reworked iop_corr, added explainability tools and plotting tools, cleanup codebase
This commit is contained in:
rpotter6298
@@ -45,6 +45,13 @@ def main():
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if not first_run:
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cli.append("--persist-img-crop-cache")
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args = base_parser.parse_args(cli)
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# Skip if this mode is already fully complete.
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if args.run_name:
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tm_dir = Path(args.output_root) / args.run_name / eval_mode / tower_mode
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if (tm_dir / "summary.json").exists():
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print(f"[compare] {eval_mode}:{tower_mode} already complete — skipping.")
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first_run = False # treat as done so cache is preserved for later runs
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continue
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V2HyperTower(args).run()
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first_run = False
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+1318
-35
File diff suppressed because it is too large
Load Diff
@@ -13,7 +13,7 @@ REPO_ROOT = Path(__file__).resolve().parents[3]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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from classes.unet_segmenter import UNetSegmenter
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from classes.v2.unet_segmenter import UNetSegmenter
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def parse_args() -> argparse.Namespace:
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Executable
+68
@@ -0,0 +1,68 @@
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#!/usr/bin/env bash
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set -euo pipefail
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# Image-only runs v2.21 (6 total):
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# No crop: binary | multiclass
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# GT crop: binary | multiclass
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# UNet crop: binary | multiclass
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#
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# Purpose: isolate the effect of ROI cropping at the single-CNN level,
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# without any MD tower contribution (bridge-mode=image_only).
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ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
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cd "$ROOT_DIR"
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MANIFEST="manifest.csv"
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UNET_WEIGHTS="models/v2/refuge/segmentation/per_image/best.pt"
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COMMON=(
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--epochs 40
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--n-splits 5
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--batch-size 8
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--backbone refugelike
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--tower-mode single
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--bridge-mode image_only
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--single-warmup-tower-epochs 4
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--single-warmup-fused-epochs 0
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--img-crop-manifest "$MANIFEST"
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)
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# ── No crop ──────────────────────────────────────────────────────────────────
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echo "[1/6] No crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary \
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--run-name v2.21_imgonly_binary_nocrop_40ep_5fold
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echo "[2/6] No crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass \
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--run-name v2.21_imgonly_multiclass_nocrop_40ep_5fold
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# ── GT crop ──────────────────────────────────────────────────────────────────
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echo "[3/6] GT crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary --img-crop-gt \
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--run-name v2.21_imgonly_binary_gt_40ep_5fold
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echo "[4/6] GT crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass --img-crop-gt \
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--run-name v2.21_imgonly_multiclass_gt_40ep_5fold
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# ── UNet crop ────────────────────────────────────────────────────────────────
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echo "[5/6] UNet crop — binary, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode binary \
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--img-crop-weights "$UNET_WEIGHTS" \
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--run-name v2.21_imgonly_binary_unet_40ep_5fold
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echo "[6/6] UNet crop — multiclass, image-only..."
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python3 scripts/main/v2/run_multifold_v2.py \
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"${COMMON[@]}" --eval-mode multiclass \
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--img-crop-weights "$UNET_WEIGHTS" \
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--run-name v2.21_imgonly_multiclass_unet_40ep_5fold
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echo "Image-only v2.21 runs complete."
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Executable
+20
@@ -0,0 +1,20 @@
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#!/usr/bin/env bash
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set -e
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echo "=== v2.3 ensemble binary nocrop ==="
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python scripts/basic_analysis/compare_hypertower_modes.py \
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--tower-modes ensemble --eval-modes binary \
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--epochs 40 --n-splits 5 \
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--backbone refugelike \
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--img-crop-manifest analysis_data/unet_manifest.csv \
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--run-name v2.3_ensemble_binary_nocrop
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echo "=== v2.3 ensemble multiclass nocrop ==="
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python scripts/basic_analysis/compare_hypertower_modes.py \
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--tower-modes ensemble --eval-modes multiclass \
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--epochs 40 --n-splits 5 \
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--backbone refugelike \
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--img-crop-manifest analysis_data/unet_manifest.csv \
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--run-name v2.3_ensemble_multiclass_nocrop
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echo "=== done ==="
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Executable
+24
@@ -0,0 +1,24 @@
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#!/usr/bin/env bash
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set -e
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echo "=== v2.3 fused binary nocrop ==="
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python scripts/basic_analysis/compare_hypertower_modes.py \
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--tower-modes ensemble --eval-modes binary \
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--epochs 40 --n-splits 5 \
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--backbone refugelike \
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--img-crop-manifest analysis_data/unet_manifest.csv \
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--warmup-md-epochs 50 \
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--fused-head \
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--run-name v2.3_fused_binary_nocrop
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echo "=== v2.3 fused multiclass nocrop ==="
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python scripts/basic_analysis/compare_hypertower_modes.py \
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--tower-modes ensemble --eval-modes multiclass \
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--epochs 40 --n-splits 5 \
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--backbone refugelike \
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--img-crop-manifest analysis_data/unet_manifest.csv \
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--warmup-md-epochs 50 \
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--fused-head \
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--run-name v2.3_fused_multiclass_nocrop
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echo "=== done ==="
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@@ -611,84 +611,70 @@ def run_fusion_event_analysis(
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pred_i = pi.argmax(axis=1)
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pred_m = pm.argmax(axis=1)
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corrections = (pred_f == y_true) & (pred_i != y_true) & (pred_m != y_true)
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errors = (pred_f != y_true) & (pred_i == y_true) & (pred_m == y_true)
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n_corr = corrections.sum()
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n_err = errors.sum()
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both_wrong = ((pred_i != y_true) & (pred_m != y_true)).sum()
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both_correct = ((pred_i == y_true) & (pred_m == y_true)).sum()
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# confidence of the predicted class for each head
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conf_f = np.take_along_axis(pf, pred_f[:, None], axis=1).squeeze(1)
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conf_i = np.take_along_axis(pi, pred_i[:, None], axis=1).squeeze(1)
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conf_m = np.take_along_axis(pm, pred_m[:, None], axis=1).squeeze(1)
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# how much did fusion shift confidence vs the average of the two towers?
