{ "cells": [ { "cell_type": "markdown", "id": "90375de7", "metadata": {}, "source": [ "# E1 — ISIC 2017 UNet2D Architecture Sweep Summary (all Learning Rates)\n", "\n", "**Question.** Across a full 3×3 grid of encoder × merge architectures swept over four learning rates, which (architecture, LR) pair produces the best ISIC 2017 segmentation model — and is the best-performing LR the same for every architecture?\n", "\n", "**Design.** 36 single-seed runs: a 3×3 grid of **encoder** (`classical`, `se`, `he2`) × **merge** (`he2`, `attention_gate`, `classical`), each trained at four fixed learning rates (`1e-4`, `3e-4`, `6e-4`, `1e-3`) — 9 architectures × 4 LRs. The performance metric is the Dice coefficient at threshold 0.5 on the ISIC 2017 validation set. This is a joint architecture × LR sweep: every architecture is evaluated at all four LRs, so the analysis selects both the architecture **and** its accompanying LR. This notebook aggregates across the four per-LR experiments to compare architectures within and across LRs and to read off the best LR for each.\n", "\n", "**Source.** Four MLflow databases under `mlruns/`, one per LR — `E1-isic2017-unet2d-model-lr-1seed.db` (`3e-4`), `…-modelsw-lr_6e-4-1seed.db`, `…-lr_1e-3-1seed.db`, `…-lr_1e-4-1seed.db` — 36 runs (9 architectures × 4 LRs), all `FINISHED`. Each per-LR notebook (`E1_isic2017_unet2d_modelsw_lr_*_1seed_analysis.ipynb`) holds the full run-level breakdown." ] }, { "cell_type": "markdown", "id": "bbfbbde7", "metadata": {}, "source": [ "## Executive summary\n", "\n", "- Screen: 3×3 encoder × merge grid for 4 LRs ({`1e-4`, `3e-4`, `6e-4`, `1e-3`}), 36 single-seed runs; metric = val Dice @ 0.5\n", "- Selection criterion: highest tail-mean (last-10-epoch) Dice, tie-broken on cross-LR std, overfitting gap and throughput (§2)\n", "\n", "| Criterion (at `lr=3e-4`) | `classical+he2` | `classical+attention_gate` |\n", "|---|---|---|\n", "| Peak Dice | **0.8427** | 0.8412 |\n", "| Tail-mean Dice | 0.8259 | **0.8265** |\n", "| Mean tail-Dice across 4 LRs | 0.8205 | **0.8211** |\n", "| Std across 4 LRs | **0.0039** | 0.0056 |\n", "| Overfitting gap | **0.078** | 0.084 |\n", "| Throughput (sps) | **140** | 123 |\n", "\n", "**Findings:**\n", "- `lr=3e-4` is the best LR for the recommended `classical` architectures and the modal winner overall (best plateau LR for 6 of 9 architectures); 3 architectures peak elsewhere (`1e-3` or `6e-4`), so the optimal LR is not uniform across the grid\n", "- `classical` is the top encoder at every LR (+`0.010–0.016` over `se`);\n", "- `he2` and `attention_gate` merges tie within single-seed noise (Δ `0.0006`); `classical` merge lags and is eliminated\n", "- `he2` wins every cost/reliability axis: 14 % faster, lower overfitting, lower cross-LR std\n", "\n", "**Decision: `classical` encoder + `he2` merge at `lr=3e-4`.** The `he2`/`attention_gate` tie-break is resolved at multi-seed in E2." ] }, { "cell_type": "markdown", "id": "e9c79f6d", "metadata": {}, "source": [ "## 1. Data\n", "\n", "Load and concatenate the four per-LR MLflow databases into a single sweep table (`all_runs`), one row per run, tagged with its learning rate. `summarize_runs` reduces each run to peak / tail-plateau / final metrics; `load_sweep_runs` stacks the four experiments and adds the `lr` column the cross-LR helpers pivot on." ] }, { "cell_type": "code", "execution_count": 10, "id": "9b202f12", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:09.855091Z", "iopub.status.busy": "2026-05-31T15:28:09.854824Z", "iopub.status.idle": "2026-05-31T15:28:10.605120Z", "shell.execute_reply": "2026-05-31T15:28:10.604656Z" } }, "outputs": [], "source": [ "# ── Imports ──────────────────────────────────────────────────────────────────\n", "import sys\n", "from pathlib import Path\n", "\n", "PROJECT_ROOT = Path('/teamspace/studios/this_studio/repos/SkiNet')\n", "sys.path.insert(0, str(PROJECT_ROOT))\n", "\n", "import