| |
| """Create vector figures for the method overview and allocation ablations.""" |
|
|
| from __future__ import annotations |
|
|
| import csv |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| from matplotlib.patches import FancyArrowPatch, FancyBboxPatch, Circle |
|
|
|
|
| ROOT = Path(__file__).resolve().parent.parent |
| RESULTS = ROOT / "results" / "controlled" / "ablations" |
| FIGURES = ROOT / "figures" |
| BLUE = "#0072B2" |
| GREEN = "#009E73" |
| ORANGE = "#E69F00" |
| PURPLE = "#CC79A7" |
| GRAY = "#777777" |
| RED = "#D55E00" |
|
|
|
|
| def paper_style() -> None: |
| plt.rcParams.update({ |
| "font.family": "DejaVu Sans", |
| "font.size": 8, |
| "axes.titlesize": 9, |
| "axes.labelsize": 8, |
| "xtick.labelsize": 7, |
| "ytick.labelsize": 7, |
| "legend.fontsize": 7, |
| "axes.spines.top": False, |
| "axes.spines.right": False, |
| "pdf.fonttype": 42, |
| "ps.fonttype": 42, |
| }) |
|
|
|
|
| def overview() -> None: |
| paper_style() |
| fig, ax = plt.subplots(figsize=(7.1, 2.35)) |
| ax.set_xlim(0, 12) |
| ax.set_ylim(0, 4) |
| ax.axis("off") |
|
|
| nodes = { |
| "retrieve": (0.8, 2.0), |
| "plan A": (2.2, 3.0), |
| "plan B": (2.2, 1.0), |
| "merge": (3.8, 2.0), |
| "write": (5.2, 2.0), |
| } |
| edges = [("retrieve", "plan A"), ("retrieve", "plan B"), |
| ("plan A", "merge"), ("plan B", "merge"), ("merge", "write")] |
| for left, right in edges: |
| x1, y1 = nodes[left] |
| x2, y2 = nodes[right] |
| ax.add_patch(FancyArrowPatch((x1 + .25, y1), (x2 - .25, y2), |
| arrowstyle="-|>", mutation_scale=8, |
| linewidth=1.15, color="#5B6573")) |
| for label, (x, y) in nodes.items(): |
| selected = label == "retrieve" |
| terminal = label == "write" |
| face = "#DDF3EA" if selected else ("#FCE8DF" if terminal else "white") |
| edge = GREEN if selected else (RED if terminal else "#5B6573") |
| ax.add_patch(Circle((x, y), .31, facecolor=face, edgecolor=edge, linewidth=1.6)) |
| ax.text(x, y, label.replace(" ", "\n"), ha="center", va="center", fontsize=7.2) |
| ax.text(2.9, 3.64, "Dependency graph", ha="center", fontsize=9.2, fontweight="bold") |
| ax.text(.8, 1.54, "$p=.22$", ha="center", fontsize=6.8, color=GREEN) |
| ax.text(5.2, 1.54, "terminal", ha="center", fontsize=6.8, color=RED) |
| ax.text(2.9, .20, "Two paths reconverge at one terminal", |
| ha="center", fontsize=7.2, color="#3D4652") |
|
|
| boxes = [ |
| (6.15, 2.70, 1.50, .76, "1 Propagate risk\nto terminals", BLUE), |
| (7.95, 2.70, 1.70, .76, "2 Score marginal\nloss reduction", ORANGE), |
| (9.95, 2.70, 1.35, .76, "3 Select within\nbudget", PURPLE), |
| (8.02, 1.13, 1.60, .76, "4 Verify and\nupdate graph", GREEN), |
| ] |
| for x, y, w, h, text, color in boxes: |
| ax.add_patch(FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.05", |
| facecolor="white", edgecolor=color, linewidth=1.4)) |
| ax.text(x + w / 2, y + h / 2, text, ha="center", va="center", fontsize=7.25) |
| for p1, p2 in [((7.66, 3.08), (7.93, 3.08)), ((9.66, 3.08), (9.93, 3.08)), |
| ((10.62, 2.68), (8.84, 1.92)), ((8.02, 1.51), (5.92, 2.0))]: |
| ax.add_patch(FancyArrowPatch(p1, p2, arrowstyle="-|>", mutation_scale=8, |
