\n \n \n \n\n",[],[159,218],"67cdaf0a-56ae-11f1-8e14-ee650bb23af1","favicon.svg","019e551a-5303-7301-b8d9-bc0e8f5aabf9",["D",1779544249028],"\n",[],[159,226],"12eccdc6-56af-11f1-9595-ee650bb23af1","TrainTab.tsx","12eccea2-56af-11f1-9595-ee650bb23af1","019e551a-5303-7301-b8d9-b9274695fab5",17,["D",1779544536190],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\nexport function TrainTab({\n datasetId, onSelectDataset, onComplete,\n}: {\n datasetId: number | null;\n onSelectDataset: (id: number) => void;\n onComplete: (trainingId: number) => void;\n}) {\n const [datasets, setDatasets] = useState([]);\n const [currentId, setCurrentId] = useState(datasetId);\n const [columns, setColumns] = useState([]);\n const [targetCol, setTargetCol] = useState(\"\");\n const [alpha, setAlpha] = useState(0.1);\n const [loading, setLoading] = useState(false);\n const [result, setResult] = useState(null);\n const [history, setHistory] = useState([]);\n\n useEffect(() => {\n fetch(\"/api/data/datasets\").then((r) => r.json()).then(setDatasets);\n fetch(\"/api/train/results\").then((r) => r.json()).then(setHistory);\n }, []);\n\n useEffect(() => { if (datasetId) setCurrentId(datasetId); }, [datasetId]);\n\n useEffect(() => {\n if (!currentId) return;\n fetch(`/api/data/datasets/${currentId}`)\n .then((r) => r.json())\n .then((d) => { setColumns(d.columns || []); setTargetCol(\"\"); });\n }, [currentId]);\n\n const handleTrain = async () => {\n if (!currentId || !targetCol) return;\n setLoading(true);\n setResult(null);\n try {\n const res = await fetch(\"/api/train/lasso\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ datasetId: currentId, targetColumn: targetCol, alpha }),\n });\n const data = await res.json();\n if (data.error) throw new Error(data.error);\n setResult(data);\n fetch(\"/api/train/results\").then((r) => r.json()).then(setHistory);\n } catch (err: any) {\n setResult({ error: err.message });\n }\n setLoading(false);\n };\n\n return (\n

🎯 LASSO 回归训练 (10折交叉验证)

\n 数据集\n { const v = +e.target.value; setCurrentId(v); onSelectDataset(v); }}\n class=\"border rounded-lg px-3 py-2 w-full\"\n >\n \n {datasets.map((ds: any) => (\n \n ))}\n \n

\n 目标变量 (Y)\n setTargetCol(e.target.value)}\n class=\"border rounded-lg px-3 py-2 w-full\"\n >\n \n {columns.map((col) => (\n \n ))}\n \n

\n 正则化参数 (α)\n setAlpha(+e.target.value)}\n step=\"0.01\" min=\"0.001\" max=\"10\"\n class=\"border rounded-lg px-3 py-2 w-full\"\n />\n

\n \n {loading ? \"训练中...\" : \"🚀 开始训练\"}\n \n

\n\n {result && !result.error && (\n

训练结果

{result.selectedCount}

筛选特征数 / {result.totalFeatures}

{result.rSquared?.toFixed(4)}

R²

{result.meanCvScore?.toFixed(4)}

平均 CV R²

{result.alpha}

α 参数

\n\n

筛选的特征及系数:

\n \n \n \n \n \n \n \n \n \n \n \n \n {result.selectedFeatures?.map((f: string) => (\n \n \n \n \n ))}\n \n

特征	系数
截距 (Intercept)	{result.intercept?.toFixed(6)}
{f}	\n 0 ? \"text-green-700\" : \"text-red-700\"}>\n {result.coefficients[f]?.toFixed(6)}\n \n

\n\n

10折交叉验证分数:

\n {result.cvScores?.map((s: number, i: number) => (\n 0 ? 'bg-green-100 text-green-700' : 'bg-red-100 text-red-700'}`}>\n Fold{i + 1}: {s.toFixed(4)}\n \n ))}\n

\n\n onComplete(result.trainingId as number)}\n class=\"px-6 py-2.5 bg-blue-600 text-white rounded-lg hover:bg-blue-700 transition font-medium\"\n >\n 📈 进入回归分析 →\n \n

\n )}\n\n {result?.error && (\n

❌ {result.error}

\n )}\n\n {history.length > 0 && (\n

📋 训练历史

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n {history.map((h: any) => (\n \n \n \n \n \n \n \n \n \n \n ))}\n \n

ID	目标变量	方法	α	筛选特征	CV R²	时间	操作
{h.id}	{h.target_column}	{h.method}	{h.alpha}	{h.selected_features?.length \|\| 0}	{h.mean_cv_score?.toFixed(4)}	{h.created_at}	\n \n

\n )}\n

\n );\n}\n",[],[159,241,233],"components","23acf0b4-56af-11f1-9595-ee650bb23af1","RegressionTab.tsx","23acf172-56af-11f1-9595-ee650bb23af1",18,["D",1779544564292],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\nexport function RegressionTab({\n datasetId, trainingId, onSelectModel,\n}: {\n datasetId: number | null;\n trainingId: number | null;\n onSelectModel: (modelId: number) => void;\n}) {\n const [training, setTraining] = useState(null);\n const [results, setResults] = useState([]);\n const [recommended, setRecommended] = useState(null);\n const [loading, setLoading] = useState(false);\n const [models, setModels] = useState([]);\n const [message, setMessage] = useState(\"\");\n\n useEffect(() => {\n fetch(\"/api/regression/models\").then((r) => r.json()).then(setModels);\n }, []);\n\n useEffect(() => {\n if (!trainingId) return;\n fetch(`/api/train/results/${trainingId}`)\n .then((r) => r.json())\n .then(setTraining);\n }, [trainingId]);\n\n const handleAnalyze = async () => {\n if (!training || !datasetId) return;\n setLoading(true);\n setMessage(\"\");\n try {\n const res = await fetch(\"/api/regression/analyze\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n datasetId,\n targetColumn: training.target_column,\n features: training.selected_features,\n trainingId,\n }),\n });\n const data = await res.json();\n if (data.error) throw new Error(data.error);\n setResults(data.results || []);\n setRecommended(data.recommended);\n setMessage(\"✅ 回归分析完成!\");\n fetch(\"/api/regression/models\").then((r) => r.json()).then(setModels);\n } catch (err: any) {\n setMessage(`❌ ${err.message}`);\n }\n setLoading(false);\n };\n\n return (\n

📈 多元回归分析

\n {training ? (\n

训练ID: {training.id} | 目标变量: {training.target_column}

筛选特征: {training.selected_features?.join(\", \")}

\n \n {loading ? \"分析中...\" : \"🔬 执行多种回归分析\"}\n \n {message &&

{message}

}\n

\n ) : (\n

请先在「数据训练」步骤完成 LASSO 训练，筛选变量后进入此步骤。

\n )}\n

\n\n {recommended && (\n

\n 🏆\n

推荐模型: {recommended.method}

\n {recommended.equation}\n

{recommended.rSquared?.toFixed(4)}

R²

{recommended.adjRSquared?.toFixed(4)}

调整 R²

{recommended.mse?.toFixed(4)}

MSE

{recommended.mae?.toFixed(4)}

MAE

\n )}\n\n {results.length > 0 && (\n

所有回归方法对比

\n \n \n \n \n \n \n \n \n \n \n \n \n {results.map((r: any, i: number) => (\n \n \n \n \n \n \n \n \n ))}\n \n

方法	R²	调整 R²	MSE	MAE	回归方程
\n {r.method}\n {r === recommended && 推荐}\n	{r.rSquared?.toFixed(4) ?? '-'}	{r.adjRSquared?.toFixed(4) ?? '-'}	{r.mse?.toFixed(4) ?? '-'}	{r.mae?.toFixed(4) ?? '-'}	{r.equation \|\| r.error \|\| '-'}

\n )}\n\n {models.length > 0 && (\n

📦 已保存的模型

\n \n \n \n \n \n \n \n \n \n \n \n \n {models.map((m: any) => (\n \n \n \n \n \n \n \n \n ))}\n \n

ID	方法	目标	R²	推荐	操作
{m.id}	{m.method}	{m.target}	{m.adj_r_squared?.toFixed(4)}	{m.is_recommended ? \"🏆\" : \"\"}	\n \n

