Add new regression and ensemble experiment configurations for V2-M and OrthoBridge
- Introduced multiple regression experiment configurations targeting vf_md, including: - cd_solo_reg_set.json: CD tower only regression setup. - img_solo_reg_set.json: Image tower only regression setup. - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100). - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations. - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis. - Added ensemble configurations for OrthoBridge with different inner bridges: - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge. - ortho_alts_tritower.json: Tritower tests with the same inner bridges. - Created V2-M specific configurations: - baseline_reg_nt50.json: Regression baseline with V2-M backbone. - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M. - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types. - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks. - Promoted existing experiments to higher repetitions for robustness.
This commit is contained in:
rpotter6298
@@ -0,0 +1,298 @@
|
||||
"""F3 — Single-mode comparison via confidence strips.
|
||||
|
||||
Four panels showing per-eye predicted P(Glaucoma) coloured by VF-MD severity
|
||||
(when known), in the style of v3/figures/explainability/confidence_strips_comparison.png.
|
||||
|
||||
Panels (left → right):
|
||||
Clinical only (cd_solo_single)
|
||||
Image only (img_solo_single_refugelike)
|
||||
Hadamard fusion (ensemble_single_refugelike — baseline)
|
||||
Concat fusion (phase3_v4/single_bcd_concat)
|
||||
|
||||
Each panel shows test predictions pooled across all reps × folds. Points are
|
||||
jittered around their true-class column and coloured by VF-MD severity tier
|
||||
(early / moderate / severe / unknown for glaucoma rows; grey for healthy).
|
||||
|
||||
Re-run anytime:
|
||||
python -m v4.figures.F3_hyperfeature_ablation
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import h5py
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.patches as mpatches
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.metrics import roc_auc_score
|
||||
|
||||
from v4.figures.util.loaders import REPO_ROOT, RESULTS_ROOT
|
||||
|
||||
OUT = Path(__file__).parent / "output" / "F3_single_mode_strips.png"
|
||||
|
||||
# ── Palette (matches v3 confidence_strips) ───────────────────────────────────
|
||||
C_NORMAL = "#78909C"
|
||||
C_EARLY = "#29B6F6"
|
||||
C_MODERATE = "#FFB300"
|
||||
C_SEVERE = "#E53935"
|
||||
C_UNKNOWN = "#BDBDBD"
|
||||
|
||||
SEV_LABELS = {
|
||||
"normal": "Normal",
|
||||
"early": "Glaucoma — early (VF_MD > −6)",
|
||||
"moderate": "Glaucoma — moderate (−12 to −6)",
|
||||
"severe": "Glaucoma — severe (VF_MD < −12)",
|
||||
"unknown": "Glaucoma — VF_MD not recorded",
|
||||
}
|
||||
SEV_COLORS = {
|
||||
"normal": C_NORMAL,
|
||||
"early": C_EARLY,
|
||||
"moderate": C_MODERATE,
|
||||
"severe": C_SEVERE,
|
||||
"unknown": C_UNKNOWN,
|
||||
}
|
||||
|
||||
SEV_ORDER = ["normal", "unknown", "early", "moderate", "severe"]
|
||||
SEV_ALPHA = {"normal": 0.40, "unknown": 0.35, "early": 0.55, "moderate": 0.70, "severe": 0.85}
|
||||
SEV_SIZE = {"normal": 6, "unknown": 6, "early": 8, "moderate": 10, "severe": 12}
|
||||
|
||||
# ── Per-panel definitions: (label, results dir, eval_stage) ──────────────────
|
