From 3acac4bf36442af3172225ca0b67d37f4cac5de7 Mon Sep 17 00:00:00 2001 From: yunji Date: Fri, 22 Nov 2024 10:23:08 +0800 Subject: [PATCH] style: format --- .github/workflows/ci.yml | 3 + .prettierignore | 4 +- README.md | 7 +- README.zh-CN.md | 1 + evaluations/datastes/recommend/README.en.md | 6 + evaluations/datastes/recommend/README.md | 40 +- .../datastes/recommend/column/01_base.json | 1458 +++-- .../datastes/recommend/column/02_split.json | 5340 ++++++++--------- evaluations/datastes/recommend/eval.json | 2607 ++------ .../recommend/flow-diagram/01_base.json | 636 +- .../datastes/recommend/funnel/01_base.json | 450 +- .../heatmap/01_two_dim_one_measure.json | 1054 ++-- .../recommend/histogram/01_values.json | 35 +- .../datastes/recommend/line/01_base.json | 940 ++- .../recommend/line/02_multi_measure.json | 854 +-- .../datastes/recommend/line/03_split.json | 344 +- .../datastes/recommend/map/01_long_lat.json | 448 +- .../datastes/recommend/mind-map/01_base.json | 140 +- .../recommend/multiple-axes/01_base.json | 2025 +++---- .../recommend/network-graph/01_base.json | 258 +- .../datastes/recommend/pie/01_base.json | 334 +- .../recommend/radar/01_single_item.json | 514 +- .../recommend/radar/02_measure_dimension.json | 154 +- .../datastes/recommend/radar/03_split.json | 378 +- .../scatter/01_two_measure_correlate.json | 684 +-- .../datastes/recommend/scatter/02_split.json | 252 +- .../recommend/scatter/03_split_and_size.json | 475 +- .../datastes/recommend/treemap/01_base.json | 250 +- .../recommend/word-cloud/01_base.json | 4 +- evaluations/scripts/eval/eval-recommend.js | 76 +- .../scripts/helpers/evaluate-chart-encode.js | 2 +- prompts/chart-recommend.md | 6 + 32 files changed, 9233 insertions(+), 10546 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 250ddb0..23c7d97 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,6 +12,9 @@ on: paths: - 'src/**' - '__tests__/**' + - 'evaluations/**' + - 'knowledges/**' + - 'docs/**' permissions: contents: read # to fetch code (actions/checkout) diff --git a/.prettierignore b/.prettierignore index 69efd79..64952df 100644 --- a/.prettierignore +++ b/.prettierignore @@ -18,7 +18,5 @@ dist/** # evaluations evaluations/datastes/recommend/gpt_vis_train.json -evaluations/datastes/recommend/evalResult.json -evaluations/datastes/recommend/eval-result.json -evaluations/datastes/recommend/eval_result.json +evaluations/datastes/recommend/metrics.json diff --git a/README.md b/README.md index 8a078ab..8dcf65d 100644 --- a/README.md +++ b/README.md @@ -138,6 +138,10 @@ The purpose of the [Visual Knowledge Base](https://github.com/antvis/GPT-Vis/tre Note: The numbers in the format of X/Y represent the metrics of the respective chart types when evaluated against the dataset. +## 🤖 Chart Recommendation Dataset + +The chart recommendation dataset is designed to evaluate or fine-tune large language models on their ability to recommend chart types based on given data. The dataset currently encompasses 16 types of charts, with 1-3 different data scenarios per chart type, and more than 15 chart data instances for each scenario. The dataset is continuously updated, and we welcome contributions of chart data collected from your own use cases. For more detailed information about the dataset, please visit [evaluations/recommend](https://github.com/antvis/GPT-Vis/tree/main/evaluations/recommend/README.md). + ## 💻 Development ```bash @@ -151,9 +155,6 @@ $ pnpm dev $ pnpm build ``` -## 🤖 Chart Recommendation Dataset -The chart recommendation dataset is designed to evaluate or fine-tune large language models on their ability to recommend chart types based on given data. The dataset currently encompasses 16 types of charts, with 1-3 different data scenarios per chart type, and more than 15 chart data instances for each scenario. The dataset is continuously updated, and we welcome contributions of chart data collected from your own use cases. For more detailed information about the dataset, please visit [evaluations/recommend](https://github.com/antvis/GPT-Vis/tree/main/evaluations/recommend/README.md). - ## License [MIT](./LICENSE) diff --git a/README.zh-CN.md b/README.zh-CN.md index 970b535..8eaf617 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -137,6 +137,7 @@ set_gpt_vis(content) | | | | | | | | ## 🤖 图表模型推荐数据集 + 图表推荐数据集用于评测/微调大模型在“给定数据，推荐图表类型”任务上的能力。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。数据集详细信息见 [evaluations/recommend](https://github.com/antvis/GPT-Vis/tree/main/evaluations/recommend/README.md) ## 💻 本地开发 diff --git a/evaluations/datastes/recommend/README.en.md b/evaluations/datastes/recommend/README.en.md index 68a5c0c..9ad8ebd 100644 --- a/evaluations/datastes/recommend/README.en.md +++ b/evaluations/datastes/recommend/README.en.md @@ -1,10 +1,13 @@ # 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: @@ -30,12 +33,15 @@ In each data entry, source represents the user input. source.data contains the o ``` ### 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 `metrics.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 89% and an encode accuracy of 82% 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 index 845e223..ae4fc34 100644 --- a/evaluations/datastes/recommend/README.md +++ b/evaluations/datastes/recommend/README.md @@ -7,36 +7,48 @@ ## 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 - } - } + "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 + `metrics.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，`correctness` 为图表类型是否推荐正确，`encodeScore` 为图表配置推荐结果的打分。评测指标的计算参考 `eval/eval-recommend.js` 文件。 ## 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 89%，`encode` 准确率达 82%。 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 index 5feb764..bfca961 100644 --- a/evaluations/datastes/recommend/column/01_base.json +++ b/evaluations/datastes/recommend/column/01_base.json @@ -1,808 +1,736 @@ [ { - "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": "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": "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": "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": "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": "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": "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": "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": "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": "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 - } - ] + "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 + }, { - "type": "column", - "encode": { - "x": [ - "site" - ], - "y": [ - "yield" - ] - } + "城市": "深圳", + "人口": 10969000 + } + ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": ["城市"], + "y": ["人口"] } + } ] -}, - { - "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] + "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 + } + ] }, - { - "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" - ] - } - } - ] - } + "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 index ccc52c8..fd9034a 100644 --- a/evaluations/datastes/recommend/column/02_split.json +++ b/evaluations/datastes/recommend/column/02_split.json @@ -37,15 +37,9 @@ { "type": "column", "encode": { - "x": [ - "地区" - ], - "y": [ - "销售额" - ], - "s": [ - "产品类别" - ] + "x": ["地区"], + "y": ["销售额"], + "s": ["产品类别"] } } ] @@ -93,15 +87,9 @@ { "type": "column", "encode": { - "x": [ - "City" - ], - "y": [ - "Population" - ], - "s": [ - "Continent" - ] + "x": ["City"], + "y": ["Population"], + "s": ["Continent"] } } ] @@ -144,15 +132,9 @@ { "type": "column", "encode": { - "x": [ - "Month" - ], - "y": [ - "Rainfall" - ], - "s": [ - "City" - ] + "x": ["Month"], + "y": ["Rainfall"], + "s": ["City"] } } ] @@ -195,15 +177,9 @@ { "type": "column", "encode": { - "x": [ - "国家" - ], - "y": [ - "GDP" - ], - "s": [ - "洲" - ] + "x": ["国家"], + "y": ["GDP"], + "s": ["洲"] } } ] @@ -246,15 +222,9 @@ { "type": "column", "encode": { - "x": [ - "Year" - ], - "y": [ - "Sales" - ], - "s": [ - "Region" - ] + "x": ["Year"], + "y": ["Sales"], + "s": ["Region"] } } ] @@ -302,15 +272,9 @@ { "type": "column", "encode": { - "x": [ - "产品" - ], - "y": [ - "库存量" - ], - "s": [ - "仓库" - ] + "x": ["产品"], + "y": ["库存量"], + "s": ["仓库"] } } ] @@ -353,15 +317,9 @@ { "type": "column", "encode": { - "x": [ - "Genre" - ], - "y": [ - "Sales" - ], - "s": [ - "Platform" - ] + "x": ["Genre"], + "y": ["Sales"], + "s": ["Platform"] } } ] @@ -409,15 +367,9 @@ { "type": "column", "encode": { - "x": [ - "纪元" - ], - "y": [ - "发电量" - ], - "s": [ - "能源类型" - ] + "x": ["纪元"], + "y": ["发电量"], + "s": ["能源类型"] } } ] @@ -460,15 +412,9 @@ { "type": "column", "encode": { - "x": [ - "Vehicle" - ], - "y": [ - "Units Sold" - ], - "s": [ - "Region" - ] + "x": ["Vehicle"], + "y": ["Units Sold"], + "s": ["Region"] } } ] @@ -516,2667 +462,2621 @@ { "type": "column", "encode": { - "x": [ - "医院" - ], - "y": [ - "患者人数" - ], - "s": [ - "科室" - ] + "x": ["医院"], + "y": ["患者人数"], + "s": ["科室"] } } ] }, { - "source": { - "metas": [ - { - "name": "state", - "dataType": "string" - }, - { - "name": "population", - "dataType": "number" - }, - { - "name": "age", - "dataType": "string" - } - ], - "data": [ - { - "state": "AL", - "age": "<10", - "population": 598478 - }, - { - "state": "AK", - "age": "<10", - "population": 106741 - }, - { - "state": "AZ", - "age": "<10", - "population": 892083 - }, - { - "state": "AR", - "age": "<10", - "population": 392177 - }, - { - "state": "CA", - "age": "<10", - "population": 5038433 - }, - { - "state": "CO", - "age": "<10", - "population": 690830 - }, - { - "state": "CT", - "age": "<10", - "population": 399369 - }, - { - "state": "DE", - "age": "<10", - "population": 112177 - }, - { - "state": "DC", - "age": "<10", - "population": 74377 - }, - { - "state": "FL", - "age": "<10", - "population": 2211012 - }, - { - "state": "GA", - "age": "<10", - "population": 1363631 - }, - { - "state": "HI", - "age": "<10", - "population": 176484 - }, - { - "state": "ID", - "age": "<10", - "population": 236658 - }, - { - "state": "IL", - "age": "<10", - "population": 1619682 - }, - { - "state": "IN", - "age": "<10", - "population": 863029 - }, - { - "state": "IA", - "age": "<10", - "population": 401712 - }, - { - "state": "KS", - "age": "<10", - "population": 401751 - }, - { - "state": "KY", - "age": "<10", - "population": 555615 - }, - { - "state": "LA", - "age": "<10", - "population": 622061 - }, - { - "state": "ME", - "age": "<10", - "population": 137954 - }, - { - "state": "MD", - "age": "<10", - "population": 741952 - }, - { - "state": "MA", - "age": "<10", - "population": 737748 - }, - { - "state": "MI", - "age": "<10", - "population": 1181424 - }, - { - "state": "MN", - "age": "<10", - "population": 711604 - }, - { - "state": "MS", - "age": "<10", - "population": 400288 - }, - { - "state": "MO", - "age": "<10", - "population": 763948 - }, - { - "state": "MT", - "age": "<10", - "population": 126020 - }, - { - "state": "NE", - "age": "<10", - "population": 263518 - }, - { - "state": "NV", - "age": "<10", - "population": 369362 - }, - { - "state": "NH", - "age": "<10", - "population": 138762 - }, - { - "state": "NJ", - "age": "<10", - "population": 1079136 - }, - { - "state": "NM", - "age": "<10", - "population": 276468 - }, - { - "state": "NY", - "age": "<10", - "population": 2319945 - }, - { - "state": "NC", - "age": "<10", - "population": 1250298 - }, - { - "state": "ND", - "age": "<10", - "population": 99477 - }, - { - "state": "OH", - "age": "<10", - "population": 1422838 - }, - { - "state": "OK", - "age": "<10", - "population": 534481 - }, - { - "state": "OR", - "age": "<10", - "population": 474456 - }, - { - "state": "PA", - "age": "<10", - 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"population": 117637 + }, + { + "state": "KY", + "age": "≥80", + "population": 155074 + }, + { + "state": "LA", + "age": "≥80", + "population": 152063 + }, + { + "state": "ME", + "age": "≥80", + "population": 62007 + }, + { + "state": "MD", + "age": "≥80", + "population": 208079 + }, + { + "state": "MA", + "age": "≥80", + "population": 288408 + }, + { + "state": "MI", + "age": "≥80", + "population": 398303 + }, + { + "state": "MN", + "age": "≥80", + "population": 215985 + }, + { + "state": "MS", + "age": "≥80", + "population": 100689 + }, + { + "state": "MO", + "age": "≥80", + "population": 242554 + }, + { + "state": "MT", + "age": "≥80", + "population": 41920 + }, + { + "state": "NE", + "age": "≥80", + "population": 78504 + }, + { + "state": "NV", + "age": "≥80", + "population": 82273 + }, + { + "state": "NH", + "age": "≥80", + "population": 52552 + }, + { + "state": "NJ", + "age": "≥80", + "population": 367432 + }, + { + "state": "NM", + "age": "≥80", + "population": 74393 + }, + { + "state": "NY", + "age": "≥80", + "population": 804091 + }, + { + "state": "NC", + "age": "≥80", + "population": 342497 + }, + { + "state": "ND", + "age": "≥80", + "population": 32206 + }, + { + "state": "OH", + "age": "≥80", + "population": 481890 + }, + { + "state": "OK", + "age": "≥80", + "population": 138055 + }, + { + "state": "OR", + "age": "≥80", + "population": 157153 + }, + { + "state": "PA", + "age": "≥80", + "population": 615069 + }, + { + "state": "RI", + "age": "≥80", + "population": 51021 + }, + { + "state": "SC", + "age": "≥80", + "population": 166727 + }, + { + "state": "SD", + "age": "≥80", + "population": 38540 + }, + { + "state": "TN", + "age": "≥80", + "population": 227483 + }, + { + "state": "TX", + "age": "≥80", + "population": 732179 + }, + { + "state": "UT", + "age": "≥80", + "population": 70711 + }, + { + "state": "VT", + "age": "≥80", + "population": 26656 + }, + { + "state": "VA", + "age": "≥80", + "population": 274606 + }, + { + "state": "WA", + "age": "≥80", + "population": 242589 + }, + { + "state": "WV", + "age": "≥80", + "population": 79201 + }, + { + "state": "WI", + "age": "≥80", + "population": 241747 + }, + { + "state": "WY", + "age": "≥80", + "population": 19682 + }, + { + "state": "PR", + "age": "≥80", + "population": 148963 + } ] + }, + "target": [ + { + "type": "column", + "encode": { + "x": ["state"], + "y": ["population"], + "s": ["age"] + } + } + ] }, - { - "source": { - "metas": [ - { - "name": "Month", - "dataType": "string" - }, - { - "name": "Revenue", - "dataType": "number" - }, - { - "name": "Department", - "dataType": "string" - } - ], - "data": [ - { - "Month": "January", - "Revenue": 80000, - "Department": "Electronics" - }, - { - "Month": "February", - "Revenue": 75000, - "Department": "Furniture" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Month" - ], - "y": [ - "Revenue" - ], - "s": [ - "Department" - ] - } + { + "source": { + "metas": [ + { + "name": "Month", + "dataType": "string" + }, + { + "name": "Revenue", + "dataType": "number" + }, + { + "name": "Department", + "dataType": "string" + } + ], + "data": [ + { + "Month": "January", + "Revenue": 80000, + "Department": "Electronics" + }, + { + "Month": "February", + "Revenue": 75000, + "Department": "Furniture" } ] }, - { - "source": { - "metas": [ - { - "name": "Country", - "dataType": "string" - }, - { - "name": "GDP", - "dataType": "number" - }, - { - "name": "Year", - "dataType": "string" - } - ], - "data": [ - { - "Country": "USA", - "GDP": 21433000, - "Year": "2022" - }, - { - "Country": "China", - "GDP": 14342000, - "Year": "2022" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Country" - ], - "y": [ - "GDP" - ], - "s": [ - "Year" - ] - } + "target": [ + { + "type": "column", + "encode": { + "x": ["Month"], + "y": ["Revenue"], + "s": ["Department"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Country", + "dataType": "string" + }, + { + "name": "GDP", + "dataType": "number" + }, + { + "name": "Year", + "dataType": "string" + } + ], + "data": [ + { + "Country": "USA", + "GDP": 21433000, + "Year": "2022" + }, + { + "Country": "China", + "GDP": 14342000, + "Year": "2022" } ] }, - { - "source": { - "metas": [ - { - "name": "Day", - "dataType": "string" - }, - { - "name": "Visitors", - "dataType": "number" - }, - { - "name": "Location", - "dataType": "string" - } - ], - "data": [ - { - "Day": "Monday", - "Visitors": 200, - "Location": "Park" - }, - { - "Day": "Monday", - "Visitors": 150, - "Location": "Museum" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Day" - ], - "y": [ - "Visitors" - ], - "s": [ - "Location" - ] - } + "target": [ + { + "type": "column", + "encode": { + "x": ["Country"], + "y": ["GDP"], + "s": ["Year"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Day", + "dataType": "string" + }, + { + "name": "Visitors", + "dataType": "number" + }, + { + "name": "Location", + "dataType": "string" + } + ], + "data": [ + { + "Day": "Monday", + "Visitors": 200, + "Location": "Park" + }, + { + "Day": "Monday", + "Visitors": 150, + "Location": "Museum" } ] }, - { - "source": { - "metas": [ - { - "name": "Quarter", - "dataType": "string" - }, - { - "name": "Earnings", - "dataType": "number" - }, - { - "name": "Sector", - "dataType": "string" - } - ], - "data": [ - { - "Quarter": "Q1", - "Earnings": 50000, - "Sector": "Tech" - }, - { - "Quarter": "Q1", - "Earnings": 75000, - "Sector": "Finance" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Quarter" - ], - "y": [ - "Earnings" - ], - "s": [ - "Sector" - ] - } + "target": [ + { + "type": "column", + "encode": { + "x": ["Day"], + "y": ["Visitors"], + "s": ["Location"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Quarter", + "dataType": "string" + }, + { + "name": "Earnings", + "dataType": "number" + }, + { + "name": "Sector", + "dataType": "string" + } + ], + "data": [ + { + "Quarter": "Q1", + "Earnings": 