Test first order FA tangent hierarchy

1 files changed, 166 insertions, 0 deletions

diff --git a/scripts/plot_tangent_hierarchy_first_order_try.py b/scripts/plot_tangent_hierarchy_first_order_try.py
new file mode 100644
index 0000000..c74c8d2
--- /dev/null
+++ b/scripts/plot_tangent_hierarchy_first_order_try.py
@@ -0,0 +1,166 @@
+#!/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()