style <> ">"
- , " " <> gkLabel fit <> badge <> " "
- , " " <> fmt4 (gpLengthScale (gkParams fit)) <> " "
- , " " <> fmt4 (sqrt (gpSignalVar (gkParams fit))) <> " "
- , " " <> fmt4 (sqrt (gpNoiseVar (gkParams fit))) <> " "
- , " " <> (if hasPer then fmt4 (gpPeriod (gkParams fit)) else "—") <> " "
- , " " <> fmt4 (gkLML fit) <> " "
- , " #" <> T.pack (show (rank :: Int)) <> " "
- , " "
- ]
-
--- | WAIC/LOO-CV の結果をスタットボックスで表示する HTML フラグメント。
-waicLooSection :: Maybe (WAICResult, LOOResult) -> [Text]
-waicLooSection Nothing = []
-waicLooSection (Just (w, l)) =
- let kBad = looKHatBad l
- kAlert = kBad > 0
- in [ " モデル比較指標 (WAIC / LOO-CV) "
- , " "
- , statBox "WAIC ↓" (fmt4 (waicValue w)) False
- , statBox "LOO ↓" (fmt4 (looValue l)) False
- , statBox "p_WAIC" (fmt4 (waicPwaic w)) False
- , statBox "LOO SE" (fmt4 (looSE l)) False
- , statBox ("k̂>0.7") (T.pack (show kBad) <> "件") kAlert
- , "
"
- , " "
- , " WAIC/LOO は小さいほど良い。p_WAIC = 実効パラメータ数。"
- , if kAlert
- then "k̂>0.7 の観測値が多い場合は LOO 推定の信頼性が低下する。"
- else "k̂>0.7 の観測値はなく LOO は安定。"
- , "
"
- ]
-
-blupTable :: [(Text, Double)] -> Text
-blupTable blups = T.unlines $
- [ " "
- , " グループ BLUP (û_j) "
- ] ++ map row blups ++
- [ " "
- , "
"
- ]
- where
- row (g, v) = " " <> g <> " " <> fmtSigned v <> " "
- , " 📈 3. 回帰結果青い帯 = 平均 ± 2σ (≈95% 信用区間)。黒点 = 訓練データ。
"
- , " "
- ] ++
- zipWith (gpTabBtn (gfKernelFits gf)) [0..] (gfKernelFits gf) ++
- [ "
" ] ++
- concatMap (gpTabContent gf) (zip [0..] (gfKernelFits gf)) ++
- [ " " ]
-resultsSection fit plots = T.unlines $
- [ ""
- , " 📈 3. 回帰結果 " ]
-
-gpTabBtn :: [GPKernelFit] -> Int -> GPKernelFit -> Text
-gpTabBtn fits i fit =
- let bestLML = maximum (map gkLML fits)
- star = if gkLML fit == bestLML then " ⭐" else ""
- active = if i == 0 then " active" else ""
- in " active <> "\" onclick=\"showGPTab(" <> T.pack (show i) <> ")\">"
- <> gkLabel fit <> star <> " "
-
-gpTabContent :: GPFitSummary -> (Int, GPKernelFit) -> [Text]
-gpTabContent gf (i, fit) =
- let active = if i == 0 then " active" else ""
- pCfg = PlotConfig
- { plotTitle = gkLabel fit <> " — GP Regression"
- , plotWidth = 700
- , plotHeight = 320
- }
- spec = gpPlot pCfg (gfXCol gf) (gfYCol gf)
- (zip (gfTrainXs gf) (gfTrainYs gf)) (gkResult fit)
- json = specJson spec
- hasPer = gkKernel fit == Periodic
- in [ " T.pack (show i) <> "\" class=\"tab-content" <> active <> "\">"
- , "
"
- , " "
- , "
"
- , " カーネル: " <> gkLabel fit <> "ℓ = " <> fmt4 (gpLengthScale (gkParams fit)) <> "σ_f = " <> fmt4 (sqrt (gpSignalVar (gkParams fit))) <> "σ_n = " <> fmt4 (sqrt (gpNoiseVar (gkParams fit))) <> "p = " <> fmt4 (gpPeriod (gkParams fit)) <> "LML = " <> fmt4 (gkLML fit) <> "
"
- , "
係数 "
- , " "
- , " パラメータ 推定値 "
- ] ++
- map (\(l,v) -> " " <> l <> " " <> fmtSigned v <> " "
- , "
"
- , " "