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conf_delta = conf_f - 0.5 * (conf_i + conf_m)
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print(f" N={N} corrections={n_corr} errors={n_err} ratio={n_corr}/{n_err}", flush=True)
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print(f" correction rate: {n_corr}/{both_wrong} = {n_corr/max(both_wrong,1):.2%} of both-wrong cases", flush=True)
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print(f" error rate: {n_err}/{both_correct} = {n_err/max(both_correct,1):.2%} of both-correct cases", flush=True)
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f_ok = pred_f == y_true
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i_ok = pred_i == y_true
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m_ok = pred_m == y_true
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# ---- cache intermediate hm/hi vectors for all patients ----
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model.eval()
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hm1_list, hm2_list, hi1_list, hi2_list = [], [], [], []
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with torch.no_grad():
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for batch in loader:
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x1 = batch.get("image_1"); m1 = batch.get("matrix_1")
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x2 = batch.get("image_2"); m2 = batch.get("matrix_2")
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if not (torch.is_tensor(x1) and torch.is_tensor(m1)):
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continue
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hi1 = model.bridge.ln_img(model.bridge.W_img(model.img_tower(x1.to(device))))
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hi2 = model.bridge.ln_img(model.bridge.W_img(model.img_tower(x2.to(device))))
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hm1 = model.bridge.ln_md(model.bridge.W_md(model.md_tower(m1.to(device))))
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hm2 = model.bridge.ln_md(model.bridge.W_md(model.md_tower(m2.to(device))))
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hi1_list.append(hi1.cpu()); hi2_list.append(hi2.cpu())
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hm1_list.append(hm1.cpu()); hm2_list.append(hm2.cpu())
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# 6 non-trivial bridge-effect event types
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full_correction = f_ok & ~i_ok & ~m_ok # both towers wrong → fused right
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img_assist = f_ok & ~i_ok & m_ok # img wrong, md right → fused right (md carried it)
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md_assist = f_ok & i_ok & ~m_ok # md wrong, img right → fused right (img carried it)
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full_error = ~f_ok & i_ok & m_ok # both towers right → fused wrong
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img_drag = ~f_ok & ~i_ok & m_ok # img wrong, md right → fused wrong (img dragged it down)
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md_drag = ~f_ok & i_ok & ~m_ok # md wrong, img right → fused wrong (md dragged it down)
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concordant_ok = f_ok & i_ok & m_ok
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concordant_bad = ~f_ok & ~i_ok & ~m_ok
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hi1 = torch.cat(hi1_list) # [N, fusion_dim]
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hi2 = torch.cat(hi2_list)
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hm1 = torch.cat(hm1_list) # [N, fusion_dim]
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hm2 = torch.cat(hm2_list)
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event_labels = [
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"full correction\n(both wrong→fused right)",
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"img assist\n(img wrong, md right→right)",
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"md assist\n(md wrong, img right→right)",
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"full error\n(both right→fused wrong)",
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"img drag\n(img wrong, md right→wrong)",
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"md drag\n(md wrong, img right→wrong)",
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]
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event_masks = [full_correction, img_assist, md_assist, full_error, img_drag, md_drag]
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event_colors = ["#2ca02c", "#98df8a", "#b5d46e", "#d62728", "#ff9896", "#ffbf9b"]
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event_keys = ["full_correction", "img_assist", "md_assist",
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"full_error", "img_drag", "md_drag"]
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counts = [int(m.sum()) for m in event_masks]
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hm1_mean = hm1.mean(dim=0, keepdim=True)
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hm2_mean = hm2.mean(dim=0, keepdim=True)
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print(f" N={N}", flush=True)
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for label, count in zip(event_labels, counts):
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print(f" {label.replace(chr(10), ' '):55s}: {count}", flush=True)
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n_corr, n_err = counts[0], counts[3]
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ratio_str = f"{n_corr}/{n_err}" if n_err > 0 else f"{n_corr}/0"
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print(f" full correction/error ratio: {ratio_str}", flush=True)
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print(f" conf_delta mean={conf_delta.mean():+.4f} median={np.median(conf_delta):+.4f}",
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flush=True)
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# ---- for each patient: compare logit[true_class] with real hm vs mean hm ----
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gains = []
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with torch.no_grad():
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for idx in range(N):
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true_cls = int(y_true[idx])
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# patient-level average of OD/OS fused vectors (SE skipped: hard to replicate outside forward)
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fused_real = (hi1[idx:idx+1] * hm1[idx:idx+1] + hi2[idx:idx+1] * hm2[idx:idx+1]) * 0.5
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fused_mean = (hi1[idx:idx+1] * hm1_mean + hi2[idx:idx+1] * hm2_mean) * 0.5
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logit_real = model.bridge.classifier_fused(fused_real.to(device))
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logit_mean = model.bridge.classifier_fused(fused_mean.to(device))
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gain = (logit_real[0, true_cls] - logit_mean[0, true_cls]).item()
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gains.append(gain)
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gains = np.array(gains)
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if n_corr > 0:
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corr_gains = gains[corrections]
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helped = (corr_gains > 0).sum()
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print(f"\n Fusion corrections — MD gate gain vs mean gate:", flush=True)
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print(f" mean gain = {corr_gains.mean():+.4f} median = {np.median(corr_gains):+.4f}", flush=True)
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print(f" real MD helped {helped}/{n_corr} correction patients ({helped/n_corr:.0%})", flush=True)
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if n_err > 0:
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err_gains = gains[errors]
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print(f"\n Fusion errors — MD gate gain vs mean gate:", flush=True)
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print(f" mean gain = {err_gains.mean():+.4f} median = {np.median(err_gains):+.4f}", flush=True)
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# ---- save CSV ----
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# ---- CSV ----
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import csv
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event_type = np.where(concordant_ok, "concordant_correct",
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np.where(concordant_bad, "concordant_wrong", "other")).astype(object)
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for mask, key in zip(event_masks, event_keys):
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event_type[mask] = key
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rows = []
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for idx in range(N):
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rows.append({
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"patient_idx": idx,
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"y_true": int(y_true[idx]),
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"pred_fused": int(pred_f[idx]),
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"pred_img": int(pred_i[idx]),