50000, + "Sector": "Tech" + }, + { + "Quarter": "Q1", + "Earnings": 75000, + "Sector": "Finance" } ] }, - { - "source": { - "metas": [ - { - "name": "Continent", - "dataType": "string" - }, - { - "name": "Area", - "dataType": "number" - }, - { - "name": "Land Type", - "dataType": "string" - } - ], - "data": [ - { - "Continent": "Asia", - "Area": 44579000, - "Land Type": "Land Mass" - }, - { - "Continent": "Africa", - "Area": 30370000, - "Land Type": "Land Mass" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Continent" - ], - "y": [ - "Area" - ], - "s": [ - "Land Type" - ] - } + "target": [ + { + "type": "column", + "encode": { + "x": ["Quarter"], + "y": ["Earnings"], + "s": ["Sector"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Continent", + "dataType": "string" + }, + { + "name": "Area", + "dataType": "number" + }, + { + "name": "Land Type", + "dataType": "string" + } + ], + "data": [ + { + "Continent": "Asia", + "Area": 44579000, + "Land Type": "Land Mass" + }, + { + "Continent": "Africa", + "Area": 30370000, + "Land Type": "Land Mass" } ] }, - { - "source": { - "metas": [ - { - "name": "Region", - "dataType": "string" - }, - { - "name": "Temperature", - "dataType": "number" - }, - { - "name": "Month", - "dataType": "string" - } - ], - "data": [ - { - "Region": "Europe", - "Temperature": 15, - "Month": "June" - }, - { - "Region": "Africa", - "Temperature": 25, - "Month": "June" - } - ] - }, - "target": [ - { - "type": "column", - "encode": { - "x": [ - "Region" - ], - "y": [ - "Temperature" - ], - "s": [ - "Month" - ] - } + "target": [ + { + "type": "column", + "encode": { + "x": ["Continent"], + "y": ["Area"], + "s": ["Land Type"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Region", + "dataType": "string" + }, + { + "name": "Temperature", + "dataType": "number" + }, + { + "name": "Month", + "dataType": "string" + } + ], + "data": [ + { + "Region": "Europe", + "Temperature": 15, + "Month": "June" + }, + { + "Region": "Africa", + "Temperature": 25, + "Month": "June" } ] - } + }, + "target": [ + { + "type": "column", + "encode": { + "x": ["Region"], + "y": ["Temperature"], + "s": ["Month"] + } + } + ] + } ] diff --git a/evaluations/datastes/recommend/eval.json b/evaluations/datastes/recommend/eval.json index e8342b9..cec946b 100644 --- a/evaluations/datastes/recommend/eval.json +++ b/evaluations/datastes/recommend/eval.json @@ -47,15 +47,9 @@ { "type": "multiple", "encode": { - "x": [ - "月份" - ], - "y": [ - "收入" - ], - "y2": [ - "花费" - ] + "x": ["月份"], + "y": ["收入"], + "y2": ["花费"] } } ], @@ -63,13 +57,8 @@ { "type": "multiple", "encode": { - "x": [ - "月份" - ], - "y": [ - "收入", - "花费" - ] + "x": ["月份"], + "y": ["收入", "花费"] } } ] @@ -122,15 +111,9 @@ { "type": "multiple", "encode": { - "x": [ - "发表时间" - ], - "y": [ - "评论数" - ], - "y2": [ - "转发数" - ] + "x": ["发表时间"], + "y": ["评论数"], + "y2": ["转发数"] } } ], @@ -138,13 +121,8 @@ { "type": "line", "encode": { - "x": [ - "发表时间" - ], - "y": [ - "评论数", - "转发数" - ] + "x": ["发表时间"], + "y": ["评论数", "转发数"] } } ] @@ -197,15 +175,9 @@ { "type": "multiple", "encode": { - "x": [ - "Category" - ], - "y": [ - "Sales" - ], - "y2": [ - "Returns" - ] + "x": ["Category"], + "y": ["Sales"], + "y2": ["Returns"] } } ], @@ -213,13 +185,8 @@ { "type": "column", "encode": { - "x": [ - "Category" - ], - "y": [ - "Sales", - "Returns" - ] + "x": ["Category"], + "y": ["Sales", "Returns"] } } ] @@ -272,15 +239,9 @@ { "type": "multiple", "encode": { - "x": [ - "有效期至" - ], - "y": [ - "成本价" - ], - "y2": [ - "折扣率" - ] + "x": ["有效期至"], + "y": ["成本价"], + "y2": ["折扣率"] } } ], @@ -288,15 +249,9 @@ { "type": "multiple", "encode": { - "x": [ - "有效期至" - ], - "y": [ - "成本价" - ], - "y2": [ - "折扣率" - ] + "x": ["有效期至"], + "y": ["成本价"], + "y2": ["折扣率"] } } ] @@ -349,15 +304,9 @@ { "type": "multiple", "encode": { - "x": [ - "车型" - ], - "y": [ - "销售量" - ], - "y2": [ - "市场份额" - ] + "x": ["车型"], + "y": ["销售量"], + "y2": ["市场份额"] } } ], @@ -365,15 +314,9 @@ { "type": "multiple", "encode": { - "x": [ - "车型" - ], - "y": [ - "销售量" - ], - "y2": [ - "市场份额" - ] + "x": ["车型"], + "y": ["销售量"], + "y2": ["市场份额"] } } ] @@ -426,15 +369,9 @@ { "type": "multiple", "encode": { - "x": [ - "日期" - ], - "y": [ - "销售额" - ], - "y2": [ - "客户数" - ] + "x": ["日期"], + "y": ["销售额"], + "y2": ["客户数"] } } ], @@ -442,13 +379,8 @@ { "type": "multiple", "encode": { - "x": [ - "日期" - ], - "y": [ - "销售额", - "客户数" - ] + "x": ["日期"], + "y": ["销售额", "客户数"] } } ] @@ -501,15 +433,9 @@ { "type": "multiple", "encode": { - "x": [ - "Company" - ], - "y": [ - "MarketCap" - ], - "y2": [ - "Employees" - ] + "x": ["Company"], + "y": ["MarketCap"], + "y2": ["Employees"] } } ], @@ -517,15 +443,9 @@ { "type": "multiple", "encode": { - "x": [ - "Company" - ], - "y": [ - "MarketCap" - ], - "y2": [ - "Employees" - ] + "x": ["Company"], + "y": ["MarketCap"], + "y2": ["Employees"] } } ] @@ -573,15 +493,9 @@ { "type": "multiple", "encode": { - "x": [ - "Date" - ], - "y": [ - "CaloriesBurned" - ], - "y2": [ - "Steps" - ] + "x": ["Date"], + "y": ["CaloriesBurned"], + "y2": ["Steps"] } } ], @@ -589,13 +503,8 @@ { "type": "multiple", "encode": { - "x": [ - "Date" - ], - "y": [ - "CaloriesBurned", - "Steps" - ] + "x": ["Date"], + "y": ["CaloriesBurned", "Steps"] } } ] @@ -648,15 +557,9 @@ { "type": "multiple", "encode": { - "x": [ - "有效期至" - ], - "y": [ - "成本价" - ], - "y2": [ - "折扣率" - ] + "x": ["有效期至"], + "y": ["成本价"], + "y2": ["折扣率"] } } ], @@ -664,15 +567,9 @@ { "type": "multiple", "encode": { - "x": [ - "有效期至" - ], - "y": [ - "成本价" - ], - "y2": [ - "折扣率" - ] + "x": ["有效期至"], + "y": ["成本价"], + "y2": ["折扣率"] } } ] @@ -725,15 +622,9 @@ { "type": "multiple", "encode": { - "x": [ - "州名" - ], - "y": [ - "工作岗位" - ], - "y2": [ - "失业率" - ] + "x": ["州名"], + "y": ["工作岗位"], + "y2": ["失业率"] } } ], @@ -741,15 +632,9 @@ { "type": "multiple", "encode": { - "x": [ - "州名" - ], - "y": [ - "工作岗位" - ], - "y2": [ - "失业率" - ] + "x": ["州名"], + "y": ["工作岗位"], + "y2": ["失业率"] } } ] @@ -789,12 +674,8 @@ { "type": "column", "encode": { - "x": [ - "Country" - ], - "y": [ - "GDP" - ] + "x": ["Country"], + "y": ["GDP"] } } ], @@ -802,12 +683,8 @@ { "type": "column", "encode": { - "x": [ - "Country" - ], - "y": [ - "GDP" - ] + "x": ["Country"], + "y": ["GDP"] } } ] @@ -855,15 +732,9 @@ { "type": "column", "encode": { - "x": [ - "产品" - ], - "y": [ - "库存量" - ], - "s": [ - "仓库" - ] + "x": ["产品"], + "y": ["库存量"], + "s": ["仓库"] } } ], @@ -871,15 +742,9 @@ { "type": "column", "encode": { - "x": [ - "产品" - ], - "y": [ - "库存量" - ], - "s": [ - "仓库" - ] + "x": ["产品"], + "y": ["库存量"], + "s": ["仓库"] } } ] @@ -919,12 +784,8 @@ { "type": "column", "encode": { - "x": [ - "部门" - ], - "y": [ - "员工数" - ] + "x": ["部门"], + "y": ["员工数"] } } ], @@ -932,12 +793,8 @@ { "type": "column", "encode": { - "x": [ - "部门" - ], - "y": [ - "员工数" - ] + "x": ["部门"], + "y": ["员工数"] } } ] @@ -975,15 +832,9 @@ { "type": "column", "encode": { - "x": [ - "Quarter" - ], - "y": [ - "Earnings" - ], - "s": [ - "Sector" - ] + "x": ["Quarter"], + "y": ["Earnings"], + "s": ["Sector"] } } ], @@ -991,15 +842,9 @@ { "type": "column", "encode": { - "x": [ - "Quarter" - ], - "y": [ - "Earnings" - ], - "s": [ - "Sector" - ] + "x": ["Quarter"], + "y": ["Earnings"], + "s": ["Sector"] } } ] @@ -1039,12 +884,8 @@ { "type": "column", "encode": { - "x": [ - "星座" - ], - "y": [ - "人数" - ] + "x": ["星座"], + "y": ["人数"] } } ], @@ -1052,12 +893,8 @@ { "type": "column", "encode": { - "x": [ - "星座" - ], - "y": [ - "人数" - ] + "x": ["星座"], + "y": ["人数"] } } ] @@ -1097,12 +934,8 @@ { "type": "column", "encode": { - "x": [ - "颜色" - ], - "y": [ - "数量" - ] + "x": ["颜色"], + "y": ["数量"] } } ], @@ -1110,12 +943,8 @@ { "type": "column", "encode": { - "x": [ - "颜色" - ], - "y": [ - "数量" - ] + "x": ["颜色"], + "y": ["数量"] } } ] @@ -1163,15 +992,9 @@ { "type": "column", "encode": { - "x": [ - "医院" - ], - "y": [ - "患者人数" - ], - "s": [ - "科室" - ] + "x": ["医院"], + "y": ["患者人数"], + "s": ["科室"] } } ], @@ -1179,15 +1002,9 @@ { "type": "column", "encode": { - "x": [ - "医院" - ], - "y": [ - "患者人数" - ], - "s": [ - "科室" - ] + "x": ["医院"], + "y": ["患者人数"], + "s": ["科室"] } } ] @@ -1223,12 +1040,8 @@ { "type": "column", "encode": { - "x": [ - "交通工具" - ], - "y": [ - "数量" - ] + "x": ["交通工具"], + "y": ["数量"] } } ], @@ -1236,12 +1049,8 @@ { "type": "column", "encode": { - "x": [ - "交通工具" - ], - "y": [ - "数量" - ] + "x": ["交通工具"], + "y": ["数量"] } } ] @@ -1281,12 +1090,8 @@ { "type": "column", "encode": { - "x": [ - "城市" - ], - "y": [ - "人口" - ] + "x": ["城市"], + "y": ["人口"] } } ], @@ -1294,12 +1099,8 @@ { "type": "column", "encode": { - "x": [ - "城市" - ], - "y": [ - "人口" - ] + "x": ["城市"], + "y": ["人口"] } } ] @@ -1342,15 +1143,9 @@ { "type": "column", "encode": { - "x": [ - "Genre" - ], - "y": [ - "Sales" - ], - "s": [ - "Platform" - ] + "x": ["Genre"], + "y": ["Sales"], + "s": ["Platform"] } } ], @@ -1358,15 +1153,9 @@ { "type": "column", "encode": { - "x": [ - "Genre" - ], - "y": [ - "Sales" - ], - "s": [ - "Platform" - ] + "x": ["Genre"], + "y": ["Sales"], + "s": ["Platform"] } } ] @@ -1404,15 +1193,9 @@ { "type": "column", "encode": { - "x": [ - "Day" - ], - "y": [ - "Visitors" - ], - "s": [ - "Location" - ] + "x": ["Day"], + "y": ["Visitors"], + "s": ["Location"] } } ], @@ -1420,15 +1203,9 @@ { "type": "column", "encode": { - "x": [ - "Day" - ], - "y": [ - "Visitors" - ], - "s": [ - "Location" - ] + "x": ["Day"], + "y": ["Visitors"], + "s": ["Location"] } } ] @@ -1464,12 +1241,8 @@ { "type": "column", "encode": { - "x": [ - "城市" - ], - "y": [ - "人口" - ] + "x": ["城市"], + "y": ["人口"] } } ], @@ -1477,12 +1250,8 @@ { "type": "column", "encode": { - "x": [ - "城市" - ], - "y": [ - "人口" - ] + "x": ["城市"], + "y": ["人口"] } } ] @@ -1525,15 +1294,9 @@ { "type": "column", "encode": { - "x": [ - "Vehicle" - ], - "y": [ - "Units Sold" - ], - "s": [ - "Region" - ] + "x": ["Vehicle"], + "y": ["Units Sold"], + "s": ["Region"] } } ], @@ -1541,15 +1304,9 @@ { "type": "column", "encode": { - "x": [ - "Vehicle" - ], - "y": [ - "UnitsSold" - ], - "s": [ - "Region" - ] + "x": ["Vehicle"], + "y": ["UnitsSold"], + "s": ["Region"] } } ] @@ -1585,12 +1342,8 @@ { "type": "column", "encode": { - "x": [ - "Activity" - ], - "y": [ - "Duration (hrs)" - ] + "x": ["Activity"], + "y": ["Duration (hrs)"] } } ], @@ -1598,12 +1351,8 @@ { "type": "column", "encode": { - "x": [ - "Activity" - ], - "y": [ - "Duration(hrs)" - ] + "x": ["Activity"], + "y": ["Duration(hrs)"] } } ] @@ -1641,15 +1390,9 @@ { "type": "column", "encode": { - "x": [ - "Region" - ], - "y": [ - "Temperature" - ], - "s": [ - "Month" - ] + "x": ["Region"], + "y": ["Temperature"], + "s": ["Month"] } } ], @@ -1657,15 +1400,9 @@ { "type": "column", "encode": { - "x": [ - "Region" - ], - "y": [ - "Temperature" - ], - "s": [ - "Month" - ] + "x": ["Region"], + "y": ["Temperature"], + "s": ["Month"] } } ] @@ -1705,12 +1442,8 @@ { "type": "column", "encode": { - "x": [ - "学科" - ], - "y": [ - "平均分数" - ] + "x": ["学科"], + "y": ["平均分数"] } } ], @@ -1718,12 +1451,8 @@ { "type": "column", "encode": { - "x": [ - "学科" - ], - "y": [ - "平均分数" - ] + "x": ["学科"], + "y": ["平均分数"] } } ] @@ -1763,12 +1492,8 @@ { "type": "column", "encode": { - "x": [ - "项目阶段" - ], - "y": [ - "完成百分比" - ] + "x": ["项目阶段"], + "y": ["完成百分比"] } } ], @@ -1776,12 +1501,8 @@ { "type": "progress", "encode": { - "x": [ - "项目阶段" - ], - "y": [ - "完成百分比" - ] + "x": ["项目阶段"], + "y": ["完成百分比"] } } ] @@ -1829,15 +1550,9 @@ { "type": "column", "encode": { - "x": [ - "City" - ], - "y": [ - "Population" - ], - "s": [ - "Continent" - ] + "x": ["City"], + "y": ["Population"], + "s": ["Continent"] } } ], @@ -1845,15 +1560,9 @@ { "type": "heatmap", "encode": { - "x": [ - "City" - ], - "y": [ - "Continent" - ], - "size": [ - "Population" - ] + "x": ["City"], + "y": ["Continent"], + "size": ["Population"] } } ] @@ -2219,12 +1928,7 @@ { "type": "funnel", "encode": { - "y": [ - "注册用户", - "加入购物车", - "下单成功", - "购买成功" - ] + "y": ["注册用户", "加入购物车", "下单成功", "购买成功"] } } ], @@ -2232,12 +1936,7 @@ { "type": "funnel", "encode": { - "y": [ - "注册用户", - "加入购物车", - "下单成功", - "购买成功" - ] + "y": ["注册用户", "加入购物车", "下单成功", "购买成功"] } } ] @@ -2275,12 +1974,7 @@ { "type": "funnel", "encode": { - "y": [ - "Leads Generated", - "Contacted", - "Qualified", - "Converted" - ] + "y": ["Leads Generated", "Contacted", "Qualified", "Converted"] } } ], @@ -2288,12 +1982,7 @@ { "type": "funnel", "encode": { - "y": [ - "LeadsGenerated", - "Contacted", - "Qualified", - "Converted" - ] + "y": ["LeadsGenerated", "Contacted", "Qualified", "Converted"] } } ] @@ -2331,12 +2020,7 @@ { "type": "funnel", "encode": { - "y": [ - "广告展示", - "广告点击", - "进入网站", - "完成注册" - ] + "y": ["广告展示", "广告点击", "进入网站", "完成注册"] } } ], @@ -2344,12 +2028,7 @@ { "type": "funnel", "encode": { - "y": [ - "广告展示", - "广告点击", - "进入网站", - "完成注册" - ] + "y": ["广告展示", "广告点击", "进入网站", "完成注册"] } } ] @@ -2387,12 +2066,7 @@ { "type": "funnel", "encode": { - "y": [ - "注册会展", - "到场参与", - "深度交谈", - "后续联系" - ] + "y": ["注册会展", "到场参与", "深度交谈", "后续联系"] } } ], @@ -2400,12 +2074,7 @@ { "type": "funnel", "encode": { - "y": [ - "注册会展", - "到场参与", - "深度交谈", - "后续联系" - ] + "y": ["注册会展", "到场参与", "深度交谈", "后续联系"] } } ] @@ -2443,12 +2112,7 @@ { "type": "funnel", "encode": { - "y": [ - "浏览新闻", - "文章点击", - "阅读完成", - "分享" - ] + "y": ["浏览新闻", "文章点击", "阅读完成", "分享"] } } ], @@ -2456,12 +2120,7 @@ { "type": "funnel", "encode": { - "y": [ - "浏览新闻", - "文章点击", - "阅读完成", - "分享" - ] + "y": ["浏览新闻", "文章点击", "阅读完成", "分享"] } } ] @@ -2500,13 +2159,7 @@ { "type": "funnel", "encode": { - "y": [ - "咨询金额", - "可用金额", - "曝光金额", - "提交金额", - "成交金额" - ] + "y": ["咨询金额", "可用金额", "曝光金额", "提交金额", "成交金额"] } } ], @@ -2514,13 +2167,7 @@ { "type": "funnel", "encode": { - "y": [ - "咨询金额", - "可用金额", - "曝光金额", - "提交金额", - "成交金额" - ] + "y": ["咨询金额", "可用金额", "曝光金额", "提交金额", "成交金额"] } } ] @@ -2558,12 +2205,7 @@ { "type": "funnel", "encode": { - "y": [ - "Application Received", - "First Review", - "Interview", - "Accept Offer" - ] + "y": ["Application Received", "First Review", "Interview", "Accept Offer"] } } ], @@ -2571,12 +2213,7 @@ { "type": "funnel", "encode": { - "y": [ - "ApplicationReceived", - "FirstReview", - "Interview", - "AcceptOffer" - ] + "y": ["ApplicationReceived", "FirstReview", "Interview", "AcceptOffer"] } } ] @@ -2614,12 +2251,7 @@ { "type": "funnel", "encode": { - "y": [ - "Campaign Views", - "Clicks", - "Conversions", - "Repeat Customers" - ] + "y": ["Campaign Views", "Clicks", "Conversions", "Repeat Customers"] } } ], @@ -2627,12 +2259,7 @@ { "type": "funnel", "encode": { - "y": [ - "CampaignViews", - "Clicks", - "Conversions", - "RepeatCustomers" - ] + "y": ["CampaignViews", "Clicks", "Conversions", "RepeatCustomers"] } } ] @@ -2670,12 +2297,7 @@ { "type": "funnel", "encode": { - "y": [ - "Free Trial Signups", - "Usage After Trial", - "Upgrades", - "Long-term Subscribers" - ] + "y": ["Free Trial Signups", "Usage After Trial", "Upgrades", "Long-term Subscribers"] } } ], @@ -2683,12 +2305,7 @@ { "type": "funnel", "encode": { - "y": [ - "FreeTrialSignups", - "UsageAfterTrial", - "Upgrades", - "Long-termSubscribers" - ] + "y": ["FreeTrialSignups", "UsageAfterTrial", "Upgrades", "Long-termSubscribers"] } } ] @@ -2741,29 +2358,17 @@ { "type": "heatmap", "encode": { - "x": [ - "行业" - ], - "y": [ - "月份" - ], - "size": [ - "增长率" - ] + "x": ["行业"], + "y": ["月份"], + "size": ["增长率"] } }, { "type": "scatter", "encode": { - "x": [ - "行业" - ], - "y": [ - "月份" - ], - "size": [ - "增长率" - ] + "x": ["行业"], + "y": ["月份"], + "size": ["增长率"] } } ], @@ -2771,15 +2376,9 @@ { "type": "heatmap", "encode": { - "x": [ - "行业" - ], - "y": [ - "月份" - ], - "size": [ - "增长率" - ] + "x": ["行业"], + "y": ["月份"], + "size": ["增长率"] } } ] @@ -2832,29 +2431,17 @@ { "type": "heatmap", "encode": { - "x": [ - "年龄" - ], - "y": [ - "收入" - ], - "size": [ - "消费金额" - ] + "x": ["年龄"], + "y": ["收入"], + "size": ["消费金额"] } }, { "type": "scatter", "encode": { - "x": [ - "年龄" - ], - "y": [ - "收入" - ], - "size": [ - "消费金额" - ] + "x": ["年龄"], + "y": ["收入"], + "size": ["消费金额"] } } ], @@ -2862,15 +2449,9 @@ { "type": "scatter", "encode": { - "x": [ - "年龄" - ], - "y": [ - "收入" - ], - "size": [ - "消费金额" - ] + "x": ["年龄"], + "y": ["收入"], + "size": ["消费金额"] } } ] @@ -2923,29 +2504,17 @@ { "type": "heatmap", "encode": { - "x": [ - "产品" - ], - "y": [ - "季度" - ], - "size": [ - "销售额" - ] + "x": ["产品"], + "y": ["季度"], + "size": ["销售额"] } }, { "type": "scatter", "encode": { - "x": [ - "产品" - ], - "y": [ - "季度" - ], - "size": [ - "销售额" - ] + "x": ["产品"], + "y": ["季度"], + "size": ["销售额"] } } ], @@ -2953,12 +2522,8 @@ { "type": "column", "encode": { - "x": [ - "产品" - ], - "y": [ - "销售额" - ] + "x": ["产品"], + "y": ["销售额"] } } ] @@ -3052,29 +2617,17 @@ { "type": "heatmap", "encode": { - "x": [ - "Entity" - ], - "y": [ - "Year" - ], - "size": [ - "Deaths" - ] + "x": ["Entity"], + "y": ["Year"], + "size": ["Deaths"] } }, { "type": "scatter", "encode": { - "x": [ - "Entity" - ], - "y": [ - "Year" - ], - "size": [ - "Deaths" - ] + "x": ["Entity"], + "y": ["Year"], + "size": ["Deaths"] } } ], @@ -3082,12 +2635,8 @@ { "type": "line", "encode": { - "x": [ - "Year" - ], - "y": [ - "Deaths" - ] + "x": ["Year"], + "y": ["Deaths"] } } ] @@ -3155,29 +2704,17 @@ { "type": "heatmap", "encode": { - "x": [ - "月" - ], - "y": [ - "类别" - ], - "size": [ - "销量" - ] + "x": ["月"], + "y": ["类别"], + "size": ["销量"] } }, { "type": "scatter", "encode": { - "x": [ - "月" - ], - "y": [ - "类别" - ], - "size": [ - "销量" - ] + "x": ["月"], + "y": ["类别"], + "size": ["销量"] } } ], @@ -3185,15 +2722,9 @@ { "type": "line", "encode": { - "x": [ - "月" - ], - "y": [ - "销量" - ], - "s": [ - "类别" - ] + "x": ["月"], + "y": ["销量"], + "s": ["类别"] } } ] @@ -3251,29 +2782,17 @@ { "type": "heatmap", "encode": { - "x": [ - "budgetYear" - ], - "y": [ - "forecastYear" - ], - "size": [ - "value" - ] + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] } }, { "type": "scatter", "encode": { - "x": [ - "budgetYear" - ], - "y": [ - "forecastYear" - ], - "size": [ - "value" - ] + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] } } ], @@ -3281,29 +2800,17 @@ { "type": "heatmap", "encode": { - "x": [ - "budgetYear" - ], - "y": [ - "forecastYear" - ], - "size": [ - "value" - ] + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] } }, { "type": "line", "encode": { - "x": [ - "budgetYear" - ], - "y": [ - "forecastYear" - ], - "size": [ - "value" - ] + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] } } ] @@ -3335,9 +2842,7 @@ { "type": "histogram", "encode": { - "y": [ - "grades" - ] + "y": ["grades"] } } ], @@ -3345,9 +2850,7 @@ { "type": "histogram", "encode": { - "y": [ - "grades" - ] + "y": ["grades"] } } ] @@ -3376,9 +2879,7 @@ { "type": "histogram", "encode": { - "y": [ - "时长" - ] + "y": ["时长"] } } ], @@ -3386,9 +2887,7 @@ { "type": "histogram", "encode": { - "y": [ - "时长" - ] + "y": ["时长"] } } ] @@ -3417,9 +2916,7 @@ { "type": "histogram", "encode": { - "y": [ - "重量" - ] + "y": ["重量"] } } ], @@ -3427,9 +2924,7 @@ { "type": "histogram", "encode": { - "y": [ - "重量" - ] + "y": ["重量"] } } ] @@ -3458,9 +2953,7 @@ { "type": "histogram", "encode": { - "y": [ - "price" - ] + "y": ["price"] } } ], @@ -3468,9 +2961,7 @@ { "type": "histogram", "encode": { - "y": [ - "price" - ] + "y": ["price"] } } ] @@ -3499,9 +2990,7 @@ { "type": "histogram", "encode": { - "y": [ - "revenue" - ] + "y": ["revenue"] } } ], @@ -3509,9 +2998,7 @@ { "type": "histogram", "encode": { - "y": [ - "revenue" - ] + "y": ["revenue"] } } ] @@ -3540,9 +3027,7 @@ { "type": "histogram", "encode": { - "y": [ - "温度" - ] + "y": ["温度"] } } ], @@ -3550,9 +3035,7 @@ { "type": "histogram", "encode": { - "y": [ - "温度" - ] + "y": ["温度"] } } ] @@ -3581,9 +3064,7 @@ { "type": "histogram", "encode": { - "y": [ - "humidity" - ] + "y": ["humidity"] } } ], @@ -3591,9 +3072,7 @@ { "type": "histogram", "encode": { - "y": [ - "humidity" - ] + "y": ["humidity"] } } ] @@ -3622,9 +3101,7 @@ { "type": "histogram", "encode": { - "y": [ - "duration" - ] + "y": ["duration"] } } ], @@ -3632,9 +3109,7 @@ { "type": "histogram", "encode": { - "y": [ - "duration" - ] + "y": ["duration"] } } ] @@ -3663,15 +3138,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "城市" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["城市"] } } ], @@ -3679,15 +3148,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "城市" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["城市"] } } ] @@ -3716,15 +3179,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "color": [ - "median_income" - ] + "x": ["longitude"], + "y": ["latitude"], + "color": ["median_income"] } } ], @@ -3732,15 +3189,9 @@ { "type": "scatter_map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "color": [ - "median_income" - ] + "x": ["longitude"], + "y": ["latitude"], + "color": ["median_income"] } } ] @@ -3824,15 +3275,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "景点" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["景点"] } } ], @@ -3840,15 +3285,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "景点" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["景点"] } } ] @@ -3880,18 +3319,10 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "城市" - ], - "color": [ - "PM2.5" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["城市"], + "color": ["PM2.5"] } } ], @@ -3899,15 +3330,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "color": [ - "PM2.5" - ] + "x": ["longitude"], + "y": ["latitude"], + "color": ["PM2.5"] } } ] @@ -3939,18 +3364,10 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "地区" - ], - "color": [ - "AQI" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["地区"], + "color": ["AQI"] } } ], @@ -3958,15 +3375,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "color": [ - "AQI" - ] + "x": ["longitude"], + "y": ["latitude"], + "color": ["AQI"] } } ] @@ -3998,15 +3409,9 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "City" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["City"] } } ], @@ -4014,18 +3419,10 @@ { "type": "map", "encode": { - "x": [ - "longitude" - ], - "y": [ - "latitude" - ], - "label": [ - "City" - ], - "color": [ - "population_density" - ] + "x": ["longitude"], + "y": ["latitude"], + "label": ["City"], + "color": ["population_density"] } } ] @@ -4527,12 +3924,8 @@ { "type": "pie", "encode": { - "x": [ - "交通方式" - ], - "y": [ - "份额" - ] + "x": ["交通方式"], + "y": ["份额"] } } ], @@ -4540,12 +3933,8 @@ { "type": "pie", "encode": { - "x": [ - "交通方式" - ], - "y": [ - "份额" - ] + "x": ["交通方式"], + "y": ["份额"] } } ] @@ -4599,12 +3988,8 @@ { "type": "pie", "encode": { - "x": [ - "type" - ], - "y": [ - "value" - ] + "x": ["type"], + "y": ["value"] } } ], @@ -4612,12 +3997,8 @@ { "type": "pie", "encode": { - "x": [ - "type" - ], - "y": [ - "value" - ] + "x": ["type"], + "y": ["value"] } } ] @@ -4647,12 +4028,8 @@ { "type": "pie", "encode": { - "x": [ - "文化活动" - ], - "y": [ - "人数" - ] + "x": ["文化活动"], + "y": ["人数"] } } ], @@ -4660,12 +4037,8 @@ { "type": "pie", "encode": { - "x": [ - "文化活动" - ], - "y": [ - "人数" - ] + "x": ["文化活动"], + "y": ["人数"] } } ] @@ -4695,12 +4068,8 @@ { "type": "pie", "encode": { - "x": [ - "社交媒体平台" - ], - "y": [ - "用户数" - ] + "x": ["社交媒体平台"], + "y": ["用户数"] } } ], @@ -4708,12 +4077,8 @@ { "type": "pie", "encode": { - "x": [ - "社交媒体平台" - ], - "y": [ - "用户数" - ] + "x": ["社交媒体平台"], + "y": ["用户数"] } } ] @@ -4743,12 +4108,8 @@ { "type": "pie", "encode": { - "x": [ - "品牌" - ], - "y": [ - "市场份额" - ] + "x": ["品牌"], + "y": ["市场份额"] } } ], @@ -4756,12 +4117,8 @@ { "type": "pie", "encode": { - "x": [ - "品牌" - ], - "y": [ - "市场份额" - ] + "x": ["品牌"], + "y": ["市场份额"] } } ] @@ -4787,12 +4144,8 @@ { "type": "pie", "encode": { - "x": [ - "platform" - ], - "y": [ - "users" - ] + "x": ["platform"], + "y": ["users"] } } ], @@ -4800,12 +4153,8 @@ { "type": "pie", "encode": { - "x": [ - "platform" - ], - "y": [ - "users" - ] + "x": ["platform"], + "y": ["users"] } } ] @@ -4839,12 +4188,8 @@ { "type": "pie", "encode": { - "x": [ - "Energy Source" - ], - "y": [ - "Usage" - ] + "x": ["Energy Source"], + "y": ["Usage"] } } ], @@ -4852,12 +4197,8 @@ { "type": "pie", "encode": { - "x": [ - "EnergySource" - ], - "y": [ - "Usage" - ] + "x": ["EnergySource"], + "y": ["Usage"] } } ] @@ -4887,12 +4228,8 @@ { "type": "pie", "encode": { - "x": [ - "Transport Mode" - ], - "y": [ - "Count" - ] + "x": ["Transport Mode"], + "y": ["Count"] } } ], @@ -4900,12 +4237,8 @@ { "type": "pie", "encode": { - "x": [ - "TransportMode" - ], - "y": [ - "Count" - ] + "x": ["TransportMode"], + "y": ["Count"] } } ] @@ -4935,12 +4268,8 @@ { "type": "pie", "encode": { - "x": [ - "宠物类别" - ], - "y": [ - "数量" - ] + "x": ["宠物类别"], + "y": ["数量"] } } ], @@ -4948,12 +4277,8 @@ { "type": "pie", "encode": { - "x": [ - "宠物类别" - ], - "y": [ - "数量" - ] + "x": ["宠物类别"], + "y": ["数量"] } } ] @@ -4983,12 +4308,8 @@ { "type": "pie", "encode": { - "x": [ - "语言" - ], - "y": [ - "比例" - ] + "x": ["语言"], + "y": ["比例"] } } ], @@ -4996,12 +4317,8 @@ { "type": "pie", "encode": { - "x": [ - "语言" - ], - "y": [ - "比例" - ] + "x": ["语言"], + "y": ["比例"] } } ] @@ -5023,12 +4340,8 @@ { "type": "pie", "encode": { - "x": [ - "渠道" - ], - "y": [ - "占比" - ] + "x": ["渠道"], + "y": ["占比"] } } ], @@ -5036,12 +4349,8 @@ { "type": "pie", "encode": { - "x": [ - "渠道" - ], - "y": [ - "占比" - ] + "x": ["渠道"], + "y": ["占比"] } } ] @@ -5075,29 +4384,17 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "budget" - ], - "s": [ - "sector" - ] + "x": ["date"], + "y": ["budget"], + "s": ["sector"] } }, { "type": "area", "encode": { - "x": [ - "date" - ], - "y": [ - "budget" - ], - "s": [ - "sector" - ] + "x": ["date"], + "y": ["budget"], + "s": ["sector"] } } ], @@ -5105,15 +4402,9 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "budget" - ], - "s": [ - "sector" - ] + "x": ["date"], + "y": ["budget"], + "s": ["sector"] } } ] @@ -5152,13 +4443,8 @@ { "type": "line", "encode": { - "x": [ - "制造日期" - ], - "y": [ - "成本", - "售价" - ] + "x": ["制造日期"], + "y": ["成本", "售价"] } } ], @@ -5166,13 +4452,8 @@ { "type": "line", "encode": { - "x": [ - "制造日期" - ], - "y": [ - "成本", - "售价" - ] + "x": ["制造日期"], + "y": ["成本", "售价"] } } ] @@ -5211,13 +4492,8 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "rainfall", - "snowfall" - ] + "x": ["date"], + "y": ["rainfall", "snowfall"] } } ], @@ -5225,13 +4501,8 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "rainfall", - "snowfall" - ] + "x": ["date"], + "y": ["rainfall", "snowfall"] } } ] @@ -5275,29 +4546,17 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "温度" - ], - "s": [ - "城市" - ] + "x": ["日期"], + "y": ["温度"], + "s": ["城市"] } }, { "type": "area", "encode": { - "x": [ - "日期" - ], - "y": [ - "温度" - ], - "s": [ - "城市" - ] + "x": ["日期"], + "y": ["温度"], + "s": ["城市"] } } ], @@ -5305,15 +4564,9 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "温度" - ], - "s": [ - "城市" - ] + "x": ["日期"], + "y": ["温度"], + "s": ["城市"] } } ] @@ -5357,12 +4610,8 @@ { "type": "line", "encode": { - "x": [ - "星期" - ], - "y": [ - "访客数" - ] + "x": ["星期"], + "y": ["访客数"] } } ], @@ -5370,12 +4619,8 @@ { "type": "line", "encode": { - "x": [ - "星期" - ], - "y": [ - "访客数" - ] + "x": ["星期"], + "y": ["访客数"] } } ] @@ -5533,14 +4778,8 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "wounds", - "other", - "disease" - ] + "x": ["date"], + "y": ["wounds", "other", "disease"] } } ], @@ -5548,14 +4787,8 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "wounds", - "other", - "disease" - ] + "x": ["date"], + "y": ["wounds", "other", "disease"] } } ] @@ -5594,13 +4827,8 @@ { "type": "line", "encode": { - "x": [ - "销售日期" - ], - "y": [ - "收入", - "支出" - ] + "x": ["销售日期"], + "y": ["收入", "支出"] } } ], @@ -5608,13 +4836,8 @@ { "type": "line", "encode": { - "x": [ - "销售日期" - ], - "y": [ - "收入", - "支出" - ] + "x": ["销售日期"], + "y": ["收入", "支出"] } } ] @@ -5653,13 +4876,8 @@ { "type": "line", "encode": { - "x": [ - "week" - ], - "y": [ - "profit", - "loss" - ] + "x": ["week"], + "y": ["profit", "loss"] } } ], @@ -5667,13 +4885,8 @@ { "type": "line", "encode": { - "x": [ - "week" - ], - "y": [ - "profit", - "loss" - ] + "x": ["week"], + "y": ["profit", "loss"] } } ] @@ -5712,13 +4925,8 @@ { "type": "line", "encode": { - "x": [ - "month" - ], - "y": [ - "production", - "defects" - ] + "x": ["month"], + "y": ["production", "defects"] } } ], @@ -5726,15 +4934,9 @@ { "type": "multiple", "encode": { - "x": [ - "month" - ], - "y": [ - "production" - ], - "y2": [ - "defects" - ] + "x": ["month"], + "y": ["production"], + "y2": ["defects"] } } ] @@ -5768,29 +4970,17 @@ { "type": "line", "encode": { - "x": [ - "semester" - ], - "y": [ - "score" - ], - "s": [ - "subject" - ] + "x": ["semester"], + "y": ["score"], + "s": ["subject"] } }, { "type": "area", "encode": { - "x": [ - "semester" - ], - "y": [ - "score" - ], - "s": [ - "subject" - ] + "x": ["semester"], + "y": ["score"], + "s": ["subject"] } } ], @@ -5798,15 +4988,9 @@ { "type": "line", "encode": { - "x": [ - "semester" - ], - "y": [ - "score" - ], - "s": [ - "subject" - ] + "x": ["semester"], + "y": ["score"], + "s": ["subject"] } } ] @@ -5845,13 +5029,8 @@ { "type": "line", "encode": { - "x": [ - "time" - ], - "y": [ - "sales", - "profit" - ] + "x": ["time"], + "y": ["sales", "profit"] } } ], @@ -5859,13 +5038,8 @@ { "type": "line", "encode": { - "x": [ - "time" - ], - "y": [ - "sales", - "profit" - ] + "x": ["time"], + "y": ["sales", "profit"] } } ] @@ -5899,13 +5073,8 @@ { "type": "line", "encode": { - "x": [ - "quarter" - ], - "y": [ - "revenue", - "expenses" - ] + "x": ["quarter"], + "y": ["revenue", "expenses"] } } ], @@ -5913,13 +5082,8 @@ { "type": "line", "encode": { - "x": [ - "quarter" - ], - "y": [ - "revenue", - "expenses" - ] + "x": ["quarter"], + "y": ["revenue", "expenses"] } } ] @@ -5958,29 +5122,17 @@ { "type": "line", "encode": { - "x": [ - "年份" - ], - "y": [ - "GDP增长" - ], - "s": [ - "国家" - ] + "x": ["年份"], + "y": ["GDP增长"], + "s": ["国家"] } }, { "type": "area", "encode": { - "x": [ - "年份" - ], - "y": [ - "GDP增长" - ], - "s": [ - "国家" - ] + "x": ["年份"], + "y": ["GDP增长"], + "s": ["国家"] } } ], @@ -5988,15 +5140,9 @@ { "type": "line", "encode": { - "x": [ - "年份" - ], - "y": [ - "GDP增长" - ], - "s": [ - "国家" - ] + "x": ["年份"], + "y": ["GDP增长"], + "s": ["国家"] } } ] @@ -6035,13 +5181,8 @@ { "type": "line", "encode": { - "x": [ - "timestamp" - ], - "y": [ - "users", - "new_users" - ] + "x": ["timestamp"], + "y": ["users", "new_users"] } } ], @@ -6049,13 +5190,8 @@ { "type": "line", "encode": { - "x": [ - "timestamp" - ], - "y": [ - "users", - "new_users" - ] + "x": ["timestamp"], + "y": ["users", "new_users"] } } ] @@ -6094,13 +5230,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "销售额", - "成本" - ] + "x": ["日期"], + "y": ["销售额", "成本"] } } ], @@ -6108,13 +5239,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "销售额", - "成本" - ] + "x": ["日期"], + "y": ["销售额", "成本"] } } ] @@ -6158,12 +5284,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "温度" - ] + "x": ["日期"], + "y": ["温度"] } } ], @@ -6171,12 +5293,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "温度" - ] + "x": ["日期"], + "y": ["温度"] } } ] @@ -6210,12 +5328,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "降水量" - ] + "x": ["日期"], + "y": ["降水量"] } } ], @@ -6223,12 +5337,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "降水量" - ] + "x": ["日期"], + "y": ["降水量"] } } ] @@ -6302,29 +5412,17 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "price" - ], - "s": [ - "symbol" - ] + "x": ["date"], + "y": ["price"], + "s": ["symbol"] } }, { "type": "area", "encode": { - "x": [ - "date" - ], - "y": [ - "price" - ], - "s": [ - "symbol" - ] + "x": ["date"], + "y": ["price"], + "s": ["symbol"] } } ], @@ -6332,12 +5430,8 @@ { "type": "line", "encode": { - "x": [ - "date" - ], - "y": [ - "price" - ] + "x": ["date"], + "y": ["price"] } } ] @@ -6381,12 +5475,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "步数" - ] + "x": ["日期"], + "y": ["步数"] } } ], @@ -6394,12 +5484,8 @@ { "type": "line", "encode": { - "x": [ - "日期" - ], - "y": [ - "步数" - ] + "x": ["日期"], + "y": ["步数"] } } ] @@ -6443,29 +5529,17 @@ { "type": "line", "encode": { - "x": [ - "时间" - ], - "y": [ - "销量" - ], - "s": [ - "产品" - ] + "x": ["时间"], + "y": ["销量"], + "s": ["产品"] } }, { "type": "area", "encode": { - "x": [ - "时间" - ], - "y": [ - "销量" - ], - "s": [ - "产品" - ] + "x": ["时间"], + "y": ["销量"], + "s": ["产品"] } } ], @@ -6473,15 +5547,9 @@ { "type": "line", "encode": { - "x": [ - "时间" - ], - "y": [ - "销量" - ], - "s": [ - "产品" - ] + "x": ["时间"], + "y": ["销量"], + "s": ["产品"] } } ] @@ -6499,12 +5567,8 @@ { "type": "progress", "encode": { - "y": [ - "当月成本使用" - ], - "target": [ - "月度预算" - ] + "y": ["当月成本使用"], + "target": ["月度预算"] } } ], @@ -6512,12 +5576,8 @@ { "type": "progress", "encode": { - "y": [ - "当月成本使用" - ], - "target": [ - "月度预算" - ] + "y": ["当月成本使用"], + "target": ["月度预算"] } } ] @@ -6535,12 +5595,8 @@ { "type": "progress", "encode": { - "y": [ - "季度利润" - ], - "target": [ - "利润目标" - ] + "y": ["季度利润"], + "target": ["利润目标"] } } ], @@ -6548,12 +5604,8 @@ { "type": "progress", "encode": { - "y": [ - "季度利润" - ], - "target": [ - "利润目标" - ] + "y": ["季度利润"], + "target": ["利润目标"] } } ] @@ -6571,12 +5623,8 @@ { "type": "progress", "encode": { - "y": [ - "10分钟内响应率" - ], - "target": [ - "10分钟内响应率_目标" - ] + "y": ["10分钟内响应率"], + "target": ["10分钟内响应率_目标"] } } ], @@ -6584,12 +5632,8 @@ { "type": "progress", "encode": { - "y": [ - "10分钟内响应率" - ], - "target": [ - "10分钟内响应率_目标" - ] + "y": ["10分钟内响应率"], + "target": ["10分钟内响应率_目标"] } } ] @@ -6607,12 +5651,8 @@ { "type": "progress", "encode": { - "y": [ - "库存利用率" - ], - "target": [ - "库存满负荷" - ] + "y": ["库存利用率"], + "target": ["库存满负荷"] } } ], @@ -6620,12 +5660,8 @@ { "type": "progress", "encode": { - "y": [ - "库存利用率" - ], - "target": [ - "库存满负荷" - ] + "y": ["库存利用率"], + "target": ["库存满负荷"] } } ] @@ -6643,12 +5679,8 @@ { "type": "progress", "encode": { - "y": [ - "Battery Level" - ], - "target": [ - "Full Capacity" - ] + "y": ["Battery Level"], + "target": ["Full Capacity"] } } ], @@ -6656,12 +5688,8 @@ { "type": "progress", "encode": { - "y": [ - "BatteryLevel" - ], - "target": [ - "FullCapacity" - ] + "y": ["BatteryLevel"], + "target": ["FullCapacity"] } } ] @@ -6715,9 +5743,7 @@ "Growth", "Aesthetics" ], - "y": [ - "value" - ] + "y": ["value"] } } ] @@ -6771,9 +5797,7 @@ "Finance", "ArtDirection" ], - "y": [ - "value" - ] + "y": ["value"] } } ] @@ -6837,15 +5861,9 @@ { "type": "radar", "encode": { - "x": [ - "aspect" - ], - "y": [ - "score" - ], - "s": [ - "department" - ] + "x": ["aspect"], + "y": ["score"], + "s": ["department"] } } ], @@ -6853,15 +5871,9 @@ { "type": "radar", "encode": { - "x": [ - "aspect" - ], - "y": [ - "score" - ], - "s": [ - "department" - ] + "x": ["aspect"], + "y": ["score"], + "s": ["department"] } } ] @@ -6925,15 +5937,9 @@ { "type": "radar", "encode": { - "x": [ - "指标" - ], - "y": [ - "分数" - ], - "s": [ - "类别" - ] + "x": ["指标"], + "y": ["分数"], + "s": ["类别"] } } ], @@ -6941,15 +5947,9 @@ { "type": "radar", "encode": { - "x": [ - "指标" - ], - "y": [ - "分数" - ], - "s": [ - "类别" - ] + "x": ["指标"], + "y": ["分数"], + "s": ["类别"] } } ] @@ -6983,12 +5983,8 @@ { "type": "radar", "encode": { - "x": [ - "skill" - ], - "y": [ - "score" - ] + "x": ["skill"], + "y": ["score"] } } ], @@ -6996,12 +5992,8 @@ { "type": "radar", "encode": { - "x": [ - "skill" - ], - "y": [ - "score" - ] + "x": ["skill"], + "y": ["score"] } } ] @@ -7035,12 +6027,8 @@ { "type": "radar", "encode": { - "x": [ - "aspect" - ], - "y": [ - "score" - ] + "x": ["aspect"], + "y": ["score"] } } ], @@ -7048,12 +6036,8 @@ { "type": "radar", "encode": { - "x": [ - "aspect" - ], - "y": [ - "score" - ] + "x": ["aspect"], + "y": ["score"] } } ] @@ -7117,15 +6101,9 @@ { "type": "radar", "encode": { - "x": [ - "category" - ], - "y": [ - "score" - ], - "s": [ - "division" - ] + "x": ["category"], + "y": ["score"], + "s": ["division"] } } ], @@ -7133,15 +6111,9 @@ { "type": "radar", "encode": { - "x": [ - "category" - ], - "y": [ - "score" - ], - "s": [ - "division" - ] + "x": ["category"], + "y": ["score"], + "s": ["division"] } } ] @@ -7205,15 +6177,9 @@ { "type": "radar", "encode": { - "x": [ - "方面" - ], - "y": [ - "得分" - ], - "s": [ - "团队" - ] + "x": ["方面"], + "y": ["得分"], + "s": ["团队"] } } ], @@ -7221,18 +6187,10 @@ { "type": "column", "encode": { - "x": [ - "方面" - ], - "y": [ - "得分" - ], - "y2": [ - "得分" - ], - "s": [ - "团队" - ] + "x": ["方面"], + "y": ["得分"], + "y2": ["得分"], + "s": ["团队"] } } ] @@ -7344,17 +6302,11 @@ }, "target": [ { - "type": "radar", - "encode": { - "x": [ - "skill" - ], - "y": [ - "score" - ], - "s": [ - "gender" - ] + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"], + "s": ["gender"] } } ], @@ -7362,18 +6314,10 @@ { "type": "heatmap", "encode": { - "x": [ - "item" - ], - "y": [ - "skill" - ], - "size": [ - "score" - ], - "color": [ - "score" - ] + "x": ["item"], + "y": ["skill"], + "size": ["score"], + "color": ["score"] } } ] @@ -7427,9 +6371,7 @@ "Sales", "UX" ], - "y": [ - "value" - ] + "y": ["value"] } } ] @@ -7547,12 +6489,8 @@ { "type": "radar", "encode": { - "x": [ - "skill" - ], - "y": [ - "score" - ] + "x": ["skill"], + "y": ["score"] } } ], @@ -7560,12 +6498,8 @@ { "type": "radar", "encode": { - "x": [ - "skill" - ], - "y": [ - "score" - ] + "x": ["skill"], + "y": ["score"] } } ] @@ -7629,15 +6563,9 @@ { "type": "radar", "encode": { - "x": [ - "指标" - ], - "y": [ - "分数" - ], - "s": [ - "员工" - ] + "x": ["指标"], + "y": ["分数"], + "s": ["员工"] } } ], @@ -7645,15 +6573,9 @@ { "type": "radar", "encode": { - "x": [ - "指标" - ], - "y": [ - "分数" - ], - "s": [ - "员工" - ] + "x": ["指标"], + "y": ["分数"], + "s": ["员工"] } } ] @@ -7678,17 +6600,7 @@ { "type": "radar", "encode": { - "y": [ - "开发", - "市场", - "用户", - "测试", - "语言", - "技术", - "支持", - "销售", - "用户体验" - ] + "y": ["开发", "市场", "用户", "测试", "语言", "技术", "支持", "销售", "用户体验"] } } ], @@ -7696,20 +6608,8 @@ { "type": "radar", "encode": { - "x": [ - "开发", - "市场", - "用户", - "测试", - "语言", - "技术", - "支持", - "销售", - "用户体验" - ], - "y": [ - "值" - ] + "x": ["开发", "市场", "用户", "测试", "语言", "技术", "支持", "销售", "用户体验"], + "y": ["值"] } } ] @@ -7763,17 +6663,7 @@ "财务指标", "艺术表现" ], - "y": [ - 65, - 70, - 60, - 85, - 75, - 90, - 85, - 65, - 80 - ] + "y": [65, 70, 60, 85, 75, 90, 85, 65, 80] } } ] @@ -7807,12 +6697,8 @@ { "type": "radar", "encode": { - "x": [ - "ability" - ], - "y": [ - "score" - ] + "x": ["ability"], + "y": ["score"] } } ], @@ -7820,12 +6706,8 @@ { "type": "radar", "encode": { - "x": [ - "ability" - ], - "y": [ - "score" - ] + "x": ["ability"], + "y": ["score"] } } ] @@ -7859,12 +6741,8 @@ { "type": "radar", "encode": { - "x": [ - "评分维度" - ], - "y": [ - "打分" - ] + "x": ["评分维度"], + "y": ["打分"] } } ], @@ -7872,12 +6750,8 @@ { "type": "radar", "encode": { - "x": [ - "评分维度" - ], - "y": [ - "打分" - ] + "x": ["评分维度"], + "y": ["打分"] } } ] @@ -7948,12 +6822,8 @@ { "type": "column", "encode": { - "x": [ - "地区" - ], - "y": [ - "石油产量" - ] + "x": ["地区"], + "y": ["石油产量"] } } ] @@ -8004,12 +6874,8 @@ { "type": "pie", "encode": { - "x": [ - "Department" - ], - "y": [ - "Budget" - ] + "x": ["Department"], + "y": ["Budget"] } } ] @@ -8319,12 +7185,8 @@ { "type": "histogram", "encode": { - "y": [ - "count()" - ], - "x": [ - "sepal_length" - ] + "y": ["count()"], + "x": ["sepal_length"] } } ] @@ -8701,15 +7563,9 @@ { "type": "scatter", "encode": { - "x": [ - "Weight" - ], - "y": [ - "Price" - ], - "s": [ - "商品类型" - ] + "x": ["Weight"], + "y": ["Price"], + "s": ["商品类型"] } } ], @@ -8717,15 +7573,9 @@ { "type": "scatter", "encode": { - "x": [ - "Weight" - ], - "y": [ - "Price" - ], - "s": [ - "商品类型" - ] + "x": ["Weight"], + "y": ["Price"], + "s": ["商品类型"] } } ] @@ -8763,12 +7613,8 @@ { "type": "scatter", "encode": { - "x": [ - "咖啡摄入量" - ], - "y": [ - "工作效率" - ] + "x": ["咖啡摄入量"], + "y": ["工作效率"] } } ], @@ -8776,12 +7622,8 @@ { "type": "scatter", "encode": { - "x": [ - "咖啡摄入量" - ], - "y": [ - "工作效率" - ] + "x": ["咖啡摄入量"], + "y": ["工作效率"] } } ] @@ -8834,15 +7676,9 @@ { "type": "scatter", "encode": { - "x": [ - "时间投入" - ], - "y": [ - "任务完成数量" - ], - "s": [ - "项目" - ] + "x": ["时间投入"], + "y": ["任务完成数量"], + "s": ["项目"] } } ], @@ -8850,15 +7686,9 @@ { "type": "scatter", "encode": { - "x": [ - "时间投入" - ], - "y": [ - "任务完成数量" - ], - "s": [ - "项目" - ] + "x": ["时间投入"], + "y": ["任务完成数量"], + "s": ["项目"] } } ] @@ -8897,15 +7727,9 @@ { "type": "scatter", "encode": { - "x": [ - "Popularity" - ], - "y": [ - "Number of Users" - ], - "s": [ - "Programming Language" - ] + "x": ["Popularity"], + "y": ["Number of Users"], + "s": ["Programming Language"] } } ], @@ -8913,13 +7737,8 @@ { "type": "multiple", "encode": { - "x": [ - "ProgrammingLanguage" - ], - "y": [ - "Popularity", - "NumberofUsers" - ] + "x": ["ProgrammingLanguage"], + "y": ["Popularity", "NumberofUsers"] } } ] @@ -8972,15 +7791,9 @@ { "type": "scatter", "encode": { - "x": [ - "Age" - ], - "y": [ - "Income" - ], - "s": [ - "Category" - ] + "x": ["Age"], + "y": ["Income"], + "s": ["Category"] } } ], @@ -8988,15 +7801,9 @@ { "type": "scatter", "encode": { - "x": [ - "Age" - ], - "y": [ - "Income" - ], - "s": [ - "Category" - ] + "x": ["Age"], + "y": ["Income"], + "s": ["Category"] } } ] @@ -9070,15 +7877,9 @@ { "type": "scatter", "encode": { - "x": [ - "员工满意度" - ], - "y": [ - "年停工率" - ], - "s": [ - "部门" - ], + "x": ["员工满意度"], + "y": ["年停工率"], + "s": ["部门"], "size": "员工人数" } } @@ -9087,15 +7888,9 @@ { "type": "scatter", "encode": { - "x": [ - "年停工率" - ], - "y": [ - "员工满意度" - ], - "s": [ - "部门" - ], + "x": ["年停工率"], + "y": ["员工满意度"], + "s": ["部门"], "size": "员工人数" } } @@ -9134,12 +7929,8 @@ { "type": "scatter", "encode": { - "x": [ - "Temperature" - ], - "y": [ - "IceCreamSales" - ] + "x": ["Temperature"], + "y": ["IceCreamSales"] } } ], @@ -9147,12 +7938,8 @@ { "type": "scatter", "encode": { - "x": [ - "Temperature" - ], - "y": [ - "IceCreamSales" - ] + "x": ["Temperature"], + "y": ["IceCreamSales"] } } ] @@ -9194,12 +7981,8 @@ { "type": "scatter", "encode": { - "x": [ - "学习时长" - ], - "y": [ - "记忆力得分" - ] + "x": ["学习时长"], + "y": ["记忆力得分"] } } ], @@ -9207,12 +7990,8 @@ { "type": "scatter", "encode": { - "x": [ - "学习时长" - ], - "y": [ - "记忆力得分" - ] + "x": ["学习时长"], + "y": ["记忆力得分"] } } ] @@ -9286,15 +8065,9 @@ { "type": "scatter", "encode": { - "x": [ - "Average Speed" - ], - "y": [ - "Fuel Consumption" - ], - "s": [ - "Transport Type" - ], + "x": ["Average Speed"], + "y": ["Fuel Consumption"], + "s": ["Transport Type"], "size": "Distance" } } @@ -9303,15 +8076,9 @@ { "type": "scatter", "encode": { - "x": [ - "AverageSpeed" - ], - "y": [ - "FuelConsumption" - ], - "s": [ - "TransportType" - ], + "x": ["AverageSpeed"], + "y": ["FuelConsumption"], + "s": ["TransportType"], "size": "Distance" } } @@ -9386,15 +8153,9 @@ { "type": "scatter", "encode": { - "x": [ - "Salary" - ], - "y": [ - "Experience" - ], - "s": [ - "Industry" - ], + "x": ["Salary"], + "y": ["Experience"], + "s": ["Industry"], "size": "Age" } } @@ -9403,18 +8164,10 @@ { "type": "scatter", "encode": { - "x": [ - "Experience" - ], - "y": [ - "Salary" - ], - "color": [ - "Industry" - ], - "size": [ - "Age" - ] + "x": ["Experience"], + "y": ["Salary"], + "color": ["Industry"], + "size": ["Age"] } } ] @@ -9467,15 +8220,9 @@ { "type": "scatter", "encode": { - "x": [ - "用户满意度" - ], - "y": [ - "销售量" - ], - "s": [ - "性别" - ] + "x": ["用户满意度"], + "y": ["销售量"], + "s": ["性别"] } } ], @@ -9483,15 +8230,9 @@ { "type": "scatter", "encode": { - "x": [ - "用户满意度" - ], - "y": [ - "销售量" - ], - "s": [ - "性别" - ] + "x": ["用户满意度"], + "y": ["销售量"], + "s": ["性别"] } } ] @@ -9537,12 +8278,8 @@ { "type": "scatter", "encode": { - "x": [ - "hours_of_sleep" - ], - "y": [ - "concentration_levels" - ] + "x": ["hours_of_sleep"], + "y": ["concentration_levels"] } } ], @@ -9550,12 +8287,8 @@ { "type": "scatter", "encode": { - "x": [ - "hours_of_sleep" - ], - "y": [ - "concentration_levels" - ] + "x": ["hours_of_sleep"], + "y": ["concentration_levels"] } } ] @@ -9594,15 +8327,9 @@ { "type": "scatter", "encode": { - "x": [ - "访问次数" - ], - "y": [ - "购买量" - ], - "s": [ - "创意类型" - ] + "x": ["访问次数"], + "y": ["购买量"], + "s": ["创意类型"] } } ], @@ -9610,15 +8337,9 @@ { "type": "scatter", "encode": { - "x": [ - "访问次数" - ], - "y": [ - "购买量" - ], - "s": [ - "创意类型" - ] + "x": ["访问次数"], + "y": ["购买量"], + "s": ["创意类型"] } } ] @@ -9664,12 +8385,8 @@ { "type": "scatter", "encode": { - "x": [ - "广告费用" - ], - "y": [ - "销售额" - ] + "x": ["广告费用"], + "y": ["销售额"] } } ], @@ -9677,12 +8394,8 @@ { "type": "scatter", "encode": { - "x": [ - "广告费用" - ], - "y": [ - "销售额" - ] + "x": ["广告费用"], + "y": ["销售额"] } } ] @@ -9756,15 +8469,9 @@ { "type": "scatter", "encode": { - "x": [ - "评论数" - ], - "y": [ - "评级" - ], - "s": [ - "产品类别" - ], + "x": ["评论数"], + "y": ["评级"], + "s": ["产品类别"], "size": "销售量" } } @@ -9773,15 +8480,9 @@ { "type": "scatter", "encode": { - "x": [ - "评论数" - ], - "y": [ - "评级" - ], - "s": [ - "产品类别" - ], + "x": ["评论数"], + "y": ["评级"], + "s": ["产品类别"], "size": "销售量" } } @@ -9828,12 +8529,8 @@ { "type": "scatter", "encode": { - "x": [ - "温度" - ], - "y": [ - "冰淇淋销售量" - ] + "x": ["温度"], + "y": ["冰淇淋销售量"] } } ], @@ -9841,12 +8538,8 @@ { "type": "scatter", "encode": { - "x": [ - "温度" - ], - "y": [ - "冰淇淋销售量" - ] + "x": ["温度"], + "y": ["冰淇淋销售量"] } } ] @@ -9913,24 +8606,20 @@ ] }, "target": [ - { - "type": "scatter", - "encode": { - "x": ["排队时间"], - "y": ["满意度"] - } + { + "type": "scatter", + "encode": { + "x": ["排队时间"], + "y": ["满意度"] } + } ], "generation": [ { "type": "scatter", "encode": { - "x": [ - "排队时间" - ], - "y": [ - "满意度" - ] + "x": ["排队时间"], + "y": ["满意度"] } } ] @@ -9984,12 +8673,8 @@ { "type": "scatter", "encode": { - "x": [ - "study_time" - ], - "y": [ - "exam_score" - ] + "x": ["study_time"], + "y": ["exam_score"] } } ], @@ -9997,12 +8682,8 @@ { "type": "scatter", "encode": { - "x": [ - "study_time" - ], - "y": [ - "exam_score" - ] + "x": ["study_time"], + "y": ["exam_score"] } } ] @@ -10048,12 +8729,8 @@ { "type": "scatter", "encode": { - "x": [ - "time_spent_watching_tv" - ], - "y": [ - "physical_activity_level" - ] + "x": ["time_spent_watching_tv"], + "y": ["physical_activity_level"] } } ], @@ -10061,12 +8738,8 @@ { "type": "scatter", "encode": { - "x": [ - "time_spent_watching_tv" - ], - "y": [ - "physical_activity_level" - ] + "x": ["time_spent_watching_tv"], + "y": ["physical_activity_level"] } } ] @@ -10104,12 +8777,8 @@ { "type": "scatter", "encode": { - "x": [ - "演讲时间" - ], - "y": [ - "观众满意度" - ] + "x": ["演讲时间"], + "y": ["观众满意度"] } } ], @@ -10117,12 +8786,8 @@ { "type": "scatter", "encode": { - "x": [ - "演讲时间" - ], - "y": [ - "观众满意度" - ] + "x": ["演讲时间"], + "y": ["观众满意度"] } } ] @@ -10196,15 +8861,9 @@ { "type": "scatter", "encode": { - "x": [ - "使用率" - ], - "y": [ - "平均点击次数" - ], - "s": [ - "业务分类" - ], + "x": ["使用率"], + "y": ["平均点击次数"], + "s": ["业务分类"], "size": "平均耗时" } } @@ -10213,18 +8872,10 @@ { "type": "scatter", "encode": { - "x": [ - "平均点击次数" - ], - "y": [ - "平均耗时" - ], - "color": [ - "业务分类" - ], - "size": [ - "使用率" - ] + "x": ["平均点击次数"], + "y": ["平均耗时"], + "color": ["业务分类"], + "size": ["使用率"] } } ] diff --git a/evaluations/datastes/recommend/flow-diagram/01_base.json b/evaluations/datastes/recommend/flow-diagram/01_base.json index 59f0491..ef2d728 100644 --- a/evaluations/datastes/recommend/flow-diagram/01_base.json +++ b/evaluations/datastes/recommend/flow-diagram/01_base.json @@ -22,13 +22,13 @@ { "source": { "data": [ - { "上一步": "访问注册页面", "下一步": "填写并提交注册表单", "提示": "信息无误" }, - { "上一步": "填写并提交注册表单", "下一步": "验证用户信息", "提示": "信息无误" }, + { "上一步": "访问注册页面", "下一步": "填写并提交注册表单", "提示": "信息无误" }, + { "上一步": "填写并提交注册表单", "下一步": "验证用户信息", "提示": "信息无误" }, { "上一步": "验证用户信息", "下一步": "创建新用户账户", "提示": "信息无误" }, { "上一步": "验证用户信息", "下一步": "提示修改错误信息", "提示": "信息有误" }, - { "上一步": "创建新用户账户", "下一步": "发送验证邮件", "提示": "信息有误" }, - { "上一步": "发送验证邮件", "下一步": "点击验证链接", "提示": "信息无误" }, - { "上一步": "点击验证链接", "下一步": "注册成功，跳转到登录页面", "提示": "信息无误" } + { "上一步": "创建新用户账户", "下一步": "发送验证邮件", "提示": "信息有误" }, + { "上一步": "发送验证邮件", "下一步": "点击验证链接", "提示": "信息无误" }, + { "上一步": "点击验证链接", "下一步": "注册成功，跳转到登录页面", "提示": "信息无误" } ] }, "target": [ @@ -41,354 +41,354 @@ } ] }, - { - "source": { - "data": [ - { "from": "Start", "to": "Prepare Ingredients" }, - { "from": "Prepare Ingredients", "to": "Cook Ingredients" }, - { "from": "Cook Ingredients", "to": "Serve Dish" }, - { "from": "Serve Dish", "to": "Finish" } - ] - }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "from", - "target": "to" - } + { + "source": { + "data": [ + { "from": "Start", "to": "Prepare Ingredients" }, + { "from": "Prepare Ingredients", "to": "Cook Ingredients" }, + { "from": "Cook Ingredients", "to": "Serve Dish" }, + { "from": "Serve Dish", "to": "Finish" } + ] + }, + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "from", + "target": "to" } + } + ] + }, + { + "source": { + "data": [ + { "步骤": "查找产品", "下一步": "选择产品" }, + { "步骤": "选择产品", "下一步": "加入购物车" }, + { "步骤": "加入购物车", "下一步": "下单支付" }, + { "步骤": "下单支付", "下一步": "完成购买" } ] }, - { - "source": { - "data": [ - { "步骤": "查找产品", "下一步": "选择产品" }, - { "步骤": "选择产品", "下一步": "加入购物车" }, - { "步骤": "加入购物车", "下一步": "下单支付" }, - { "步骤": "下单支付", "下一步": "完成购买" } - ] - }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "步骤", - "target": "下一步" - } + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "步骤", + "target": "下一步" } + } + ] + }, + { + "source": { + "data": [ + { "from": "Research Topic", "to": "Draft Outline" }, + { "from": "Draft Outline", "to": "Write Content" }, + { "from": "Write Content", "to": "Edit Draft" }, + { "from": "Edit Draft", "to": "Publish" } ] }, - { - "source": { - "data": [ - { "from": "Research Topic", "to": "Draft Outline" }, - { "from": "Draft Outline", "to": "Write Content" }, - { "from": "Write Content", "to": "Edit Draft" }, - { "from": "Edit Draft", "to": "Publish" } - ] - }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "from", - "target": "to" - } + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "from", + "target": "to" } + } + ] + }, + { + "source": { + "data": [ + { "步骤": "创建项目", "下一步": "设计原型" }, + { "步骤": "设计原型", "下一步": "开发应用" }, + { "步骤": "开发应用", "下一步": "测试应用" }, + { "步骤": "测试应用", "下一步": "部署应用" } ] }, - { - "source": { - "data": [ - { "步骤": "创建项目", "下一步": "设计原型" }, - { "步骤": "设计原型", "下一步": "开发应用" }, - { "步骤": "开发应用", "下一步": "测试应用" }, - { "步骤": "测试应用", "下一步": "部署应用" } - ] - }, - "target": [ + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "步骤", + "target": "下一步" + } + } + ] + }, + { + "source": { + "data": [ { - "type": "flow_diagram", - "encode": { - "source": "步骤", - "target": "下一步" - } + "source": "加载数据", + "target": "逻辑回归" + }, + { + "source": "逻辑回归", + "target": "模型预测" + }, + { + "source": "逻辑回归", + "target": "读取参数" } ] }, - { - "source": { - "data": [ - { - "source": "加载数据", - "target": "逻辑回归" - }, - { - "source": "逻辑回归", - "target": "模型预测" - }, - { - "source": "逻辑回归", - "target": "读取参数" - } - ] - }, - "target": [ + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "source", + "target": "target" + } + } + ] + }, + { + "source": { + "data": [ + { + "环节": "请假申请", + "下一步": "领导审批" + }, + { + "环节": "请假申请", + "下一步": "一级审批" + }, + { + "环节": "领导审批", + "下一步": "二级审批" + }, { - "type": "flow_diagram", - "encode": { - "source": "source", - "target": "target" - } + "环节": "一级审批", + "下一步": "二级审批" } ] }, - { - "source": { - "data": [ - { - "环节": "请假申请", - "下一步": "领导审批" - }, - { - "环节": "请假申请", - "下一步": "一级审批" - }, - { - "环节": "领导审批", - "下一步": "二级审批" - }, - { - "环节": "一级审批", - "下一步": "二级审批" - } - ] - }, - "target": [ + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "环节", + "target": "下一步" + } + } + ] + }, + { + "source": { + "data": [ + { + "stage": "Start", + "next": "Analysis" + }, + { + "stage": "Analysis", + "next": "Design" + }, { - "type": "flow_diagram", - "encode": { - "source": "环节", - "target": "下一步" - } + "stage": "Design", + "next": "Implementation" } ] }, + "target": [ { - "source": { - "data": [ - { - "stage": "Start", - "next": "Analysis" - }, - { - "stage": "Analysis", - "next": "Design" - }, - { - "stage": "Design", - "next": "Implementation" - } - ] + "type": "flow_diagram", + "encode": { + "source": "stage", + "target": "next" + } + } + ] + }, + { + "source": { + "data": [ + { + "过程": "调查", + "后续": "评估" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "stage", - "target": "next" - } - } - ] - }, - { - "source": { - "data": [ - { - "过程": "调查", - "后续": "评估" - }, - { - "过程": "评估", - "后续": "决策" - }, - { - "过程": "决策", - "后续": "执行" - } - ] + { + "过程": "评估", + "后续": "决策" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "过程", - "target": "后续" - } - } - ] - }, + { + "过程": "决策", + "后续": "执行" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "source": "Import Data", - "target": "Preprocessing" - }, - { - "source": "Preprocessing", - "target": "Feature Extraction" - }, - { - "source": "Feature Extraction", - "target": "Training" - } - ] + "type": "flow_diagram", + "encode": { + "source": "过程", + "target": "后续" + } + } + ] + }, + { + "source": { + "data": [ + { + "source": "Import Data", + "target": "Preprocessing" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "source", - "target": "target" - } - } - ] - }, + { + "source": "Preprocessing", + "target": "Feature Extraction" + }, + { + "source": "Feature