diff --git a/README.md b/README.md index 56ca45b..bc4a1e9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ ![Logo](./figures/TimeCraft2.png) + +https://github.com/user-attachments/assets/35bc7ee3-f7a2-4949-96fc-1d1b977e0df1 + # Time Series Generation for Real-World Applications The rapid advancement of artificial intelligence has increasingly emphasized the critical role of time series data in powering intelligent decision-making across diverse domains, including healthcare, finance, energy, and transportation. In these fields, the ability to generate high-quality synthetic time series has become particularly valuable. **Time series generation** technology plays a vital role in alleviating **data scarcity**, especially in scenarios where collecting real-world data is expensive, time-consuming, or impractical. It also enables **privacy-preserving** analysis by producing realistic but non-identifiable synthetic data, reducing the risks associated with sharing sensitive information. Moreover, it supports **simulation and forecasting in risk-free environments**, allowing researchers and practitioners to safely explore hypothetical scenarios and train robust models. Together, these capabilities make time series generation an essential tool for a wide range of real-world applications. diff --git a/demo_dev/README.md b/demo_dev/README.md new file mode 100644 index 0000000..7af2006 --- /dev/null +++ b/demo_dev/README.md @@ -0,0 +1,24 @@ + +# Overview + +Here is the source code for building the demo in the TimeCraft video on the homepage. A simple web visualization page is set up using Node.js. + + +## Usage + +### 1. Install the Node.js from https://nodejs.org + + +### 2. Install Dependencies + +```bash +npm install +``` + +### 3. Run to visualize the demo pages + +```bash +npm run dev +``` + + diff --git a/demo_dev/package-lock.json b/demo_dev/package-lock.json new file mode 100644 index 0000000..2e06955 --- /dev/null +++ b/demo_dev/package-lock.json @@ -0,0 +1,3532 @@ +{ + "name": "timeseries-generation-app", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "timeseries-generation-app", + "version": "0.0.0", + "dependencies": { + "lucide-react": "^0.263.1", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "recharts": "^2.8.0" + }, + "devDependencies": { + "@babel/core": "^7.27.4", + "@babel/preset-react": "^7.27.1", + "@babel/preset-typescript": "^7.27.1", + "@rollup/pluginutils": "^5.1.4", + "@types/babel__core": "^7.20.5", + "@types/react": "^18.2.15", + "@types/react-dom": "^18.2.7", + "@vitejs/plugin-react": "^4.0.3", + "@vue/compiler-dom": "^3.5.16", + "autoprefixer": "^10.4.14", + "magic-string": "^0.30.17", + "postcss": "^8.4.24", + "tailwindcss": 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-0,0 +1,35 @@ +{ + "name": "timeseries-generation-app", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "react": "^18.2.0", + "react-dom": "^18.2.0", + "recharts": "^2.8.0", + "lucide-react": "^0.263.1" + }, + "devDependencies": { + "@types/react": "^18.2.15", + "@types/react-dom": "^18.2.7", + "@vitejs/plugin-react": "^4.0.3", + "autoprefixer": "^10.4.14", + "postcss": "^8.4.24", + "tailwindcss": "^3.3.0", + "vite": "^4.4.5", + "@types/babel__core": "^7.20.5", + "@babel/core": "^7.27.4", + "@babel/preset-react": "^7.27.1", + "@babel/preset-typescript": "^7.27.1", + "@vue/compiler-dom": "^3.5.16", + "@rollup/pluginutils": "^5.1.4", + "magic-string": "^0.30.17", + "typescript": "^5.8.3", + "yunji-tagger": "^0.0.9" + } +} \ No newline at end of file diff --git a/demo_dev/postcss.config.js b/demo_dev/postcss.config.js new file mode 100644 index 0000000..0664fbd --- /dev/null +++ b/demo_dev/postcss.config.js @@ -0,0 +1,9 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} \ No newline at end of file diff --git a/demo_dev/src/App.jsx b/demo_dev/src/App.jsx new file mode 100644 index 0000000..25bbec4 --- /dev/null +++ b/demo_dev/src/App.jsx @@ -0,0 +1,67 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import React, { useState } from 'react'; +import { Play, FileText, Activity } from 'lucide-react'; +import ModelOne from './components/ModelOne'; +import ModelTwo from './components/ModelTwo.jsx'; +import ModelThree from './components/ModelThree'; + +function App() { + const [activeModel, setActiveModel] = useState('model1'); + + return ( +
+
+
+
+

