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Add weight-trap histogram plotting helpers and integrate into WW checkpoint plots #47

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Add weight-trap histogram plotting helpers and integrate into WW checkpoint plots #47

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127 changes: 127 additions & 0 deletions XGBWW_OpenML_1049_W7W8_Alpha_Checkpoint_WWPlots.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -190,6 +190,7 @@
"import xgboost as xgb\n",
"import torch\n",
"import weightwatcher as ww\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, log_loss\n",
Expand Down Expand Up @@ -228,6 +229,126 @@
" return None\n",
"\n",
"\n",
"\n",
"def _extract_weight_matrix(layer):\n",
" candidates = []\n",
"\n",
" if hasattr(layer, 'weight'):\n",
" candidates.append(layer.weight)\n",
"\n",
" if hasattr(layer, 'modules'):\n",
" for sublayer in layer.modules():\n",
" if sublayer is layer:\n",
" continue\n",
" if hasattr(sublayer, 'weight'):\n",
" candidates.append(sublayer.weight)\n",
"\n",
" for w in candidates:\n",
" if hasattr(w, 'detach'):\n",
" arr = w.detach().cpu().numpy()\n",
" else:\n",
" arr = np.asarray(w)\n",
" if arr.ndim == 2:\n",
" return arr\n",
"\n",
" return None\n",
"\n",
"\n",
"def _dominant_weight_values(weight_matrix, max_traps=8):\n",
" if weight_matrix is None:\n",
" return []\n",
"\n",
" flat = np.asarray(weight_matrix, dtype=float).ravel()\n",
" if flat.size == 0:\n",
" return []\n",
"\n",
" order = np.argsort(np.abs(flat))[::-1]\n",
" selected = []\n",
" for idx in order:\n",
" value = float(flat[idx])\n",
" if not np.isfinite(value):\n",
" continue\n",
" if any(np.isclose(value, prev, atol=1e-12) for prev in selected):\n",
" continue\n",
" selected.append(value)\n",
" if len(selected) >= int(max_traps):\n",
" break\n",
" return selected\n",
"\n",
"\n",
"def plot_trap_lines_on_weight_histogram(layer, matrix_name, round_idx, output_dir, max_traps=8, trap_values=None, message_prefix='analyze traps'):\n",
" weight_matrix = _extract_weight_matrix(layer)\n",
" if weight_matrix is None:\n",
" print(f'round={round_idx:4d} | skipping trap histogram for {matrix_name}: no 2D weight matrix found')\n",
" return\n",
"\n",
" values = np.asarray(weight_matrix, dtype=float).ravel()\n",
" values = values[np.isfinite(values)]\n",
" if values.size == 0:\n",
" print(f'round={round_idx:4d} | skipping trap histogram for {matrix_name}: empty/invalid weights')\n",
" return\n",
"\n",
" if trap_values is None:\n",
" selected_traps = _dominant_weight_values(weight_matrix, max_traps=max_traps)\n",
" else:\n",
" selected_traps = [float(v) for v in trap_values if np.isfinite(v)]\n",
" if not selected_traps:\n",
" print(f'round={round_idx:4d} | skipping trap histogram for {matrix_name}: no trap values selected')\n",
" return\n",
"\n",
" max_abs_weight = float(values[np.argmax(np.abs(values))])\n",
"\n",
" fig, ax = plt.subplots(figsize=(9, 6))\n",
" ax.hist(values, bins=60, density=True, alpha=0.6, color='tab:blue', edgecolor='none')\n",
" ax.set_xlabel('Weight value')\n",
" ax.set_ylabel('Density')\n",
" ax.set_title(f'Layer {matrix_name} ({round_idx}) \u2014 trap lines on weight histogram')\n",
"\n",
" cmap = plt.get_cmap('viridis')\n",
" y_max = ax.get_ylim()[1]\n",
"\n",
" for i, trap_value in enumerate(selected_traps, start=1):\n",
" color = cmap((i - 1) / max(1, len(selected_traps) - 1))\n",
" is_global_max = np.isclose(trap_value, max_abs_weight, atol=1e-12)\n",
" linestyle = (0, (7, 2, 2, 2)) if is_global_max else '--'\n",
" linewidth = 2.6 if is_global_max else 1.6\n",
"\n",
" ax.axvline(trap_value, color=color, linestyle=linestyle, linewidth=linewidth, alpha=0.95)\n",
"\n",
" label_text = f'#{i}: ({trap_value:.4g})\\n' + ('max |w|' if is_global_max else '\\u03b5=0.000')\n",
" y_pos = y_max * (0.93 - 0.08 * ((i - 1) % 5))\n",
" ax.text(\n",
" trap_value,\n",
" y_pos,\n",
" label_text,\n",
" rotation=90,\n",
" va='top',\n",
" ha='center',\n",
" fontsize=8,\n",
" color='black',\n",
" bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor='none', alpha=0.55),\n",
" )\n",
"\n",
" output_dir = Path(output_dir)\n",
" output_dir.mkdir(parents=True, exist_ok=True)\n",
" figure_path = output_dir / f'{matrix_name.lower()}_trap_hist.png'\n",
" fig.tight_layout()\n",
" fig.savefig(figure_path, dpi=160)\n",
" plt.show()\n",
" plt.close(fig)\n",
" print(f'round={round_idx:4d} | {message_prefix} ({matrix_name}): saved trap histogram to {figure_path}')\n",
"\n",
"\n",
"def plot_removed_traps_on_weight_histogram(layer, matrix_name, round_idx, output_dir, removed_trap_values):\n",
" return plot_trap_lines_on_weight_histogram(\n",
" layer=layer,\n",
" matrix_name=matrix_name,\n",
" round_idx=round_idx,\n",
" output_dir=output_dir,\n",
" trap_values=removed_trap_values,\n",
" message_prefix='remove traps',\n",
" )\n",
"\n",
"def layer_min_matrix_dim(layer):\n",
" shape = _extract_weight_shape(layer)\n",
" if shape is None:\n",
Expand Down Expand Up @@ -450,6 +571,7 @@
"MIN_WW_EIGENVALUES = 10 # Wait until constructed matrix has enough spectral support\n",
"WW_PLOTS_DIR = DRIVE_ROOT / 'ww_plots'\n",
"WW_PLOTS_DIR.mkdir(parents=True, exist_ok=True)\n",
"TRAP_HIST_MAX_LINES = 8\n",
"\n",
"start_time = time.time()\n",
"for r in range(start_round + 1, MAX_ROUNDS + 1):\n",
Expand Down Expand Up @@ -507,6 +629,11 @@
" ww_w7 = ww_stats_for_matrix(layer_w7, 'W7', make_plot=make_ww_plots, plot_dir=round_plot_dir)\n",
" ww_w8 = ww_stats_for_matrix(layer_w8, 'W8', make_plot=make_ww_plots, plot_dir=round_plot_dir)\n",
"\n",
" if make_ww_plots:\n",
" plot_trap_lines_on_weight_histogram(layer_w2, 'W2', r, round_plot_dir, max_traps=TRAP_HIST_MAX_LINES)\n",
" plot_trap_lines_on_weight_histogram(layer_w7, 'W7', r, round_plot_dir, max_traps=TRAP_HIST_MAX_LINES)\n",
" plot_trap_lines_on_weight_histogram(layer_w8, 'W8', r, round_plot_dir, max_traps=TRAP_HIST_MAX_LINES)\n",
"\n",
" alpha_w2 = ww_w2['alpha']\n",
" alpha_w7 = ww_w7['alpha']\n",
" alpha_w8 = ww_w8['alpha']\n",
Expand Down