recipes/plotting/v1/plot_qwen3_4b_matched.py

#!/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()