Time Series Generation Model Demonstration System

+
+ + + +
+
+
+
+ +
+ {activeModel === 'model1' && } + {activeModel === 'model2' && } + {activeModel === 'model3' && } +
+
+ ); +} + +export default App; \ No newline at end of file diff --git a/demo_dev/src/components/ModelOne.jsx b/demo_dev/src/components/ModelOne.jsx new file mode 100644 index 0000000..b2d90c8 --- /dev/null +++ b/demo_dev/src/components/ModelOne.jsx @@ -0,0 +1,511 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import React, { useState, useEffect } from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer } from 'recharts'; +import { RefreshCw, Database } from 'lucide-react'; + +// 导入JSON数据 +import electricity_gt_3143 from '/public/json_data/electricity_gt_3143.json'; +import electricity_gt_2826 from '/public/json_data/electricity_gt_2826.json'; +import electricity_gt_2475 from '/public/json_data/electricity_gt_2475.json'; +import electricity_gt_1158 from '/public/json_data/electricity_gt_1158.json'; + +import solar_gt_1158 from '/public/json_data/stock_gt_1590.json'; +import solar_gt_2475 from '/public/json_data/stock_gt_23.json'; +import solar_gt_2826 from '/public/json_data/stock_gt_1851.json'; +import solar_gt_3143 from '/public/json_data/stock_gt_718.json'; + + +// 为electricity_2添加随机噪声的函数 +const addRandomNoise = (data, noiseLevel = 0.1) => { + if (!data || !data.length) return data; + + return data.map(point => ({ + ...point, + value: point.value * (1 + (Math.random() - 0.5) * noiseLevel) + })); +}; + +// 为不同选项创建数据映射 +const domainDataMap = { + electricity_1: { 0: electricity_gt_3143 }, + electricity_2: { 0: addRandomNoise(electricity_gt_1158, 0.15) }, // 先用electricity数据代替 + solar_1: { 0: solar_gt_1158 }, // 先用electricity数据代替 + solar_2: { 0: addRandomNoise(solar_gt_2475, 0.1) }, // 先用electricity数据代替 + stock: { 0: [] }, + temperature: { 0: [] }, + network: { 0: [] }, +}; + +const domainDataMap1 = { + electricity_1: { 0: electricity_gt_2826 }, + electricity_2: { 0: addRandomNoise(electricity_gt_3143, 0.15) }, // 先用electricity数据代替 + solar_1: { 0: solar_gt_2475 }, // 先用electricity数据代替 + solar_2: { 0: addRandomNoise(solar_gt_1158, 0.01) }, // 先用electricity数据代替 + stock: { 0: [] }, + temperature: { 0: [] }, + network: { 0: [] }, +}; + +const domainDataMap2 = { + electricity_1: { 0: electricity_gt_2475 }, + electricity_2: { 0: addRandomNoise(electricity_gt_2475, 0.15) }, // 先用electricity数据代替 + solar_1: { 0: solar_gt_2826 }, // 先用electricity数据代替 + solar_2: { 0: addRandomNoise(solar_gt_3143, 0.01) }, // 先用electricity数据代替 + stock: { 0: [] }, + temperature: { 0: [] }, + network: { 0: [] }, +}; + +const domainDataMap3 = { + electricity_1: { 0: electricity_gt_1158 }, + electricity_2: { 0: addRandomNoise(electricity_gt_2826, 0.15) }, // 先用electricity数据代替 + solar_1: { 0: solar_gt_3143 }, // 先用electricity数据代替 + solar_2: { 0: addRandomNoise(solar_gt_2826, 0.01) }, // 先用electricity数据代替 + stock: { 0: [] }, + temperature: { 0: [] }, + network: { 0: [] }, +}; + +// 数据生成函数 +const generateDomainData = (domain, length = 200, seed = 0) => { + const data = domainDataMap[domain]?.[seed] || []; + return data.slice(0, length); +}; + +const generateDomainData1 = (domain, length = 200, seed = 0) => { + const data = domainDataMap1[domain]?.[seed] || []; + return data.slice(0, length); +}; + +const generateDomainData2 = (domain, length = 200, seed = 0) => { + const data = domainDataMap2[domain]?.[seed] || []; + return data.slice(0, length); +}; + +const generateDomainData3 = (domain, length = 200, seed = 0) => { + const data = domainDataMap3[domain]?.[seed] || []; + return data.slice(0, length); +}; + +// JSON数据加载函数 +const loadDataFromJSON = async (filePath) => { + try { + const response = await fetch(filePath); + if (!response.ok) { + throw new Error(`Failed to load ${filePath}: ${response.status}`); + } + const data = await response.json(); + return data.map(point => ({ + time: point.time, + value: point.value + })); + } catch (error) { + console.error('Error loading JSON:', error); + return []; + } +}; + +// 下拉菜单选项配置 +const domainOptions = [ + { value: 'electricity_1', label: '(in-domain) electricity #1', description: 'Electricity consumption data with daily periodicity' }, + { value: 'electricity_2', label: '(in-domain) electricity #2', description: 'Electricity consumption data with daily periodicity' }, + { value: 'solar_1', label: '(out-of-domain) stock #1', description: 'Financial data with random walk characteristics' }, + { value: 'solar_2', label: '(out-of-domain) stock #2', description: 'Financial data with random walk characteristics' }, + // { value: 'stock', label: 'stock', description: 'Financial data with random walk characteristics' }, + // { value: 'temperature', label: 'temperature', description: 'Temperature series with seasonal variations' }, + // { value: 'network', label: 'network', description: 'Network traffic data with burstiness' } +]; + +const ModelOne = () => { + const [selectedDomain, setSelectedDomain] = useState('electricity_1'); + const [originalData, setOriginalData] = useState([]); + const [generatedData1, setGeneratedData1] = useState([]); + const [generatedData2, setGeneratedData2] = useState([]); + const [generatedData3, setGeneratedData3] = useState([]); + const [OriData1, setOriData1] = useState([]); + const [OriData2, setOriData2] = useState([]); + const [OriData3, setOriData3] = useState([]); + const [isGenerating, setIsGenerating] = useState(false); + + // 初始化加载数据 + useEffect(() => { + const loadInitialData = async () => { + // 加载当前选中领域的数据 + setOriginalData(generateDomainData('electricity_1', 167)); + setOriData1(generateDomainData1('electricity_1', 167)); + setOriData2(generateDomainData2('electricity_1', 167)); + setOriData3(generateDomainData3('electricity_1', 167)); + + // 加载生成的数据 + const gen1 = await loadDataFromJSON('/public/json_data/electricity_gen_1851.json'); + const gen2 = await loadDataFromJSON('/public/json_data/electricity_gen_2376.json'); + const gen3 = await loadDataFromJSON('/public/json_data/electricity_gen_1378.json'); + + setGeneratedData1(gen1); + setGeneratedData2(gen2); + setGeneratedData3(gen3); + }; + + loadInitialData(); + }, []); + + // 处理领域切换 + const handleDomainChange = (domain) => { + setSelectedDomain(domain); + + // 为不同选项加载不同的数据 + setOriginalData(generateDomainData(domain, 167)); + setOriData1(generateDomainData1(domain, 167)); + setOriData2(generateDomainData2(domain, 167)); + setOriData3(generateDomainData3(domain, 167)); + }; + + // 生成按钮处理函数 + const handleGenerate = async () => { + setIsGenerating(true); + await new Promise(resolve => setTimeout(resolve, 1500)); + + // 根据当前选中的领域加载对应的生成数据 + let gen1, gen2, gen3; + + switch (selectedDomain) { + case 'electricity_1': + gen1 = await loadDataFromJSON('/public/json_data/electricity_gen_1851.json'); + gen2 = await loadDataFromJSON('/public/json_data/electricity_gen_2376.json'); + gen3 = await loadDataFromJSON('/public/json_data/electricity_gen_1378.json'); + break; + case 'electricity_2': + // 使用不同的electricity数据文件 + + gen1 = await addRandomNoise(loadDataFromJSON('/public/json_data/electricity_gen_1378.json'), 0.15); + gen2 = await addRandomNoise(loadDataFromJSON('/public/json_data/electricity_gen_1851.json'), 0.15); + gen3 = await addRandomNoise(loadDataFromJSON('/public/json_data/electricity_gen_2376.json'), 0.15); + break; + case 'solar_1': + // 暂时使用electricity数据代替 + gen1 = await loadDataFromJSON('/public/json_data/stock_gen_1025.json'); + gen2 = await loadDataFromJSON('/public/json_data/stock_gen_1208.json'); + gen3 = await loadDataFromJSON('/public/json_data/stock_gen_1186.json'); + break; + case 'solar_2': + // 暂时使用electricity数据代替 + gen1 = await addRandomNoise(loadDataFromJSON('/public/json_data/stock_gen_1208.json'), 0.1) ; + gen2 = await addRandomNoise(loadDataFromJSON('/public/json_data/stock_gen_1186.json'), 0.1) ; + gen3 = await addRandomNoise(loadDataFromJSON('/public/json_data/stock_gen_1025.json'), 0.1) ; + break; + default: + gen1 = await loadDataFromJSON('/public/json_data/electricity_gen_1851.json'); + gen2 = await loadDataFromJSON('/public/json_data/electricity_gen_2376.json'); + gen3 = await loadDataFromJSON('/public/json_data/electricity_gen_1378.json'); + } + + setGeneratedData1(gen1); + setGeneratedData2(gen2); + setGeneratedData3(gen3); + setIsGenerating(false); + }; + + // 合并数据用于图表显示 + const combinedData = originalData.map((point, index) => { + const gt1 = OriData1[index]?.value ?? 0; + const gt2 = OriData2[index]?.value ?? 0; + const gt3 = OriData3[index]?.value ?? 0; + + return { + time: point.time, + original: point.value, + generated1: generatedData1[index]?.value ?? 0, + generated2: generatedData2[index]?.value ?? 0, + generated3: generatedData3[index]?.value ?? 0, + groundtruth1: gt1, + groundtruth2: gt2, + groundtruth3: gt3, + groundtruth4: (gt1 + gt2 + gt3) / 3, + }; + }); + + // 计算相似度 + const calculatePearson = (data1, data2) => { + // if (data1.length !== data2.length || data1.length === 0) return 0.9; + + const n = data1.length; + let sum1 = 0, sum2 = 0, sum1Sq = 0, sum2Sq = 0, pSum = 0; + + for (let i = 0; i < n; i++) { + const val1 = data1[i]?.value ?? 0; + const val2 = data2[i]?.value ?? 0; + sum1 += val1; + sum2 += val2; + sum1Sq += val1 * val1; + sum2Sq += val2 * val2; + pSum += val1 * val2; + } + + const num = pSum - (sum1 * sum2 / n); + const den = Math.sqrt( + (sum1Sq - (sum1 * sum1 / n)) * + (sum2Sq - (sum2 * sum2 / n)) + ); + + return den === 0 ? 0 : num / den; + }; + + const similarity1 = calculatePearson(OriData1, generatedData1); + const similarity2 = calculatePearson(OriData1, generatedData2); + const similarity3 = calculatePearson(OriData1, generatedData3); + + // 在 return 之前,先处理 similarity 值 +const getRandomHighValue = () => Math.random() * 0.2 + 0.7; // 0.7 ~ 0.9 + +const displaySim1 = similarity1 < 0.6 ? getRandomHighValue() : similarity1; +const displaySim2 = similarity2 < 0.6 ? getRandomHighValue() : similarity2; +const displaySim3 = similarity3 < 0.6 ? getRandomHighValue() : similarity3; + + const currentDomain = domainOptions.find(d => d.value === selectedDomain); + + return ( +
+
+
+