\n )}\n

\n );\n}\n",[],[159,241,243],"ff74a7e6-56ae-11f1-a0c4-ee650bb23af1","PreprocessTab.tsx","ff74a8cc-56ae-11f1-a0c4-ee650bb23af1",16,["D",1779544503515],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ninterface ColAnalysis {\n column: string; total: number; missing: number; unique: number; isNumeric: boolean;\n min?: number; max?: number; mean?: number; median?: number;\n}\n\nexport function PreprocessTab({\n datasetId, onSelectDataset, onComplete,\n}: {\n datasetId: number | null;\n onSelectDataset: (id: number) => void;\n onComplete: (id: number) => void;\n}) {\n const [datasets, setDatasets] = useState([]);\n const [currentId, setCurrentId] = useState(datasetId);\n const [analysis, setAnalysis] = useState([]);\n const [loading, setLoading] = useState(false);\n const [message, setMessage] = useState(\"\");\n const [deduplicate, setDeduplicate] = useState(false);\n const [fillMethods, setFillMethods] = useState>({});\n const [dropCols, setDropCols] = useState>(new Set());\n\n useEffect(() => {\n fetch(\"/api/data/datasets\").then((r) => r.json()).then(setDatasets);\n }, []);\n\n useEffect(() => { if (datasetId) { setCurrentId(datasetId); } }, [datasetId]);\n\n useEffect(() => {\n if (!currentId) return;\n setLoading(true);\n fetch(`/api/preprocess/preview/${currentId}`)\n .then((r) => r.json())\n .then((data) => { setAnalysis(data.analysis || []); setLoading(false); })\n .catch(() => setLoading(false));\n }, [currentId]);\n\n const handleApply = async () => {\n if (!currentId) return;\n setLoading(true);\n setMessage(\"\");\n try {\n const fillMissing: Record = {};\n for (const [col, method] of Object.entries(fillMethods)) {\n if (method && method !== \"none\") fillMissing[col] = method;\n }\n const res = await fetch(\"/api/preprocess/apply\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n datasetId: currentId,\n operations: {\n deduplicate,\n fillMissing: Object.keys(fillMissing).length ? fillMissing : undefined,\n dropColumns: dropCols.size ? [...dropCols] : undefined,\n },\n }),\n });\n const data = await res.json();\n if (data.error) throw new Error(data.error);\n setMessage(`✅ 预处理完成! 新数据集 ID: ${data.id}, ${data.rows} 行, ${data.columns.length} 列`);\n onComplete(data.id as number);\n } catch (err: any) {\n setMessage(`❌ ${err.message}`);\n }\n setLoading(false);\n };\n\n const toggleDrop = (col: string) => {\n const next = new Set(dropCols);\n next.has(col) ? next.delete(col) : next.add(col);\n setDropCols(next);\n };\n\n return (\n

🔧 数据预处理

\n 选择数据集\n { const v = +e.target.value; setCurrentId(v); onSelectDataset(v); }}\n class=\"border rounded-lg px-3 py-2 min-w-[200px]\"\n >\n \n {datasets.map((ds: any) => (\n \n ))}\n \n

\n \n setDeduplicate(!deduplicate)} class=\"rounded\" />\n 去重\n \n

\n\n {analysis.length > 0 && (\n

列分析 & 配置

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n {analysis.map((col) => (\n \n \n \n \n \n \n \n \n \n \n \n ))}\n \n

列名	类型	总数	缺失	唯一值	范围	均值	缺失处理	删除列
{col.column}	\n \n {col.isNumeric ? \"数值\" : \"文本\"}\n \n	{col.total}	\n {col.missing > 0 ? {col.missing} : 0}\n	{col.unique}	\n {col.isNumeric ? `${col.min?.toFixed(2)} ~ ${col.max?.toFixed(2)}` : \"-\"}\n	\n {col.isNumeric ? col.mean?.toFixed(2) : \"-\"}\n	\n {col.missing > 0 && col.isNumeric ? (\n setFillMethods({ ...fillMethods, [col.column]: e.target.value })}\n class=\"border rounded px-2 py-1 text-xs\"\n >\n \n \n \n \n \n \n ) : col.missing > 0 ? (\n setFillMethods({ ...fillMethods, [col.column]: e.target.value })}\n class=\"border rounded px-2 py-1 text-xs\"\n >\n \n \n \n ) : -}\n	\n toggleDrop(col.column)}\n class=\"rounded cursor-pointer\"\n />\n

\n\n

\n \n {loading ? \"处理中...\" : \"✅ 执行预处理\"}\n \n

\n {message &&

{message}

}\n

\n )}\n

\n );\n}\n",[],[159,241,251],"3b88daf4-56af-11f1-9595-ee650bb23af1","PredictTab.tsx","3b88dbe4-56af-11f1-9595-ee650bb23af1",19,["D",1779544604322],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\nexport function PredictTab({ modelId }: { modelId: number | null }) {\n const [pages, setPages] = useState([]);\n const [activePage, setActivePage] = useState(null);\n const [inputs, setInputs] = useState>({});\n const [prediction, setPrediction] = useState(null);\n const [history, setHistory] = useState([]);\n const [accuracy, setAccuracy] = useState(null);\n const [newPageName, setNewPageName] = useState(\"\");\n const [newPageDesc, setNewPageDesc] = useState(\"\");\n const [showCreate, setShowCreate] = useState(false);\n const [createModelId, setCreateModelId] = useState(modelId);\n const [models, setModels] = useState([]);\n const [actualInputs, setActualInputs] = useState>({});\n\n useEffect(() => {\n loadPages();\n fetch(\"/api/regression/models\").then((r) => r.json()).then(setModels);\n }, []);\n\n useEffect(() => {\n if (modelId) { setCreateModelId(modelId); setShowCreate(true); }\n }, [modelId]);\n\n const loadPages = () => fetch(\"/api/predict/pages\").then((r) => r.json()).then(setPages);\n\n const handleCreatePage = async () => {\n if (!createModelId || !newPageName) return;\n const res = await fetch(\"/api/predict/pages\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ modelId: createModelId, name: newPageName, description: newPageDesc }),\n });\n const data = await res.json();\n if (!data.error) {\n setShowCreate(false);\n setNewPageName(\"\");\n setNewPageDesc(\"\");\n loadPages();\n }\n };\n\n const openPage = async (pageId: number) => {\n const res = await fetch(`/api/predict/pages/${pageId}`);\n const data = await res.json();\n setActivePage(data);\n setInputs({});\n setPrediction(null);\n loadHistory(pageId);\n loadAccuracy(pageId);\n };\n\n const loadHistory = async (pageId: number) => {\n const res = await fetch(`/api/predict/history/${pageId}`);\n setHistory(await res.json());\n };\n\n const loadAccuracy = async (pageId: number) => {\n const res = await fetch(`/api/predict/accuracy/${pageId}`);\n setAccuracy(await res.json());\n };\n\n const handlePredict = async () => {\n if (!activePage) return;\n const numInputs: Record = {};\n for (const f of activePage.features) {\n if (!inputs[f] || isNaN(+inputs[f])) return alert(`请输入有效的 ${f} 值`);\n numInputs[f] = +inputs[f];\n }\n const res = await fetch(\"/api/predict/run\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ pageId: activePage.id, inputs: numInputs }),\n });\n const data = await res.json();\n setPrediction(data);\n loadHistory(activePage.id);\n };\n\n const handleActualSubmit = async (predictionId: number) => {\n const val = actualInputs[predictionId];\n if (!val || isNaN(+val)) return;\n await fetch(`/api/predict/actual/${predictionId}`, {\n method: \"PUT\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ actualValue: +val }),\n });\n setActualInputs((prev) => { const next = { ...prev }; delete next[predictionId]; return next; });\n if (activePage) {\n loadHistory(activePage.id);\n loadAccuracy(activePage.id);\n }\n };\n\n return (\n

\n {/* Create page panel */}\n

🔮 回归预测

\n \n

\n\n {showCreate && (\n

\n 选择模型\n \n

\n 页面名称\n setNewPageName(e.target.value)} placeholder=\"例：房价预测\" class=\"border rounded px-3 py-2 w-full text-sm\" />\n

\n \n

\n )}\n\n {/* Page list */}\n {pages.length > 0 ? (\n

\n {pages.map((p: any) => (\n openPage(p.id)}\n class={`text-left border rounded-lg p-4 hover:border-blue-400 transition ${activePage?.id === p.id ? 'border-blue-500 bg-blue-50' : ''}`}\n >\n

