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ba0faed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | #!/usr/bin/env python3
"""Render the Qwen3-4B matched-volume trajectory for the AAAI supplement."""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
GENERALIST_COLOR = "#5B5B5B"
MOS_COLOR = "#6F5AA8"
GRID_COLOR = "#D9D9D9"
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--input",
type=Path,
default=Path(
"paper/submission/evidence/b5_qwen3_4b/"
"matched_volume_trajectory.csv"
),
)
parser.add_argument(
"--output",
type=Path,
default=Path("paper/submission/figures/fig_qwen3_4b_matched"),
help="Output stem; both PDF and PNG are written.",
)
return parser.parse_args()
def load_rows(path: Path) -> dict[str, np.ndarray]:
with path.open(newline="") as handle:
rows = list(csv.DictReader(handle))
if len(rows) != 29:
raise ValueError(f"expected 29 matched points, found {len(rows)}")
keys = (
"training_samples",
"generalist_overall_al",
"arm_a_overall_al",
"delta_overall_al",
)
arrays = {
key: np.asarray([float(row[key]) for row in rows], dtype=np.float64)
for key in keys
}
if not np.all(np.diff(arrays["training_samples"]) > 0):
raise ValueError("training_samples must be strictly increasing")
recomputed = arrays["arm_a_overall_al"] - arrays["generalist_overall_al"]
if not np.allclose(recomputed, arrays["delta_overall_al"], atol=5e-5):
raise ValueError("stored deltas disagree with trajectory values")
if not np.all(arrays["delta_overall_al"] > 0):
raise ValueError("the publication annotation assumes 29/29 positive deltas")
return arrays
def configure_style() -> None:
mpl.rcParams.update(
{
"font.family": "serif",
"font.serif": ["Times New Roman", "Times", "Nimbus Roman", "DejaVu Serif"],
"font.size": 8.0,
"axes.labelsize": 8.0,
"axes.titlesize": 8.0,
"xtick.labelsize": 7.2,
"ytick.labelsize": 7.2,
"legend.fontsize": 7.1,
"axes.linewidth": 0.7,
"lines.linewidth": 1.5,
"lines.markersize": 3.4,
"pdf.fonttype": 42,
"ps.fonttype": 42,
"savefig.bbox": "tight",
"savefig.pad_inches": 0.02,
}
)
def render(data: dict[str, np.ndarray], output: Path) -> None:
configure_style()
samples_m = data["training_samples"] / 1_000_000.0
generalist = data["generalist_overall_al"]
mos = data["arm_a_overall_al"]
delta = data["delta_overall_al"]
median_delta = float(np.median(delta))
fig, (ax_curve, ax_delta) = plt.subplots(
1,
2,
figsize=(7.0, 2.42),
gridspec_kw={"width_ratios": [1.16, 0.84], "wspace": 0.31},
)
ax_curve.plot(
samples_m,
generalist,
color=GENERALIST_COLOR,
linestyle="--",
marker="o",
markerfacecolor="white",
markeredgewidth=0.75,
markevery=2,
label="Generalist",
zorder=2,
)
ax_curve.plot(
samples_m,
mos,
color=MOS_COLOR,
linestyle="-",
marker="s",
markerfacecolor=MOS_COLOR,
markeredgewidth=0.0,
markevery=2,
label="D0-MoS (5 groups)",
zorder=3,
)
ax_curve.set_xlabel("Training samples (millions)")
ax_curve.set_ylabel("Five-domain mean AL")
ax_curve.set_xlim(0.0, 2.4)
ymin = min(float(generalist.min()), float(mos.min())) - 0.025
ymax = max(float(generalist.max()), float(mos.max())) + 0.025
ax_curve.set_ylim(ymin, ymax)
ax_curve.grid(axis="y", color=GRID_COLOR, linewidth=0.55, alpha=0.8)
ax_curve.legend(loc="lower right", frameon=False, handlelength=2.2)
ax_delta.axhline(0.0, color=GENERALIST_COLOR, linewidth=0.75, linestyle=":")
ax_delta.plot(
samples_m,
delta,
color=MOS_COLOR,
linestyle="-",
marker="D",
markerfacecolor="white",
markeredgewidth=0.75,
markevery=2,
zorder=3,
)
ax_delta.axhline(
median_delta,
color=MOS_COLOR,
linewidth=0.9,
linestyle="--",
alpha=0.8,
)
ax_delta.text(
0.98,
0.08,
f"29/29 matched points > 0\nmedian $\\Delta$ = {median_delta:.3f}",
transform=ax_delta.transAxes,
ha="right",
va="bottom",
fontsize=7.0,
)
ax_delta.set_xlabel("Training samples (millions)")
ax_delta.set_ylabel(r"$\Delta$ AL (MoS $-$ generalist)")
ax_delta.set_xlim(0.0, 2.4)
ax_delta.set_ylim(0.0, max(0.12, float(delta.max()) + 0.01))
ax_delta.grid(axis="y", color=GRID_COLOR, linewidth=0.55, alpha=0.8)
for label, axis in (("(a)", ax_curve), ("(b)", ax_delta)):
axis.text(
-0.14,
1.03,
label,
transform=axis.transAxes,
ha="left",
va="bottom",
fontweight="bold",
)
axis.spines["top"].set_visible(False)
axis.spines["right"].set_visible(False)
axis.tick_params(width=0.7, length=3.0)
output.parent.mkdir(parents=True, exist_ok=True)
metadata = {
"Title": "Qwen3-4B matched-volume MoS replication",
"Subject": "Five-domain acceptance length over matched training volume",
}
fig.savefig(output.with_suffix(".pdf"), metadata=metadata)
fig.savefig(output.with_suffix(".png"), dpi=450)
plt.close(fig)
def main() -> None:
args = parse_args()
render(load_rows(args.input), args.output)
if __name__ == "__main__":
main()
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