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"pred_md": int(pred_m[idx]),
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"conf_fused": float(pf[idx].max()),
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"conf_img": float(pi[idx].max()),
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"conf_md": float(pm[idx].max()),
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"is_correction": bool(corrections[idx]),
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"is_error": bool(errors[idx]),
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"md_gate_gain": float(gains[idx]),
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"patient_idx": idx,
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"y_true": int(y_true[idx]),
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"pred_fused": int(pred_f[idx]),
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"pred_img": int(pred_i[idx]),
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"pred_md": int(pred_m[idx]),
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"conf_fused": float(conf_f[idx]),
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"conf_img": float(conf_i[idx]),
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"conf_md": float(conf_m[idx]),
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"conf_delta": float(conf_delta[idx]),
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"event_type": event_type[idx],
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})
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csv_path = out_dir / "fusion_events.csv"
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with csv_path.open("w", newline="") as f:
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@@ -697,25 +683,61 @@ def run_fusion_event_analysis(
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writer.writerows(rows)
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print(f" Saved → {csv_path}", flush=True)
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# ---- chart ----
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fig, axes = plt.subplots(1, 2, figsize=(11, 4))
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# ---- plot ----
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fig, axes = plt.subplots(1, 3, figsize=(15, 4))
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categories = ["corrections\n(both wrong→fused right)", "errors\n(both right→fused wrong)"]
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counts = [int(n_corr), int(n_err)]
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axes[0].bar(categories, counts, color=["#e05c5c", "#5c9ee0"], width=0.5)
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# Panel 1: stacked bar — positive events vs negative events
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pos_counts = counts[:3]
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neg_counts = counts[3:]
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pos_colors = event_colors[:3]
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neg_colors = event_colors[3:]
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for bar_x, bar_counts, bar_colors in ((0, pos_counts, pos_colors),
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(1, neg_counts, neg_colors)):
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bot = 0
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for c, col in zip(bar_counts, bar_colors):
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axes[0].bar(bar_x, c, bottom=bot, color=col, width=0.5)
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if c > 0:
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axes[0].text(bar_x, bot + c / 2, str(c), ha="center", va="center",
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fontsize=9, fontweight="bold")
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bot += c
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axes[0].set_xticks([0, 1])
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axes[0].set_xticklabels(["Positive\nevents", "Negative\nevents"])
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axes[0].set_ylabel("Count")
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axes[0].set_title(f"Fusion Events (N={N})")
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for i, v in enumerate(counts):
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axes[0].text(i, v + 0.1, str(v), ha="center", fontsize=11)
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patches = [mpatches.Patch(color=c, label=l.replace("\n", " "))
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for c, l in zip(event_colors, event_labels)]
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axes[0].legend(handles=patches, fontsize=6, loc="upper right")
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if n_corr > 0:
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axes[1].hist(gains[corrections], bins=10, alpha=0.7, color="#e05c5c", label=f"corrections (n={n_corr})")
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if n_err > 0:
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axes[1].hist(gains[errors], bins=10, alpha=0.7, color="#5c9ee0", label=f"errors (n={n_err})")
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axes[1].axvline(0, color="black", linewidth=0.8)
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axes[1].set_xlabel("MD gate gain vs mean gate\n(logit[true class]: real − mean)")
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axes[1].set_title("Does real MD help the fused prediction?")
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axes[1].legend(fontsize=9)
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# Panel 2: conf_delta boxplot per event type (only non-empty)
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box_data = [conf_delta[m] for m in event_masks if m.sum() > 0]
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box_labels = [l.split("\n")[0] for m, l in zip(event_masks, event_labels) if m.sum() > 0]
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box_cols = [c for m, c in zip(event_masks, event_colors) if m.sum() > 0]
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if box_data:
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bp = axes[1].boxplot(box_data, patch_artist=True, widths=0.5)
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for patch, color in zip(bp["boxes"], box_cols):
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patch.set_facecolor(color)
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axes[1].set_xticks(range(1, len(box_labels) + 1))
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axes[1].set_xticklabels(box_labels, rotation=35, ha="right", fontsize=7)
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axes[1].axhline(0, color="black", linewidth=0.8, linestyle="--")
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axes[1].set_ylabel("conf_delta\n(fused − avg(img, md))")
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axes[1].set_title("Confidence delta by event type")
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# Panel 3: img vs md confidence space, coloured by event type
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for mask, color, label in zip(event_masks, event_colors, event_labels):
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if mask.sum() > 0:
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axes[2].scatter(conf_i[mask], conf_m[mask], c=color,
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label=label.split("\n")[0], alpha=0.85, s=45, edgecolors="none")
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if concordant_ok.sum() > 0:
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axes[2].scatter(conf_i[concordant_ok], conf_m[concordant_ok],
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c="lightgrey", alpha=0.4, s=20, edgecolors="none", label="concordant correct")
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if concordant_bad.sum() > 0:
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axes[2].scatter(conf_i[concordant_bad], conf_m[concordant_bad],
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c="darkgrey", alpha=0.4, s=20, edgecolors="none", label="concordant wrong")
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axes[2].plot([0, 1], [0, 1], "k--", linewidth=0.5, alpha=0.4)
|
||||
axes[2].set_xlabel("conf_img")
|
||||
axes[2].set_ylabel("conf_md")
|
||||
axes[2].set_title("Tower confidence space\ncoloured by fusion event")
|
||||
axes[2].legend(fontsize=6, loc="lower right")
|
||||
|
||||
fig.tight_layout()
|
||||
fig.savefig(out_dir / "fusion_events.png", dpi=150)
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,652 @@
|
||||
"""
|
||||
Plot predicted-probability strip charts for V2 runs.
|
||||
|
||||
Three styles available via --style:
|
||||
|
||||
strips (default)
|
||||
X = true class, Y = P(Glaucoma), color = true class × fold shade.
|
||||
Works for binary and multiclass.
|
||||
|
||||
confidence
|
||||
X = predicted class (major) subdivided by true class (minor sub-column).
|
||||
Y = model confidence in its own prediction (P of the predicted class).
|
||||
Color = true class × fold shade.
|
||||
Makes high-confidence mistakes immediately visible.
|
||||
|
||||
triangle (multiclass only)
|
||||
Ternary / simplex plot. Each corner = 100% probability for one class.
|
||||
Every sample is a dot placed at its softmax probability vector
|
||||
(p_H, p_G, p_S) using barycentric coordinates. The centroid is maximum
|
||||
uncertainty (1/3, 1/3, 1/3). Correctly classified samples cluster near
|
||||
their true-class corner; mistakes drift toward the wrong corner.
