diff --git a/analysis/stage5-families/FINDINGS-families.md b/analysis/stage5-families/FINDINGS-families.md new file mode 100644 index 0000000000000000000000000000000000000000..79b51428fde2416c7d3d52846172e4d352f7d572 --- /dev/null +++ b/analysis/stage5-families/FINDINGS-families.md @@ -0,0 +1,243 @@ +# 第5段階・サンプラー系比較 応答曲面分析（ERSDE-Simple / Euler-Beta） + +- データ: `Judge-results/Stage5-Families_20260907-235041/merged-results.csv`（3,330行 = 2系 x 15条件 x 111枚、欠損なし） +- 対象: ERSDE-Simple、Euler-Beta x Prompt001〜005 x seed {5193, 6111, 9676}、各系 N=15 +- 主要指標: LPIPS-VGG（小さいほど良い）。副次: SSIM win11 (Wang)、散布図は SSIM win7 (skimage) +- 損傷スケールの物差し: アンカー（±1ステップの自然揺らぎ）= 0.121 +- 生成スクリプト: `analyze_families.py`、`scatter/make_scatter.py`（いずれも再実行可能） +- 比較対象: ERSDE-Beta（走査1）の既分析結果 `analysis-stage5-scan1/tables/`（再計算していない） + +## 0. 分析上の扱い（走査1と同一） + +**strength=0 行の扱い**: strength=0 では予測項が消えるため smoothing は無効になる。実生成でも `reuse.png` 1枚しか撮っていない。11x11 グリッドの strength=0 行には **reuse の実測値を11列すべてに複製**して埋めた（11点の独立測定ではなく1点の複製）。したがって「条件の最適点が strength=0 に来た」という結果は、**その条件では格子上のどの予測設定も reuse を上回れなかった**ことを意味する。 + +**局所最小の数え方**: 実測された strength>=0.1 の 10x11 ブロックについて、8近傍のいずれよりも真に小さいセルを局所最小として数えた（複製行 strength=0 は除外）。 + +**y軸レンジの扱い（散布図）**: 走査1のレンジ（LPIPS 0.255〜0.993 / SSIM-win7 0.000〜0.881）では本データがはみ出す（実測 LPIPS 0.078〜1.043、SSIM-win7 0.0005〜0.971）。そこで**3系を通した全系共通の新レンジ**に統一した: + +- **LPIPS-VGG: 0.058 〜 1.063** +- **SSIM (win7): 0.000 〜 0.991** + +走査1の散布図は既存ファイル変更禁止のため再描画していない。**3系を並べて目視比較する際は、走査1のCSVにも同じレンジを適用して描き直す必要がある**（`scatter/make_scatter.py` 冒頭の `UNIFIED_*` を流用可）。 + +--- + +# I. ERSDE-Simple + +出力: ヒートマップ `surfaces/surface_ERSDE-Simple_*.png` と `surface_MEDIAN-15cond_ERSDE-Simple.png` / `surface_MEAN-15cond_ERSDE-Simple.png`、散布図 `scatter/scatter-lpips_ERSDE-Simple_*.png`・`scatter/scatter-ssim_ERSDE-Simple_*.png` と `scatter/sheet-lpips_ERSDE-Simple.png`・`scatter/sheet-ssim_ERSDE-Simple.png`、表 `tables/*_ERSDE-Simple.csv`、`tables/summary_ERSDE-Simple.json` + +## I-1. 条件別曲面と最適点 + +reuse 基線: LPIPS 中央値 **0.324**、範囲 0.160〜0.504（ERSDE-Beta の 0.413 より軽い）。 + +| 条件 | strength | smoothing | LPIPS | reuse | 改善 | +|---|---|---|---|---|---| +| P001-schoolgirl-5193 | 0.1 | 0.4 | 0.2972 | 0.3059 | +0.0087 | +| P001-schoolgirl-6111 | **0.0 (reuse)** | - | 0.4248 | 0.4248 | 0 | +| P001-schoolgirl-9676 | 0.6 | 0.4 | 0.3774 | 0.4294 | +0.0520 | +| P002-android-5193 | 0.2 | 0.5 | 0.3042 | 0.3312 | +0.0270 | +| P002-android-6111 | **0.0 (reuse)** | - | 0.3332 | 0.3332 | 0 | +| P002-android-9676 | 0.6 | 0.1 | 0.2380 | 0.2841 | +0.0461 | +| P003-woman-5193 | 0.2 | 0.4 | 0.1556 | 0.1605 | +0.0048 | +| P003-woman-6111 | 0.8 | 0.1 | 0.4386 | 0.4395 | +0.0009 | +| P003-woman-9676 | 0.1 | 0.0 | 0.1944 | 0.1978 | +0.0034 | +| P004-shop-5193 | 0.2 | 0.5 | 0.3561 | 0.3777 | +0.0215 | +| P004-shop-6111 | **0.0 (reuse)** | - | 0.5045 | 0.5045 | 0 | +| P004-shop-9676 | 0.1 | 0.0 | 0.3135 | 0.3235 | +0.0101 | +| P005-inkwash-5193 | 0.2 | 0.5 | 0.1865 | 0.2113 | +0.0247 | +| P005-inkwash-6111 | **0.0 (reuse)** | - | 0.2762 | 0.2762 | 0 | +| P005-inkwash-9676 | 0.1 | 0.1 | 0.2433 | 0.2571 | +0.0138 | + +**4条件（P001-6111 / P002-6111 / P004-6111 / P005-6111）で121セルのどこにも reuse を上回る設定がなかった。** ERSDE-Beta では負ける条件がプロンプト（shop）に固まっていたのに対し、ERSDE-Simple では **seed 6111 の5条件中4条件**に固まっている（偏りの軸がプロンプト側からシード側へ移っている）。 + +- **strength 軸**: 範囲 0.0〜0.8、中央値 0.1、IQR 0.05〜0.20 +- **smoothing 軸**: 範囲 0.0〜0.5、中央値 0.1、IQR 0.0〜0.40 + +## I-2. 共通1点 + +- **中央値基準: (strength 0.1, smoothing 0.0)、集約LPIPS 0.3135**（reuse行 0.3235 より良い） +- **平均基準: (strength 0.0) = reuse そのもの、集約LPIPS 0.3238**。予測をかける前提（strength>=0.1）に制限した平均最小セルは **(0.1, 0.3)、平均LPIPS 0.3330**（reuse行の平均 0.3238 より悪い） +- 上位5セル（中央値基準）は 0.3135〜0.3163 の 0.003 差に密集し、**strength 0.1〜0.2 x smoothing 0.0〜0.4** にばらける。位置は集約方法にあまり頑健ではない（中央値 (0.1,0.0) と制限付き平均 (0.1,0.3) が食い違う）。 + +**共通点 (0.1, 0.0) における各条件の損失**（`tables/common-point-loss_ERSDE-Simple.csv`）: 中央値 **+0.0204（アンカー比 16.9%）**、最大 **+0.1041（86.0%）**。 + +## I-3. Reuse 比の改善 + +`tables/reuse-comparison_ERSDE-Simple.csv`。共通点 (0.1, 0.0) と各条件の reuse の対応差（正=改善）: + +- **改善の中央値: -0.0049（アンカー比 -4.1%、中央値では悪化）** +- 範囲: -0.0816 〜 +0.0121 +- **改善 6 / 15、悪化 9 / 15** +- **Wilcoxon 符号順位検定 p = 0.208**（有意差なし） +- 制限付き平均基準の共通点 (0.1, 0.3) でも: 改善中央値 +0.0007（+0.6%）、改善 8/15、**p = 0.762**（有意差なし） + +プロンプト別改善中央値: schoolgirl -0.0008、android -0.0272、woman -0.0049、shop -0.0100、inkwash +0.0043 +シード別改善中央値: 5193 -0.0060、6111 -0.0272、9676 +0.0074 + +**チェリーピック上限**（各条件が格子から自条件最良の予測セル（strength>=0.1）を後から選ぶ場合、`tables/best-prediction-vs-reuse_ERSDE-Simple.csv`）: + +- 改善中央値 **+0.0087（アンカー比 7.2%）**、範囲 -0.0816 〜 +0.0520、改善 11/15、**Wilcoxon p = 0.359** +- strength=0（改善0）も選択肢に含めた自条件最適では中央値 +0.0087、p = 0.0033 + +## I-4. 単峰性 + +`tables/unimodality_ERSDE-Simple.csv`。**単峰仮説は文字どおりには成立しない**が、ERSDE-Beta より凹凸は少ない: 局所最小の個数は 2個が6条件、3個が3条件、4個が2条件、5個が1条件、6個が2条件、7個が1条件（**中央値3個**、ERSDE-Beta は中央値5個）。低損傷側（その条件の実測レンジの下半分）に限れば 1〜2個が9条件（中央値2個）。 + +中央値曲面の局所最小: (0.1,0.0)=0.3135、(0.1,0.4)=0.3144、(0.6,0.4)=0.3774、(1.0,0.99)=0.8774。低損傷帯の2点は 0.0009 差の実質同点で、**低損傷帯は概ね一つのなだらかな谷**である。 + +**二層構造**: 中央値曲面の EMA別プロファイル（strength>=0.1 の中央値）は 0.0→0.410、0.4→0.380、0.5→0.392、0.6→0.430、0.7→0.547、0.8→0.668、0.9→0.855、0.99→0.878。**EMA 0.0〜0.5 が安定層（0.38〜0.41）、EMA 0.7 以降が崩壊層（0.55→0.88）** という二層構造が明確に存在する。 + +## I-5. 平坦さ + +`tables/curvature-near-common_ERSDE-Simple.csv`。共通点 (0.1, 0.0) の4近傍（±0.1）: + +- 近傍差の絶対値平均: 条件ごとの中央値 **0.0053**、最大 0.0498 +- 変化幅のアンカー比: 条件ごとの中央値 **10.8%** +- 中央値曲面での近傍差: strength 0.0 側 +0.0101、strength 0.2 側 +0.0024、smoothing 0.1 側 +0.0028 + +**ERSDE-Beta（中央値 30.5%）よりさらに平坦**。格子解像度は結論を動かす要因ではない。 + +## I-6. SSIM との整合 + +`tables/ssim-vs-lpips-optima_ERSDE-Simple.csv`。中央値SSIM曲面のピークは **(0.1, 0.5)**、LPIPS の共通ボトム (0.1, 0.0) と smoothing で 0.5 ずれる（ただし LPIPS 側でも (0.1,0.4) が 0.0009 差の実質同点であり、谷が smoothing 方向に平坦なことの反映）。条件別では 7/15 で完全一致。 + +--- + +# II. Euler-Beta + +出力: ヒートマップ `surfaces/surface_Euler-Beta_*.png` と `surface_MEDIAN-15cond_Euler-Beta.png` / `surface_MEAN-15cond_Euler-Beta.png`、散布図 `scatter/scatter-lpips_Euler-Beta_*.png`・`scatter/scatter-ssim_Euler-Beta_*.png` と `scatter/sheet-lpips_Euler-Beta.png`・`scatter/sheet-ssim_Euler-Beta.png`、表 `tables/*_Euler-Beta.csv`、`tables/summary_Euler-Beta.json` + +## II-1. 条件別曲面と最適点 + +reuse 基線: LPIPS 中央値 **0.286**、範囲 0.182〜0.399（3系で最も軽く、最もばらつきが小さい）。 + +| 条件 | strength | smoothing | LPIPS | reuse | 改善 | +|---|---|---|---|---|---| +| P001-schoolgirl-5193 | 0.7 | 0.2 | 0.3090 | 0.3437 | +0.0347 | +| P001-schoolgirl-6111 | 0.4 | 0.4 | 0.1904 | 0.3433 | +0.1529 | +| P001-schoolgirl-9676 | 0.7 | 0.0 | 0.3055 | 0.3987 | +0.0932 | +| P002-android-5193 | 1.0 | 0.0 | 0.1333 | 0.2862 | +0.1528 | +| P002-android-6111 | 0.6 | 0.3 | 0.3169 | 0.3683 | +0.0514 | +| P002-android-9676 | 0.1 | 0.7 | 0.3454 | 0.3654 | +0.0200 | +| P003-woman-5193 | 0.9 | 0.0 | 0.0778 | 0.1815 | +0.1037 | +| P003-woman-6111 | 0.7 | 0.0 | 0.1459 | 0.2102 | +0.0643 | +| P003-woman-9676 | 0.6 | 0.1 | 0.1288 | 0.1924 | +0.0636 | +| P004-shop-5193 | 0.7 | 0.2 | 0.2574 | 0.3001 | +0.0427 | +| P004-shop-6111 | 0.3 | 0.2 | 0.2270 | 0.2589 | +0.0319 | +| P004-shop-9676 | 0.3 | 0.1 | 0.2503 | 0.2698 | +0.0195 | +| P005-inkwash-5193 | 0.9 | 0.0 | 0.1585 | 0.1995 | +0.0410 | +| P005-inkwash-6111 | 0.3 | 0.1 | 0.2795 | 0.2923 | +0.0129 | +| P005-inkwash-9676 | **0.0 (reuse)** | - | 0.2138 | 0.2138 | 0 | + +**reuse を上回れなかったのは1条件のみ**（P005-inkwash-9676）。ERSDE-Beta の4条件、ERSDE-Simple の4条件と対照的である。 + +- **strength 軸**: 範囲 0.0〜1.0、中央値 **0.6**、IQR 0.3〜0.7 — 他2系（中央値 0.1）と大きく異なり、**強い予測が有利** +- **smoothing 軸**: 範囲 0.0〜0.7、中央値 0.1、IQR 0.0〜0.2 — 他2系より**低平滑寄り** + +## II-2. 共通1点 + +- **中央値基準: (strength 0.3, smoothing 0.1)、集約LPIPS 0.2503**（reuse行 0.2862 より 0.0359 良い） +- **平均基準: (strength 0.6, smoothing 0.1)、集約LPIPS 0.2487**（reuse行平均 0.2816 より 0.0329 良い） +- **どちらの基準でも strength>0 が選ばれる**（他2系では平均基準が reuse そのものを選んだ） +- 上位5セル（中央値基準）は 0.2503〜0.2572 の 0.007 差で **strength 0.3〜0.4 x smoothing 0.0〜0.2** に密集。位置は頑健。 + +**共通点 (0.3, 0.1) における各条件の損失**: 中央値 **+0.0286（アンカー比 23.6%）**、最大 **+0.1013（83.7%）**。損失自体は3系で最大だが、後述のとおり reuse を上回る幅がそれを超えている。 + +## II-3. Reuse 比の改善 + +`tables/reuse-comparison_Euler-Beta.csv`。共通点 (0.3, 0.1): + +- **改善の中央値: +0.0271（アンカー比 +22.4%）** +- 範囲: -0.0365 〜 +0.0727 +- **改善 13 / 15、悪化 2 / 15** +- **Wilcoxon 符号順位検定 p = 0.015（有意）** +- 平均基準の共通点 (0.6, 0.1) でも: 改善中央値 **+0.0349（+28.8%）**、改善 12/15、**p = 0.030（有意）** + +プロンプト別改善中央値: schoolgirl +0.0704、android +0.0324、woman +0.0409、shop +0.0195、inkwash +0.0129（**全プロンプトで正**） +シード別改善中央値: 5193 +0.0143、6111 +0.0324、9676 +0.0195（**全シードで正**） + +**チェリーピック上限**（`tables/best-prediction-vs-reuse_Euler-Beta.csv`）: + +- 改善中央値 **+0.0427（アンカー比 35.3%）**、範囲 -0.0039 〜 +0.1529、改善 **14/15**、**Wilcoxon p = 0.0001** + +## II-4. 単峰性 + +`tables/unimodality_Euler-Beta.csv`。局所最小の個数は 1個が1条件、2個が2条件、3個が3条件、4個が2条件、5個が3条件、7個/9個/10個/14個が各1条件（**中央値4個**）。局所最小が最も多い条件（14個）が現れる一方、1個の条件もあり、**条件間の曲面形状のばらつきが3系で最大**である。低損傷側の下半分に限れば 1〜2個が8条件（中央値2個）。 + +中央値曲面の局所最小は3個: (0.3,0.1)=0.2503、(0.7,0.2)=0.2574、(0.1,0.5)=0.2693。**3個とも低損傷帯にあり**、高平滑帯の浅い凹凸が中央値曲面から消えている点が他2系と異なる。ただし低損傷帯に (0.3,0.1) と (0.7,0.2) という strength で 0.4 離れた2つの谷が並存しており、**低損傷帯が単一の谷とは言い切れない**（他2系では低損傷帯の複数最小が互いに 0.001 以内の実質同点だったのに対し、ここは 0.007 差の別々の谷）。 + +**二層構造**: 中央値曲面の EMA別プロファイル 0.0→0.271、0.2→0.267、0.3→0.276、0.4→0.304、0.5→0.338、0.6→0.426、0.7→0.543、0.8→0.703、0.9→0.737、0.99→0.759。**EMA 0.0〜0.3 が安定層（0.267〜0.276）、EMA 0.6 以降が崩壊層（0.43→0.76）**。安定層が3系で最も狭い（Simple が 0.0〜0.5 なのに対し 0.0〜0.3）。二層構造そのものは3系すべてに存在する。 + +## II-5. 平坦さ + +共通点 (0.3, 0.1) の4近傍: 近傍差の絶対値平均は条件ごとの中央値 **0.0065**、最大 0.0236。変化幅のアンカー比の中央値 **11.1%**。中央値曲面での近傍差は +0.0048〜+0.0079。ERSDE-Simple 同様に平坦。 + +## II-6. SSIM との整合 + +中央値SSIM曲面のピークは **(0.1, 0.0)**、LPIPS の共通ボトム (0.3, 0.1) と strength で 0.2 ずれる。