- , statBox (fsR2Label fs) (fmt4 (fsR2 fs)) True
- , "
"
- ]
-fitTable (HBMFit hs) =
- let fs = hbmsFit hs
- ch = hbmsChain hs
- total = chainTotalOf ch
- accepted = chainAcceptedOf ch
- acceptR = if total == 0 then 0
- else fromIntegral accepted / fromIntegral total :: Double
- nSamp = chainNSamples ch
- in [ " 事後分布サマリー "
- , " "
- , " 各潜在変数の事後平均・標準偏差・95% 信用区間 (2.5% / 97.5% 分位点)。"
- , "
"
- , " "
- , " パラメータ 事後平均 "
- <> "事後 SD 2.5% 97.5% "
- ] ++
- map posteriorRowHtml (hbmsPosteriorRows hs) ++
- [ " "
- , "
"
- , " "
- , statBox (fsR2Label fs) (fmt4 (fsR2 fs)) True
- , statBox "サンプル数" (T.pack (show nSamp)) False
- , statBox "受容率" (fmt1 (acceptR * 100) <> "%") False
- , "
"
- ]
- where
- posteriorRowHtml (n, m, sd_, lo, hi) =
- " " <> n <> " "
- <> "" <> fmtSigned m <> " "
- <> "" <> fmt4 sd_ <> " "
- <> "" <> fmtSigned lo <> " "
- <> "" <> fmtSigned hi <> " 固定効果係数 "
- , " "
- , " パラメータ 推定値 "
- ] ++
- map (\(l,v) -> " " <> l <> " " <> fmtSigned v <> " "
- , "
"
- , " "
- , statBox (gsR2Label gs) (fmt4 (gsR2 gs)) True
- , statBox "ICC" (fmt4 (gsICC gs)) False
- , "
"
- ]
-
-chainNSamples :: Chain -> Int
-chainNSamples = length . chainSamples
-
-chainTotalOf :: Chain -> Int
-chainTotalOf = chainTotal
-
-chainAcceptedOf :: Chain -> Int
-chainAcceptedOf = chainAccepted
-
-residualSummary :: ModelFit -> Text
-residualSummary fit =
- let resids = case fit of
- RegFit fs -> fsResiduals fs
- MixFit gs -> gsResiduals gs
- HBMFit hs -> fsResiduals (hbmsFit hs)
- NoRegFit -> []
- n = fromIntegral (length resids) :: Double
- rmse = if n == 0 then 0 else sqrt (sum (map (^(2::Int)) resids) / n)
- mx = if null resids then 0 else maximum (map abs resids)
- in if null resids then ""
- else T.unlines
- [ " 残差サマリー "
- , " "
- , statBox "RMSE" (fmt4 rmse) False
- , statBox "最大絶対残差" (fmt4 mx) False
- , "
"
- ]
-
-plotDiv :: (Int, NamedPlot) -> [Text]
-plotDiv (i, np) =
- let divId = npName np <> "-" <> T.pack (show i)
- in [ " " <> npTitle np <> " "
- , " "
- , " "
- ]
-
--- ---------------------------------------------------------------------------
--- Section 4: Interactive prediction
--- ---------------------------------------------------------------------------
-
-predictionSection :: DXD.DataFrame -> [Text] -> Text -> ModelFit -> [Text]
-predictionSection _ _ _ NoRegFit = []
-predictionSection _ _ _ (GPFit gf) = gpPredictionSection gf
-predictionSection df xCols yCol fit =
- let -- データ範囲を ±50% 拡張してスライダーに使う
- xRanges = [ (col, mn, mx, smin, smax)
- | col <- xCols
- , Just v <- [getDoubleVec col df]
- , let mn = V.minimum v
- , let mx = V.maximum v
- , let ext = max 1e-8 (mx - mn) * 0.5
- , let smin = mn - ext
- , let smax = mx + ext
- ]
- groups = case fit of
- MixFit gs -> map fst (gsBLUPs gs)
- _ -> []
- hasSingle = length xCols == 1 && case smoothDataFor fit of { Just _ -> True; Nothing -> False }
