{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92acdd79",
   "metadata": {},
   "source": [
    "# E2 — ISIC 2017 UNet2D Architecture Tie-Break (10 seeds)\n",
    "\n",
    "**Question.** Between `classical + he2` (HE2) and `classical + attention_gate` (AG) (both at `lr = 3e-4`), which architecture produces the better segmentation model?\n",
    "\n",
    "**Design.** 10 shared seeds (100–109). Within each seed both architectures share weight initialisation and data ordering, so the *only* difference inside a seed pair is the architecture. We therefore analyse the **paired** differences Δᵢ = AGᵢ − HE2ᵢ using ISIC 2017 validation set\n",
    "\n",
    "**Source.** `E2-isic2017-unet2d-model-tiebreak-5seed.db` (seeds 100–104) + `…_part2.db` (seeds 105–109) — 20 runs (2 architectures × 10 seeds), all `FINISHED`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cfd656",
   "metadata": {},
   "source": [
    "## Executive summary\n",
    "\n",
    "- Paired across 10 shared seeds, Δ = AG − HE2\n",
    "- Primary test = Wilcoxon signed-rank\n",
    "- CI = BCa bootstrap 95 % (10 000 resamples, `rng=42`)\n",
    "- d_z = Cohen's paired effect size.\n",
    "\n",
    "| Metric | AG | HE2 | Δ (AG−HE2) | 95 % BCa CI | Wilcoxon p | d_z | Verdict |\n",
    "|---|---|---|---|---|---|---|---|\n",
    "| **Plateau Dice** — *primary, pre-registered* [1] | 0.8300 | 0.8275 | **+0.0025** | [+0.0006, +0.0048] | **0.037** | +0.70 | **AG** ✓ |\n",
    "| Peak Dice | 0.8458 | 0.8464 | −0.0006 | [−0.0024, +0.0009] | 0.70 | −0.20 | tie |\n",
    "| Peak IoU | 0.7392 | 0.7397 | −0.0005 | [−0.0035, +0.0022] | 0.85 | −0.10 | tie |\n",
    "| Generalisation gap *(lower = better)* | 0.0921 | 0.0945 | −0.0024 | [−0.0068, +0.0030] | 0.38 | −0.29 | tie |\n",
    "| Throughput [2] | 119.7 sps | 135.3 sps | −15.6 (−13 %) | [−17.3, −12.7] | **0.002** | −4.1 | **HE2** ✓ |\n",
    "\n",
    "[1] Plateau Dice is the primary metrics; tested standalone (k = 1, α = 0.05).  \n",
    "\n",
    "Secondary quality metrics: Peak Dice, Peak IoU and Gen-gap form a Holm family (k = 3) — none of the paired differences are significant.\n",
    "\n",
    "[2] Training-cost hypothesis; standalone (k = 1, α = 0.05).\n",
    "\n",
    "**Finding.** \n",
    "- AG wins on the pre-registered primary metric (plateau Dice: p = 0.037, CI excludes 0, d_z = +0.70, 8 out of 10 seeds)\n",
    "- Peak Dice and IoU are statistical ties (|Δ| < 0.001, p > 0.7)\n",
    "- HE2 trains 13 % faster (p=0.002)\n",
    "\n",
    "**Decision: lock `classical + attention_gate` at `lr = 3e-4`.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1a4a209",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T10:44:48.259608Z",
     "iopub.status.busy": "2026-06-24T10:44:48.258607Z",
     "iopub.status.idle": "2026-06-24T10:44:49.878193Z",
     "shell.execute_reply": "2026-06-24T10:44:49.877636Z"
    }
   },
   "outputs": [],
   "source": [
    "# ── Path bootstrap (must run before any SkiNet import) ───────────────────────\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "# Resolve the repo root regardless of kernel working directory.\n",
    "# VS Code injects __vsc_ipynb_file__; nbconvert sets CWD = notebook directory.\n",
    "try:\n",
    "    _nb_dir = Path(__vsc_ipynb_file__).resolve().parent  # VS Code interactive\n",
    "except NameError:\n",
    "    _nb_dir = Path().resolve()                            # nbconvert / CLI\n",
    "\n",
    "PROJECT_ROOT = _nb_dir.parents[1].resolve()\n",
    "sys.path.insert(0, str(PROJECT_ROOT))\n",
    "\n",
    "# ── Imports ───────────────────────────────────────────────────────────────────\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from SkiNet.Utils.analysis.aggregation import load_runs\n",
    "from SkiNet.Utils.analysis.stats import build_comparison_table\n",