条件別完全一致は 5/15。**Euler-Beta では2指標のズレが3系で最も大きい**（ERSDE-Beta は完全一致、Simple は smoothing のみズレ）。SSIM は LPIPS より弱い予測を好む方向にずれている。強い予測（strength 0.6〜1.0）は LPIPS では有利だが SSIM ではそこまで有利にならない、という指標間の食い違いが Euler-Beta に固有に現れている。 + +--- + +# III. 3系比較 + +すべて N=15（Prompt001〜005 x seed 3種）、11x11格子、アンカー 0.121。ERSDE-Beta の数値は `analysis-stage5-scan1/tables/summary.json` からの引用（再計算していない）。 + +## III-1. 突き合わせ表 + +| 項目 | ERSDE-Beta（走査1） | ERSDE-Simple | Euler-Beta | +|---|---|---|---| +| reuse 基線 LPIPS 中央値（範囲） | 0.413（0.285〜0.625） | 0.324（0.160〜0.504） | **0.286（0.182〜0.399）** | +| 共通最適点（中央値基準） | (0.1, 0.5) | (0.1, 0.0) | **(0.3, 0.1)** | +| 共通最適点（平均基準） | (0.0)=reuse | (0.0)=reuse | **(0.6, 0.1)** | +| 平均基準が予測を選ぶか | No | No | **Yes** | +| 条件別最適 strength 中央値（IQR） | 0.1（0.05〜0.30） | 0.1（0.05〜0.20） | **0.6（0.3〜0.7）** | +| 条件別最適 smoothing 中央値（IQR） | 0.4（0.0〜0.55） | 0.1（0.0〜0.40） | 0.1（0.0〜0.2） | +| reuse に勝てなかった条件数 | 4/15 | 4/15 | **1/15** | +| **共通点での reuse 比改善 中央値** | **-0.0025（-2.1%）** | **-0.0049（-4.1%）** | **+0.0271（+22.4%）** | +| **改善/悪化 条件数** | 7 / 8 | 6 / 9 | **13 / 2** | +| **Wilcoxon p（共通点 vs reuse）** | 0.359 | 0.208 | **0.015（有意）** | +| 平均基準共通点での改善中央値・p | 0（reuse自身） | +0.0007 / p=0.762 | **+0.0349 / p=0.030（有意）** | +| **チェリーピック上限 改善中央値** | +0.0130（10.7%） | +0.0087（7.2%） | **+0.0427（35.3%）** | +| チェリーピック上限 改善条件数・p | 11/15、p=0.095 | 11/15、p=0.359 | **14/15、p=0.0001** | +| 共通点損失（中央値 / 最大, アンカー比） | 21.6% / 116.4% | 16.9% / 86.0% | 23.6% / 83.7% | +| 局所最小の個数（条件別中央値） | 5 | 3 | 4 | +| 中央値曲面の局所最小 | 4個（うち2個は高平滑帯） | 4個（うち1個は高平滑帯） | 3個（**全て低損傷帯**） | +| **二層構造の有無** | あり | **あり**（安定 EMA<=0.5 / 崩壊 EMA>=0.7） | **あり**（安定 EMA<=0.3 / 崩壊 EMA>=0.6） | +| 低損傷帯が単一の谷か | 概ね単一 | 概ね単一（2点が 0.0009 差） | **単一とは言えない**（strength 0.3 と 0.7 に別の谷） | +| 共通点近傍の変化幅（アンカー比中央値） | 30.5% | 10.8% | 11.1% | +| SSIM ピークと LPIPS ボトムの一致 | 完全一致（条件別 6/15） | smoothing のみズレ（7/15） | strength 0.2 ズレ（5/15） | +| 偏りが強く出る軸 | プロンプト（shop が全滅） | シード（6111 が4/5全滅） | なし（全プロンプト・全シードで正） | + +## III-2. 事前登録仮説への答え + +**仮説「ER SDE系1系で見えた『予測は Reuse に勝てない』が Euler 決定論系でも再現するか」** + +**再現しなかった。** 事実として: + +1. **ER SDE系の2系（Beta / Simple）では再現した。** 共通1点での reuse 比改善中央値はいずれも負（Beta -0.0025、Simple -0.0049）、改善条件数は半数以下（7/15、6/15）、Wilcoxon はいずれも有意でない（p=0.359、p=0.208）。平均基準の集約曲面は両系とも予測を一切かけない（strength=0）を選ぶ。**スケジュールが Beta から Simple に変わっても、ER SDE の枠内では結論は同一方向である。** + +2. **Euler-Beta では逆転した。** 共通1点 (0.3, 0.1) での改善中央値は **+0.0271（アンカー比 +22.4%）**、13/15 の条件で改善、**Wilcoxon p = 0.015 で有意**。平均基準の共通点 (0.6, 0.1) でも +0.0349、p = 0.030 で有意。全5プロンプト・全3シードで改善中央値が正であり、偏りの方向が揃っている。 + +3. **効く予測の設定そのものが違う。** ER SDE系2系の最適 strength 中央値は 0.1（弱い予測）だが、Euler-Beta は **0.6**（IQR 0.3〜0.7、strength=1.0 が最適の条件もある）。**弱い予測を少しだけ足す ER SDE系と、強い予測を主に使う Euler 系で、応答曲面の性格が定性的に異なる。** smoothing は3系とも低〜中（中央値 0.1〜0.4）で、EMA を強くかけると崩壊する二層構造は3系すべてに共通する。 + +4. **チェリーピック上限でも差は開く。** 条件ごとに後から最良セルを選ぶ上限性能では、ER SDE系が +0.0087〜+0.0130（アンカー比 7〜11%）にとどまるのに対し、Euler-Beta は **+0.0427（35.3%）、14/15 改善、p=0.0001**。H4（スキップ1回あたり損傷2〜3割減）の水準は、ER SDE系では上限ケースでも届かないが、**Euler-Beta では共通1点運用（22.4%）で下限側に、上限ケース（35.3%）で上振れ側に触れている。** + +## III-3. 注意すべき交絡・限界（事実として） + +- **reuse 基線の重さが3系で違う**（中央値 0.413 / 0.324 / 0.286）。Euler-Beta は基線が最も軽い。改善幅は絶対値（LPIPS差）で比較しており、基線に対する相対比では見ていない。基線が軽い系ほど改善の絶対幅が出やすいのか出にくいのかは、本データからは判定できない。 +- **Beta スケジュールは ERSDE-Beta と Euler-Beta で共通、ER SDE は Beta と Simple で共通**という 2x2 の欠けた設計であり、Euler vs ER SDE の効果と Beta vs Simple の効果は完全には分離できない。ただし ER SDE内で Beta から Simple に変えても結論が変わらなかったこと（III-2 第1項）は、**スケジュール側の寄与が小さく、サンプラー側（Euler か ER SDE か）の寄与が大きい**方向の材料である。Euler-Simple は未測定である。 +- **Euler-Beta では SSIM と LPIPS が3系で最も食い違う**（ピーク strength 差 0.2、条件別一致 5/15）。SSIM は Euler-Beta でも予測側を選ぶ（ピークは strength 0.1 で reuse ではない）が、LPIPS ほど強い予測を支持しない。指標選択で改善幅の見積もりは変わりうる。 +- **1複製・formula=Linear 固定**である。条件は決定論的（同一シード・同一パターン）で再現ノイズはないが、他の formula や複数複製での確認はしていない。 +- **Euler-Beta の低損傷帯には strength 0.3 と 0.7 の2つの谷が並存する**（0.007 差）。共通1点をどちらに置くかは集約方法で変わり（中央値なら 0.3、平均なら 0.6）、いずれも reuse に有意に勝つが、両者の間で運用上の意味が同じかは本データでは判定していない。 + +## 所見（事実ベース。採否の裁定はユーザーの領分） + +1. **事前登録仮説は棄却される側の結果である。** 予測は Reuse に勝てない、という像は ER SDE系（Beta / Simple）に限定された現象であり、Euler-Beta では**逆に有意な改善**（共通1点で中央値 +22.4%、13/15改善、p=0.015）が観測された。 +2. **ER SDE系内部では結論が安定している。** Beta から Simple へのスケジュール変更で、共通点の位置（strength 0.1）、改善中央値が負であること、平均基準が reuse を選ぶこと、いずれも変わらなかった。 +3. **Euler-Beta は最適 strength が中央値 0.6 と高い。** 3系を横断する単一の推奨設定は存在せず、少なくともサンプラー系ごとに別の較正が要る、という像になる。系をまたいだプロンプト非依存の共通1点を要件とするなら、この差そのものが論点である。 +4. **EMA の二層構造は3系すべてに共通する。** 安定層の上限は ERSDE-Beta で概ね EMA 0.5〜0.6、ERSDE-Simple で 0.5、Euler-Beta で 0.3。**高EMAでの崩壊は系に依存しない構造的な性質**である一方、**安定層の広さは系依存**である。 +5. **単峰仮説は3系いずれでも文字どおりには成立しない。** ただし ER SDE系2系は低損傷帯が概ね一つの谷であるのに対し、Euler-Beta は低損傷帯に別々の2つの谷を持つ。 +6. **格子解像度は3系いずれでもボトルネックではない。** 共通点近傍±0.1 での変化幅はアンカー比で 10.8%〜30.5%、中央値曲面では 0.002〜0.019 しか動かない。密走査で詰められる余地は、系間の差（Euler-Beta の +0.027 と ER SDE系の -0.005、その差 0.032）より一桁小さい。 +7. **未測定の穴は Euler-Simple である。** 2x2 の残り1マスであり、Euler かどうか、と、Simple かどうか、の分離を最も直接に詰められる条件である（実施の可否は裁定事項）。 + +（本報告は Stage5-Families の実測を要約したものであり、走査2の実施可否・Euler-Simple の追試可否・ResRefine の採否・実装方針の変更についての裁定は行わない。） diff --git a/analysis/stage5-families/analyze_families.py b/analysis/stage5-families/analyze_families.py new file mode 100644 index 0000000000000000000000000000000000000000..361b888f255189e1132eb9373af885b5b9fb24dc --- /dev/null +++ b/analysis/stage5-families/analyze_families.py @@ -0,0 +1,335 @@ +# -*- coding: utf-8 -*- +"""Stage5 Families response-surface analysis (ERSDE-Simple / Euler-Beta). + +Framework identical to analysis-stage5-scan1/analyze_scan1.py, run per family. +Does not modify any existing file. +""" +import os, re, json +import numpy as np, pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from scipy.stats import wilcoxon + +ROOT = r"S:\30_OriginalApps\16_HareSkip\experiment-HareSkip" +CSV = os.path.join(ROOT, "Judge-results", "Stage5-Families_20260907-235041", "merged-results.csv") +OUT = os.path.join(ROOT, "analysis-stage5-families") +SURF = os.path.join(OUT, "surfaces"); TAB = os.path.join(OUT, "tables") +os.makedirs(SURF, exist_ok=True); os.makedirs(TAB, exist_ok=True) +ANCHOR = 0.121 + +LP = "LPIPS - VGG" +SS11 = "SSIM - win size 11 (Wang)" + +df_all = pd.read_csv(CSV, encoding="utf-8-sig") +for c in df_all.columns: + if c not in ("Condition", "Job ID", "File name"): + df_all[c] = pd.to_numeric(df_all[c], errors="coerce") + + +def parse(fn): + if fn == "reuse.png": + return 0.0, np.nan + m = re.match(r"str-(\d+)_ema-(\d+)\.png$", fn) + s = int(m.group(1)) / 10.0 + e = m.group(2) + e = 0.99 if e == "099" else int(e) / 10.0 + return s, e + + +df_all[["strength", "smoothing"]] = df_all["File name"].apply(lambda f: pd.Series(parse(f))) +df_all["prompt"] = df_all["Condition"].str.extract(r"_(Prompt\d+-[a-z]+)-") +df_all["seed"] = df_all["Condition"].str.extract(r"-(\d+)$") +df_all["family"] = df_all["Condition"].str.split("_").str[0] + +STR = [round(x, 1) for x in np.arange(0.0, 1.01, 0.1)] +SMO = [round(x, 1) for x in np.arange(0.0, 0.91, 0.1)] + [0.99] + +FAMILIES = sorted(df_all["family"].unique()) + + +def heat(g, title, path, cbl="LPIPS-VGG (lower=better)"): + fig, ax = plt.subplots(figsize=(7.6, 6.2)) + im = ax.imshow(g, origin="lower", aspect="auto", cmap="viridis_r") + ax.set_xticks(range(len(SMO))); ax.set_xticklabels([str(s) for s in SMO], fontsize=8) + ax.set_yticks(range(len(STR))); ax.set_yticklabels([str(s) for s in STR], fontsize=8) + ax.set_xlabel("EMA smoothing"); ax.set_ylabel("prediction strength") + ax.set_title(title, fontsize=9) + idx = np.unravel_index(np.argmin(g), g.shape) + ax.plot(idx[1], idx[0], marker="*", ms=18, color="red", mec="white", mew=1.0) + mid = (g.min() + g.max()) / 2 + for a in range(g.shape[0]): + for b in range(g.shape[1]): + ax.text(b, a, ("%.3f" % g[a, b]).lstrip("0"), ha="center", va="center", + fontsize=5.2, color="white" if g[a, b] > mid else "black") + fig.colorbar(im, ax=ax, label=cbl) + fig.tight_layout(); fig.savefig(path, dpi=130); plt.close(fig) + + +def local_minima(g, include_row0=False): + sub = g if include_row0 else g[1:, :] + n, m = sub.shape; out = [] + for a in range(n): + for b in range(m): + v = sub[a, b]; ok = True + for da in (-1, 0, 1): + for db in (-1, 0, 1): + if da == 0 and db == 0: + continue + aa, bb = a + da, b + db + if 0 <= aa < n and 0 <= bb < m and sub[aa, bb] < v: + ok = False + if ok: + out.append((a + (0 if include_row0 else 1), b, v)) + return out + + +ALL = {} + +for FAM in FAMILIES: + df = df_all[df_all["family"] == FAM].copy() + conds = sorted(df["Condition"].unique()) + grids, reuse = {}, {} + for c in conds: + d = df[df["Condition"] == c] + r = float(d[d["strength"] == 0][LP].iloc[0]) + reuse[c] = r + g = np.full((len(STR), len(SMO)), np.nan) + g[0, :] = r + for _, row in d[d["strength"] > 0].iterrows(): + g[STR.index(round(row["strength"], 1)), SMO.index(round(row["smoothing"], 2))] = row[LP] + assert not np.isnan(g).any(), c + grids[c] = g + G = np.stack([grids[c] for c in conds]) + + # ---------- 1. per-condition optima + heatmaps ---------- + rows = [] + for i, c in enumerate(conds): + g = grids[c] + idx = np.unravel_index(np.argmin(g), g.shape) + rows.append(dict(Condition=c, prompt=df[df.Condition == c]["prompt"].iloc[0], + seed=df[df.Condition == c]["seed"].iloc[0], + best_strength=STR[idx[0]], best_smoothing=SMO[idx[1]], + best_LPIPS=g[idx], reuse_LPIPS=reuse[c], + improvement_vs_reuse=reuse[c] - g[idx], + improvement_pct_anchor=100 * (reuse[c] - g[idx]) / ANCHOR)) + opt = pd.DataFrame(rows) + opt.to_csv(os.path.join(TAB, "per-condition-optima_%s.csv" % FAM), index=False, encoding="utf-8-sig") + + for c in conds: + heat(grids[c], c + "\nLPIPS-VGG surface (strength x smoothing); row 