- in [ ""
- , " 🎯 4. 対話的予測"
- , " スライダーまたは入力欄で説明変数の値を変えると、"
- , " 回帰曲線上の予測点がリアルタイムで移動します。"
- , " スライダーはデータ範囲の ±50% まで外挿できます。"
- , "
"
- , " "
- , "
"
- , "
"
- ] ++
- concatMap xSlider xRanges ++
- (if null groups then []
- else [ "
"
- , " グループ (" <> grpCol fit <> "): "
- , " "
- , T.concat [ " g <> "\">" <> g <> " \n"
- | g <- groups ]
- , " "
- , "
"
- ]) ++
- [ "
"
- , "
"
- , "
"
- , "
予測値 (" <> yCol <> ")"
- , " "
- , "
"
- , "
—
"
- , "
g⁻¹(η)
"
- , "
"
- , "
"
- , "
線形予測子 (η)
"
- , "
—
"
- , "
Xβ
"
- , "
"
- , if hasSingle
- then "
"
- <> "
95% CI
"
- <> "
—
"
- <> "
—
"
- <> "
"
- else ""
- , "
"
- , "
"
- , if hasSingle
- then "
"
- else ""
- , "
"
- ]
- where
- grpCol (MixFit gs) = gsGroupCol gs
- grpCol _ = ""
-
-gpPredictionSection :: GPFitSummary -> [Text]
-gpPredictionSection gf =
- let xs = gfTrainXs gf
- xMin = minimum xs
- xMax = maximum xs
- ext = max 1e-8 (xMax - xMin) * 0.5
- smin = xMin - ext
- smax = xMax + ext
- step = (smax - smin) / 500
- mid = (smin + smax) / 2
- xCol = gfXCol gf
- yCol = gfYCol gf
- in [ ""
- , " 🎯 4. 対話的予測スライダーまたは入力欄で x 値を変えると、選択したカーネルの GP 事後平均と信用区間をリアルタイムで計算します。曲線はベストカーネルを表示。
"
- , " "
- , "
"
- , "
"
- , "
"
- , " カーネル: "
- , " "
- , T.concat [ " T.pack (show i) <> "\">"
- <> gkLabel fit
- <> " (LML=" <> fmt4 (gkLML fit) <> ")"
- <> " \n"
- | (i, fit) <- zip [0 :: Int ..] (gfKernelFits gf) ]
- , " "
- , "
"
- , "
"
- , " " <> xCol <> ": "
- , "
"
- , "
"
- , "
"
- , "
"
- , "
事後平均 (" <> yCol <> ")"
- , " "
- , "
"
- , "
—
"
- , "
μ(x*)
"
- , "
"
- , "
"
- , "
標準偏差
"
- , "
—
"
- , "
σ(x*)
"
- , "
"
- , "
"
- , "
95% 信用区間
"
- , "
—
"
- , "
—
"
- , "
"
- , "
"
- , "
"
- , "
"
- , "
"
- ]
-
-smoothDataFor :: ModelFit -> Maybe (Text, SmoothData)
-smoothDataFor (RegFit fs) = fsSmoothData fs
-smoothDataFor (MixFit gs) = gsSmoothData gs
-smoothDataFor (HBMFit hs) = fsSmoothData (hbmsFit hs)
-smoothDataFor NoRegFit = Nothing
-
--- (col, data_min, data_max, slider_min, slider_max)
-xSlider :: (Text, Double, Double, Double, Double) -> [Text]
-xSlider (col, _mn, _mx, smin, smax) =
- let step = (smax - smin) / 500
- mid = (smin + smax) / 2
- sid = "x-" <> col
- in [ " "
- , " " <> col <> ": "
- , "
"
- ]
-
--- ---------------------------------------------------------------------------
--- Section 5: Appendix
--- ---------------------------------------------------------------------------
-
-appendixSection :: ModelFit -> Text
-appendixSection fit = T.unlines
- [ ""
- , " 📚 5. 付録: モデルの原理 "
- ]
-
-appendixContent :: ModelFit -> Text
-appendixContent NoRegFit = " 回帰モデルなし。
"
-appendixContent (RegFit fs) = T.unlines $
- [ " "
- , "
" <> fsModelType fs <> " モデル "
- , "
一般化線形モデル (GLM) は線形予測子 η = Xβ をリンク関数 g で連結します:
"
- , "
g(E[y]) = β₀ + β₁x₁ + β₂x₁² + ...
