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#!/usr/bin/env python3
"""Plot trajectory-level capacity transition at two training horizons."""
from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
RUNS = [
(
"T=10000, 1024 FA trajectories",
Path("outputs/phase_transition_zoom_T10000_1024traj"),
"#c65f16",
),
(
"T=30000, 256 FA trajectories",
Path("outputs/phase_transition_zoom_T30000_256traj"),
"#174f91",
),
]
def summarize_gaps(runs: pd.DataFrame) -> pd.DataFrame:
fa = runs[runs["run_type"] == "fa"].copy()
records = []
for margin, group in fa.groupby("fa_capacity_margin"):
gaps = group["train_gap_to_bp"].to_numpy(dtype=np.float64)
records.append(
{
"margin": float(margin),
"mean": float(gaps.mean()),
"q05": float(np.quantile(gaps, 0.05)),
"q25": float(np.quantile(gaps, 0.25)),
"q75": float(np.quantile(gaps, 0.75)),
"q95": float(np.quantile(gaps, 0.95)),
}
)
return pd.DataFrame(records).sort_values("margin", ascending=False)
def add_summary(ax: plt.Axes, runs: pd.DataFrame, label: str, color: str) -> pd.DataFrame:
summary = summarize_gaps(runs)
x = summary["margin"].to_numpy(dtype=np.float64)
ax.fill_between(
x,
summary["q05"],
summary["q95"],
color=color,
alpha=0.13,
linewidth=0,
)
ax.fill_between(
x,
summary["q25"],
summary["q75"],
color=color,
alpha=0.24,
linewidth=0,
)
ax.plot(
x,
summary["mean"],
color=color,
marker="o",
linewidth=2.3,
label=label,
)
return summary
def add_trajectories(ax: plt.Axes, runs: pd.DataFrame, color: str) -> None:
fa = runs[runs["run_type"] == "fa"].copy()
fa["_tid"] = fa["init_seed"].astype(str) + ":" + fa["feedback_seed"].astype(str)
for _tid, group in fa.groupby("_tid"):
group = group.sort_values("fa_capacity_margin", ascending=False)
ax.plot(
group["fa_capacity_margin"],
group["train_gap_to_bp"],
color=color,
alpha=0.11,
linewidth=0.85,
)
def main() -> None:
outdir = Path("outputs/phase_transition_zoom_comparison")
outdir.mkdir(parents=True, exist_ok=True)
fig, axes = plt.subplots(1, 2, figsize=(13.2, 5.2), dpi=180)
for ax, (label, run_dir, color) in zip(axes, RUNS, strict=True):
runs = pd.read_csv(run_dir / "runs.csv")
add_trajectories(ax, runs, color)
summary = add_summary(ax, runs, label, color)
ax.axvline(0, color="black", linestyle="--", linewidth=1.1)
ax.axhline(0, color="black", linewidth=1.0)
ax.set_xlabel("hard FA capacity margin P - K_FA - N*out")
ax.set_title(label)
ax.grid(alpha=0.18)
ax.legend(frameon=True)
fa = runs[runs["run_type"] == "fa"]
ymax = max(
0.01,
float(summary["q95"].max()) * 1.18,
float(fa["train_gap_to_bp"].max()) * 1.03,
)
ax.set_ylim(-0.004 * ymax, ymax)
axes[0].set_xlim(285, -280)
axes[1].set_xlim(90, -150)
for ax in axes:
ax.set_ylabel("FA train MSE - BP train MSE")
fig.suptitle("More trajectories separate finite-time ramp from capacity-limited gap")
fig.tight_layout()
path = outdir / "trajectory_gap_clouds_T10000_vs_T30000.png"
fig.savefig(path)
plt.close(fig)
fig2, ax = plt.subplots(figsize=(8.8, 5.3), dpi=180)
for label, run_dir, color in RUNS:
runs = pd.read_csv(run_dir / "runs.csv")
add_summary(ax, runs, label, color)
ax.axvline(0, color="black", linestyle="--", linewidth=1.1, label="hard margin=0")
ax.axhline(0, color="black", linewidth=1.0)
ax.set_xlim(285, -280)
ax.set_xlabel("hard FA capacity margin P - K_FA - N*out")
ax.set_ylabel("FA train MSE - BP train MSE")
ax.set_title("Training horizon compresses the apparent transition")
ax.grid(alpha=0.18)
ax.legend(frameon=True)
fig2.tight_layout()
path2 = outdir / "trajectory_gap_quantiles_T10000_vs_T30000.png"
fig2.savefig(path2)
plt.close(fig2)
print(f"plot: {path}")
print(f"plot: {path2}")
if __name__ == "__main__":
main()
|