0.0 = reuse, replicated", + os.path.join(SURF, "surface_%s.png" % c)) + Gmed = np.median(G, axis=0); Gmean = G.mean(axis=0) + heat(Gmed, "%s / 15 conditions: MEDIAN LPIPS-VGG surface" % FAM, + os.path.join(SURF, "surface_MEDIAN-15cond_%s.png" % FAM)) + heat(Gmean, "%s / 15 conditions: MEAN LPIPS-VGG surface" % FAM, + os.path.join(SURF, "surface_MEAN-15cond_%s.png" % FAM)) + pd.DataFrame(Gmed, index=STR, columns=SMO).to_csv( + os.path.join(TAB, "median-surface_%s.csv" % FAM), encoding="utf-8-sig") + + # ---------- 2. common single point ---------- + im_ = np.unravel_index(np.argmin(Gmed), Gmed.shape) + ia_ = np.unravel_index(np.argmin(Gmean), Gmean.shape) + common = {"median": (STR[im_[0]], SMO[im_[1]], float(Gmed[im_])), + "mean": (STR[ia_[0]], SMO[ia_[1]], float(Gmean[ia_]))} + # restricted (strength >= 0.1) aggregate minima + def restricted(A): + sub = A[1:, :] + j = np.unravel_index(np.argmin(sub), sub.shape) + return (STR[j[0] + 1], SMO[j[1]], float(sub[j])) + common_restricted = {"median": restricted(Gmed), "mean": restricted(Gmean)} + + def topk(A, k=5): + flat = [(float(A[a, b]), STR[a], SMO[b]) for a in range(11) for b in range(11)] + return sorted(flat)[:k] + + loss_rows = [] + for key, (s, e, _) in common.items(): + si, ei = STR.index(s), SMO.index(e) + for i, c in enumerate(conds): + own = opt.loc[i, "best_LPIPS"]; at = G[i, si, ei] + loss_rows.append(dict(criterion=key, common_strength=s, common_smoothing=e, Condition=c, + prompt=opt.loc[i, "prompt"], seed=opt.loc[i, "seed"], + own_best_LPIPS=own, own_best_strength=opt.loc[i, "best_strength"], + own_best_smoothing=opt.loc[i, "best_smoothing"], + LPIPS_at_common=at, loss_vs_own_best=at - own, + loss_pct_anchor=100 * (at - own) / ANCHOR)) + loss = pd.DataFrame(loss_rows) + loss.to_csv(os.path.join(TAB, "common-point-loss_%s.csv" % FAM), index=False, encoding="utf-8-sig") + + # ---------- 3. reuse comparison ---------- + cmp_rows = [] + for key, (s, e, _) in common.items(): + si, ei = STR.index(s), SMO.index(e) + for i, c in enumerate(conds): + at = G[i, si, ei]; r = reuse[c] + cmp_rows.append(dict(criterion=key, common_strength=s, common_smoothing=e, Condition=c, + prompt=opt.loc[i, "prompt"], seed=opt.loc[i, "seed"], + reuse_LPIPS=r, common_LPIPS=at, improvement=r - at, + improvement_pct_anchor=100 * (r - at) / ANCHOR, + improved=bool(at < r))) + # also record the restricted-median common point explicitly + for key, (s, e, _) in common_restricted.items(): + si, ei = STR.index(s), SMO.index(e) + for i, c in enumerate(conds): + at = G[i, si, ei]; r = reuse[c] + cmp_rows.append(dict(criterion=key + "_restricted", common_strength=s, common_smoothing=e, + Condition=c, prompt=opt.loc[i, "prompt"], seed=opt.loc[i, "seed"], + reuse_LPIPS=r, common_LPIPS=at, improvement=r - at, + improvement_pct_anchor=100 * (r - at) / ANCHOR, + improved=bool(at < r))) + cmpdf = pd.DataFrame(cmp_rows) + cmpdf.to_csv(os.path.join(TAB, "reuse-comparison_%s.csv" % FAM), index=False, encoding="utf-8-sig") + + stats = {} + for key in cmpdf.criterion.unique(): + d = cmpdf[cmpdf.criterion == key] + if (d.reuse_LPIPS - d.common_LPIPS).abs().sum() == 0: + wstat, wp = 0.0, float("nan") + else: + w = wilcoxon(d.reuse_LPIPS, d.common_LPIPS); wstat, wp = float(w.statistic), float(w.pvalue) + stats[key] = dict(point=(float(d.common_strength.iloc[0]), float(d.common_smoothing.iloc[0])), + median_improvement=float(d.improvement.median()), + min_improvement=float(d.improvement.min()), + max_improvement=float(d.improvement.max()), + median_pct_anchor=float(d.improvement_pct_anchor.median()), + wilcoxon_stat=wstat, wilcoxon_p=wp, + n_improved=int(d.improved.sum()), n_worse=int((~d.improved).sum()), + by_prompt={k: float(v) for k, v in d.groupby("prompt").improvement.median().items()}, + by_seed={k: float(v) for k, v in d.groupby("seed").improvement.median().items()}) + + # cherry-pick ceiling: per-condition best prediction cell (strength>=0.1) + bp_rows = [] + for i, c in enumerate(conds): + sub = G[i][1:, :] + j = np.unravel_index(np.argmin(sub), sub.shape) + v = float(sub[j]); r = reuse[c] + bp_rows.append(dict(Condition=c, prompt=opt.loc[i, "prompt"], seed=opt.loc[i, "seed"], + reuse_LPIPS=r, best_pred_strength=STR[j[0] + 1], best_pred_smoothing=SMO[j[1]], + best_pred_LPIPS=v, delta_vs_reuse=r - v, + delta_pct_anchor=100 * (r - v) / ANCHOR, prediction_helps=bool(v < r))) + bp = pd.DataFrame(bp_rows) + bp.to_csv(os.path.join(TAB, "best-prediction-vs-reuse_%s.csv" % FAM), index=False, encoding="utf-8-sig") + bp_stats = dict(median=float(bp.delta_vs_reuse.median()), min=float(bp.delta_vs_reuse.min()), + max=float(bp.delta_vs_reuse.max()), + median_pct_anchor=float(bp.delta_pct_anchor.median()), + wilcoxon_p=float(wilcoxon(bp.reuse_LPIPS, bp.best_pred_LPIPS).pvalue), + n_helps=int(bp.prediction_helps.sum()), n_hurts=int((~bp.prediction_helps).sum()), + by_prompt={k: float(v) for k, v in bp.groupby("prompt").delta_vs_reuse.median().items()}, + by_seed={k: float(v) for k, v in bp.groupby("seed").delta_vs_reuse.median().items()}) + + own_stats = dict(median=float(opt.improvement_vs_reuse.median()), + min=float(opt.improvement_vs_reuse.min()), max=float(opt.improvement_vs_reuse.max()), + wilcoxon_p=(float(wilcoxon(opt.reuse_LPIPS, opt.best_LPIPS).pvalue) + if (opt.reuse_LPIPS - opt.best_LPIPS).abs().sum() > 0 else float("nan")), + by_prompt={k: float(v) for k, v in opt.groupby("prompt").improvement_vs_reuse.median().items()}, + by_seed={k: float(v) for k, v in opt.groupby("seed").improvement_vs_reuse.median().items()}) + + # ---------- 4. unimodality ---------- + uni_rows = [] + for i, c in enumerate(conds): + lm = local_minima(grids[c]) + # split by low-damage half of that condition's own range + thr = grids[c][1:, :].min() + 0.5 * (grids[c][1:, :].max() - grids[c][1:, :].min()) + lm_low = [t for t in lm if t[2] <= thr] + uni_rows.append(dict(Condition=c, n_local_minima=len(lm), + n_local_minima_low_damage_half=len(lm_low), + minima=";".join("(%s,%s,%.4f)" % (STR[a], SMO[b], v) + for a, b, v in sorted(lm, key=lambda t: t[2])))) + uni = pd.DataFrame(uni_rows) + uni.to_csv(os.path.join(TAB, "unimodality_%s.csv" % FAM), index=False, encoding="utf-8-sig") + lm_med = local_minima(Gmed); lm_mean = local_minima(Gmean) + thr_med = Gmed[1:, :].min() + 0.5 * (Gmed[1:, :].max() - Gmed[1:, :].min()) + lm_med_low = [t for t in lm_med if t[2] <= thr_med] + + # ---------- 5. curvature around the median common optimum ---------- + si, ei = STR.index(common["median"][0]), SMO.index(common["median"][1]) + curv = [] + for i, c in enumerate(conds): + g = G[i]; v = g[si, ei]; nb = [] + for da, db in ((-1, 0), (1, 0), (0, -1), (0, 1)): + a, b = si + da, ei + db + if 0 <= a < 11 and 0 <= b < 11: + nb.append(g[a, b] - v) + curv.append(dict(Condition=c, LPIPS_at_common=v, max_neighbor_delta=max(nb), + min_neighbor_delta=min(nb), mean_abs_neighbor_delta=float(np.mean(np.abs(nb))), + range_pct_anchor=100 * (max(nb) - min(nb)) / ANCHOR)) + curvdf = pd.DataFrame(curv) + curvdf.to_csv(os.path.join(TAB, "curvature-near-common_%s.csv" % FAM), index=False, encoding="utf-8-sig") + vmedc = Gmed[si, ei] + med_nb = {"s%s_e%s" % (STR[si + da], SMO[ei + db]): float(Gmed[si + da, ei + db] - vmedc) + for da, db in ((-1, 0), (1, 0), (0, -1), (0, 1)) + if 0 <= si + da < 11 and 0 <= ei + db < 11} + + # ---------- 6. SSIM (win11) ---------- + ssim_rows = [] + Gs = np.full((len(conds), 11, 11), np.nan) + for i, c in enumerate(conds): + d = df[df["Condition"] == c] + rs = float(d[d["strength"] == 0][SS11].iloc[0]); Gs[i, 0, :] = rs + for _, row in d[d["strength"] > 0].iterrows(): + Gs[i, STR.index(round(row["strength"], 1)), SMO.index(round(row["smoothing"], 2))] = row[SS11] + idx = np.unravel_index(np.argmax(Gs[i]), Gs[i].shape) + ssim_rows.append(dict(Condition=c, ssim_peak_strength=STR[idx[0]], ssim_peak_smoothing=SMO[idx[1]], + ssim_peak=Gs[i][idx], + lpips_bottom_strength=opt.loc[i, "best_strength"], + lpips_bottom_smoothing=opt.loc[i, "best_smoothing"], + d_strength=STR[idx[0]] - opt.loc[i, "best_strength"], + d_smoothing=SMO[idx[1]] - opt.loc[i, "best_smoothing"])) + ssdf = pd.DataFrame(ssim_rows) + ssdf.to_csv(os.path.join(TAB, "ssim-vs-lpips-optima_%s.csv" % FAM), index=False, encoding="utf-8-sig") + Gsmed = np.median(Gs, axis=0) + si2 = np.unravel_index(np.argmax(Gsmed), Gsmed.shape) + ssim_common = (STR[si2[0]], SMO[si2[1]], float(Gsmed[si2])) + + # ---------- two-layer structure: EMA-wise aggregate ---------- + ema_profile = {str(SMO[b]): float(np.median(Gmed[1:, b])) for b in range(11)} + str_profile = {str(STR[a]): float(np.median(Gmed[a, :])) for a in range(11)} + + summary = dict( + family=FAM, common=common, common_restricted=common_restricted, stats=stats, + best_prediction_vs_reuse=bp_stats, own_best_vs_reuse=own_stats, ssim_common=ssim_common, + ssim_agree_exact=int(((ssdf.d_strength.abs() < 1e-9) & (ssdf.d_smoothing.abs() < 1e-9)).sum()), + n_local_minima_median_surface=len(lm_med), n_local_minima_mean_surface=len(lm_mean), + n_local_minima_median_surface_low_half=len(lm_med_low), + minima_median_surface=[(STR[a], SMO[b], float(v)) for a, b, v in sorted(lm_med, key=lambda t: t[2])], + minima_mean_surface=[(STR[a], SMO[b], float(v)) for a, b, v in sorted(lm_mean, key=lambda t: t[2])], + median_surface_neighbors=med_nb, + top5_median_cells=topk(Gmed), top5_mean_cells=topk(Gmean), + median_surface_reuse_row=float(Gmed[0, 0]), mean_surface_reuse_row=float(Gmean[0, 