|
||||
|
||||
Colour families (light → dark = fold 0 → fold N-1):
|
||||
Blues = Healthy / Normal eyes
|
||||
Reds = Glaucoma eyes
|
||||
Greens = Suspect eyes
|
||||
|
||||
Usage
|
||||
-----
|
||||
python scripts/output_analysis/visualizations/plot_prob_strips.py \
|
||||
--run-dir analysis_data/v2.3_single_multiclass_nocrop/multiclass/single \
|
||||
--style triangle --head fused
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.ticker as ticker
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
# ── colour families ────────────────────────────────────────────────────────────
|
||||
_CLASS_FAMILIES = {
|
||||
0: ("#aac4ff", "#0a3d91"), # blues (healthy / normal)
|
||||
1: ("#ffaaaa", "#8b0000"), # reds (glaucoma)
|
||||
2: ("#aaffcc", "#1a6b3c"), # greens (suspect)
|
||||
}
|
||||
|
||||
_CLASS_LABELS = {
|
||||
0: "Healthy",
|
||||
1: "Glaucoma",
|
||||
2: "Suspect",
|
||||
}
|
||||
|
||||
_CLASS_LABELS_SHORT = {
|
||||
0: "H",
|
||||
1: "G",
|
||||
2: "S",
|
||||
}
|
||||
|
||||
|
||||
def _lerp_hex(c1: str, c2: str, t: float) -> tuple:
|
||||
def h(c): return tuple(int(c.lstrip("#")[i*2:i*2+2], 16) / 255 for i in range(3))
|
||||
r1, g1, b1 = h(c1)
|
||||
r2, g2, b2 = h(c2)
|
||||
return (r1 + t*(r2-r1), g1 + t*(g2-g1), b1 + t*(b2-b1))
|
||||
|
||||
|
||||
def _fold_colours(n_folds: int) -> dict[int, dict[int, tuple]]:
|
||||
out: dict[int, dict[int, tuple]] = {}
|
||||
for cls, (light, dark) in _CLASS_FAMILIES.items():
|
||||
out[cls] = {}
|
||||
for f in range(n_folds):
|
||||
t = f / max(n_folds - 1, 1)
|
||||
out[cls][f] = _lerp_hex(light, dark, t)
|
||||
return out
|
||||
|
||||
|
||||
def _load_folds(run_dir: Path, head: str) -> list[pd.DataFrame]:
|
||||
fold_dirs = sorted(
|
||||
[p for p in run_dir.glob("fold*") if p.is_dir() and re.search(r"\d+", p.name)],
|
||||
key=lambda p: int(re.search(r"\d+", p.name).group()),
|
||||
)
|
||||
if not fold_dirs:
|
||||
raise FileNotFoundError(f"No fold* directories found under {run_dir}")
|
||||
frames = []
|
||||
for fd in fold_dirs:
|
||||
csv = fd / "predictions_classic.csv"
|
||||
if not csv.exists():
|
||||
print(f" [warn] {csv} not found, skipping")
|
||||
continue
|
||||
df = pd.read_csv(csv)
|
||||
df["_fold"] = int(re.search(r"\d+", fd.name).group())
|
||||
frames.append(df)
|
||||
return frames
|
||||
|
||||
|
||||
def _detect_num_classes(df: pd.DataFrame, head: str) -> int:
|
||||
return len([c for c in df.columns if c.startswith(f"prob_{head}_c")])
|
||||
|
||||
|
||||
def _draw_grid_legend(ax_leg: plt.Axes, n_folds: int, classes: list[int],
|
||||
colours: dict, title: str = "True class") -> None:
|
||||
"""Rows = folds, columns = classes grid of coloured dots."""
|
||||
from matplotlib.patches import FancyBboxPatch
|
||||
ax_leg.axis("off")
|
||||
|
||||
col_xs = np.linspace(0.55, 0.88, len(classes)) if len(classes) > 1 else [0.72]
|
||||
row_ys = np.linspace(0.88, 0.05, n_folds + 1)
|
||||
header_y, dot_ys = row_ys[0], row_ys[1:]
|
||||
|
||||
for ci, cls in enumerate(classes):
|
||||
ax_leg.text(col_xs[ci], header_y, _CLASS_LABELS_SHORT.get(cls, f"C{cls}"),
|
||||
ha="center", va="bottom", fontsize=8, fontweight="bold",
|
||||
transform=ax_leg.transAxes)
|
||||
|
||||
for fi in range(n_folds):
|
||||
y = dot_ys[fi]
|
||||
ax_leg.text(0.05, y, f"Fold {fi}", ha="left", va="center", fontsize=9,
|
||||
transform=ax_leg.transAxes)
|
||||
for ci, cls in enumerate(classes):
|
||||
ax_leg.scatter([col_xs[ci]], [y], color=colours[cls][fi], s=70, zorder=3,
|
||||
transform=ax_leg.transAxes, clip_on=False)
|
||||
|
||||
ax_leg.add_patch(FancyBboxPatch((0, 0), 1, 1, boxstyle="round,pad=0.02",
|
||||
linewidth=0.8, edgecolor="#aaaaaa",
|
||||
facecolor="#f9f9f9", zorder=0,
|
||||
transform=ax_leg.transAxes))
|
||||
ax_leg.set_title(title, fontsize=9, pad=4)
|
||||
|
||||
|
||||
# ── strips style ───────────────────────────────────────────────────────────────
|
||||
|
||||
def plot_strip(run_dir: Path, head: str = "fused", out_dir: Path | None = None,
|
||||
jitter_strength: float = 0.08) -> Path:
|
||||
"""X = true class, Y = P(Glaucoma)."""
|
||||
frames = _load_folds(run_dir, head)
|
||||
all_true = sorted({int(v) for df in frames for v in df["y_true"]})
|
||||
n_folds = len(frames)
|
||||
colours = _fold_colours(n_folds)
|
||||
rng = np.random.default_rng(seed=0)
|
||||
prob_col = f"prob_{head}_c1"
|
||||
x_pos = {cls: i for i, cls in enumerate(all_true)}
|
||||
|
||||
leg_width = 0.8 + 0.55 * len(all_true)
|
||||
fig = plt.figure(figsize=(max(6, 2.2 * len(all_true) + 1), 7))
|
||||
gs = fig.add_gridspec(1, 2, width_ratios=[max(6, 2.2 * len(all_true)), leg_width],
|
||||
wspace=0.08)
|
||||
ax = fig.add_subplot(gs[0])
|
||||
ax_leg = fig.add_subplot(gs[1])
|
||||
|
||||
fig.suptitle(f"{run_dir.parent.parent.name} — P(Glaucoma) [{head}]",
|
||||
fontsize=11, y=1.01)
|
||||
|
||||
for fold_idx, df in enumerate(frames):
|
||||
for cls in all_true:
|
||||
sub = df[df["y_true"] == cls]
|
||||
if sub.empty:
|
||||
continue
|
||||
probs = sub[prob_col].values
|
||||
jitter = rng.uniform(-jitter_strength, jitter_strength, size=len(probs))
|
||||
ax.scatter(x_pos[cls] + jitter, probs, color=colours[cls][fold_idx],
|
||||
s=45, alpha=0.88, linewidths=0, zorder=3)
|
||||
|
||||
ax.set_xticks(list(x_pos.values()))
|
||||
ax.set_xticklabels([_CLASS_LABELS.get(c, f"C{c}") for c in all_true], fontsize=12)
|
||||
ax.set_xlim(-0.5, len(all_true) - 0.5)
|
||||
ax.set_ylim(-0.05, 1.05)
|
||||
ax.set_ylabel("Predicted P(Glaucoma)", fontsize=12)
|
||||
ax.set_xlabel("True class", fontsize=12)
|
||||
ax.axhline(0.5, color="grey", linestyle="--", linewidth=0.8, alpha=0.5)
|
||||
ax.grid(axis="y", linestyle=":", alpha=0.4)
|
||||
|
||||
_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="Legend")
|
||||
|
||||
fig.tight_layout()
|
||||
return _save(fig, out_dir or run_dir / "plots", f"prob_strips_{head}.png")
|
||||
|
||||
|
||||
# ── confidence style ───────────────────────────────────────────────────────────
|
||||
|
||||
def plot_confidence(run_dir: Path, head: str = "fused", out_dir: Path | None = None,
|
||||
jitter_strength: float = 0.06) -> Path:
|
||||
"""
|
||||
X = predicted class (major) × true class (minor sub-column).