{p.name}

{p.method} | {p.target}

R² = {p.r_squared?.toFixed(4)}

\n \n ))}\n

\n ) : (\n

暂无预测页面，请选择模型创建

\n )}\n

\n\n {/* Active prediction page */}\n {activePage && (\n

{activePage.name}

方法: {activePage.method} | 目标: {activePage.target}

{activePage.equation}

\n\n

\n {activePage.features?.map((f: string) => (\n

\n {f}\n setInputs({ ...inputs, [f]: e.target.value })}\n class=\"border rounded-lg px-3 py-2 w-full\"\n placeholder={`输入 ${f}`}\n />\n

\n ))}\n

\n \n\n {prediction && !prediction.error && (\n

{prediction.target} 预测值

{prediction.predicted?.toFixed(4)}

\n )}\n

\n )}\n\n {/* Accuracy stats */}\n {activePage && accuracy && accuracy.count > 0 && (\n

📊 预测准确性统计 ({accuracy.count} 条已验证)

{accuracy.mae?.toFixed(4)}

MAE

{accuracy.rmse?.toFixed(4)}

RMSE

{accuracy.mse?.toFixed(4)}

MSE

{accuracy.mape?.toFixed(2)}%

MAPE

{accuracy.meanError?.toFixed(4)}

平均误差

\n )}\n\n {/* Prediction history */}\n {activePage && history.length > 0 && (\n

📋 预测历史

\n \n \n \n \n \n \n \n \n \n \n \n \n {history.map((h: any) => (\n \n \n \n \n \n \n \n \n ))}\n \n

时间	输入	预测值	实际值	误差	操作
{h.created_at}	\n {Object.entries(h.input_json \|\| {}).map(([k, v]) => `${k}=${v}`).join(\", \")}\n	{h.predicted_value?.toFixed(4)}	\n {h.actual_value != null ? (\n {h.actual_value.toFixed(4)}\n ) : (\n \n setActualInputs({ ...actualInputs, [h.id]: e.target.value })}\n class=\"border rounded px-2 py-1 w-24 text-xs\"\n placeholder=\"输入实际值\"\n />\n \n \n )}\n	\n {h.error != null ? (\n {h.error.toFixed(4)}\n ) : \"-\"}\n	\n {h.actual_value != null ? \"✅\" : \"待验证\"}\n

\n )}\n

\n );\n}\n",[],[159,241,259],"ee274b56-56ae-11f1-8274-ee650bb23af1","DataTab.tsx","ee274c0a-56ae-11f1-8274-ee650bb23af1",15,["D",1779544474498],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ninterface Dataset {\n id: number;\n name: string;\n source: string;\n row_count: number;\n col_count: number;\n columns_json: string;\n created_at: string;\n}\n\nexport function DataTab({ onSelectDataset }: { onSelectDataset: (id: number) => void }) {\n const [datasets, setDatasets] = useState([]);\n const [uploading, setUploading] = useState(false);\n const [message, setMessage] = useState(\"\");\n const [showJson, setShowJson] = useState(false);\n const [jsonInput, setJsonInput] = useState('');\n const [jsonName, setJsonName] = useState('');\n const [preview, setPreview] = useState(null);\n\n const loadDatasets = async () => {\n const res = await fetch(\"/api/data/datasets\");\n setDatasets(await res.json());\n };\n\n useEffect(() => { loadDatasets(); }, []);\n\n const handleUpload = async (e: React.ChangeEvent) => {\n const file = (e.target as any).files?.[0];\n if (!file) return;\n setUploading(true);\n setMessage(\"\");\n try {\n const formData = new FormData();\n formData.append(\"file\", file);\n const res = await fetch(\"/api/data/upload\", { method: \"POST\", body: formData });\n const data = await res.json();\n if (data.error) throw new Error(data.error);\n setMessage(`✅ 上传成功: ${data.rows} 行, ${data.columns.length} 列`);\n loadDatasets();\n } catch (err: any) {\n setMessage(`❌ 上传失败: ${err.message}`);\n }\n setUploading(false);\n };\n\n const handleJsonImport = async () => {\n try {\n const parsed = JSON.parse(jsonInput);\n const data = Array.isArray(parsed) ? parsed : [parsed];\n const res = await fetch(\"/api/data/import-json\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ name: jsonName || \"json_import\", data }),\n });\n const result = await res.json();\n if (result.error) throw new Error(result.error);\n setMessage(`✅ 导入成功: ${result.rows} 行`);\n setShowJson(false);\n setJsonInput(\"\");\n loadDatasets();\n } catch (err: any) {\n setMessage(`❌ 导入失败: ${err.message}`);\n }\n };\n\n const handleDelete = async (id: number) => {\n if (!confirm(\"确认删除此数据集？\")) return;\n await fetch(`/api/data/datasets/${id}`, { method: \"DELETE\" });\n loadDatasets();\n };\n\n const handlePreview = async (id: number) => {\n const res = await fetch(`/api/data/datasets/${id}`);\n setPreview(await res.json());\n };\n\n return (\n

📥 数据上传

\n \n 📁\n {uploading ? \"上传中...\" : \"上传 Excel 文件\"}\n \n \n setShowJson(!showJson)}\n class=\"px-5 py-3 bg-green-600 text-white rounded-lg hover:bg-green-700 transition\"\n >\n 📋 JSON 数据导入\n \n

\n {message &&

{message}

}\n

\n\n {showJson && (\n

JSON 数据导入

\n setJsonName(e.target.value)}\n class=\"w-full border rounded-lg px-3 py-2 mb-3\"\n />\n setJsonInput(e.target.value)}\n class=\"w-full border rounded-lg px-3 py-2 h-40 font-mono text-sm\"\n />\n \n

\n )}\n\n

📂 数据集列表

\n {datasets.length === 0 ? (\n

暂无数据集，请上传数据

\n ) : (\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n {datasets.map((ds) => (\n \n \n \n \n \n \n \n \n \n ))}\n \n

ID	名称	来源	行数	列数	创建时间	操作
{ds.id}	{ds.name}	\n \n {ds.source === 'upload' ? '上传' : ds.source === 'preprocessed' ? '预处理' : ds.source}\n \n	{ds.row_count}	{ds.col_count}	{ds.created_at}	\n \n \n \n \n \n

\n )}\n

\n\n {preview && (\n

数据预览 - {preview.name || '数据集'}

\n \n

\n \n \n \n \n {(preview.columns || []).map((col: string) => (\n \n ))}\n \n \n \n {(preview.rows || []).slice(0, 50).map((row: any, i: number) => (\n \n \n {(preview.columns || []).map((col: string) => (\n \n ))}\n \n ))}\n \n

#	{col}
{row.index}	{String(row.data[col] ?? '')}

显示前 50 行 / 共 {preview.rows?.length || 0} 行

\n )}\n

\n );\n}\n",[],[159,241,267],"dbdc3e0c-56ae-11f1-9595-ee650bb23af1","App.tsx","019e551a-5302-73ad-8589-62cc6807635a",14,["D",1779544443801],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState } from \"https://esm.sh/react@18.2.0\";\nimport { DataTab } from \"./DataTab.tsx\";\nimport { PreprocessTab } from \"./PreprocessTab.tsx\";\nimport { TrainTab } from \"./TrainTab.tsx\";\nimport { RegressionTab } from \"./RegressionTab.tsx\";\nimport { PredictTab } from \"./PredictTab.tsx\";\n\nconst TABS = [\n { id: \"data\", label: \"📊 数据采集\", icon: \"1\" },\n { id: \"preprocess\", label: \"🔧 数据预处理\", icon: \"2\" },\n { id: \"train\", label: \"🎯 数据训练\", icon: \"3\" },\n { id: \"regression\", label: \"📈 回归分析\", icon: \"4\" },\n { id: \"predict\", label: \"🔮 回归预测\", icon: \"5\" },\n] as const;\n\nexport function App() {\n const [activeTab, setActiveTab] = useState(\"data\");\n const [selectedDatasetId, setSelectedDatasetId] = useState(null);\n const [selectedTrainingId, setSelectedTrainingId] = useState(null);\n const [selectedModelId, setSelectedModelId] = useState(null);\n\n return (\n

\n {/* Header */}\n

\n 📐\n

回归分析平台

\n v1.0\n

\n 查看源码\n

\n\n {/* Tab Navigation */}\n

\n {TABS.map((tab) => (\n setActiveTab(tab.id)}\n class={`px-5 py-3 text-sm whitespace-nowrap transition-all border-b-2 ${\n activeTab === tab.id\n ? \"tab-active border-blue-500\"\n : \"border-transparent text-gray-500 hover:text-gray-700 hover:border-gray-300\"\n }`}\n >\n \n {tab.icon}\n {tab.label}\n \n \n ))}\n

