diff --git a/evaluations/datastes/recommend/README.en.md b/evaluations/datastes/recommend/README.en.md new file mode 100644 index 0000000..c6a66a7 --- /dev/null +++ b/evaluations/datastes/recommend/README.en.md @@ -0,0 +1,41 @@ +# Chart Recommendation Dataset +This folder provides datasets for evaluating/fine-tuning large models on the "Chart Recommendation" task. Here, the chart recommendation task refers to recommending suitable charts to display data based on the given data. + +Currently, the dataset covers 16 types of charts, with 1-3 distinct data scenarios for each chart type. Each scenario contains 15+ sets of chart data. The data will be continually updated, and we welcome contributions of chart data collected from your use scenarios. + +## Dataset Description +### Original Chart Data +Data is organized into different folders based on chart types, and chart data is divided into different files based on data scenarios. For example, the column chart folder column contains two files: 01_base.json for basic column chart data, and 02_split.json for split column chart data. + +In each data entry, source represents the user input. source.data contains the original data, and source.meta includes metadata about the input data, mainly field names and data types. One of these pieces of information can be missing. target represents the recommended chart type and the field mappings in the chart configuration. Here is an example: + +```json +{ + "source": { + // Metadata about data fields + "metas": [{"name": "City", "dataType": "string"}, {"name": "Population", "dataType": "number"}], + // Original data + "data": [{"City": "Beijing", "Population": 2154}, {"City": "Shanghai", "Population": 2424}, {"City": "Guangzhou", "Population": 1530}] + }, + "target": [ + { + "type": "column", + "encode": { + "x": ["City"], // Field for x-axis + "y": ["Population"] // Field for y-axis + } + } + ] +} +``` + +### Model Fine-Tuning Dataset +The gpt_vis_train.jsonl file is a fine-tuning training dataset generated from the above original chart data. The generation strategy is as follows: randomly select half of the cases for each chart type (the remaining data is used for evaluation). Since the number of original data entries varies for each chart type, to avoid imbalanced chart quantities affecting recommendation results, some chart data entries are repeated a certain number of times to ensure there are 60 entries for each chart type in the training set. + +### Evaluation Result File +The evalResult.json file contains the results of our model evaluation after fine-tuning. In this file, every source entry is the original input, target is the expected output, and generation is the model's output. Comparing these entries allows the evaluation of recommendation accuracy. + +### Model's Performance on Chart Recommendation Task +Using the above datasets, we achieved a chart type accuracy of 85% and an encode accuracy of 70% with fine-tuning based on the `qwen2.5-14b-instruct`. + +It is important to note that the model recommendations can satisfy the requirement of "providing data and returning chart and configuration" in most scenarios. However, the model's output is not entirely controlled, which may result in invalid output or charts that cannot be successfully rendered. We recommend combining these with the recommendation modules in [@antv/ava](https://ava.antv.antgroup.com/api/advice/advisor). In scenarios where the model performance is suboptimal or where traditional rules fulfill the recommendation requirements, rule-based recommendation pipelines can be used as a fallback. diff --git a/evaluations/datastes/recommend/README.md b/evaluations/datastes/recommend/README.md new file mode 100644 index 0000000..eb0fcd1 --- /dev/null +++ b/evaluations/datastes/recommend/README.md @@ -0,0 +1,42 @@ +# 图表推荐数据集 + +该文件夹中提供用于评测/微调大模型在“图表推荐”任务上能力的数据集。