||||
# Top row: single-modality reference runs
|
||||
TOP_ROW = [
|
||||
("Clinical only", RESULTS_ROOT / "phase2_v4" / "cd_solo_single", "cd_fuse"),
|
||||
("Image only", RESULTS_ROOT / "refuge_v2m_baseline" / "img_solo_single_refugelike", "img_fuse"),
|
||||
]
|
||||
|
||||
# Bottom row: L1 fusion bridge variants (eye-level img+cd ensembles)
|
||||
BOTTOM_ROW = [
|
||||
("Concat fusion", RESULTS_ROOT / "phase3_v4" / "single_bcd_concat", "nt"),
|
||||
("Pairwise fusion", RESULTS_ROOT / "phase3_v4" / "single_bcd_pairwise", "nt"),
|
||||
("Gated fusion", RESULTS_ROOT / "phase3_v4" / "single_bcd_gated", "nt"),
|
||||
("Hadamard fusion", RESULTS_ROOT / "refuge_v2m_baseline" / "ensemble_single_refugelike", "nt"),
|
||||
]
|
||||
|
||||
CLINICAL_DIR = REPO_ROOT / "Papila" / "ClinicalData"
|
||||
|
||||
|
||||
# ── VFI loader (patient-level worst-eye severity, matches v3) ────────────────
|
||||
|
||||
def load_vfi() -> pd.DataFrame:
|
||||
od = pd.read_excel(CLINICAL_DIR / "patient_data_od.xlsx", header=1)
|
||||
os_= pd.read_excel(CLINICAL_DIR / "patient_data_os.xlsx", header=1)
|
||||
|
||||
def _clean(df):
|
||||
df = df.copy()
|
||||
if "Patient ID" not in df.columns and "ID" in df.columns:
|
||||
df.rename(columns={"ID": "Patient ID"}, inplace=True)
|
||||
df["Patient ID"] = df["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
|
||||
df["Diagnosis"] = pd.to_numeric(df["Diagnosis"], errors="coerce")
|
||||
df["VF_MD"] = pd.to_numeric(df["VF_MD"], errors="coerce")
|
||||
# PAPILA: 0=Normal, 1=Glaucoma, 2=Suspect — keep only binary subjects
|
||||
df = df[df["Diagnosis"].isin([0, 1])].copy()
|
||||
return df[["Patient ID", "Diagnosis", "VF_MD"]]
|
||||
|
||||
both = pd.concat([_clean(od), _clean(os_)], ignore_index=True)
|
||||
diag = both.groupby("Patient ID")["Diagnosis"].agg(lambda x: x.mode().iloc[0]).reset_index()
|
||||
vf = both.groupby("Patient ID")["VF_MD"].min().reset_index()
|
||||
out = diag.merge(vf, on="Patient ID").rename(
|
||||
columns={"Patient ID": "patient_id", "Diagnosis": "diagnosis", "VF_MD": "vf_md"}
|
||||
)
|
||||
|
||||
def _sev(row):
|
||||
if int(row["diagnosis"]) == 0: return "normal"
|
||||
v = row["vf_md"]
|
||||
if pd.isna(v): return "unknown"
|
||||
if v > -6: return "early"
|
||||
if v > -12: return "moderate"
|
||||
return "severe"
|
||||
out["severity"] = out.apply(_sev, axis=1)
|
||||
return out
|
||||
|
||||
|
||||
# ── Prediction pooler ────────────────────────────────────────────────────────
|
||||
|
||||
def collect_predictions(run_dir: Path, eval_stage: str) -> pd.DataFrame:
|
||||
"""Pool test rows across reps × folds. Returns DataFrame with
|
||||
patient_id, y_true, prob_glaucoma, rep, fold.
|
||||
Applies softmax to the 2-class logits."""
|
||||
if not run_dir.exists():
|
||||
return pd.DataFrame()
|
||||
rows: list[dict] = []
|
||||
for rep in sorted(run_dir.glob("rep*")):
|
||||
fp = next(iter(rep.rglob("predictions.h5")), None)
|
||||
if fp is None: continue
|
||||
with h5py.File(fp, "r") as f:
|
||||
if eval_stage not in f: continue
|
||||
grp = f[eval_stage]
|
||||
logits = grp["logits"][:]
|
||||
y_true = grp["y_true"][:].astype(int)
|
||||
split = grp["split"][:]
|
||||
eid0 = grp["entity_id_0"][:]
|
||||
n_folds, n_epochs, n_samples, n_heads, n_outputs = logits.shape