"
- , "
リンク関数 " <> fsLinkName fs <> " を使用しています。
"
- , "
"
- , "
" <> gsModelType gs <> " モデル "
- , "
混合効果モデルはグループ固有のランダム切片 û_j を固定効果に加えます:
"
- , "
g(E[y_ij]) = β₀ + β₁x + ... + û_j, û_j ~ N(0, σ²_u)
"
- , "
ICC = σ²_u / (σ²_u + σ²) = " <> fmt4 (gsICC gs) <> "
"
- , "
"
- , "
" <> fsModelType (hbmsFit hs) <> " "
- , "
ベイズ線形回帰では係数を点推定ではなく 事後分布 として推定します:
"
- , "
"
- , " α ~ Normal(0, σ_α), β ~ Normal(0, σ_β), σ ~ Exponential(1)
"
- , "
推論は NUTS (No-U-Turn Sampler, AD 勾配) で実行。"
- , " 各パラメータの 95% 信用区間 = 事後分布の 2.5%/97.5% 分位点。
"
- , "
予測曲線の 信用区間バンド は、グリッド点 x* に対して"
- , " 全事後サンプル (α^(s), β^(s)) で μ^(s) = α^(s) + β^(s)·x* を計算し、"
- , " その分布の 2.5%/97.5% 分位点を取ったものです。
"
- , "
"
- , "
ガウス過程 (Gaussian Process) とは "
- , "
ガウス過程は関数に対する確率分布です。平均関数 m(x) とカーネル k(x,x') によって定義されます:
"
- , "
f(x) ~ GP( m(x), k(x, x') )
"
- , "
訓練データ (X, y) を条件付けた事後分布:
"
- , "
"
- , " μ(x*) = K(x*, X) · [K(X,X) + σ²_n I]⁻¹ · y
"
- , "
"
- , "
対数周辺尤度 (LML) によるモデル選択 "
- , "
log p(y|X,θ) = −½ yᵀ K⁻¹ y − ½ log|K| − n/2 · log(2π)
"
- , "
LML はデータ適合度とモデル複雑度ペナルティのバランスを取ります。
"
- , "
"
- , "
使用したカーネル関数 "
- ] ++
- concatMap kernelDesc (map gkKernel (gfKernelFits gf)) ++
- [ " " ]
- where
- kernelDesc RBF =
- [ " RBF (二乗指数カーネル)
"
- , " k(x, x') = σ²_f · exp( −(x−x')² / (2ℓ²) )
"
- ]
- kernelDesc Matern52 =
- [ " Matérn 5/2 カーネル
"
- , " k(x, x') = σ²_f · (1 + √5·r/ℓ + 5r²/(3ℓ²)) · exp(−√5·r/ℓ)
"
- ]
- kernelDesc Periodic =
- [ " Periodic カーネル
"
- , " k(x, x') = σ²_f · exp( −2 sin²(π|x−x'|/p) / ℓ² )
"
- ]
- kernelDesc Linear =
- [ " Linear (内積) カーネル
"
- , " k(x, x') = σ²_f · (x·x')
"
- ]
- kernelDesc (Poly d) =
- [ " Polynomial カーネル (次数 " <> T.pack (show d) <> ")
"
- , " k(x, x') = (γ·(x·x') + 1)^" <> T.pack (show d) <> " (γ = 1/(2ℓ²))
"
- ]
-
-lmAppendix :: Text -> Text
-lmAppendix link = T.unlines
- [ " "
- , "
リンク関数とその逆関数 "
- , "
"
- , " リンク g(μ) g⁻¹(η) (予測値変換) "
- , " markActive "identity" link <> ">identity μ η "
- , " markActive "log" link <> ">log log(μ) exp(η) "
- , " markActive "logit" link <> ">logit log(μ/(1-μ)) 1/(1+exp(-η)) "
- , " markActive "sqrt" link <> ">sqrt √μ η² "
- , " "
- , "
"
- , "
"
- , "
WAIC と LOO-CV "
- , "
WAIC (Widely Applicable Information Criterion) は"
- , " 事後予測分布に基づくモデル比較指標です:
"
- , "
"
- , " WAIC = −2 × (lppd − p_WAIC)
"
- , "
LOO-CV (PSIS-LOO) は各観測を1つ除いた予測精度の推定値です。"
- , " Pareto k̂ 診断: k̂ < 0.5 = 良好、0.5–0.7 = 許容、> 0.7 = 要注意。
"
- , "
いずれも 値が小さいほど良い 。WAIC ≈ LOO であれば両者は一致。
"
- , "
LM では flat prior の解析的事後分布からサンプリング。"
- , " GLM では Laplace 近似 β ~ MVN(β̂, Fisher⁻¹) を使用。
"
- , "
(if hi then " highlight" else "") <> "\">"
- , "
" <> lbl <> "
"
- , "
" <> val <> "
"
- , "
"
- , "
" <> lbl <> "
"
- , "
" <> val <> "
"
- , "
" <> arcTitle cfg <> " "
- , " "
- , " "
- , " "
- , " "
- , ""
- , ""
- , compareNavBar cfg
- , ""
- , dataSummarySection df xCols yCol