图表推荐任务在这里指，根据数据推荐适合展示数据的图表 + +数据集目前涵盖了 16 种图表类型，每种图表类型下 1-3 个不同数据场景，每个场景下 15+ 个图表数据。数据会持续更新，也欢迎向我们贡献你的使用场景中收集的图表数据～ + +## 数据集说明 + +### 原始图表数据 +数据按照图表类型进行图表类型分为不同文件夹，图表数据按照数据场景划分为不同文件。例如柱形图 `column` 包含两个文件 `01_base.json` 为基础柱形图数据，`02_split.json` 为带拆分的柱形图。 + +每条数据中 `source` 为用户输入，`source.data` 为输入的原始数据，`source.meta` 为输入的数据的元信息，主要是字段名和字段数值类型，推荐时两个信息可以缺少其中之一。`target` 为推荐的图表类型和图表配置中的字段映射。数据示例： +```json +{ + "source": { + // 数据字段元信息 + "metas": [{"name": "城市", "dataType": "string"},{"name": "人口","dataType": "number"}], + // 原始数据 + "data": [{"城市": "北京","人口": 2154},{"城市": "上海","人口": 2424},{"城市": "广州","人口": 1530}] + }, + "target": [ + { + "type": "column", + "encode": { + "x": ["城市"], // x 轴字段 + "y": ["人口"] // y 轴字段 + } + } + ] +} +``` + +### 模型微调数据集 +`gpt_vis_train.jsonl` 文件是我们使用上述原始图表数据集生成的微调训练数据集。生成策略如下：每种图表随机抽取一半的 case （剩余数据用作评测），由于每种图表原始数据条数不同，为避免图表数量不均衡影响推荐结果，通过将部分图表数据重复一定次数，保证每种图表在训练集中有 60 条数据。 + +### 评测结果文件 +`evalResult.json` 文件是我们执行模型微调后，用模型评测的结果，其中每条数据的 `source` 为原始输入，`target` 为期望输出，`generation` 为模型输出，通过比对可以评估推荐的准确率。 + +## 模型推荐图表效果说明 +我们使用上述数据集，基于 `qwen2.5-14b-instruct` 微调后的图表类型准确率可达 85%，`encode` 准确率达 70%。 + +需要注意的是，模型推荐在大部分场景下能够满足“给出数据，返回图表及配置”的需求，但模型输出不完全可控，存在输出结果不合法、输出的图表无法绘制成功等情况。推荐结合 [@antv/ava](https://ava.antv.antgroup.com/api/advice/advisor) 中的推荐模块使用，在模型效果不佳或者传统规则已满足推荐需求的场景下，可以使用规则推荐的工程链路进行兜底。 diff --git a/evaluations/datastes/recommend/column/01_base.json b/evaluations/datastes/recommend/column/01_base.json new file mode 100644 index 0000000..5feb764 --- /dev/null +++ b/evaluations/datastes/recommend/column/01_base.json @@ -0,0 +1,808 @@ +[ + { + "source": { + "metas": [ + { + "name": "城市", + "dataType": "string" + }, + { + "name": "人口", + "dataType": "number" + } + ], + "data": [ + { + "城市": "北京", + "人口": 2154 + }, + { + "城市": "上海", + "人口": 2424 + }, + { + "城市": "广州", + "人口": 1530 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "城市" + ], + "y": [ + "人口" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Country", + "dataType": "string" + }, + { + "name": "GDP", + "dataType": "number" + } + ], + "data": [ + { + "Country": "USA", + "GDP": 21433 + }, + { + "Country": "China", + "GDP": 14140 + }, + { + "Country": "Japan", + "GDP": 5065 + }, + { + "Country": "Germany", + "GDP": 3861 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "Country" + ], + "y": [ + "GDP" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "季节", + "dataType": "string" + }, + { + "name": "温度", + "dataType": "number" + } + ], + "data": [ + { + "季节": "春", + "温度": 15.0 + }, + { + "季节": "夏", + "温度": 28.0 + }, + { + "季节": "秋", + "温度": 20.0 + }, + { + "季节": "冬", + "温度": 5.0 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "季节" + ], + "y": [ + "温度" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Product", + "dataType": "string" + }, + { + "name": "Sales", + "dataType": "number" + } + ], + "data": [ + { + "Product": "Laptop", + "Sales": 250 + }, + { + "Product": "Tablet", + "Sales": 150 + }, + { + "Product": "Smartphone", + "Sales": 300 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "Product" + ], + "y": [ + "Sales" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "颜色", + "dataType": "string" + }, + { + "name": "数量", + "dataType": "number" + } + ], + "data": [ + { + "颜色": "红色", + "数量": 100 + }, + { + "颜色": "蓝色", + "数量": 200 + }, + { + "颜色": "绿色", + "数量": 150 + }, + { + "颜色": "黄色", + "数量": 75 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "颜色" + ], + "y": [ + "数量" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "城市", + "dataType": "string" + }, + { + "name": "平均气温", + "dataType": "number" + } + ], + "data": [ + { + "城市": "伦敦", + "平均气温": 11 + }, + { + "城市": "东京", + "平均气温": 15 + }, + { + "城市": "纽约", + "平均气温": 13 + }, + { + "城市": "巴黎", + "平均气温": 12 + }, + { + "城市": "悉尼", + "平均气温": 17 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "城市" + ], + "y": [ + "平均气温" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "星座", + "dataType": "string" + }, + { + "name": "人数", + "dataType": "number" + } + ], + "data": [ + { + "星座": "白羊座", + "人数": 120 + }, + { + "星座": "金牛座", + "人数": 130 + }, + { + "星座": "双子座", + "人数": 110 + }, + { + "星座": "巨蟹座", + "人数": 90 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "星座" + ], + "y": [ + "人数" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Activity", + "dataType": "string" + }, + { + "name": "Duration (hrs)", + "dataType": "number" + } + ], + "data": [ + { + "Activity": "Running", + "Duration (hrs)": 5 + }, + { + "Activity": "Swimming", + "Duration (hrs)": 3 + }, + { + "Activity": "Cycling", + "Duration (hrs)": 4 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "Activity" + ], + "y": [ + "Duration (hrs)" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "交通工具", + "dataType": "string" + }, + { + "name": "数量", + "dataType": "number" + } + ], + "data": [ + { + "交通工具": "汽车", + "数量": 120 + }, + { + "交通工具": "自行车", + "数量": 80 + }, + { + "交通工具": "公交车", + "数量": 50 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "交通工具" + ], + "y": [ + "数量" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "site", + "dataType": "string" + }, + { + "name": "yield", + "dataType": "number" + } + ], + "data": [ + { + "site": "University Farm", + "yield": 27 + }, + { + "site": "Waseca", + "yield": 48.86667 + }, + { + "site": "Morris", + "yield": 27.43334 + }, + { + "site": "Crookston", + "yield": 39.93333 + }, + { + "site": "Grand Rapids", + "yield": 32.96667 + }, + { + "site": "Duluth", + "yield": 28.96667 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "site" + ], + "y": [ + "yield" + ] + } + } + ] +}, + { + "source": { + "metas": [ + { + "name": "城市", + "dataType": "string" + }, + { + "name": "人口", + "dataType": "number" + } + ], + "data": [ + { + "城市": "北京", + "人口": 21542000 + }, + { + "城市": "上海", + "人口": 24256800 + }, + { + "城市": "广州", + "人口": 13081000 + }, + { + "城市": "深圳", + "人口": 10969000 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "城市" + ], + "y": [ + "人口" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "产品类别", + "dataType": "string" + }, + { + "name": "销量", + "dataType": "number" + } + ], + "data": [ + { + "产品类别": "电子产品", + "销量": 500 + }, + { + "产品类别": "家居用品", + "销量": 300 + }, + { + "产品类别": "服装", + "销量": 450 + }, + { + "产品类别": "食品", + "销量": 600 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "产品类别" + ], + "y": [ + "销量" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "项目阶段", + "dataType": "string" + }, + { + "name": "完成百分比", + "dataType": "number" + } + ], + "data": [ + { + "项目阶段": "需求分析", + "完成百分比": 100 + }, + { + "项目阶段": "设计", + "完成百分比": 80 + }, + { + "项目阶段": "开发", + "完成百分比": 60 + }, + { + "项目阶段": "测试", + "完成百分比": 40 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "项目阶段" + ], + "y": [ + "完成百分比" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "学科", + "dataType": "string" + }, + { + "name": "平均分数", + "dataType": "number" + } + ], + "data": [ + { + "学科": "数学", + "平均分数": 85 + }, + { + "学科": "英语", + "平均分数": 80 + }, + { + "学科": "物理", + "平均分数": 88 + }, + { + "学科": "化学", + "平均分数": 82 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "学科" + ], + "y": [ + "平均分数" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "年龄段", + "dataType": "string" + }, + { + "name": "用户数", + "dataType": "number" + } + ], + "data": [ + { + "年龄段": "18-24岁", + "用户数": 1500 + }, + { + "年龄段": "25-34岁", + "用户数": 2000 + }, + { + "年龄段": "35-44岁", + "用户数": 