    "from SkiNet.Utils.analysis.reporting import show_run_table, show_comparison_table, show_family_verdicts\n",
    "from SkiNet.Utils.analysis.plotting import set_paper_style, plot_paired_slopegraph, plot_paired_forest\n",
    "from SkiNet.Utils.analysis.schema import VAL_DICE_MAX, VAL_DICE_TAIL_MEAN, VAL_IOU_MAX, GENERALIZATION_GAP_FINAL, SAMPLES_PER_SEC\n",
    "\n",
    "# ── Configuration — every tunable argument lives in this cell ────────────────\n",
    "FIG_DIR  = _nb_dir / '_static/model_selection'\n",
    "DB_PATHS = [\n",
    "    PROJECT_ROOT / 'mlruns' / 'E2-isic2017-unet2d-model-tiebreak-5seed.db',        # seeds 100–104\n",
    "    PROJECT_ROOT / 'mlruns' / 'E2-isic2017-unet2d-model-tiebreak-5seed_part2.db',  # seeds 105–109\n",
    "]\n",
    "\n",
    "AG, HE2 = 'classical+attention_gate', 'classical+he2'\n",
    "EXPERIMENT_MAP = {1: HE2, 2: AG} # MLflow experiment_id  -  architecture\n",
    "PALETTE = {AG: '#d1495b', HE2: '#30638e'} # color palette for the two architectures\n",
    "\n",
    "ALPHA   = 0.05 # significance level for confidence intervals and hypothesis tests\n",
    "N_BOOT  = 10_000 # bootstrap samples for confidence intervals and p-values\n",
    "RNG     = np.random.default_rng(42) # random number generator for reproducibility\n",
    "\n",
    "# Metric column names are imported from SkiNet.Utils.analysis.schema; the\n",
    "# spec lists below stay here because the choice of metrics, family sizes and\n",
    "# display names is specific to this E2 paired comparison.\n",
    "PRIMARY_METRIC    = VAL_DICE_TAIL_MEAN\n",
    "SECONDARY_METRICS = [VAL_DICE_MAX, VAL_IOU_MAX, GENERALIZATION_GAP_FINAL]\n",
    "# (metric, higher_is_better, family_size_k)\n",
    "METRICS_SPEC = [\n",
    "    (VAL_DICE_MAX,             True,  len(SECONDARY_METRICS)),\n",
    "    (VAL_IOU_MAX,              True,  len(SECONDARY_METRICS)),\n",
    "    (VAL_DICE_TAIL_MEAN,       True,  1),\n",
    "    (GENERALIZATION_GAP_FINAL, False, len(SECONDARY_METRICS)),\n",
    "    (SAMPLES_PER_SEC,          True,  1),\n",
    "]\n",
    "SLOPE_METRICS = [\n",
    "    (VAL_DICE_MAX,       'Peak Dice (best checkpoint)'),\n",
    "    (VAL_DICE_TAIL_MEAN, 'Plateau Dice (last-10-epoch mean)'),\n",
    "]\n",
    "# (display_name, metric, higher_is_better)\n",
    "FOREST_SPECS = [\n",
    "    ('Peak Dice',         VAL_DICE_MAX,             False),\n",
    "    ('Peak IoU',          VAL_IOU_MAX,              False),\n",
    "    ('Plateau Dice',      VAL_DICE_TAIL_MEAN,       False),\n",
    "    ('Gen-gap reduction', GENERALIZATION_GAP_FINAL, True),\n",
    "]\n",
    "\n",
    "# ── Presentation ─────────────────────────────────────────────────────────────\n",
    "set_paper_style(context='notebook')\n",
    "pd.set_option('display.width', 220)\n",
    "pd.set_option('display.float_format', '{:.4f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f30d3a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T10:44:49.880865Z",
     "iopub.status.busy": "2026-06-24T10:44:49.880508Z",
     "iopub.status.idle": "2026-06-24T10:44:52.711578Z",
     "shell.execute_reply": "2026-06-24T10:44:52.710733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 20 runs: {'classical+he2': 10, 'classical+attention_gate': 10} | seeds: [100, 101, 102, 103, 104, 105, 106, 107, 108, 109]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# ── Load: 20 runs = 2 architectures × 10 seeds ───────────────────────────────\n",
    "runs = load_runs(*DB_PATHS, exp_map=EXPERIMENT_MAP, monitor=\"val_dice\")\n",
    "SEEDS, N = sorted(runs['seed'].unique()), runs['seed'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be295bd8",
   "metadata": {},
   "source": [
    "## 1. Data\n",
    "\n",
    "One row per (seed, architecture). Columns:\n",
    "\n",
    "- **`val_dice_max`** — peak Dice of the best-saved checkpoint (the model that would actually be deployed).\n",
