maze_vis_sort.py

import os
import argparse
import pickle
import torch
import numpy as np
import networkx as nx
import random
import matplotlib.pyplot as plt
from tqdm import tqdm
from torch.nn.utils.rnn import pad_sequence
from collections import defaultdict
from sklearn.decomposition import PCA

# Import project modules
from model.transformer import GPTConfig, GPT
from model.transformer_rope import GPTRoPEConfig, GPTRoPE
from model.transformer_nextlat import TransformerNextLatConfig, TransformerNextLat
from model.mamba import MambaConfig, Mamba
from model.mamba2 import Mamba2Config, Mamba2
from model.gated_deltanet import GatedDeltaNetConfig, GatedDeltaNet
from model.gru import GRUConfig, GRU
from cli_utils import parse_count, format_count


def parse_args():
    parser = argparse.ArgumentParser(
        description='Analyze hidden state clustering by wall structure (legal moves).')

    # --- Model & Data Configuration ---
    parser.add_argument('--model', type=str, default='transformer',
                        choices=['transformer', 'transformer-rope', 'transformer-nextlat',
                                 'mamba', 'mamba2', 'gated-deltanet', 'gru'],
                        help='Model architecture (matches out/<model>/ dir). Default: transformer')
    parser.add_argument('--ckpt_iter', type=int, default=10000)
    parser.add_argument('--config', type=str, default='12_12_576')
    parser.add_argument('--device', type=str, default='cuda:0')
    parser.add_argument('--num_nodes', type=int, default=100)
    parser.add_argument('--num_of_paths', type=int, default=20)
    parser.add_argument('--multitasks', action=argparse.BooleanOptionalAction, default=True)
    parser.add_argument('--num_train_dataset', type=parse_count, default="10M")
    parser.add_argument('--tasks', type=str, default='C1')
    parser.add_argument('--CL', action=argparse.BooleanOptionalAction, default=False)
    parser.add_argument('--path_type', type=str, default='RWs', choices=['RWc', 'RWa', 'RWs'])
    parser.add_argument('--no_task_tag', action='store_true', default=False)
    parser.add_argument('--local', action=argparse.BooleanOptionalAction, default=False)
    parser.add_argument('--NLS', action='store_true', default=False,
                        help='Use NLS model checkpoint (adds _NLS suffix to filenames)')

    # --- Clustering Specific ---
    parser.add_argument('--num_samples', type=int, default=5000,
                        help='Number of prefix sequences to sample.')
    parser.add_argument('--batch_size', type=int, default=128)
    parser.add_argument('--min_samples_per_class', type=int, default=30,
                        help='Minimum number of samples required to keep a class.')
    parser.add_argument('--cluster_by', type=str, default='current_token',
                        choices=['node', 'wall_type', 'predicted_token', 'current_token'],
                        help='Clustering criteria: node, wall_type, predicted_token, or current_token '
                             '(the last/probed token of the prefix). '
                             '(For Task-specific criteria see --cluster_by_orientation / --cluster_by_taskE_token_type.)')
    parser.add_argument('--probe_tasks', type=str, default='C',
                        help='Which task identifier to probe (e.g., A, C, E, H, or I)')

    # --- Task C additions ---
    parser.add_argument('--rel_wall_type', action=argparse.BooleanOptionalAction, default=False,
                        help='Task C only: cluster by legal moves expressed as L/R/F/T relative to current orientation. Overrides --cluster_by when set.')
    parser.add_argument('--cluster_by_orientation', action=argparse.BooleanOptionalAction, default=False,
                        help='Task C only: cluster by current orientation (N/S/E/W) instead of --cluster_by. Overrides --cluster_by when set.')

    # --- Task E additions ---
    parser.add_argument('--taskE_probe_type', type=str, default='label', choices=['dir', 'label'],
                        help='For Task E, probe at the "dir" token or the "label" token position.')
    parser.add_argument('--cluster_by_taskE_token_type', action=argparse.BooleanOptionalAction, default=False,
                        help='Task E only: cluster by the taskE token type (dir vs label) instead of --cluster_by. Overrides --cluster_by when set.')

    # --- Task I additions ---
    parser.add_argument('--cluster_by_legal_next', action=argparse.BooleanOptionalAction, default=False,
                        help='Task I only: cluster by the set of legal next steps expressed as open fixed-scan '
                             'slot indices (0=N,1=E,2=S,3=W), e.g. "0,2,3". Overrides --cluster_by when set.')