Extraction", + "target": "Training" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "任务": "准备食材", - "步骤": "切割" - }, - { - "任务": "切割", - "步骤": "烹饪" - }, - { - "任务": "烹饪", - "步骤": "摆盘" - } - ] + "type": "flow_diagram", + "encode": { + "source": "source", + "target": "target" + } + } + ] + }, + { + "source": { + "data": [ + { + "任务": "准备食材", + "步骤": "切割" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "任务", - "target": "步骤" - } - } - ] - }, + { + "任务": "切割", + "步骤": "烹饪" + }, + { + "任务": "烹饪", + "步骤": "摆盘" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "phase": "Discovery", - "next": "Definition" - }, - { - "phase": "Definition", - "next": "Development" - }, - { - "phase": "Development", - "next": "Delivery" - } - ] + "type": "flow_diagram", + "encode": { + "source": "任务", + "target": "步骤" + } + } + ] + }, + { + "source": { + "data": [ + { + "phase": "Discovery", + "next": "Definition" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "phase", - "target": "next" - } - } - ] - }, + { + "phase": "Definition", + "next": "Development" + }, + { + "phase": "Development", + "next": "Delivery" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "阶段": "需求收集", - "下一步": "需求分析" - }, - { - "阶段": "需求分析", - "下一步": "系统设计" - }, - { - "阶段": "系统设计", - "下一步": "实现" - } - ] + "type": "flow_diagram", + "encode": { + "source": "phase", + "target": "next" + } + } + ] + }, + { + "source": { + "data": [ + { + "阶段": "需求收集", + "下一步": "需求分析" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "阶段", - "target": "下一步" - } - } - ] - }, + { + "阶段": "需求分析", + "下一步": "系统设计" + }, + { + "阶段": "系统设计", + "下一步": "实现" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "source": "Idea", - "target": "Research" - }, - { - "source": "Research", - "target": "Prototype" - }, - { - "source": "Prototype", - "target": "Testing" - } - ] + "type": "flow_diagram", + "encode": { + "source": "阶段", + "target": "下一步" + } + } + ] + }, + { + "source": { + "data": [ + { + "source": "Idea", + "target": "Research" + }, + { + "source": "Research", + "target": "Prototype" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "source", - "target": "target" - } - } - ] - }, + { + "source": "Prototype", + "target": "Testing" + } + ] + }, + "target": [ { - "source": { - "data": [ - { - "步骤": "起草合同", - "后续": "内部评审" - }, - { - "步骤": "内部评审", - "后续": "客户确认" - }, - { - "步骤": "客户确认", - "后续": "合同签署" - } - ] + "type": "flow_diagram", + "encode": { + "source": "source", + "target": "target" + } + } + ] + }, + { + "source": { + "data": [ + { + "步骤": "起草合同", + "后续": "内部评审" + }, + { + "步骤": "内部评审", + "后续": "客户确认" }, - "target": [ - { - "type": "flow_diagram", - "encode": { - "source": "步骤", - "target": "后续" - } - } - ] + { + "步骤": "客户确认", + "后续": "合同签署" + } + ] + }, + "target": [ + { + "type": "flow_diagram", + "encode": { + "source": "步骤", + "target": "后续" + } } + ] + } ] diff --git a/evaluations/datastes/recommend/funnel/01_base.json b/evaluations/datastes/recommend/funnel/01_base.json index 6b17e98..643a409 100644 --- a/evaluations/datastes/recommend/funnel/01_base.json +++ b/evaluations/datastes/recommend/funnel/01_base.json @@ -1,60 +1,56 @@ [ { "source": { - "metas": [ - { - "name": "注册用户", - "dataType": "number" - }, - { - "name": "加入购物车", - "dataType": "number" - }, - { - "name": "下单成功", - "dataType": "number" - }, - { - "name": "购买成功", - "dataType": "number" - } - ], - "data": [ - {"注册用户": 20000, "加入购物车": 12000, "下单成功": 3600, "购买成功": 2800} - ] + "metas": [ + { + "name": "注册用户", + "dataType": "number" + }, + { + "name": "加入购物车", + "dataType": "number" + }, + { + "name": "下单成功", + "dataType": "number" + }, + { + "name": "购买成功", + "dataType": "number" + } + ], + "data": [{ "注册用户": 20000, "加入购物车": 12000, "下单成功": 3600, "购买成功": 2800 }] }, "target": [ - { - "type": "funnel", - "encode": { - "y": ["注册用户", "加入购物车", "下单成功", "购买成功"] - } + { + "type": "funnel", + "encode": { + "y": ["注册用户", "加入购物车", "下单成功", "购买成功"] } + } ] -}, + }, { "source": { "metas": [ - { - "name": "Visits", - "dataType": "number" - }, - { - "name": "Signups", - "dataType": "number" - }, - { - "name": "Purchased", - "dataType": "number" - }, - { - "name": "Retained Customers", - "dataType": "number" - } - ], - "data": [ - {"Visits": 5000, "Signups": 3000, "Purchased": 1500, "Retained Customers": 800} - ] + { + "name": "Visits", + "dataType": "number" + }, + { + "name": "Signups", + "dataType": "number" + }, + { + "name": "Purchased", + "dataType": "number" + }, + { + "name": "Retained Customers", + "dataType": "number" + } + ], + "data": [{ "Visits": 5000, "Signups": 3000, "Purchased": 1500, "Retained Customers": 800 }] }, "target": [ { @@ -69,25 +65,23 @@ "source": { "metas": [ { - "name": "收到简历", - "dataType": "number" + "name": "收到简历", + "dataType": "number" }, { - "name": "初筛通过", - "dataType": "number" + "name": "初筛通过", + "dataType": "number" }, { - "name": "进入面试", - "dataType": "number" + "name": "进入面试", + "dataType": "number" }, { "name": "最终录用", "dataType": "number" - } - ], - "data": [ - {"收到简历": 1000, "初筛通过": 400, "进入面试": 200, "最终录用": 50} - ] + } + ], + "data": [{ "收到简历": 1000, "初筛通过": 400, "进入面试": 200, "最终录用": 50 }] }, "target": [ { @@ -101,26 +95,24 @@ { "source": { "metas": [ - { - "name": "Page Views", - "dataType": "number" - }, - { - "name": "Clicks", - "dataType": "number" - }, - { - "name": "Cart Additions", - "dataType": "number" - }, - { - "name": "Checkouts", - "dataType": "number" - } - ], - "data": [ - {"Page Views": 10000, "Clicks": 7000, "Cart Additions": 3500, "Checkouts": 2500} - ] + { + "name": "Page Views", + "dataType": "number" + }, + { + "name": "Clicks", + "dataType": "number" + }, + { + "name": "Cart Additions", + "dataType": "number" + }, + { + "name": "Checkouts", + "dataType": "number" + } + ], + "data": [{ "Page Views": 10000, "Clicks": 7000, "Cart Additions": 3500, "Checkouts": 2500 }] }, "target": [ { @@ -134,25 +126,25 @@ { "source": { "metas": [ - { - "name": "Leads Generated", - "dataType": "number" - }, - { - "name": "Contacted", - "dataType": "number" - }, - { - "name": "Qualified", - "dataType": "number" - }, - { - "name": "Converted", - "dataType": "number" - } - ], + { + "name": "Leads Generated", + "dataType": "number" + }, + { + "name": "Contacted", + "dataType": "number" + }, + { + "name": "Qualified", + "dataType": "number" + }, + { + "name": "Converted", + "dataType": "number" + } + ], "data": [ - {"Leads Generated": 12000, "Contacted": 6000, "Qualified": 3000, "Converted": 1200} + { "Leads Generated": 12000, "Contacted": 6000, "Qualified": 3000, "Converted": 1200 } ] }, "target": [ @@ -167,25 +159,25 @@ { "source": { "metas": [ - { - "name": "Application Received", - "dataType": "number" - }, - { - "name": "First Review", - "dataType": "number" - }, - { - "name": "Interview", - "dataType": "number" - }, - { - "name": "Accept Offer", - "dataType": "number" - } - ], + { + "name": "Application Received", + "dataType": "number" + }, + { + "name": "First Review", + "dataType": "number" + }, + { + "name": "Interview", + "dataType": "number" + }, + { + "name": "Accept Offer", + "dataType": "number" + } + ], "data": [ - {"Application Received": 500, "First Review": 300, "Interview": 100, "Accept Offer": 30} + { "Application Received": 500, "First Review": 300, "Interview": 100, "Accept Offer": 30 } ] }, "target": [ @@ -201,25 +193,23 @@ "source": { "metas": [ { - "name": "浏览新闻", - "dataType": "number" + "name": "浏览新闻", + "dataType": "number" }, { - "name": "文章点击", - "dataType": "number" + "name": "文章点击", + "dataType": "number" }, { - "name": "阅读完成", - "dataType": "number" + "name": "阅读完成", + "dataType": "number" }, { "name": "分享", "dataType": "number" - } - ], - "data": [ - {"浏览新闻": 8000, "文章点击": 6000, "阅读完成": 3000, "分享": 500} - ] + } + ], + "data": [{ "浏览新闻": 8000, "文章点击": 6000, "阅读完成": 3000, "分享": 500 }] }, "target": [ { @@ -234,24 +224,29 @@ "source": { "metas": [ { - "name": "Session Starts", - "dataType": "number" + "name": "Session Starts", + "dataType": "number" }, { - "name": "Games Played", - "dataType": "number" + "name": "Games Played", + "dataType": "number" }, { - "name": "In-Game Purchase", - "dataType": "number" + "name": "In-Game Purchase", + "dataType": "number" }, { "name": "Return User", "dataType": "number" - } - ], + } + ], "data": [ - {"Session Starts": 9000, "Games Played": 5000, "In-Game Purchase": 2000, "Return Users": 1000} + { + "Session Starts": 9000, + "Games Played": 5000, + "In-Game Purchase": 2000, + "Return Users": 1000 + } ] }, "target": [ @@ -267,24 +262,29 @@ "source": { "metas": [ { - "name": "Free Trial Signups", - "dataType": "number" + "name": "Free Trial Signups", + "dataType": "number" }, { - "name": "Usage After Trial", - "dataType": "number" + "name": "Usage After Trial", + "dataType": "number" }, { - "name": "Upgrades", - "dataType": "number" + "name": "Upgrades", + "dataType": "number" }, { "name": "Long-term Subscribers", "dataType": "number" - } - ], + } + ], "data": [ - {"Free Trial Signups": 600, "Usage After Trial": 400, "Upgrades": 150, "Long-term Subscribers": 85} + { + "Free Trial Signups": 600, + "Usage After Trial": 400, + "Upgrades": 150, + "Long-term Subscribers": 85 + } ] }, "target": [ @@ -300,25 +300,23 @@ "source": { "metas": [ { - "name": "信息传播", - "dataType": "number" + "name": "信息传播", + "dataType": "number" }, { - "name": "接受搜索", - "dataType": "number" + "name": "接受搜索", + "dataType": "number" }, { - "name": "阅读条款", - "dataType": "number" + "name": "阅读条款", + "dataType": "number" }, { "name": "尝试服务", "dataType": "number" - } - ], - "data": [ - {"信息传播": 20000, "接受搜索": 12000, "阅读条款": 5000, "尝试服务": 1800} - ] + } + ], + "data": [{ "信息传播": 20000, "接受搜索": 12000, "阅读条款": 5000, "尝试服务": 1800 }] }, "target": [ { @@ -333,24 +331,29 @@ "source": { "metas": [ { - "name": "Campaign Views", - "dataType": "number" + "name": "Campaign Views", + "dataType": "number" }, { - "name": "Clicks", - "dataType": "number" + "name": "Clicks", + "dataType": "number" }, { - "name": "Conversions", - "dataType": "number" + "name": "Conversions", + "dataType": "number" }, { "name": "Repeat Customers", "dataType": "number" - } - ], + } + ], "data": [ - {"Campaign Views": 100000, "Clicks": 50000, "Conversions": 20000, "Repeat Customers": 5000} + { + "Campaign Views": 100000, + "Clicks": 50000, + "Conversions": 20000, + "Repeat Customers": 5000 + } ] }, "target": [ @@ -366,25 +369,23 @@ "source": { "metas": [ { - "name": "潜在客户", - "dataType": "number" + "name": "潜在客户", + "dataType": "number" }, { - "name": "营销邮件打开", - "dataType": "number" + "name": "营销邮件打开", + "dataType": "number" }, { - "name": "点击链接", - "dataType": "number" + "name": "点击链接", + "dataType": "number" }, { "name": "签约", "dataType": "number" - } - ], - "data": [ - {"潜在客户": 7000, "营销邮件打开": 4000, "点击链接": 1500, "签约": 300} - ] + } + ], + "data": [{ "潜在客户": 7000, "营销邮件打开": 4000, "点击链接": 1500, "签约": 300 }] }, "target": [ { @@ -399,25 +400,23 @@ "source": { "metas": [ { - "name": "广告展示", - "dataType": "number" + "name": "广告展示", + "dataType": "number" }, { - "name": "广告点击", - "dataType": "number" + "name": "广告点击", + "dataType": "number" }, { - "name": "进入网站", - "dataType": "number" + "name": "进入网站", + "dataType": "number" }, { "name": "完成注册", "dataType": "number" - } - ], - "data": [ - {"广告展示": 500000, "广告点击": 100000, "进入网站": 20000, "完成注册": 1000} - ] + } + ], + "data": [{ "广告展示": 500000, "广告点击": 100000, "进入网站": 20000, "完成注册": 1000 }] }, "target": [ { @@ -432,24 +431,29 @@ "source": { "metas": [ { - "name": "Social Media Followers", - "dataType": "number" + "name": "Social Media Followers", + "dataType": "number" }, { - "name": "Engaged Users", - "dataType": "number" + "name": "Engaged Users", + "dataType": "number" }, { - "name": "Content Shares", - "dataType": "number" + "name": "Content Shares", + "dataType": "number" }, { "name": "Conversions", "dataType": "number" - } - ], + } + ], "data": [ - {"Social Media Followers": 10000, "Engaged Users": 4000, "Content Shares": 1000, "Conversions": 200} + { + "Social Media Followers": 10000, + "Engaged Users": 4000, + "Content Shares": 1000, + "Conversions": 200 + } ] }, "target": [ @@ -465,25 +469,23 @@ "source": { "metas": [ { - "name": "文章阅读", - "dataType": "number" + "name": "文章阅读", + "dataType": "number" }, { - "name": "深入阅读", - "dataType": "number" + "name": "深入阅读", + "dataType": "number" }, { - "name": "推荐他人", - "dataType": "number" + "name": "推荐他人", + "dataType": "number" }, { "name": "新增用户", "dataType": "number" - } - ], - "data": [ - {"文章阅读": 2500, "深入阅读": 1500, "推荐他人": 800, "新增用户": 200} - ] + } + ], + "data": [{ "文章阅读": 2500, "深入阅读": 1500, "推荐他人": 800, "新增用户": 200 }] }, "target": [ { @@ -498,25 +500,23 @@ "source": { "metas": [ { - "name": "注册会展", - "dataType": "number" + "name": "注册会展", + "dataType": "number" }, { - "name": "到场参与", - "dataType": "number" + "name": "到场参与", + "dataType": "number" }, { - "name": "深度交谈", - "dataType": "number" + "name": "深度交谈", + "dataType": "number" }, { "name": "后续联系", "dataType": "number" - } - ], - "data": [ - {"注册会展": 1000, "到场参与": 600, "深度交谈": 300, "后续联系": 100} - ] + } + ], + "data": [{ "注册会展": 1000, "到场参与": 600, "深度交谈": 300, "后续联系": 100 }] }, "target": [ { @@ -531,24 +531,24 @@ "source": { "metas": [ { - "name": "咨询金额", - "dataType": "number" + "name": "咨询金额", + "dataType": "number" }, { - "name": "可用金额", - "dataType": "number" + "name": "可用金额", + "dataType": "number" }, { - "name": "曝光金额", - "dataType": "number" + "name": "曝光金额", + "dataType": "number" }, { "name": "成交金额", "dataType": "number" - } - ], + } + ], "data": [ - {"咨询金额": 1000, "可用金额": 600, "曝光金额": 300, "提交金额": 100, "成交金额": 100} + { "咨询金额": 1000, "可用金额": 600, "曝光金额": 300, "提交金额": 100, "成交金额": 100 } ] }, "target": [ diff --git a/evaluations/datastes/recommend/heatmap/01_two_dim_one_measure.json b/evaluations/datastes/recommend/heatmap/01_two_dim_one_measure.json index 11353b7..3fc9c19 100644 --- a/evaluations/datastes/recommend/heatmap/01_two_dim_one_measure.json +++ b/evaluations/datastes/recommend/heatmap/01_two_dim_one_measure.json @@ -1,583 +1,583 @@ [ { "source": { - "metas": [ - { - "name": "budgetYear", - "dataType": "date" - }, - { - "name": "forecastYear", - "dataType": "date" - }, - { - "name": "value", - "dataType": "number" - } - ], - "data": [ - {"budgetYear": 1980, "forecastYear": 1980, "value": -0.103}, - {"budgetYear": 1980, "forecastYear": 1981, "value": -0.037}, - {"budgetYear": 1980, "forecastYear": 1982, "value": 0.01}, - {"budgetYear": 1980, "forecastYear": 1983, "value": 0.051}, - {"budgetYear": 1981, "forecastYear": 1980, "value": -0.192}, - {"budgetYear": 1981, "forecastYear": 1981, "value": -0.129} - ] - }, - "target": [ + "metas": [ + { + "name": "budgetYear", + "dataType": "date" + }, { - "type": "heatmap", - "encode": { - "x": ["budgetYear"], - "y": ["forecastYear"], - "size": ["value"] - } + "name": "forecastYear", + "dataType": "date" }, { - "type": "scatter", - "encode": { - "x": ["budgetYear"], - "y": ["forecastYear"], - "size": ["value"] - } + "name": "value", + "dataType": "number" } - ] -}, -{ - "source": { - "data": [ - { - "Entity": "All natural disasters", - "Year": 1900, - "Deaths": 1267360 - }, - { - "Entity": "All natural disasters", - "Year": 1901, - "Deaths": 200018 - }, - { - "Entity": "All natural disasters", - "Year": 1902, - "Deaths": 46037 - }, - { - "Entity": "All natural disasters", - "Year": 1903, - "Deaths": 6506 - }, + ], + "data": [ + { "budgetYear": 1980, "forecastYear": 1980, "value": -0.103 }, + { "budgetYear": 1980, "forecastYear": 1981, "value": -0.037 }, + { "budgetYear": 1980, "forecastYear": 1982, "value": 0.01 }, + { "budgetYear": 1980, "forecastYear": 1983, "value": 0.051 }, + { "budgetYear": 1981, "forecastYear": 1980, "value": -0.192 }, + { "budgetYear": 1981, "forecastYear": 1981, "value": -0.129 } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1905, - "Deaths": 22758 + "type": "heatmap", + "encode": { + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] + } }, { - "Entity": "All natural disasters", - "Year": 1906, - "Deaths": 42970 - }, + "type": "scatter", + "encode": { + "x": ["budgetYear"], + "y": ["forecastYear"], + "size": ["value"] + } + } + ] + }, + { + "source": { + "data": [ + { + "Entity": "All natural disasters", + "Year": 1900, + "Deaths": 1267360 + }, + { + "Entity": "All natural disasters", + "Year": 1901, + "Deaths": 200018 + }, + { + "Entity": "All natural disasters", + "Year": 1902, + "Deaths": 46037 + }, + { + "Entity": "All natural disasters", + "Year": 1903, + "Deaths": 6506 + }, + { + "Entity": "All natural disasters", + "Year": 1905, + "Deaths": 22758 + }, + { + "Entity": "All natural disasters", + "Year": 1906, + "Deaths": 42970 + }, + { + "Entity": "All natural disasters", + "Year": 1907, + "Deaths": 1325641 + }, + { + "Entity": "All natural disasters", + "Year": 1908, + "Deaths": 75033 + }, + { + "Entity": "All natural disasters", + "Year": 1909, + "Deaths": 1511524 + }, + { + "Entity": "All natural disasters", + "Year": 1910, + "Deaths": 148233 + }, + { + "Entity": "All natural disasters", + "Year": 1911, + "Deaths": 102408 + }, + { + "Entity": "All natural disasters", + "Year": 1912, + "Deaths": 52093 + }, + { + "Entity": "All natural disasters", + "Year": 1913, + "Deaths": 882 + }, + { + "Entity": "All natural disasters", + "Year": 1914, + "Deaths": 289 + }, + { + "Entity": "All natural disasters", + "Year": 1915, + "Deaths": 32167 + }, + { + "Entity": "All natural disasters", + "Year": 1916, + "Deaths": 300 + } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1907, - "Deaths": 1325641 + "type": "heatmap", + "encode": { + "x": ["Entity"], + "y": ["Year"], + "size": ["Deaths"] + } }, { - "Entity": "All natural disasters", - "Year": 1908, - "Deaths": 75033 - }, + "type": "scatter", + "encode": { + "x": ["Entity"], + "y": ["Year"], + "size": ["Deaths"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "年龄", + "dataType": "number" + }, + { + "name": "收入", + "dataType": "number" + }, + { + "name": "消费金额", + "dataType": "number" + } + ], + "data": [ + { "年龄": 25, "收入": 5000, "消费金额": 2000 }, + { "年龄": 30, "收入": 7000, "消费金额": 3000 }, + { "年龄": 35, "收入": 10000, "消费金额": 5000 }, + { "年龄": 40, "收入": 15000, "消费金额": 7000 }, + { "年龄": 45, "收入": 20000, "消费金额": 8000 } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1909, - "Deaths": 1511524 + "type": "heatmap", + "encode": { + "x": ["年龄"], + "y": ["收入"], + "size": ["消费金额"] + } }, { - "Entity": "All natural disasters", - "Year": 1910, - "Deaths": 148233 - }, + "type": "scatter", + "encode": { + "x": ["年龄"], + "y": ["收入"], + "size": ["消费金额"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "城市", + "dataType": "string" + }, + { + "name": "年份", + "dataType": "number" + }, + { + "name": "人口", + "dataType": "number" + } + ], + "data": [ + { "城市": "北京", "年份": 2010, "人口": 19612368 }, + { "城市": "上海", "年份": 2010, "人口": 23019148 }, + { "城市": "广州", "年份": 2010, "人口": 12700800 }, + { "城市": "深圳", "年份": 2010, "人口": 10357813 }, + { "城市": "成都", "年份": 2010, "人口": 14043972 } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1911, - "Deaths": 102408 + "type": "heatmap", + "encode": { + "x": ["城市"], + "y": ["年份"], + "size": ["人口"] + } }, { - "Entity": "All natural disasters", - "Year": 1912, - "Deaths": 52093 - }, + "type": "scatter", + "encode": { + "x": ["城市"], + "y": ["年份"], + "size": ["人口"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "Country", + "dataType": "string" + }, + { + "name": "Year", + "dataType": "number" + }, + { + "name": "GDP", + "dataType": "number" + } + ], + "data": [ + { "Country": "USA", "Year": 2020, "GDP": 21137518 }, + { "Country": "China", "Year": 2020, "GDP": 14722801 }, + { "Country": "Japan", "Year": 2020, "GDP": 5064873 }, + { "Country": "Germany", "Year": 2020, "GDP": 3845630 }, + { "Country": "India", "Year": 2020, "GDP": 2875142 } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1913, - "Deaths": 882 + "type": "heatmap", + "encode": { + "x": ["Country"], + "y": ["Year"], + "size": ["GDP"] + } }, { - "Entity": "All natural disasters", - "Year": 1914, - "Deaths": 289 - }, + "type": "scatter", + "encode": { + "x": ["Country"], + "y": ["Year"], + "size": ["GDP"] + } + } + ] + }, + { + "source": { + "metas": [ + { + "name": "产品", + "dataType": "string" + }, + { + "name": "季度", + "dataType": "number" + }, + { + "name": "销售额", + "dataType": "number" + } + ], + "data": [ + { "产品": "手机", "季度": 1, "销售额": 100000 }, + { "产品": "笔记本", "季度": 1, "销售额": 80000 }, + { "产品": "平板", "季度": 1, "销售额": 40000 }, + { "产品": "电视", "季度": 1, "销售额": 60000 }, + { "产品": "耳机", "季度": 1, "销售额": 20000 } + ] + }, + "target": [ { - "Entity": "All natural disasters", - "Year": 1915, - "Deaths": 32167 + "type": "heatmap", + "encode": { + "x": ["产品"], + "y": ["季度"], + "size": ["销售额"] + } }, { - "Entity": "All natural disasters", - "Year": 1916, - "Deaths": 300 + "type": "scatter", + "encode": { + "x": ["产品"], + "y": ["季度"], + "size": ["销售额"] + } } ] }, - "target": [ - { + { + "source": { + "metas": [ + { + "name": "国家", + "dataType": "string" + }, + { + "name": "年份", + "dataType": "number" + }, + { + "name": "CO2 排放", + "dataType": "number" + } + ], + "data": [ + { "国家": "美国", "年份": 2015, "CO2 排放": 5000 }, + { "国家": "中国", "年份": 2015, "CO2 排放": 10000 }, + { "国家": "印度", "年份": 2015, "CO2 排放": 2500 }, + { "国家": "俄罗斯", "年份": 2015, "CO2 排放": 1600 }, + { "国家": "日本", "年份": 2015, "CO2 排放": 1200 } + ] + }, + "target": [ + { "type": "heatmap", "encode": { - "x": ["Entity"], - "y": ["Year"], - "size": ["Deaths"] + "x": ["国家"], + "y": ["年份"], + "size": ["CO2 排放"] } - }, - { + }, + { "type": "scatter", "encode": { - "x": ["Entity"], - "y": ["Year"], - "size": ["Deaths"] + "x": ["国家"], + "y": ["年份"], + "size": ["CO2 排放"] } - } + } ] -}, - { - "source": { - "metas": [ - { - "name": "年龄", - "dataType": "number" - }, - { - "name": "收入", - "dataType": "number" - }, - { - "name": "消费金额", - "dataType": "number" - } - ], - "data": [ - {"年龄": 25, "收入": 5000, "消费金额": 2000}, - {"年龄": 30, "收入": 7000, "消费金额": 3000}, - {"年龄": 35, "收入": 10000, "消费金额": 5000}, - {"年龄": 40, "收入": 15000, "消费金额": 7000}, - {"年龄": 45, "收入": 20000, "消费金额": 8000} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["年龄"], - "y": ["收入"], - "size": ["消费金额"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["年龄"], - "y": ["收入"], - "size": ["消费金额"] - } - } - ] }, { - "source": { - "metas": [ - { - "name": "城市", - "dataType": "string" - }, - { - "name": "年份", - "dataType": "number" - }, - { - "name": "人口", - "dataType": "number" - } - ], - "data": [ - {"城市": "北京", "年份": 2010, "人口": 19612368}, - {"城市": "上海", "年份": 2010, "人口": 23019148}, - {"城市": "广州", "年份": 2010, "人口": 12700800}, - {"城市": "深圳", "年份": 2010, "人口": 10357813}, - {"城市": "成都", "年份": 2010, "人口": 14043972} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["城市"], - "y": ["年份"], - "size": ["人口"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["城市"], - "y": ["年份"], - "size": ["人口"] - } - } + "source": { + "metas": [ + { + "name": "Sector", + "dataType": "string" + }, + { + "name": "Year", + "dataType": "number" + }, + { + "name": "Profit", + "dataType": "number" + } + ], + "data": [ + { "Sector": "Technology", "Year": 2021, "Profit": 1500 }, + { "Sector": "Healthcare", "Year": 2021, "Profit": 830 }, + { "Sector": "Finance", "Year": 2021, "Profit": 970 }, + { "Sector": "Energy", "Year": 2021, "Profit": 650 }, + { "Sector": "Retail", "Year": 2021, "Profit": 510 } ] - }, - { - "source": { - "metas": [ - { - "name": "Country", - "dataType": "string" - }, - { - "name": "Year", - "dataType": "number" - }, - { - "name": "GDP", - "dataType": "number" - } - ], - "data": [ - {"Country": "USA", "Year": 2020, "GDP": 21137518}, - {"Country": "China", "Year": 2020, "GDP": 14722801}, - {"Country": "Japan", "Year": 2020, "GDP": 5064873}, - {"Country": "Germany", "Year": 2020, "GDP": 3845630}, - {"Country": "India", "Year": 2020, "GDP": 2875142} - ] + }, + "target": [ + { + "type": "heatmap", + "encode": { + "x": ["Sector"], + "y": ["Year"], + "size": ["Profit"] + } }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["Country"], - "y": ["Year"], - "size": ["GDP"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["Country"], - "y": ["Year"], - "size": ["GDP"] - } - } - ] + { + "type": "scatter", + "encode": { + "x": ["Sector"], + "y": ["Year"], + "size": ["Profit"] + } + } + ] }, { - "source": { - "metas": [ - { - "name": "产品", - "dataType": "string" - }, - { - "name": "季度", - "dataType": "number" - }, - { - "name": "销售额", - "dataType": "number" - } - ], - "data": [ - {"产品": "手机", "季度": 1, "销售额": 100000}, - {"产品": "笔记本", "季度": 1, "销售额": 80000}, - {"产品": "平板", "季度": 1, "销售额": 40000}, - {"产品": "电视", "季度": 1, "销售额": 60000}, - {"产品": "耳机", "季度": 1, "销售额": 20000} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["产品"], - "y": ["季度"], - "size": ["销售额"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["产品"], - "y": ["季度"], - "size": ["销售额"] - } - } + "source": { + "metas": [ + { + "name": "行业", + "dataType": "string" + }, + { + "name": "月份", + "dataType": "number" + }, + { + "name": "增长率", + "dataType": "number" + } + ], + "data": [ + { "行业": "科技", "月份": 1, "增长率": 5.2 }, + { "行业": "医疗", "月份": 1, "增长率": 3.8 }, + { "行业": "金融", "月份": 1, "增长率": 2.4 }, + { "行业": "能源", "月份": 1, "增长率": 1.9 }, + { "行业": "零售", "月份": 1, "增长率": 4.1 } ] - }, - { - "source": { - "metas": [ - { - "name": "国家", - "dataType": "string" - }, - { - "name": "年份", - "dataType": "number" - }, - { - "name": "CO2 排放", - "dataType": "number" - } - ], - "data": [ - {"国家": "美国", "年份": 2015, "CO2 排放": 5000}, - {"国家": "中国", "年份": 2015, "CO2 排放": 10000}, - {"国家": "印度", "年份": 2015, "CO2 排放": 2500}, - {"国家": "俄罗斯", "年份": 2015, "CO2 排放": 1600}, - {"国家": "日本", "年份": 2015, "CO2 排放": 1200} - ] + }, + "target": [ + { + "type": "heatmap", + "encode": { + "x": ["行业"], + "y": ["月份"], + "size": ["增长率"] + } }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["国家"], - "y": ["年份"], - "size": ["CO2 排放"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["国家"], - "y": ["年份"], - "size": ["CO2 排放"] - } - } - ] + { + "type": "scatter", + "encode": { + "x": ["行业"], + "y": ["月份"], + "size": ["增长率"] + } + } + ] }, { - "source": { - "metas": [ - { - "name": "Sector", - "dataType": "string" - }, - { - "name": "Year", - "dataType": "number" - }, - { - "name": "Profit", - "dataType": "number" - } - ], - "data": [ - {"Sector": "Technology", "Year": 2021, "Profit": 1500}, - {"Sector": "Healthcare", "Year": 2021, "Profit": 830}, - {"Sector": "Finance", "Year": 2021, "Profit": 970}, - {"Sector": "Energy", "Year": 2021, "Profit": 650}, - {"Sector": "Retail", "Year": 2021, "Profit": 510} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["Sector"], - "y": ["Year"], - "size": ["Profit"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["Sector"], - "y": ["Year"], - "size": ["Profit"] - } - } + "source": { + "metas": [ + { + "name": "City", + "dataType": "string" + }, + { + "name": "Year", + "dataType": "number" + }, + { + "name": "Temperature", + "dataType": "number" + } + ], + "data": [ + { "City": "London", "Year": 2018, "Temperature": 15.5 }, + { "City": "Paris", "Year": 2018, "Temperature": 17.0 }, + { "City": "New York", "Year": 2018, "Temperature": 22.1 }, + { "City": "Tokyo", "Year": 2018, "Temperature": 19.3 }, + { "City": "Sydney", "Year": 2018, "Temperature": 23.1 } ] - }, - { - "source": { - "metas": [ - { - "name": "行业", - "dataType": "string" - }, - { - "name": "月份", - "dataType": "number" - }, - { - "name": "增长率", - "dataType": "number" - } - ], - "data": [ - {"行业": "科技", "月份": 1, "增长率": 5.2}, - {"行业": "医疗", "月份": 1, "增长率": 3.8}, - {"行业": "金融", "月份": 1, "增长率": 2.4}, - {"行业": "能源", "月份": 1, "增长率": 1.9}, - {"行业": "零售", "月份": 1, "增长率": 4.1} - ] + }, + "target": [ + { + "type": "heatmap", + "encode": { + "x": ["City"], + "y": ["Year"], + "size": ["Temperature"] + } }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["行业"], - "y": ["月份"], - "size": ["增长率"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["行业"], - "y": ["月份"], - "size": ["增长率"] - } - } - ] + { + "type": "scatter", + "encode": { + "x": ["City"], + "y": ["Year"], + "size": ["Temperature"] + } + } + ] }, { - "source": { - "metas": [ - { - "name": "City", - "dataType": "string" - }, - { - "name": "Year", - "dataType": "number" - }, - { - "name": "Temperature", - "dataType": "number" - } - ], - "data": [ - {"City": "London", "Year": 2018, "Temperature": 15.5}, - {"City": "Paris", "Year": 2018, "Temperature": 17.0}, - {"City": "New York", "Year": 2018, "Temperature": 22.1}, - {"City": "Tokyo", "Year": 2018, "Temperature": 19.3}, - {"City": "Sydney", "Year": 2018, "Temperature": 23.1} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["City"], - "y": ["Year"], - "size": ["Temperature"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["City"], - "y": ["Year"], - "size": ["Temperature"] - } - } + "source": { + "metas": [ + { + "name": "Country", + "dataType": "string" + }, + { + "name": "Year", + "dataType": "number" + }, + { + "name": "Unemployment", + "dataType": "number" + } + ], + "data": [ + { "Country": "USA", "Year": 2019, "Unemployment": 3.5 }, + { "Country": "Canada", "Year": 2019, "Unemployment": 5.7 }, + { "Country": "Germany", "Year": 2019, "Unemployment": 3.2 }, + { "Country": "UK", "Year": 2019, "Unemployment": 3.8 }, + { "Country": "France", "Year": 2019, "Unemployment": 8.5 } ] - }, - { - "source": { - "metas": [ - { - "name": "Country", - "dataType": "string" - }, - { - "name": "Year", - "dataType": "number" - }, - { 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"dataType": "number" - } - ], - "data": [ - {"月": 1, "类别": "A", "销量": 305}, - {"月": 1, "类别": "B", "销量": 220}, - {"月": 2, "类别": "A", "销量": 310}, - {"月": 2, "类别": "B", "销量": 215}, - {"月": 3, "类别": "A", "销量": 275}, - {"月": 3, "类别": "B", "销量": 230}, - {"月": 4, "类别": "A", "销量": 295}, - {"月": 4, "类别": "B", "销量": 240} - ] - }, - "target": [ - { - "type": "heatmap", - "encode": { - "x": ["月"], - "y": ["类别"], - "size": ["销量"] - } - }, - { - "type": "scatter", - "encode": { - "x": ["月"], - "y": ["类别"], - "size": ["销量"] - } - } + "source": { + "metas": [ + { + "name": "月", + "dataType": "number" + }, + { + "name": "类别", + "dataType": "string" + }, + { + "name": "销量", + "dataType": "number" + } + ], + "data": [ + { "月": 1, "类别": "A", "销量": 305 }, + { "月": 1, "类别": "B", "销量": 220 }, + { "月": 2, "类别": "A", "销量": 310 }, + { "月": 2, "类别": "B", "销量": 215 }, + { "月": 3, "类别": "A", "销量": 275 }, + { "月": 3, "类别": "B", "销量": 230 }, + { "月": 4, "类别": "A", "销量": 295 }, + { "月": 4, "类别": "B", "销量": 240 } ] + }, + "target": [ + { + "type": "heatmap", + "encode": { + "x": ["月"], + "y": ["类别"], + "size": ["销量"] + } + }, + { + "type": "scatter", + "encode": { + "x": ["月"], + "y": ["类别"], + "size": ["销量"] + } + } + ] } ] diff --git a/evaluations/datastes/recommend/histogram/01_values.json b/evaluations/datastes/recommend/histogram/01_values.json index 113fa50..ac895f1 100644 --- a/evaluations/datastes/recommend/histogram/01_values.json +++ b/evaluations/datastes/recommend/histogram/01_values.json @@ -1,4 +1,3 @@ - [ { "source": { @@ -99,13 +98,7 @@ }, { "source": { - "data": [ - { "重量": 2.5 }, - { "重量": 3.0 }, - { "重量": 3.5 }, - { "重量": 4.0 }, - { "重量": 3.2 } - ] + "data": [{ "重量": 2.5 }, { "重量": 3.0 }, { "重量": 3.5 }, { "重量": 4.0 }, { "重量": 3.2 }] }, "target": [ { @@ -175,13 +168,7 @@ }, { "source": { - "data": [ - { "水费": 48 }, - { "水费": 50 }, - { "水费": 45 }, - { "水费": 52 }, - { "水费": 47 } - ] + "data": [{ "水费": 48 }, { "水费": 50 }, { "水费": 45 }, { "水费": 52 }, { "水费": 47 }] }, "target": [ { @@ -232,13 +219,7 @@ }, { "source": { - "data": [ - { "speed": 50 }, - { "speed": 55 }, - { "speed": 52 }, - { "speed": 56 }, - { "speed": 54 } - ] + "data": [{ "speed": 50 }, { "speed": 55 }, { "speed": 52 }, { "speed": 56 }, { "speed": 54 }] }, "target": [ { @@ -251,13 +232,7 @@ }, { "source": { - "data": [ - { "时长": 77 }, - { "时长": 84 }, - { "时长": 72 }, - { "时长": 80 }, - { "时长": 76 } - ] + "data": [{ "时长": 77 }, { "时长": 84 }, { "时长": 72 }, { "时长": 80 }, { "时长": 76 }] }, "target": [ { @@ -286,5 +261,5 @@ } } ] - } + } ] diff --git a/evaluations/datastes/recommend/line/01_base.json b/evaluations/datastes/recommend/line/01_base.json index 6cb3bd5..90aab67 100644 --- a/evaluations/datastes/recommend/line/01_base.json +++ b/evaluations/datastes/recommend/line/01_base.json @@ -1,554 +1,508 @@ [ -{ + { "source": { - "metas": [ - { - "name": "月份", - "dataType": "string" - }, - { - "name": "降雨量", - "dataType": "number" - } - ], - "data": [ - { - "月份": "一月", - "降雨量": 30 - }, - { - "月份": "二月", - 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"y": [ - "学员人数" - ] - } + { + "type": "line", + "encode": { + "x": ["学期"], + "y": ["学员人数"] } + } ] -}, -{ + }, + { "source": { - "data": [ - { - "日期": "2023/02/01", - "降水量": 25 - }, - { - "日期": "2023/02/02", - "降水量": 30 - }, - { - "日期": "2023/02/03", - "降水量": 18 - }, - { - "日期": "2023/02/04", - "降水量": 20 - }, - { - "日期": "2023/02/05", - "降水量": 22 - } - ] + "data": [ + { + "日期": "2023/02/01", + "降水量": 25 + }, + { + "日期": "2023/02/02", + "降水量": 30 + }, + { + "日期": "2023/02/03", + "降水量": 18 + }, + { + "日期": "2023/02/04", + "降水量": 20 + }, + { + "日期": "2023/02/05", + "降水量": 22 + } + ] }, "target": [ - { - "type": "line", - "encode": { - "x": [ - "日期" - ], - "y": [ - "降水量" - ] - } + { + "type": "line", + "encode": { + "x": ["日期"], + "y": ["降水量"] } + } ] -} + } ] diff --git a/evaluations/datastes/recommend/line/02_multi_measure.json b/evaluations/datastes/recommend/line/02_multi_measure.json index 1718c73..ec6ab9b 100644 --- a/evaluations/datastes/recommend/line/02_multi_measure.json +++ b/evaluations/datastes/recommend/line/02_multi_measure.json @@ -1,475 +1,485 @@ [ { - "source": { - "data": [ - { - "dateCollected": "2023-03-16", - "temperature": 22.5, - "humidity": 60.5 - }, - { - "dateCollected": "2023-03-17", - "temperature": 25.1, - "humidity": 55.2 - }, - { - "dateCollected": "2023-03-18", - "temperature": 18.3, - "humidity": 65.7 - }, - { - "dateCollected": "2023-03-19", - "temperature": 20.7, - "humidity": 70.3 - }, - { - "dateCollected": "2023-03-20", - "temperature": 24.8, - "humidity": 50.1 - } - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["dateCollected"], - "y": ["temperature", "humidity"] - } - } + "source": { + "data": [ + { + "dateCollected": "2023-03-16", + "temperature": 22.5, + "humidity": 60.5 + }, + { + "dateCollected": "2023-03-17", + "temperature": 25.1, + "humidity": 55.2 + }, + { + "dateCollected": "2023-03-18", + "temperature": 18.3, + "humidity": 65.7 + }, + { + "dateCollected": "2023-03-19", + "temperature": 20.7, + "humidity": 70.3 + }, + { + "dateCollected": "2023-03-20", + "temperature": 24.8, + "humidity": 50.1 + } ] + }, + "target": [ + { + "type": "line", + "encode": { + "x": ["dateCollected"], + "y": ["temperature", "humidity"] + } + } + ] }, { - "source": { - "data": [ - { - "time": "04/01/2023", - "sales": 10000, - "profit": 2000 - }, - { - "time": "04/02/2023", - "sales": 20000, - "profit": 3000 - }, - { - "time": "04/03/2023", - "sales": 30000, - "profit": 4000 - }, - { - "time": "04/04/2023", - "sales": 40000, - "profit": 5000 - }, - { - "time": "04/05/2023", - "sales": 50000, - "profit": 6000 - } - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["time"], - "y": ["sales", "profit"] - } - } + "source": { + "data": [ + { + "time": "04/01/2023", + "sales": 10000, + "profit": 2000 + }, + { + "time": "04/02/2023", + "sales": 20000, + "profit": 3000 + }, + { + "time": "04/03/2023", + "sales": 30000, + "profit": 4000 + }, + { + "time": "04/04/2023", + "sales": 40000, + "profit": 5000 + }, + { + "time": "04/05/2023", + "sales": 50000, + "profit": 6000 + } ] + }, + "target": [ + { + "type": "line", + "encode": { + "x": ["time"], + "y": ["sales", "profit"] + } + } + ] }, { "source": { - "data": [ - { - "制造日期": "2023-01-01", - "成本": 123, - "售价": 567 - }, - { - "制造日期": "2023-01-02", - "成本": 345, - "售价": 1098 - }, - { - "制造日期": "2023-01-03", - "成本": 567, - "售价": 1234 - }, - { - "制造日期": "2023-01-04", - "成本": 789, - "售价": 1456 - }, - { - "制造日期": "2023-01-05", - "成本": 901, - "售价": 1678 - } - ] + "data": [ + { + "制造日期": "2023-01-01", + "成本": 123, + "售价": 567 + }, + { + "制造日期": "2023-01-02", + "成本": 345, + "售价": 1098 + }, + { + "制造日期": "2023-01-03", + "成本": 567, + "售价": 1234 + }, + { + "制造日期": "2023-01-04", + "成本": 789, + "售价": 1456 + }, + { + "制造日期": "2023-01-05", + "成本": 901, + "售价": 1678 + } + ] }, "target": [ - { - "type": "line", - "encode": { - "x": [ - "制造日期" - ], - "y": [ - "成本", - "售价" - ] - } + { + "type": "line", + "encode": { + "x": ["制造日期"], + "y": ["成本", "售价"] } + } ] -}, -{ - "source": { - "data": [ - { - "销售日期": "2023-05-01", - "收入": 423, - "支出": 123 - }, - { - "销售日期": "2023-05-02", - "收入": 489, - "支出": 178 - }, - { - "销售日期": "2023-05-03", - "收入": 512, - "支出": 230 - }, - { - "销售日期": "2023-05-04", - "收入": 678, - "支出": 340 - }, - { - "销售日期": "2023-05-05", - "收入": 789, - "支出": 450 - } - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": [ - "销售日期" - ], - "y": [ - "收入", - "支出" - ] - } - } - ] }, { - "source": { - "data": [ - { - "日期": "01/06/2023", - "温度": 22, - "湿度": 68 - }, - { - "日期": "02/06/2023", - "温度": 24, - "湿度": 70 - }, - { - "日期": "03/06/2023", - "温度": 26, - "湿度": 74 - }, - { - "日期": "04/06/2023", - "温度": 27, - "湿度": 72 - }, - { - "日期": "05/06/2023", - "温度": 25, - "湿度": 69 - } - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": [ - "日期" - ], - "y": [ - "温度", - "湿度" - ] - } - } + "source": { + "data": [ + { + "销售日期": "2023-05-01", + "收入": 423, + "支出": 123 + }, + { + "销售日期": "2023-05-02", + "收入": 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8500, "loss": 3100}, - {"week": "20230605", "profit": 8700, "loss": 2900} - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["week"], - "y": ["profit", "loss"] - } + "target": [ + { + "type": "line", + "encode": { + "x": ["timestamp"], + "y": ["users", "new_users"] } + } + ] + }, + { + "source": { + "data": [ + { "week": "20230601", "profit": 8000, "loss": 3000 }, + { "week": "20230602", "profit": 8200, "loss": 3400 }, + { "week": "20230603", "profit": 7900, "loss": 3200 }, + { "week": "20230604", "profit": 8500, "loss": 3100 }, + { "week": "20230605", "profit": 8700, "loss": 2900 } ] }, - { - "source": { - "data": [ - {"date": "1/03/2023", "rainfall": 10, "snowfall": 0}, - {"date": "2/03/2023", "rainfall": 15, "snowfall": 0}, - {"date": "3/03/2023", "rainfall": 5, "snowfall": 3}, - {"date": "4/03/2023", "rainfall": 0, "snowfall": 10}, - {"date": "5/03/2023", "rainfall": 12, "snowfall": 1} - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["date"], - "y": ["rainfall", "snowfall"] - } + "target": [ + { + "type": "line", + "encode": { + "x": ["week"], + "y": ["profit", "loss"] } + } + ] + }, + { + "source": { + "data": [ + { "date": "1/03/2023", "rainfall": 10, "snowfall": 0 }, + { "date": "2/03/2023", "rainfall": 15, "snowfall": 0 }, + { "date": "3/03/2023", "rainfall": 5, "snowfall": 3 }, + { "date": "4/03/2023", "rainfall": 0, "snowfall": 10 }, + { "date": "5/03/2023", "rainfall": 12, "snowfall": 1 } ] }, - { - "source": { - "data": [ - {"quarter": "Q1 2023", "revenue": 20000, "expenses": 12000}, - {"quarter": "Q2 2023", "revenue": 22000, "expenses": 13000}, - {"quarter": "Q3 2023", "revenue": 25000, "expenses": 15000}, - {"quarter": "Q4 2023", "revenue": 27000, "expenses": 16000} - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["quarter"], - "y": ["revenue", "expenses"] - } + "target": [ + { + "type": "line", + "encode": { + "x": ["date"], + "y": ["rainfall", "snowfall"] } + } + ] + }, + { + "source": { + "data": [ + { "quarter": "Q1 2023", "revenue": 20000, "expenses": 12000 }, + { "quarter": "Q2 2023", "revenue": 22000, "expenses": 13000 }, + { "quarter": "Q3 2023", "revenue": 25000, "expenses": 15000 }, + { "quarter": "Q4 2023", "revenue": 27000, "expenses": 16000 } ] }, - { - "source": { - "data": [ - {"month": "January", "production": 500, "defects": 5}, - {"month": "February", "production": 550, "defects": 6}, - {"month": "March", "production": 600, "defects": 7}, - {"month": "April", "production": 580, "defects": 6}, - {"month": "May", "production": 620, "defects": 8} - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["month"], - "y": ["production", "defects"] - } + "target": [ + { + "type": "line", + "encode": { + "x": ["quarter"], + "y": ["revenue", "expenses"] } + } + ] + }, + { + "source": { + "data": [ + { "month": "January", "production": 500, "defects": 5 }, + { "month": "February", "production": 550, "defects": 6 }, + { "month": "March", "production": 600, "defects": 7 }, + { "month": "April", "production": 580, "defects": 6 }, + { "month": "May", "production": 620, "defects": 8 } ] }, - { - "source": { - "data": [ - {"date": "2023.11.01", "score_math": 80, "score_english": 85, "score_music": 80, "score_writing": 85}, - {"date": "2023.11.02", "score_math": 78, "score_english": 90, "score_music": 80, "score_writing": 85}, - {"date": "2023.11.03", "score_math": 85, "score_english": 87, "score_music": 80, "score_writing": 85}, - {"date": "2023.11.04", "score_math": 90, "score_english": 82, "score_music": 80, "score_writing": 85}, - {"date": "2023.11.05", "score_math": 88, "score_english": 89, "score_music": 80, "score_writing": 85} - ] - }, - "target": [ - { - "type": "line", - "encode": { - "x": ["date"], - "y": ["score_math", "score_english", "score_music", "score_writing"] - } + "target": [ + { + "type": "line", + "encode": { + "x": ["month"], + "y": ["production", "defects"] + } + } + ] + }, + { + "source": { + "data": [ + { + "date": "2023.11.01", + "score_math": 80, + "score_english": 85, + "score_music": 80, + "score_writing": 85 + }, + { + "date": "2023.11.02", + "score_math": 78, + "score_english": 90, + "score_music": 80, + "score_writing": 85 + }, + { + "date": "2023.11.03", + "score_math": 85, + "score_english": 87, + "score_music": 80, + "score_writing": 85 + }, + { + "date": "2023.11.04", + "score_math": 90, + "score_english": 82, + "score_music": 80, + "score_writing": 85 + }, + { + "date": "2023.11.05", + "score_math": 88, + "score_english": 89, + "score_music": 80, + "score_writing": 85 } ] - } + }, + "target": [ + { + "type": "line", + "encode": { + "x": ["date"], + "y": ["score_math", "score_english", "score_music", "score_writing"] + } + } + ] + } ] diff --git a/evaluations/datastes/recommend/line/03_split.json b/evaluations/datastes/recommend/line/03_split.json index 41f9bd4..a87f2ff 100644 --- a/evaluations/datastes/recommend/line/03_split.json +++ b/evaluations/datastes/recommend/line/03_split.json @@ -65,181 +65,181 @@ ] }, "target": [ - { - "type": "line", - "encode": { - "x": ["date"], - "y": ["price"], - "s": ["symbol"] - } - }, - { - "type": "area", - "encode": { - "x": ["date"], - "y": ["price"], - "s": ["symbol"] - } + { + "type": "line", + "encode": { + "x": ["date"], + "y": ["price"], + "s": ["symbol"] + } + }, + { + "type": "area", + "encode": { + "x": ["date"], + "y": ["price"], + "s": ["symbol"] } + } + ] + }, + { + "source": { + "data": [ + { "城市": "北京", "日期": "2020-01", "温度": -5 }, + { "城市": "北京", "日期": "2020-02", "温度": 0 }, + { "城市": "北京", "日期": "2020-03", "温度": 8 }, + { "城市": "北京", "日期": "2020-04", "温度": 15 }, + { "城市": "北京", "日期": "2020-05", "温度": 20 }, + { "城市": "北京", "日期": "2020-06", "温度": 25 } ] + }, + "target": [ + { "type": "line", "encode": { "x": ["日期"], "y": ["温度"], "s": ["城市"] } }, + { "type": "area", "encode": { "x": ["日期"], "y": ["温度"], "s": ["城市"] } } + ] }, - { - "source": { - "data": [ - { "城市": "北京", "日期": "2020-01", "温度": -5 }, - { "城市": "北京", "日期": "2020-02", "温度": 0}, - { "城市": "北京", "日期": "2020-03", "温度": 8}, - { "城市": "北京", "日期": "2020-04", "温度": 15}, - { "城市": "北京", "日期": "2020-05", "温度": 20}, - { "城市": "北京", "日期": "2020-06", "温度": 25} - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["日期"], "y": ["温度"], "s": ["城市"] } }, - { "type": "area", "encode": { "x": ["日期"], "y": ["温度"], "s": ["城市"] } } + { + "source": { + "data": [ + { "产品": "手机", "时间": "2022-01", "销量": 1200 }, + { "产品": "手机", "时间": "2022-02", "销量": 1500 }, + { "产品": "手机", "时间": "2022-03", "销量": 1800 }, + { "产品": "手机", "时间": "2022-04", "销量": 1600 }, + { "产品": "手机", "时间": "2022-05", "销量": 2000 }, + { "产品": "手机", "时间": "2022-06", "销量": 1700 } ] }, - { - "source": { - "data": [ - { "产品": "手机", "时间": "2022-01", "销量": 1200 }, - { "产品": "手机", "时间": "2022-02", "销量": 1500 }, - { "产品": "手机", "时间": "2022-03", "销量": 1800 }, - { "产品": "手机", "时间": "2022-04", "销量": 1600 }, - { "产品": "手机", "时间": "2022-05", "销量": 2000 }, - { "产品": "手机", "时间": "2022-06", "销量": 1700 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["时间"], "y": ["销量"], "s": ["产品"] } }, - { "type": "area", "encode": { "x": ["时间"], "y": ["销量"], "s": ["产品"] } } - ] - }, - { - "source": { - "data": [ - { "category": "A", "quarter": "Q1 2021", "profit": 3000 }, - { "category": "A", "quarter": "Q2 2021", "profit": 4500 }, - { "category": "A", "quarter": "Q3 2021", "profit": 5000 }, - { "category": "A", "quarter": "Q4 2021", "profit": 4800 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["quarter"], "y": ["profit"], "s": ["category"] } }, - { "type": "area", "encode": { "x": ["quarter"], "y": ["profit"], "s": ["category"] } } - ] - }, - { - "source": { - "data": [ - { "国家": "中国", "年份": "2015", "GDP增长": 6.9 }, - { "国家": "中国", "年份": "2016", "GDP增长": 6.7 }, - { "国家": "中国", "年份": "2017", "GDP增长": 6.9 }, - { "国家": "中国", "年份": "2018", "GDP增长": 6.6 }, - { "国家": "中国", "年份": "2019", "GDP增长": 6.0 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["年份"], "y": ["GDP增长"], "s": ["国家"] } }, - { "type": "area", "encode": { "x": ["年份"], "y": ["GDP增长"], "s": ["国家"] } } - ] - }, - { - "source": { - "data": [ - { "region": "North", "year": "2020", "sales": 7000 }, - { "region": "North", "year": "2021", "sales": 8500 }, - { "region": "North", "year": "2022", "sales": 9200 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["year"], "y": ["sales"], "s": ["region"] } }, - { "type": "area", "encode": { "x": ["year"], "y": ["sales"], "s": ["region"] } } - ] - }, - { - "source": { - "data": [ - { "公司": "TechCorp", "月度": "2021-07", "盈利": 450 }, - { "公司": "TechCorp", "月度": "2021-08", "盈利": 520 }, - { "公司": "TechCorp", "月度": "2021-09", "盈利": 480 }, - { "公司": "TechCorp", "月度": "2021-10", "盈利": 530 }, - { "公司": "TechCorp", "月度": "2021-11", "盈利": 600 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["月度"], "y": ["盈利"], "s": ["公司"] } }, - { "type": "area", "encode": { "x": ["月度"], "y": ["盈利"], "s": ["公司"] } } - ] - }, - { - "source": { - "data": [ - { "sector": "Education", "date": "2021-Q1", "budget": 320 }, - { "sector": "Education", "date": "2021-Q2", "budget": 400 }, - { "sector": "Education", "date": "2021-Q3", "budget": 450 }, - { "sector": "Education", "date": "2021-Q4", "budget": 390 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["date"], "y": ["budget"], "s": ["sector"] } }, - { "type": "area", "encode": { "x": ["date"], "y": ["budget"], "s": ["sector"] } } - ] - }, - { - "source": { - "data": [ - { "地区": "南方", "季度": "2020-Q1", "税收": 5000 }, - { "地区": "南方", "季度": "2020-Q2", "税收": 5300 }, - { "地区": "南方", "季度": "2020-Q3", "税收": 5800 }, - { "地区": "南方", "季度": "2020-Q4", "税收": 5600 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["季度"], "y": ["税收"], "s": ["地区"] } }, - { "type": "area", "encode": { "x": ["季度"], "y": ["税收"], "s": ["地区"] } } - ] - }, - { - "source": { - "data": [ - { "城市": "上海", "时间": "Jan 2021", "游客数": 120 }, - { "城市": "上海", "时间": "Feb 2021", "游客数": 110 }, - { "城市": "上海", "时间": "Mar 2021", "游客数": 130 }, - { "城市": "上海", "时间": "Apr 2021", "游客数": 150 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["时间"], "y": ["游客数"], "s": ["城市"] } }, - { "type": "area", "encode": { "x": ["时间"], "y": ["游客数"], "s": ["城市"] } } - ] - }, - { - "source": { - "data": [ - { "subject": "Math", "semester": "S1 2019", "score": 85 }, - { "subject": "Math", "semester": "S2 2019", "score": 87 }, - { "subject": "Math", "semester": "S1 2020", "score": 90 }, - { "subject": "Math", "semester": "S2 2020", "score": 88 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["semester"], "y": ["score"], "s": ["subject"] } }, - { "type": "area", "encode": { "x": ["semester"], "y": ["score"], "s": ["subject"] } } - ] - }, - { - "source": { - "data": [ - { "类别": "电子产品", "月份": "2022-01", "库存量": 600 }, - { "类别": "电子产品", "月份": "2022-02", "库存量": 580 }, - { "类别": "电子产品", "月份": "2022-03", "库存量": 620 }, - { "类别": "电子产品", "月份": "2022-04", "库存量": 610 } - ] - }, - "target": [ - { "type": "line", "encode": { "x": ["月份"], "y": ["库存量"], "s": ["类别"] } }, - { "type": "area", "encode": { "x": ["月份"], "y": ["库存量"], "s": ["类别"] } } - ] - } + "target": [ + { "type": "line", "encode": { "x": ["时间"], "y": ["销量"], "s": ["产品"] } }, + { "type": "area", "encode": { "x": ["时间"], "y": ["销量"], "s": ["产品"] } } + ] + }, + { + "source": { + "data": [ + { "category": "A", "quarter": "Q1 2021", "profit": 3000 }, + { "category": "A", "quarter": "Q2 2021", "profit": 4500 }, + { "category": "A", "quarter": "Q3 2021", "profit": 5000 }, + { "category": "A", "quarter": "Q4 2021", "profit": 4800 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["quarter"], "y": ["profit"], "s": ["category"] } }, + { "type": "area", "encode": { "x": ["quarter"], "y": ["profit"], "s": ["category"] } } + ] + }, + { + "source": { + "data": [ + { "国家": "中国", "年份": "2015", "GDP增长": 6.9 }, + { "国家": "中国", "年份": "2016", "GDP增长": 6.7 }, + { "国家": "中国", "年份": "2017", "GDP增长": 6.9 }, + { "国家": "中国", "年份": "2018", "GDP增长": 6.6 }, + { "国家": "中国", "年份": "2019", "GDP增长": 6.0 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["年份"], "y": ["GDP增长"], "s": ["国家"] } }, + { "type": "area", "encode": { "x": ["年份"], "y": ["GDP增长"], "s": ["国家"] } } + ] + }, + { + "source": { + "data": [ + { "region": "North", "year": "2020", "sales": 7000 }, + { "region": "North", "year": "2021", "sales": 8500 }, + { "region": "North", "year": "2022", "sales": 9200 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["year"], "y": ["sales"], "s": ["region"] } }, + { "type": "area", "encode": { "x": ["year"], "y": ["sales"], "s": ["region"] } } + ] + }, + { + "source": { + "data": [ + { "公司": "TechCorp", "月度": "2021-07", "盈利": 450 }, + { "公司": "TechCorp", "月度": "2021-08", "盈利": 520 }, + { "公司": "TechCorp", "月度": "2021-09", "盈利": 480 }, + { "公司": "TechCorp", "月度": "2021-10", "盈利": 530 }, + { "公司": "TechCorp", "月度": "2021-11", "盈利": 600 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["月度"], "y": ["盈利"], "s": ["公司"] } }, + { "type": "area", "encode": { "x": ["月度"], "y": ["盈利"], "s": ["公司"] } } + ] + }, + { + "source": { + "data": [ + { "sector": "Education", "date": "2021-Q1", "budget": 320 }, + { "sector": "Education", "date": "2021-Q2", "budget": 400 }, + { "sector": "Education", "date": "2021-Q3", "budget": 450 }, + { "sector": "Education", "date": "2021-Q4", "budget": 390 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["date"], "y": ["budget"], "s": ["sector"] } }, + { "type": "area", "encode": { "x": ["date"], "y": ["budget"], "s": ["sector"] } } + ] + }, + { + "source": { + "data": [ + { "地区": "南方", "季度": "2020-Q1", "税收": 5000 }, + { "地区": "南方", "季度": "2020-Q2", "税收": 5300 }, + { "地区": "南方", "季度": "2020-Q3", "税收": 5800 }, + { "地区": "南方", "季度": "2020-Q4", "税收": 5600 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["季度"], "y": ["税收"], "s": ["地区"] } }, + { "type": "area", "encode": { "x": ["季度"], "y": ["税收"], "s": ["地区"] } } + ] + }, + { + "source": { + "data": [ + { "城市": "上海", "时间": "Jan 2021", "游客数": 120 }, + { "城市": "上海", "时间": "Feb 2021", "游客数": 110 }, + { "城市": "上海", "时间": "Mar 2021", "游客数": 130 }, + { "城市": "上海", "时间": "Apr 2021", "游客数": 150 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["时间"], "y": ["游客数"], "s": ["城市"] } }, + { "type": "area", "encode": { "x": ["时间"], "y": ["游客数"], "s": ["城市"] } } + ] + }, + { + "source": { + "data": [ + { "subject": "Math", "semester": "S1 2019", "score": 85 }, + { "subject": "Math", "semester": "S2 2019", "score": 87 }, + { "subject": "Math", "semester": "S1 2020", "score": 90 }, + { "subject": "Math", "semester": "S2 2020", "score": 88 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["semester"], "y": ["score"], "s": ["subject"] } }, + { "type": "area", "encode": { "x": ["semester"], "y": ["score"], "s": ["subject"] } } + ] + }, + { + "source": { + "data": [ + { "类别": "电子产品", "月份": "2022-01", "库存量": 600 }, + { "类别": "电子产品", "月份": "2022-02", "库存量": 580 }, + { "类别": "电子产品", "月份": "2022-03", "库存量": 620 }, + { "类别": "电子产品", "月份": "2022-04", "库存量": 610 } + ] + }, + "target": [ + { "type": "line", "encode": { "x": ["月份"], "y": ["库存量"], "s": ["类别"] } }, + { "type": "area", "encode": { "x": ["月份"], "y": ["库存量"], "s": ["类别"] } } + ] + } ] diff --git a/evaluations/datastes/recommend/map/01_long_lat.json b/evaluations/datastes/recommend/map/01_long_lat.json index 74e67fa..bf8025a 100644 --- a/evaluations/datastes/recommend/map/01_long_lat.json +++ b/evaluations/datastes/recommend/map/01_long_lat.json @@ -1,65 +1,65 @@ [ { "source": { - "data": [ - {"name": "Kingston upon