- , compareModelsSection entries
- , compareOverlaySection xCols yCol
- , compareCoefSection entries
- , compareWaicSection entries
- , " "
- , ""
- , ""
- , ""
- ]
-
-compareNavBar :: AnalysisReportConfig -> Text
-compareNavBar cfg = T.unlines
- [ ""
- , " 📊 " <> arcTitle cfg <> " "
- , " データ "
- , " モデル一覧 "
- , " 予測比較 "
- , " 係数比較 "
- , " WAIC/LOO "
- , " "
- ]
-
--- | モデル一覧表
-compareModelsSection :: [CompareEntry] -> Text
-compareModelsSection entries = T.unlines $
- [ ""
- , " ⚖ 2. モデル一覧本レポートで比較する " <> T.pack (show (length entries))
- <> " モデルの概要。色はオーバーレイ図と凡例で共通。
"
- , " "
- , " "
- , " モデル 種別 回帰式 "
- , " R² "
- , " "
- ] ++
- map modelRowHtml entries ++
- [ " "
- , "
"
- , " "
- ]
- where
- modelRowHtml e = T.unlines
- [ " "
- , " ceColor e <> "\"> " <> ceLabel e <> " " <> modelTypeOf (ceFit e) <> " "
- , " " <> modelFormulaOf (ceFit e) <> " "
- , " " <> fmt4 (modelR2Of (ceFit e)) <> " "
- , " "
- ]
-
-modelTypeOf :: ModelFit -> Text
-modelTypeOf (RegFit fs) = fsModelType fs
-modelTypeOf (MixFit gs) = gsModelType gs
-modelTypeOf (HBMFit hs) = fsModelType (hbmsFit hs)
-modelTypeOf (GPFit _) = "Gaussian Process"
-modelTypeOf NoRegFit = "—"
-
-modelFormulaOf :: ModelFit -> Text
-modelFormulaOf (RegFit fs) = fsFormula fs
-modelFormulaOf (MixFit gs) = gsFormula gs
-modelFormulaOf (HBMFit hs) = fsFormula (hbmsFit hs)
-modelFormulaOf (GPFit _) = "y ~ GP(m, k)"
-modelFormulaOf NoRegFit = "—"
-
-modelR2Of :: ModelFit -> Double
-modelR2Of (RegFit fs) = fsR2 fs
-modelR2Of (MixFit gs) = gsR2 gs
-modelR2Of (HBMFit hs) = fsR2 (hbmsFit hs)
-modelR2Of _ = 0
-
--- | 予測曲線オーバーレイ (描画は JS で実装)
-compareOverlaySection :: [Text] -> Text -> Text
-compareOverlaySection xCols yCol = T.unlines
- [ ""
- , " 📈 3. 予測曲線比較同一データに対する各モデルの予測曲線を重ね描き。"
- , " HBM など信用区間を持つモデルはバンドも表示。
"
- , " "
- , if length xCols /= 1
- then " x 列が単一でないため曲線比較は省略しました。
"
- else " x = "
- <> head xCols <> ", y = " <> yCol <> "
"
- , " "
- ]
-
--- | 係数比較表 (HBM は CI 付き)
-compareCoefSection :: [CompareEntry] -> Text
-compareCoefSection entries = T.unlines $
- [ ""
- , " 🔬 4. 係数比較各モデルが推定したパラメータの一覧。"
- , " HBM は事後平均と 95% 信用区間 [2.5%, 97.5%] を表示。
"
- , " "
- , " "
- , " モデル パラメータ "
- , " 推定値 95% CI "
- , " "
- ] ++
- concatMap coefRowsForEntry entries ++
- [ " "
- , "
"
- , " "
- ]
- where
- coefRowsForEntry e =
- let coefs = extractCoefRows (ceFit e)
- n = length coefs
- in zipWith (mkCoefRow e n) [0 :: Int ..] coefs
-
- mkCoefRow e n i (cname, val, mci) =
- let firstCol = if i == 0
- then " T.pack (show n) <> "\">"
- <> " ceColor e <> "\"> "
- <> " T.pack (show n) <> "\">"
- <> ceLabel e <> " "
- else ""
- ciCell = case mci of
- Just (lo, hi) -> "[" <> fmtSigned lo
- <> ", " <> fmtSigned hi <> "] "
- Nothing -> "— "
- in T.unlines
- [ " "
- , " " <> firstCol