    # --- Visualization ---
    parser.add_argument('--vis_num_nodes', type=int, default=0,
                        help='When clustering by node, randomly show at most this many node classes with distinct colors. '
                             'Set to 0 or negative to show all nodes (continuous colormap).')
    parser.add_argument('--pca_full_data', action='store_true', default=True,
                        help='When clustering by node and using --vis_num_nodes > 0, perform PCA on all samples (not just the subset) '
                             'and then display only the subset. This preserves the global PCA structure.')

    return parser.parse_args()


def load_lines(path):
    if not os.path.exists(path):
        return []
    try:
        with open(path, 'r', encoding='gbk') as f:
            return [line.strip() for line in f if line.strip()]
    except:
        with open(path, 'r', encoding='utf-8') as f:
            return [line.strip() for line in f if line.strip()]


def get_legal_dirs(G, node, grid_n):
    legal = []
    for nb_str in G.neighbors(str(node)):
        nb = int(nb_str)
        if nb == node - grid_n:
            legal.append('N')
        elif nb == node + grid_n:
            legal.append('S')
        elif nb == node + 1:
            legal.append('E')
        elif nb == node - 1:
            legal.append('W')
    return sorted(legal)


def extract_data_for_clustering(lines, stoi, max_samples, no_task_tag, grid_n, num_nodes, maze_graph, probe_tasks,

                                taskE_probe_type, cluster_by):
    labels_chars = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'}
    turn_chars = {'L', 'R', 'F', 'T'}
    data = []

    lines = list(lines)
    random.shuffle(lines)

    for line in lines:
        if len(data) >= max_samples:
            break

        parts = line.split()
        if ':' not in parts:
            continue
        colon_idx = parts.index(':')

        if no_task_tag:
            action_tokens = parts[colon_idx + 1:]
            if any(c in turn_chars for c in action_tokens):
                line_task = 'C'
            elif len(parts) > colon_idx + 2 and parts[colon_idx + 2] in labels_chars:
                line_task = 'E'
            else:
                line_task = 'A'
            try:
                source = int(parts[0])
            except:
                continue
        else:
            line_task = parts[0]
            if line_task not in ['A', 'E', 'C', 'H', 'I']:
                continue
            try:
                source = int(parts[1])
            except:
                continue

        if line_task != probe_tasks:
            continue

        actions = parts[colon_idx + 1:]
        if not actions:
            continue

        try:
            token_ids = [stoi[t] for t in parts]
        except KeyError:
            continue

        curr = source

        if probe_tasks == 'A':
            for i, move in enumerate(actions):
                if move == 'N':
                    curr -= grid_n
                elif move == 'S':
                    curr += grid_n
                elif move == 'E':
                    curr += 1
                elif move == 'W':
                    curr -= 1

                if not (0 <= curr < num_nodes):
                    break

                legal = get_legal_dirs(maze_graph, curr, grid_n)
                wall_type = ','.join(legal)

                prefix_ids = token_ids[:colon_idx + 2 + i]
                data.append({
                    'ids': torch.tensor(prefix_ids, dtype=torch.long),
                    'node': curr,
                    'wall_type': wall_type,
                    'current_token': move
                })

                if len(data) >= max_samples:
                    break

        elif probe_tasks == 'C':
            # Task C：相对转向，起始朝东 (E)，每个 action 先转向再前进一步
            left_of = {'N': 'W', 'W': 'S', 'S': 'E', 'E': 'N'}
            right_of = {v: k for k, v in left_of.items()}
            opposite_of = {'N': 'S', 'S': 'N', 'E': 'W', 'W': 'E'}
            delta = {'N': -grid_n, 'S': grid_n, 'E': 1, 'W': -1}
            orientation = 'E'

            for i, action in enumerate(actions):
                if action not in turn_chars:
                    break

                if action == 'F':
                    next_orientation = orientation
                elif action == 'L':
                    next_orientation = left_of[orientation]
                elif action == 'R':
                    next_orientation = right_of[orientation]
                else:  # 'T'
                    next_orientation = opposite_of[orientation]

                next_node = curr + delta[next_orientation]
                if not (0 <= next_node < num_nodes):
                    break

                orientation = next_orientation
                curr = next_node

                legal = get_legal_dirs(maze_graph, curr, grid_n)
                # 同时记录绝对方向和相对方向的 wall_type
                abs_wall_type = ','.join(legal)
                abs_to_rel = {
                    orientation: 'F',
                    left_of[orientation]: 'L',
                    right_of[orientation]: 'R',
                    opposite_of[orientation]: 'T',
                }
                rel_legal = sorted({abs_to_rel[d] for d in legal})
                rel_wall_type_str = ','.join(rel_legal)

                prefix_ids = token_ids[:colon_idx + 2 + i]
                data.append({
                    'ids': torch.tensor(prefix_ids, dtype=torch.long),
                    'node': curr,
                    'wall_type': abs_wall_type,
                    'rel_wall_type': rel_wall_type_str,
                    'orientation': orientation,
                    'current_token': action
                })

                if len(data) >= max_samples:
                    break

        elif probe_tasks == 'E':
            for i in range(0, len(actions), 2):
                direction = actions[i]
                if i + 1 >= len(actions):
                    break
                target_lab = actions[i + 1]