pandas as pd\n", "\n", "from SkiNet.Utils.analysis.lr_sweep import (\n", " load_sweep_runs,\n", " best_run_per_group,\n", " rank_all_runs,\n", " pivot_dim_effect,\n", " arch_consistency,\n", ")\n", "from SkiNet.Utils.analysis.plotting import plot_group_bar, plot_sweep_facet\n", "\n", "# ── Configuration — every tunable argument lives in this cell ────────────────\n", "MLRUNS = PROJECT_ROOT / 'mlruns' # directory holding the per-LR MLflow databases\n", "MONITOR = 'val_dice' # validation metric summarised per run\n", "\n", "# group label (learning rate) → (db_filename, mlflow_experiment_name)\n", "EXPERIMENTS = {\n", " '3e-4': ('E1-isic2017-unet2d-model-lr-1seed.db', 'E1-isic2017-unet2d-model-lr-1seed'),\n", " '6e-4': ('E1-isic2017-unet2d-modelsw-lr_6e-4-1seed.db', 'E1-isic2017-unet2d-modelsw-lr_6e-4-1seed'),\n", " '1e-3': ('E1-isic2017-unet2d-modelsw-lr_1e-3-1seed.db', 'E1-isic2017-unet2d-modelsw-lr_1e-3-1seed'),\n", " '1e-4': ('E1-isic2017-unet2d-modelsw-lr_1e-4-1seed.db', 'E1-isic2017-unet2d-modelsw-lr_1e-4-1seed'),\n", "}\n", "LR_ORDER = ['1e-4', '3e-4', '6e-4', '1e-3'] # canonical LR ordering for tables and facets\n", "\n", "# ── Presentation ─────────────────────────────────────────────────────────────\n", "pd.set_option('display.max_columns', 80)\n", "pd.set_option('display.width', 200)\n", "pd.set_option('display.float_format', '{:.4f}'.format)" ] }, { "cell_type": "code", "execution_count": 11, "id": "5f60b287", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:10.607811Z", "iopub.status.busy": "2026-05-31T15:28:10.607441Z", "iopub.status.idle": "2026-05-31T15:28:12.162023Z", "shell.execute_reply": "2026-05-31T15:28:12.159868Z" } }, "outputs": [ { "data": { "text/plain": [ "\"Loaded 36 runs across 4 LRs: ['1e-3', '1e-4', '3e-4', '6e-4']\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ── Load: 36 runs = 9 architectures × 4 learning rates ───────────────────────\n", "all_runs = load_sweep_runs(EXPERIMENTS, MLRUNS, group_by='lr', monitor=MONITOR)\n", "f\"Loaded {len(all_runs)} runs across {all_runs['lr'].nunique()} LRs: {sorted(all_runs['lr'].unique())}\"" ] }, { "cell_type": "markdown", "id": "69998920", "metadata": {}, "source": [ "## 2. Selection methodology\n", "\n", "### 2.1 Primary ranking metric\n", "\n", "At one seed, runs are ranked on **`val_dice_tail_mean`** — the mean validation Dice (threshold 0.5) over the **last 10 epochs**, i.e. the stable plateau — rather than the single best epoch (`val_dice_max`). Peak Dice is retained as a secondary, reported column.\n", "\n", "### 2.2 Selection rules\n", "\n", "The architecture is selected in two passes over the 9 encoder × merge combinations:\n", "\n", "| # | Criterion | Operational definition |\n", "|---|---|---|\n", "| 1 | **Best plateau Dice** | Highest `val_dice_tail_mean` at the best LR, evaluated per encoder and per merge family (§3.4–3.5) and per full architecture (§3.6) |\n", "| 2 | **Cross-LR robustness** | Lowest std of `val_dice_tail_mean` across the 4 LRs — prefers architectures that do not depend on LR tuning (§3.6) |\n", "\n", "When two architectures are tied on plateau Dice **within single-seed noise** (Δ ≲ `0.001`), the decision falls to the secondary cost/reliability axes:\n", "\n", "| Tie-break axis | Column | Preference |\n", "|---|---|---|\n", "| Generalisation | `generalization_gap_final` (`train_dice − val_dice`) | lower |\n", "| Stability from peak | `drop_peak_to_final` (`val_dice_max − final_val_dice`) | lower |\n", "| Throughput | `samples_per_sec` | higher |\n", "\n", "### 2.3 Why a single seed is sufficient here\n", "\n", "E1 is a combined architecture-learning rate **screen**: its job is to eliminate clearly inferior combinations and surface the one or two finalists. Resolving a remaining tie-break requires the paired multi-seed design, which is carried out in **E2**." ] }, { "cell_type": "markdown", "id": "30633042", "metadata": {}, "source": [ "## 