""" pca_all_layers.py — Generate 2D and 3D PCA plots for ALL layers from existing NPZ files. For each results/{model}/npz/vectors_{scale}.npz, produces: results/{model}/plots/all/pca/pca_{scale}_L{layer}.png (2D, 3-panel) results/{model}/plots/all/pca_3d/pca_{scale}_L{layer}.png (3D, 3-panel) for every layer stored in the NPZ. NOTE: NPZ files must have all layers saved. If you only see 5 representative layers in existing results, re-run the main pipeline — save_vectors_npz() now saves all layers. Usage: python pca_all_layers.py # all models, all scales python pca_all_layers.py --model qwen # one model, all scales python pca_all_layers.py --model qwen --scale vanilla # one model, one scale python pca_all_layers.py --overwrite # regenerate existing plots """ import argparse import os import re import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # noqa: F401 import numpy as np from sklearn.decomposition import PCA # --------------------------------------------------------------------------- # Constants # --------------------------------------------------------------------------- RESULTS_DIR = os.path.join(os.path.dirname(__file__), 'results') CATEGORY_ORDER = ['left', 'right', 'above', 'under', 'far', 'close'] GROUP_ORDER = ['horizontal', 'vertical', 'distance'] CAT_COLORS = { 'left': '#ff7f0e', 'right': '#ffbb78', # horizontal → orange 'above': '#2ca02c', 'under': '#98df8a', # vertical → green 'far': '#9467bd', 'close': '#c5b0d5', # distance → purple } GROUP_COLORS = { 'horizontal': '#ff7f0e', 'vertical': '#2ca02c', 'distance': '#9467bd', } # --------------------------------------------------------------------------- # 2D PCA # --------------------------------------------------------------------------- def _plot_2d(data, layer, scale, model, save_path): orig = data.get(f'orig_L{layer}') swap = data.get(f'swap_L{layer}') labels = data.get(f'labels_L{layer}') deltas = data.get(f'delta_L{layer}') cats = data.get(f'categories_L{layer}') groups = data.get(f'groups_L{layer}') if orig is None or swap is None: return False fig, axes = plt.subplots(1, 3, figsize=(24, 7)) # ── Panel 1: embeddings ─────────────────────────────────────────────── pca_emb = PCA(n_components=2) all_proj = pca_emb.fit_transform(np.vstack([orig, swap])) orig_proj = all_proj[:len(orig)] swap_proj = all_proj[len(orig):] ev = pca_emb.explained_variance_ratio_ ax = axes[0] for cat in CATEGORY_ORDER: mask = np.array([str(l) == cat for l in labels]) if mask.any(): ax.scatter(orig_proj[mask, 0], orig_proj[mask, 1], c=CAT_COLORS.get(cat, 'gray'), label=f'{cat} (orig)', alpha=0.5, s=15, marker='o') ax.scatter(swap_proj[mask, 0], swap_proj[mask, 1], c=CAT_COLORS.get(cat, 'gray'), alpha=0.5, s=15, marker='x') ax.set_title('Embeddings by Category\n(o=orig, x=swap)', fontsize=11) ax.set_xlabel(f'PC1 ({ev[0]:.1%})', fontsize=9) ax.set_ylabel(f'PC2 ({ev[1]:.1%})', fontsize=9) ax.legend(fontsize=7, ncol=2) ax.grid(True, alpha=0.2) # ── Panels 2+3: delta vectors ───────────────────────────────────────── has_delta = deltas is not None and cats is not None and len(deltas) >= 2 if has_delta: pca_d = PCA(n_components=2) delta_proj = pca_d.fit_transform(deltas) ev_d = pca_d.explained_variance_ratio_ ax = axes[1] if has_delta and groups is not None: for group in GROUP_ORDER: mask = np.array([str(g) == group for g in groups]) if mask.any(): ax.scatter(delta_proj[mask, 0], delta_proj[mask, 1], c=GROUP_COLORS.get(group, 'gray'), label=group, alpha=0.5, s=15) ax.set_title('Delta Vectors by Group', fontsize=11) ax.set_xlabel(f'PC1 ({ev_d[0]:.1%})', fontsize=9) ax.set_ylabel(f'PC2 ({ev_d[1]:.1%})', fontsize=9) ax.legend(fontsize=9) ax.grid(True, alpha=0.2) else: ax.set_title('Delta Vectors by Group\n(no data)', fontsize=11) ax = axes[2] if has_delta and cats is not None: for cat in CATEGORY_ORDER: mask = np.array([str(c) == cat for c in cats]) if mask.any(): ax.scatter(delta_proj[mask, 0], delta_proj[mask, 1], c=CAT_COLORS.get(cat, 'gray'), label=cat, alpha=0.5, s=15) ax.set_title('Delta Vectors by Category', fontsize=11) ax.set_xlabel(f'PC1 ({ev_d[0]:.1%})', fontsize=9) ax.set_ylabel(f'PC2 ({ev_d[1]:.1%})', fontsize=9) ax.legend(fontsize=8, ncol=2) ax.grid(True, alpha=0.2) else: ax.set_title('Delta Vectors by Category\n(no data)', fontsize=11) fig.suptitle(f'{model.upper()} ({scale}) — Layer {layer} — 2D PCA', fontweight='bold') plt.tight_layout() plt.savefig(save_path, dpi=200, bbox_inches='tight') plt.close() return True # --------------------------------------------------------------------------- # 3D PCA # --------------------------------------------------------------------------- def _plot_3d(data, layer, scale, model, save_path): orig = data.get(f'orig_L{layer}') swap = data.get(f'swap_L{layer}') labels = data.get(f'labels_L{layer}') deltas = data.get(f'delta_L{layer}') cats = data.get(f'categories_L{layer}') groups = data.get(f'groups_L{layer}') if orig is None or swap is None: return False fig = plt.figure(figsize=(30, 8)) # ── Panel 1: embeddings ─────────────────────────────────────────────── pca_emb = PCA(n_components=3) all_proj = pca_emb.fit_transform(np.vstack([orig, swap])) orig_proj = all_proj[:len(orig)] swap_proj = all_proj[len(orig):] ev1 = pca_emb.explained_variance_ratio_ ax1 = fig.add_subplot(131, projection='3d') for cat in CATEGORY_ORDER: mask = np.array([str(l) == cat for l in labels]) if mask.any(): ax1.scatter(orig_proj[mask, 0], orig_proj[mask, 1], orig_proj[mask, 2], c=CAT_COLORS.get(cat, 'gray'), label=f'{cat} (orig)', alpha=0.45, s=12, marker='o') ax1.scatter(swap_proj[mask, 0], swap_proj[mask, 1], swap_proj[mask, 2], c=CAT_COLORS.get(cat, 'gray'), label=f'{cat} (swap)', alpha=0.45, s=12, marker='^') ax1.set_title('Embeddings by Category\n(o=orig, ^=swap)', fontsize=10) ax1.set_xlabel(f'PC1 ({ev1[0]:.1%})', fontsize=8) ax1.set_ylabel(f'PC2 ({ev1[1]:.1%})', fontsize=8) ax1.set_zlabel(f'PC3 ({ev1[2]:.1%})', fontsize=8) ax1.legend(fontsize=6, ncol=2, loc='upper left') # ── Panels 2+3: delta vectors ───────────────────────────────────────── has_delta = deltas is not None and len(deltas) >= 3 if has_delta: pca_d = PCA(n_components=3) delta_proj = pca_d.fit_transform(deltas) ev2 = pca_d.explained_variance_ratio_ else: delta_proj = None ev2 = None ax2 = fig.add_subplot(132, projection='3d') if has_delta and groups is not None: for group in GROUP_ORDER: mask = np.array([str(g) == group for g in groups]) if mask.any(): ax2.scatter(delta_proj[mask, 0], delta_proj[mask, 1], delta_proj[mask, 2], c=GROUP_COLORS.get(group, 'gray'), label=group, alpha=0.45, s=12) ax2.set_title('Delta Vectors by Group', fontsize=10) ax2.set_xlabel(f'PC1 ({ev2[0]:.1%})', fontsize=8) ax2.set_ylabel(f'PC2 ({ev2[1]:.1%})', fontsize=8) ax2.set_zlabel(f'PC3 ({ev2[2]:.1%})', fontsize=8) ax2.legend(fontsize=8) else: ax2.set_title('Delta Vectors by Group\n(no data)', fontsize=10) ax3 = fig.add_subplot(133, projection='3d') if has_delta and cats is not None: for cat in CATEGORY_ORDER: mask = np.array([str(c) == cat for c in cats]) if mask.any(): ax3.scatter(delta_proj[mask, 0], delta_proj[mask, 1], delta_proj[mask, 2], c=CAT_COLORS.get(cat, 'gray'), label=cat, alpha=0.45, s=12) ax3.set_title('Delta Vectors by Category', fontsize=10) ax3.set_xlabel(f'PC1 ({ev2[0]:.1%})', fontsize=8) ax3.set_ylabel(f'PC2 ({ev2[1]:.1%})', fontsize=8) ax3.set_zlabel(f'PC3 ({ev2[2]:.1%})', fontsize=8) ax3.legend(fontsize=7, ncol=2) else: ax3.set_title('Delta Vectors by Category\n(no data)', fontsize=10) fig.suptitle(f'{model.upper()} ({scale}) — Layer {layer} — 3D PCA', fontweight='bold') plt.tight_layout() plt.savefig(save_path, dpi=200, bbox_inches='tight', pad_inches=0.4) plt.close() return True # --------------------------------------------------------------------------- # Per-NPZ processing # --------------------------------------------------------------------------- def process_npz(npz_path, scale, model, plots_all_dir, overwrite=False): data = np.load(npz_path, allow_pickle=True) layer_keys = [k for k in data.files if k.startswith('orig_L')] layers = sorted([int(k.replace('orig_L', '')) for k in layer_keys]) if not layers: print(f' [skip] No orig_L* keys in {npz_path}') return 0 print(f' [{model}] {scale}: {len(layers)} layers (L{layers[0]}–L{layers[-1]})') pca_2d_dir = os.path.join(plots_all_dir, 'pca') pca_3d_dir = os.path.join(plots_all_dir, 'pca_3d') os.makedirs(pca_2d_dir, exist_ok=True) os.makedirs(pca_3d_dir, exist_ok=True) saved = 0 for i, layer in enumerate(layers): path_2d = os.path.join(pca_2d_dir, f'pca_{scale}_L{layer}.png') path_3d = os.path.join(pca_3d_dir, f'pca_{scale}_L{layer}.png') skip_2d = not overwrite and os.path.exists(path_2d) skip_3d = not overwrite and os.path.exists(path_3d) if skip_2d and skip_3d: continue print(f' L{layer:>3} ({i+1}/{len(layers)})', end='\r', flush=True) if not skip_2d: if _plot_2d(data, layer, scale, model, path_2d): saved += 1 if not skip_3d: if _plot_3d(data, layer, scale, model, path_3d): saved += 1 print() # newline after progress return saved # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- def scale_from_name(filename): m = re.match(r'vectors_(.+)\.npz$', filename) return m.group(1) if m else None def main(): parser = argparse.ArgumentParser( description='Generate 2D+3D PCA plots for all layers from NPZ files') parser.add_argument('--model', help='Restrict to this model directory (e.g. qwen)') parser.add_argument('--scale', help='Restrict to this scale (e.g. vanilla, 80k)') parser.add_argument('--overwrite', action='store_true', help='Regenerate plots even if they already exist') parser.add_argument('--results-dir', default=RESULTS_DIR, help='Path to results/ directory') args = parser.parse_args() results_dir = args.results_dir if not os.path.isdir(results_dir): print(f'Results directory not found: {results_dir}') return model_dirs = sorted( m for m in os.listdir(results_dir) if os.path.isdir(os.path.join(results_dir, m)) ) if args.model: model_dirs = [m for m in model_dirs if m == args.model] if not model_dirs: print(f"Model '{args.model}' not found in {results_dir}") return total_saved = 0 total_npz = 0 for model in model_dirs: model_dir = os.path.join(results_dir, model) npz_dir = os.path.join(model_dir, 'npz') plots_all_dir = os.path.join(model_dir, 'plots', 'all') if not os.path.isdir(npz_dir): print(f'[{model}] no npz/ directory, skipping') continue npz_files = sorted( f for f in os.listdir(npz_dir) if f.startswith('vectors_') and f.endswith('.npz') ) if args.scale: npz_files = [f for f in npz_files if scale_from_name(f) == args.scale] if not npz_files: print(f'[{model}] no matching NPZ files, skipping') continue for npz_file in npz_files: scale = scale_from_name(npz_file) if scale is None: continue npz_path = os.path.join(npz_dir, npz_file) os.makedirs(plots_all_dir, exist_ok=True) n = process_npz(npz_path, scale, model, plots_all_dir, overwrite=args.overwrite) total_saved += n total_npz += 1 print(f'\nDone. Processed {total_npz} NPZ file(s), saved {total_saved} plots.') if __name__ == '__main__': main()