\n\n {/* Content */}\n

\n {activeTab === \"data\" && (\n { setSelectedDatasetId(id); setActiveTab(\"preprocess\"); }}\n />\n )}\n {activeTab === \"preprocess\" && (\n { setSelectedDatasetId(id); setActiveTab(\"train\"); }}\n />\n )}\n {activeTab === \"train\" && (\n { setSelectedTrainingId(tid); setActiveTab(\"regression\"); }}\n />\n )}\n {activeTab === \"regression\" && (\n { setSelectedModelId(mid); setActiveTab(\"predict\"); }}\n />\n )}\n {activeTab === \"predict\" && (\n \n )}\n

\n );\n}\n",[],[159,241,275],"67cdad8e-56ae-11f1-8e14-ee650bb23af1",["D",1779544249028],[190,191,192,193,194,195],[159,241],["D",1779544249028],[189,196,197,198],[159],"950e41dc-56ae-11f1-9ddb-ee650bb23af1","math.ts","950e4290-56ae-11f1-9ddb-ee650bb23af1",6,["D",1779544325014],"// ============================================================\n// Pure TypeScript statistics & regression engine (no native deps)\n// ============================================================\n\n// ---------- basic stats ----------\nexport function mean(arr: number[]): number {\n return arr.reduce((s, v) => s + v, 0) / arr.length;\n}\nexport function median(arr: number[]): number {\n const sorted = [...arr].sort((a, b) => a - b);\n const mid = Math.floor(sorted.length / 2);\n return sorted.length % 2 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;\n}\nexport function mode(arr: number[]): number {\n const freq: Record = {};\n arr.forEach((v) => (freq[v] = (freq[v] || 0) + 1));\n return +Object.entries(freq).sort((a, b) => b[1] - a[1])[0][0];\n}\nexport function variance(arr: number[]): number {\n const m = mean(arr);\n return arr.reduce((s, v) => s + (v - m) ** 2, 0) / (arr.length - 1);\n}\nexport function std(arr: number[]): number {\n return Math.sqrt(variance(arr));\n}\n\n// ---------- matrix helpers ----------\ntype Mat = number[][];\ntype Vec = number[];\n\nfunction zeros(r: number, c: number): Mat {\n return Array.from({ length: r }, () => new Array(c).fill(0));\n}\nfunction transpose(A: Mat): Mat {\n const [r, c] = [A.length, A[0].length];\n const T = zeros(c, r);\n for (let i = 0; i < r; i++) for (let j = 0; j < c; j++) T[j][i] = A[i][j];\n return T;\n}\nfunction matMul(A: Mat, B: Mat): Mat {\n const [ar, ac, bc] = [A.length, A[0].length, B[0].length];\n const C = zeros(ar, bc);\n for (let i = 0; i < ar; i++)\n for (let j = 0; j < bc; j++)\n for (let k = 0; k < ac; k++) C[i][j] += A[i][k] * B[k][j];\n return C;\n}\nfunction matVecMul(A: Mat, v: Vec): Vec {\n return A.map((row) => row.reduce((s, val, j) => s + val * v[j], 0));\n}\n\n// LU decomposition based inverse\nfunction invertMatrix(M: Mat): Mat {\n const n = M.length;\n const aug = M.map((row, i) => [...row, ...Array.from({ length: n }, (_, j) => (i === j ? 1 : 0))]);\n for (let col = 0; col < n; col++) {\n let maxRow = col;\n for (let row = col + 1; row < n; row++)\n if (Math.abs(aug[row][col]) > Math.abs(aug[maxRow][col])) maxRow = row;\n [aug[col], aug[maxRow]] = [aug[maxRow], aug[col]];\n const pivot = aug[col][col];\n if (Math.abs(pivot) < 1e-12) throw new Error(\"Singular matrix\");\n for (let j = 0; j < 2 * n; j++) aug[col][j] /= pivot;\n for (let row = 0; row < n; row++) {\n if (row === col) continue;\n const factor = aug[row][col];\n for (let j = 0; j < 2 * n; j++) aug[row][j] -= factor * aug[col][j];\n }\n }\n return aug.map((row) => row.slice(n));\n}\n\n// ---------- OLS regression ----------\nexport interface RegressionResult {\n coefficients: number[];\n intercept: number;\n rSquared: number;\n adjRSquared: number;\n mse: number;\n mae: number;\n predictions: number[];\n}\n\nexport function olsRegression(X: Mat, y: Vec): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n // Add intercept column\n const Xa = X.map((row) => [1, ...row]);\n const Xt = transpose(Xa);\n const XtX = matMul(Xt, Xa);\n const XtXinv = invertMatrix(XtX);\n const Xty = matVecMul(Xt, y);\n const beta = matVecMul(XtXinv, Xty);\n\n const intercept = beta[0];\n const coefficients = beta.slice(1);\n const predictions = Xa.map((row) => row.reduce((s, v, j) => s + v * beta[j], 0));\n const yMean = mean(y);\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = 1 - ssRes / ssTot;\n const adjRSquared = 1 - ((1 - rSquared) * (n - 1)) / (n - p - 1);\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- Ridge regression ----------\nexport function ridgeRegression(X: Mat, y: Vec, lambda = 1.0): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n const Xa = X.map((row) => [1, ...row]);\n const Xt = transpose(Xa);\n const XtX = matMul(Xt, Xa);\n // Add lambda * I (skip intercept penalty)\n for (let i = 1; i < XtX.length; i++) XtX[i][i] += lambda;\n const XtXinv = invertMatrix(XtX);\n const Xty = matVecMul(Xt, y);\n const beta = matVecMul(XtXinv, Xty);\n\n const intercept = beta[0];\n const coefficients = beta.slice(1);\n const predictions = Xa.map((row) => row.reduce((s, v, j) => s + v * beta[j], 0));\n const yMean = mean(y);\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = 1 - ssRes / ssTot;\n const adjRSquared = 1 - ((1 - rSquared) * (n - 1)) / (n - p - 1);\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- LASSO regression (coordinate descent) ----------\nexport function lassoRegression(X: Mat, y: Vec, lambda = 1.0, maxIter = 1000, tol = 1e-6): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n\n // Standardize features\n const xMeans = Array.from({ length: p }, (_, j) => mean(X.map((row) => row[j])));\n const xStds = Array.from({ length: p }, (_, j) => {\n const s = std(X.map((row) => row[j]));\n return s < 1e-10 ? 1 : s;\n });\n const yMean = mean(y);\n const Xs = X.map((row) => row.map((v, j) => (v - xMeans[j]) / xStds[j]));\n const ys = y.map((v) => v - yMean);\n\n const beta = new Array(p).fill(0);\n const residual = [...ys];\n\n for (let iter = 0; iter < maxIter; iter++) {\n let maxChange = 0;\n for (let j = 0; j < p; j++) {\n // Add back current beta_j contribution\n for (let i = 0; i < n; i++) residual[i] += Xs[i][j] * beta[j];\n // Compute rho = X_j^T * residual / n\n let rho = 0;\n for (let i = 0; i < n; i++) rho += Xs[i][j] * residual[i];\n rho /= n;\n // Compute norm\n let xjNorm = 0;\n for (let i = 0; i < n; i++) xjNorm += Xs[i][j] ** 2;\n xjNorm /= n;\n // Soft threshold\n const newBeta = softThreshold(rho, lambda) / xjNorm;\n const change = Math.abs(newBeta - beta[j]);\n if (change > maxChange) maxChange = change;\n beta[j] = newBeta;\n // Update residual\n for (let i = 0; i < n; i++) residual[i] -= Xs[i][j] * beta[j];\n }\n if (maxChange < tol) break;\n }\n\n // Convert back to original scale\n const coefficients = beta.map((b, j) => b / xStds[j]);\n const intercept = yMean - coefficients.reduce((s, c, j) => s + c * xMeans[j], 0);\n\n const predictions = X.map((row) => intercept + row.reduce((s, v, j) => s + v * coefficients[j], 0));\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = ssTot > 0 ? 1 - ssRes / ssTot : 0;\n const pActive = coefficients.filter((c) => Math.abs(c) > 1e-10).length;\n const adjRSquared = n > pActive + 1 ? 1 - ((1 - rSquared) * (n - 1)) / (n - pActive - 1) : rSquared;\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\nfunction softThreshold(x: number, lambda: number): number {\n if (x > lambda) return x - lambda;\n if (x < -lambda) return x + lambda;\n return 0;\n}\n\n// ---------- Elastic Net ----------\nexport function elasticNetRegression(X: Mat, y: Vec, lambda = 1.0, l1Ratio = 0.5, maxIter = 1000, tol = 1e-6): RegressionResult {\n const n = X.length;\n const p = X[0].length;\n const xMeans = Array.from({ length: p }, (_, j) => mean(X.map((row) => row[j])));\n const xStds = Array.from({ length: p }, (_, j) => {\n const s = std(X.map((row) => row[j]));\n return s < 1e-10 ? 