3. Results" ] }, { "cell_type": "markdown", "id": "859be58a", "metadata": {}, "source": [ "### 3.1 Best run per LR\n", "\n", "Top-ranked run at each learning rate (by `val_dice_tail_mean`) with key secondary metrics — the per-LR champions feeding the cross-LR comparison." ] }, { "cell_type": "code", "execution_count": 12, "id": "f165b7be", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.164386Z", "iopub.status.busy": "2026-05-31T15:28:12.163891Z", "iopub.status.idle": "2026-05-31T15:28:12.221783Z", "shell.execute_reply": "2026-05-31T15:28:12.221211Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 lrencodermergeval_dice_tail_meanval_dice_maxval_dice_max_epochgeneralization_gap_finalsamples_per_sec
03e-4classicalattention_gate0.82650.8412740.084123.2
16e-4classicalattention_gate0.82520.8375990.098114.2
21e-3classicalclassical0.82130.8306920.081158.9
31e-4classicalhe20.81880.8273970.091128.0
\n" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_run_per_group(all_runs)" ] }, { "cell_type": "markdown", "id": "9549635b", "metadata": {}, "source": [ "### 3.2 All 36 runs ranked\n", "\n", "Full cross-LR ranking sorted by `val_dice_tail_mean` (stable-plateau Dice). Columns:\n", "\n", "| column | what it measures |\n", "|---|---|\n", "| `val_dice_max` | Single best-epoch Dice at fixed threshold 0.5 — the peak this run reached |\n", "| `val_dice_tail_mean` | Mean Dice over last 10 epochs — the stable plateau |\n", "| `val_dice_tail_std` | Std over the same tail window — convergence noise |\n", "| `val_dice_max_epoch` | Epoch at which the peak occurred |\n", "| `gen_gap` | `train_dice − val_dice` at the final epoch — overfitting signal |\n", "| `drop` | `val_dice_max − final_val_dice` — how much the run fell from peak to last epoch |" ] }, { "cell_type": "code", "execution_count": 13, "id": "6d9b32a6", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.224101Z", "iopub.status.busy": "2026-05-31T15:28:12.223823Z", "iopub.status.idle": "2026-05-31T15:28:12.242332Z", "shell.execute_reply": "2026-05-31T15:28:12.241781Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 lrencodermergeval_dice_maxval_dice_tail_meantail_stdepochgen_gapdropsamples_per_sec
03e-4classicalattention_gate0.84120.82650.0107740.0840.019123.2
13e-4classicalhe20.84270.82590.0109970.0780.017140.3
26e-4classicalattention_gate0.83750.82520.0114990.0980.032114.2
31e-3classicalclassical0.83060.82130.0085920.0810.012158.9
46e-4classicalhe20.83340.82060.0078970.0820.011131.9
51e-4classicalhe20.82730.81880.0058970.0910.018128.0
61e-3classicalattention_gate0.82770.81780.0068880.0820.008116.6
71e-3classicalhe20.83200.81680.0092920.0770.009138.3
83e-4classicalclassical0.82990.81650.0107840.0900.014157.3
96e-4classicalclassical0.83190.81530.0106990.0940.021157.1
101e-4classicalattention_gate0.83020.81490.0082870.1020.033117.6
113e-4seclassical0.83070.81280.0123990.0950.030144.5
123e-4he2attention_gate0.82560.81240.0071990.0920.023117.2
131e-3he2he20.82400.81190.0079920.0750.003136.6
143e-4he2he20.82220.81150.0076990.0780.011136.8
151e-4seclassical0.82480.81020.0071920.0830.013134.5
166e-4sehe20.83230.80950.0091700.0780.017125.1
173e-4he2classical0.82380.80830.0093990.0870.019145.0
183e-4sehe20.82430.80830.0115910.0890.018129.5
196e-4seclassical0.82400.80770.0109700.0910.021136.4
206e-4he2attention_gate0.82250.80720.0098990.0920.020114.7
211e-4classicalclassical0.81780.80690.0060840.1000.018148.7
226e-4he2classical0.81910.80650.0108990.0740.003136.7
236e-4he2he20.82480.80590.0122840.0800.014129.1
243e-4seattention_gate0.81950.80560.0094830.0870.010114.8
256e-4seattention_gate0.82540.80490.0104870.0880.020114.5
261e-3seattention_gate0.82500.80440.0156990.0950.028111.1
271e-4he2attention_gate0.81690.80350.0080990.0940.023114.7
281e-3he2attention_gate0.81650.80300.0104930.0960.019115.2
291e-4sehe20.82770.80250.0099700.0970.028120.4
301e-4he2he20.81840.80180.0086990.0860.020129.8
311e-3sehe20.81830.80160.0150700.0750.006128.3
