File size: 5,180 Bytes
ea8bfa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | #!/usr/bin/env python3
"""Plot confusion matrix and ablation figure for MVSA-Single results."""
from __future__ import annotations
import argparse
import csv
from pathlib import Path
from typing import List, Tuple
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Plot MVSA-Single result figures")
parser.add_argument("--results-dir", default="results/mvsa_single")
parser.add_argument("--dpi", type=int, default=150)
return parser.parse_args()
def read_confusion_matrix(path: Path) -> Tuple[List[str], np.ndarray]:
with path.open("r", encoding="utf-8") as f:
rows = list(csv.reader(f))
if len(rows) < 2:
raise ValueError(f"Invalid confusion matrix file: {path}")
labels = [cell.strip() for cell in rows[0][1:] if cell.strip()]
values = []
for row in rows[1:]:
values.append([int(float(x)) for x in row[1 : 1 + len(labels)]])
cm = np.array(values, dtype=np.int64)
return labels, cm
def read_ablation(path: Path) -> List[Tuple[str, float, float]]:
rows: List[Tuple[str, float, float]] = []
with path.open("r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
rows.append(
(
str(row["variant"]),
float(row["accuracy"]),
float(row["f1_weighted"]),
)
)
if not rows:
raise ValueError(f"Empty ablation file: {path}")
return rows
def plot_confusion_matrix(labels: List[str], cm: np.ndarray, out_path: Path, dpi: int) -> None:
fig, ax = plt.subplots(figsize=(5.2, 4.4))
row_sums = cm.sum(axis=1, keepdims=True).astype(np.float64)
with np.errstate(divide="ignore", invalid="ignore"):
cm_norm = np.divide(cm, row_sums, where=row_sums > 0)
cm_norm = np.nan_to_num(cm_norm)
im = ax.imshow(cm_norm, cmap="Blues", vmin=0.0, vmax=1.0)
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
pct = cm_norm[i, j] * 100.0
count = int(cm[i, j])
color = "white" if cm_norm[i, j] > 0.55 else "black"
ax.text(j, i, f"{count}\n({pct:.1f}%)", ha="center", va="center", fontsize=10, color=color)
ax.set_xticks(np.arange(len(labels)))
ax.set_yticks(np.arange(len(labels)))
ax.set_xticklabels([label.capitalize() for label in labels], fontsize=10)
ax.set_yticklabels([label.capitalize() for label in labels], fontsize=10)
ax.set_xlabel("Predicted", fontsize=11)
ax.set_ylabel("True", fontsize=11)
cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=9)
fig.tight_layout()
fig.savefig(out_path, dpi=dpi, bbox_inches="tight")
plt.close(fig)
def plot_ablation_table(rows: List[Tuple[str, float, float]], out_path: Path, dpi: int) -> None:
navy = "#2c3e6b"
orange = "#d4a017"
light_grey = "#f0f0f0"
fig, ax = plt.subplots(figsize=(6.2, 3.4))
ax.axis("off")
col_labels = ["Variant", "Acc", "F1-Weighted"]
table_data = [[name, f"{acc:.4f}", f"{f1w:.4f}"] for name, acc, f1w in rows]
table = ax.table(
cellText=table_data,
colLabels=col_labels,
loc="center",
cellLoc="center",
)
table.auto_set_font_size(False)
table.set_fontsize(10)
table.scale(1.3, 1.8)
for col_idx in range(len(col_labels)):
cell = table[0, col_idx]
cell.set_facecolor(navy)
cell.set_text_props(color="white", fontweight="bold")
cell.set_edgecolor("white")
full_f1 = rows[0][2]
for row_idx, (variant, _, f1w) in enumerate(rows, start=1):
is_full = variant == "Full"
for col_idx in range(len(col_labels)):
cell = table[row_idx, col_idx]
cell.set_edgecolor("#cccccc")
if is_full:
cell.set_facecolor("#dce6f1")
elif row_idx % 2 == 0:
cell.set_facecolor(light_grey)
if (f1w > full_f1) and (not is_full):
table[row_idx, 2].set_facecolor(orange)
table[row_idx, 2].set_text_props(fontweight="bold")
fig.tight_layout()
fig.savefig(out_path, dpi=dpi, bbox_inches="tight")
plt.close(fig)
def main() -> None:
args = parse_args()
results_dir = Path(args.results_dir)
cm_path = results_dir / "confusion_matrix.csv"
ablation_path = results_dir / "ablation_summary.csv"
if not cm_path.exists():
raise FileNotFoundError(f"Missing: {cm_path}")
if not ablation_path.exists():
raise FileNotFoundError(f"Missing: {ablation_path}")
labels, cm = read_confusion_matrix(cm_path)
ablation_rows = read_ablation(ablation_path)
out_cm = results_dir / "figure4a_confusion_matrix.png"
out_ablation = results_dir / "figure4b_ablation_study.png"
plot_confusion_matrix(labels, cm, out_cm, args.dpi)
plot_ablation_table(ablation_rows, out_ablation, args.dpi)
print(f"Saved -> {out_cm}")
print(f"Saved -> {out_ablation}")
if __name__ == "__main__":
main()
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