                step_count = 0
                temp_curr = curr
                found = False

                while True:
                    if direction == 'N':
                        temp_curr -= grid_n
                    elif direction == 'S':
                        temp_curr += grid_n
                    elif direction == 'E':
                        temp_curr += 1
                    elif direction == 'W':
                        temp_curr -= 1

                    if not (0 <= temp_curr < num_nodes):
                        break
                    step_count += 1
                    if step_count > num_nodes + 5:
                        break

                    if maze_graph.nodes[str(temp_curr)]['label'] == target_lab:
                        curr = temp_curr
                        found = True
                        break

                if not found:
                    break

                legal = get_legal_dirs(maze_graph, curr, grid_n)
                wall_type = ','.join(legal)

                if cluster_by == 'taskE_token_type':
                    # Extract BOTH 'dir' and 'label' positions
                    prefix_ids_dir = token_ids[:colon_idx + 2 + i]
                    data.append({
                        'ids': torch.tensor(prefix_ids_dir, dtype=torch.long),
                        'node': curr,
                        'wall_type': wall_type,
                        'taskE_token_type': 'dir',
                        'current_token': direction
                    })

                    prefix_ids_lab = token_ids[:colon_idx + 3 + i]
                    data.append({
                        'ids': torch.tensor(prefix_ids_lab, dtype=torch.long),
                        'node': curr,
                        'wall_type': wall_type,
                        'taskE_token_type': 'label',
                        'current_token': target_lab
                    })
                else:
                    # Extract only the specified one
                    if taskE_probe_type == 'dir':
                        prefix_ids = token_ids[:colon_idx + 2 + i]
                        cur_tok = direction
                    else:
                        prefix_ids = token_ids[:colon_idx + 3 + i]
                        cur_tok = target_lab

                if len(data) >= max_samples:
                    break

        elif probe_tasks in ('H', 'I'):
            # Index-token tasks: replay (node[, facing]) state from 1-based scan indices.
            # Task H: clockwise scan starting from current facing (state = node + facing).
            # Task I: fixed North->E->S->W scan, no facing tracking (state = node only).
            CLOCKWISE_SCAN = {
                'N': ['N', 'E', 'S', 'W'],
                'E': ['E', 'S', 'W', 'N'],
                'S': ['S', 'W', 'N', 'E'],
                'W': ['W', 'N', 'E', 'S'],
            }
            FIXED_SCAN = ['N', 'E', 'S', 'W']
            delta = {'N': -grid_n, 'S': grid_n, 'E': 1, 'W': -1}
            facing = 'E'  # Task H starts facing East; unused for Task I

            for i, idx_tok in enumerate(actions):
                try:
                    choice = int(idx_tok)
                except ValueError:
                    break

                scan_order = CLOCKWISE_SCAN[facing] if probe_tasks == 'H' else FIXED_SCAN
                feasible = []
                for d in scan_order:
                    nb = curr + delta[d]
                    if 0 <= nb < num_nodes and maze_graph.has_edge(str(curr), str(nb)):
                        feasible.append(d)

                if not (1 <= choice <= len(feasible)):
                    break

                move = feasible[choice - 1]
                curr = curr + delta[move]
                if probe_tasks == 'H':
                    facing = move

                legal = get_legal_dirs(maze_graph, curr, grid_n)
                wall_type = ','.join(legal)

                prefix_ids = token_ids[:colon_idx + 2 + i]
                item = {
                    'ids': torch.tensor(prefix_ids, dtype=torch.long),
                    'node': curr,
                    'wall_type': wall_type,
                    'current_token': idx_tok,
                }
                if probe_tasks == 'H':
                    item['orientation'] = facing
                if probe_tasks == 'I':
                    # Legal next steps as open fixed-scan slot indices (0=N,1=E,2=S,3=W),
                    # matching the index tokens the model emits for Task I.
                    legal_set = set(legal)
                    item['legal_next'] = ','.join(str(j) for j, d in enumerate(FIXED_SCAN) if d in legal_set)
                data.append(item)

                if len(data) >= max_samples:
                    break

    return data[:max_samples]


activations = {}


def get_layer_hook(layer_idx):
    def hook(model, input, output):
        activations[layer_idx] = output.detach()

    return hook


def get_block_list(model):
    """Per-layer block ModuleList (transformer: .transformer.h, recurrent: .layers)."""
    if hasattr(model, 'transformer'):
        return model.transformer.h
    return model.layers


def get_final_norm(model):
    """Final pre-head norm (transformer: .transformer.ln_f, recurrent: .out_norm)."""
    if hasattr(model, 'transformer') and hasattr(model.transformer, 'ln_f'):
        return model.transformer.ln_f
    return model.out_norm