    "- **`val_dice_tail_mean` / `…_std`** — mean and SD of Dice over the last 10 epochs: the convergent *plateau* level and its noise.\n",
    "- **`val_iou_max`** — peak IoU of the best checkpoint.\n",
    "- **`generalization_gap_final`** — final `train_dice − val_dice` (overfitting signal; lower is better).\n",
    "- **`samples_per_sec` / `duration_min`** — training throughput and wall-clock cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "958f9121",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T10:44:52.714016Z",
     "iopub.status.busy": "2026-06-24T10:44:52.713785Z",
     "iopub.status.idle": "2026-06-24T10:44:52.729759Z",
     "shell.execute_reply": "2026-06-24T10:44:52.728993Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>arch</th>\n",
       "      <th>seed</th>\n",
       "      <th>val_dice_max</th>\n",
       "      <th>val_dice_tail_mean</th>\n",
       "      <th>val_dice_tail_std</th>\n",
       "      <th>val_iou_max</th>\n",
       "      <th>generalization_gap_final</th>\n",
       "      <th>samples_per_sec</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>100</td>\n",
       "      <td>0.8446</td>\n",
       "      <td>0.8323</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.7375</td>\n",
       "      <td>0.0836</td>\n",
       "      <td>120.6179</td>\n",
       "      <td>47.9777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>101</td>\n",
       "      <td>0.8421</td>\n",
       "      <td>0.8270</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.7350</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>120.2251</td>\n",
       "      <td>44.7637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>102</td>\n",
       "      <td>0.8459</td>\n",
       "      <td>0.8295</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.7402</td>\n",
       "      <td>0.0932</td>\n",
       "      <td>119.1804</td>\n",
       "      <td>44.9632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>103</td>\n",
       "      <td>0.8471</td>\n",
       "      <td>0.8360</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.7416</td>\n",
       "      <td>0.0926</td>\n",
       "      <td>121.8578</td>\n",
       "      <td>45.0704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>104</td>\n",
       "      <td>0.8461</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.7385</td>\n",
       "      <td>0.1012</td>\n",
       "      <td>122.7619</td>\n",
       "      <td>44.9806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>105</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8287</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.7446</td>\n",
       "      <td>0.0725</td>\n",
       "      <td>116.9509</td>\n",
       "      <td>46.6895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>106</td>\n",
       "      <td>0.8475</td>\n",
       "      <td>0.8295</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>0.7417</td>\n",
       "      <td>0.0979</td>\n",
       "      <td>119.1532</td>\n",
       "      <td>45.3808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>107</td>\n",
       "      <td>0.8460</td>\n",
       "      <td>0.8345</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.7397</td>\n",
       "      <td>0.0811</td>\n",
       "      <td>116.8750</td>\n",
       "      <td>46.6754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>108</td>\n",
       "      <td>0.8437</td>\n",
       "      <td>0.8272</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.7356</td>\n",
       "      <td>0.1094</td>\n",
       "      <td>119.5617</td>\n",
       "      <td>46.8568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>classical+attention_gate</td>\n",
       "      <td>109</td>\n",
       "      <td>0.8450</td>\n",
