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#!/usr/bin/env python3
"""Plot first-order FA tangent-hierarchy predictor diagnostics."""
from __future__ import annotations
import argparse
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--csv",
type=Path,
default=Path("outputs/fa_tangent_hierarchy_first_order_try/compressed_operator_rows.csv"),
)
parser.add_argument(
"--outdir",
type=Path,
default=Path("outputs/fa_tangent_hierarchy_first_order_try"),
)
return parser.parse_args()
def metrics(df: pd.DataFrame) -> pd.DataFrame:
rows = []
for early, group in df.groupby("early_steps"):
for label, column in [
("fixed K0", "fixed_gap"),
("linear velocity", "linear_gap"),
("retangent", "retangent_gap"),
("compressed", "compressed_gap"),
]:
error = group[column] - group["empirical_gap"]
rows.append(
{
"early_steps": int(early),
"predictor": label,
"mae": float(error.abs().mean()),
"bias": float(error.mean()),
"rmse": float(np.sqrt(np.mean(np.square(error)))),
"corr": float(group[column].corr(group["empirical_gap"])),
}
)
return pd.DataFrame(rows)
def plot_scatter(df: pd.DataFrame, outdir: Path) -> Path:
early_values = sorted(df["early_steps"].unique())
fig, axes = plt.subplots(1, len(early_values) + 1, figsize=(5.0 * (len(early_values) + 1), 4.6), dpi=170)
panels = [("fixed K0", "fixed_gap", df)]
for early in early_values:
panels.append((f"linear velocity s={early}", "linear_gap", df[df["early_steps"] == early]))
values = []
for _title, column, group in panels:
values.extend(group[column].tolist())
values.extend(group["empirical_gap"].tolist())
lo, hi = float(min(values)), float(max(values))
pad = 0.04 * (hi - lo + 1e-12)
for ax, (title, column, group) in zip(axes, panels):
ax.scatter(group[column], group["empirical_gap"], s=26, alpha=0.72, color="#1f5a9d")
ax.plot([lo - pad, hi + pad], [lo - pad, hi + pad], color="black", linewidth=1.0)
err = group[column] - group["empirical_gap"]
ax.set_title(f"{title}\nMAE={err.abs().mean():.4f}, bias={err.mean():+.4f}")
ax.set_xlabel("predicted gap")
ax.set_ylabel("empirical gap")
ax.grid(alpha=0.18)
ax.set_xlim(lo - pad, hi + pad)
ax.set_ylim(lo - pad, hi + pad)
fig.suptitle("First-order tangent-hierarchy predictor", y=1.02)
fig.tight_layout()
path = outdir / "first_order_prediction_scatter_by_early_step.png"
fig.savefig(path, bbox_inches="tight")
plt.close(fig)
return path
def plot_metrics(metric_df: pd.DataFrame, outdir: Path) -> Path:
fig, axes = plt.subplots(1, 3, figsize=(13.5, 4.4), dpi=170)
predictors = ["fixed K0", "linear velocity", "retangent", "compressed"]
colors = {
"fixed K0": "#777777",
"linear velocity": "#1f5a9d",
"retangent": "#2b8a3e",
"compressed": "#8a4fb0",
}
for ax, metric_name in zip(axes, ["mae", "bias", "corr"]):
for predictor in predictors:
sub = metric_df[metric_df["predictor"] == predictor].sort_values("early_steps")
ax.plot(
sub["early_steps"],
sub[metric_name],
marker="o",
linewidth=1.8,
color=colors[predictor],
label=predictor,
)
if metric_name == "bias":
ax.axhline(0.0, color="black", linewidth=1.0)
ax.set_xlabel("early operator step s")
ax.set_title(metric_name.upper())
ax.grid(alpha=0.18)
axes[0].set_ylabel("metric value")
axes[0].legend(fontsize=8)
fig.suptitle("Prediction metrics vs early operator horizon", y=1.02)
fig.tight_layout()
path = outdir / "first_order_error_metrics_by_early_step.png"
fig.savefig(path, bbox_inches="tight")
plt.close(fig)
return path
def plot_distributions(df: pd.DataFrame, outdir: Path) -> Path:
early_values = sorted(df["early_steps"].unique())
fig, axes = plt.subplots(1, len(early_values), figsize=(5.0 * len(early_values), 4.4), dpi=170, squeeze=False)
for ax, early in zip(axes.ravel(), early_values):
group = df[df["early_steps"] == early]
bins = np.linspace(
min(group["empirical_gap"].min(), group["linear_gap"].min(), group["fixed_gap"].min()),
max(group["empirical_gap"].max(), group["linear_gap"].max(), group["fixed_gap"].max()),
18,
)
ax.hist(group["empirical_gap"], bins=bins, alpha=0.45, density=True, color="#c65f16", label="empirical")
ax.hist(group["fixed_gap"], bins=bins, alpha=0.32, density=True, color="#777777", label="fixed K0")
ax.hist(group["linear_gap"], bins=bins, alpha=0.36, density=True, color="#1f5a9d", label="linear velocity")
ax.set_title(f"gap distribution, s={early}")
ax.set_xlabel("FA/BP train gap")
ax.set_ylabel("density")
ax.legend(fontsize=8)
ax.grid(alpha=0.18)
fig.tight_layout()
path = outdir / "first_order_gap_distribution_overlay.png"
fig.savefig(path, bbox_inches="tight")
plt.close(fig)
return path
def main() -> None:
args = parse_args()
args.outdir.mkdir(parents=True, exist_ok=True)
df = pd.read_csv(args.csv)
metric_df = metrics(df)
metric_path = args.outdir / "first_order_metrics.csv"
metric_df.to_csv(metric_path, index=False)
paths = [
plot_scatter(df, args.outdir),
plot_metrics(metric_df, args.outdir),
plot_distributions(df, args.outdir),
]
print(f"metrics: {metric_path}")
for path in paths:
print(f"plot: {path}")
print(metric_df.to_string(index=False))
if __name__ == "__main__":
main()
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