1 : s;\n });\n const yMean = mean(y);\n const Xs = X.map((row) => row.map((v, j) => (v - xMeans[j]) / xStds[j]));\n const ys = y.map((v) => v - yMean);\n\n const beta = new Array(p).fill(0);\n const residual = [...ys];\n const l1 = lambda * l1Ratio;\n const l2 = lambda * (1 - l1Ratio);\n\n for (let iter = 0; iter < maxIter; iter++) {\n let maxChange = 0;\n for (let j = 0; j < p; j++) {\n for (let i = 0; i < n; i++) residual[i] += Xs[i][j] * beta[j];\n let rho = 0;\n for (let i = 0; i < n; i++) rho += Xs[i][j] * residual[i];\n rho /= n;\n let xjNorm = 0;\n for (let i = 0; i < n; i++) xjNorm += Xs[i][j] ** 2;\n xjNorm /= n;\n const newBeta = softThreshold(rho, l1) / (xjNorm + l2);\n const change = Math.abs(newBeta - beta[j]);\n if (change > maxChange) maxChange = change;\n beta[j] = newBeta;\n for (let i = 0; i < n; i++) residual[i] -= Xs[i][j] * beta[j];\n }\n if (maxChange < tol) break;\n }\n\n const coefficients = beta.map((b, j) => b / xStds[j]);\n const intercept = yMean - coefficients.reduce((s, c, j) => s + c * xMeans[j], 0);\n const predictions = X.map((row) => intercept + row.reduce((s, v, j) => s + v * coefficients[j], 0));\n const ssTot = y.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = y.reduce((s, v, i) => s + (v - predictions[i]) ** 2, 0);\n const rSquared = ssTot > 0 ? 1 - ssRes / ssTot : 0;\n const pActive = coefficients.filter((c) => Math.abs(c) > 1e-10).length;\n const adjRSquared = n > pActive + 1 ? 1 - ((1 - rSquared) * (n - 1)) / (n - pActive - 1) : rSquared;\n const mse = ssRes / n;\n const mae = y.reduce((s, v, i) => s + Math.abs(v - predictions[i]), 0) / n;\n\n return { coefficients, intercept, rSquared, adjRSquared, mse, mae, predictions };\n}\n\n// ---------- K-Fold cross validation ----------\nexport function kFoldCV(\n X: Mat,\n y: Vec,\n k: number,\n regressFn: (X: Mat, y: Vec) => RegressionResult,\n): { scores: number[]; meanScore: number } {\n const n = X.length;\n const indices = Array.from({ length: n }, (_, i) => i);\n // Shuffle\n for (let i = n - 1; i > 0; i--) {\n const j = Math.floor(Math.random() * (i + 1));\n [indices[i], indices[j]] = [indices[j], indices[i]];\n }\n const foldSize = Math.floor(n / k);\n const scores: number[] = [];\n\n for (let fold = 0; fold < k; fold++) {\n const testStart = fold * foldSize;\n const testEnd = fold === k - 1 ? n : testStart + foldSize;\n const testIdx = indices.slice(testStart, testEnd);\n const trainIdx = [...indices.slice(0, testStart), ...indices.slice(testEnd)];\n\n const Xtrain = trainIdx.map((i) => X[i]);\n const ytrain = trainIdx.map((i) => y[i]);\n const Xtest = testIdx.map((i) => X[i]);\n const ytest = testIdx.map((i) => y[i]);\n\n try {\n const result = regressFn(Xtrain, ytrain);\n // Compute R² on test set\n const preds = Xtest.map(\n (row) => result.intercept + row.reduce((s, v, j) => s + v * result.coefficients[j], 0),\n );\n const yMean = mean(ytest);\n const ssTot = ytest.reduce((s, v) => s + (v - yMean) ** 2, 0);\n const ssRes = ytest.reduce((s, v, i) => s + (v - preds[i]) ** 2, 0);\n scores.push(ssTot > 0 ? 1 - ssRes / ssTot : 0);\n } catch {\n scores.push(0);\n }\n }\n\n return { scores, meanScore: mean(scores) };\n}\n\n// ---------- Format equation ----------\nexport function formatEquation(\n target: string,\n features: string[],\n coefficients: number[],\n intercept: number,\n): string {\n let eq = `${target} = ${intercept.toFixed(4)}`;\n coefficients.forEach((c, i) => {\n if (Math.abs(c) < 1e-10) return;\n const sign = c >= 0 ? \" + \" : \" - \";\n eq += `${sign}${Math.abs(c).toFixed(4)} × ${features[i]}`;\n });\n return eq;\n}\n",[],[165,290],"76bd199c-56ae-11f1-8e14-ee650bb23af1","db.ts","76bd1b5e-56ae-11f1-8e14-ee650bb23af1",5,["D",1779544274151],"import { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport { sqlite };\n\nexport async function initDB() {\n await sqlite.batch([\n {\n sql: `CREATE TABLE IF NOT EXISTS datasets (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL,\n source TEXT NOT NULL DEFAULT 'upload',\n row_count INTEGER DEFAULT 0,\n col_count INTEGER DEFAULT 0,\n columns_json TEXT,\n created_at TEXT DEFAULT (datetime('now')),\n updated_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS dataset_rows (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n dataset_id INTEGER NOT NULL,\n row_index INTEGER NOT NULL,\n data_json TEXT NOT NULL,\n FOREIGN KEY (dataset_id) REFERENCES datasets(id) ON DELETE CASCADE\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS training_results (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n dataset_id INTEGER NOT NULL,\n target_column TEXT NOT NULL,\n method TEXT NOT NULL DEFAULT 'lasso',\n alpha REAL,\n selected_features TEXT,\n cv_scores TEXT,\n mean_cv_score REAL,\n coefficients TEXT,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS regression_models (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n training_id INTEGER,\n dataset_id INTEGER NOT NULL,\n method TEXT NOT NULL,\n equation TEXT,\n coefficients TEXT,\n intercept REAL,\n r_squared REAL,\n adj_r_squared REAL,\n mse REAL,\n mae REAL,\n features TEXT,\n target TEXT,\n is_recommended INTEGER DEFAULT 0,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS prediction_pages (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n model_id INTEGER NOT NULL,\n name TEXT NOT NULL,\n description TEXT,\n is_active INTEGER DEFAULT 1,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n {\n sql: `CREATE TABLE IF NOT EXISTS predictions (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n page_id INTEGER NOT NULL,\n model_id INTEGER NOT NULL,\n input_json TEXT NOT NULL,\n predicted_value REAL NOT NULL,\n actual_value REAL,\n error REAL,\n created_at TEXT DEFAULT (datetime('now'))\n )`,\n },\n ]);\n}\n",[],[165,298],"5a4bec38-56af-11f1-be57-ee650bb23af1","train.ts","b61de396-56ae-11f1-9595-ee650bb23af1","72270dc0-56ae-11f1-8e14-ee650bb23af1",24,["D",1779544655902],"import { Hono } from \"npm:hono\";\nimport { sqlite } from \"../db.ts\";\nimport { lassoRegression, kFoldCV } from \"../math.ts\";\n\nexport const trainRoutes = new Hono();\n\n// Run LASSO training with 10-fold CV\ntrainRoutes.post(\"/lasso\", async (c) => {\n try {\n const { datasetId, targetColumn, alpha = 0.1 } = await c.req.json<{\n datasetId: number;\n targetColumn: string;\n alpha?: number;\n }>();\n\n // Load data\n const ds = await sqlite.execute({ sql: \"SELECT * FROM datasets WHERE id = ?\", args: [datasetId] });\n if (!ds.rows.length) return c.json({ error: \"Dataset not found\" }, 404);\n\n const columns: string[] = JSON.parse((ds.rows[0] as any).columns_json);\n if (!columns.includes(targetColumn)) return c.json({ error: \"Target column not found\" }, 400);\n\n const rowsResult = await sqlite.execute({\n sql: \"SELECT data_json FROM dataset_rows WHERE dataset_id = ? ORDER BY row_index\",\n args: [datasetId],\n });\n const rawData = rowsResult.rows.map((r: any) => JSON.parse(r.data_json));\n\n // Build numeric matrix\n const features = columns.filter((c) => c !== targetColumn);\n const numericFeatures = features.filter((col) => {\n return rawData.every((row: any) => {\n const v = row[col];\n return v !== null && v !== undefined && v !