Add aligned missingness analyses and results

math/Q2/plot_missingness_effects.py

@@ -0,0 +1,90 @@
+"""Plot the aligned-data rate sweep and matched missing-type response."""
+from __future__ import annotations
+
+import csv
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+RESULTS = Path(__file__).resolve().parent / "results"
+
+
+def read_csv(name: str) -> list[dict[str, str]]:
+    with (RESULTS / name).open(encoding="utf-8-sig", newline="") as stream:
+        return list(csv.DictReader(stream))
+
+
+def main() -> None:
+    rate_rows = read_csv("controlled_missingness.csv")
+    rate_bootstrap = read_csv("controlled_group_bootstrap.csv")
+    type_bootstrap = read_csv("matched_missing_type_bootstrap.csv")
+
+    colors = {"single": "#3b82f6", "sync": "#dc2626", "partial": "#16a34a", "async": "#9333ea"}
+    labels = {"single": "Single modality", "sync": "Synchronous", "partial": "Partial overlap", "async": "Asynchronous"}
+    fig, (ax_rate, ax_type) = plt.subplots(1, 2, figsize=(12.4, 4.8), gridspec_kw={"width_ratios": [1.35, 1.0]})
+
+    baseline = next(row for row in rate_rows if row["model"] == "C5" and row["mask_pattern"] == "none")
+    baseline_ci = next(row for row in rate_bootstrap if row["model"] == "C5" and row["scenario"] == "0.0/none" and row["metric"] == "mae")
+    for mode in ("single", "sync", "partial", "async"):
+        rows = [baseline] + sorted(
+            (row for row in rate_rows if row["model"] == "C5" and row["mask_pattern"] == mode),
+            key=lambda row: float(row["rate_requested_per_selected_source"]),
+        )
+        x, y, lower, upper = [], [], [], []
+        for row in rows:
+            if row["mask_pattern"] == "none":
+                ci = baseline_ci
+                scenario = "0.0/none"
+            else:
+                scenario = f"{float(row['rate_requested_per_selected_source']):.1f}/{mode}"
+                ci = next(item for item in rate_bootstrap if item["model"] == "C5" and item["scenario"] == scenario and item["metric"] == "mae")
+            x.append(float(row["rate_realized_additional_global"]))
+            y.append(float(row["regression_mae"]))
+            lower.append(float(ci["ci_2_5"]))
+            upper.append(float(ci["ci_97_5"]))
+        ax_rate.errorbar(
+            x, y, yerr=[np.asarray(y) - np.asarray(lower), np.asarray(upper) - np.asarray(y)],
+            color=colors[mode], marker="o", linewidth=1.7, markersize=4.5,
+            capsize=2.5, label=labels[mode], alpha=0.95,
+        )
+    ax_rate.set_title("C5 performance across missing rates")
+    ax_rate.set_xlabel("Added missing rate (paper definition)")
+    ax_rate.set_ylabel("Regression MAE (95% group-bootstrap CI)")
+    ax_rate.grid(axis="both", color="#d1d5db", linewidth=0.7, alpha=0.65)
+    ax_rate.legend(frameon=False, fontsize=8.5, loc="upper left")
+
+    type_order = ("T", "A", "V", "TA", "TV", "AV", "TAV")
+    point, low, high = [], [], []
+    for label in type_order:
+        scenario = f"matched_type_{label}"
+        boot = next(row for row in type_bootstrap if row["model"] == "C5" and row["scenario"] == scenario and row["metric"] == "mae")
+        point.append(float(boot["delta_to_natural"]))
+        low.append(float(boot["delta_to_natural_ci_2_5"]))
+        high.append(float(boot["delta_to_natural_ci_97_5"]))
+    positions = np.arange(len(type_order))
+    ax_type.errorbar(
+        positions, point, yerr=[np.asarray(point) - low, high - np.asarray(point)],
+        fmt="o", color="#2563eb", ecolor="#2563eb", capsize=3, linewidth=1.4,
+        markersize=5,
+    )
+    ax_type.axhline(0, color="#374151", linewidth=1, linestyle="--")
+    ax_type.set_xticks(positions, type_order)
+    ax_type.set_title("Matched missing-modality types")
+    ax_type.set_xlabel("Hidden modality set")
+    ax_type.set_ylabel("MAE change from natural condition")
+    ax_type.grid(axis="y", color="#d1d5db", linewidth=0.7, alpha=0.65)
+    ax_type.text(
+        0.02, 0.02, "Same added feature-row count per sample and type",
+        transform=ax_type.transAxes, fontsize=7.5, color="#4b5563",
+    )
+
+    fig.tight_layout(pad=1.2)
+    output = RESULTS / "aligned_missingness_effects.png"
+    fig.savefig(output, dpi=200, bbox_inches="tight", facecolor="white")
+    print(output)
+
+
+if __name__ == "__main__":
+    main()