| """Demon Attack training cross-task evaluation companion figure.""" |
|
|
| from __future__ import annotations |
|
|
| import csv |
| from pathlib import Path |
| import sys |
|
|
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from matplotlib.lines import Line2D |
| from matplotlib.ticker import FuncFormatter |
|
|
|
|
| HERE = Path(__file__).resolve().parent |
| PAPER = HERE.parents[1] |
| sys.path.insert(0, str(PAPER)) |
| import paper_colors |
|
|
|
|
| TASKS = ("Demon Attack", "Asterix", "Atlantis", "AirRaid") |
| SERIES = ( |
| ("zero_latency_train", "Zero-latency training", paper_colors.GRAY), |
| ("mixed_latency_train", "Mixed-latency training", paper_colors.PURPLE), |
| ) |
|
|
|
|
| def read_rows() -> dict[str, list[dict[str, float]]]: |
| by_task: dict[str, list[dict[str, float]]] = {task: [] for task in TASKS} |
| with (HERE / "data.csv").open(newline="", encoding="utf-8") as stream: |
| for row in csv.DictReader(stream): |
| by_task[row["task"]].append({ |
| "eval_latency": float(row["eval_latency"]), |
| "zero_latency_train": float(row["zero_latency_train"]), |
| "mixed_latency_train": float(row["mixed_latency_train"]), |
| }) |
| for rows in by_task.values(): |
| rows.sort(key=lambda item: item["eval_latency"]) |
| return by_task |
|
|
|
|
| def compact_int(value: float, _position: int) -> str: |
| if abs(value) >= 1000: |
| return f"{value / 1000:g}k" |
| return f"{value:g}" |
|
|
|
|
| def main() -> None: |
| plt.style.use(PAPER / "plot_style.mplstyle") |
| plt.rcParams.update(paper_colors.RC_COLORS) |
| data = read_rows() |
|
|
| fig, axes = plt.subplots(1, 4, figsize=(7.2, 2.25), sharex=True) |
| fig.subplots_adjust(left=0.075, right=0.995, top=0.76, bottom=0.24, wspace=0.32) |
|
|
| for ax, task in zip(axes, TASKS): |
| rows = data[task] |
| xs = [row["eval_latency"] for row in rows] |
| max_value = max(row[key] for row in rows for key, _label, _color in SERIES) |
| ymin = 0 |
| ymax = max_value * 1.18 |
|
|
| for key, label, color in SERIES: |
| ys = [row[key] for row in rows] |
| ax.plot( |
| xs, |
| ys, |
| marker="o", |
| markersize=3.1, |
| linewidth=1.25, |
| color=color, |
| label=label, |
| ) |
| ax.set_title(task, fontsize=8.4, weight="bold", pad=6) |
| ax.set_xlim(-0.2, 4.2) |
| ax.set_ylim(ymin, ymax) |
| ax.set_xticks((0, 2, 4)) |
| ax.yaxis.set_major_formatter(FuncFormatter(compact_int)) |
| ax.tick_params(axis="both", labelsize=6.5) |
| ax.grid(True, axis="y") |
| ax.grid(False, axis="x") |
|
|
| axes[0].set_ylabel("Mean return", fontsize=7.2) |
| fig.supxlabel("Evaluation latency (raw frames)", fontsize=7.2, y=0.07) |
| handles = [ |
| Line2D([0], [0], color=color, marker="o", markersize=3.2, linewidth=1.25, label=label) |
| for _key, label, color in SERIES |
| ] |
| fig.legend( |
| handles=handles, |
| loc="upper center", |
| bbox_to_anchor=(0.52, 0.995), |
| ncols=2, |
| columnspacing=1.6, |
| handlelength=1.8, |
| fontsize=6.5, |
| ) |
|
|
| for extension in ("pdf", "png"): |
| fig.savefig(HERE / f"fig.{extension}", dpi=300, bbox_inches="tight", pad_inches=0.025) |
| plt.close(fig) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