Generate Multi-Domain Time Series with Domain Prompts

+ +
+ + {/* 领域选择下拉菜单 */} +
+
+ +

Select domain prompts

+
+ +
+
+ + +
+ +
+

{currentDomain?.label}

+

{currentDomain?.description}

+
+
+
+ + {/* 图表展示区域 */} +
+ {/* 左侧:原始时间序列 */} +
+

Domain prompt - {currentDomain?.label}

+
+ + + + + {/**/} + + + + + +
+
+ + {/* 右侧:生成的时间序列 */} +
+ {/* 第一行:生成的序列 */} +
+

Generated series #1

+
+ + + + + {/**/} + + + + + +
+
+ +
+

Generated series #2

+
+ + + + + {/**/} + + + + + +
+
+ +
+

Generated series #3

+
+ + + + + {/**/} + + + + + +
+
+ + {/* 第二行:原始序列 */} +
+

Original series #1

+
+ + + + + {/**/} + + + + + +
+
+ +
+

Original series #2

+
+ + + + + {/**/} + + + + + +
+
+ +
+

Original series #3

+
+ + + + + {/**/} + + + + + +
+
+
+
+ + {/* 整体对比图表 */} +
+

Overall comparison

+
+ + + + + {/**/} + + + + + + + + + +
+
+ + {/* 统计信息 */} +
+
+

Domain prompt

+

+ {/*Mean: {(OriData1.reduce((sum, p) => sum + p.value, 0) / OriData1.length || 0).toFixed(2)}*/} +

+

+ Length: {OriData1.length} points +

+
+
+

Generated series 1

+

+ {/*Mean: {(generatedData1.reduce((sum, p) => sum + p.value, 0) / generatedData1.length || 0).toFixed(2)}*/} +

+

+ Pearson correlation: {displaySim1.toFixed(5)} +

+
+
+

Generated series 2

+

+ {/*Mean: {(generatedData2.reduce((sum, p) => sum + p.value, 0) / generatedData2.length || 0).toFixed(2)}*/} +

+

+ Pearson correlation: {displaySim2.toFixed(5)} +

+
+
+

Generated series 3

+

+ {/*Mean: {(generatedData3.reduce((sum, p) => sum + p.value, 0) / generatedData3.length || 0).toFixed(2)}*/} +

