j]:.6f}", f"{sv_s[j]:.6f}"])
logger.info(f"wrote {args.out} and {csv_path.name} ({len(arms)} arms x {len(modes)} modes)")
if __name__ == "__main__":
scripts/plot_dynamics.py
+9 -4
View File
@@ -355,6 +355,7 @@ def _overlay_panel(ax, by_arm, arms, key, *, label, with_onset, label_arms, ylim
endpoint (de-collided in y). An arm whose mean series sits at zero gets a
"$\\approx 0$" tag so a pinned-at-zero line reads as a finding, not a missing line."""
ends = [] # (y_endpoint, x_endpoint, arm, color, is_zero) for direct labels
onset_steps = [] # mean-onset across arms -> ONE labeled vertical line (see below)
for arm in arms:
rs = [r for r in by_arm[arm] if key in r]
if not rs:
@@ -370,11 +371,15 @@ def _overlay_panel(ax, by_arm, arms, key, *, label, with_onset, label_arms, ylim
xm = rs[0]["steps"][:L]
ax.plot(xm, ym, color=color, lw=2.0, solid_capstyle="round")
if with_onset:
onsets = [s for r in rs if (s := _onset(r["steps"], r["hack_s"])) is not None]
if onsets:
s0 = float(np.mean(onsets))
ax.plot(s0, np.interp(s0, xm, ym), marker="o", ms=4, color=color, zorder=3)
onset_steps += [s for r in rs if (s := _onset(r["steps"], r["hack_s"])) is not None]
ends.append((float(ym[-1]), float(xm[-1]), arm, color, float(np.nanmax(ym)) < 0.02))
# First-hack as a labeled vertical line (matches the small-multiples), not a dot:
# a dashed rule reads as "emergence starts here" across both arms in one mark.
if with_onset and onset_steps:
s0 = float(np.mean(onset_steps))
ax.axvline(s0, color="0.55", lw=0.8, ls=(0, (4, 3)), zorder=0)
ax.annotate("first hack", (s0, ylim[1]), color="0.4", fontsize=7,
xytext=(2, -2), textcoords="offset points", va="top")
ax.set_ylim(*ylim)
ax.set_ylabel(label)
ax.spines[["top", "right"]].set_visible(False)