0]), + opt_strength=dict(min=float(opt.best_strength.min()), max=float(opt.best_strength.max()), + q1=float(opt.best_strength.quantile(.25)), q3=float(opt.best_strength.quantile(.75)), + median=float(opt.best_strength.median())), + opt_smoothing=dict(min=float(opt.best_smoothing.min()), max=float(opt.best_smoothing.max()), + q1=float(opt.best_smoothing.quantile(.25)), q3=float(opt.best_smoothing.quantile(.75)), + median=float(opt.best_smoothing.median())), + unimodal_counts={int(k): int(v) for k, v in uni.n_local_minima.value_counts().items()}, + unimodal_counts_low_half={int(k): int(v) for k, v in uni.n_local_minima_low_damage_half.value_counts().items()}, + loss_summary={k: dict(median=float(loss[loss.criterion == k].loss_vs_own_best.median()), + max=float(loss[loss.criterion == k].loss_vs_own_best.max()), + median_pct_anchor=float(loss[loss.criterion == k].loss_pct_anchor.median()), + max_pct_anchor=float(loss[loss.criterion == k].loss_pct_anchor.max())) + for k in ("median", "mean")}, + reuse_stats=dict(median=float(np.median(list(reuse.values()))), + min=float(min(reuse.values())), max=float(max(reuse.values()))), + curvature=dict(median_of_mean_abs=float(curvdf.mean_abs_neighbor_delta.median()), + max_of_mean_abs=float(curvdf.mean_abs_neighbor_delta.max()), + median_range_pct_anchor=float(curvdf.range_pct_anchor.median())), + ema_profile_median_surface=ema_profile, strength_profile_median_surface=str_profile, + n_conditions_reuse_unbeatable=int((opt.best_strength == 0.0).sum())) + ALL[FAM] = summary + with open(os.path.join(TAB, "summary_%s.json" % FAM), "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2, ensure_ascii=False, default=str) + print("=" * 70); print(FAM) + print(json.dumps(summary, indent=2, ensure_ascii=False, default=str)) + print("\n--- per-condition optima ---"); print(opt.to_string()) + print("\n--- best prediction (str>=0.1) vs reuse ---"); print(bp.to_string()) + print("\n--- unimodality ---"); print(uni[["Condition", "n_local_minima", "n_local_minima_low_damage_half"]].to_string()) + print("\n--- median surface ---") + print(pd.DataFrame(Gmed, index=STR, columns=SMO).round(4).to_string()) + print("\n--- SSIM vs LPIPS optima ---"); print(ssdf.to_string()) + print("\n--- curvature ---"); print(curvdf.round(4).to_string()) + +with open(os.path.join(TAB, "summary_ALL-families.json"), "w", encoding="utf-8") as f: + json.dump(ALL, f, indent=2, ensure_ascii=False, default=str) +print("\nDONE") diff --git a/analysis/stage5-families/run.log b/analysis/stage5-families/run.log new file mode 100644 index 0000000000000000000000000000000000000000..4f8012d15c754ec7d3eb811fc6571c62823315ec --- /dev/null +++ b/analysis/stage5-families/run.log @@ -0,0 +1,926 @@ +====================================================================== +ERSDE-Simple +{ + "family": "ERSDE-Simple", + "common": { + "median": [ + 0.1, + 0.0, + 0.313459 + ], + "mean": [ + 0.0, + 0.0, + 0.32377713333333336 + ] + }, + "common_restricted": { + "median": [ + 0.1, + 0.0, + 0.313459 + ], + "mean": [ + 0.1, + 0.3, + 0.3329523999999999 + ] + }, + "stats": { + "median": { + "point": [ + 0.1, + 0.0 + ], + "median_improvement": -0.004917999999999978, + "min_improvement": -0.08158299999999996, + "max_improvement": 0.012102000000000002, + "median_pct_anchor": -4.064462809917337, + "wilcoxon_stat": 37.0, + "wilcoxon_p": 0.207763671875, + "n_improved": 6, + "n_worse": 9, + "by_prompt": { + "Prompt001-schoolgirl": -0.0007900000000000129, + "Prompt002-android": -0.027158000000000015, + "Prompt003-woman": -0.004917999999999978, + "Prompt004-shop": -0.009969000000000006, + "Prompt005-inkwash": 0.0043330000000000035 + }, + "by_seed": { + "5193": -0.006027000000000005, + "6111": -0.027158000000000015, + "9676": 0.007409999999999972 + } + }, + "mean": { + "point": [ + 0.0, + 0.0 + ], + "median_improvement": 0.0, + "min_improvement": 0.0, + "max_improvement": 0.0, + "median_pct_anchor": 0.0, + "wilcoxon_stat": 0.0, + "wilcoxon_p": NaN, + "n_improved": 0, + "n_worse": 15, + "by_prompt": { + "Prompt001-schoolgirl": 0.0, + "Prompt002-android": 0.0, + "Prompt003-woman": 0.0, + "Prompt004-shop": 0.0, + "Prompt005-inkwash": 0.0 + }, + "by_seed": { + "5193": 0.0, + "6111": 0.0, + "9676": 0.0 + } + }, + "median_restricted": { + "point": [ + 0.1, + 0.0 + ], + "median_improvement": -0.004917999999999978, + "min_improvement": -0.08158299999999996, + "max_improvement": 0.012102000000000002, + "median_pct_anchor": -4.064462809917337, + "wilcoxon_stat": 37.0, + "wilcoxon_p": 0.207763671875, + "n_improved": 6, + "n_worse": 9, + "by_prompt": { + "Prompt001-schoolgirl": -0.0007900000000000129, + "Prompt002-android": -0.027158000000000015, + "Prompt003-woman": -0.004917999999999978, + "Prompt004-shop": -0.009969000000000006, + "Prompt005-inkwash": 0.0043330000000000035 + }, + "by_seed": { + "5193": -0.006027000000000005, + "6111": -0.027158000000000015, + "9676": 0.007409999999999972 + } + }, + "mean_restricted": { + "point": [ + 0.1, + 0.3 + ], + "median_improvement": 0.0006999999999999784, + "min_improvement": -0.09985199999999994, + "max_improvement": 0.01750299999999999, + "median_pct_anchor": 0.5785123966941971, + "wilcoxon_stat": 54.0, + "wilcoxon_p": 0.76153564453125, + "n_improved": 8, + "n_worse": 7, + "by_prompt": { + "Prompt001-schoolgirl": 0.0041200000000000125, + "Prompt002-android": 0.0044319999999999915, + "Prompt003-woman": -0.0018320000000000003, + "Prompt004-shop": 0.0006999999999999784, + "Prompt005-inkwash": 0.0023120000000000085 + }, + "by_seed": { + "5193": 0.0023120000000000085, + "6111": -0.023474999999999968, + "9676": 0.005861000000000005 + } + } + }, + "best_prediction_vs_reuse": { + "median": 0.008681999999999968, + "min": -0.08158299999999996, + "max": 0.05197499999999999, + "median_pct_anchor": 7.175206611570221, + "wilcoxon_p": 0.359130859375, + "n_helps": 11, + "n_hurts": 4, + "by_prompt": { + "Prompt001-schoolgirl": 0.008681999999999968, + "Prompt002-android": 0.02703099999999997, + "Prompt003-woman": 0.003361000000000003, + "Prompt004-shop": 0.010055000000000036, + "Prompt005-inkwash": 0.013811000000000018 + }, + "by_seed": { + "5193": 0.02154299999999998, + "6111": -0.023474999999999968, + "9676": 0.013811000000000018 + } + }, + "own_best_vs_reuse": { + "median": 0.008681999999999968, + "min": 0.0, + "max": 0.05197499999999999, + "wilcoxon_p": 0.0033456181158508813, + "by_prompt": { + "Prompt001-schoolgirl": 0.008681999999999968, + "Prompt002-android": 0.02703099999999997, + "Prompt003-woman": 0.003361000000000003, + "Prompt004-shop": 0.010055000000000036, + "Prompt005-inkwash": 0.013811000000000018 + }, + "by_seed": { + "5193": 0.02154299999999998, + "6111": 0.0, + "9676": 0.013811000000000018 + } + }, + "ssim_common": [ + 0.1, + 0.5, + 0.735741 + ], + "ssim_agree_exact": 7, + "n_local_minima_median_surface": 4, + "n_local_minima_mean_surface": 3, + "n_local_minima_median_surface_low_half": 3, + "minima_median_surface": [ + [ + 0.1, + 0.0, + 0.313459 + ], + [ + 0.1, + 0.4, + 0.314361 + ], + [ + 0.6, + 0.4, + 0.377386 + ], + [ + 1.0, + 0.99, + 0.877416 + ] + ], + "minima_mean_surface": [ + [ + 0.1, + 0.3, + 0.3329523999999999 + ], + [ + 0.1, + 0.1, + 0.3368154666666667 + ], + [ + 1.0, + 0.99, + 0.8568986666666666 + ] + ], + "median_surface_neighbors": { + "s0.0_e0.0": 0.010055000000000036, + "s0.2_e0.0": 0.0023900000000000032, + "s0.1_e0.1": 0.002817999999999987 + }, + "top5_median_cells": [ + [ + 0.313459, + 0.1, + 0.0 + ], + [ + 0.314361, + 0.1, + 0.4 + ], + [ + 0.3145, + 0.2, + 0.1 + ], + [ + 0.315849, + 0.2, + 0.0 + ], + [ + 0.316277, + 0.1, + 0.1 + ] + ], + "top5_mean_cells": [ + [ + 0.32377713333333336, + 0.0, + 0.0 + ], + [ + 0.32377713333333336, + 0.0, + 0.1 + ], + [ + 0.32377713333333336, + 0.0, + 0.2 + ], + [ + 0.32377713333333336, + 0.0, + 0.3 + ], + [ + 0.32377713333333336, + 0.0, + 0.4 + ] + ], + "median_surface_reuse_row": 0.323514, + "mean_surface_reuse_row": 0.32377713333333336, + "opt_strength": { + "min": 0.0, + "max": 0.8, + "q1": 0.05, + "q3": 0.2, + "median": 0.1 + }, + "opt_smoothing": { + "min": 0.0, + "max": 0.5, + "q1": 0.0, + "q3": 0.4, + "median": 0.1 + }, + "unimodal_counts": { + "2": 6, + "3": 3, + "6": 2, + "4": 2, + "7": 1, + "5": 1 + }, + "unimodal_counts_low_half": { + "2": 5, + "1": 4, + "6": 2, + "5": 2, + "3": 1, + "4": 1 + }, + "loss_summary": { + "median": { + "median": 0.020405000000000006, + "max": 0.10408, + "median_pct_anchor": 16.86363636363637, + "max_pct_anchor": 86.01652892561985 + }, + "mean": { + "median": 0.008681999999999968, + "max": 0.05197499999999999, + "median_pct_anchor": 7.175206611570221, + "max_pct_anchor": 42.95454545454545 + } + }, + "reuse_stats": { + "median": 0.323514, + "min": 0.16046, + "max": 0.504483 + }, + "curvature": { + "median_of_mean_abs": 0.005258000000000013, + "max_of_mean_abs": 0.04979300000000001, + "median_range_pct_anchor": 10.830578512396722 + }, + "ema_profile_median_surface": { + "0.0": 0.4102455, + "0.1": 0.40275099999999997, + "0.2": 0.39505599999999996, + "0.3": 0.3930055, + "0.4": 0.3800735, + "0.5": 0.3923375, + "0.6": 0.430079, + "0.7": 0.547435, + "0.8": 0.6677615, + "0.9": 0.8550845, + "0.99": 0.8781954999999999 + }, + "strength_profile_median_surface": { + "0.0": 0.323514, + "0.1": 0.318385, + "0.2": 0.340133, + "0.3": 0.37769, + "0.4": 0.389693, + "0.5": 0.405798, + "0.6": 0.414693, + "0.7": 0.423736, + "0.8": 0.439799, + "0.9": 0.461622, + "1.0": 0.495764 + }, + "n_conditions_reuse_unbeatable": 4 +} + +--- per-condition optima --- + Condition prompt seed best_strength best_smoothing best_LPIPS reuse_LPIPS improvement_vs_reuse improvement_pct_anchor +0 ERSDE-Simple_Prompt001-schoolgirl-5193 Prompt001-schoolgirl 