#!/usr/bin/env python3 """ Aggregate spatial 5-fold CV results into side-by-side model comparison plots. Directory structure expected: results/{model_config}/simple_decoder/cv_s{fold}_v{vvv}.json where vvv is zero-padded integer percent (e.g., 000, 005, 010, 025, 050, 100) Each JSON should include: - val_rmse_history (list[float]) - var_pct_target (float) - k_chosen (int) - cum_evr_at_k (float) """ import argparse, json, os, glob import numpy as np import matplotlib.pyplot as plt from scipy import stats from pathlib import Path # -------------------------- # IO # -------------------------- def _safe_load_json(path): try: with open(path, "r") as f: return json.load(f) except Exception as e: print(f"Error loading {path}: {e}") return None def load_results_for_model(base_dir, model_config): """ Returns: dict[var_pct] -> list[result_dict] """ model_dir = os.path.join(base_dir, model_config, "simple_decoder") if not os.path.exists(model_dir): print(f"Directory not found: {model_dir}") return {} # New naming: cv_s{fold}_v{vvv}.json paths = sorted(glob.glob(os.path.join(model_dir, "cv_s*_v*.json"))) if not paths: print(f"No result files found in {model_dir}") return {} print(f"Found {len(paths)} result files for {model_config}/simple_decoder") by_var = {} for p in paths: r = _safe_load_json(p) if not r: continue if "val_rmse_history" not in r: print(f"Warning: {p} missing val_rmse_history, skipping") continue # Prefer explicit field; fallback to filename parse if needed if "var_pct_target" in r: vp = float(r["var_pct_target"]) else: # Fallback: parse ..._v{vvv}.json stem = Path(p).stem try: vtag = stem.split("_v")[-1] vp = float(int(vtag)) except Exception: print(f"Warning: could not infer var_pct from {p}; skipping") continue by_var.setdefault(vp, []).append(r) return by_var # -------------------------- # CV metrics # -------------------------- def compute_cv_metrics(fold_results): """ Given a list of result dicts (one per fold), compute: mean RMSE at the epoch minimizing the mean RMSE across folds, plus 95% t-CI across folds at that epoch. """ if not fold_results: return None, None, None histories = [] for r in fold_results: h = r.get("val_rmse_history", []) if h: histories.append(h) if not histories: return None, None, None min_len = min(len(h) for h in histories) H = np.array([h[:min_len] for h in histories]) # [n_folds, n_epochs] n_folds = H.shape[0] mean_per_epoch = H.mean(axis=0) best_epoch = int(np.argmin(mean_per_epoch)) fold_rmses = H[:, best_epoch] mean_rmse = float(fold_rmses.mean()) std_rmse = float(fold_rmses.std(ddof=1)) if n_folds > 1 else 0.0 if n_folds > 1: t_crit = stats.t.ppf(0.975, df=n_folds - 1) margin = float(t_crit * std_rmse / np.sqrt(n_folds)) ci_lower, ci_upper = mean_rmse - margin, mean_rmse + margin else: ci_lower = ci_upper = mean_rmse return mean_rmse, ci_lower, ci_upper # -------------------------- # Plot # -------------------------- def create_comparison_plot(dinov2_results, dinov3_results, output_path): """ Combined comparison of DINOv2 vs DINOv3 across % variance removed. X-axis: percent variance removed (0..100). """ v2_keys = set(dinov2_results.keys()) if dinov2_results else set() v3_keys = set(dinov3_results.keys()) if dinov3_results else set() all_vps = sorted(v2_keys | v3_keys) if not all_vps: print("No variance-percent keys found; nothing to plot.") return vps = np.array(all_vps, dtype=float) def collect(model_dict): means, lows, ups = [], [], [] for vp in all_vps: fr = model_dict.get(vp, []) m, lo, up = compute_cv_metrics(fr) means.append(np.nan if m is None else m) lows.append(np.nan if lo is None else lo) ups.append(np.nan if up is None else up) return np.array(means), np.array(lows), np.array(ups) v2_mean, v2_low, v2_up = collect(dinov2_results) v3_mean, v3_low, v3_up = collect(dinov3_results) fig, ax = plt.subplots(1, 1, figsize=(10, 6)) # DINOv2 - Python blue (C0) mask2 = ~np.isnan(v2_mean) if np.any(mask2): ax.plot(vps[mask2], v2_mean[mask2], color='C0', linewidth=2, marker='o', label='DINOv2-base') ax.fill_between(vps[mask2], v2_low[mask2], v2_up[mask2], color='C0', alpha=0.25) # DINOv3 - Python orange (C1) mask3 = ~np.isnan(v3_mean) if np.any(mask3): ax.plot(vps[mask3], v3_mean[mask3], color='C1', linewidth=2, marker='o', label='DINOv3-sat') ax.fill_between(vps[mask3], v3_low[mask3], v3_up[mask3], color='C1', alpha=0.25) ax.set_xlabel('Percent of lighting subspace variance removed') ax.set_ylabel('Plant canopy height RMSE (cm)') ax.grid(True, alpha=0.3) ax.set_xlim(-2, 102) # Set y-limits based on available data if np.any(mask2) or np.any(mask3): all_lows = [] all_ups = [] if np.any(mask2): all_lows.extend(v2_low[mask2]) all_ups.extend(v2_up[mask2]) if np.any(mask3): all_lows.extend(v3_low[mask3]) all_ups.extend(v3_up[mask3]) y_min = np.nanmin(all_lows) y_max = np.nanmax(all_ups) pad = 0.05 * (y_max - y_min) if y_max > y_min else 0.1 ax.set_ylim(y_min - pad, y_max + pad) # Legend in lower left with Model title legend = ax.legend(title='Model', loc='upper left', framealpha=0.9) legend.get_title().set_fontweight('bold') # Main title plt.tight_layout() os.makedirs(os.path.dirname(output_path), exist_ok=True) plt.savefig(output_path, dpi=300, bbox_inches='tight') plt.close() print(f"Saved comparison plot: {output_path}") # -------------------------- # Main # -------------------------- def main(): parser = argparse.ArgumentParser(description="Aggregate spatial CV results into model comparison plots") parser.add_argument("--results_dir", type=str, default="results/light_subspace_removal", help="Base results directory") parser.add_argument("--output_dir", type=str, default="plots", help="Output directory for plots") args = parser.parse_args() model_configs = ["dinov2_base", "dinov3_sat"] # Load results dinov2_results = load_results_for_model(args.results_dir, "dinov2_base") dinov3_results = load_results_for_model(args.results_dir, "dinov3_sat") if not dinov2_results and not dinov3_results: print("No results found for either model; nothing to plot.") return # Plot out_path = os.path.join(args.output_dir, "cv_comparison_variance_pct.png") create_comparison_plot(dinov2_results, dinov3_results, out_path) # Console summary print("\nSummary by % variance removed:") for mc, res in zip(model_configs, [dinov2_results, dinov3_results]): if not res: print(f" {mc}: No results") continue print(f" {mc}:") for vp in sorted(res.keys()): m, lo, up = compute_cv_metrics(res[vp]) if m is not None: n_folds = len(res[vp]) ci = (up - lo) / 2.0 if up is not None and lo is not None else np.nan print(f" v={int(vp):3d}%: {m:.2f} ± {ci:.2f} cm (n={n_folds})") else: print(f" v={int(vp):3d}%: No valid results") if __name__ == "__main__": main()