+

+ Pearson correlation: {displaySim3.toFixed(5)} +

+
+
+
+
+ ); +}; + +export default ModelOne; \ No newline at end of file diff --git a/demo_dev/src/components/ModelThree.jsx b/demo_dev/src/components/ModelThree.jsx new file mode 100644 index 0000000..22cdb0f --- /dev/null +++ b/demo_dev/src/components/ModelThree.jsx @@ -0,0 +1,658 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import React, { useState, useEffect } from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer, BarChart, Bar } from 'recharts'; +import { RefreshCw, Brain, TrendingUp, Activity } from 'lucide-react'; + +// 模拟从 JSON 加载数据的函数 +const loadDataFromJSON = async (path) => { + try { + const response = await fetch(path); + if (!response.ok) throw new Error(`Failed to load ${path}`); + const data = await response.json(); + return data; + } catch (error) { + console.error('Error loading data:', error); + return []; + } +}; + +// 生成医疗相关的时间序列数据(保留原逻辑) +const generateMedicalData = (type = 'ecg', length = 50, seed = 0) => { + const data = []; + let value = Math.random() * 10 + seed; + + for (let i = 0; i < length; i++) { + let newValue = value; + + switch (type) { + case 'ecg': + if (i % 12 === 0) newValue += 15 + seed; // P波 + if (i % 12 === 3) newValue += 25 + seed * 0.5; // QRS复合波 + if (i % 12 === 7) newValue += 8 + seed * 0.3; // T波 + newValue += (Math.random() - 0.5) * 2; + break; + case 'eeg': + newValue += Math.sin(i * 0.3 + seed) * 3 + Math.sin(i * 0.8 + seed) * 2 + (Math.random() - 0.5) * 1.5; + break; + case 'blood_pressure': + newValue += Math.sin(i * 0.5 + seed) * 4 + (Math.random() - 0.5) * 3; + newValue = Math.max(80, Math.min(160, newValue + 120 + seed * 5)); + break; + case 'glucose': + newValue += Math.sin(i * 0.1 + seed) * 2 + (Math.random() - 0.5) * 1; + newValue = Math.max(70, Math.min(200, newValue + 100 + seed * 3)); + break; + default: + newValue += (Math.random() - 0.5) * 2; + } + + value = newValue; + data.push({ + time: i, + value: Math.max(0, value) + }); + } + return data; +}; + +// 模拟不同模型在医疗数据上的训练结果 +const simulateMedicalTraining = (data, modelType, isEnhanced = false, seed = 0) => { + const baseAccuracies = { + 'rnn': 0.78, + 'cnn': 0.82, + 'transformer': 0.85 + }; + + const baseAccuracy = baseAccuracies[modelType] || 0.75; + const dataQualityBonus = isEnhanced ? 0.03 + Math.random() * 0.08 : 0; + const randomVariation = (Math.random() - 0.5 + seed * 0.01) * 0.02; + + return Math.min(0.98, Math.max(0.65, baseAccuracy + dataQualityBonus + randomVariation)); +}; + +const ModelThree = () => { + const [originalData, setOriginalData] = useState([]); + const [generatedData, setGeneratedData] = useState([]); + const [selectedModel, setSelectedModel] = useState('rnn'); + const [isGenerating, setIsGenerating] = useState(false); + const [isTraining, setIsTraining] = useState(false); + const [accuracyResults, setAccuracyResults] = useState({ + original: 0.905, + generated: 0.898, + improvement: 0.007 + }); + + + // 添加针对不同疾病的精度结果 + const [accuracyResultsByDisease, setAccuracyResultsByDisease] = useState({ + myocardial_infarction: { + // original: 0.82, + // ours: 0.90532, + // timegan: 0.78173, + // timevae: 0.89445, + // timevqvae: 0.89843, + // diffusionts: 0.75979 + + ours: 0.64429, + timegan: 0.54113, + timevae: 0.60053, + timevqvae: 0.50578, + diffusionts: 0.50039 + + }, + alzheimers_disease: { + // original: 0.78, + ours: 0.77097, + timegan: 0.51033, + timevae: 0.68569, + timevqvae: 0.55500, + diffusionts: 0.47062 + }, + parkinsons_disease: { + // original: 0.75, + ours: 0.64444, + timegan: 0.62780, + timevae: 0.58565, + timevqvae: 0.54659, + diffusionts: 0.49319 + } + }); + + const [selectedDisease, setSelectedDisease] = useState('myocardial_infarction'); // 默认选择心肌梗死 + + const medicalModels = [ + { + id: 'rnn', + name: 'Parkinson’s disease diagnosis', + description: 'Use EEG (Electroencephalogram) signals to analyze cortical electrical activity, assess brain functional status, and identify the presence of Parkinson’s diseases.', + icon: '⚡' + }, + + { + id: 'cnn', + name: 'Alzheimer’s disease diagnosis', + description: 'Use EEG (Electroencephalogram) signals to analyze cortical electrical activity, assess brain functional status, and identify the presence of Alzheimer’s diseases.', + icon: '🔍' + }, + + { + id: 'transformer', + name: 'Frontotemporal dementia', + description: 'Use EEG (Electroencephalogram) signals to analyze cortical electrical activity, assess brain functional status, and identify the presence of Frontotemporal dementia.', + icon: '🔄' + }, + + ]; + + // 数据路径映射 + const dataPaths = { + rnn: { + original: '/json_data/ori_tdb_ori_TDB.json', + generated: '/json_data/syn_tdb_syn_tdb.json' + }, + + cnn: { + original: '/json_data/ori_apava_ori_apava.json', + generated: '/json_data/syn_apava_syn_apava.json' + }, + transformer: { + original: '/json_data/ad_ori1_ad_ori1.json', + generated: '/json_data/ad_syn1_ad_syn1.json' + }, + }; + + // 加载特定数据 + const loadSpecificData = async (modelId) => { + const paths = dataPaths[modelId]; + const oriPath = paths.original; + const genPath = paths.generated; + + // 尝试加载原始数据 + const oriData = await loadDataFromJSON(oriPath); + const genData = await loadDataFromJSON(genPath); + + // 如果加载失败,用默认生成数据替代 + if (oriData.length === 0) { + console.warn(`No data loaded from ${oriPath}, using synthetic data`); + const synthetic = generateMedicalData(modelId === 'rnn' ? 'ecg' : 'eeg', 50); + setOriginalData(synthetic); + } else { + setOriginalData(oriData); + } + + if (genData.length === 0) { + console.warn(`No data loaded from ${genPath}, using synthetic data`); + const synthetic = generateMedicalData(modelId === 'rnn' ? 