5193 0.1 0.4 0.297233 0.305915 0.008682 7.175207 +1 ERSDE-Simple_Prompt001-schoolgirl-6111 Prompt001-schoolgirl 6111 0.0 0.0 0.424783 0.424783 0.000000 0.000000 +2 ERSDE-Simple_Prompt001-schoolgirl-9676 Prompt001-schoolgirl 9676 0.6 0.4 0.377386 0.429361 0.051975 42.954545 +3 ERSDE-Simple_Prompt002-android-5193 Prompt002-android 5193 0.2 0.5 0.304188 0.331219 0.027031 22.339669 +4 ERSDE-Simple_Prompt002-android-6111 Prompt002-android 6111 0.0 0.0 0.333236 0.333236 0.000000 0.000000 +5 ERSDE-Simple_Prompt002-android-9676 Prompt002-android 9676 0.6 0.1 0.237991 0.284118 0.046127 38.121488 +6 ERSDE-Simple_Prompt003-woman-5193 Prompt003-woman 5193 0.2 0.4 0.155648 0.160460 0.004812 3.976860 +7 ERSDE-Simple_Prompt003-woman-6111 Prompt003-woman 6111 0.8 0.1 0.438640 0.439541 0.000901 0.744628 +8 ERSDE-Simple_Prompt003-woman-9676 Prompt003-woman 9676 0.1 0.0 0.194427 0.197788 0.003361 2.777686 +9 ERSDE-Simple_Prompt004-shop-5193 Prompt004-shop 5193 0.2 0.5 0.356122 0.377665 0.021543 17.804132 +10 ERSDE-Simple_Prompt004-shop-6111 Prompt004-shop 6111 0.0 0.0 0.504483 0.504483 0.000000 0.000000 +11 ERSDE-Simple_Prompt004-shop-9676 Prompt004-shop 9676 0.1 0.0 0.313459 0.323514 0.010055 8.309917 +12 ERSDE-Simple_Prompt005-inkwash-5193 Prompt005-inkwash 5193 0.2 0.5 0.186543 0.211281 0.024738 20.444628 +13 ERSDE-Simple_Prompt005-inkwash-6111 Prompt005-inkwash 6111 0.0 0.0 0.276156 0.276156 0.000000 0.000000 +14 ERSDE-Simple_Prompt005-inkwash-9676 Prompt005-inkwash 9676 0.1 0.1 0.243326 0.257137 0.013811 11.414050 + +--- best prediction (str>=0.1) vs reuse --- + Condition prompt seed reuse_LPIPS best_pred_strength best_pred_smoothing best_pred_LPIPS delta_vs_reuse delta_pct_anchor prediction_helps +0 ERSDE-Simple_Prompt001-schoolgirl-5193 Prompt001-schoolgirl 5193 0.305915 0.1 0.4 0.297233 0.008682 7.175207 True +1 ERSDE-Simple_Prompt001-schoolgirl-6111 Prompt001-schoolgirl 6111 0.424783 0.1 0.0 0.439057 -0.014274 -11.796694 False +2 ERSDE-Simple_Prompt001-schoolgirl-9676 Prompt001-schoolgirl 9676 0.429361 0.6 0.4 0.377386 0.051975 42.954545 True +3 ERSDE-Simple_Prompt002-android-5193 Prompt002-android 5193 0.331219 0.2 0.5 0.304188 0.027031 22.339669 True +4 ERSDE-Simple_Prompt002-android-6111 Prompt002-android 6111 0.333236 0.1 0.0 0.360394 -0.027158 -22.444628 False +5 ERSDE-Simple_Prompt002-android-9676 Prompt002-android 9676 0.284118 0.6 0.1 0.237991 0.046127 38.121488 True +6 ERSDE-Simple_Prompt003-woman-5193 Prompt003-woman 5193 0.160460 0.2 0.4 0.155648 0.004812 3.976860 True +7 ERSDE-Simple_Prompt003-woman-6111 Prompt003-woman 6111 0.439541 0.8 0.1 0.438640 0.000901 0.744628 True +8 ERSDE-Simple_Prompt003-woman-9676 Prompt003-woman 9676 0.197788 0.1 0.0 0.194427 0.003361 2.777686 True +9 ERSDE-Simple_Prompt004-shop-5193 Prompt004-shop 5193 0.377665 0.2 0.5 0.356122 0.021543 17.804132 True +10 ERSDE-Simple_Prompt004-shop-6111 Prompt004-shop 6111 0.504483 0.1 0.0 0.586066 -0.081583 -67.423967 False +11 ERSDE-Simple_Prompt004-shop-9676 Prompt004-shop 9676 0.323514 0.1 0.0 0.313459 0.010055 8.309917 True +12 ERSDE-Simple_Prompt005-inkwash-5193 Prompt005-inkwash 5193 0.211281 0.2 0.5 0.186543 0.024738 20.444628 True +13 ERSDE-Simple_Prompt005-inkwash-6111 Prompt005-inkwash 6111 0.276156 0.1 0.3 0.299631 -0.023475 -19.400826 False +14 ERSDE-Simple_Prompt005-inkwash-9676 Prompt005-inkwash 9676 0.257137 0.1 0.1 0.243326 0.013811 11.414050 True + +--- unimodality --- + Condition n_local_minima n_local_minima_low_damage_half +0 ERSDE-Simple_Prompt001-schoolgirl-5193 6 6 +1 ERSDE-Simple_Prompt001-schoolgirl-6111 2 1 +2 ERSDE-Simple_Prompt001-schoolgirl-9676 5 5 +3 ERSDE-Simple_Prompt002-android-5193 7 6 +4 ERSDE-Simple_Prompt002-android-6111 2 1 +5 ERSDE-Simple_Prompt002-android-9676 3 2 +6 ERSDE-Simple_Prompt003-woman-5193 4 3 +7 ERSDE-Simple_Prompt003-woman-6111 4 4 +8 ERSDE-Simple_Prompt003-woman-9676 2 2 +9 ERSDE-Simple_Prompt004-shop-5193 6 5 +10 ERSDE-Simple_Prompt004-shop-6111 2 1 +11 ERSDE-Simple_Prompt004-shop-9676 3 2 +12 ERSDE-Simple_Prompt005-inkwash-5193 2 2 +13 ERSDE-Simple_Prompt005-inkwash-6111 2 1 +14 ERSDE-Simple_Prompt005-inkwash-9676 3 2 + +--- median surface --- + 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.99 +0.0 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 0.3235 +0.1 0.3135 0.3163 0.3184 0.3177 0.3144 0.3168 0.3202 0.3408 0.3886 0.4663 0.7025 +0.2 0.3158 0.3145 0.3206 0.3263 0.3401 0.3364 0.3809 0.4125 0.4810 0.6460 0.8170 +0.3 0.3275 0.3489 0.3619 0.3777 0.3660 0.3730 0.4051 0.4304 0.4943 0.7385 0.8565 +0.4 0.3868 0.3897 0.3891 0.3712 0.3685 0.3893 0.4099 0.4932 0.5544 0.8184 0.8759 +0.5 0.4058 0.3982 0.3929 0.3923 0.3828 0.3890 0.4158 0.5128 0.6382 0.8496 0.8836 +0.6 0.4147 0.4073 0.3972 0.3938 0.3774 0.3954 0.4444 0.5821 0.6973 0.8605 0.8852 +0.7 0.4237 0.4176 0.4062 0.4005 0.3933 0.4082 0.4829 0.6086 0.7524 0.8663 0.8828 +0.8 0.4398 0.4168 0.4044 0.4015 0.4023 0.4143 0.5106 0.6329 0.8135 0.8809 0.8805 +0.9 0.4417 0.4303 0.4150 0.4125 0.4164 0.4616 0.5524 0.6739 0.8351 0.8844 0.8790 +1.0 0.4434 0.4424 0.4236 0.4183 0.4464 0.4958 0.5848 0.7085 0.8542 0.8845 0.8774 + +--- SSIM vs LPIPS optima --- + Condition ssim_peak_strength ssim_peak_smoothing ssim_peak lpips_bottom_strength lpips_bottom_smoothing d_strength d_smoothing +0 ERSDE-Simple_Prompt001-schoolgirl-5193 0.1 0.6 0.770072 0.1 0.4 0.0 0.2 +1 ERSDE-Simple_Prompt001-schoolgirl-6111 0.0 0.0 0.647326 0.0 0.0 0.0 0.0 +2 ERSDE-Simple_Prompt001-schoolgirl-9676 0.6 0.4 0.732172 0.6 0.4 0.0 0.0 +3 ERSDE-Simple_Prompt002-android-5193 0.1 0.7 0.751116 0.2 0.5 -0.1 0.2 +4 ERSDE-Simple_Prompt002-android-6111 0.0 0.0 0.733333 0.0 0.0 0.0 0.0 +5 ERSDE-Simple_Prompt002-android-9676 0.6 0.1 0.765157 0.6 0.1 0.0 0.0 +6 ERSDE-Simple_Prompt003-woman-5193 0.1 0.0 0.919054 0.2 0.4 -0.1 -0.4 +7 ERSDE-Simple_Prompt003-woman-6111 0.5 0.0 0.722273 0.8 0.1 -0.3 -0.1 +8 ERSDE-Simple_Prompt003-woman-9676 0.1 0.0 0.918260 0.1 0.0 0.0 0.0 +9 ERSDE-Simple_Prompt004-shop-5193 0.2 0.7 0.663236 0.2 0.5 0.0 0.2 +10 ERSDE-Simple_Prompt004-shop-6111 0.0 0.0 0.614867 0.0 0.0 0.0 0.0 +11 ERSDE-Simple_Prompt004-shop-9676 0.0 0.0 0.672762 0.1 0.0 -0.1 0.0 +12 ERSDE-Simple_Prompt005-inkwash-5193 0.3 0.0 0.794313 0.2 0.5 0.1 -0.5 +13 ERSDE-Simple_Prompt005-inkwash-6111 0.0 0.0 0.786036 0.0 0.0 0.0 0.0 +14 ERSDE-Simple_Prompt005-inkwash-9676 0.1 0.2 0.753666 0.1 0.1 0.0 0.1 + +--- curvature --- + Condition LPIPS_at_common max_neighbor_delta min_neighbor_delta mean_abs_neighbor_delta range_pct_anchor +0 ERSDE-Simple_Prompt001-schoolgirl-5193 0.3067 0.0074 -0.0057 0.0046 10.8157 +1 ERSDE-Simple_Prompt001-schoolgirl-6111 0.4391 0.0180 -0.0143 0.0114 26.6521 +2 ERSDE-Simple_Prompt001-schoolgirl-9676 0.4220 0.0074 -0.0040 0.0050 9.4413 +3 ERSDE-Simple_Prompt002-android-5193 0.4083 -0.0008 -0.0770 0.0285 62.9785 +4 ERSDE-Simple_Prompt002-android-6111 0.3604 0.0389 -0.0272 0.0222 54.6107 +5 ERSDE-Simple_Prompt002-android-9676 0.2720 0.0121 -0.0097 0.0079 18.0000 +6 ERSDE-Simple_Prompt003-woman-5193 0.1665 0.0021 -0.0060 0.0034 6.7058 +7 ERSDE-Simple_Prompt003-woman-6111 0.4445 0.0022 -0.0049 0.0027 5.8702 +8 ERSDE-Simple_Prompt003-woman-9676 0.1944 0.0046 0.0019 0.0033 2.2132 +9 ERSDE-Simple_Prompt004-shop-5193 0.3876 0.0031 -0.0100 0.0065 10.8306 +10 ERSDE-Simple_Prompt004-shop-6111 0.5861 0.0632 -0.0816 0.0498 119.6174 +11 ERSDE-Simple_Prompt004-shop-9676 0.3135 0.0101 0.0001 0.0043 8.2331 +12 ERSDE-Simple_Prompt005-inkwash-5193 0.2069 0.0043 -0.0042 0.0036 7.0331 +13 ERSDE-Simple_Prompt005-inkwash-6111 0.3075 0.0084 -0.0313 0.0142 32.8041 +14 ERSDE-Simple_Prompt005-inkwash-9676 0.2510 0.0062 -0.0077 0.0053 11.4140 +====================================================================== +Euler-Beta +{ + "family": "Euler-Beta", + "common": { + "median": [ + 0.3, + 0.1, + 0.250345 + ], + "mean": [ + 0.6, + 0.1, + 0.24866366666666673 + ] + }, + "common_restricted": { + "median": [ + 0.3, + 0.1, + 0.250345 + ], + "mean": [ + 0.6, + 0.1, + 0.24866366666666673 + ] + }, + "stats": { + "median": { + "point": [ + 0.3, + 0.1 + ], + "median_improvement": 0.027129999999999987, + "min_improvement": -0.036507999999999985, + "max_improvement": 0.07270100000000002, + "median_pct_anchor": 22.421487603305774, + "wilcoxon_stat": 18.0, + "wilcoxon_p": 0.01507568359375, + "n_improved": 13, + "n_worse": 2, + "by_prompt": { + "Prompt001-schoolgirl": 0.07035000000000002, + "Prompt002-android": 0.03243600000000002, + "Prompt003-woman": 0.04086600000000001, + "Prompt004-shop": 0.019498000000000015, + "Prompt005-inkwash": 0.012865999999999989 + }, + "by_seed": { + "5193": 0.014325000000000004, + "6111": 0.03243600000000002, + "9676": 0.019498000000000015 + } + }, + "mean": { + "point": [ + 0.6, + 0.1 + ], + "median_improvement": 0.03487100000000004, + "min_improvement": -0.07802999999999996, + "max_improvement": 0.10211100000000004, + "median_pct_anchor": 28.819008264462845, + "wilcoxon_stat": 22.0, + "wilcoxon_p": 0.0301513671875, + "n_improved": 12, + "n_worse": 3, + "by_prompt": { + "Prompt001-schoolgirl": 0.077036, + "Prompt002-android": 0.048068, + "Prompt003-woman": 0.063614, + "Prompt004-shop": 0.02020199999999997, + "Prompt005-inkwash": -0.023415999999999992 + }, + "by_seed": { + "5193": 0.03487100000000004, + "6111": 0.048068, + "9676": 0.004047000000000023 + } + }, + "median_restricted": { + "point": [ + 0.3, + 0.1 + ], + "median_improvement": 0.027129999999999987, + "min_improvement": -0.036507999999999985, + "max_improvement": 0.07270100000000002, + "median_pct_anchor": 22.421487603305774, + "wilcoxon_stat": 18.0, + "wilcoxon_p": 