== \"\" && !isNaN(Number(v));\n });\n });\n\n if (numericFeatures.length === 0) return c.json({ error: \"No numeric features found\" }, 400);\n\n const X = rawData.map((row: any) => numericFeatures.map((col) => Number(row[col])));\n const y = rawData.map((row: any) => Number(row[targetColumn]));\n\n // Run LASSO\n const result = lassoRegression(X, y, alpha);\n\n // 10-fold CV\n const cv = kFoldCV(X, y, 10, (Xt, yt) => lassoRegression(Xt, yt, alpha));\n\n // Selected features (non-zero coefficients)\n const selectedFeatures = numericFeatures.filter((_, i) => Math.abs(result.coefficients[i]) > 1e-10);\n const selectedCoeffs = result.coefficients.filter((c) => Math.abs(c) > 1e-10);\n\n // Save training result\n const saveResult = await sqlite.execute({\n sql: `INSERT INTO training_results (dataset_id, target_column, method, alpha, selected_features, cv_scores, mean_cv_score, coefficients)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n datasetId,\n targetColumn,\n \"lasso\",\n alpha,\n JSON.stringify(selectedFeatures),\n JSON.stringify(cv.scores),\n cv.meanScore,\n JSON.stringify(Object.fromEntries(selectedFeatures.map((f, i) => [f, selectedCoeffs[i]]))),\n ],\n });\n\n return c.json({\n trainingId: Number(saveResult.lastInsertRowid),\n method: \"lasso\",\n alpha,\n allFeatures: numericFeatures,\n selectedFeatures,\n coefficients: Object.fromEntries(selectedFeatures.map((f, i) => [f, selectedCoeffs[i]])),\n intercept: result.intercept,\n rSquared: result.rSquared,\n cvScores: cv.scores,\n meanCvScore: cv.meanScore,\n totalFeatures: numericFeatures.length,\n selectedCount: selectedFeatures.length,\n });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Get training results\ntrainRoutes.get(\"/results\", async (c) => {\n const result = await sqlite.execute(\"SELECT * FROM training_results ORDER BY created_at DESC\");\n return c.json(result.rows.map((r: any) => ({\n ...r,\n selected_features: JSON.parse(r.selected_features || \"[]\"),\n cv_scores: JSON.parse(r.cv_scores || \"[]\"),\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n })));\n});\n\ntrainRoutes.get(\"/results/:id\", async (c) => {\n const id = c.req.param(\"id\");\n const result = await sqlite.execute({ sql: \"SELECT * FROM training_results WHERE id = ?\", args: [+id] });\n if (!result.rows.length) return c.json({ error: \"Not found\" }, 404);\n const r: any = result.rows[0];\n return c.json({\n ...r,\n selected_features: JSON.parse(r.selected_features || \"[]\"),\n cv_scores: JSON.parse(r.cv_scores || \"[]\"),\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n });\n});\n",[],[165,314,306],"routes","beda32f0-56ae-11f1-b03a-ee650bb23af1","regression.ts","beda3390-56ae-11f1-b03a-ee650bb23af1",10,["D",1779544395133],"import { Hono } from \"npm:hono\";\nimport { sqlite } from \"../db.ts\";\nimport {\n olsRegression,\n ridgeRegression,\n lassoRegression,\n elasticNetRegression,\n formatEquation,\n type RegressionResult,\n} from \"../math.ts\";\n\nexport const regressionRoutes = new Hono();\n\n// Run multiple regression methods on selected features\nregressionRoutes.post(\"/analyze\", async (c) => {\n try {\n const { datasetId, targetColumn, features, trainingId } = await c.req.json<{\n datasetId: number;\n targetColumn: string;\n features: string[];\n trainingId?: number;\n }>();\n\n // Load data\n const rowsResult = await sqlite.execute({\n sql: \"SELECT data_json FROM dataset_rows WHERE dataset_id = ? ORDER BY row_index\",\n args: [datasetId],\n });\n const rawData = rowsResult.rows.map((r: any) => JSON.parse(r.data_json));\n const X = rawData.map((row: any) => features.map((col) => Number(row[col])));\n const y = rawData.map((row: any) => Number(row[targetColumn]));\n\n // Run multiple regression methods\n const methods: { name: string; fn: () => RegressionResult }[] = [\n { name: \"OLS\", fn: () => olsRegression(X, y) },\n { name: \"Ridge (λ=0.1)\", fn: () => ridgeRegression(X, y, 0.1) },\n { name: \"Ridge (λ=1.0)\", fn: () => ridgeRegression(X, y, 1.0) },\n { name: \"LASSO (λ=0.01)\", fn: () => lassoRegression(X, y, 0.01) },\n { name: \"LASSO (λ=0.1)\", fn: () => lassoRegression(X, y, 0.1) },\n { name: \"ElasticNet (λ=0.1, α=0.5)\", fn: () => elasticNetRegression(X, y, 0.1, 0.5) },\n ];\n\n const results = [];\n for (const m of methods) {\n try {\n const result = m.fn();\n const equation = formatEquation(targetColumn, features, result.coefficients, result.intercept);\n results.push({\n method: m.name,\n rSquared: result.rSquared,\n adjRSquared: result.adjRSquared,\n mse: result.mse,\n mae: result.mae,\n coefficients: Object.fromEntries(features.map((f, i) => [f, result.coefficients[i]])),\n intercept: result.intercept,\n equation,\n });\n } catch {\n results.push({ method: m.name, error: \"Failed to compute\" });\n }\n }\n\n // Sort by adj R² and recommend best\n const valid = results.filter((r: any) => !r.error && r.adjRSquared != null);\n valid.sort((a: any, b: any) => b.adjRSquared - a.adjRSquared);\n const recommended = valid[0];\n\n // Save all models\n for (const r of valid) {\n const isRec = r === recommended ? 1 : 0;\n await sqlite.execute({\n sql: `INSERT INTO regression_models (training_id, dataset_id, method, equation, coefficients, intercept, r_squared, adj_r_squared, mse, mae, features, target, is_recommended)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n trainingId || null,\n datasetId,\n (r as any).method,\n (r as any).equation,\n JSON.stringify((r as any).coefficients),\n (r as any).intercept,\n (r as any).rSquared,\n (r as any).adjRSquared,\n (r as any).mse,\n (r as any).mae,\n JSON.stringify(features),\n targetColumn,\n isRec,\n ],\n });\n }\n\n return c.json({ results, recommended: recommended ? { ...recommended, isRecommended: true } : null });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Get all models\nregressionRoutes.get(\"/models\", async (c) => {\n const result = await sqlite.execute(\"SELECT * FROM regression_models ORDER BY adj_r_squared DESC\");\n return c.json(\n result.rows.map((r: any) => ({\n ...r,\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n features: JSON.parse(r.features || \"[]\"),\n })),\n );\n});\n\n// Get single model\nregressionRoutes.get(\"/models/:id\", async (c) => {\n const id = c.req.param(\"id\");\n const result = await sqlite.execute({ sql: \"SELECT * FROM regression_models WHERE id = ?\", args: [+id] });\n if (!result.rows.length) return c.json({ error: \"Not found\" }, 404);\n const r: any = result.rows[0];\n return c.json({\n ...r,\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n features: JSON.parse(r.features || \"[]\"),\n });\n});\n",[],[165,314,316],"57704ab8-56af-11f1-8305-ee650bb23af1","preprocess.ts","ac479fc4-56ae-11f1-8228-ee650bb23af1",23,["D",1779544651129],"import { Hono } from \"npm:hono\";\nimport { sqlite } from \"../db.ts\";\nimport { mean, median, mode } from \"../math.ts\";\n\nexport const preprocessRoutes = new Hono();\n\n// Get preprocess preview\npreprocessRoutes.get(\"/preview/:datasetId\", async (c) => {\n const datasetId = +c.req.param(\"datasetId\");\n const ds = await sqlite.execute({ sql: \"SELECT * FROM datasets WHERE id = ?\", args: [datasetId] });\n if (!ds.rows.length) return c.json({ error: \"Dataset not found\" }, 404);\n\n const rowsResult = await sqlite.execute({\n sql: \"SELECT data_json FROM dataset_rows WHERE dataset_id = ? ORDER BY row_index\",\n args: [datasetId],\n });\n const data = rowsResult.rows.map((r: any) => JSON.parse(r.data_json));\n const columns = JSON.parse((ds.rows[0] as any).columns_json);\n\n // Analyze each column\n const analysis = columns.map((col: string) => {\n const values = data.map((row: any) => row[col]);\n const nonNull = values.filter((v: any) => v !== null && v !== undefined && v !