""" pca_new.py - Generate single-panel PCA visualizations from existing NPZ files. Accepts a path argument pointing to either: - A single model directory (e.g. .../results_short_answer/nvila) - A parent directory containing multiple model directories (e.g. .../results_short_answer) Use --3d to extract the rightmost panel from pca_3d/ plots → saves to pca_3d_new/. Use --2d to extract the rightmost panel from pca/ plots → saves to pca_new/. Only the "Delta Vectors by Category" panel is produced (previously the rightmost of 3). 'under' has been renamed 'below' throughout. Usage: python pca_new.py --3d /data/shared/Qwen/experiments/swap_analysis/results_short_answer python pca_new.py --3d /data/shared/Qwen/experiments/swap_analysis/results_short_answer/nvila python pca_new.py --2d /data/shared/Qwen/experiments/swap_analysis/results_short_answer python pca_new.py --2d /data/shared/Qwen/experiments/swap_analysis/results_short_answer/nvila """ import argparse import os import re import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # noqa: F401 from sklearn.decomposition import PCA # ── Label / colour config ───────────────────────────────────────────────────── CATEGORY_ORDER = ['left', 'right', 'above', 'below', 'far', 'close'] GROUP_ORDER = ['horizontal', 'vertical', 'distance'] CAT_COLORS = { 'above': '#2ca02c', 'below': '#98df8a', # horizontal → green 'left': '#ff7f0e', 'right': '#ffbb78', # vertical → orange (was 'under') 'far': '#9467bd', 'close': '#c5b0d5', # distance → purple } GROUP_COLORS = { 'horizontal': '#2ca02c', 'vertical': '#ff7f0e', 'distance': '#9467bd', } # ── Font / marker sizes ─────────────────────────────────────────────────────── TITLE_FS = 22 # subplot title AXIS_FS = 18 # axis labels (PC1 / PC2 / PC3) TICK_FS = 14 # tick labels LEGEND_FS = 16 # legend text SUPTITLE_FS = 24 # figure-level suptitle SCATTER_S = 30 # marker size def _normalise_label(raw): """Map 'under' → 'below'; everything else passes through unchanged.""" return 'below' if str(raw) == 'under' else str(raw) def scatter3d(ax, xs, ys, zs, c, label, alpha=0.55, s=SCATTER_S, marker='o'): ax.scatter(xs, ys, zs, c=c, label=label, alpha=alpha, s=s, marker=marker) def plot_pca_3d(vectors_npz_path, scale, model_type, save_dir): """Generate single-panel 3D PCA figure (Delta Vectors by Category) per layer.""" data = np.load(vectors_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 found in {vectors_npz_path}") return os.makedirs(save_dir, exist_ok=True) for layer in layers: deltas = data.get(f'delta_L{layer}') cats = data.get(f'categories_L{layer}') has_delta = (deltas is not None and len(deltas) >= 3) if not has_delta: print(f" [skip] Layer {layer}: no delta vectors") continue # ── PCA on delta vectors ────────────────────────────────────────────── pca_d = PCA(n_components=3) delta_proj = pca_d.fit_transform(deltas) ev = pca_d.explained_variance_ratio_ # ── Figure ──────────────────────────────────────────────────────────── fig = plt.figure(figsize=(13, 10)) ax = fig.add_subplot(111, projection='3d') if cats is not None: for cat in CATEGORY_ORDER: # support both 'under' (legacy) and 'below' in stored labels mask = np.array([_normalise_label(c) == cat for c in cats]) if not mask.any(): continue scatter3d(ax, delta_proj[mask, 0], delta_proj[mask, 1], delta_proj[mask, 2], c=CAT_COLORS.get(cat, 'gray'), label=cat) ax.set_title('Delta Vectors by Category', fontsize=TITLE_FS, pad=12) ax.set_xlabel(f'PC1 ({ev[0]:.1%})', fontsize=AXIS_FS, labelpad=25) ax.set_ylabel(f'PC2 ({ev[1]:.1%})', fontsize=AXIS_FS, labelpad=25) # set_zlabel is unreliable in 3D — use fig.text for guaranteed visibility ax.set_zlabel('') ax.tick_params(axis='both', labelsize=TICK_FS) ax.legend(fontsize=LEGEND_FS, ncol=2, loc='upper right') # ax.legend(fontsize=LEGEND_FS, ncol=1, loc='upper left', # bbox_to_anchor=(1.02, 1.0), borderaxespad=0) # ── Draw everything so we can read accurate bbox positions ──────────── fig.canvas.draw() ax_pos = ax.get_position() # Bbox in figure-fraction coords # PC3 label: place with a bit of gap (0.04) from the axes right edge pc3_x = ax_pos.x1 + 0.04 fig.text( pc3_x, (ax_pos.y0 + ax_pos.y1) / 2, f'PC3 ({ev[2]:.1%})', fontsize=AXIS_FS, va='center', ha='center', rotation=90, ) # Centre both titles over the axes area (not the full figure width) ax_cx = (ax_pos.x0 + ax_pos.x1) / 2 fig.suptitle( f'{model_type.upper()} ({scale}) — L{layer}', fontsize=SUPTITLE_FS, fontweight='bold', x=ax_cx, y=1.01, ) # Re-centre the axes title (set_title is relative to axes, already centred; # but the axes itself may be off-centre in the figure, so suptitle fix is enough) out_path = os.path.join(save_dir, f'pca_{scale}_L{layer}.png') plt.savefig(out_path, dpi=200, bbox_inches='tight', pad_inches=0.5) plt.close() print(f" Saved {out_path}") def plot_pca_2d(vectors_npz_path, scale, model_type, save_dir): """Generate single-panel 2D PCA figure (Delta Vectors by Category) per layer.""" data = np.load(vectors_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 found in {vectors_npz_path}") return os.makedirs(save_dir, exist_ok=True) for layer in layers: deltas = data.get(f'delta_L{layer}') cats = data.get(f'categories_L{layer}') has_delta = (deltas is not None and len(deltas) >= 2) if not has_delta: print(f" [skip] Layer {layer}: no delta vectors") continue # ── PCA on delta vectors ────────────────────────────────────────────── pca_d = PCA(n_components=2) delta_proj = pca_d.fit_transform(deltas) ev = pca_d.explained_variance_ratio_ # ── Figure ──────────────────────────────────────────────────────────── fig, ax = plt.subplots(figsize=(10, 8)) if cats is not None: for cat in CATEGORY_ORDER: mask = np.array([_normalise_label(c) == cat for c in cats]) if not mask.any(): continue ax.scatter(delta_proj[mask, 0], delta_proj[mask, 1], c=CAT_COLORS.get(cat, 'gray'), label=cat, alpha=0.55, s=SCATTER_S) ax.set_title('Delta Vectors by Category', fontsize=TITLE_FS, pad=12) ax.set_xlabel(f'PC1 ({ev[0]:.1%})', fontsize=AXIS_FS) ax.set_ylabel(f'PC2 ({ev[1]:.1%})', fontsize=AXIS_FS) ax.tick_params(axis='both', labelsize=TICK_FS) ax.legend(fontsize=LEGEND_FS, ncol=2, loc='upper right') ax.grid(True, alpha=0.2) fig.suptitle( f'{model_type.upper()} ({scale}) — L{layer}', fontsize=SUPTITLE_FS, fontweight='bold', ) plt.tight_layout() out_path = os.path.join(save_dir, f'pca_{scale}_L{layer}.png') plt.savefig(out_path, dpi=200, bbox_inches='tight', pad_inches=0.3) plt.close() print(f" Saved {out_path}") def scale_from_npz_name(name): """'vectors_80k.npz' -> '80k'""" m = re.match(r'vectors_(.+)\.npz$', name) return m.group(1) if m else None def is_model_dir(path): """Return True if path looks like a single model directory (has npz/ sub-dir).""" return os.path.isdir(os.path.join(path, 'npz')) def process_model(model_dir, mode): """Process all scales for a single model directory. mode: '3d' → pca_3d/ → pca_3d_new/ (3D single-panel) '2d' → pca/ → pca_new/ (2D single-panel) """ model = os.path.basename(model_dir) npz_dir = os.path.join(model_dir, 'npz') plots_dir = os.path.join(model_dir, 'plots') if not os.path.isdir(npz_dir): print(f"[{model}] no npz/ dir, skipping") return if not os.path.isdir(plots_dir): print(f"[{model}] no plots/ dir, skipping") return npz_files = sorted( f for f in os.listdir(npz_dir) if f.startswith('vectors_') and f.endswith('.npz') ) if not npz_files: print(f"[{model}] no vectors_*.npz files, skipping") return if mode == '3d': ref_dir_name = 'pca_3d' new_dir_name = 'pca_3d_new' plot_fn = plot_pca_3d else: ref_dir_name = 'pca' new_dir_name = 'pca_new' plot_fn = plot_pca_2d # Locate ref dirs to mirror structure into new sibling dir ref_dirs = [] for dirpath, dirnames, _ in os.walk(plots_dir): if os.path.basename(dirpath) == ref_dir_name: ref_dirs.append(dirpath) if not ref_dirs: print(f"[{model}] no {ref_dir_name}/ dirs found under plots/, skipping") return for npz_file in npz_files: scale = scale_from_npz_name(npz_file) if scale is None: continue npz_path = os.path.join(npz_dir, npz_file) for ref_dir in ref_dirs: parent = os.path.dirname(ref_dir) # e.g. plots/all out_dir = os.path.join(parent, new_dir_name) print(f"[{model}] scale={scale} -> {out_dir}") plot_fn(npz_path, scale, model, out_dir) def main(): parser = argparse.ArgumentParser( description='Generate single-panel PCA plots (rightmost panel) from existing NPZ files.') parser.add_argument('path', help='Model directory or parent directory containing model dirs') group = parser.add_mutually_exclusive_group(required=True) group.add_argument('--3d', dest='mode', action='store_const', const='3d', help='Extract from pca_3d/ → pca_3d_new/ (3D Delta Vectors by Category)') group.add_argument('--2d', dest='mode', action='store_const', const='2d', help='Extract from pca/ → pca_new/ (2D Delta Vectors by Category)') args = parser.parse_args() root = args.path.rstrip('/') mode = args.mode if not os.path.isdir(root): print(f"Error: '{root}' is not a directory.") raise SystemExit(1) if is_model_dir(root): # Single model directory supplied process_model(root, mode) else: # Parent directory: iterate over sub-directories processed = 0 for name in sorted(os.listdir(root)): sub = os.path.join(root, name) if os.path.isdir(sub) and is_model_dir(sub): process_model(sub, mode) processed += 1 if processed == 0: print(f"No model directories (with npz/ sub-dir) found under '{root}'.") if __name__ == '__main__': main()