- , " " <> cname <> " "
- , " " <> fmtSigned val <> " "
- , " " <> ciCell <> " "
- , " "
- ]
-
-extractCoefRows :: ModelFit -> [(Text, Double, Maybe (Double, Double))]
-extractCoefRows (RegFit fs) = [(n, v, Nothing) | (n, v) <- fsCoeffs fs]
-extractCoefRows (MixFit gs) = [(n, v, Nothing) | (n, v) <- gsFixed gs]
-extractCoefRows (HBMFit hs) =
- [ (n, m, Just (lo, hi))
- | (n, m, _, lo, hi) <- hbmsPosteriorRows hs ]
-extractCoefRows _ = []
-
--- | WAIC / LOO 比較 (どれか 1 つでも持っていれば表示)
-compareWaicSection :: [CompareEntry] -> Text
-compareWaicSection entries =
- let rows = [ (e, w, l) | e <- entries
- , let mws = waicLooOf (ceFit e)
- , Just (w, l) <- [mws] ]
- in if null rows
- then ""
- else
- let bestWaic = minimum [waicValue w | (_, w, _) <- rows]
- bestLoo = minimum [looValue l | (_, _, l) <- rows]
- in T.unlines $
- [ ""
- , " 📊 5. WAIC / LOO 比較情報量規準が小さいほど良い。"
- <> "ΔWAIC ≈ 0 のモデル群は実質的に同等。最良モデルを ★ で示す。
"
- , " "
- , " "
- , " モデル WAIC "
- , " ΔWAIC LOO "
- , " ΔLOO "
- , " "
- ] ++
- map (waicRowHtml bestWaic bestLoo) rows ++
- [ " "
- , "
"
- , " "
- ]
- where
- waicRowHtml bw bl (e, w, l) =
- let dw = waicValue w - bw
- dl = looValue l - bl
- star x = if x == 0 then " ★" else ""
- in T.unlines
- [ " "
- , " ceColor e <> "\"> " <> ceLabel e <> " " <> fmt4 (waicValue w) <> " "
- , " " <> fmt4 dw <> star dw <> " "
- , " " <> fmt4 (looValue l) <> " "
- , " " <> fmt4 dl <> star dl <> " "
- , " "
- ]
-
-waicLooOf :: ModelFit -> Maybe (WAICResult, LOOResult)
-waicLooOf (RegFit fs) = fsModelSelect fs
-waicLooOf (HBMFit hs) = fsModelSelect (hbmsFit hs)
-waicLooOf (MixFit gs) = gsModelSelect gs
-waicLooOf _ = Nothing
-
--- | オーバーレイ用の Vega-Lite spec を組み立てる JS (data URL 経由)
-compareOverlayJS :: DXD.DataFrame -> [Text] -> Text -> [CompareEntry] -> Text
-compareOverlayJS df xCols yCol entries
- | length xCols /= 1 = ""
- | otherwise =
- let xCol = head xCols
- (xs, ys) = case (getDoubleVec xCol df, getDoubleVec yCol df) of
- (Just xv, Just yv) -> (V.toList xv, V.toList yv)
- _ -> ([], [])
- gs = case getTextVec "group" df of
- Just gv -> map Just (V.toList gv)
- Nothing -> map (const Nothing) xs
- dataPoints = T.intercalate "," $
- zipWith3 (\x y mg ->
- let g = maybe "" (\t -> ",\"g\":\"" <> t <> "\"") mg
- in "{\"x\":" <> fmtJS x <> ",\"y\":" <> fmtJS y <> g <> "}")
- xs ys gs
- hasGroups = any ( /= Nothing) gs
- -- 各 modelLayer は ",{band},{line}" 形式で返す (リーディングカンマあり)
- modelLayers = T.concat (map (modelLayer xCol yCol) entries)
- legendItems = T.intercalate "," (map legendItem entries)
- in T.unlines
- [ "(function() {"
- , " const spec = {"
- , " '$schema':'https://vega.github.io/schema/vega-lite/v5.json',"
- , " width: 720, height: 400, background:'transparent',"
- , " layer: ["
- , " {"
- , " data:{values:[" <> dataPoints <> "]},"
