rpotter6298
715 lines
29 KiB
Python
715 lines
29 KiB
Python
"""
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Plot predicted-probability strip charts for V2 runs.
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Three styles available via --style:
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strips (default)
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X = true class, Y = P(Glaucoma), color = true class × fold shade.
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Works for binary and multiclass.
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confidence
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X = predicted class (major) subdivided by true class (minor sub-column).
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Y = model confidence in its own prediction (P of the predicted class).
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Color = true class × fold shade.
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Makes high-confidence mistakes immediately visible.
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triangle (multiclass only)
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Ternary / simplex plot. Each corner = 100% probability for one class.
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Every sample is a dot placed at its softmax probability vector
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(p_H, p_G, p_S) using barycentric coordinates. The centroid is maximum
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uncertainty (1/3, 1/3, 1/3). Correctly classified samples cluster near
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their true-class corner; mistakes drift toward the wrong corner.
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Colour families (light → dark = fold 0 → fold N-1):
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Blues = Healthy / Normal eyes
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Reds = Glaucoma eyes
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Greens = Suspect eyes
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Usage
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-----
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python scripts/output_analysis/visualizations/plot_prob_strips.py \
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--run-dir analysis_data/v2.3_single_multiclass_nocrop/multiclass/single \
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--style triangle --head fused
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"""
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from __future__ import annotations
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import argparse
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import re
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from pathlib import Path
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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import numpy as np
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import pandas as pd
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# ── colour families ────────────────────────────────────────────────────────────
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_CLASS_FAMILIES = {
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0: ("#aac4ff", "#0a3d91"), # blues (healthy / normal)
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1: ("#ffaaaa", "#8b0000"), # reds (glaucoma)
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2: ("#aaffcc", "#1a6b3c"), # greens (suspect)
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}
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_CLASS_LABELS = {
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0: "Healthy",
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1: "Glaucoma",
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2: "Suspect",
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}
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_CLASS_LABELS_SHORT = {
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0: "H",
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1: "G",
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2: "S",
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}
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def _lerp_hex(c1: str, c2: str, t: float) -> tuple:
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def h(c): return tuple(int(c.lstrip("#")[i*2:i*2+2], 16) / 255 for i in range(3))
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r1, g1, b1 = h(c1)
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r2, g2, b2 = h(c2)
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return (r1 + t*(r2-r1), g1 + t*(g2-g1), b1 + t*(b2-b1))
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def _fold_colours(n_folds: int) -> dict[int, dict[int, tuple]]:
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out: dict[int, dict[int, tuple]] = {}
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for cls, (light, dark) in _CLASS_FAMILIES.items():
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out[cls] = {}
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for f in range(n_folds):
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t = f / max(n_folds - 1, 1)
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out[cls][f] = _lerp_hex(light, dark, t)
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return out
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def _fold_dirs(run_dir: Path) -> list[Path]:
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return sorted(
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[p for p in run_dir.glob("fold*") if p.is_dir() and re.search(r"\d+", p.name)],
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key=lambda p: int(re.search(r"\d+", p.name).group()),
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)
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def _detect_heads(run_dir: Path) -> list[str]:
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"""Return all head names available in the first non-empty fold dir."""
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for fd in _fold_dirs(run_dir):
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found: list[str] = []
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# Standard heads come from the predictions CSV — prefer per-eye
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csv = fd / "predictions_pereye.csv"
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if not csv.exists():
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csv = fd / "predictions_classic.csv"
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if not csv.exists():
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csv = fd / "predictions.csv"
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if csv.exists():
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cols = pd.read_csv(csv, nrows=0).columns.tolist()
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for h in ["fused", "img", "md"]:
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if f"prob_{h}_c0" in cols:
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found.append(h)
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# Fusion head lives in a separate npy
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if (fd / "probs_fused_head.npy").exists():
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found.append("fused_head")
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if found:
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return found
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return ["fused"] # safe fallback
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def _load_folds(run_dir: Path, head: str) -> list[pd.DataFrame]:
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dirs = _fold_dirs(run_dir)
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if not dirs:
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raise FileNotFoundError(f"No fold* directories found under {run_dir}")
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frames = []
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for fd in dirs:
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fold_num = int(re.search(r"\d+", fd.name).group())
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# fused_head is stored as npy, not in the predictions CSV
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if head == "fused_head":
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y_path = fd / "y_true.npy"
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p_path = fd / "probs_fused_head.npy"
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if not y_path.exists() or not p_path.exists():
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print(f" [warn] fused_head npy not found in {fd}, skipping")
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continue
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y = np.load(y_path)
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p = np.load(p_path)
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df = pd.DataFrame({"y_true": y})
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for c in range(p.shape[1]):
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df[f"prob_fused_head_c{c}"] = p[:, c]
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df["_fold"] = fold_num
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frames.append(df)
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continue
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# Standard heads from predictions CSV — prefer per-eye (2× dots, no OD/OS averaging)
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csv = fd / "predictions_pereye.csv"
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if not csv.exists():
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csv = fd / "predictions_classic.csv"
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if not csv.exists():
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csv = fd / "predictions.csv"
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if not csv.exists():
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print(f" [warn] no predictions CSV found in {fd}, skipping")
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continue
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df = pd.read_csv(csv)
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df["_fold"] = fold_num
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frames.append(df)
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if not frames:
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raise FileNotFoundError(
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f"No data found for head='{head}' in any fold dir under {run_dir}"
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)
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return frames
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def _detect_num_classes(df: pd.DataFrame, head: str) -> int:
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return len([c for c in df.columns if c.startswith(f"prob_{head}_c")])
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def _draw_grid_legend(ax_leg: plt.Axes, n_folds: int, classes: list[int],
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colours: dict, title: str = "True class") -> None:
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"""Rows = folds, columns = classes grid of coloured dots."""
