pca_new.py

"""
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()