1800 + }, + { + "年龄段": "45岁以上", + "用户数": 1200 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "年龄段" + ], + "y": [ + "用户数" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "季度", + "dataType": "string" + }, + { + "name": "利润", + "dataType": "number" + } + ], + "data": [ + { + "季度": "Q1", + "利润": 500000 + }, + { + "季度": "Q2", + "利润": 550000 + }, + { + "季度": "Q3", + "利润": 600000 + }, + { + "季度": "Q4", + "利润": 650000 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "季度" + ], + "y": [ + "利润" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "国家", + "dataType": "string" + }, + { + "name": "GDP", + "dataType": "number" + } + ], + "data": [ + { + "国家": "美国", + "GDP": 21439453 + }, + { + "国家": "中国", + "GDP": 14140163 + }, + { + "国家": "日本", + "GDP": 5177086 + }, + { + "国家": "德国", + "GDP": 3885000 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "国家" + ], + "y": [ + "GDP" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "部门", + "dataType": "string" + }, + { + "name": "员工数", + "dataType": "number" + } + ], + "data": [ + { + "部门": "销售部", + "员工数": 50 + }, + { + "部门": "技术部", + "员工数": 70 + }, + { + "部门": "市场部", + "员工数": 40 + }, + { + "部门": "人力资源部", + "员工数": 30 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "部门" + ], + "y": [ + "员工数" + ] + } + } + ] + } +] diff --git a/evaluations/datastes/recommend/column/02_split.json b/evaluations/datastes/recommend/column/02_split.json new file mode 100644 index 0000000..ccc52c8 --- /dev/null +++ b/evaluations/datastes/recommend/column/02_split.json @@ -0,0 +1,3182 @@ +[ + { + "source": { + "metas": [ + { + "name": "地区", + "dataType": "string" + }, + { + "name": "销售额", + "dataType": "number" + }, + { + "name": "产品类别", + "dataType": "string" + } + ], + "data": [ + { + "地区": "北区", + "销售额": 750, + "产品类别": "电子产品" + }, + { + "地区": "南区", + "销售额": 300, + "产品类别": "生活用品" + }, + { + "地区": "西区", + "销售额": 900, + "产品类别": "服装" + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "地区" + ], + "y": [ + "销售额" + ], + "s": [ + "产品类别" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "City", + "dataType": "string" + }, + { + "name": "Population", + "dataType": "number" + }, + { + "name": "Continent", + "dataType": "string" + } + ], + "data": [ + { + "City": "New York", + "Population": 8419000, + "Continent": "North America" + }, + { + "City": "London", + "Population": 8982000, + "Continent": "Europe" + }, + { + "City": "Tokyo", + "Population": 13929000, + "Continent": "Asia" + }, + { + "City": "Cairo", + "Population": 9908000, + "Continent": "Africa" + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "City" + ], + "y": [ + "Population" + ], + "s": [ + "Continent" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Month", + "dataType": "string" + }, + { + "name": "Rainfall", + "dataType": "number" + }, + { + "name": "City", + "dataType": "string" + } + ], + "data": [ + { + "Month": "January", + "Rainfall": 120, + "City": "Shanghai" + }, + { + "Month": "February", + "Rainfall": 85, + "City": "Shanghai" + }, + { + "Month": "January", + "Rainfall": 200, + "City": "London" + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": [ + "Month" + ], + "y": [ + "Rainfall" + ], + "s": [ + "City" + ] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "国家", + "dataType": "string" + }, + { + "name": "GDP", + "dataType": "number" + }, + { + "name": "洲", + "dataType": "string" + } + ], + "data": [ + { + "国家": "中国", + "GDP": 14342903, + "洲": "亚洲" + 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b/evaluations/datastes/recommend/radar/02_measure_dimension.json new file mode 100644 index 0000000..05fc3cf --- /dev/null +++ b/evaluations/datastes/recommend/radar/02_measure_dimension.json @@ -0,0 +1,205 @@ +[ + { + "source": { + "data": [ + { "skill": "Design", "score": 70 }, + { "skill": "Development", "score": 60 }, + { "skill": "Marketing", "score": 50 }, + { "skill": "Users", "score": 40 }, + { "skill": "Test", "score": 60 }, + { "skill": "Language", "score": 70 }, + { "skill": "Technology", "score": 50 }, + { "skill": "Support", "score": 30 }, + { "skill": "Sales", "score": 60 }, + { "skill": "UX", "score": 50 } + ] + }, + "target": [{ + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } + }] + }, + { + "source": { + "data": [ + { "skill": "沟通", "score": 75 }, + { "skill": "领导", "score": 88 }, + { "skill": "解决问题", "score": 65 }, + { "skill": "团队合作", "score": 92 }, + { "skill": "创新", "score": 78 } + ] + }, + "target": [{ + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } + }] + }, + { + "source": { + "data": [ + { "skill": "Communication", "score": 82 }, + { "skill": "Leadership", "score": 90 }, + { "skill": "Problem Solving", "score": 70 }, + { "skill": "Teamwork", "score": 85 }, + { "skill": "Creativity", "score": 80 } + ] + }, + "target": [{ + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } + }] + }, + { + "source": { + "data": [ + { "skill": "数据分析", "score": 63 }, + { "skill": "编程", "score": 55 }, + { "skill": "项目管理", "score": 77 }, + { "skill": "市场研究", "score": 70 }, + { "skill": "财务管理", "score": 60 } + ] + }, + "target": [{ + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } + }] + }, + { + "source": { + "data": [ + { "skill": "Data Analysis", "score": 68 }, + { "skill": "Coding", "score": 72 }, + { "skill": "Project Management", "score": 90 }, + { "skill": "Market Research", "score": 65 }, + { "skill": "Financial Management", "score": 73 } + ] + }, + "target": [{ + 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b/evaluations/datastes/recommend/scatter/02_split.json @@ -0,0 +1,411 @@ +[ + { + "source": { + "data": [ + {"Series":"I", "X":10.0, "Y":8.04}, + {"Series":"I", "X":8.0, "Y":6.95}, + {"Series":"I", "X":13.0, "Y":7.58}, + {"Series":"I", "X":9.0, "Y":8.81}, + {"Series":"I", "X":11.0, "Y":8.33}, + {"Series":"I", "X":14.0, "Y":9.96}, + {"Series":"I", "X":6.0, "Y":7.24}, + {"Series":"I", "X":4.0, "Y":4.26}, + {"Series":"II", "X":10.0, "Y":9.14}, + {"Series":"II", "X":8.0, "Y":8.14}, + {"Series":"II", "X":13.0, "Y":8.74}, + {"Series":"II", "X":9.0, "Y":8.77}, + {"Series":"II", "X":11.0, "Y":9.26}, + {"Series":"II", "X":14.0, "Y":8.10}, + {"Series":"II", "X":6.0, "Y":6.13}, + {"Series":"II", "X":4.0, "Y":3.10}, + {"Series":"III", "X":10.0, "Y":7.46}, + {"Series":"III", "X":8.0, "Y":6.77}, + {"Series":"III", "X":13.0, "Y":12.74}, + {"Series":"III", "X":9.0, "Y":7.11}, + {"Series":"III", "X":11.0, "Y":7.81}, + {"Series":"III", "X":14.0, "Y":8.84}, + {"Series":"III", "X":6.0, "Y":6.08}, + 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11, "keyword": "Steak" }, + { "frequency": 10, "keyword": "BBQ" }, + { "frequency": 9, "keyword": "Seafood" }, + { "frequency": 8, "keyword": "Sandwiches" }, + { "frequency": 8, "keyword": "Burgers" }, + { "frequency": 7, "keyword": "Curry" }, + { "frequency": 6, "keyword": "Sushi Rolls" }, + { "frequency": 5, "keyword": "Desserts" }, + { "frequency": 5, "keyword": "Appetizers" }, + { "frequency": 5, "keyword": "Main Courses" }, + { "frequency": 4, "keyword": "Sides" }, + { "frequency": 4, "keyword": "Soups" }, + { "frequency": 4, "keyword": "Vegetarian" } + ] + }, + "target": [ + { + "type": "wordCloud", + "encode": { + "y": "frequency", + "label": "Popular Dishes" + } + } + ] + }, + { + "source": { + "data": [ + { "frequency": 25, "keyword": "Earth" }, + { "frequency": 22, "keyword": "Jupiter" }, + { "frequency": 20, "keyword": "Mars" }, + { "frequency": 18, "keyword": "Venus" }, + { "frequency": 