       "      <td>0.8263</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>0.7371</td>\n",
       "      <td>0.1004</td>\n",
       "      <td>119.6275</td>\n",
       "      <td>46.7054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>100</td>\n",
       "      <td>0.8484</td>\n",
       "      <td>0.8242</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.7427</td>\n",
       "      <td>0.0903</td>\n",
       "      <td>134.3562</td>\n",
       "      <td>42.5046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>101</td>\n",
       "      <td>0.8408</td>\n",
       "      <td>0.8261</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.7311</td>\n",
       "      <td>0.0921</td>\n",
       "      <td>134.0201</td>\n",
       "      <td>42.2701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>102</td>\n",
       "      <td>0.8439</td>\n",
       "      <td>0.8285</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.7366</td>\n",
       "      <td>0.0940</td>\n",
       "      <td>138.8387</td>\n",
       "      <td>41.8726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>103</td>\n",
       "      <td>0.8465</td>\n",
       "      <td>0.8282</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>0.7383</td>\n",
       "      <td>0.1042</td>\n",
       "      <td>136.8446</td>\n",
       "      <td>42.3139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>104</td>\n",
       "      <td>0.8488</td>\n",
       "      <td>0.8285</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.1074</td>\n",
       "      <td>129.5884</td>\n",
       "      <td>42.5380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>105</td>\n",
       "      <td>0.8462</td>\n",
       "      <td>0.8245</td>\n",
       "      <td>0.0092</td>\n",
       "      <td>0.7384</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>135.0456</td>\n",
       "      <td>42.4626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>106</td>\n",
       "      <td>0.8471</td>\n",
       "      <td>0.8287</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>136.7004</td>\n",
       "      <td>41.7020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>107</td>\n",
       "      <td>0.8466</td>\n",
       "      <td>0.8352</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.7411</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>136.6124</td>\n",
       "      <td>41.9020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>108</td>\n",
       "      <td>0.8495</td>\n",
       "      <td>0.8223</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.1122</td>\n",
       "      <td>135.4170</td>\n",
       "      <td>41.0072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>classical+he2</td>\n",
       "      <td>109</td>\n",
       "      <td>0.8459</td>\n",
       "      <td>0.8289</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.7406</td>\n",
       "      <td>0.0883</td>\n",
       "      <td>135.3955</td>\n",
       "      <td>41.2499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        arch  seed  val_dice_max  val_dice_tail_mean  val_dice_tail_std  val_iou_max  generalization_gap_final  samples_per_sec  duration_min\n",
       "0   classical+attention_gate   100        0.8446              0.8323             0.0091       0.7375                    0.0836         120.6179       47.9777\n",
       "1   classical+attention_gate   101        0.8421              0.8270             0.0075       0.7350                    0.0895         120.2251       44.7637\n",
       "2   classical+attention_gate   102        0.8459              0.8295             0.0141       0.7402                    0.0932         119.1804       44.9632\n",
       "3   classical+attention_gate   103        0.8471              0.8360             0.0088       0.7416                    0.0926         121.8578       45.0704\n",