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from matplotlib.patches import FancyBboxPatch
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ax_leg.axis("off")
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col_xs = np.linspace(0.55, 0.88, len(classes)) if len(classes) > 1 else [0.72]
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row_ys = np.linspace(0.88, 0.05, n_folds + 1)
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header_y, dot_ys = row_ys[0], row_ys[1:]
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for ci, cls in enumerate(classes):
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ax_leg.text(col_xs[ci], header_y, _CLASS_LABELS_SHORT.get(cls, f"C{cls}"),
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ha="center", va="bottom", fontsize=8, fontweight="bold",
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transform=ax_leg.transAxes)
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for fi in range(n_folds):
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y = dot_ys[fi]
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ax_leg.text(0.05, y, f"Fold {fi}", ha="left", va="center", fontsize=9,
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transform=ax_leg.transAxes)
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for ci, cls in enumerate(classes):
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ax_leg.scatter([col_xs[ci]], [y], color=colours[cls][fi], s=70, zorder=3,
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transform=ax_leg.transAxes, clip_on=False)
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ax_leg.add_patch(FancyBboxPatch((0, 0), 1, 1, boxstyle="round,pad=0.02",
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linewidth=0.8, edgecolor="#aaaaaa",
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facecolor="#f9f9f9", zorder=0,
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transform=ax_leg.transAxes))
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ax_leg.set_title(title, fontsize=9, pad=4)
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# ── strips style ───────────────────────────────────────────────────────────────
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def plot_strip(run_dir: Path, head: str = "fused", out_dir: Path | None = None,
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jitter_strength: float = 0.08) -> Path:
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"""X = true class, Y = P(Glaucoma)."""
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frames = _load_folds(run_dir, head)
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all_true = sorted({int(v) for df in frames for v in df["y_true"]})
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n_folds = len(frames)
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colours = _fold_colours(n_folds)
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rng = np.random.default_rng(seed=0)
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prob_col = f"prob_{head}_c1"
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x_pos = {cls: i for i, cls in enumerate(all_true)}
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leg_width = 0.8 + 0.55 * len(all_true)
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fig = plt.figure(figsize=(max(6, 2.2 * len(all_true) + 1), 7))
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gs = fig.add_gridspec(1, 2, width_ratios=[max(6, 2.2 * len(all_true)), leg_width],
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wspace=0.08)
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ax = fig.add_subplot(gs[0])
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ax_leg = fig.add_subplot(gs[1])
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fig.suptitle(f"{run_dir.parent.parent.name} — P(Glaucoma) [{head}]",
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fontsize=11, y=1.01)
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for fold_idx, df in enumerate(frames):
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for cls in all_true:
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sub = df[df["y_true"] == cls]
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if sub.empty:
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continue
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probs = sub[prob_col].values
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jitter = rng.uniform(-jitter_strength, jitter_strength, size=len(probs))
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ax.scatter(x_pos[cls] + jitter, probs, color=colours[cls][fold_idx],
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s=45, alpha=0.88, linewidths=0, zorder=3)
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ax.set_xticks(list(x_pos.values()))
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ax.set_xticklabels([_CLASS_LABELS.get(c, f"C{c}") for c in all_true], fontsize=12)
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ax.set_xlim(-0.5, len(all_true) - 0.5)
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ax.set_ylim(-0.05, 1.05)
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ax.set_ylabel("Predicted P(Glaucoma)", fontsize=12)
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ax.set_xlabel("True class", fontsize=12)
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ax.axhline(0.5, color="grey", linestyle="--", linewidth=0.8, alpha=0.5)
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ax.grid(axis="y", linestyle=":", alpha=0.4)
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_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="Legend")
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fig.tight_layout()
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return _save(fig, out_dir or run_dir / "plots", f"prob_strips_{head}.png")
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# ── confidence style ───────────────────────────────────────────────────────────
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def plot_confidence(run_dir: Path, head: str = "fused", out_dir: Path | None = None,
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jitter_strength: float = 0.06) -> Path:
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"""
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X = predicted class (major) × true class (minor sub-column).