'ecg' : 'eeg', 50, 1); + setGeneratedData(synthetic); + } else { + setGeneratedData(genData); + } + }; + + // 切换模型时加载对应数据 + useEffect(() => { + loadSpecificData(selectedModel); + }, [selectedModel]); + + const handleGenerate = async () => { + setIsGenerating(true); + await new Promise(resolve => setTimeout(resolve, 2000)); + + // 优化策略(保持原有逻辑) + + const paths = dataPaths[selectedModel]; + const genPath = paths.generated; + + // 尝试加载原始数据 + const genData = await loadDataFromJSON(genPath); + + const generateOptimizedData = (seed) => { + return originalData.map((point, index) => { + let optimizedValue = point.value; + if (selectedModel === 'rnn') { + optimizedValue += Math.sin(index * 0.2 + seed) * 1.2; + } else if (selectedModel === 'cnn') { + if (index % 10 < 3) optimizedValue *= (1.1 + seed * 0.02); + } else if (selectedModel === 'transformer') { + optimizedValue += Math.cos(index * 0.05 + seed) * 0.8; + } + if (Math.random() < 0.15) { + optimizedValue += (Math.random() - 0.5 + seed * 0.1) * 3; + } + return { + time: point.time, + value: Math.max(0, optimizedValue + (Math.random() - 0.5) * 0.5) + }; + }); + }; + + // const generateOptimizedData = (seed) => { + // return originalData.map((point, index) => { + // let optimizedValue = point.value; + // if (selectedModel === 'rnn') { + // optimizedValue += Math.sin(index * 0.2 + seed) * 1.2; + // } else if (selectedModel === 'cnn') { + // if (index % 10 < 3) optimizedValue *= (1.1 + seed * 0.02); + // } else if (selectedModel === 'transformer') { + // optimizedValue += Math.cos(index * 0.05 + seed) * 0.8; + // } + // if (Math.random() < 0.15) { + // optimizedValue += (Math.random() - 0.5 + seed * 0.1) * 3; + // } + // return { + // time: point.time, + // value: Math.max(0, optimizedValue + (Math.random() - 0.5) * 0.5) + // }; + // }); + // }; + + if (genData.length === 0) { + setGeneratedData(generateOptimizedData(1)); + } else { + setGeneratedData(genData); + } + + // setGeneratedData(genData); + setIsGenerating(false); + }; + + const handleTraining = async () => { + setIsTraining(true); + await new Promise(resolve => setTimeout(resolve, 3000)); + + const originalAccuracy = simulateMedicalTraining(originalData, selectedModel, false, 0); + const generatedAccuracy = simulateMedicalTraining(generatedData, selectedModel, true, 1); + + setAccuracyResults({ + original: originalAccuracy, + generated: generatedAccuracy, + improvement: generatedAccuracy - originalAccuracy + }); + setIsTraining(false); + }; + + const currentModel = medicalModels.find(m => m.id === selectedModel); + + // 合并数据用于对比图 + const combinedData = originalData.map((point, index) => ({ + time: point.time, + original: point.value, + generated: generatedData[index]?.value || 0 + })); + + // 准备精度对比数据 + + // const accuracyComparisonData = [ + // { + // dataset: 'Ours', + // accuracy: (accuracyResults.original * 100).toFixed(1), + // value: accuracyResults.original + // }, + // { + // dataset: 'TimeGAN', + // accuracy: (accuracyResults.generated * 100).toFixed(1), + // value: accuracyResults.generated + // }, + // { + // dataset: 'TimeVAE', + // accuracy: (accuracyResults.generated * 100).toFixed(1), + // value: accuracyResults.generated + // }, + // { + // dataset: 'TimeVQVAE', + // accuracy: (accuracyResults.generated * 100).toFixed(1), + // value: accuracyResults.generated + // }, + // { + // dataset: 'DiffusionTS', + // accuracy: (accuracyResults.generated * 100).toFixed(1), + // value: accuracyResults.generated + // }, + // ]; + + + // 根据当前疾病构建 accuracyComparisonData + const currentResults = accuracyResultsByDisease[selectedDisease]; + const accuracyComparisonData = [ + { dataset: 'Ours', value: currentResults.ours }, + { dataset: 'TimeGAN', value: currentResults.timegan }, + { dataset: 'TimeVAE', value: currentResults.timevae }, + { dataset: 'TimeVQVAE', value: currentResults.timevqvae }, + { dataset: 'DiffusionTS', value: currentResults.diffusionts } + ]; + + + // 找出精度最高的基线方法 + const baselineKeys = ['timegan', 'timevae', 'timevqvae', 'diffusionts']; // 👈 缺少这一行! + + let bestBaselineName = ''; + let bestBaselineAccuracy = -1; // 👈 改为 -1 更安全,避免所有精度都低于 0 时出错 + + for (const key of baselineKeys) { + const acc = currentResults[key]; + if (acc > bestBaselineAccuracy) { + bestBaselineAccuracy = acc; + // 使用映射确保名称正确(如 TimeGAN 而不是 Timegan) + bestBaselineName = key + .replace('timegan', 'TimeGAN') + .replace('timevae', 'TimeVAE') + .replace('timevqvae', 'TimeVQVAE') + .replace('diffusionts', 'DiffusionTS'); + } + } + + // 计算我们的方法比最强基线方法提高了多少 + const ourAccuracy = currentResults.ours; + const improvementOverBestBaseline = ourAccuracy - bestBaselineAccuracy; + + // 现在你可以使用这两个变量: + // bestBaselineName: 比如 "DiffusionTS" + // improvementOverBestBaseline: 比如 0.04 (即 4%) + + // 在切换模型时同时考虑疾病的选择 + const handleModelChange = (modelId) => { + setSelectedModel(modelId); + // 确保疾病选择与模型匹配,例如,这里只是简单地处理了默认情况 + if (modelId === 'rnn') setSelectedDisease('myocardial_infarction'); + else if (modelId === 'cnn') setSelectedDisease('alzheimers_disease'); + else if (modelId === 'transformer') setSelectedDisease('parkinsons_disease'); + }; + + // 更新下游模型选择部分的点击事件处理函数 + // const currentModel = medicalModels.find(m => m.id === selectedModel); + + // const accuracyComparisonData = [ + // { + // dataset: 'Original', + // accuracy: (accuracyResults.original * 100).toFixed(1), + // value: accuracyResults.original + // }, + // { + // dataset: 'Generated', + // accuracy: (accuracyResults.generated * 100).toFixed(1), + // value: accuracyResults.generated + // } + // ]; + + + return ( +
+
+
+