|
||||
Y = model confidence = P(predicted class).
|
||||
Color = true class × fold shade.
|
||||
"""
|
||||
frames = _load_folds(run_dir, head)
|
||||
num_cls = _detect_num_classes(frames[0], head)
|
||||
all_cls = list(range(num_cls))
|
||||
all_true = sorted({int(v) for df in frames for v in df["y_true"]})
|
||||
n_folds = len(frames)
|
||||
colours = _fold_colours(n_folds)
|
||||
rng = np.random.default_rng(seed=0)
|
||||
|
||||
# Sub-column spacing within each predicted-class group
|
||||
# E.g. for 3 classes: sub-offsets at -0.25, 0, +0.25
|
||||
n_sub = len(all_true)
|
||||
sub_spacing = 0.22
|
||||
sub_offsets = np.linspace(-(n_sub - 1) * sub_spacing / 2,
|
||||
(n_sub - 1) * sub_spacing / 2,
|
||||
n_sub)
|
||||
sub_off = {cls: sub_offsets[i] for i, cls in enumerate(all_true)}
|
||||
|
||||
# Major x positions for each predicted class, spaced so sub-columns don't bleed
|
||||
group_gap = sub_spacing * n_sub + 0.35
|
||||
major_x = {pc: i * group_gap for i, pc in enumerate(all_cls)}
|
||||
|
||||
leg_width = 0.8 + 0.55 * n_sub
|
||||
fig_w = max(7, group_gap * len(all_cls) * 1.8 + 1)
|
||||
fig = plt.figure(figsize=(fig_w, 7))
|
||||
gs = fig.add_gridspec(1, 2, width_ratios=[fig_w - leg_width, leg_width],
|
||||
wspace=0.08)
|
||||
ax = fig.add_subplot(gs[0])
|
||||
ax_leg = fig.add_subplot(gs[1])
|
||||
|
||||
fig.suptitle(f"{run_dir.parent.parent.name} — Prediction confidence [{head}]",
|
||||
fontsize=11, y=1.01)
|
||||
|
||||
for fold_idx, df in enumerate(frames):
|
||||
prob_cols = [f"prob_{head}_c{c}" for c in all_cls]
|
||||
pred_col = f"pred_{head}"
|
||||
for true_cls in all_true:
|
||||
sub = df[df["y_true"] == true_cls].copy()
|
||||
if sub.empty:
|
||||
continue
|
||||
for pred_cls in all_cls:
|
||||
rows = sub[sub[pred_col] == pred_cls]
|
||||
if rows.empty:
|
||||
continue
|
||||
# confidence = probability assigned to the predicted class
|
||||
conf = rows[f"prob_{head}_c{pred_cls}"].values
|
||||
x_base = major_x[pred_cls] + sub_off[true_cls]
|
||||
jitter = rng.uniform(-jitter_strength * 0.5,
|
||||
jitter_strength * 0.5, size=len(conf))
|
||||
ax.scatter(x_base + jitter, conf, color=colours[true_cls][fold_idx],
|
||||
s=45, alpha=0.88, linewidths=0, zorder=3)
|
||||
|
||||
# X-axis: major ticks with predicted-class labels, minor sub-column markers
|
||||
ax.set_xlim(-group_gap * 0.5, group_gap * len(all_cls) - group_gap * 0.5)
|
||||
ax.set_xticks([major_x[pc] for pc in all_cls])
|
||||
ax.set_xticklabels([_CLASS_LABELS.get(pc, f"C{pc}") for pc in all_cls], fontsize=12)
|
||||
|
||||
# Light vertical separators between predicted-class groups
|
||||
for i in range(1, len(all_cls)):
|
||||
sep_x = (major_x[all_cls[i-1]] + major_x[all_cls[i]]) / 2
|
||||
ax.axvline(sep_x, color="#cccccc", linewidth=1.0, zorder=1)
|
||||
|
||||
# Sub-column labels (H/G/S) just below the x-axis
|
||||
for pred_cls in all_cls:
|
||||
for true_cls in all_true:
|
||||
lbl = _CLASS_LABELS_SHORT.get(true_cls, f"C{true_cls}")
|
||||
ax.text(major_x[pred_cls] + sub_off[true_cls], -0.085, lbl,
|
||||
ha="center", va="top", fontsize=7, color="#555555",
|
||||
transform=ax.get_xaxis_transform())
|
||||
|
||||
ax.set_ylim(-0.05, 1.05)
|
||||
ax.set_ylabel("Confidence P(predicted class)", fontsize=12)
|
||||
ax.set_xlabel("Predicted class", fontsize=12, labelpad=18)
|
||||
ax.axhline(0.5, color="grey", linestyle="--", linewidth=0.8, alpha=0.5)
|
||||
ax.grid(axis="y", linestyle=":", alpha=0.4)
|
||||
|
||||
_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="True class")
|
||||
|
||||
fig.tight_layout()
|
||||
return _save(fig, out_dir or run_dir / "plots", f"prob_confidence_{head}.png")
|
||||
|
||||
|
||||
# ── triangle / ternary style ───────────────────────────────────────────────────
|
||||
|
||||
# Equilateral triangle vertices in Cartesian space:
|
||||
# H (Healthy) = bottom-left (0, 0)
|
||||
# G (Glaucoma) = bottom-right (1, 0)
|
||||
# S (Suspect) = apex (0.5, sqrt(3)/2)
|
||||
_TRI_VERTICES = np.array([
|
||||
[0.0, 0.0], # class 0 – Healthy
|
||||
[1.0, 0.0], # class 1 – Glaucoma
|
||||
[0.5, np.sqrt(3) / 2], # class 2 – Suspect
|
||||
])
|
||||
|
||||
|
||||
def _bary_to_cart(probs: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert Nx3 barycentric coordinates (softmax probs) to Nx2 Cartesian.