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Y = model confidence = P(predicted class).
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Color = true class × fold shade.
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"""
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frames = _load_folds(run_dir, head)
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num_cls = _detect_num_classes(frames[0], head)
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all_cls = list(range(num_cls))
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all_true = sorted({int(v) for df in frames for v in df["y_true"]})
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n_folds = len(frames)
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colours = _fold_colours(n_folds)
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rng = np.random.default_rng(seed=0)
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# Sub-column spacing within each predicted-class group
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# E.g. for 3 classes: sub-offsets at -0.25, 0, +0.25
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n_sub = len(all_true)
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sub_spacing = 0.22
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sub_offsets = np.linspace(-(n_sub - 1) * sub_spacing / 2,
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(n_sub - 1) * sub_spacing / 2,
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n_sub)
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sub_off = {cls: sub_offsets[i] for i, cls in enumerate(all_true)}
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# Major x positions for each predicted class, spaced so sub-columns don't bleed
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group_gap = sub_spacing * n_sub + 0.35
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major_x = {pc: i * group_gap for i, pc in enumerate(all_cls)}
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leg_width = 0.8 + 0.55 * n_sub
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fig_w = max(7, group_gap * len(all_cls) * 1.8 + 1)
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fig = plt.figure(figsize=(fig_w, 7))
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gs = fig.add_gridspec(1, 2, width_ratios=[fig_w - leg_width, leg_width],
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wspace=0.08)
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ax = fig.add_subplot(gs[0])
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ax_leg = fig.add_subplot(gs[1])
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fig.suptitle(f"{run_dir.parent.parent.name} — Prediction confidence [{head}]",
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fontsize=11, y=1.01)
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for fold_idx, df in enumerate(frames):
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prob_cols = [f"prob_{head}_c{c}" for c in all_cls]
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pred_col = f"pred_{head}"
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for true_cls in all_true:
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sub = df[df["y_true"] == true_cls].copy()
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if sub.empty:
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continue
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for pred_cls in all_cls:
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rows = sub[sub[pred_col] == pred_cls]
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if rows.empty:
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continue
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# confidence = probability assigned to the predicted class
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conf = rows[f"prob_{head}_c{pred_cls}"].values
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x_base = major_x[pred_cls] + sub_off[true_cls]
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jitter = rng.uniform(-jitter_strength * 0.5,
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jitter_strength * 0.5, size=len(conf))
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ax.scatter(x_base + jitter, conf, color=colours[true_cls][fold_idx],
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s=45, alpha=0.88, linewidths=0, zorder=3)
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# X-axis: major ticks with predicted-class labels, minor sub-column markers
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ax.set_xlim(-group_gap * 0.5, group_gap * len(all_cls) - group_gap * 0.5)
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ax.set_xticks([major_x[pc] for pc in all_cls])
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ax.set_xticklabels([_CLASS_LABELS.get(pc, f"C{pc}") for pc in all_cls], fontsize=12)
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# Light vertical separators between predicted-class groups
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for i in range(1, len(all_cls)):
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sep_x = (major_x[all_cls[i-1]] + major_x[all_cls[i]]) / 2
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ax.axvline(sep_x, color="#cccccc", linewidth=1.0, zorder=1)
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# Sub-column labels (H/G/S) just below the x-axis
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for pred_cls in all_cls:
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for true_cls in all_true:
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lbl = _CLASS_LABELS_SHORT.get(true_cls, f"C{true_cls}")
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ax.text(major_x[pred_cls] + sub_off[true_cls], -0.085, lbl,
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ha="center", va="top", fontsize=7, color="#555555",
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transform=ax.get_xaxis_transform())
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ax.set_ylim(-0.05, 1.05)
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ax.set_ylabel("Confidence P(predicted class)", fontsize=12)
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ax.set_xlabel("Predicted class", fontsize=12, labelpad=18)
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ax.axhline(0.5, color="grey", linestyle="--", linewidth=0.8, alpha=0.5)
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ax.grid(axis="y", linestyle=":", alpha=0.4)
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_draw_grid_legend(ax_leg, n_folds, all_true, colours, title="True class")
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fig.tight_layout()
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return _save(fig, out_dir or run_dir / "plots", f"prob_confidence_{head}.png")
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# ── triangle / ternary style ───────────────────────────────────────────────────
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# Equilateral triangle vertices in Cartesian space:
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# H (Healthy) = bottom-left (0, 0)
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# G (Glaucoma) = bottom-right (1, 0)
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# S (Suspect) = apex (0.5, sqrt(3)/2)
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_TRI_VERTICES = np.array([
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[0.0, 0.0], # class 0 – Healthy
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[1.0, 0.0], # class 1 – Glaucoma
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[0.5, np.sqrt(3) / 2], # class 2 – Suspect
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])
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def _bary_to_cart(probs: np.ndarray) -> np.ndarray:
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"""
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Convert Nx3 barycentric coordinates (softmax probs) to Nx2 Cartesian.
