| """Plot k-step detour metrics vs k for the architectures on one figure per metric. |
| |
| Data is loaded automatically from the kdetour result files saved by |
| maze_kstep_detour_test.py (out/maze_kdetour/kdetour_*.npz, key 'table' with |
| column names in 'columns'). You only need to set TASK / DATASET / configs |
| (via the command line, see --help). One PNG is produced per metric in |
| METRICS, written to out/plot/. |
| """ |
| import os |
| import argparse |
| import numpy as np |
| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
|
|
| |
| |
| |
| TASK = 'I1' |
| DATASET = '10M' |
|
|
| |
| TF_CONFIG = '6_6_384' |
| RNN_CONFIG = '12_384' |
|
|
| |
| CKPT_ITER = 10000 |
| PATH_TYPE = 'RWs' |
| KDETOUR_DIR = 'out/maze_kdetour' |
| OUT_DIR = 'out/plot' |
|
|
| |
| MODEL_SPECS = [ |
| ('Transformer', 'transformer', True), |
| ('Transformer-NextLat', 'transformer_nextlat', True), |
| ('Mamba', 'mamba', False), |
| ('Mamba-2', 'mamba2', False), |
| ('Gated-DeltaNet', 'gated_deltanet', False), |
| ('GRU', 'gru', False), |
| ] |
|
|
| MARKERS = ['o', 's', '^', 'D', 'P', 'v'] |
|
|
| |
| |
| METRICS = [ |
| ('probe_acc', 'probe accuracy (%)', (-2, 105)), |
| ('c_match_jsd', 'c_match_jsd', None), |
| ('reach_acc', 'reach accuracy (%)', (-2, 105)), |
| ] |
|
|
|
|
| def parse_args(): |
| p = argparse.ArgumentParser(description='Plot k-step detour metrics from kdetour npz files.') |
| p.add_argument('--task', default=TASK) |
| p.add_argument('--dataset', default=DATASET) |
| p.add_argument('--tf_config', default=TF_CONFIG) |
| p.add_argument('--rnn_config', default=RNN_CONFIG) |
| p.add_argument('--ckpt_iter', type=int, default=CKPT_ITER) |
| p.add_argument('--path_type', default=PATH_TYPE) |
| p.add_argument('--kdetour_dir', default=KDETOUR_DIR) |
| p.add_argument('--out_dir', default=OUT_DIR) |
| return p.parse_args() |
|
|
|
|
| def load_table(args, model_key, config): |
| """Return (k array, {col_name: values}) from the kdetour npz, or (None, None).""" |
| fname = f'kdetour_{args.task}_{args.path_type}_{args.ckpt_iter}_{args.dataset}_{model_key}_{config}.npz' |
| path = os.path.join(args.kdetour_dir, fname) |
| if not os.path.exists(path): |
| print(f"[missing] {path}") |
| return None, None |
| d = np.load(path, allow_pickle=True) |
| if 'table' not in d or 'columns' not in d: |
| print(f"[no table] {path}") |
| return None, None |
| table = np.asarray(d['table'], dtype=float) |
| cols = [str(c) for c in d['columns']] |
| by_col = {c: table[:, i] for i, c in enumerate(cols)} |
| k = by_col.get('k') |
| return k, by_col |
|
|
|
|
| def plot_metric(args, loaded, metric, ylabel, ylim): |
| plt.figure(figsize=(9, 5.5)) |
| all_k = None |
| plotted = 0 |
| for (name, key, is_tf), marker in zip(MODEL_SPECS, MARKERS): |
| k, by_col = loaded[key] |
| if k is None or metric not in by_col: |
| continue |
| config = args.tf_config if is_tf else args.rnn_config |
| plt.plot(k, by_col[metric], marker=marker, markersize=6, linewidth=2, |
| label=f'{name} ({config})') |
| if all_k is None or len(k) > len(all_k): |
| all_k = k |
| plotted += 1 |
|
|
| if plotted == 0: |
| print(f"[{metric}] nothing to plot: no kdetour npz files found.") |
| plt.close() |
| return |
|
|
| plt.xlabel('detour length k (steps)', fontsize=12) |
| plt.ylabel(ylabel, fontsize=12) |
| plt.title(f'k-step detour {metric} (Task {args.task}, {args.dataset})', fontsize=13) |
| ax = plt.gca() |
| ticks = [int(x) for x in all_k] |
| ax.set_xticks(ticks) |
| ax.set_xticklabels([str(t) for t in ticks]) |
| for t, lbl in zip(ticks, ax.get_xticklabels()): |
| if t >= 100: |
| lbl.set_color('red') |
| if ylim is not None: |
| plt.ylim(*ylim) |
| plt.grid(True, alpha=0.3) |
| plt.legend(fontsize=10, framealpha=0.9) |
| plt.tight_layout() |
| out = os.path.join( |
| args.out_dir, |
| f'kstep_{metric}_{args.task}_{args.dataset}_{args.tf_config}_{args.rnn_config}.png') |
| plt.savefig(out, dpi=150) |
| plt.close() |
| print(f"Saved figure to {out}") |
|
|
|
|
| def main(): |
| args = parse_args() |
| os.makedirs(args.out_dir, exist_ok=True) |
| loaded = {} |
| for name, key, is_tf in MODEL_SPECS: |
| config = args.tf_config if is_tf else args.rnn_config |
| loaded[key] = load_table(args, key, config) |
| for metric, ylabel, ylim in METRICS: |
| plot_metric(args, loaded, metric, ylabel, ylim) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