|
||||
probs rows must sum to 1.
|
||||
"""
|
||||
return probs @ _TRI_VERTICES
|
||||
|
||||
|
||||
def _draw_triangle_grid(ax: plt.Axes, levels: tuple = (0.25, 0.5, 0.75)) -> None:
|
||||
"""Draw the triangle border and iso-probability grid lines."""
|
||||
from matplotlib.patches import Polygon
|
||||
from matplotlib.lines import Line2D
|
||||
|
||||
# Outer triangle
|
||||
tri = Polygon(_TRI_VERTICES, fill=False, edgecolor="#333333", linewidth=1.5, zorder=2)
|
||||
ax.add_patch(tri)
|
||||
|
||||
# Grid lines: for each class, lines where p_class = level,
|
||||
# parallel to the opposite edge.
|
||||
for level in levels:
|
||||
for cls in range(3):
|
||||
# Points on the two edges adjacent to this vertex at distance `level`
|
||||
v0 = _TRI_VERTICES[cls]
|
||||
v1 = _TRI_VERTICES[(cls + 1) % 3]
|
||||
v2 = _TRI_VERTICES[(cls + 2) % 3]
|
||||
# A line at p_cls = level divides the triangle:
|
||||
# p1 = level * v0 + (1-level) * v1
|
||||
# p2 = level * v0 + (1-level) * v2
|
||||
p1 = level * v0 + (1 - level) * v1
|
||||
p2 = level * v0 + (1 - level) * v2
|
||||
ax.plot([p1[0], p2[0]], [p1[1], p2[1]],
|
||||
color="#cccccc", linewidth=0.6, zorder=1, linestyle="--")
|
||||
|
||||
|
||||
def plot_triangle(run_dir: Path, head: str = "fused", out_dir: Path | None = None) -> Path:
|
||||
"""
|
||||
Ternary simplex plot of softmax probabilities for a 3-class run.
|
||||
Each dot = one eye; position = (p_H, p_G, p_S) in barycentric coords.
|
||||
Color = true class × fold shade.
|
||||
"""
|
||||
frames = _load_folds(run_dir, head)
|
||||
num_cls = _detect_num_classes(frames[0], head)
|
||||
if num_cls != 3:
|
||||
raise ValueError(f"Triangle plot requires 3 classes; got {num_cls}. "
|
||||
"Use --style strips for binary.")
|
||||
|
||||
all_true = sorted({int(v) for df in frames for v in df["y_true"]})
|
||||
n_folds = len(frames)
|
||||
colours = _fold_colours(n_folds)
|
||||
|
||||
leg_width = 0.8 + 0.55 * len(all_true)
|
||||
fig = plt.figure(figsize=(8, 7))
|
||||
gs = fig.add_gridspec(1, 2, width_ratios=[8 - leg_width, leg_width], wspace=0.05)
|
||||
ax = fig.add_subplot(gs[0], aspect="equal")
|
||||
ax_leg = fig.add_subplot(gs[1])
|
||||
|
||||
fig.suptitle(f"{run_dir.parent.parent.name} — Simplex [{head}]",
|
||||
fontsize=11, y=1.01)
|
||||
|
||||
_draw_triangle_grid(ax)
|
||||
|
||||
# Vertex labels — placed just outside each corner
|
||||
offsets = [(-0.07, -0.06), (0.07, -0.06), (0.0, 0.06)]
|
||||
for cls_idx in range(3):
|
||||
lbl = _CLASS_LABELS.get(cls_idx, f"C{cls_idx}")
|
||||
vx, vy = _TRI_VERTICES[cls_idx]
|
||||
dx, dy = offsets[cls_idx]
|
||||
ax.text(vx + dx, vy + dy, lbl, ha="center", va="center",
|
||||
fontsize=12, fontweight="bold")
|
||||
|
||||
# Centroid marker
|
||||
cx, cy = _TRI_VERTICES.mean(axis=0)
|
||||
ax.scatter([cx], [cy], color="#aaaaaa", s=30, marker="+", zorder=2, linewidths=1)
|
||||
ax.text(cx + 0.02, cy - 0.04, "1/3 each", fontsize=7, color="#999999", ha="left")
|
||||
|
||||
# Data dots
|
||||
rng = np.random.default_rng(seed=0)
|
||||
for fold_idx, df in enumerate(frames):
|
||||
prob_cols = [f"prob_{head}_c{c}" for c in range(3)]
|
||||
probs_all = df[prob_cols].values # Nx3
|
||||
y_true = df["y_true"].values.astype(int)
|
||||
for true_cls in all_true:
|
||||
mask = y_true == true_cls
|
||||
if not mask.any():
|
||||
continue
|
||||
probs = probs_all[mask] # Kx3
|
||||
xy = _bary_to_cart(probs) # Kx2
|
||||
noise = rng.normal(0, 0.004, xy.shape)
|
||||
ax.scatter(xy[:, 0] + noise[:, 0],
|
||||
xy[:, 1] + noise[:, 1],
|
||||
color=colours[true_cls][fold_idx],
|
||||
s=30, alpha=0.80, linewidths=0, zorder=3)
|
||||
|
||||
ax.set_xlim(-0.18, 1.18)
|
||||
ax.set_ylim(-0.12, 1.02)
|
||||
ax.axis("off")
|
||||
|
||||
_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="True class")
|
||||
|
||||
fig.tight_layout()
|
||||
return _save(fig, out_dir or run_dir / "plots", f"prob_triangle_{head}.png")
|
||||
|
||||
|
||||
# ── triangle3d style ──────────────────────────────────────────────────────────
|
||||
|
||||
# Triangle vertices for the 3D "bread-slice" view.
|
||||
# Triangles stand upright in the x-z plane; y is the depth (layer) axis.
|
||||
# H (Healthy) = bottom-left (0, 0) — back corner of the base
|
||||
# G (Glaucoma) = top-centre (0.5, √3/2) — apex (visually "at the top")
|
||||
# S (Suspect) = bottom-right (1, 0) — front corner toward the viewer
|
||||
_TRI3D_VERTS = np.array([
|
||||
[0.0, 0.0], # class 0 – Healthy (bottom-left / back)
|
||||
[0.5, np.sqrt(3) / 2], # class 1 – Glaucoma (top)
|
||||
[1.0, 0.0], # class 2 – Suspect (bottom-right / front)
|
||||
])
|
||||
|
||||
|
||||
def _bary_to_cart_3d(probs: np.ndarray) -> np.ndarray:
|
||||
"""Convert Nx3 softmax probs to Nx2 Cartesian using the 3D vertex layout."""