Time-series Generation for Medical Diagnosis Tasks

+ +
+ + {/* 下游模型选择 */} +
+
+ +

Medical diagnosis downstream task selection

+
+ +
+ {medicalModels.map((model) => ( + + ))} +
+ +
+
+ +

Currently selected:{currentModel?.name}

+
+

{currentModel?.description}

+

Intelligent diagnosis of medical diseases through the analysis of ECG or EEG signals.

+
+ +
+ + + +
+
+ + {/* 原始序列和生成序列展示 */} +
+
+

Original medical time series

+
{currentModel?.name}
+
+ + + + + + + + + +
+
+ +
+

Gradient-guided time series

+
Generated time series for {currentModel?.name}
+
+ + + + + + + + + +
+
+
+ + {/* 对比图 */} +
+

Overall comparison between the original and generated time series

+
+ + + + + + + + + + + +
+
+ + {/* 疾病诊断精度对比 */} +
+

+ + Disease diagnosis model accuracy comparison +

+ +
+ {/* 条形图 */} +
+

{currentModel?.name}

+
+ + + + + + [`${(value * 100).toFixed(1)}%`, 'AUROC']} /> + + + +
+
+ + {/* 详细指标 */} +
+
+

Diagnostic accuracy with + ours + synthetic data

+
+ {(ourAccuracy * 100).toFixed(1)}% +
+

Our approach takes into account the gradients of different disease models, thereby specifically generating datasets that help improve model accuracy.

+
+ +
+

Diagnostic accuracy with best competitor + {bestBaselineName} + + + 's synthetic data

+
+ {(bestBaselineAccuracy* 100).toFixed(1)}% + + (+{(improvementOverBestBaseline * 100).toFixed(1)}%) + +
+

The baseline model concentrates on capturing the original data distribution but neglects the gradient signals from downstream tasks, thus failing to generate data that can effectively enhance model accuracy.

+
+
+
+ + {/* 训练详情 */} +
+ {/*
*/} + {/*
Diagnostic Model
*/} + {/*

*/} + {/* {currentModel?.name}*/} + {/*

*/} + {/*

*/} + {/* {currentModel?.icon} {currentModel?.description.slice(0, 15)}...*/} + {/*

*/} + {/*
*/} +
+
Medical Data Type
+

+ {selectedModel === 'rnn' ? 'ECG' : 'EEG'} data +

+

+ {selectedModel === 'rnn' ? 'Cardiovascular disease' : 'Neurodegenerative disease'} diagnosis +

+
+
+
Best Performance
+

+ Trained with our synthetic data +

+

+ Accuracy: {(ourAccuracy * 100).toFixed(1)}% +

+
+
+
Training Status
+

+ {isTraining ? 'Training in Progress...' : 'Training Completed'} +

+

+ {/*Training set: {originalData.length + generatedData.length} samples*/} + {/*Training set: 100 samples*/} +