321e-3seclassical0.81640.80110.0073700.0930.019139.3
331e-4he2classical0.81760.80080.0091990.0900.021105.2
341e-3he2classical0.81110.80080.0074840.0850.006140.3
351e-4seattention_gate0.81330.79820.0119920.1160.033109.0
\n" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rank_all_runs(all_runs)" ] }, { "cell_type": "markdown", "id": "0008d4d2", "metadata": {}, "source": [ "### 3.3 LR comparison — tail-mean Dice per architecture\n", "\n", "Bar chart of tail-mean (plateau) Dice per architecture, grouped by LR, to visualise the LR effect on each combination." ] }, { "cell_type": "code", "execution_count": 14, "id": "c5ebc196", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.244838Z", "iopub.status.busy": "2026-05-31T15:28:12.244636Z", "iopub.status.idle": "2026-05-31T15:28:12.539479Z", "shell.execute_reply": "2026-05-31T15:28:12.538956Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_group_bar(\n", " all_runs,\n", " group_col='lr',\n", " group_order=LR_ORDER,\n", " value_col='val_dice_tail_mean',\n", " title='E1 Architecture Screen: Tail-mean Dice by LR (all 36 runs)',\n", " ylabel='val Dice tail-mean (last 10 epochs)',\n", " ylim=(0.800, 0.830),\n", ")" ] }, { "cell_type": "markdown", "id": "9fd311f7", "metadata": {}, "source": [ "### 3.4 Encoder family effect by LR\n", "\n", "Mean tail-mean (plateau) Dice per encoder family at each LR (averaged over the 3 merge modes)." ] }, { "cell_type": "code", "execution_count": 15, "id": "98f9ea4e", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.541928Z", "iopub.status.busy": "2026-05-31T15:28:12.541439Z", "iopub.status.idle": "2026-05-31T15:28:12.553528Z", "shell.execute_reply": "2026-05-31T15:28:12.552922Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 classicalsehe2
 meanstdmeanstdmeanstd
lr      
1e-40.81350.00600.80360.00610.80210.0014
3e-40.82300.00560.80890.00370.81070.0021
6e-40.82040.00490.80740.00230.80650.0007
1e-30.81870.00240.80240.00170.80520.0059
\n" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivot_dim_effect(\n", " all_runs, 'encoder',\n", " group_order=LR_ORDER,\n", " dim_order=['classical', 'se', 'he2'],\n", ")" ] }, { "cell_type": "markdown", "id": "1c5b4c11", "metadata": {}, "source": [ "### 3.5 Merge family effect by LR\n", "\n", "Mean tail-mean (plateau) Dice per merge family at each LR (averaged over the 3 encoder modes)." ] }, { "cell_type": "code", "execution_count": 16, "id": "6ceeea19", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.555511Z", "iopub.status.busy": "2026-05-31T15:28:12.555302Z", "iopub.status.idle": "2026-05-31T15:28:12.564742Z", "shell.execute_reply": "2026-05-31T15:28:12.564267Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 he2attention_gateclassical
 meanstdmeanstdmeanstd
lr      
1e-40.80770.00960.80560.00860.80600.0048
3e-40.81520.00940.81480.01070.81250.0041
6e-40.81200.00770.81240.01110.80980.0048
1e-30.81010.00770.80840.00820.80770.0118
\n" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivot_dim_effect(\n", " all_runs, 'merge',\n", " group_order=LR_ORDER,\n", " dim_order=['he2', 'attention_gate', 'classical'],\n", ")" ] }, { "cell_type": "markdown", "id": "2ced1b04", "metadata": {}, "source": [ "### 3.6 Architecture consistency across LRs\n", "\n", "Aggregated across all 4 LRs, each architecture's mean / std / min / max tail-mean Dice (the metric `arch_consistency` ranks on). Low std means the architecture is robust to LR choice; high std means it benefits from LR tuning.\n", "\n", "**Key finding:** the two `classical`-encoder architectures lead and are separated by noise. `classical+attention_gate` has the highest mean (`0.8211`) but the larger cross-LR spread (std `0.0056`); `classical+he2` trails by `0.0006` on the mean (`0.8205`) while being the more stable of the two (std `0.0039`). `attention_gate`'s edge is driven mainly by its result at `lr=3e-4`." ] }, { "cell_type": "code", "execution_count": 17, "id": "74ac5a91", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.567167Z", "iopub.status.busy": "2026-05-31T15:28:12.566813Z", "iopub.status.idle": "2026-05-31T15:28:12.578178Z", "shell.execute_reply": "2026-05-31T15:28:12.577665Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 encodermergemean_tail_meanstd_tail_meanmin_tail_meanmax_tail_mean