       "4   classical+attention_gate   104        0.8461              0.8294             0.0098       0.7385                    0.1012         122.7619       44.9806\n",
       "5   classical+attention_gate   105        0.8500              0.8287             0.0164       0.7446                    0.0725         116.9509       46.6895\n",
       "6   classical+attention_gate   106        0.8475              0.8295             0.0080       0.7417                    0.0979         119.1532       45.3808\n",
       "7   classical+attention_gate   107        0.8460              0.8345             0.0074       0.7397                    0.0811         116.8750       46.6754\n",
       "8   classical+attention_gate   108        0.8437              0.8272             0.0110       0.7356                    0.1094         119.5617       46.8568\n",
       "9   classical+attention_gate   109        0.8450              0.8263             0.0067       0.7371                    0.1004         119.6275       46.7054\n",
       "10             classical+he2   100        0.8484              0.8242             0.0106       0.7427                    0.0903         134.3562       42.5046\n",
       "11             classical+he2   101        0.8408              0.8261             0.0075       0.7311                    0.0921         134.0201       42.2701\n",
       "12             classical+he2   102        0.8439              0.8285             0.0123       0.7366                    0.0940         138.8387       41.8726\n",
       "13             classical+he2   103        0.8465              0.8282             0.0133       0.7383                    0.1042         136.8446       42.3139\n",
       "14             classical+he2   104        0.8488              0.8285             0.0112       0.7440                    0.1074         129.5884       42.5380\n",
       "15             classical+he2   105        0.8462              0.8245             0.0092       0.7384                    0.0869         135.0456       42.4626\n",
       "16             classical+he2   106        0.8471              0.8287             0.0112       0.7400                    0.0895         136.7004       41.7020\n",
       "17             classical+he2   107        0.8466              0.8352             0.0065       0.7411                    0.0803         136.6124       41.9020\n",
       "18             classical+he2   108        0.8495              0.8223             0.0130       0.7440                    0.1122         135.4170       41.0072\n",
       "19             classical+he2   109        0.8459              0.8289             0.0038       0.7406                    0.0883         135.3955       41.2499"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_run_table(runs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8946280c",
   "metadata": {},
   "source": [
    "## 2. Statistical methods\n",
    "\n",
    "### 2.1 Paired design\n",
    "\n",
    "Within each seed, AG and HE2 share weight initialisation and data ordering, so everything that varies run-to-run **except the architecture** is held constant. Subtracting within the pair, Δᵢ = AGᵢ − HE2ᵢ, removes seed-to-seed noise and we analyse 10 paired differences to compute statistics.\n",
    "\n",
    "> **Scope caveat.** All seeds reuse a single fixed ISIC-2017 train/validation split; only initialisation varies — this is *not* k-fold cross-validation. Per Rainio et al. (2024), fixed-split p-values can understate variance, so treat every interval and p-value below as an **optimistic lower bound** on the true uncertainty.\n",
    "\n",
    "### 2.2 Three independent hypothesis families\n",
    "\n",
    "Each family controls its own family-wise error rate at α = 0.05, so a win in one never borrows evidence from another.\n",