def get_lm_head(model):
    """Output projection head. Most models expose .lm_head; TransformerNextLat

    wraps the backbone, so its head lives at .gpt.lm_head."""
    if hasattr(model, 'lm_head'):
        return model.lm_head
    if hasattr(model, 'gpt') and hasattr(model.gpt, 'lm_head'):
        return model.gpt.lm_head
    raise AttributeError(f"{type(model).__name__} has no lm_head")


def build_model_from_checkpoint(checkpoint, model_type, local=False):
    """Reconstruct the right architecture from a checkpoint, honoring its stored model_type."""
    ckpt_model_type = checkpoint.get('model_type', model_type)
    margs = checkpoint['model_args']
    if ckpt_model_type == 'mamba':
        if local and 'use_cuda' in margs:
            margs['use_cuda'] = False  # fall back to pure-PyTorch parallel scan
        conf = MambaConfig(**margs); model = Mamba(conf)
    elif ckpt_model_type == 'mamba2':
        if local and 'use_cuda' in margs:
            margs['use_cuda'] = False  # fall back to pure-PyTorch chunked SSD
        conf = Mamba2Config(**margs); model = Mamba2(conf)
    elif ckpt_model_type == 'gated-deltanet':
        conf = GatedDeltaNetConfig(**margs); model = GatedDeltaNet(conf)
    elif ckpt_model_type == 'gru':
        conf = GRUConfig(**margs); model = GRU(conf)
    elif ckpt_model_type == 'transformer-nextlat':
        if local and 'use_flash' in margs:
            margs['use_flash'] = False
        conf = TransformerNextLatConfig(**margs); model = TransformerNextLat(conf)
    elif ckpt_model_type == 'transformer-rope':
        conf = GPTRoPEConfig(**margs)
        if local:
            conf.use_flash = False
        model = GPTRoPE(conf)
    else:
        conf = GPTConfig(**margs)
        if local:
            conf.use_flash = False
        model = GPT(conf)
    model.load_state_dict({k.replace('_orig_mod.', ''): v for k, v in checkpoint['model'].items()})
    return model, conf


def normalize_features(feats):
    norms = np.linalg.norm(feats, axis=1, keepdims=True)
    norms[norms == 0] = 1e-12
    return feats / norms


def compute_intra_inter_distances(feats, labels, unique_labels):
    centroids = {}
    for lab in unique_labels:
        mask = (labels == lab)
        if np.sum(mask) == 0:
            continue
        centroids[lab] = np.mean(feats[mask], axis=0)
        centroids[lab] = centroids[lab] / (np.linalg.norm(centroids[lab]) + 1e-12)

    intra_dists = []
    for lab in unique_labels:
        mask = (labels == lab)
        if np.sum(mask) == 0:
            continue
        centroid = centroids[lab]
        sim = np.dot(feats[mask], centroid)
        dists = 1 - sim
        intra_dists.append(np.mean(dists))
    intra_avg = np.mean(intra_dists) if intra_dists else 0.0

    inter_dists = []
    centroids_list = list(centroids.values())
    for i in range(len(centroids_list)):
        for j in range(i + 1, len(centroids_list)):
            sim = np.dot(centroids_list[i], centroids_list[j])
            dist = 1 - sim
            inter_dists.append(dist)
    inter_avg = np.mean(inter_dists) if inter_dists else 0.0

    return intra_avg, inter_avg


def main():
    args = parse_args()

    # 解析任务专用的 cluster_by 覆盖选项
    overrides_set = sum([args.cluster_by_orientation, args.cluster_by_taskE_token_type,
                         args.rel_wall_type, args.cluster_by_legal_next])
    if overrides_set > 1:
        print("[Warning] --cluster_by_orientation / --cluster_by_taskE_token_type / --rel_wall_type / "
              "--cluster_by_legal_next are mutually exclusive; "
              "preferring --cluster_by_orientation > --rel_wall_type > --cluster_by_legal_next > --cluster_by_taskE_token_type.")
        if args.cluster_by_orientation:
            args.rel_wall_type = False
            args.cluster_by_legal_next = False
            args.cluster_by_taskE_token_type = False
        elif args.rel_wall_type:
            args.cluster_by_legal_next = False
            args.cluster_by_taskE_token_type = False
        elif args.cluster_by_legal_next:
            args.cluster_by_taskE_token_type = False
    if args.cluster_by_orientation:
        args.cluster_by = 'orientation'
    elif args.rel_wall_type:
        args.cluster_by = 'rel_wall_type'
    elif args.cluster_by_legal_next:
        args.cluster_by = 'legal_next'
    elif args.cluster_by_taskE_token_type:
        args.cluster_by = 'taskE_token_type'