0classicalattention_gate0.82110.00560.81490.8265
1classicalhe20.82050.00390.81680.8259
2classicalclassical0.81500.00600.80690.8213
3seclassical0.80800.00500.80110.8128
4he2he20.80780.00480.80180.8119
5he2attention_gate0.80650.00430.80300.8124
6sehe20.80550.00400.80160.8095
7he2classical0.80410.00390.80080.8083
8seattention_gate0.80330.00340.79820.8056
\n" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arch_consistency(all_runs)" ] }, { "cell_type": "markdown", "id": "8de864ff", "metadata": {}, "source": [ "### 3.7 Throughput vs accuracy frontier\n", "\n", "Four panels share the same y-axis — this makes the LR effect directly readable as vertical shifts between panels.\n", "\n", "**Encoding:** colour = merge mode, marker shape = encoder family. Error bars show ±`val_dice_tail_std` (convergence noise over the last 10 epochs). The ring marks `classical+he2` — the recommended architecture.\n", "\n", "**How to read this:**\n", "- Top-right corner of any panel = best accuracy *and* fastest training.\n", "- The `lr=3e-4` panel sits highest overall - it is the best LR for the leading (`classical`) architectures\n", "- Within that panel, `classical+he2` sits furthest right among the high-Dice points - it is the fastest architecture that does not sacrifice Dice.\n", "- `classical+attention_gate` is the only competitor: slightly higher Dice in this panel, but visibly slower (further left) and with a wider error bar — less stable convergence." ] }, { "cell_type": "code", "execution_count": 18, "id": "5f364427", "metadata": { "execution": { "iopub.execute_input": "2026-05-31T15:28:12.580332Z", "iopub.status.busy": "2026-05-31T15:28:12.580101Z", "iopub.status.idle": "2026-05-31T15:28:12.909063Z", "shell.execute_reply": "2026-05-31T15:28:12.908437Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_sweep_facet(\n", " all_runs,\n", " facet_col='lr',\n", " facet_order=LR_ORDER,\n", " y_col='val_dice_tail_mean',\n", " yerr_col='val_dice_tail_std',\n", " color_col='merge',\n", " marker_col='encoder',\n", " color_order=['he2', 'attention_gate', 'classical'],\n", " marker_order=['classical', 'se', 'he2'],\n", " highlight={'encoder': 'classical', 'merge': 'he2', 'label': '★'},\n", " xlabel='Samples / sec',\n", " ylabel='Tail-mean val Dice ± std (last 10 epochs)',\n", ")" ] }, { "cell_type": "markdown", "id": "1387df72", "metadata": {}, "source": [ "## 4. Conclusion\n", "\n", "### Recommendation: `classical` encoder + `he2` merge at `lr=3e-4`\n", "\n", "| criterion | `classical+he2` | `classical+attention_gate` | winner |\n", "|---|---|---|---|\n", "| Mean tail-Dice across 4 LRs | 0.8205 | **0.8211** | att_gate (+0.0006) |\n", "| Std tail-Dice across 4 LRs | **0.0039** | 0.0056 | **he2** |\n", "| Best single-run Dice at lr=3e-4 | **0.8427** | 0.8412 | **he2** (+0.0015) |\n", "| Tail-mean at lr=3e-4 | 0.8259 | **0.8265** | att_gate (+0.0006) |\n", "| Overfitting gap at lr=3e-4 | **0.078** | 0.084 | **he2** |\n", "| Throughput at lr=3e-4 | **140 sps** | 123 sps | **he2** (+14 %) |\n", "\n", "`classical+attention_gate` has a marginal edge on mean and tail-mean Dice (`+0.0006`) — a difference well within single-seed noise. Against that, `classical+he2` wins on every cost and reliability axis: lower cross-LR std (more robust to LR choice), higher peak Dice, lower overfitting, and 14 % faster training. At multi-seed the `+0.0006` Dice gap is expected to vanish; the throughput and stability advantages are structural. The multi-seed tie-break is carried out in E2." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 }