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probs rows must sum to 1.
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"""
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return probs @ _TRI_VERTICES
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def _draw_triangle_grid(ax: plt.Axes, levels: tuple = (0.25, 0.5, 0.75)) -> None:
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"""Draw the triangle border and iso-probability grid lines."""
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from matplotlib.patches import Polygon
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from matplotlib.lines import Line2D
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# Outer triangle
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tri = Polygon(_TRI_VERTICES, fill=False, edgecolor="#333333", linewidth=1.5, zorder=2)
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ax.add_patch(tri)
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# Grid lines: for each class, lines where p_class = level,
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# parallel to the opposite edge.
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for level in levels:
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for cls in range(3):
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# Points on the two edges adjacent to this vertex at distance `level`
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v0 = _TRI_VERTICES[cls]
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v1 = _TRI_VERTICES[(cls + 1) % 3]
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v2 = _TRI_VERTICES[(cls + 2) % 3]
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# A line at p_cls = level divides the triangle:
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# p1 = level * v0 + (1-level) * v1
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# p2 = level * v0 + (1-level) * v2
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p1 = level * v0 + (1 - level) * v1
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p2 = level * v0 + (1 - level) * v2
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ax.plot([p1[0], p2[0]], [p1[1], p2[1]],
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color="#cccccc", linewidth=0.6, zorder=1, linestyle="--")
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def plot_triangle(run_dir: Path, head: str = "fused", out_dir: Path | None = None) -> Path:
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"""
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Ternary simplex plot of softmax probabilities for a 3-class run.
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Each dot = one eye; position = (p_H, p_G, p_S) in barycentric coords.
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Color = true class × fold shade.
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"""
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frames = _load_folds(run_dir, head)
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num_cls = _detect_num_classes(frames[0], head)
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if num_cls != 3:
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raise ValueError(f"Triangle plot requires 3 classes; got {num_cls}. "
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"Use --style strips for binary.")
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all_true = sorted({int(v) for df in frames for v in df["y_true"]})
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n_folds = len(frames)
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colours = _fold_colours(n_folds)
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leg_width = 0.8 + 0.55 * len(all_true)
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fig = plt.figure(figsize=(8, 7))
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gs = fig.add_gridspec(1, 2, width_ratios=[8 - leg_width, leg_width], wspace=0.05)
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ax = fig.add_subplot(gs[0], aspect="equal")
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ax_leg = fig.add_subplot(gs[1])
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fig.suptitle(f"{run_dir.parent.parent.name} — Simplex [{head}]",
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fontsize=11, y=1.01)
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_draw_triangle_grid(ax)
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# Vertex labels — placed just outside each corner
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offsets = [(-0.07, -0.06), (0.07, -0.06), (0.0, 0.06)]
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for cls_idx in range(3):
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lbl = _CLASS_LABELS.get(cls_idx, f"C{cls_idx}")
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vx, vy = _TRI_VERTICES[cls_idx]
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dx, dy = offsets[cls_idx]
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ax.text(vx + dx, vy + dy, lbl, ha="center", va="center",
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fontsize=12, fontweight="bold")
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# Centroid marker
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cx, cy = _TRI_VERTICES.mean(axis=0)
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ax.scatter([cx], [cy], color="#aaaaaa", s=30, marker="+", zorder=2, linewidths=1)
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ax.text(cx + 0.02, cy - 0.04, "1/3 each", fontsize=7, color="#999999", ha="left")
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# 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=None,
|
||
choices=["fused", "fused_head", "img", "md"],
|
||
help="Head to plot. Omit to auto-detect and plot all available heads.")
|
||
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
|
||
|
||
heads = [args.head] if args.head else _detect_heads(rd)
|
||
print(f"Heads to plot: {heads}")
|
||
|
||
plot_fn = {
|
||
"confidence": plot_confidence,
|
||
"triangle": plot_triangle,
|
||
"triangle3d": plot_triangle_3d,
|
||
}.get(args.style, plot_strip)
|
||
|
||
for head in heads:
|
||
print(f"\n--- {head} ---")
|
||
try:
|
||
plot_fn(rd, head=head, out_dir=od)
|
||
except Exception as exc:
|
||
print(f" [skip] {head}: {exc}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|