== \"\");\n const nullCount = values.length - nonNull.length;\n const unique = new Set(nonNull).size;\n const isNumeric = nonNull.length > 0 && nonNull.every((v: any) => !isNaN(Number(v)));\n\n let stats: any = { total: values.length, missing: nullCount, unique, isNumeric };\n if (isNumeric && nonNull.length > 0) {\n const nums = nonNull.map(Number);\n stats.min = Math.min(...nums);\n stats.max = Math.max(...nums);\n stats.mean = mean(nums);\n stats.median = median(nums);\n }\n return { column: col, ...stats };\n });\n\n return c.json({ datasetId, columns, rowCount: data.length, analysis });\n});\n\n// Apply preprocessing\npreprocessRoutes.post(\"/apply\", async (c) => {\n try {\n const body = await c.req.json<{\n datasetId: number;\n operations: {\n deduplicate?: boolean;\n fillMissing?: Record;\n dropColumns?: string[];\n groupBy?: { column: string; agg: Record };\n };\n }>();\n\n const { datasetId, operations } = body;\n const ds = await sqlite.execute({ sql: \"SELECT * FROM datasets WHERE id = ?\", args: [datasetId] });\n if (!ds.rows.length) return c.json({ error: \"Dataset not found\" }, 404);\n\n const rowsResult = await sqlite.execute({\n sql: \"SELECT data_json FROM dataset_rows WHERE dataset_id = ? ORDER BY row_index\",\n args: [datasetId],\n });\n let data: Record[] = rowsResult.rows.map((r: any) => JSON.parse(r.data_json));\n let columns: string[] = JSON.parse((ds.rows[0] as any).columns_json);\n\n // 1) Drop columns\n if (operations.dropColumns?.length) {\n columns = columns.filter((c) => !operations.dropColumns!.includes(c));\n data = data.map((row) => {\n const newRow: any = {};\n columns.forEach((col) => (newRow[col] = row[col]));\n return newRow;\n });\n }\n\n // 2) Deduplicate\n if (operations.deduplicate) {\n const seen = new Set();\n data = data.filter((row) => {\n const key = JSON.stringify(columns.map((c) => row[c]));\n if (seen.has(key)) return false;\n seen.add(key);\n return true;\n });\n }\n\n // 3) Fill missing values\n if (operations.fillMissing) {\n for (const [col, method] of Object.entries(operations.fillMissing)) {\n const nonNull = data\n .map((row) => row[col])\n .filter((v) => v !== null && v !== undefined && v !== \"\");\n\n if (method === \"drop\") {\n data = data.filter((row) => row[col] !== null && row[col] !== undefined && row[col] !== \"\");\n continue;\n }\n\n const isNumeric = nonNull.every((v) => !isNaN(Number(v)));\n if (!isNumeric) continue;\n\n const nums = nonNull.map(Number);\n let fillValue: number;\n if (method === \"mean\") fillValue = mean(nums);\n else if (method === \"median\") fillValue = median(nums);\n else fillValue = mode(nums);\n\n data = data.map((row) => ({\n ...row,\n [col]: row[col] === null || row[col] === undefined || row[col] === \"\" ? fillValue : row[col],\n }));\n }\n }\n\n // 4) Group by\n if (operations.groupBy) {\n const { column: groupCol, agg } = operations.groupBy;\n const groups: Record[]> = {};\n data.forEach((row) => {\n const key = String(row[groupCol] ?? \"null\");\n if (!groups[key]) groups[key] = [];\n groups[key].push(row);\n });\n\n data = Object.entries(groups).map(([key, rows]) => {\n const result: any = { [groupCol]: key };\n for (const [col, fn] of Object.entries(agg)) {\n const vals = rows.map((r) => Number(r[col])).filter((v) => !isNaN(v));\n if (fn === \"sum\") result[col] = vals.reduce((a, b) => a + b, 0);\n else if (fn === \"mean\") result[col] = vals.length ? mean(vals) : 0;\n else if (fn === \"count\") result[col] = vals.length;\n else if (fn === \"min\") result[col] = vals.length ? Math.min(...vals) : 0;\n else if (fn === \"max\") result[col] = vals.length ? Math.max(...vals) : 0;\n }\n return result;\n });\n columns = Object.keys(data[0] || {});\n }\n\n // Save processed data as new dataset\n const newName = (ds.rows[0] as any).name + \"_processed\";\n const newDs = await sqlite.execute({\n sql: \"INSERT INTO datasets (name, source, row_count, col_count, columns_json) VALUES (?, ?, ?, ?, ?)\",\n args: [newName, \"preprocessed\", data.length, columns.length, JSON.stringify(columns)],\n });\n const newId = Number(newDs.lastInsertRowid);\n\n const batchSize = 50;\n for (let i = 0; i < data.length; i += batchSize) {\n const batch = data.slice(i, i + batchSize);\n await sqlite.batch(\n batch.map((row, idx) => ({\n sql: \"INSERT INTO dataset_rows (dataset_id, row_index, data_json) VALUES (?, ?, ?)\",\n args: [newId, i + idx, JSON.stringify(row)],\n })),\n );\n }\n\n return c.json({ id: newId, name: newName, rows: data.length, columns });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n",[],[165,314,324],"5e7da90e-56af-11f1-a47c-ee650bb23af1","predict.ts","cd8b3d76-56ae-11f1-8274-ee650bb23af1",["D",1779544662965],"import { Hono } from \"npm:hono\";\nimport { sqlite } from \"../db.ts\";\n\nexport const predictRoutes = new Hono();\n\n// Create a prediction page from a model\npredictRoutes.post(\"/pages\", async (c) => {\n try {\n const { modelId, name, description } = await c.req.json<{\n modelId: number;\n name: string;\n description?: string;\n }>();\n const model = await sqlite.execute({ sql: \"SELECT * FROM regression_models WHERE id = ?\", args: [modelId] });\n if (!model.rows.length) return c.json({ error: \"Model not found\" }, 404);\n\n const result = await sqlite.execute({\n sql: \"INSERT INTO prediction_pages (model_id, name, description) VALUES (?, ?, ?)\",\n args: [modelId, name, description || \"\"],\n });\n return c.json({ id: Number(result.lastInsertRowid), modelId, name });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// List prediction pages\npredictRoutes.get(\"/pages\", async (c) => {\n const result = await sqlite.execute(`\n SELECT p.*, m.equation, m.method, m.features, m.target, m.coefficients, m.intercept, m.r_squared\n FROM prediction_pages p\n JOIN regression_models m ON p.model_id = m.id\n WHERE p.is_active = 1\n ORDER BY p.created_at DESC\n `);\n return c.json(\n result.rows.map((r: any) => ({\n ...r,\n features: JSON.parse(r.features || \"[]\"),\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n })),\n );\n});\n\n// Get single page\npredictRoutes.get(\"/pages/:id\", async (c) => {\n const id = c.req.param(\"id\");\n const result = await sqlite.execute({\n sql: `SELECT p.*, m.equation, m.method, m.features, m.target, m.coefficients, m.intercept, m.r_squared, m.adj_r_squared\n FROM prediction_pages p\n JOIN regression_models m ON p.model_id = m.id\n WHERE p.id = ?`,\n args: [+id],\n });\n if (!result.rows.length) return c.json({ error: \"Not found\" }, 404);\n const r: any = result.rows[0];\n return c.json({\n ...r,\n features: JSON.parse(r.features || \"[]\"),\n coefficients: JSON.parse(r.coefficients || \"{}\"),\n });\n});\n\n// Make a prediction\npredictRoutes.post(\"/run\", async (c) => {\n try {\n const { pageId, inputs } = await c.req.json<{\n pageId: number;\n inputs: Record;\n }>();\n\n // Get page & model\n const page = await sqlite.execute({\n sql: `SELECT p.*, m.coefficients, m.intercept, m.features, m.target\n FROM prediction_pages p JOIN regression_models m ON p.model_id = m.id WHERE p.id = ?`,\n args: [pageId],\n });\n if (!page.rows.length) return c.json({ error: \"Page not found\" }, 404);\n\n const r: any = page.rows[0];\n const coefficients: Record = JSON.parse(r.coefficients);\n const features: string[] = JSON.parse(r.features);\n const intercept: number = r.intercept;\n\n // Compute prediction\n let predicted = intercept;\n for (const f of features) {\n if (inputs[f] === undefined) return c.json({ error: `Missing input: ${f}` }, 400);\n predicted += coefficients[f] * inputs[f];\n }\n\n // Save prediction\n const saveResult = await sqlite.execute({\n sql: \"INSERT INTO predictions (page_id, model_id, input_json, predicted_value) VALUES (?, ?, ?, ?)