|
||||
return probs @ _TRI3D_VERTS
|
||||
|
||||
|
||||
def plot_triangle_3d(
|
||||
run_dir: Path,
|
||||
head: str = "fused",
|
||||
out_dir: Path | None = None,
|
||||
elev: float = 20,
|
||||
azim: float = -45,
|
||||
y_spacing: float = 0.4,
|
||||
) -> Path:
|
||||
"""
|
||||
3-D ternary "bread-slice" plot.
|
||||
|
||||
Each true class gets its own vertical triangle slice standing in the x-z
|
||||
plane, stacked along the y (depth) axis (camera at ~4:30 / SE position):
|
||||
• Healthy layer is at y=0 (front-left from 4:30 view)
|
||||
• Glaucoma layer is at y=1
|
||||
• Suspect layer is at y=2 (back-right from 4:30 view)
|
||||
|
||||
Within every slice the simplex corners are:
|
||||
• G (Glaucoma) at the top apex
|
||||
• H (Healthy) at the bottom-left (back corner of the base)
|
||||
• S (Suspect) at the bottom-right (front corner toward viewer)
|
||||
|
||||
View with elev/azim to see the slices as near-vertical planes with slight
|
||||
perspective depth.
|
||||
"""
|
||||
from mpl_toolkits.mplot3d import Axes3D # noqa: F401
|
||||
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
|
||||
from matplotlib.patches import Patch
|
||||
|
||||
frames = _load_folds(run_dir, head)
|
||||
num_cls = _detect_num_classes(frames[0], head)
|
||||
if num_cls != 3:
|
||||
raise ValueError("triangle3d requires 3 classes.")
|
||||
|
||||
all_true = sorted({int(v) for df in frames for v in df["y_true"]})
|
||||
n_folds = len(frames)
|
||||
colours = _fold_colours(n_folds)
|
||||
rng = np.random.default_rng(seed=0)
|
||||
|
||||
# Layer y positions: H front-left (y=0), S back-right (y=2) from 4:30 view
|
||||
y_pos = {cls: i * y_spacing for i, cls in enumerate(all_true)}
|
||||
|
||||
# Triangle wireframe in (x, z) — loop closed
|
||||
wire_x = np.append(_TRI3D_VERTS[:, 0], _TRI3D_VERTS[0, 0])
|
||||
wire_z = np.append(_TRI3D_VERTS[:, 1], _TRI3D_VERTS[0, 1])
|
||||
|
||||
fig = plt.figure(figsize=(9, 7))
|
||||
ax = fig.add_subplot(111, projection="3d")
|
||||
ax.view_init(elev=elev, azim=azim)
|
||||
|
||||
fig.suptitle(run_dir.parent.parent.name, fontsize=11)
|
||||
|
||||
# ── pre-compute all xz points per class (needed for KDE) ─────────────────
|
||||
from scipy.stats import gaussian_kde
|
||||
import matplotlib.colors as mcolors
|
||||
|
||||
_xz_by_cls: dict[int, np.ndarray] = {}
|
||||
for df in frames:
|
||||
prob_cols_all = [f"prob_{head}_c{c}" for c in range(3)]
|
||||
p_all = df[prob_cols_all].values
|
||||
yt_all = df["y_true"].values.astype(int)
|
||||
for cls in all_true:
|
||||
m = yt_all == cls
|
||||
if m.any():
|
||||
xz_pts = _bary_to_cart_3d(p_all[m])
|
||||
_xz_by_cls[cls] = (
|
||||
np.vstack([_xz_by_cls[cls], xz_pts])
|
||||
if cls in _xz_by_cls else xz_pts
|
||||
)
|
||||
|
||||
# KDE grid setup — evaluate on a 40×40 grid masked to the triangle interior
|
||||
_G = 40
|
||||
_x_lin = np.linspace(0.0, 1.0, _G)
|
||||
_z_lin = np.linspace(0.0, np.sqrt(3) / 2, _G)
|
||||
_dx = _x_lin[1] - _x_lin[0]
|
||||
_dz = _z_lin[1] - _z_lin[0]
|
||||
_XX, _ZZ = np.meshgrid(_x_lin, _z_lin) # (_G, _G)
|
||||
|
||||
# Vectorised inside-triangle test (barycentric)
|
||||
vH, vG, vS = _TRI3D_VERTS
|
||||
_denom = (vG[1] - vS[1]) * (vH[0] - vS[0]) + (vS[0] - vG[0]) * (vH[1] - vS[1])
|
||||
def _inside(px, pz):
|
||||
la = ((vG[1] - vS[1]) * (px - vS[0]) + (vS[0] - vG[0]) * (pz - vS[1])) / _denom
|
||||
lb = ((vS[1] - vH[1]) * (px - vS[0]) + (vH[0] - vS[0]) * (pz - vS[1])) / _denom
|
||||
return (la >= 0) & (lb >= 0) & ((1 - la - lb) >= 0)
|
||||
|
||||
_inside_mask = _inside(_XX.ravel(), _ZZ.ravel()).reshape(_G, _G)
|
||||
_grid_pts = np.vstack([_XX.ravel(), _ZZ.ravel()]) # 2×(_G²)
|
||||
|
||||
# ── draw triangle wireframe + density heatmap at each y layer ─────────────
|
||||
for cls in all_true:
|
||||
y = y_pos[cls]
|
||||
base_col = colours[cls][n_folds // 2]
|
||||
rgba_base = np.array(mcolors.to_rgba(base_col))
|
||||
|
||||
# Wireframe
|
||||
ax.plot(wire_x, np.full_like(wire_x, y), wire_z,
|
||||
color=base_col, linewidth=1.2, alpha=0.65, zorder=1)
|
||||
|
||||
# ── density heatmap ────────────────────────────────────────────────
|
||||
xz_pts = _xz_by_cls.get(cls)
|
||||
if xz_pts is not None and len(xz_pts) >= 2:
|
||||
kde = gaussian_kde(xz_pts.T, bw_method="silverman")
|
||||
density = kde(_grid_pts).reshape(_G, _G)
|
||||
density[~_inside_mask] = 0.0
|
||||
inside_vals = density[_inside_mask]
|
||||
d_max = inside_vals.max()
|
||||
if d_max > 0:
|
||||
# Normalise by 95th-percentile density so a tight peak at one
|
||||
# corner doesn't wash out the rest of the triangle. Values
|
||||
# above the cap are clipped to the max alpha.
|
||||
d_ref = np.percentile(inside_vals[inside_vals > 0], 95)
|
||||
if d_ref == 0:
|
||||
d_ref = d_max
|
||||
alpha_grid = np.clip(density / d_ref, 0, 1) * 0.50
|
||||
# Build one quad per inside cell, coloured by density alpha
|
||||
quads, face_cols = [], []
|
||||
for i in range(_G):
|
||||
for j in range(_G):
|
||||
if not _inside_mask[i, j]:
|
||||
continue
|
||||
xi, zj = _x_lin[j], _z_lin[i]
|
||||
x0, x1 = xi - _dx / 2, xi + _dx / 2
|
||||
z0, z1 = zj - _dz / 2, zj + _dz / 2
|
||||
quads.append([(x0, y, z0), (x1, y, z0),
|
||||
(x1, y, z1), (x0, y, z1)])
|
||||
fc = rgba_base.copy()
|
||||
fc[3] = float(alpha_grid[i, j])
|
||||
face_cols.append(fc)
|
||||
heat = Poly3DCollection(quads, facecolors=face_cols,
|
||||
edgecolors="none", zorder=0)
|
||||
ax.add_collection3d(heat)
|
||||
|
||||
# Iso-prob grid lines
|
||||
for level in (0.25, 0.5, 0.75):
|
||||
for vi in range(3):
|
||||
v0 = _TRI3D_VERTS[vi]
|
||||
v1 = _TRI3D_VERTS[(vi + 1) % 3]
|
||||
v2 = _TRI3D_VERTS[(vi + 2) % 3]
|
||||
p1 = level * v0 + (1 - level) * v1
|
||||
p2 = level * v0 + (1 - level) * v2
|
||||
ax.plot([p1[0], p2[0]], [y, y], [p1[1], p2[1]],
|
||||
color="#cccccc", linewidth=0.4, linestyle="--",
|
||||
alpha=0.45, zorder=1)
|
||||
|
||||
# ── vertex labels just outside the front face of the merged volume ────────
|
||||
# Place labels at the H-layer plane (y=0, front-left) but pushed slightly
|
||||
# in front of the y-axis so they don't collide with the wireframe.
|
||||
lbl_info = [
|
||||
(0, -0.13, -0.08), # H: bottom-left
|
||||
(1, 0.00, 0.10), # G: top
|
||||
(2, 0.13, -0.08), # S: bottom-right
|
||||
]
|
||||
front_y = min(y_pos.values()) - 0.08
|
||||
for ci, dx, dz in lbl_info:
|
||||
vx, vz = _TRI3D_VERTS[ci]
|
||||
ax.text(vx + dx, front_y, vz + dz,
|
||||
_CLASS_LABELS.get(ci, f"C{ci}"),
|
||||
fontsize=10, fontweight="bold", ha="center", va="center",
|
||||
zorder=10)
|
||||
|
||||
# ── data dots ─────────────────────────────────────────────────────────────
|
||||
for fold_idx, df in enumerate(frames):
|
||||
prob_cols = [f"prob_{head}_c{c}" for c in range(3)]
|
||||
probs_all = df[prob_cols].values
|
||||
y_true = df["y_true"].values.astype(int)
|
||||
for true_cls in all_true:
|
||||
mask = y_true == true_cls
|
||||
if not mask.any():
|
||||
continue
|
||||
probs = probs_all[mask]
|
||||
xz = _bary_to_cart_3d(probs) # Nx2 (x, z)
|
||||
noise = rng.normal(0, 0.004, xz.shape)
|
||||
y_vals = np.full(mask.sum(), y_pos[true_cls])
|
||||
ax.scatter(xz[:, 0] + noise[:, 0],
|
||||
y_vals,
|
||||
xz[:, 1] + noise[:, 1],
|
||||
color=colours[true_cls][fold_idx],
|
||||
s=22, alpha=0.82, linewidths=0,
|
||||
depthshade=False, zorder=5)
|
||||
|
||||
ax.set_xlim(-0.15, 1.15)
|
||||
ax.set_ylim(-0.3, max(y_pos.values()) + 0.3)
|
||||
ax.set_zlim(-0.1, np.sqrt(3) / 2 + 0.1)
|
||||
ax.set_xlabel("")
|
||||
ax.set_ylabel("")
|
||||
ax.set_zlabel("")
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
ax.set_zticks([])
|
||||
ax.xaxis.pane.fill = False
|
||||
ax.yaxis.pane.fill = False
|
||||
ax.zaxis.pane.fill = False
|
||||
ax.grid(False)
|
||||
|
||||
# ── legend: same grid style as 2D plots, placed as an inset axes ─────────
|
||||
ax_leg = fig.add_axes([0.68, 0.65, 0.28, 0.30])
|
||||
_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="True class")
|
||||
|
||||
fig.tight_layout()
|
||||
return _save(fig, out_dir or run_dir / "plots", f"prob_triangle3d_{head}.png")
|
||||
|
||||
|
||||
# ── shared save helper ─────────────────────────────────────────────────────────
|
||||
|
||||
def _save(fig: plt.Figure, out_dir: Path, filename: str) -> Path:
|
||||
out_dir = Path(out_dir)
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
out_path = out_dir / filename
|
||||
fig.savefig(out_path, dpi=150, bbox_inches="tight")
|
||||
plt.close(fig)
|
||||
print(f"Saved: {out_path}")
|
||||
return out_path
|
||||
|
||||
|
||||
# ── CLI ────────────────────────────────────────────────────────────────────────
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser(description=__doc__,
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--run-dir", required=True)
|
||||
ap.add_argument("--head", default="fused", choices=["fused", "img", "md"])
|
||||
ap.add_argument("--style", default="strips",
|
||||
choices=["strips", "confidence", "triangle", "triangle3d"],
|
||||
help="strips: X=true class, Y=P(Glaucoma). "
|
||||
"confidence: X=predicted class × true sub-column, Y=confidence. "
|
||||
"triangle: ternary simplex plot (multiclass only).")
|
||||
ap.add_argument("--out", default=None)
|
||||
args = ap.parse_args()
|
||||
|
||||
rd = Path(args.run_dir)
|
||||
od = Path(args.out) if args.out else None
|
||||
if args.style == "confidence":
|
||||
plot_confidence(rd, head=args.head, out_dir=od)
|
||||
elif args.style == "triangle":
|
||||
plot_triangle(rd, head=args.head, out_dir=od)
|
||||
elif args.style == "triangle3d":
|
||||
plot_triangle_3d(rd, head=args.head, out_dir=od)
|
||||
else:
|
||||
plot_strip(rd, head=args.head, out_dir=od)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user