    # 强制修正：如果是 taskE_token_type，则必须提取 Task E
    if args.cluster_by == 'taskE_token_type':
        args.probe_tasks = 'E'
    # 'rel_wall_type' 仅对 Task C 有意义；'orientation' 对 Task C / H 有意义
    if args.cluster_by == 'rel_wall_type' and args.probe_tasks != 'C':
        print(f"[Warning] cluster_by='rel_wall_type' is only meaningful for Task C; forcing probe_tasks='C'.")
        args.probe_tasks = 'C'
    elif args.cluster_by == 'orientation' and args.probe_tasks not in ('C', 'H'):
        print(f"[Warning] cluster_by='orientation' is only meaningful for Task C/H; forcing probe_tasks='C'.")
        args.probe_tasks = 'C'
    elif args.cluster_by == 'legal_next' and args.probe_tasks != 'I':
        print(f"[Warning] cluster_by='legal_next' is only meaningful for Task I; forcing probe_tasks='I'.")
        args.probe_tasks = 'I'

    seed = 44
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    grid_n = int(args.num_nodes ** 0.5)

    tasks_tag = f"{args.tasks}_CL" if args.CL else args.tasks
    tasks_tag = f"{tasks_tag}_{args.path_type}"
    if args.no_task_tag:
        tasks_tag += "_NT"
    # NLS only affects checkpoint naming (per train_maze.py)
    ckpt_tasks_tag = tasks_tag
    if args.model == 'transformer-nextlat':
        ckpt_tasks_tag = f"{ckpt_tasks_tag}_NL"
    if args.NLS:
        ckpt_tasks_tag = f"{ckpt_tasks_tag}_NLS"

    data_dir = f'data/maze/{args.num_nodes}'
    nt_suffix = '_NT' if args.no_task_tag else ''
    model_dir = args.model.replace('-', '_')
    out_dir = f'out/{model_dir}/maze_{args.config}_{args.num_nodes}{nt_suffix}/'
    os.makedirs(out_dir, exist_ok=True)

    def pick_file(candidates):
        for path in candidates:
            if os.path.exists(path):
                return path
        return candidates[0]

    maze_graph_path = pick_file([f'{data_dir}/maze_graph_{tasks_tag}.graphml',
                                 f'{data_dir}/maze_graph.graphml'])
    print(f"Loading Graph from: {maze_graph_path}")
    maze_graph = nx.read_graphml(maze_graph_path)

    meta = pickle.load(open(pick_file([f'{data_dir}/meta_{tasks_tag}.pkl',
                                       f'{data_dir}/meta.pkl']), 'rb'))
    stoi, itos = meta['stoi'], meta['itos']

    train_label = format_count(args.num_train_dataset)
    ckpt_path = pick_file([os.path.join(out_dir, f'{args.ckpt_iter}_ckpt_maze_{ckpt_tasks_tag}_{train_label}.pt'),
                           os.path.join(out_dir, f'{args.ckpt_iter}_ckpt_maze_{tasks_tag}_{train_label}.pt'),
                           os.path.join(out_dir, f'{args.ckpt_iter}_ckpt_maze_{args.num_of_paths}.pt')])
    print(f"Loading Model from: {ckpt_path}")
    checkpoint = torch.load(ckpt_path, map_location=args.device, weights_only=False)
    model, conf = build_model_from_checkpoint(checkpoint, args.model, local=args.local)
    model.to(args.device).eval()
    for p in model.parameters():
        p.requires_grad = False

    block_list = get_block_list(model)
    for i in range(conf.n_layer):
        block_list[i].register_forward_hook(get_layer_hook(i))

    train_txt = pick_file([os.path.join(data_dir, f"train_{tasks_tag}_{train_label}.txt"),
                           os.path.join(data_dir, f'train_{args.num_of_paths}.txt')])
    print(f"Extracting samples for clustering...")
    dataset = extract_data_for_clustering(load_lines(train_txt), stoi, args.num_samples,
                                          args.no_task_tag, grid_n, args.num_nodes, maze_graph,
                                          args.probe_tasks, args.taskE_probe_type, args.cluster_by)
    print(f"Extracted {len(dataset)} samples.")

    print("\n" + "=" * 60)
    print(f"SAMPLE DATA EXAMPLES (Cluster by: {args.cluster_by})")
    print("=" * 60)
    for i in range(min(5, len(dataset))):
        item = dataset[i]
        ids = item['ids'].tolist()
        tokens = [itos[idx] for idx in ids]
        prefix_str = " ".join(tokens)
        label_val = item.get(args.cluster_by,
                             "N/A (needs model inference)") if args.cluster_by != 'predicted_token' else "N/A (needs model inference)"

        print(f"Example {i + 1}:")
        print(f"  Current Node : {item['node']}")
        print(f"  Wall Type    : {item['wall_type']}")
        print(f"  Selected Label: {label_val}")
        print(f"  Prefix       : {prefix_str}")
        print("-" * 60)