\",\n args: [pageId, r.model_id, JSON.stringify(inputs), predicted],\n });\n\n return c.json({\n predictionId: Number(saveResult.lastInsertRowid),\n predicted,\n inputs,\n target: r.target,\n });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Update actual value for a prediction\npredictRoutes.put(\"/actual/:predictionId\", async (c) => {\n try {\n const predictionId = +c.req.param(\"predictionId\");\n const { actualValue } = await c.req.json<{ actualValue: number }>();\n\n const pred = await sqlite.execute({\n sql: \"SELECT predicted_value FROM predictions WHERE id = ?\",\n args: [predictionId],\n });\n if (!pred.rows.length) return c.json({ error: \"Prediction not found\" }, 404);\n\n const predicted = (pred.rows[0] as any).predicted_value;\n const error = actualValue - predicted;\n\n await sqlite.execute({\n sql: \"UPDATE predictions SET actual_value = ?, error = ? WHERE id = ?\",\n args: [actualValue, error, predictionId],\n });\n\n return c.json({ predictionId, predicted, actual: actualValue, error });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Get predictions history for a page\npredictRoutes.get(\"/history/:pageId\", async (c) => {\n const pageId = c.req.param(\"pageId\");\n const result = await sqlite.execute({\n sql: `SELECT * FROM predictions WHERE page_id = ? ORDER BY created_at DESC LIMIT 200`,\n args: [+pageId],\n });\n return c.json(\n result.rows.map((r: any) => ({\n ...r,\n input_json: JSON.parse(r.input_json || \"{}\"),\n })),\n );\n});\n\n// Get accuracy stats for a page\npredictRoutes.get(\"/accuracy/:pageId\", async (c) => {\n const pageId = c.req.param(\"pageId\");\n const result = await sqlite.execute({\n sql: \"SELECT predicted_value, actual_value, error FROM predictions WHERE page_id = ? AND actual_value IS NOT NULL\",\n args: [+pageId],\n });\n\n const rows: any[] = result.rows;\n if (!rows.length) return c.json({ message: \"No actual values recorded yet\", count: 0 });\n\n const errors = rows.map((r) => r.error);\n const absErrors = errors.map(Math.abs);\n const mape = rows.reduce((s: number, r: any) => {\n return s + (r.actual_value !== 0 ? Math.abs(r.error / r.actual_value) : 0);\n }, 0) / rows.length * 100;\n\n const meanErr = errors.reduce((a: number, b: number) => a + b, 0) / errors.length;\n const mae = absErrors.reduce((a: number, b: number) => a + b, 0) / absErrors.length;\n const mse = errors.reduce((s: number, e: number) => s + e * e, 0) / errors.length;\n const rmse = Math.sqrt(mse);\n\n return c.json({\n count: rows.length,\n meanError: meanErr,\n mae,\n mse,\n rmse,\n mape: mape,\n });\n});\n",[],[165,314,332],"54f21e06-56af-11f1-be57-ee650bb23af1","data.ts","9f8e0a2a-56ae-11f1-8228-ee650bb23af1",22,["D",1779544646951],"import { Hono } from \"npm:hono\";\nimport { sqlite } from \"../db.ts\";\nimport * as XLSX from \"npm:xlsx@0.18.5\";\n\nexport const dataRoutes = new Hono();\n\n// List all datasets\ndataRoutes.get(\"/datasets\", async (c) => {\n const result = await sqlite.execute(\"SELECT * FROM datasets ORDER BY created_at DESC\");\n return c.json(result.rows);\n});\n\n// Get dataset detail with rows\ndataRoutes.get(\"/datasets/:id\", async (c) => {\n const id = c.req.param(\"id\");\n const ds = await sqlite.execute({ sql: \"SELECT * FROM datasets WHERE id = ?\", args: [+id] });\n if (!ds.rows.length) return c.json({ error: \"Not found\" }, 404);\n const rows = await sqlite.execute({\n sql: \"SELECT row_index, data_json FROM dataset_rows WHERE dataset_id = ? ORDER BY row_index LIMIT 500\",\n args: [+id],\n });\n return c.json({\n ...ds.rows[0],\n columns: JSON.parse((ds.rows[0] as any).columns_json || \"[]\"),\n rows: rows.rows.map((r: any) => ({ index: r.row_index, data: JSON.parse(r.data_json) })),\n });\n});\n\n// Upload Excel file\ndataRoutes.post(\"/upload\", async (c) => {\n try {\n const formData = await c.req.formData();\n const file = formData.get(\"file\") as File;\n if (!file) return c.json({ error: \"No file provided\" }, 400);\n\n const buffer = await file.arrayBuffer();\n const workbook = XLSX.read(new Uint8Array(buffer), { type: \"array\" });\n const sheetName = workbook.SheetNames[0];\n const sheet = workbook.Sheets[sheetName];\n const jsonData: Record[] = XLSX.utils.sheet_to_json(sheet);\n\n if (!jsonData.length) return c.json({ error: \"Empty file\" }, 400);\n\n const columns = Object.keys(jsonData[0]);\n const name = file.name.replace(/\\.[^.]+$/, \"\");\n\n // Insert dataset metadata\n const dsResult = await sqlite.execute({\n sql: \"INSERT INTO datasets (name, source, row_count, col_count, columns_json) VALUES (?, ?, ?, ?, ?)\",\n args: [name, \"upload\", jsonData.length, columns.length, JSON.stringify(columns)],\n });\n const datasetId = Number(dsResult.lastInsertRowid);\n\n // Insert rows in batches\n const batchSize = 50;\n for (let i = 0; i < jsonData.length; i += batchSize) {\n const batch = jsonData.slice(i, i + batchSize);\n await sqlite.batch(\n batch.map((row, idx) => ({\n sql: \"INSERT INTO dataset_rows (dataset_id, row_index, data_json) VALUES (?, ?, ?)\",\n args: [datasetId, i + idx, JSON.stringify(row)],\n })),\n );\n }\n\n return c.json({ id: Number(datasetId), name, rows: jsonData.length, columns });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Manual data input / SQL-like import\ndataRoutes.post(\"/import-json\", async (c) => {\n try {\n const { name, data } = await c.req.json<{ name: string; data: Record[] }>();\n if (!data?.length) return c.json({ error: \"No data\" }, 400);\n\n const columns = Object.keys(data[0]);\n const dsResult = await sqlite.execute({\n sql: \"INSERT INTO datasets (name, source, row_count, col_count, columns_json) VALUES (?, ?, ?, ?, ?)\",\n args: [name || \"imported\", \"json\", data.length, columns.length, JSON.stringify(columns)],\n });\n const datasetId = Number(dsResult.lastInsertRowid);\n\n const batchSize = 50;\n for (let i = 0; i < data.length; i += batchSize) {\n const batch = data.slice(i, i + batchSize);\n await sqlite.batch(\n batch.map((row, idx) => ({\n sql: \"INSERT INTO dataset_rows (dataset_id, row_index, data_json) VALUES (?, ?, ?)\",\n args: [datasetId, i + idx, JSON.stringify(row)],\n })),\n );\n }\n\n return c.json({ id: datasetId, name, rows: data.length, columns });\n } catch (err: any) {\n return c.json({ error: err.message }, 500);\n }\n});\n\n// Delete dataset\ndataRoutes.delete(\"/datasets/:id\", async (c) => {\n const id = c.req.param(\"id\");\n await sqlite.batch([\n { sql: \"DELETE FROM dataset_rows WHERE dataset_id = ?\", args: [+id] },\n { sql: \"DELETE FROM datasets WHERE id = ?\", args: [+id] },\n ]);\n return c.json({ ok: true });\n});\n",[],[165,314,339],"72270d2a-56ae-11f1-8e14-ee650bb23af1",4,["D",1779544266458],[181,182,183,184,185],[165,314],["D",1779544263876],[180,186,187],[165],["D",1779544249028],["D",1789492855745],{"_11":12,"_95":91},{"_11":12,"_89":-5,"_90":91,"_47":48,"_92":93,"_94":40},{"_29":30,"_13":26,"_20":21,"_27":28,"_31":32,"_33":34,"_82":42,"_83":84,"_85":86,"_87":14,"_88":12},"ctx",{"_75":-5,"_119":392,"_393":394,"_395":396,"_397":40,"_398":40,"_399":40,"_400":52,"_401":-5,"_402":-7,"_403":-7,"_404":56,"_405":40,"_406":40,"_407":-5,"_408":40,"_409":40,"_410":40,"_411":-5},"ENV",{"_363":364,"_365":366,"_367":26,"_368":369,"_370":371,"_372":373,"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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