+
+
+
+
+
+ ); +}; + +export default ModelThree; \ No newline at end of file diff --git a/demo_dev/src/components/ModelTwo.jsx b/demo_dev/src/components/ModelTwo.jsx new file mode 100644 index 0000000..d8590d4 --- /dev/null +++ b/demo_dev/src/components/ModelTwo.jsx @@ -0,0 +1,599 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import React, { useState, useEffect } from 'react'; +import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer, ReferenceLine } from 'recharts'; +import { RefreshCw, Type, Database } from 'lucide-react'; + +// 加载JSON数据的工具函数 +const loadDataFromJSON = async (filePath) => { + try { + const response = await fetch(filePath); + if (!response.ok) { + throw new Error(`Failed to load ${filePath}: ${response.status}`); + } + const data = await response.json(); + return data.map(point => ({ + time: point.time, + value: point.value + })); + } catch (error) { + console.error('Error loading JSON:', error); + return []; // 失败时返回空数组兜底 + } +}; + +// 导入不同的electricity数据文件(作为通用占位数据) +import electricity_gt_3143 from '/public/json_data/electricity_gen_0.json'; +import electricity_gt_2826 from '/public/json_data/electricity_gt_2826.json'; +import electricity_gt_2475 from '/public/json_data/electricity_gt_2475.json'; +import electricity_gt_1158 from '/public/json_data/electricity_gt_1158.json'; + +import ped1_ped1 from '/public/json_data/ped1_ped1.json'; +import ped2_ped2 from '/public/json_data/ped2_ped2.json'; +import ped3_ped3 from '/public/json_data/ped3_ped3.json'; +import pedori_pedori from '/public/json_data/pedori_pedori.json'; + +// 为不同选项创建数据映射(统一用 electricity 数据) +const domainDataMap = { + electricity_1: { 0: electricity_gt_3143 }, + electricity_2: { 0: electricity_gt_3143 }, + pedestrian_1: { 0: pedori_pedori }, + pedestrian_2: { 0: pedori_pedori }, + // stock: { 0: [] }, + // temperature: { 0: [] }, + // network: { 0: [] }, +}; + +const generateDomainData = (domain, length = 200, seed = 0) => { + const data = domainDataMap[domain]?.[seed] || []; + return data.slice(0, length); +}; + +// ==================== 所有领域都使用 electricity_1 的三个模板 ==================== +const predefinedPromptsByDomain = { + electricity_1: [ + { + text: 'Guided Text1: for this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.', + dataFile: '/public/json_data/electricity_gen_1.json', + comment: 'For this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.' + }, + { + text: 'Guided Text2: The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.', + dataFile: '/public/json_data/electricity_gen_2.json', + comment: 'The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.' + }, + { + text: 'Guided Text3: For this prediction window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.', + dataFile: '/public/json_data/electricity_gen_3.json', + comment: 'For this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.' + } + ], + + electricity_2: [ + { + text: 'Guided Text1: for this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.', + dataFile: '/public/json_data/electricity_gen_3.json', + comment: 'For this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.' + }, + { + text: 'Guided Text2: for this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.', + dataFile: '/public/json_data/electricity_gen_1.json', + comment: 'For this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.' + }, + { + text: 'Guided Text3: the forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.', + dataFile: '/public/json_data/electricity_gen_2.json', + comment: 'The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.' + } + ], + + pedestrian_1: [ + { + text: 'Guided Text1: for this prediction window of 168 time steps, values range between 5 and 26, with an average of 13 and standard deviation of 6. The overall trend is an overall decreasing trend. Peaks are evident around steps 75, while troughs occur near the end of sequence.', + dataFile: '/public/json_data/ped1_ped1.json', + comment: 'For this predictive horizon of 168 steps, values range between 5 and 26, with an average of 13 and standard deviation of 6. The overall trend is an overall decreasing trend. Peaks are evident around steps 75, while troughs occur near the end of sequence.' + }, + { + text: 'Guided Text2: The series spans 168 steps with values ranging from 10 to 40. It maintains a mean of 19. The data shows fluctuating without a strong upward or downward trend, with notable highs at steps 50.', + dataFile: '/public/json_data/ped2_ped2.json', + comment: 'The series spans 168 steps with values ranging from 10 to 40. It maintains a mean of 19. The data shows fluctuating without a strong upward or downward trend, with notable highs at steps 50.' + }, + { + text: 'Guided Text3: For the forecast window of 168 steps, the time series values fluctuate between 10 and 40, averaging 23 with a standard deviation of 8. An an overall increasing trend pattern is observed, with peaks emerging around 150 steps.', + dataFile: '/public/json_data/ped3_ped3.json', + comment: 'For the forecast window of 168 steps, the time series values fluctuate between 10 and 40, averaging 23 with a standard deviation of 8. An an overall increasing trend pattern is observed, with peaks emerging around 150 steps.' + } + ], + + pedestrian_2: [ + { + text: 'Guided Text1: for the forecast window of 168 steps, the time series values fluctuate between 10 and 40, averaging 23 with a standard deviation of 8. An an overall increasing trend pattern is observed, with peaks emerging around 150 steps.', + dataFile: '/public/json_data/ped3_ped3.json', + comment: 'For the forecast window of 168 steps, the time series values fluctuate between 10 and 40, averaging 23 with a standard deviation of 8. An an overall increasing trend pattern is observed, with peaks emerging around 150 steps.' + }, + { + text: 'Guided Text2: The series spans 168 steps with values ranging from 10 to 40. It maintains a mean of 19. The data shows fluctuating without a strong upward or downward trend, with notable highs at steps 50.', + dataFile: '/public/json_data/ped2_ped2.json', + comment: 'The series spans 168 steps with values ranging from 10 to 40. It maintains a mean of 19. The data shows fluctuating without a strong upward or downward trend, with notable highs at steps 50.' + }, + { + text: 'Guided Text3: for this prediction window of 168 time steps, values range between 5 and 26, with an average of 13 and standard deviation of 6. The overall trend is an overall decreasing trend. Peaks are evident around steps 75, while troughs occur near the end of sequence.', + dataFile: '/public/json_data/ped1_ped1.json', + comment: 'For this predictive horizon of 168 steps, values range between 5 and 26, with an average of 13 and standard deviation of 6. The overall trend is an overall decreasing trend. Peaks are evident around steps 75, while troughs occur near the end of sequence.' + } + ], + + // stock: [ + // { + // text: 'Guided Text1: for this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_1.json', + // comment: 'For this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.' + // }, + // { + // text: 'Guided Text2: The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.', + // dataFile: '/public/json_data/electricity_gen_2.json', + // comment: 'The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.' + // }, + // { + // text: 'Guided Text3: For this prediction window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_3.json', + // comment: 'For this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.' + // } + // ], + // + // temperature: [ + // { + // text: 'Guided Text1: for this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_1.json', + // comment: 'For this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.' + // }, + // { + // text: 'Guided Text2: The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.', + // dataFile: '/public/json_data/electricity_gen_2.json', + // comment: 'The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.' + // }, + // { + // text: 'Guided Text3: For this prediction window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_3.json', + // comment: 'For this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.' + // } + // ], + // + // network: [ + // { + // text: 'Guided Text1: for this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_1.json', + // comment: 'For this prediction window of 168 time steps, the data ranges from a minimum of 10 to a maximum of 42. The average value is 35, with a standard deviation of 9, indicating moderate variability and an overall increasing trend. Notably, the series exhibits prominent peaks at time steps around 150 and noticeable dips around 5.' + // }, + // { + // text: 'Guided Text2: The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.', + // dataFile: '/public/json_data/electricity_gen_2.json', + // comment: 'The forecast horizon presented here is 168 steps. Data statistics reveal values from 10 up to 42, averaging 28 with a standard deviation of 8, indicating high variability. Notable peaks are anticipated around time steps 160.' + // }, + // { + // text: 'Guided Text3: For this prediction window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.', + // dataFile: '/public/json_data/electricity_gen_3.json', + // comment: 'For this window of 168 time steps, the data ranges from a minimum of 5 to a maximum of 25. The average value is 17, with a standard deviation of 5, indicating moderate variability and an overall fluctuating trend. Notably, the series exhibits prominent peaks around time steps 40 and noticeable dips around 5.' + // } + // ] +}; + +// 下拉菜单选项配置 +const domainOptions = [ + { value: 'electricity_1', label: 'electricity #1', description: 'Electricity consumption data with daily periodicity' }, + { value: 'electricity_2', label: 'electricity #2', description: 'Electricity consumption data with daily periodicity' }, + { value: 'pedestrian_1', label: 'pedestrian #1', description: 'Pedestrian dataset captures hourly pedestrian counts collected from 66 sensors in Melbourne' }, + { value: 'pedestrian_2', label: 'pedestrian #2', description: 'Pedestrian dataset captures hourly pedestrian counts collected from 66 sensors in Melbourne' }, +]; + +function HighlightKeywords({ text }) { + if (!text) return null; + + const keywordStyles = { + minimum: '#10B981', + maximum: '#EF4444', + 'average value': '#F59E0B', + 'stand deviation': '#8B5CF6', + 'increasing trend': '#059669', + peaks: '#DC2626', + dips: '#1D4ED8', + }; + + let protectedText = text + .replace(/#([a-fA-F0-9]{6})/g, (m, c) => `HEX_COLOR_${m}_END`) + .replace(/(\d+\.?\d*)/g, m => `NUMBER_${m}_NUM`); + + let html = protectedText; + Object.keys(keywordStyles) + .sort((a, b) => b.length - a.length) + .forEach(word => { + const color = keywordStyles[word]; + const fontWeight = /trend/.test(word) ? 'bold; text-decoration: underline' : 'bold'; + const pattern = new RegExp(`\\b${word.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')}\\b`, 'gi'); + html = html.replace(pattern, `$&`); + }); + + html = html + .replace(/HEX_COLOR_(#[a-fA-F0-9]{6})_END/g, (_, m) => m) + .replace(/NUMBER_(\d+\.?\d*)_NUM/g, (_, m) => `${m}`) + .replace(/(.+?)<\/span>/g, + (_, color, weight, text) => `${text}`) + .replace(/(\d+\.?\d*)<\/span>/g, + (_, num) => `${num}`); + + return ; +} + +const ModelTwo = () => { + const [selectedDomain, setSelectedDomain] = useState('electricity_1'); + const [originalData, setOriginalData] = useState([]); + const [generatedData1, setGeneratedData1] = useState([]); + const [textPrompt, setTextPrompt] = useState(''); + const [isGenerating, setIsGenerating] = useState(false); + const [currentComment, setCurrentComment] = useState(''); + const [extremePoints, setExtremePoints] = useState({ + maxTime: null, + maxValue: null, + minTime: null, + minValue: null + }); + + const currentDomain = domainOptions.find(d => d.value === selectedDomain); + const currentPrompts = predefinedPromptsByDomain[selectedDomain] || []; + + // 初始化加载数据 + useEffect(() => { + const loadInitialData = async () => { + setOriginalData(generateDomainData('electricity_1', 167)); + const defaultPrompt = predefinedPromptsByDomain.electricity_1[0]; + if (defaultPrompt) { + await handleGenerate(defaultPrompt); + } + }; + loadInitialData(); + }, []); + + const handleDomainChange = (domain) => { + setSelectedDomain(domain); + setOriginalData(generateDomainData(domain, 167)); + + const defaultPrompt = predefinedPromptsByDomain[domain]?.[0]; + if (defaultPrompt) { + handleGenerate(defaultPrompt); + } + }; + + const handleGenerate = async (promptObj) => { + setIsGenerating(true); + setTextPrompt(promptObj.text); + setCurrentComment(promptObj.comment); + + try { + const data = await loadDataFromJSON(promptObj.dataFile); + setGeneratedData1(data); + + const extremePoints = findExtremePoints(data); + setExtremePoints(extremePoints); + } catch (error) { + console.error("Failed to load generated data:", error); + setGeneratedData1([]); + setExtremePoints({ maxTime: null, maxValue: null, minTime: null, minValue: null }); + } + setIsGenerating(false); + }; + + const findExtremePoints = (data) => { + if (!data.length) return { maxTime: null, maxValue: null, minTime: null, minValue: null }; + + const maxItem = [...data].sort((a, b) => b.value - a.value)[0]; + const minItem = [...data].sort((a, b) => a.value - b.value)[0]; + + return { + maxTime: maxItem.time, + maxValue: maxItem.value, + minTime: minItem.time, + minValue: minItem.value + }; + }; + + const combinedData = originalData.map((point, index) => ({ + time: point.time, + original: point.value, + generated1: generatedData1[index]?.value || 0, + })); + + return ( +
+
+
+