    all_feats = {i: [] for i in range(conf.n_layer + 1)}
    cluster_labels = []
    all_wall_types = []   # 记录每个样本的墙壁类型（用于节点模式图例标注）

    for b_start in tqdm(range(0, len(dataset), args.batch_size), desc="Inference"):
        batch = dataset[b_start:b_start + args.batch_size]
        x_padded = pad_sequence([item['ids'] for item in batch], batch_first=True, padding_value=0).to(args.device)
        seq_lengths = [len(item['ids']) for item in batch]

        current_batch_size = len(batch)

        with torch.no_grad():
            _ = model(x_padded)

            batch_indices = torch.arange(current_batch_size, device=args.device)
            last_token_idx = torch.tensor(seq_lengths, device=args.device) - 1

            h_final = activations[conf.n_layer - 1]
            if h_final.shape[0] != current_batch_size and h_final.shape[1] == current_batch_size:
                h_final = h_final.transpose(0, 1)

            h_last = h_final[batch_indices, last_token_idx, :]

            h_norm = get_final_norm(model)(h_last)
            logits = get_lm_head(model)(h_norm)

            if args.cluster_by == 'predicted_token':
                pred_ids = torch.argmax(logits, dim=-1).cpu().numpy()
                batch_labels = [itos[idx] for idx in pred_ids]
                cluster_labels.extend(batch_labels)
                # 填充 all_wall_types，保持长度一致
                for item in batch:
                    all_wall_types.append(item['wall_type'])

            for l in range(conf.n_layer):
                h = activations[l]
                if h.shape[0] != current_batch_size and h.shape[1] == current_batch_size:
                    h = h.transpose(0, 1)

                h_last_layer = h[batch_indices, last_token_idx, :].cpu().numpy()
                all_feats[l].extend(h_last_layer)

            h_norm_np = h_norm.cpu().numpy()
            all_feats[conf.n_layer].extend(h_norm_np)

        if args.cluster_by != 'predicted_token':
            for item in batch:
                if args.cluster_by == 'node':
                    cluster_labels.append(str(item['node']))
                elif args.cluster_by == 'wall_type':
                    cluster_labels.append(item['wall_type'])
                elif args.cluster_by == 'taskE_token_type':
                    cluster_labels.append(item['taskE_token_type'])
                elif args.cluster_by == 'orientation':
                    cluster_labels.append(item['orientation'])
                elif args.cluster_by == 'rel_wall_type':
                    cluster_labels.append(item['rel_wall_type'])
                elif args.cluster_by == 'legal_next':
                    cluster_labels.append(item['legal_next'])
                elif args.cluster_by == 'current_token':
                    cluster_labels.append(item['current_token'])
                # 无论哪种模式，只要后续需要显示墙壁类型（仅用于 node 模式），都记录 wall_type
                all_wall_types.append(item['wall_type'])

    cluster_labels = np.array(cluster_labels)
    all_wall_types = np.array(all_wall_types)

    unique, counts = np.unique(cluster_labels, return_counts=True)
    valid_classes = [u for u, c in zip(unique, counts) if c >= args.min_samples_per_class]
    print(f"Retained {len(valid_classes)} classes with >= {args.min_samples_per_class} samples.")
    if len(valid_classes) < 2:
        print("Not enough classes for inter-class distance. Exiting.")
        return

    mask = np.isin(cluster_labels, valid_classes)
    filtered_labels = cluster_labels[mask]
    # 仅在 node 模式下才需要墙壁类型信息（用于图例标注）
    if args.cluster_by == 'node':
        filtered_wall_types = all_wall_types[mask]
    else:
        filtered_wall_types = None  # 其他模式下不使用
    print(f"Total samples after filtering: {np.sum(mask)}")

    intra_distances = []
    inter_distances = []

    for l in range(conf.n_layer + 1):
        feats = np.array(all_feats[l])[mask]
        feats_norm = normalize_features(feats)

        intra, inter = compute_intra_inter_distances(feats_norm, filtered_labels, valid_classes)
        intra_distances.append(intra)
        inter_distances.append(inter)

    print("\n" + "=" * 60)
    print(f"CLUSTERING BY {args.cluster_by.upper()} (cosine distance)")
    print("=" * 60)
    print(f"{'Layer':<6} | {'Intra-class Dist':<18} | {'Inter-class Dist':<18}")
    print("-" * 60)
    for l, (intra, inter) in enumerate(zip(intra_distances, inter_distances)):
        layer_name = f"L_{l + 1}" if l < conf.n_layer else "L_Norm"
        print(f"{layer_name:<6} | {intra:>16.4f} | {inter:>16.4f}")
    print("=" * 60)

    if args.cluster_by == 'taskE_token_type':
        task_suffix = f"{args.probe_tasks}_both"
    elif args.probe_tasks == 'E':
        task_suffix = f"{args.probe_tasks}{args.taskE_probe_type}"
    else:
        task_suffix = f"{args.probe_tasks}"