|
||||
if n_outputs != 2: continue
|
||||
ep, head = n_epochs - 1, n_heads - 1
|
||||
for fold in range(n_folds):
|
||||
labels = np.array([s.decode() if isinstance(s, bytes) else str(s) for s in split[fold]])
|
||||
test_mask = (labels == "test")
|
||||
if not test_mask.any(): continue
|
||||
lg = logits[fold, ep, test_mask, head, :] # (n_test, 2)
|
||||
# softmax
|
||||
e = np.exp(lg - lg.max(axis=1, keepdims=True))
|
||||
p = e / e.sum(axis=1, keepdims=True)
|
||||
for k, idx in enumerate(np.where(test_mask)[0]):
|
||||
rows.append({
|
||||
"rep": rep.name,
|
||||
"fold": fold,
|
||||
"patient_id": int(eid0[idx]),
|
||||
"y_true": int(y_true[idx]),
|
||||
"prob_glaucoma": float(p[k, 1]),
|
||||
})
|
||||
return pd.DataFrame(rows)
|
||||
|
||||
|
||||
# ── Panel render ─────────────────────────────────────────────────────────────
|
||||
|
||||
C_NORMAL_VIOLIN = "#4c72b0"
|
||||
C_GLAUCOMA_VIOLIN = "#c44e52"
|
||||
|
||||
|
||||
def _draw_panel(ax, df: pd.DataFrame, vfi: pd.DataFrame, label: str):
|
||||
df = df.merge(vfi[["patient_id", "severity"]], on="patient_id", how="left")
|
||||
df["severity"] = df["severity"].fillna("unknown")
|
||||
|
||||
rng = np.random.default_rng(42)
|
||||
x_pos = {0: 0.0, 1: 1.0}
|
||||
jitter_scale = 0.18
|
||||
|
||||
# Violin density behind everything (per true class)
|
||||
data_by_class = [df.loc[df["y_true"] == cls, "prob_glaucoma"].values for cls in [0, 1]]
|
||||
if all(len(d) > 0 for d in data_by_class):
|
||||
vp = ax.violinplot(data_by_class, positions=[0, 1], widths=0.7,
|
||||
showmedians=False, showextrema=False)
|
||||
for body, color in zip(vp["bodies"], [C_NORMAL_VIOLIN, C_GLAUCOMA_VIOLIN]):
|
||||
body.set_facecolor(color); body.set_alpha(0.30)
|
||||
body.set_edgecolor("none"); body.set_zorder(2)
|
||||
|
||||
for sev in SEV_ORDER:
|
||||
mask = df["severity"] == sev
|
||||
if not mask.any(): continue
|
||||
sub = df[mask]
|
||||
jitter = rng.uniform(-jitter_scale, jitter_scale, len(sub))
|
||||
x = np.array([x_pos[int(v)] for v in sub["y_true"]]) + jitter
|
||||
ax.scatter(x, sub["prob_glaucoma"].values,
|
||||
c=SEV_COLORS[sev], s=SEV_SIZE[sev],
|
||||
alpha=SEV_ALPHA[sev], linewidths=0, zorder=3)
|
||||
|
||||
# Median lines + TN/TP rate labels per class
|
||||
xtick_labels = []
|
||||
for cls, xc in x_pos.items():
|
||||
vals = df.loc[df["y_true"] == cls, "prob_glaucoma"]
|
||||
if not len(vals):
|
||||
xtick_labels.append("Normal" if cls == 0 else "Glaucoma"); continue
|
||||
med = float(np.median(vals))
|
||||
ax.plot([xc - jitter_scale - 0.04, xc + jitter_scale + 0.04],
|
||||
[med, med], color="#222", lw=2.0, zorder=5)
|
||||
if cls == 0:
|
||||
rate = (vals <= 0.5).mean() * 100
|
||||
xtick_labels.append(f"Normal\nTN {rate:.0f}%")
|
||||
else:
|
||||
rate = (vals > 0.5).mean() * 100
|
||||
xtick_labels.append(f"Glaucoma\nTP {rate:.0f}%")
|
||||
|
||||
ax.axhline(0.5, color="#888", lw=1.2, ls="--", alpha=0.7, zorder=2)
|
||||
ax.set_xticks([0, 1]); ax.set_xticklabels(xtick_labels, fontsize=10)
|
||||
ax.set_ylim(-0.04, 1.04); ax.set_xlim(-0.55, 1.55)
|
||||
ax.set_title(label, fontsize=11, fontweight="bold")
|
||||
ax.grid(axis="y", alpha=0.3, zorder=1)
|
||||
|
||||
# AUC across reps×folds (per-fold AUC averaged)
|
||||
fold_aucs = []
|
||||
for _, g in df.groupby(["rep", "fold"]):
