feat(sequence-scatter-background): add categorical background drawing…#3759
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skie1997 merged 1 commit intoVisActor:feat/sequence-scatterfrom Apr 16, 2025
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skie1997
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Mar 19, 2025
| const xExtent = { min: xMin - xExpand, max: xMax + xExpand }; | ||
| const yExtent = { min: yMin - yExpand, max: yMax + yExpand }; | ||
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| const xStep = (xExtent.max - xExtent.min) / bins; |
Contributor
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step决定了背景的填充色块的大小，这个大小其实是不变的。
现在的逻辑相当于保持每个group下填充色块的数量不变，而非色宽的大小不变。
写成定值（比如0.1），右下角group的填充色块就不会堆积在一起了。
修改后：
修改前：
| symbolType: 'rect', | ||
| x: (datum: any, ctx: any) => { | ||
| // 获取region位置 | ||
| // const regionStartPoint = ctx.chart.getAllRegions()[0].getLayoutStartPoint(); |
| { | ||
| id: 'colorScale', | ||
| type: 'ordinal', | ||
| specified: { |
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| const kdeResult: Array<{ x: number; y: number; kde: number; label: string }> = []; | ||
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| const expandRatio = 0.2; // 扩展比例 |
Contributor
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写成常量，const EXPAND_RATIO = 0.2，提出来放到同级目录下的constant.ts中
| } | ||
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| // KDE 相关的工具函数 | ||
| function gaussKernel(x: number) { |
Contributor
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工具函数可以放到同级目录下的utils.ts文件中
| xField: spec.xField, | ||
| yField: spec.yField | ||
| dataIndex: 0, | ||
| xField: 'x', |
Contributor
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从配置中读取spec.xField, spec.yField, spec.seriesField
| } | ||
| density = density / (points.length * h * h); | ||
| densities.push(density); // 先暂存 density 值 | ||
| kdeResult.push({ x, y, kde: density, label }); // 同时也先存入 kdeResult |
Contributor
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数据字段保持和spec统一，[spec.xField]: x, [spec.yField]: y....
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| } | ||
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| // // 归一化每个 label 的 KDE 密度值到 [0, 1] 范围内 |
Contributor
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maxDensity 和 minDensity 可以在caculateKDE的时候直接对比得到，类似👇，减少性能损耗。
Contributor
This was referenced Apr 16, 2025
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…(in progress)
[中文版模板 / Chinese template]
🤔 This is a ...
🔗 Related issue link
#3574
🔗 Related PR link
🐞 Bugserver case id
💡 Background and solution
初步实现分类背景绘制功能：为了展示机器学习聚类算法的效果，动态显示不同标签的数据点在不同迭代轮次的聚集程度。
创建了 SequenceScatterChartSpecTransformer 类，将特定的序列散点图配置转换为通用的 VChart 配置。
主要包括：
效果展示
图 1 为训练集 1 的效果，图 2 为训练集 2 的效果
未来改进方向
KDE 参数调优： 未来将增加手动调节 bandwidth 的选项（调节核密度估计的平滑程度，bandwith 越高，估计的结果越平滑。每个数据点的影响范围更大，会将更多的邻近点纳入考虑范围），允许用户对 KDE 结果进行更精细的控制。
归一化策略： KDE 密度值的归一化被临时注释，当前的密度值直接映射到透明度。虽然保留了不同 label 间 KDE 值的相对大小关系，但缺乏统一的密度范围，可能导致不同数据集间的密度视觉呈现不一致。需要探索更通用的策略（目前策略是为不同标签的数据统一生成 100*100 = 10000 个图元，因此比较聚集的点的背景显示不出透明度差异，如效果展示中的图 1的紫色分组）。
性能优化： 计划优化 calculateKDE 函数的计算过程，以提高大数据集下的性能。
自定义程度提升： 将提供更多的配置项，允许用户自定义 KDE 密度图的颜色、形状等属性，满足个性化的需求。
📝 Changelog
☑️ Self-Check before Merge
🚀 Summary
copilot:summary
🔍 Walkthrough
copilot:walkthrough