    # cluster_by 直接作为文件名后缀（'rel_wall_type' / 'orientation' / 'taskE_token_type' 等都是覆盖后的值）
    cluster_by_suffix = args.cluster_by
    nls_suffix = '_NLS' if args.NLS else ''

    out_txt = os.path.join(out_dir, f"clustering_{task_suffix}_{cluster_by_suffix}_{args.ckpt_iter}{nls_suffix}.txt")
    with open(out_txt, 'w') as f:
        f.write(f"CLUSTERING BY {args.cluster_by.upper()} (cosine distance)\n")
        f.write("=" * 60 + "\n")
        f.write(f"{'Layer':<6} | {'Intra-class Dist':<18} | {'Inter-class Dist':<18}\n")
        f.write("-" * 60 + "\n")
        for l, (intra, inter) in enumerate(zip(intra_distances, inter_distances)):
            layer_name = f"L_{l + 1}" if l < conf.n_layer else "L_Norm"
            f.write(f"{layer_name:<6} | {intra:>16.4f} | {inter:>16.4f}\n")
    print(f"Results saved to {out_txt}")

    print("\nGenerating 2D visualization using PCA...")

    n_layers = conf.n_layer + 1
    cols = min(3, n_layers)
    rows = (n_layers + cols - 1) // cols

    fig, axes = plt.subplots(rows, cols, figsize=(6 * cols, 5 * rows))
    if n_layers == 1:
        axes = [axes]
    else:
        axes = axes.flatten()

    # Decide how to visualize node classes
    is_node_mode = (args.cluster_by == 'node')
    use_random_nodes = False
    selected_nodes = None
    node_mask_for_vis = None
    node_to_wall_types = None   # 节点ID -> 墙壁类型集合

    if is_node_mode and args.vis_num_nodes > 0:
        unique_nodes = np.unique(filtered_labels)
        if len(unique_nodes) > args.vis_num_nodes:
            # Randomly select a subset of nodes for visualization
            rng = np.random.RandomState(seed)
            selected_nodes = rng.choice(unique_nodes, size=args.vis_num_nodes, replace=False)
            use_random_nodes = True
            node_mask_for_vis = np.isin(filtered_labels, selected_nodes)
            print(f"Randomly selected {args.vis_num_nodes} nodes out of {len(unique_nodes)} for visualization: {sorted(selected_nodes)}")

            # 预计算每个选中节点的墙壁类型集合
            node_to_wall_types = {}
            for node in selected_nodes:
                node_mask = (filtered_labels == node)
                wall_types_for_node = filtered_wall_types[node_mask]
                unique_wall_types = sorted(set(wall_types_for_node))
                node_to_wall_types[node] = unique_wall_types
        else:
            print(f"Total unique nodes ({len(unique_nodes)}) <= vis_num_nodes, showing all nodes.")
    elif is_node_mode:
        print("Showing all nodes (continuous colormap).")

    # 用于存储全局图例的句柄和标签
    legend_handles = []
    legend_labels = []

    for l in range(n_layers):
        ax = axes[l]

        # 获取该层所有样本的特征（已通过 mask 过滤）
        feats_all = np.array(all_feats[l])[mask]   # shape (N, D)

        if use_random_nodes and args.pca_full_data:
            # 情况：使用全部样本进行 PCA 拟合，然后只绘制选中节点的投影点
            # 可选子采样以提高速度（仅用于拟合，不影响投影）
            if feats_all.shape[0] > 2000:
                # 随机采样 2000 个点用于拟合 PCA
                idx_sample = np.random.choice(feats_all.shape[0], 2000, replace=False)
                pca = PCA(n_components=2)
                pca.fit(feats_all[idx_sample])
            else:
                pca = PCA(n_components=2)
                pca.fit(feats_all)

            # 对所有样本进行投影
            feats_2d_all = pca.transform(feats_all)   # shape (N, 2)
            # 提取选中节点的投影结果
            feats_vis = feats_2d_all[node_mask_for_vis]
            labels_vis = filtered_labels[node_mask_for_vis]
            if is_node_mode:
                wall_types_vis = filtered_wall_types[node_mask_for_vis]
            else:
                wall_types_vis = None
        else:
            # 原有逻辑：仅使用用于绘制的样本进行 PCA（可能是全部样本，也可能是子集）
            if use_random_nodes:
                # 只使用选中节点的特征
                feats_vis_subset = feats_all[node_mask_for_vis]
                labels_vis = filtered_labels[node_mask_for_vis]
                if is_node_mode:
                    wall_types_vis = filtered_wall_types[node_mask_for_vis]
                else:
                    wall_types_vis = None
            else:
                # 使用全部有效样本
                feats_vis_subset = feats_all
                labels_vis = filtered_labels
                if is_node_mode:
                    wall_types_vis = filtered_wall_types
                else:
                    wall_types_vis = None