0.01507568359375, + "n_improved": 13, + "n_worse": 2, + "by_prompt": { + "Prompt001-schoolgirl": 0.07035000000000002, + "Prompt002-android": 0.03243600000000002, + "Prompt003-woman": 0.04086600000000001, + "Prompt004-shop": 0.019498000000000015, + "Prompt005-inkwash": 0.012865999999999989 + }, + "by_seed": { + "5193": 0.014325000000000004, + "6111": 0.03243600000000002, + "9676": 0.019498000000000015 + } + }, + "mean_restricted": { + "point": [ + 0.6, + 0.1 + ], + "median_improvement": 0.03487100000000004, + "min_improvement": -0.07802999999999996, + "max_improvement": 0.10211100000000004, + "median_pct_anchor": 28.819008264462845, + "wilcoxon_stat": 22.0, + "wilcoxon_p": 0.0301513671875, + "n_improved": 12, + "n_worse": 3, + "by_prompt": { + "Prompt001-schoolgirl": 0.077036, + "Prompt002-android": 0.048068, + "Prompt003-woman": 0.063614, + "Prompt004-shop": 0.02020199999999997, + "Prompt005-inkwash": -0.023415999999999992 + }, + "by_seed": { + "5193": 0.03487100000000004, + "6111": 0.048068, + "9676": 0.004047000000000023 + } + } + }, + "best_prediction_vs_reuse": { + "median": 0.042718000000000034, + "min": -0.003938999999999998, + "max": 0.15287, + "median_pct_anchor": 35.304132231404985, + "wilcoxon_p": 0.0001220703125, + "n_helps": 14, + "n_hurts": 1, + "by_prompt": { + "Prompt001-schoolgirl": 0.09323999999999999, + "Prompt002-android": 0.05135299999999998, + "Prompt003-woman": 0.06426199999999999, + "Prompt004-shop": 0.03190499999999999, + "Prompt005-inkwash": 0.012865999999999989 + }, + "by_seed": { + "5193": 0.042718000000000034, + "6111": 0.05135299999999998, + "9676": 0.019969999999999988 + } + }, + "own_best_vs_reuse": { + "median": 0.042718000000000034, + "min": 0.0, + "max": 0.15287, + "wilcoxon_p": 0.0009815397525216685, + "by_prompt": { + "Prompt001-schoolgirl": 0.09323999999999999, + "Prompt002-android": 0.05135299999999998, + "Prompt003-woman": 0.06426199999999999, + "Prompt004-shop": 0.03190499999999999, + "Prompt005-inkwash": 0.012865999999999989 + }, + "by_seed": { + "5193": 0.042718000000000034, + "6111": 0.05135299999999998, + "9676": 0.019969999999999988 + } + }, + "ssim_common": [ + 0.1, + 0.0, + 0.786009 + ], + "ssim_agree_exact": 5, + "n_local_minima_median_surface": 3, + "n_local_minima_mean_surface": 1, + "n_local_minima_median_surface_low_half": 3, + "minima_median_surface": [ + [ + 0.3, + 0.1, + 0.250345 + ], + [ + 0.7, + 0.2, + 0.257376 + ], + [ + 0.1, + 0.5, + 0.26926 + ] + ], + "minima_mean_surface": [ + [ + 0.6, + 0.1, + 0.24866366666666673 + ] + ], + "median_surface_neighbors": { + "s0.2_e0.1": 0.007581000000000004, + "s0.4_e0.1": 0.006869000000000014, + "s0.3_e0.0": 0.007911000000000001, + "s0.3_e0.2": 0.004785000000000039 + }, + "top5_median_cells": [ + [ + 0.250345, + 0.3, + 0.1 + ], + [ + 0.254853, + 0.4, + 0.2 + ], + [ + 0.25513, + 0.3, + 0.2 + ], + [ + 0.256827, + 0.4, + 0.0 + ], + [ + 0.257214, + 0.4, + 0.1 + ] + ], + "top5_mean_cells": [ + [ + 0.24866366666666673, + 0.6, + 0.1 + ], + [ + 0.25032566666666667, + 0.7, + 0.2 + ], + [ + 0.2503452666666667, + 0.5, + 0.1 + ], + [ + 0.25062286666666667, + 0.4, + 0.2 + ], + [ + 0.2515322, + 0.6, + 0.2 + ] + ], + "median_surface_reuse_row": 0.286158, + "mean_surface_reuse_row": 0.2815976666666667, + "opt_strength": { + "min": 0.0, + "max": 1.0, + "q1": 0.3, + "q3": 0.7, + "median": 0.6 + }, + "opt_smoothing": { + "min": 0.0, + "max": 0.7, + "q1": 0.0, + "q3": 0.2, + "median": 0.1 + }, + "unimodal_counts": { + "3": 3, + "5": 3, + "4": 2, + "2": 2, + "1": 1, + "7": 1, + "14": 1, + "9": 1, + "10": 1 + }, + "unimodal_counts_low_half": { + "1": 4, + "2": 4, + "4": 3, + "3": 3, + "5": 1 + }, + "loss_summary": { + "median": { + "median": 0.02857699999999999, + "max": 0.101322, + "median_pct_anchor": 23.617355371900818, + "max_pct_anchor": 83.73719008264463 + }, + "mean": { + "median": 0.015450999999999993, + "max": 0.07802999999999996, + "median_pct_anchor": 12.7694214876033, + "max_pct_anchor": 64.4876033057851 + } + }, + "reuse_stats": { + "median": 0.286158, + "min": 0.181535, + "max": 0.398704 + }, + "curvature": { + "median_of_mean_abs": 0.006505250000000004, + "max_of_mean_abs": 0.023558499999999996, + "median_range_pct_anchor": 11.095041322314067 + }, + "ema_profile_median_surface": { + "0.0": 0.271027, + "0.1": 0.27001600000000003, + "0.2": 0.267112, + "0.3": 0.27561800000000003, + "0.4": 0.304351, + "0.5": 0.337778, + "0.6": 0.4256415, + "0.7": 0.543118, + "0.8": 0.7034305, + "0.9": 0.737447, + "0.99": 0.758555 + }, + "strength_profile_median_surface": { + "0.0": 0.286158, + "0.1": 0.270201, + "0.2": 0.278747, + "0.3": 0.292742, + "0.4": 0.32731, + "0.5": 0.334754, + "0.6": 0.340802, + "0.7": 0.368209, + "0.8": 0.407567, + "0.9": 0.406592, + "1.0": 0.418887 + }, + "n_conditions_reuse_unbeatable": 1 +} + +--- per-condition optima --- + Condition prompt seed best_strength best_smoothing best_LPIPS reuse_LPIPS improvement_vs_reuse improvement_pct_anchor +0 Euler-Beta_Prompt001-schoolgirl-5193 Prompt001-schoolgirl 5193 0.7 0.2 0.308964 0.343670 0.034706 28.682645 +1 Euler-Beta_Prompt001-schoolgirl-6111 Prompt001-schoolgirl 6111 0.4 0.4 0.190400 0.343270 0.152870 126.338843 +2 Euler-Beta_Prompt001-schoolgirl-9676 Prompt001-schoolgirl 9676 0.7 0.0 0.305464 0.398704 0.093240 77.057851 +3 Euler-Beta_Prompt002-android-5193 Prompt002-android 5193 1.0 0.0 0.133345 0.286158 0.152813 126.291736 +4 Euler-Beta_Prompt002-android-6111 Prompt002-android 6111 0.6 0.3 0.316939 0.368292 0.051353 42.440496 +5 Euler-Beta_Prompt002-android-9676 Prompt002-android 9676 0.1 0.7 0.345424 0.365394 0.019970 16.504132 +6 Euler-Beta_Prompt003-woman-5193 Prompt003-woman 5193 0.9 0.0 0.077788 0.181535 0.103747 85.741322 +7 Euler-Beta_Prompt003-woman-6111 Prompt003-woman 6111 0.7 0.0 0.145931 0.210193 0.064262 53.109091 +8 Euler-Beta_Prompt003-woman-9676 Prompt003-woman 9676 0.6 0.1 0.128754 0.192368 0.063614 52.573554 +9 Euler-Beta_Prompt004-shop-5193 Prompt004-shop 5193 0.7 0.2 0.257376 0.300094 0.042718 35.304132 +10 Euler-Beta_Prompt004-shop-6111 Prompt004-shop 6111 0.3 0.2 0.226953 0.258858 0.031905 26.367769 +11 Euler-Beta_Prompt004-shop-9676 Prompt004-shop 9676 0.3 0.1 0.250345 0.269843 0.019498 16.114050 +12 Euler-Beta_Prompt005-inkwash-5193 Prompt005-inkwash 5193 0.9 0.0 0.158499 0.199467 0.040968 33.857851 +13 Euler-Beta_Prompt005-inkwash-6111 Prompt005-inkwash 6111 0.3 0.1 0.279459 0.292325 0.012866 10.633058 +14 Euler-Beta_Prompt005-inkwash-9676 Prompt005-inkwash 9676 0.0 0.0 0.213794 0.213794 0.000000 0.000000 + +--- best prediction (str>=0.1) vs reuse --- + Condition prompt seed reuse_LPIPS best_pred_strength best_pred_smoothing best_pred_LPIPS delta_vs_reuse delta_pct_anchor prediction_helps +0 Euler-Beta_Prompt001-schoolgirl-5193 Prompt001-schoolgirl 5193 0.343670 0.7 0.2 0.308964 0.034706 28.682645 True +1 Euler-Beta_Prompt001-schoolgirl-6111 Prompt001-schoolgirl 6111 0.343270 0.4 0.4 0.190400 0.152870 126.338843 True +2 Euler-Beta_Prompt001-schoolgirl-9676 Prompt001-schoolgirl 9676 0.398704 0.7 0.0 0.305464 0.093240 77.057851 True +3 Euler-Beta_Prompt002-android-5193 Prompt002-android 5193 0.286158 1.0 0.0 0.133345 0.152813 126.291736 True +4 Euler-Beta_Prompt002-android-6111 Prompt002-android 6111 0.368292 0.6 0.3 0.316939 0.051353 42.440496 True +5 Euler-Beta_Prompt002-android-9676 Prompt002-android 9676 0.365394 0.1 0.7 0.345424 0.019970 16.504132 True +6 Euler-Beta_Prompt003-woman-5193 Prompt003-woman 5193 0.181535 0.9 0.0 0.077788 0.103747 85.741322 True +7 Euler-Beta_Prompt003-woman-6111 Prompt003-woman 6111 0.210193 0.7 0.0 0.145931 0.064262 53.109091 True +8 Euler-Beta_Prompt003-woman-9676 Prompt003-woman 9676 0.192368 0.6 0.1 0.128754 0.063614 52.573554 True +9 Euler-Beta_Prompt004-shop-5193 Prompt004-shop 5193 0.300094 0.7 0.2 0.257376 0.042718 35.304132 True +10 Euler-Beta_Prompt004-shop-6111 Prompt004-shop 6111 0.258858 0.3 0.2 0.226953 0.031905 26.367769 True +11 Euler-Beta_Prompt004-shop-9676 Prompt004-shop 9676 0.269843 0.3 0.1 0.250345 0.019498 16.114050 True +12 Euler-Beta_Prompt005-inkwash-5193 Prompt005-inkwash 5193 0.199467 0.9 0.0 0.158499 0.040968 33.857851 True +13 Euler-Beta_Prompt005-inkwash-6111 Prompt005-inkwash 6111 0.292325 0.3 0.1 0.279459 0.012866 10.633058 True +14 Euler-Beta_Prompt005-inkwash-9676 Prompt005-inkwash 9676 0.213794 0.1 0.0 0.217733 -0.003939 -3.255372 False + +--- unimodality --- + Condition n_local_minima n_local_minima_low_damage_half +0 Euler-Beta_Prompt001-schoolgirl-5193 4 4 +1 Euler-Beta_Prompt001-schoolgirl-6111 3 3 +2 Euler-Beta_Prompt001-schoolgirl-9676 5 4 +3 Euler-Beta_Prompt002-android-5193 2 1 +4 Euler-Beta_Prompt002-android-6111 3 3 +5 Euler-Beta_Prompt002-android-9676 5 4 +6 Euler-Beta_Prompt003-woman-5193 4 1 +7 Euler-Beta_Prompt003-woman-6111 1 1 +8 Euler-Beta_Prompt003-woman-9676 2 2 +9 Euler-Beta_Prompt004-shop-5193 7 5 +10 Euler-Beta_Prompt004-shop-6111 5 3 +11 Euler-Beta_Prompt004-shop-9676 3 1 +12 Euler-Beta_Prompt005-inkwash-5193 14 2 +13 Euler-Beta_Prompt005-inkwash-6111 9 2 +14 Euler-Beta_Prompt005-inkwash-9676 10 2 + +--- median surface --- + 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.99 +0.0 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 0.2862 +0.1 0.2678 0.2700 0.2693 0.2665 0.2702 0.2693 0.2782 0.3094 0.3552 0.4561 0.6715 +0.2 0.2598 0.2579 0.2649 0.2599 0.2711 0.2787 0.3101 0.3525 0.4515 0.6777 0.7390 +0.3 0.2583 0.2503 0.2551 0.2587 0.2636 0.2927 0.3410 0.4045 0.5687 0.7148 0.7350 +0.4 0.2568 0.2572 0.2549 0.2635 0.2713 0.3273 0.3507 0.4539 0.6479 0.7409 0.7463 +0.5 0.2594 0.2700 0.2584 0.2638 0.2899 0.3348 0.4087 