    "\n",
    "| Family | Metric(s) | k | Per-metric threshold | Correction |\n",
    "|---|---|---|---|---|\n",
    "| **Primary (pre-registered)** | `val_dice_tail_mean` | 1 | **0.05** | none |\n",
    "| **Secondary quality** | `val_dice_max`, `val_iou_max`, gen-gap | 3 | ≤ **0.0167** | Holm step-down |\n",
    "| **Training cost** | `samples_per_sec` | 1 | **0.05** | none  |\n",
    "\n",
    "*Why plateau Dice is primary:* it measures the **stable, convergent** Dice level (mean of the last 10 epochs), and we commit to this metric **before** the statistical significance results were seen.\n",
    "\n",
    "### 2.3 Inference criteria — how to read each number\n",
    "\n",
    "Three complementary statistics on the same 10 paired differences:\n",
    "\n",
    "#### 1) Wilcoxon signed-rank test\n",
    "\n",
    "- Wilcoxon test is non-parametric and works on ranks, robust to the odd outlier seed typical of small ML sweeps, and the test Rainio et al. recommend for comparing segmentation models across repeated runs.\n",
    "- Rank 10 differences by magnitude, check whether the positive and negative ones are balanced. If the two architectures were truly equal, large differences would land on each side about equally; a lopsided pile-up is evidence of a real effect.\n",
    "- **Null hypothesis H₀:** the population median of Δᵢ is 0 (both architectures yield the same result)\n",
    "- **Statistic:** T = min(W⁺, W⁻), the smaller of the summed positive / negative ranks. Small T means the wins are consistent in sign and the larger-magnitude differences point the same way.\n",
    "- **p-value:** p = 2 × min(P(W⁺ ≥ w⁺), P(W⁻ ≤ w⁻)), where P(W⁺ ≥ k) and P(W⁻ ≤ k) are cumulative probabilities from the exact Wilcoxon null distribution. The factor of 2 converts the smaller one-tailed probability to a two-tailed p-value.\n",
    "- **Reject H₀ when** the exact two-tailed p < the family threshold (§2.2).\n",
    "- **Resolution limit at n = 10:** the smallest achievable two-tailed p is 2 / 2¹⁰ = **0.00195**, reached only when all 10 differences share one sign. Both α thresholds sit safely above this floor, so the test has room to reject.\n",
    "\n",
    "#### 2) BCa bootstrap 95 % CI\n",
    "\n",
    "- **What it estimates — mean(Δ).** Draw the 10 differences *with replacement* 10 000 times, recompute the mean each time, and read off the 2.5th–97.5th percentiles of those resampled means. This brackets the *mean* effect — a different estimand from the Wilcoxon test, which assesses a rank-based location shift (the pseudomedian) robust to outliers. When the mean-based interval and the rank-based test agree, we can trust the mean isn't being dragged by skew in just one or two of the 10 seeds.\n",
    "- **Why BCa, not raw percentiles.** BCa = **b**ias-**c**orrected and **a**ccelerated, two adjustments layered on the plain percentile interval:\n",
    "  - **bias (b̂)** — shifts both endpoints when the bootstrap means sit systematically above or below the observed mean;\n",
    "  - **acceleration (â)** — stretches one tail more than the other (estimated by leave-one-out jackknife) when the estimator's variance changes with its value.\n",
    "- **How to read it.** An interval that **excludes 0** fixes the *sign* of the effect at 95 % confidence; one that **straddles 0** leaves the direction undetermined.\n",
    "\n",
    "\n",
    "#### 3) Cohen's d_z\n",
    "\n",
    "$$d_z = \\frac{\\overline{\\Delta}}{\\mathrm{SD}(\\Delta,\\ \\mathrm{ddof}=1)}$$\n",
    "\n",
    "where:\n",
    "- $\\overline{\\Delta} = \\frac{1}{n}\\sum_{i=1}^{n}\\Delta_i$ — mean of the $n = 10$ paired differences; the **signal** (how much better AG is on average).\n",
    "- $\\mathrm{SD}(\\Delta,\\ \\mathrm{ddof}=1) = \\sqrt{\\frac{1}{n-1}\\sum_{i=1}^{n}(\\Delta_i - \\overline{\\Delta})^2}$ — sample SD of those differences (Bessel-corrected); the **noise** — how much the per-seed advantage fluctuates.\n",