17, "keyword": "Saturn" }, + { "frequency": 16, "keyword": "Mercury" }, + { "frequency": 15, "keyword": "Neptune" }, + { "frequency": 14, "keyword": "Uranus" }, + { "frequency": 12, "keyword": "Pluto" }, + { "frequency": 11, "keyword": "Solar System" }, + { "frequency": 10, "keyword": "Moon" }, + { "frequency": 9, "keyword": "Asteroids" }, + { "frequency": 8, "keyword": "Comets" }, + { "frequency": 8, "keyword": "Galaxies" }, + { "frequency": 7, "keyword": "Black Holes" }, + { "frequency": 6, "keyword": "Star Clusters" }, + { "frequency": 5, "keyword": "Nebulae" }, + { "frequency": 5, "keyword": "Supernova" }, + { "frequency": 4, "keyword": "Exoplanets" }, + { "frequency": 4, "keyword": "Cosmos" } + ] + }, + "target": [ + { + "type": "wordCloud", + "encode": { + "y": "frequency", + "label": "Astronomy Topics" + } + } + ] + } + ] +] diff --git a/prompts/chart-recommend.md b/prompts/chart-recommend.md new file mode 100644 index 0000000..5116510 --- /dev/null +++ b/prompts/chart-recommend.md @@ -0,0 +1,46 @@ +## 角色 + +你是一个数据可视化专家，你拥有多年的数据分析和可视化经验，可以通过用户提供的原始数据和数据字段元信息，推荐合理的图表类型和图表配置。 + +## 任务 + +- 图表推荐：根据原始数据，推荐最适合用来展示数据的图表类型，并生成用于绘制图表的图表配置。 + +## 图表推荐工作流程 + +1. 理解用户问题，提取可视化意图。 +2. 理解用户的数据，提取数据的关键特点。用户可能给出原始数据 data，也可能给出数据的元信息 metas，字段元信息包含字段名、字段类型。 +3. 结合意图和数据特点，选择适合用户分析意图和场景的图表类型。如果用户直接指定了图表类型，使用用户指定的；如果用户没有指定，根据可视化的数据选择合适的图表类型。 +4. 根据图表类型和可视化数据，生成可视化图表配置。 + +## 图表知识 +{{}} + +## 要求 + +- 输出的格式为 JSON 格式，不得添加任何额外的文字描述或解释。 + +## 参考例子: +- 用户询问：用图表展示这份数据 {"metas":[{"name":"Product","dataType":"string"},{"name":"Sales","dataType":"number"}],"data":[{"Product":"Laptop","Sales":250},{"Product":"Tablet","Sales":150},{"Product":"Smartphone","Sales":300}]} +- 你回答： +```vis-chart +{"type":"column","encode":{"x":"Product","y":"Sales"}, "data":[{"Product":"Laptop","Sales":250},{"Product":"Tablet","Sales":150},{"Product":"Smartphone","Sales":300}]} +``` + +- 用户询问：可视化下列数据 {"data":[{"水果":"苹果","数量":30000},{"水果":"香蕉","数量":25000},{"水果":"橙子","数量":20000},{"水果":"葡萄","数量":15000}]} +- 你回答： +```vis-chart +{"type":"pie","encode":{"x":"水果","y":"数量"}, "data":[{"水果":"苹果","数量":30000},{"水果":"香蕉","数量":25000},{"水果":"橙子","数量":20000},{"水果":"葡萄","数量":15000}]} +``` + +- 用户询问：用图表展示这份数据 [{"月份":"一月","降雨量":30},{"月份":"二月","降雨量":28},{"月份":"三月","降雨量":45},{"月份":"四月","降雨量":60},{"月份":"五月","降雨量":75},{"月份":"六月","降雨量":90}] +- 你回答： +```vis-chart +{"type":"line","encode":{"x":"月份","y":"降雨量"}, "data":[{"月份":"一月","降雨量":30},{"月份":"二月","降雨量":28},{"月份":"三月","降雨量":45},{"月份":"四月","降雨量":60},{"月份":"五月","降雨量":75},{"月份":"六月","降雨量":90}]} +``` + +- 用户询问：[{"category":"Artificial Intelligence","subcategory":"Machine Learning"},{"category":"Artificial Intelligence","subcategory":"Natural Language Processing"},{"category":"Artificial Intelligence","subcategory":"Robotics"},{"category":"Machine Learning","subcategory":"Supervised Learning"},{"category":"Machine Learning","subcategory":"Unsupervised Learning"},{"category":"Natural Language Processing","subcategory":"Sentiment Analysis"},{"category":"Natural Language Processing","subcategory":"Machine Translation"}] +- 你回答：[{"type":"mind_map","encode":{"source":"category","target":"subcategory"}}] +```vis-chart +{"type":"mind_map","encode":{"source":"category","target":"subcategory"}, "data"[{"category":"Artificial Intelligence","subcategory":"Machine Learning"},{"category":"Artificial Intelligence","subcategory":"Natural Language Processing"},{"category":"Artificial Intelligence","subcategory":"Robotics"},{"category":"Machine Learning","subcategory":"Supervised Learning"},{"category":"Machine Learning","subcategory":"Unsupervised Learning"},{"category":"Natural Language Processing","subcategory":"Sentiment Analysis"},{"category":"Natural Language Processing","subcategory":"Machine Translation"}]} +```