0.5080 0.6989 0.7336 0.7707 +0.6 0.2742 0.2652 0.2753 0.2848 0.3188 0.3408 0.4426 0.5782 0.7274 0.7340 0.7756 +0.7 0.2942 0.2898 0.2574 0.3077 0.3281 0.3682 0.4757 0.6524 0.7079 0.7409 0.7756 +0.8 0.3177 0.3112 0.2820 0.3246 0.3360 0.4076 0.5210 0.7021 0.7395 0.7439 0.7705 +0.9 0.3264 0.3172 0.3246 0.3245 0.3519 0.4066 0.5686 0.6888 0.7377 0.7436 0.7614 +1.0 0.3233 0.3277 0.3300 0.3345 0.3934 0.4189 0.6220 0.6802 0.7369 0.7576 0.7557 + +--- SSIM vs LPIPS optima --- + Condition ssim_peak_strength ssim_peak_smoothing ssim_peak lpips_bottom_strength lpips_bottom_smoothing d_strength d_smoothing +0 Euler-Beta_Prompt001-schoolgirl-5193 0.5 0.0 0.794703 0.7 0.2 -0.2 -0.2 +1 Euler-Beta_Prompt001-schoolgirl-6111 0.4 0.4 0.891826 0.4 0.4 0.0 0.0 +2 Euler-Beta_Prompt001-schoolgirl-9676 0.4 0.0 0.768327 0.7 0.0 -0.3 0.0 +3 Euler-Beta_Prompt002-android-5193 1.0 0.2 0.862044 1.0 0.0 0.0 0.2 +4 Euler-Beta_Prompt002-android-6111 0.6 0.3 0.623154 0.6 0.3 0.0 0.0 +5 Euler-Beta_Prompt002-android-9676 0.1 0.7 0.637746 0.1 0.7 0.0 0.0 +6 Euler-Beta_Prompt003-woman-5193 0.8 0.1 0.970607 0.9 0.0 -0.1 0.1 +7 Euler-Beta_Prompt003-woman-6111 0.6 0.2 0.951576 0.7 0.0 -0.1 0.2 +8 Euler-Beta_Prompt003-woman-9676 0.7 0.3 0.930316 0.6 0.1 0.1 0.2 +9 Euler-Beta_Prompt004-shop-5193 0.4 0.2 0.750001 0.7 0.2 -0.3 0.0 +10 Euler-Beta_Prompt004-shop-6111 0.3 0.2 0.803315 0.3 0.2 0.0 0.0 +11 Euler-Beta_Prompt004-shop-9676 0.3 0.1 0.744654 0.3 0.1 0.0 0.0 +12 Euler-Beta_Prompt005-inkwash-5193 0.7 0.1 0.874141 0.9 0.0 -0.2 0.1 +13 Euler-Beta_Prompt005-inkwash-6111 0.3 0.0 0.762206 0.3 0.1 0.0 -0.1 +14 Euler-Beta_Prompt005-inkwash-9676 0.2 0.0 0.800308 0.0 0.0 0.2 0.0 + +--- curvature --- + Condition LPIPS_at_common max_neighbor_delta min_neighbor_delta mean_abs_neighbor_delta range_pct_anchor +0 Euler-Beta_Prompt001-schoolgirl-5193 0.3428 -0.0044 -0.0091 0.0070 3.9231 +1 Euler-Beta_Prompt001-schoolgirl-6111 0.2729 0.0460 -0.0130 0.0236 48.7810 +2 Euler-Beta_Prompt001-schoolgirl-9676 0.3260 0.0211 0.0077 0.0120 11.0950 +3 Euler-Beta_Prompt002-android-5193 0.2347 0.0160 -0.0190 0.0094 28.9620 +4 Euler-Beta_Prompt002-android-6111 0.3359 0.0015 -0.0095 0.0058 9.1430 +5 Euler-Beta_Prompt002-android-9676 0.4019 0.0032 -0.0098 0.0043 10.7215 +6 Euler-Beta_Prompt003-woman-5193 0.1352 0.0116 -0.0130 0.0065 20.2851 +7 Euler-Beta_Prompt003-woman-6111 0.1745 0.0120 -0.0025 0.0053 12.0000 +8 Euler-Beta_Prompt003-woman-9676 0.1515 0.0133 -0.0164 0.0091 24.6140 +9 Euler-Beta_Prompt004-shop-5193 0.2878 0.0279 -0.0105 0.0158 31.8017 +10 Euler-Beta_Prompt004-shop-6111 0.2317 0.0032 -0.0048 0.0027 6.5669 +11 Euler-Beta_Prompt004-shop-9676 0.2503 0.0079 0.0042 0.0065 3.0314 +12 Euler-Beta_Prompt005-inkwash-5193 0.1851 0.0015 -0.0087 0.0057 8.4041 +13 Euler-Beta_Prompt005-inkwash-6111 0.2795 0.0065 0.0029 0.0054 3.0190 +14 Euler-Beta_Prompt005-inkwash-9676 0.2481 0.0091 -0.0242 0.0156 27.5471 + +DONE diff --git a/analysis/stage5-families/scatter/make_scatter.py b/analysis/stage5-families/scatter/make_scatter.py new file mode 100644 index 0000000000000000000000000000000000000000..06353780d5e124de63027ba73466469432ddc206 --- /dev/null +++ b/analysis/stage5-families/scatter/make_scatter.py @@ -0,0 +1,209 @@ +""" +HareSkip Stage5-Families 散布図生成スクリプト（ERSDE-Simple / Euler-Beta） + +走査①の analysis-stage5-scan1/scatter/make_scatter.py と同一仕様。 +異なる点は y軸レンジのみ: + 走査①のレンジ（LPIPS 0.255〜0.993 / SSIM-win7 0.000〜0.881）では本データがはみ出す + （LPIPS 最大 1.043、最小 0.078 / SSIM 最大 0.971）ため、 + 3系（ERSDE-Beta / ERSDE-Simple / Euler-Beta）を通した**全系共通の新レンジ**に統一する: + LPIPS-VGG : 0.058 〜 1.063 + SSIM (win7) : 0.000 〜 0.991 + ※走査①の図は既存ファイル変更禁止のため再描画していない。3系を並べて見る際は + 本スクリプトの UNIFIED_* を走査①CSVにも適用して描き直す必要がある（別途）。 + +入力: Judge-results/Stage5-Families_20260907-235041/merged-results.csv +出力: analysis-stage5-families/scatter/ 配下に + - scatter-lpips_<条件名>.png x30 + - scatter-ssim_<条件名>.png x30 + - sheet-lpips_<系名>.png / sheet-ssim_<系名>.png （各5x3一覧、系ごと） + +再実行可能（このスクリプト自身と入力CSV以外のファイルは変更しない）。 +""" +import re +import pathlib + +import pandas as pd +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from matplotlib.cm import ScalarMappable +from matplotlib.colors import Normalize + +BASE = pathlib.Path(__file__).resolve().parent +CSV_PATH = BASE.parent.parent / "Judge-results" / "Stage5-Families_20260907-235041" / "merged-results.csv" +OUT_DIR = BASE + +LPIPS_COL = "LPIPS - VGG" +SSIM_COL = "SSIM - win size 7 (skimage)" + +LPIPS_ANCHOR = 0.121 # 参考の損傷スケールアンカー + +# 3系共通の統一 y 軸レンジ +UNIFIED_LPIPS_RANGE = (0.058, 1.063) +UNIFIED_SSIM_RANGE = (0.000, 0.991) + +FNAME_RE = re.compile(r"str-(\d+)_ema-(\d+)\.png") + + +def parse_filename(fname): + if fname == "reuse.png": + return None, None + m = FNAME_RE.match(fname) + if not m: + return None, None + str_code, ema_code = m.group(1), m.group(2) + strength = int(str_code) / 10.0 + if ema_code == "099": + ema = 0.99 + else: + ema = int(ema_code) / 10.0 + return strength, ema + + +def ema_color(ema, ema_min=0.0, ema_max=0.99): + # EMA=0.0 -> 黒 #000000, EMA=0.99 -> #cccccc の線形グラデーション + t = (ema - ema_min) / (ema_max - ema_min) + g = 0x00 + t * (0xcc - 0x00) + v = g / 255.0 + return (v, v, v) + + +def load_data(): + df = pd.read_csv(CSV_PATH) + strengths, emas = zip(*df["File name"].map(parse_filename)) + df = df.copy() + df["strength"] = strengths + df["ema"] = emas + df["family"] = df["Condition"].str.split("_").str[0] + return df + + +def plot_condition(ax, sub, value_col, y_label, y_range, ema_levels): + reuse_row = sub[sub["File name"] == "reuse.png"] + reuse_val = reuse_row[value_col].iloc[0] if len(reuse_row) else None + + grid = sub[sub["File name"] != "reuse.png"] + + # 同一EMA水準の点を細い同色線で結ぶ（strength昇順） + for ema in ema_levels: + line_sub = grid[grid["ema"] == ema].sort_values("strength") + if line_sub.empty: + continue + color = ema_color(ema) + ax.plot( + line_sub["strength"], line_sub[value_col], + color=color, linewidth=0.8, alpha=0.9, zorder=2, + ) + ax.scatter( + line_sub["strength"], line_sub[value_col], + color=color, s=26, edgecolors="white", linewidths=0.4, zorder=3, + ) + + # Reuse基準（strength=0の実測1点）: 赤い点線の水平線＋ラベル + if reuse_val is not None: + ax.axhline(reuse_val, color="red", linestyle="--", linewidth=1.0, zorder=1) + ax.text( + 0.02, reuse_val, f"Reuse={reuse_val:.3f} ", + color="red", fontsize=8, va="bottom", ha="left", + transform=ax.get_yaxis_transform(), + ) + + # LPIPS版のみ: 参考の損傷スケールアンカー + if value_col == LPIPS_COL: + ax.axhline(LPIPS_ANCHOR, color="gray", linestyle=":", linewidth=0.8, zorder=1) + ax.text( + 0.02, LPIPS_ANCHOR, f"anchor={LPIPS_ANCHOR}", + color="gray", fontsize=7, va="bottom", ha="left", + transform=ax.get_yaxis_transform(), + ) + + ax.set_xlabel("prediction strength") + ax.set_ylabel(y_label) + ax.set_xlim(0.0, 1.05) + ax.set_xticks([i / 10 for i in range(1, 11)]) + ax.set_ylim(*y_range) + ax.grid(True, linewidth=0.4, alpha=0.4) + + +def add_colorbar(fig, ax, ema_min=0.0, ema_max=0.99): + sm = ScalarMappable( + cmap=matplotlib.colors.LinearSegmentedColormap.from_list( + "ema_gray", [(0, 0, 0), (0xcc / 255, 0xcc / 255, 0xcc / 255)] + ), + norm=Normalize(vmin=ema_min, vmax=ema_max), + ) + sm.set_array([]) + cbar = fig.colorbar(sm, ax=ax, fraction=0.05, pad=0.03) + cbar.set_label("EMA smoothing (0.0=black / 0.99=light gray)") + + +def make_individual_plots(df, value_col, y_label, y_range, fname_prefix, ema_levels): + conditions = sorted(df["Condition"].unique()) + for cond in conditions: + sub = df[df["Condition"] == cond] + fig, ax = plt.subplots(figsize=(7, 5.2)) + plot_condition(ax, sub, value_col, y_label, y_range, ema_levels) + ax.set_title(cond, fontsize=10) + add_colorbar(fig, ax, min(ema_levels), max(ema_levels)) + fig.tight_layout() + fig.savefig(OUT_DIR / f"{fname_prefix}_{cond}.png", dpi=150) + plt.close(fig) + return conditions + + +def make_sheet(df, value_col, y_label, y_range, conditions, sheet_name, ema_levels, family): + fig, axes = plt.subplots(3, 5, figsize=(24, 13.5)) + for ax, cond in zip(axes.flat, conditions): + sub = df[df["Condition"] == cond] + plot_condition(ax, sub, value_col, y_label, y_range, ema_levels) + ax.set_title(cond, fontsize=8) + ax.tick_params(labelsize=7) + ax.set_xlabel("") + ax.set_ylabel("") + + for ax in axes.flat[len(conditions):]: + ax.axis("off") + + fig.suptitle(f"Stage5-Families [{family}]: {y_label} vs strength (grid=EMA)", fontsize=14) + + legend_handles = [ + Line2D([0], [0], marker="o", color=ema_color(e), linestyle="-", markersize=6, + label=f"EMA={e:.2f}" if e != 0.99 else "EMA=0.99") + for e in ema_levels + ] + fig.legend(handles=legend_handles, loc="lower center", ncol=len(ema_levels), fontsize=7, + bbox_to_anchor=(0.5, -0.02)) + + fig.tight_layout(rect=[0, 0.03, 1, 0.97]) + out_path = OUT_DIR / sheet_name + fig.savefig(out_path, dpi=110) + plt.close(fig) + return out_path + + +def main(): + df = load_data() + ema_levels = sorted(df["ema"].dropna().unique()) + + print(f"LPIPS observed: {df[LPIPS_COL].min():.4f} .. {df[LPIPS_COL].max():.4f}") + print(f"SSIM observed: {df[SSIM_COL].min():.4f} .. {df[SSIM_COL].max():.4f}") + print(f"LPIPS range used (unified): {UNIFIED_LPIPS_RANGE}") + print(f"SSIM range used (unified): {UNIFIED_SSIM_RANGE}") + + conditions = make_individual_plots( + df, LPIPS_COL, "LPIPS-VGG", UNIFIED_LPIPS_RANGE, "scatter-lpips", ema_levels) + make_individual_plots( + df, SSIM_COL, "SSIM (win7)", UNIFIED_SSIM_RANGE, "scatter-ssim", ema_levels) + + for family in sorted(df["family"].unique()): + fam_conds = [c for c in conditions if c.startswith(family + "_")] + p1 = make_sheet(df, LPIPS_COL, "LPIPS-VGG", UNIFIED_LPIPS_RANGE, fam_conds, + f"sheet-lpips_{family}.png", ema_levels, family) + p2 = make_sheet(df, SSIM_COL, "SSIM (win7)", UNIFIED_SSIM_RANGE, fam_conds, + f"sheet-ssim_{family}.png", ema_levels, family) + print(f"{family}: {len(fam_conds)} conditions -> 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b/analysis/stage5-families/tables/curvature-near-common_ERSDE-Simple.csv new file mode 100644 index 0000000000000000000000000000000000000000..5140ac4a91b38911b11b3bcaef0571260d8256bc --- /dev/null +++ b/analysis/stage5-families/tables/curvature-near-common_ERSDE-Simple.csv @@ -0,0 +1,16 @@ +Condition,LPIPS_at_common,max_neighbor_delta,min_neighbor_delta,mean_abs_neighbor_delta,range_pct_anchor +ERSDE-Simple_Prompt001-schoolgirl-5193,0.306705,0.007378999999999969,-0.005707999999999991,0.004625666666666657,10.81570247933881 +ERSDE-Simple_Prompt001-schoolgirl-6111,0.439057,0.01797500000000002,-0.014273999999999953,0.011422999999999997,26.652066115702457 +ERSDE-Simple_Prompt001-schoolgirl-9676,0.421951,0.007409999999999972,-0.0040140000000000176,0.0049746666666666646,9.441322314049579 +ERSDE-Simple_Prompt002-android-5193,0.408268,-0.0008450000000000402,-0.07704900000000003,0.028460000000000023,62.97851239669421 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b/analysis/stage5-families/tables/median-surface_ERSDE-Simple.csv new file mode 100644 index 0000000000000000000000000000000000000000..7dc7b75e77d97fda20692844727a86b861822292 --- /dev/null +++ b/analysis/stage5-families/tables/median-surface_ERSDE-Simple.csv @@ -0,0 +1,12 @@ +,0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99 +0.0,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514,0.323514 +0.1,0.313459,0.316277,0.318385,0.317653,0.314361,0.31678,0.320233,0.340822,0.388588,0.466345,0.702543 +0.2,0.315849,0.3145,0.320589,0.326331,0.340133,0.336402,0.380898,0.412506,0.481015,0.645992,0.816966 +0.3,0.327534,0.348885,0.36187,0.37769,0.365999,0.373028,0.405067,0.430415,0.494304,0.738454,0.856486 +0.4,0.386829,0.389693,0.389092,0.371245,0.368475,0.389284,0.40991,0.49323,0.554352,0.818438,0.875949 +0.5,0.405798,0.398169,0.392875,0.392252,0.382761,0.388992,0.41578,0.512779,0.638208,0.849633,0.883575 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b/analysis/stage5-families/tables/reuse-comparison_ERSDE-Simple.csv new file mode 100644 index 0000000000000000000000000000000000000000..2bc65b5249484a251f5852121d457bcda971ab7f --- /dev/null +++ b/analysis/stage5-families/tables/reuse-comparison_ERSDE-Simple.csv @@ -0,0 +1,61 @@ +criterion,common_strength,common_smoothing,Condition,prompt,seed,reuse_LPIPS,common_LPIPS,improvement,improvement_pct_anchor,improved +median,0.1,0.0,ERSDE-Simple_Prompt001-schoolgirl-5193,Prompt001-schoolgirl,5193,0.305915,0.306705,-0.0007900000000000129,-0.6528925619834818,False +median,0.1,0.0,ERSDE-Simple_Prompt001-schoolgirl-6111,Prompt001-schoolgirl,6111,0.424783,0.439057,-0.014273999999999953,-11.796694214875995,False +median,0.1,0.0,ERSDE-Simple_Prompt001-schoolgirl-9676,Prompt001-schoolgirl,9676,0.429361,0.421951,0.007409999999999972,6.123966942148738,True +median,0.1,0.0,ERSDE-Simple_Prompt002-android-5193,Prompt002-android,5193,0.331219,0.408268,-0.07704900000000003,-63.67685950413227,False 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+ERSDE-Simple_Prompt004-shop-9676,0.0,0.0,0.672762,0.1,0.0,-0.1,0.0 +ERSDE-Simple_Prompt005-inkwash-5193,0.3,0.0,0.794313,0.2,0.5,0.09999999999999998,-0.5 +ERSDE-Simple_Prompt005-inkwash-6111,0.0,0.0,0.786036,0.0,0.0,0.0,0.0 +ERSDE-Simple_Prompt005-inkwash-9676,0.1,0.2,0.753666,0.1,0.1,0.0,0.1 diff --git a/analysis/stage5-families/tables/ssim-vs-lpips-optima_Euler-Beta.csv b/analysis/stage5-families/tables/ssim-vs-lpips-optima_Euler-Beta.csv new file mode 100644 index 0000000000000000000000000000000000000000..9a1a447f554c3cfd8bf06d3d98c068f8a01ae8d1 --- /dev/null +++ b/analysis/stage5-families/tables/ssim-vs-lpips-optima_Euler-Beta.csv @@ -0,0 +1,16 @@ +Condition,ssim_peak_strength,ssim_peak_smoothing,ssim_peak,lpips_bottom_strength,lpips_bottom_smoothing,d_strength,d_smoothing +Euler-Beta_Prompt001-schoolgirl-5193,0.5,0.0,0.794703,0.7,0.2,-0.19999999999999996,-0.2 +Euler-Beta_Prompt001-schoolgirl-6111,0.4,0.4,0.891826,0.4,0.4,0.0,0.0 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a/analysis/stage5-families/tables/summary_ALL-families.json b/analysis/stage5-families/tables/summary_ALL-families.json new file mode 100644 index 0000000000000000000000000000000000000000..b2de4d9c840b3c425e32cb20288e5a5d8429f759 --- /dev/null +++ b/analysis/stage5-families/tables/summary_ALL-families.json @@ -0,0 +1,714 @@ +{ + "ERSDE-Simple": { + "family": "ERSDE-Simple", + "common": { + "median": [ + 0.1, + 0.0, + 0.313459 + ], + "mean": [ + 0.0, + 0.0, + 0.32377713333333336 + ] + }, + "common_restricted": { + "median": [ + 0.1, + 0.0, + 0.313459 + ], + "mean": [ + 0.1, + 0.3, + 0.3329523999999999 + ] + }, + "stats": { + "median": { + "point": [ + 0.1, + 0.0 + ], + "median_improvement": -0.004917999999999978, + "min_improvement": -0.08158299999999996, + "max_improvement": 0.012102000000000002, + "median_pct_anchor": -4.064462809917337, + "wilcoxon_stat": 37.0, + "wilcoxon_p": 0.207763671875, + "n_improved": 6, + "n_worse": 9, + "by_prompt": { + "Prompt001-schoolgirl": 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b/analysis/stage5-families/tables/unimodality_ERSDE-Simple.csv new file mode 100644 index 0000000000000000000000000000000000000000..41fc3846d9240effa10c446b7cbd0af3f3ed26c2 --- /dev/null +++ b/analysis/stage5-families/tables/unimodality_ERSDE-Simple.csv @@ -0,0 +1,16 @@ +Condition,n_local_minima,n_local_minima_low_damage_half,minima +ERSDE-Simple_Prompt001-schoolgirl-5193,6,6,"(0.1,0.4,0.2972);(0.1,0.2,0.2976);(0.1,0.6,0.2982);(0.5,0.5,0.3002);(0.3,0.5,0.3010);(0.3,0.3,0.3012)" +ERSDE-Simple_Prompt001-schoolgirl-6111,2,1,"(0.1,0.0,0.4391);(1.0,0.99,0.8505)" +ERSDE-Simple_Prompt001-schoolgirl-9676,5,5,"(0.6,0.4,0.3774);(0.8,0.3,0.3819);(0.6,0.0,0.4176);(0.2,0.0,0.4179);(0.1,0.2,0.4214)" +ERSDE-Simple_Prompt002-android-5193,7,6,"(0.2,0.5,0.3042);(0.1,0.7,0.3088);(0.1,0.1,0.4008);(0.6,0.1,0.4026);(0.8,0.2,0.4044);(0.1,0.9,0.4198);(0.7,0.99,0.8529)" +ERSDE-Simple_Prompt002-android-6111,2,1,"(0.1,0.0,0.3604);(1.0,0.99,0.8973)" +ERSDE-Simple_Prompt002-android-9676,3,2,"(0.6,0.1,0.2380);(0.3,0.0,0.2477);(1.0,0.99,0.8774)" +ERSDE-Simple_Prompt003-woman-5193,4,3,"(0.2,0.4,0.1556);(0.3,0.1,0.1642);(0.9,0.1,0.2017);(1.0,0.99,0.8024)" +ERSDE-Simple_Prompt003-woman-6111,4,4,"(0.8,0.1,0.4386);(0.1,0.2,0.4437);(0.1,0.0,0.4445);(0.1,0.4,0.4457)" +ERSDE-Simple_Prompt003-woman-9676,2,2,"(0.1,0.0,0.1944);(0.3,0.1,0.1948)" +ERSDE-Simple_Prompt004-shop-5193,6,5,"(0.2,0.5,0.3561);(0.1,0.7,0.3579);(0.4,0.5,0.3591);(0.1,0.1,0.3812);(0.6,0.2,0.3918);(1.0,0.99,0.8896)" +ERSDE-Simple_Prompt004-shop-6111,2,1,"(0.1,0.0,0.5861);(1.0,0.99,0.9177)" +ERSDE-Simple_Prompt004-shop-9676,3,2,"(0.1,0.0,0.3135);(0.2,0.3,0.3154);(1.0,0.99,0.9089)" +ERSDE-Simple_Prompt005-inkwash-5193,2,2,"(0.2,0.5,0.1865);(0.2,0.1,0.2003)" +ERSDE-Simple_Prompt005-inkwash-6111,2,1,"(0.1,0.3,0.2996);(1.0,0.99,0.9278)" +ERSDE-Simple_Prompt005-inkwash-9676,3,2,"(0.1,0.1,0.2433);(0.1,0.4,0.2468);(1.0,0.99,0.7665)" diff --git a/analysis/stage5-families/tables/unimodality_Euler-Beta.csv b/analysis/stage5-families/tables/unimodality_Euler-Beta.csv new file mode 100644 index 0000000000000000000000000000000000000000..8da7046b884f3289885ed641d2057b15de66163c --- /dev/null +++ b/analysis/stage5-families/tables/unimodality_Euler-Beta.csv @@ -0,0 +1,16 @@ +Condition,n_local_minima,n_local_minima_low_damage_half,minima +Euler-Beta_Prompt001-schoolgirl-5193,4,4,"(0.7,0.2,0.3090);(0.6,0.0,0.3164);(1.0,0.0,0.3193);(0.3,0.3,0.3291)" +Euler-Beta_Prompt001-schoolgirl-6111,3,3,"(0.4,0.4,0.1904);(0.8,0.3,0.1980);(0.6,0.2,0.2150)" +Euler-Beta_Prompt001-schoolgirl-9676,5,4,"(0.7,0.0,0.3055);(0.6,0.3,0.3143);(0.4,0.0,0.3173);(0.4,0.3,0.3226);(1.0,0.99,0.7419)" +Euler-Beta_Prompt002-android-5193,2,1,"(1.0,0.0,0.1333);(1.0,0.99,0.7557)" +Euler-Beta_Prompt002-android-6111,3,3,"(0.6,0.3,0.3169);(0.9,0.3,0.3304);(0.1,0.8,0.3459)" +Euler-Beta_Prompt002-android-9676,5,4,"(0.1,0.7,0.3454);(0.1,0.3,0.3645);(0.1,0.0,0.3714);(0.5,0.3,0.3980);(1.0,0.99,1.0174)" +Euler-Beta_Prompt003-woman-5193,4,1,"(0.9,0.0,0.0778);(0.5,0.99,0.7306);(0.7,0.99,0.7306);(1.0,0.9,0.7306)" +Euler-Beta_Prompt003-woman-6111,1,1,"(0.7,0.0,0.1459)" +Euler-Beta_Prompt003-woman-9676,2,2,"(0.6,0.1,0.1288);(0.7,0.3,0.1298)" +Euler-Beta_Prompt004-shop-5193,7,5,"(0.7,0.2,0.2574);(0.4,0.2,0.2655);(0.3,0.0,0.2773);(0.1,0.5,0.2835);(0.1,0.7,0.3253);(1.0,0.99,0.9640);(0.8,0.99,0.9640)" +Euler-Beta_Prompt004-shop-6111,5,3,"(0.3,0.2,0.2270);(0.3,0.0,0.2298);(0.5,0.0,0.2308);(0.4,0.99,0.7756);(1.0,0.9,0.7756)" +Euler-Beta_Prompt004-shop-9676,3,1,"(0.3,0.1,0.2503);(0.7,0.9,0.7831);(0.4,0.99,0.7832)" +Euler-Beta_Prompt005-inkwash-5193,14,2,"(0.9,0.0,0.1585);(0.7,0.1,0.1620);(0.3,0.99,0.6628);(0.4,0.99,0.6628);(0.5,0.9,0.6628);(0.5,0.99,0.6628);(0.6,0.9,0.6628);(0.6,0.99,0.6628);(0.7,0.9,0.6628);(0.7,0.99,0.6628);(0.8,0.9,0.6628);(0.9,0.9,0.6628);(1.0,0.9,0.6628);(1.0,0.99,0.6628)" +Euler-Beta_Prompt005-inkwash-6111,9,2,"(0.3,0.1,0.2795);(1.0,0.0,0.3475);(0.3,0.99,0.6866);(0.7,0.9,0.6866);(0.5,0.99,0.6866);(0.9,0.9,0.6866);(0.9,0.99,0.6866);(1.0,0.9,0.6866);(1.0,0.99,0.6866)" +Euler-Beta_Prompt005-inkwash-9676,10,2,"(0.1,0.0,0.2177);(0.1,0.4,0.2296);(0.8,0.8,0.6683);(0.5,0.9,0.6683);(0.3,0.99,0.6683);(0.7,0.99,0.6683);(0.8,0.99,0.6683);(0.9,0.99,0.6683);(1.0,0.9,0.6683);(1.0,0.99,0.6683)"