    "- $d_z$ is therefore a signal-to-noise ratio: how many standard deviations of seed-to-seed scatter the mean architecture advantage represents. Because pairing removed shared seed variance before this SD is computed, SD(Δ) is smaller than the pooled SD of the two raw groups — hence, d_z is *larger* than the one we would get if computed the avarage (not paired) variance of the two groups: \n",
    "\n",
    "$$SD_{pooled} = \\sqrt{\\frac{SD_{AG}^2 + SD_{HE2}^2}{2}},$$\n",
    "\n",
    "where $SD_{AG}^2$ and $SD_{HE2}^2$ are the sample variances of the 10 AG scores and 10 HE2 scores computed independently — before any pairing or subtraction.\n",
    "\n",
    "- **Scale (Cohen 1988):** < 0.2 negligible · 0.2–0.5 small · 0.5–0.8 medium · > 0.8 large. Reported with its own bootstrap 95 % CI, since at n = 10 the point estimate is imprecise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a13c48",
   "metadata": {},
   "source": [
    "## 3. Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4cd5a6e",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-06-24T10:44:52.731755Z",
     "iopub.status.idle": "2026-06-24T10:44:52.780426Z",
     "shell.execute_reply": "2026-06-24T10:44:52.779117Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AG</th>\n",
       "      <th>HE2</th>\n",
       "      <th>Δ (AG−HE2)</th>\n",
       "      <th>95% BCa CI</th>\n",
       "      <th>wilcoxon_p</th>\n",
       "      <th>sig</th>\n",
       "      <th>d_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>val_dice_max</th>\n",
       "      <td>0.8458</td>\n",
       "      <td>0.8464</td>\n",
       "      <td>-0.0006</td>\n",
       "      <td>[-0.0024, +0.0009]</td>\n",
       "      <td>0.6953</td>\n",
       "      <td></td>\n",
       "      <td>-0.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_iou_max</th>\n",
       "      <td>0.7392</td>\n",
       "      <td>0.7397</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>[-0.0035, +0.0022]</td>\n",
       "      <td>0.8457</td>\n",
       "      <td></td>\n",
       "      <td>-0.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>val_dice_tail_mean</th>\n",
       "      <td>0.8300</td>\n",
       "      <td>0.8275</td>\n",
       "      <td>+0.0025</td>\n",
       "      <td>[+0.0006, +0.0048]</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>✓</td>\n",
       "      <td>0.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generalization_gap_final</th>\n",
       "      <td>0.0921</td>\n",
       "      <td>0.0945</td>\n",
       "      <td>-0.0024</td>\n",
       "      <td>[-0.0069, +0.0028]</td>\n",
       "      <td>0.3750</td>\n",
       "      <td></td>\n",
       "      <td>-0.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples_per_sec</th>\n",
       "      <td>119.6811</td>\n",
       "      <td>135.2819</td>\n",
       "      <td>-15.6007</td>\n",
       "      <td>[-17.2921, -12.4790]</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>✓</td>\n",
       "      <td>-4.1400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               AG      HE2 Δ (AG−HE2)            95% BCa CI wilcoxon_p sig     d_z\n",
       "val_dice_max               0.8458   0.8464    -0.0006    [-0.0024, +0.0009]     0.6953     -0.2000\n",
       "val_iou_max                0.7392   0.7397    -0.0005    [-0.0035, +0.0022]     0.8457     -0.1000\n",
       "val_dice_tail_mean         0.8300   0.8275    +0.0025    [+0.0006, +0.0048]     0.0371   ✓  0.7000\n",
       "generalization_gap_final   0.0921   0.0945    -0.0024    [-0.0069, +0.0028]     0.3750     -0.2900\n",
       "samples_per_sec          119.6811 135.2819   -15.6007  [-17.2921, -12.4790]     0.0020   ✓ -4.1400"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary  val_dice_tail_mean  (k=1, α=0.05):\n",