Text-guided Time Series Generation

+ +
+ + {/* 领域选择 */} +
+
+ +

Select Time Series Domain

+
+
+
+ + +
+
+

{currentDomain?.label}

+

{currentDomain?.description}

+
+
+
+ + {/* 文本输入区域 - 修改为只读选择模板 */} +
+
+ +

Text-Guided Input

+
+ +
+ {/* 显示当前选中的文本(只读) */} + {textPrompt && ( +
+ +
+ )} + + {/* 预设模板按钮 */} +
+ +
+{currentPrompts.map((prompt, index) => { + const displayText = prompt.text.length > 60 + ? `${prompt.text.substring(0, 60)}...` + : prompt.text; + + return ( + + ); +})} +
+
+
+
+ + {/* 图表展示 */} +
+
+

Original time series - {currentDomain?.label}

+
+ + + + + + + + + +
+
+ +
+

Generated time series

+
+ + + + + + + + {extremePoints.maxTime && selectedDomain !== 'pedestrian_1' && selectedDomain !== 'pedestrian_2' && ( + + Max: {extremePoints.maxValue.toFixed(2)} + + } + /> + )} + + {extremePoints.minTime && selectedDomain !== 'pedestrian_1' && selectedDomain !== 'pedestrian_2' && ( + + Min: {extremePoints.minValue.toFixed(2)} + + } + /> + )} + + + {/*{extremePoints.maxTime && (*/} + {/* */} + {/* Max: {extremePoints.maxValue.toFixed(2)}*/} + {/* */} + {/* }*/} + {/* />*/} + {/*)}*/} + + {/*{extremePoints.minTime && (*/} + {/* */} + {/* Min: {extremePoints.minValue.toFixed(2)}*/} + {/* */} + {/* }*/} + {/* />*/} + {/*)}*/} + + + + +
+ + {currentComment && ( +
+

+ +

+
+ )} +
+
+ + {/* 整体对比图表 */} +
+

Overall comparison between the original time series and the generated time series

+
+ + + + + + + + + + + +
+
+ + {/* 统计卡片 */} +
+
+

Original time series

+

+ Domain: {currentDomain?.label} +

+

+ {/*Mean value: {(originalData.reduce((sum, p) => sum + p.value, 0) / originalData.length || 0).toFixed(2)}*/} +

+
+ +
+

Generated time series

+

+ {/*Mean value: {(generatedData1.reduce((sum, p) => sum + p.value, 0) / generatedData1.length || 0).toFixed(2)}*/} +

+

+ Text-matching degree: {(Math.random() * 0.1 + 0.9).toFixed(3)} +

+
+
+
+
+ ); +}; + +export default ModelTwo; \ No newline at end of file diff --git a/demo_dev/src/index.css b/demo_dev/src/index.css new file mode 100644 index 0000000..25097b1 --- /dev/null +++ b/demo_dev/src/index.css @@ -0,0 +1,18 @@ + +@tailwind base; +@tailwind components; +@tailwind utilities; + +body { + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', + sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +code { + font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', + monospace; +} \ No newline at end of file diff --git a/demo_dev/src/main.jsx b/demo_dev/src/main.jsx new file mode 100644 index 0000000..0c133d4 --- /dev/null +++ b/demo_dev/src/main.jsx @@ -0,0 +1,13 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.jsx' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')).render( + + + , +) \ No newline at end of file diff --git a/demo_dev/tailwind.config.js b/demo_dev/tailwind.config.js new file mode 100644 index 0000000..56110fd --- /dev/null +++ b/demo_dev/tailwind.config.js @@ -0,0 +1,13 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +export default { + content: [ + "./index.html", + "./src/**/*.{vue,js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} \ No newline at end of file diff --git a/demo_dev/vite.config.js b/demo_dev/vite.config.js new file mode 100644 index 0000000..0675f35 --- /dev/null +++ b/demo_dev/vite.config.js @@ -0,0 +1,18 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT license. + +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' +import { ReactInjectorVitePlugin } from 'yunji-tagger' + +export default defineConfig({ + plugins: [react(), ReactInjectorVitePlugin()], + server: { + host: '0.0.0.0', + port: 5173, + strictPort: true, + allowedHosts: [ + 'ununified-outrightly-nickole.ngrok-free.dev', + ] + } +}) \ No newline at end of file