|
||||
if g["y_true"].nunique() < 2: continue
|
||||
try: fold_aucs.append(roc_auc_score(g["y_true"], g["prob_glaucoma"]))
|
||||
except Exception: pass
|
||||
if fold_aucs:
|
||||
ax.text(0.66, 0.0,
|
||||
f"AUC = {np.mean(fold_aucs):.3f} ± {np.std(fold_aucs):.3f}",
|
||||
transform=ax.transAxes, ha="right", va="bottom",
|
||||
fontsize=9, color="#333",
|
||||
bbox=dict(facecolor="white", alpha=0.7, edgecolor="none", pad=2))
|
||||
|
||||
|
||||
def render() -> None:
|
||||
vfi = load_vfi()
|
||||
top_dfs = [(lbl, collect_predictions(p, s)) for lbl, p, s in TOP_ROW]
|
||||
bottom_dfs = [(lbl, collect_predictions(p, s)) for lbl, p, s in BOTTOM_ROW]
|
||||
|
||||
for lbl, df in top_dfs + bottom_dfs:
|
||||
if len(df):
|
||||
print(f" {lbl:<18s} n_rows={len(df):>5d} (pid={df['patient_id'].nunique()}, reps={df['rep'].nunique()})")
|
||||
else:
|
||||
print(f" {lbl:<18s} no data")
|
||||
|
||||
n_cols = len(BOTTOM_ROW)
|
||||
fig = plt.figure(figsize=(4.4 * n_cols, 12))
|
||||
fig.patch.set_facecolor("#e8e8e8")
|
||||
fig.suptitle("L1 Fusion Comparison", fontsize=14, fontweight="bold")
|
||||
|
||||
gs = fig.add_gridspec(2, n_cols, hspace=0.30, wspace=0.15)
|
||||
|
||||
# Top row: 2 reference panels at same width as bottom panels, centered.
|
||||
# In a 4-column bottom grid, that's columns 1 and 2.
|
||||
n_top = len(top_dfs)
|
||||
top_offset = (n_cols - n_top) // 2 # leading empty columns
|
||||
top_axes = []
|
||||
for i, (lbl, df) in enumerate(top_dfs):
|
||||
ax = fig.add_subplot(gs[0, top_offset + i])
|
||||
top_axes.append(ax)
|
||||
ax.set_facecolor("#e8e8e8")
|
||||
if len(df):
|
||||
_draw_panel(ax, df, vfi, lbl)
|
||||
else:
|
||||
ax.text(0.5, 0.5, "(pending)", ha="center", va="center",
|
||||
fontsize=12, color="#888", transform=ax.transAxes)
|
||||
ax.set_xticks([]); ax.set_yticks([])
|
||||
ax.set_title(lbl, fontsize=11, fontweight="bold")
|
||||
|
||||
# Bottom row: 4 fusion variants
|
||||
bottom_axes = []
|
||||
sharey = None
|
||||
for i, (lbl, df) in enumerate(bottom_dfs):
|
||||
ax = fig.add_subplot(gs[1, i], sharey=sharey)
|
||||
sharey = sharey or ax
|
||||
bottom_axes.append(ax)
|
||||
ax.set_facecolor("#e8e8e8")
|
||||
if len(df):
|
||||
_draw_panel(ax, df, vfi, lbl)
|
||||
else:
|
||||
ax.text(0.5, 0.5, "(pending)", ha="center", va="center",
|
||||
fontsize=12, color="#888", transform=ax.transAxes)
|
||||
ax.set_xticks([]); ax.set_yticks([])
|
||||
ax.set_title(lbl, fontsize=11, fontweight="bold")
|
||||
|
||||
top_axes[0].set_ylabel("Predicted P(Glaucoma)", fontsize=11)
|
||||
bottom_axes[0].set_ylabel("Predicted P(Glaucoma)", fontsize=11)
|
||||
for ax in bottom_axes[1:]:
|
||||
ax.set_yticklabels([])
|
||||
|
||||
legend_patches = [mpatches.Patch(color=SEV_COLORS[s], label=SEV_LABELS[s])
|
||||
for s in ["normal", "early", "moderate", "severe", "unknown"]]
|
||||
fig.legend(handles=legend_patches, fontsize=9,
|
||||
loc="lower center", ncol=len(legend_patches),
|
||||
framealpha=0.75, bbox_to_anchor=(0.5, -0.01))
|
||||
|
||||
fig.tight_layout(rect=[0, 0.06, 1, 0.97])
|
||||
OUT.parent.mkdir(parents=True, exist_ok=True)
|
||||
fig.savefig(OUT, dpi=180, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"saved {OUT}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
render()
|
||||
Reference in New Issue
Block a user