- , " mark:{type:'circle',size:60,opacity:0.7},"
- , " encoding:{"
- , " x:{field:'x',type:'quantitative',axis:{title:'" <> xCol <> "'}},"
- , " y:{field:'y',type:'quantitative',axis:{title:'" <> yCol <> "'}}"
- , if hasGroups
- then " ,color:{field:'g',type:'nominal',scale:{scheme:'tableau10'},legend:{title:'group'}}"
- else " ,color:{value:'#888'}"
- , " }"
- , " }"
- , modelLayers -- 各要素はすでに先頭カンマ付き
- , " ]"
- , " };"
- , " vegaEmbed('#cmp-overlay', spec, {actions:false}).catch(console.error);"
- , " // モデル凡例 (色対応をテキストで表示)"
- , " const lg = [" <> legendItems <> "];"
- , " console.log('Model legend:', lg);"
- , "})();"
- ]
- where
- legendItem e = "{\"label\":\"" <> ceLabel e <> "\",\"color\":\"" <> ceColor e <> "\"}"
-
--- | 1 モデルの予測曲線レイヤー (smoothData があれば線 + バンド)
-modelLayer :: Text -> Text -> CompareEntry -> Text
-modelLayer xCol yCol e =
- case smoothDataFor (ceFit e) of
- Nothing -> ""
- Just (_, sd) ->
- let pts = T.intercalate "," $
- zipWith4 (\x y lo hi ->
- "{\"x\":" <> fmtJS x <> ",\"y\":" <> fmtJS y
- <> ",\"lo\":" <> fmtJS lo <> ",\"hi\":" <> fmtJS hi <> "}")
- (sdXs sd) (sdYs sd) (sdLower sd) (sdUpper sd)
- color = ceColor e
- band = if sdHasBand sd
- then T.unlines
- [ " ,{"
- , " data:{values:[" <> pts <> "]},"
- , " mark:{type:'area',color:'" <> color <> "',opacity:0.18},"
- , " encoding:{"
- , " x:{field:'x',type:'quantitative'},"
- , " y:{field:'lo',type:'quantitative'},"
- , " y2:{field:'hi'}"
- , " }"
- , " }"
- ]
- else ""
- line = T.unlines
- [ " ,{"
- , " data:{values:[" <> pts <> "]},"
- , " mark:{type:'line',color:'" <> color
- <> "',strokeWidth:2.5,tooltip:{content:'data'}},"
- , " encoding:{"
- , " x:{field:'x',type:'quantitative'},"
- , " y:{field:'y',type:'quantitative'}"
- , " }"
- , " }"
- ]
- in band <> line
- where
- _ = (xCol, yCol) -- 軸タイトルは点レイヤーで設定済み
-
--- 4 引数 zipWith
-zipWith4 :: (a -> b -> c -> d -> e) -> [a] -> [b] -> [c] -> [d] -> [e]
-zipWith4 f (a:as) (b:bs) (c:cs) (d:ds) = f a b c d : zipWith4 f as bs cs ds
-zipWith4 _ _ _ _ _ = []
diff --git a/src/Hanalyze/Viz/Assets.hs b/src/Hanalyze/Viz/Assets.hs
deleted file mode 100644
--- a/src/Hanalyze/Viz/Assets.hs
+++ /dev/null
@@ -1,233 +0,0 @@
--- |
--- Module : Hanalyze.Viz.Assets
--- Description : オフライン HTML レポート用に Vega/Vega-Lite/Vega-Embed の JS 本体を同梱する自動生成モジュール
--- Copyright : (c) 2026 Aelysce Project (Toshiaki Honda)
--- License : BSD-3-Clause
---
-{-# LANGUAGE OverloadedStrings #-}
--- Auto-generated from assets/ — do not edit by hand.
--- Bundles Vega v5, Vega-Lite v5, Vega-Embed v6 for offline HTML reports.
-module Hanalyze.Viz.Assets
- ( vegaJS
- , vegaLiteJS
- , vegaEmbedJS
- ) where
-
-import Data.Text (Text)
-import qualified Data.Text as T
-
-vegaJS :: Text
-vegaJS = T.concat
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