      "  p=0.0371  →  REJECT H0 - OK\n",
      "\n",
      "Holm step-down  secondary family  (k=3, α_adj=0.0167):\n",
      "                              p  threshold  reject\n",
      "test                                              \n",
      "generalization_gap_final 0.3750     0.0167   False\n",
      "val_dice_max             0.6953     0.0250   False\n",
      "val_iou_max              0.8457     0.0500   False\n",
      "\n",
      "Throughput  samples_per_sec  (k=1, α=0.05):\n",
      "  p=0.0020  →  REJECT H0 - OK\n"
     ]
    }
   ],
   "source": [
    "results = build_comparison_table(\n",
    "    runs, METRICS_SPEC,\n",
    "    arch_a=AG, arch_b=HE2, seeds=SEEDS,\n",
    "    alpha=ALPHA, n_resamples=N_BOOT, random_state=RNG,\n",
    ")\n",
    "show_comparison_table(results)\n",
    "show_family_verdicts(results, PRIMARY_METRIC, SECONDARY_METRICS, alpha=ALPHA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "015fe1b6",
   "metadata": {},
   "source": [
    "## 4. Figures\n",
    "\n",
    "- **Fig 1 — slopegraph.** One line per seed; slope direction shows the per-seed winner. Plateau Dice tilts to AG (8/10); Peak Dice is an even split.\n",
    "- **Fig 2 — forest.** Point = mean Δ, bar = BCa 95 % CI (see §2.3 ②). Only Plateau Dice clears 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f372dcda",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-06-24T10:44:52.783162Z",
     "iopub.status.idle": "2026-06-24T10:44:53.684005Z",
     "shell.execute_reply": "2026-06-24T10:44:53.683526Z"
    }
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   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1210x660 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_paired_slopegraph(\n",
    "    runs, SLOPE_METRICS,\n",
    "    arch_a=AG, arch_b=HE2, seeds=SEEDS, palette=PALETTE,\n",
    "    title=f'Fig 1 — Per-seed paired comparison (n={N})',\n",
    "    save_path=FIG_DIR / 'E2_fig1_paired_slopegraph.png',\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5800942d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-24T10:44:53.686636Z",
     "iopub.status.busy": "2026-06-24T10:44:53.686431Z",
     "iopub.status.idle": "2026-06-24T10:44:54.134247Z",
     "shell.execute_reply": "2026-06-24T10:44:54.132584Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 990x572 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_paired_forest(\n",
    "    results, FOREST_SPECS,\n",
    "    arch_a=AG, arch_b=HE2, n=N, palette=PALETTE,\n",
    "    title=f'Fig 2 — Paired AG−HE2 differences, BCa 95% CI (n={N})',\n",
    "    save_path=FIG_DIR / 'E2_fig2_forest_paired_diff.png',\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e49396c",
   "metadata": {},
   "source": [
    "## 5. Decision\n",
    "\n",
    "> **Lock `classical` encoder + `attention_gate` merge at `lr = 3e-4`.**\n",
    "\n",
    "| Priority | Criterion | AG | HE2 | Status (n = 10) |\n",
    "|---|---|---|---|---|\n",
    "| 1 | **Plateau Dice — *pre-registered primary*** (8/10 seeds) | **0.8300** | 0.8275 | AG **confirmed** — p = 0.037, CI [+0.0006, +0.0048], d_z = +0.70; passes pre-registered primary (k = 1, α = 0.05) ✓ |\n",
    "| 2 | Peak Dice / IoU | 0.8458 | **0.8464** | tie — Δ ≈ −0.0006, p = 0.70 / 0.85, CI spans 0 |\n",
    "| 3 | Generalisation gap | 0.092 | 0.094 | tie — p = 0.38, CI spans 0 |\n",
    "| 4 | Training throughput | 119.7 sps | **135.3 sps** | HE2 **confirmed** — p = 0.002, standalone (k = 1, α = 0.05) ✓ |\n",
    "\n",
    "**Rationale.** AG wins the one pre-registered model-quality axis (plateau stability); peak accuracy is a dead tie; no secondary metric survives Holm correction. HE2's only confirmed advantage is a 13 % throughput edge"
   ]
  }
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