            # 可选子采样
            if feats_vis_subset.shape[0] > 2000:
                idx = np.random.choice(feats_vis_subset.shape[0], 2000, replace=False)
                feats_vis_subset = feats_vis_subset[idx]
                labels_vis = labels_vis[idx]
                if is_node_mode and wall_types_vis is not None:
                    wall_types_vis = wall_types_vis[idx]

            pca = PCA(n_components=2)
            feats_2d = pca.fit_transform(feats_vis_subset)
            feats_vis = feats_2d   # 此时 feats_vis 就是投影后的坐标

        # 绘图部分，根据 is_node_mode 和 use_random_nodes 选择颜色映射和标记
        if is_node_mode:
            if use_random_nodes:
                # 为每个选中的节点分配固定颜色
                unique_selected = np.unique(labels_vis)
                # 如果全局图例尚未生成，则创建颜色映射
                if not legend_handles:
                    cmap = plt.cm.tab20
                    color_map = {}
                    for i, node in enumerate(unique_selected):
                        color_map[node] = cmap(i % 20)
                    # 为每个节点生成图例句柄和标签
                    for node in unique_selected:
                        wall_types_str = ','.join(node_to_wall_types[node]) if node in node_to_wall_types else ''
                        label_text = f"{node} ({wall_types_str})" if wall_types_str else str(node)
                        handle = plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=color_map[node],
                                            markersize=8, label=label_text)
                        legend_handles.append(handle)
                        legend_labels.append(label_text)
                else:
                    # 复用颜色映射（但需要从已存储的 handle 中获取颜色）
                    # 简单起见，重新生成 color_map（因为 unique_selected 顺序可能一致）
                    cmap = plt.cm.tab20
                    color_map = {}
                    for i, node in enumerate(unique_selected):
                        color_map[node] = cmap(i % 20)

                # 绘制每个节点
                for node in unique_selected:
                    node_mask_plot = (labels_vis == node)
                    ax.scatter(feats_vis[node_mask_plot, 0], feats_vis[node_mask_plot, 1],
                               c=[color_map[node]], s=10, alpha=0.7)
            else:
                # 连续色图：不添加图例，只绘制
                int_labels_vis = np.array([int(lab) for lab in labels_vis])
                sc = ax.scatter(feats_vis[:, 0], feats_vis[:, 1],
                                c=int_labels_vis, cmap='viridis', s=10, alpha=0.7)
        else:
            # 其他聚类模式：为每个有效类别分配固定颜色
            if not legend_handles:
                colors = plt.cm.tab20(np.linspace(0, 1, len(valid_classes)))
                for i, cls_type in enumerate(valid_classes):
                    handle = plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=colors[i],
                                        markersize=8, label=cls_type)
                    legend_handles.append(handle)
                    legend_labels.append(cls_type)
            else:
                # 复用颜色
                colors = [h.get_markerfacecolor() for h in legend_handles]
            # 绘制每个类别
            for i, cls_type in enumerate(valid_classes):
                type_mask = (labels_vis == cls_type)
                if np.sum(type_mask) > 0:
                    ax.scatter(feats_vis[type_mask, 0], feats_vis[type_mask, 1],
                               c=[colors[i]], s=10, alpha=0.7)

        title = f'Layer {l + 1}' if l < conf.n_layer else 'Layer Final (Norm)'
        ax.set_title(title)
        ax.set_xlabel('Principal Component 1')
        ax.set_ylabel('Principal Component 2')
        ax.grid(True, linestyle='--', alpha=0.3)

    # 删除多余的子图
    for i in range(n_layers, len(axes)):
        fig.delaxes(axes[i])

    # 添加全局图例（如果需要）
    if legend_handles:
        fig.legend(handles=legend_handles, labels=legend_labels, loc='lower right',
                   fontsize='small', title=('Node ID (wall types)' if is_node_mode and use_random_nodes else args.cluster_by),
                   bbox_to_anchor=(0.98, 0.02))  # 右下角位置
    elif is_node_mode and not use_random_nodes and 'sc' in locals():
        # 连续色图模式添加 colorbar
        active_axes = axes[:n_layers]
        fig.colorbar(sc, ax=active_axes, orientation='vertical', fraction=0.02, pad=0.04, label='Node ID')

    out_png = os.path.join(out_dir, f"clustering_{task_suffix}_{cluster_by_suffix}_{args.ckpt_iter}{nls_suffix}.png")
    plt.savefig(out_png, dpi=300, bbox_inches='tight')
    plt.close()
    print(f"Plot saved to {out_png}")


if __name__ == "__main__":
    main()