| linewidth=1.0, color="#555555", |
| connectionstyle="arc3,rad=0.0")) |
| ax.text(8.82, .28, |
| "Recompute after every selected check", |
| ha="center", fontsize=7.3, color="#3D4652") |
| ax.text(.15, 3.72, "(a)", fontsize=9, fontweight="bold") |
| ax.text(6.0, 3.72, "(b)", fontsize=9, fontweight="bold") |
| fig.tight_layout(pad=.15) |
| fig.savefig(FIGURES / "figures_method_overview.pdf", bbox_inches="tight") |
| fig.savefig(FIGURES / "figures_method_overview.png", dpi=240, bbox_inches="tight") |
| plt.close(fig) |
|
|
|
|
| def allocation_evidence() -> None: |
| paper_style() |
| budget = list(csv.DictReader((RESULTS / "budget_curve.csv").open())) |
| optimality = list(csv.DictReader((RESULTS / "exact_optimality.csv").open())) |
| fig, axes = plt.subplots(1, 2, figsize=(7.0, 2.55)) |
|
|
| specs = [ |
| ("local-risk", "Local risk", BLUE, "o"), |
| ("risk-x-influence", "Risk × influence", RED, "s"), |
| ("static-toporisk", "Static TopoRisk", ORANGE, "D"), |
| ("toporisk", "TopoRisk", GREEN, "^"), |
| ] |
| for method, label, color, marker in specs: |
| rows = [r for r in budget if r["method"] == method] |
| x = [100 * float(r["budget"]) for r in rows] |
| y = [float(r["terminal_error"]) for r in rows] |
| e = [float(r["terminal_error_ci95"]) for r in rows] |
| axes[0].errorbar(x, y, yerr=e, label=label, color=color, marker=marker, |
| linewidth=1.4, markersize=4, capsize=2) |
| axes[0].set_xlabel("Verification budget (% of nodes)") |
| axes[0].set_ylabel("Corrupted-sink fraction ↓") |
| axes[0].grid(alpha=.25, linewidth=.5) |
| axes[0].legend(frameon=False, fontsize=6.7, ncol=2, columnspacing=.8, |
| handlelength=1.8, loc="upper right") |
| axes[0].set_title("Budget sweep on 240 forty-node DAGs", fontsize=9) |
|
|
| kinds = ["chain", "tree", "diamond", "random"] |
| x = list(range(len(kinds))) |
| greedy = [] |
| static = [] |
| for kind in kinds: |
| rows = [r for r in optimality if r["graph"] == kind] |
| greedy.append(100 * sum(float(r["greedy_fraction_of_optimal_gain"]) for r in rows) / len(rows)) |
| static.append(100 * sum(float(r["static_fraction_of_optimal_gain"]) for r in rows) / len(rows)) |
| width = .34 |
| axes[1].bar([v - width / 2 for v in x], static, width, label="Static TopoRisk", |
| color=ORANGE, edgecolor="black", linewidth=.4) |
| axes[1].bar([v + width / 2 for v in x], greedy, width, label="TopoRisk", |
| color=GREEN, edgecolor="black", linewidth=.4) |
| axes[1].set_xticks(x, [k.capitalize() for k in kinds], rotation=15) |
| axes[1].set_ylim(80, 101) |
| axes[1].set_ylabel("Fraction of optimal gain (%) ↑") |
| axes[1].set_title("Exact optimum on 200 twelve-node DAGs", fontsize=9) |
| axes[1].grid(axis="y", alpha=.25, linewidth=.5) |
| axes[1].legend(frameon=False, fontsize=7, loc="lower left") |
|
|
| fig.tight_layout(pad=.5, w_pad=1.0) |
| fig.savefig(FIGURES / "figures_allocation_evidence.pdf", bbox_inches="tight") |
| fig.savefig(FIGURES / "figures_allocation_evidence.png", dpi=240, bbox_inches="tight") |
| plt.close(fig) |
|
|
|
|
| if __name__ == "__main__": |
| overview() |
| allocation_evidence() |
|
|
