rpotter6298
3d7777f010
- Introduced `fused_importance_with_axial.py` to evaluate the importance of Axial_Length in the fused-head model. - Created JSON configurations for various experiments excluding zero-importance clinical features: - `cd_solo_bilateral_dropzero.json`: Bilateral clinical-only evaluation. - `cd_solo_single_dropzero.json`: Single-eye clinical-only evaluation. - `ensemble_refugelike_ckpt_dropzero.json`: Ensemble model with dropped zero-importance features. - `ensemble_single_refugelike_dropzero.json`: Single-eye ensemble model with dropped features.
120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
"""F8 quadrant attention chart (full-image scope).
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Reads the per-cell quadrant fractions produced by
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``v4.figures.F8_explainability`` (see CSV at
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``output/F8_quadrant_fractions.csv``) and renders a single-panel grouped bar
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chart of full-image Grad-CAM intensity by optic-disc quadrant. Within-disc
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and peri-disc scopes are present in the CSV but are not plotted here; see
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the CSV for the per-cell numbers if you need them.
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Re-run:
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python -m v4.figures.F8_quadrant_plot
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"""
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from __future__ import annotations
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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CSV = Path(__file__).parent / "output" / "F8_quadrant_fractions.csv"
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OUT = Path(__file__).parent / "output" / "F8_quadrant_attention.png"
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QUAD_ORDER = ("ST", "SN", "IT", "IN")
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QUAD_LABEL = {
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"ST": "Superotemporal",
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"SN": "Superonasal",
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"IT": "Inferotemporal",
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"IN": "Inferonasal",
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}
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CELL_ORDER = [
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("Normal", "correct"),
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("Normal", "incorrect"),
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("Glaucoma", "correct"),
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("Glaucoma", "incorrect"),
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]
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CELL_COLOR = {
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("Normal", "correct"): "#3B6FB5",
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("Normal", "incorrect"): "#8BB0DA",
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("Glaucoma", "correct"): "#c44e52",
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("Glaucoma", "incorrect"): "#e6a3a4",
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}
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CELL_LABEL = {
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("Normal", "correct"): "Normal correct",
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("Normal", "incorrect"): "Normal incorrect",
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("Glaucoma", "correct"): "Glaucoma correct",
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("Glaucoma", "incorrect"): "Glaucoma incorrect",
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}
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def render() -> None:
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if not CSV.exists():
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raise SystemExit(
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f"CSV {CSV} not found. Run `python -m v4.figures.F8_explainability "
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"--only-gradcam --run-gradcam` first."
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)
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df = pd.read_csv(CSV)
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df_full = df[df["scope"] == "full"]
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fig, ax = plt.subplots(figsize=(9.6, 6.0))
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n_quad = len(QUAD_ORDER)
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n_cell = len(CELL_ORDER)
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bar_w = 0.18
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x = np.arange(n_quad, dtype=float)
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for i, cell in enumerate(CELL_ORDER):
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means, sds = [], []
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n_eyes = None
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for q in QUAD_ORDER:
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row = df_full[
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(df_full["class"] == cell[0])
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& (df_full["outcome"] == cell[1])
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& (df_full["quadrant"] == q)
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]
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means.append(float(row["mean"].iloc[0]) if len(row) else float("nan"))
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sds.append(float(row["sd"].iloc[0]) if len(row) else float("nan"))
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if n_eyes is None and len(row):
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n_eyes = int(row["n_eyes"].iloc[0])
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offsets = (i - (n_cell - 1) / 2.0) * bar_w
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bars = ax.bar(
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x + offsets, means, width=bar_w,
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yerr=sds, capsize=2,
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color=CELL_COLOR[cell], alpha=0.92,
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edgecolor="black", linewidth=0.6,
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label=f"{CELL_LABEL[cell]} (n = {n_eyes})",
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error_kw=dict(ecolor="#444", linewidth=0.8, capthick=0.8),
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)
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for rect, m in zip(bars, means):
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if np.isnan(m):
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continue
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ax.text(rect.get_x() + rect.get_width() / 2, m + 0.005,
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f"{m:.2f}", ha="center", va="bottom",
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fontsize=8.5, color="#222")
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ax.set_xticks(x)
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ax.set_xticklabels([QUAD_LABEL[q] for q in QUAD_ORDER], fontsize=11)
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ax.set_ylabel("Mean fraction of full-image Grad-CAM intensity", fontsize=11)
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ax.grid(axis="y", alpha=0.3, linestyle="--")
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ax.set_ylim(0, max(ax.get_ylim()[1], 0.7))
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ax.axhline(0.25, color="#888", linestyle=":", linewidth=0.8, alpha=0.6, zorder=0)
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ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.10), ncol=4,
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framealpha=0.94, fontsize=10)
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fig.tight_layout(rect=(0, 0.04, 1, 0.98))
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OUT.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(OUT, dpi=180, bbox_inches="tight")
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plt.close(fig)
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print(f"saved {OUT}")
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if __name__ == "__main__":
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render()
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