Thames", "cx": -0.28691465719243003, "cy": 51.387893353778395}, - {"name": "Croydon", "cx": -0.08716546101532906, "cy": 51.355317416374}, - {"name": "Bromley", "cx": 0.051536586180461884, "cy": 51.37198402033694}, - {"name": "Hounslow", "cx": -0.3671516396902454, "cy": 51.46838045919461}, - {"name": 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"Redbridge", "cx": 0.07586498666349965, "cy": 51.58566980239283}, - {"name": "Sutton", "cx": -0.1775706458949663, "cy": 51.36208881322784}, - {"name": "Richmond upon Thames", "cx": -0.312989912939369, "cy": 51.44217087768007}, - {"name": "Merton", "cx": -0.19724686451592524, "cy": 51.40994982121643}, - {"name": "Wandsworth", "cx": -0.18644255213923716, "cy": 51.45135372974042}, - {"name": "Hammersmith and Fulham", "cx": -0.22312501860130696, "cy": 51.49760349773975}, - {"name": "Kensington and Chelsea", "cx": -0.1926146417708775, "cy": 51.50326858912867}, - {"name": "Westminster", "cx": -0.16129707300361068, "cy": 51.513897745031734}, - {"name": "Camden", "cx": -0.15741523389534484, "cy": 51.54639253962996}, - {"name": "Tower Hamlets", "cx": -0.03246795051939853, "cy": 51.515056063777294}, - {"name": "Islington", "cx": -0.10744657464865907, "cy": 51.54550386068218}, - {"name": "Hackney", "cx": -0.06564515736890775, "cy": 51.54976532427057}, - {"name": "Haringey", "cx": 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51.558554439571225 }, + { "name": "Barnet", "cx": -0.2100178905687616, "cy": 51.61601840050092 }, + { "name": "Lambeth", "cx": -0.11827879184555543, "cy": 51.45307915598091 }, + { "name": "Southwark", "cx": -0.07460248417459615, "cy": 51.47313736252929 }, + { "name": "Lewisham", "cx": -0.020251210147097444, "cy": 51.44808622366465 }, + { "name": "Greenwich", "cx": 0.05623854022441538, "cy": 51.47280349330009 }, + { "name": "Bexley", "cx": 0.14034521936844538, "cy": 51.45881060129503 }, + { "name": "Enfield", "cx": -0.08726531756552813, "cy": 51.650991897325454 }, + { "name": "Waltham Forest", "cx": -0.012645389036967391, "cy": 51.59402179942259 }, + { "name": "Redbridge", "cx": 0.07586498666349965, "cy": 51.58566980239283 }, + { "name": "Sutton", "cx": -0.1775706458949663, "cy": 51.36208881322784 }, + { "name": "Richmond upon Thames", "cx": -0.312989912939369, "cy": 51.44217087768007 }, + { "name": "Merton", "cx": -0.19724686451592524, "cy": 51.40994982121643 }, + { "name": "Wandsworth", "cx": -0.18644255213923716, "cy": 51.45135372974042 }, + { "name": "Hammersmith and Fulham", "cx": -0.22312501860130696, "cy": 51.49760349773975 }, + { "name": "Kensington and Chelsea", "cx": -0.1926146417708775, "cy": 51.50326858912867 }, + { "name": "Westminster", "cx": -0.16129707300361068, "cy": 51.513897745031734 }, + { "name": "Camden", "cx": -0.15741523389534484, "cy": 51.54639253962996 }, + { "name": "Tower Hamlets", "cx": -0.03246795051939853, "cy": 51.515056063777294 }, + { "name": "Islington", "cx": -0.10744657464865907, "cy": 51.54550386068218 }, + { "name": "Hackney", "cx": -0.06564515736890775, "cy": 51.54976532427057 }, + { "name": "Haringey", "cx": -0.10747319126297968, "cy": 51.59037386425316 }, + { "name": "Newham", "cx": 0.036385305135703754, "cy": 51.52834280272405 }, + { "name": "Barking and Dagenham", "cx": 0.13352186486629006, "cy": 51.545272267046116 }, + { "name": "City of London", "cx": -0.09232371387227843, "cy": 51.51529009582889 } + ] }, "target": [ - { - "type": "map", - "encode": { - "x": ["cx"], - "y": ["cy"], - "label": ["name"] - } + { + "type": "map", + "encode": { + "x": ["cx"], + "y": ["cy"], + "label": ["name"] } + } ] }, { "source": { - "data": [ + "data": [ { - "景点": "石屋洞", - "longitude": 120.130638, - "latitude": 30.219835 + "景点": "石屋洞", + "longitude": 120.130638, + "latitude": 30.219835 }, { - "景点": "满觉陇", - "longitude": 120.128125, - "latitude": 30.219386 + "景点": "满觉陇", + "longitude": 120.128125, + "latitude": 30.219386 }, { "景点": "杨梅岭", "longitude": 120.118362, "latitude": 30.217175 }, { "景点": "理安寺", "longitude": 120.112958, "latitude": 30.207319 }, @@ -73,196 +73,226 @@ { "景点": "虎跑公园", "longitude": 120.130095, "latitude": 30.207505 }, { "景点": "玉皇飞云", "longitude": 120.145323, "latitude": 30.214993 }, { "景点": "长桥公园", "longitude": 120.155057, "latitude": 30.232985 } - ] + ] }, "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["景点"] - } + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["景点"] } + } ] }, - { - "source": { - "data": [ - {"name": "Kingston upon Thames", "cx": -0.2869, "cy": 51.3879}, - {"name": "Croydon", "cx": -0.0872, "cy": 51.3553}, - {"name": "Bromley", "cx": 0.0515, "cy": 51.3720}, - {"name": "Hounslow", "cx": -0.3672, "cy": 51.4684}, - {"name": "Ealing", "cx": -0.3310, "cy": 51.5225}, - {"name": "Havering", "cx": 0.2211, "cy": 51.5644} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["cx"], - "y": ["cy"], - "label": ["name"] - } - } + { + "source": { + "data": [ + { "name": "Kingston upon Thames", "cx": -0.2869, "cy": 51.3879 }, + { "name": "Croydon", "cx": -0.0872, "cy": 51.3553 }, + { "name": "Bromley", "cx": 0.0515, "cy": 51.372 }, + { "name": "Hounslow", "cx": -0.3672, "cy": 51.4684 }, + { "name": "Ealing", "cx": -0.331, "cy": 51.5225 }, + { "name": "Havering", "cx": 0.2211, "cy": 51.5644 } ] }, - { - "source": { - "data": [ - {"City": "New York", "population_density": 27000, "longitude": -74.006, "latitude": 40.7128}, - {"City": "Los Angeles", "population_density": 8304, "longitude": -118.2437, "latitude": 34.0522}, - {"City": "Chicago", "population_density": 11900, "longitude": -87.6298, "latitude": 41.8781} - ] - }, - "target": [ + "target": [ + { + "type": "map", + "encode": { + "x": ["cx"], + "y": ["cy"], + "label": ["name"] + } + } + ] + }, + { + "source": { + "data": [ + { + "City": "New York", + "population_density": 27000, + "longitude": -74.006, + "latitude": 40.7128 + }, + { + "City": "Los Angeles", + "population_density": 8304, + "longitude": -118.2437, + "latitude": 34.0522 + }, { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["City"] - } + "City": "Chicago", + "population_density": 11900, + "longitude": -87.6298, + "latitude": 41.8781 } ] }, - { - "source": { - "data": [ - {"国家": "中国", "GDP": 14300000000000, "longitude": 104.1954, "latitude": 35.8617}, - {"国家": "美国", "GDP": 21430000000000, "longitude": -95.7129, "latitude": 37.0902}, - {"国家": "日本", "GDP": 5080000000000, "longitude": 138.2529, "latitude": 36.2048} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["国家"], - "color": ["GDP"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["City"] } + } + ] + }, + { + "source": { + "data": [ + { "国家": "中国", "GDP": 14300000000000, "longitude": 104.1954, "latitude": 35.8617 }, + { "国家": "美国", "GDP": 21430000000000, "longitude": -95.7129, "latitude": 37.0902 }, + { "国家": "日本", "GDP": 5080000000000, "longitude": 138.2529, "latitude": 36.2048 } ] }, - { - "source": { - "data": [ - {"Region": "California", "output": 423967, "longitude": -119.4179, "latitude": 36.7783}, - {"Region": "Texas", "output": 695662, "longitude": -99.9018, "latitude": 31.9686}, - {"Region": "Alaska", "output": 1717850, "longitude": -149.4937, "latitude": 64.2008} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["Region"], - "color": ["output"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["国家"], + "color": ["GDP"] } + } + ] + }, + { + "source": { + "data": [ + { "Region": "California", "output": 423967, "longitude": -119.4179, "latitude": 36.7783 }, + { "Region": "Texas", "output": 695662, "longitude": -99.9018, "latitude": 31.9686 }, + { "Region": "Alaska", "output": 1717850, "longitude": -149.4937, "latitude": 64.2008 } ] }, - { - "source": { - "data": [ - {"城市": "上海", "PM2.5": 56, "longitude": 121.4737, "latitude": 31.2304}, - {"城市": "北京", "PM2.5": 70, "longitude": 116.4074, "latitude": 39.9042}, - {"城市": "广州", "PM2.5": 42, "longitude": 113.2644, "latitude": 23.1291} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["城市"], - "color": ["PM2.5"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["Region"], + "color": ["output"] } + } + ] + }, + { + "source": { + "data": [ + { "城市": "上海", "PM2.5": 56, "longitude": 121.4737, "latitude": 31.2304 }, + { "城市": "北京", "PM2.5": 70, "longitude": 116.4074, "latitude": 39.9042 }, + { "城市": "广州", "PM2.5": 42, "longitude": 113.2644, "latitude": 23.1291 } ] }, - { - "source": { - "data": [ - {"Landmark": "Eiffel Tower", "visitors_per_year": 7000000, "longitude": 2.2945, "latitude": 48.8584}, - {"Landmark": "Colosseum", "visitors_per_year": 5500000, "longitude": 12.4924, "latitude": 41.8902}, - {"Landmark": "Statue of Liberty", "visitors_per_year": 4300000, "longitude": -74.0445, "latitude": 40.6892} - ] - }, - "target": [ + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["城市"], + "color": ["PM2.5"] + } + } + ] + }, + { + "source": { + "data": [ { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["Landmark"], - "color": ["visitors_per_year"] - } + "Landmark": "Eiffel Tower", + "visitors_per_year": 7000000, + "longitude": 2.2945, + "latitude": 48.8584 + }, + { + "Landmark": "Colosseum", + "visitors_per_year": 5500000, + "longitude": 12.4924, + "latitude": 41.8902 + }, + { + "Landmark": "Statue of Liberty", + "visitors_per_year": 4300000, + "longitude": -74.0445, + "latitude": 40.6892 } ] }, - { - "source": { - "data": [ - {"地区": "西湖区", "AQI": 42, "longitude": 120.1474, "latitude": 30.2729}, - {"地区": "拱墅区", "AQI": 45, "longitude": 120.1492, "latitude": 30.3188}, - {"地区": "滨江区", "AQI": 50, "longitude": 120.2106, "latitude": 30.2066} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["地区"], - "color": ["AQI"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["Landmark"], + "color": ["visitors_per_year"] } + } + ] + }, + { + "source": { + "data": [ + { "地区": "西湖区", "AQI": 42, "longitude": 120.1474, "latitude": 30.2729 }, + { "地区": "拱墅区", "AQI": 45, "longitude": 120.1492, "latitude": 30.3188 }, + { "地区": "滨江区", "AQI": 50, "longitude": 120.2106, "latitude": 30.2066 } ] }, - { - "source": { - "data": [ - {"median_income": 74000, "longitude": -84.7519, "latitude": 49.2827}, - {"median_income": 62000, "longitude": -71.2082, "latitude": 46.8139}, - {"median_income": 80000, "longitude": -122.6587, "latitude": 53.7267} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "color": ["median_income"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["地区"], + "color": ["AQI"] } + } + ] + }, + { + "source": { + "data": [ + { "median_income": 74000, "longitude": -84.7519, "latitude": 49.2827 }, + { "median_income": 62000, "longitude": -71.2082, "latitude": 46.8139 }, + { "median_income": 80000, "longitude": -122.6587, "latitude": 53.7267 } ] }, - { - "source": { - "data": [ - {"城市": "东京", "longitude": 139.6917, "latitude": 35.6895}, - {"城市": "大阪", "longitude": 135.5023, "latitude": 34.6937}, - {"城市": "名古屋", "longitude": 136.9066, "latitude": 35.1814} - ] - }, - "target": [ - { - "type": "map", - "encode": { - "x": ["longitude"], - "y": ["latitude"], - "label": ["城市"] - } + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "color": ["median_income"] } + } + ] + }, + { + "source": { + "data": [ + { "城市": "东京", "longitude": 139.6917, "latitude": 35.6895 }, + { "城市": "大阪", "longitude": 135.5023, "latitude": 34.6937 }, + { "城市": "名古屋", "longitude": 136.9066, "latitude": 35.1814 } ] - } + }, + "target": [ + { + "type": "map", + "encode": { + "x": ["longitude"], + "y": ["latitude"], + "label": ["城市"] + } + } + ] + } ] diff --git a/evaluations/datastes/recommend/mind-map/01_base.json b/evaluations/datastes/recommend/mind-map/01_base.json index 78e7f71..e6546ba 100644 --- a/evaluations/datastes/recommend/mind-map/01_base.json +++ b/evaluations/datastes/recommend/mind-map/01_base.json @@ -12,13 +12,15 @@ { "parent": "地理环境", "child": "岛屿的影响" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "parent", - "target": "child" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "parent", + "target": "child" + } } - }] + ] }, { "source": { @@ -49,13 +51,15 @@ { "主题": "Common", "子主题": "Boosting" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "主题", - "target": "子主题" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "主题", + "target": "子主题" + } } - }] + ] }, { "source": { @@ -69,13 +73,15 @@ { "parent": "定期锻炼", "child": "力量训练" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "parent", - "target": "child" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "parent", + "target": "child" + } } - }] + ] }, { "source": { @@ -89,13 +95,15 @@ { "category": "Natural Language Processing", "subcategory": "Machine Translation" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "category", - "target": "subcategory" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "category", + "target": "subcategory" + } } - }] + ] }, { "source": { @@ -108,13 +116,15 @@ { "topic": "Interpreted Languages", "subtopic": "JavaScript" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "topic", - "target": "subtopic" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "topic", + "target": "subtopic" + } } - }] + ] }, { "source": { @@ -129,13 +139,15 @@ { "子层级": "技术层", "子子层级": "基础设施管理" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "层级", - "target": "子层级" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "层级", + "target": "子层级" + } } - }] + ] }, { "source": { @@ -149,13 +161,15 @@ { "node": "Muscular System", "subnode": "Skeletal Muscles" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "header", - "target": "node" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "header", + "target": "node" + } } - }] + ] }, { "source": { @@ -169,13 +183,15 @@ { "子构思": "行程安排", "细节": "交通方式" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "主题", - "target": "子构思" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "主题", + "target": "子构思" + } } - }] + ] }, { "source": { @@ -188,13 +204,15 @@ { "category": "Insurance", "subcategory": "Health Insurance" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "category", - "target": "subcategory" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "category", + "target": "subcategory" + } } - }] + ] }, { "source": { @@ -208,12 +226,14 @@ { "子主题": "中学教育", "子子主题": "文科" } ] }, - "target": [{ - "type": "mind_map", - "encode": { - "source": "主题", - "target": "子主题" + "target": [ + { + "type": "mind_map", + "encode": { + "source": "主题", + "target": "子主题" + } } - }] + ] } ] diff --git a/evaluations/datastes/recommend/multiple-axes/01_base.json b/evaluations/datastes/recommend/multiple-axes/01_base.json index b4a66c5..052b824 100644 --- a/evaluations/datastes/recommend/multiple-axes/01_base.json +++ b/evaluations/datastes/recommend/multiple-axes/01_base.json @@ -1,1174 +1,1101 @@ [ -{ - "source": { - "metas": [ + { + "source": { + "metas": [ { - "name": "生产日期", - "dataType": "string" + "name": "生产日期", + "dataType": "string" }, { - "name": "单价", - "dataType": "number" + "name": "单价", + "dataType": "number" }, { - "name": "数量", - "dataType": "number" + "name": "数量", + "dataType": "number" } - ], + ], "data": [ - { - "生产日期": "2023-03-01", - "单价": 1234500, - "数量": 45 - }, - { - "生产日期": "2023-03-02", - "单价": 2345600, - "数量": 56 - }, - { - "生产日期": "2023-03-03", - "单价": 3456700, - "数量": 67 - }, - { - "生产日期": "2023-03-04", - "单价": 4567800, - "数量": 78 - }, - { - "生产日期": "2023-03-05", - "单价": 5678900, - "数量": 89 - } + { + "生产日期": "2023-03-01", + "单价": 1234500, + "数量": 45 + }, + { + "生产日期": "2023-03-02", + "单价": 2345600, + "数量": 56 + }, + { + "生产日期": "2023-03-03", + "单价": 3456700, + "数量": 67 + }, + { + "生产日期": "2023-03-04", + "单价": 4567800, + "数量": 78 + }, + { + "生产日期": "2023-03-05", + "单价": 5678900, + "数量": 89 + } ] - }, - "target": [ + }, + "target": [ { - "type": "multiple", - "encode": { - "x": ["生产日期"], - "y": ["单价"], - "y2": ["数量"] - } + "type": "multiple", + "encode": { + "x": ["生产日期"], + "y": ["单价"], + "y2": ["数量"] + } } - ] -}, -{ + ] + }, + { "source": { - "metas": [ + "metas": [ + { + "name": "有效期至", + "dataType": "string" + }, + { + "name": "成本价", + "dataType": "number" + }, + { + "name": "折扣率", + "dataType": "number" + } + ], + "data": [ + { + "有效期至": "2023/04/01", + "成本价": 6543210, + "折扣率": 20 + }, { - "name": "有效期至", - "dataType": "string" + "有效期至": "2023/04/02", + "成本价": 5432100, + "折扣率": 30 }, { - "name": "成本价", - "dataType": "number" + "有效期至": "2023/04/03", + "成本价": 4321000, + "折扣率": 40 }, { - "name": "折扣率", - "dataType": "number" + "有效期至": "2023/04/04", + "成本价": 3210000, + "折扣率": 50 + }, + { + "有效期至": "2023/04/05", + "成本价": 2100000, + "折扣率": 60 } - ], - "data": [ - { - "有效期至": "2023/04/01", - "成本价": 6543210, - "折扣率": 20 - }, - { - "有效期至": "2023/04/02", - "成本价": 5432100, - "折扣率": 30 - }, - { - "有效期至": "2023/04/03", - "成本价": 4321000, - "折扣率": 40 - }, - { - "有效期至": "2023/04/04", - "成本价": 3210000, - "折扣率": 50 - }, - { - "有效期至": "2023/04/05", - "成本价": 2100000, - "折扣率": 60 - } - ] + ] }, "target": [ - { - "type": "multiple", - "encode": { - "x": ["有效期至"], - "y": ["成本价"], - "y2": ["折扣率"] - } + { + "type": "multiple", + "encode": { + "x": ["有效期至"], + "y": ["成本价"], + "y2": ["折扣率"] } + } ] -}, -{ + }, + { "source": { - "metas": [ + "metas": [ + { + "name": "发布时间", + "dataType": "string" + }, + { + "name": "点击量", + "dataType": "number" + }, + { + "name": "点赞数", + "dataType": "number" + } + ], + "data": [ + { + "发布时间": "2023/02/06", + 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b/evaluations/datastes/recommend/network-graph/01_base.json @@ -40,149 +40,149 @@ } } }, - { - "source": { - "data": [ - { "起点": "伦敦", "终点": "曼彻斯特"}, - { "起点": "曼彻斯特", "终点": "利兹"}, - { "起点": "利兹", "终点": "约克"}, - { "起点": "约克", "终点": "纽卡斯尔"}, - { "起点": "纽卡斯尔", "终点": "爱丁堡"}, - { "起点": "爱丁堡", "终点": "格拉斯哥"} - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "起点", - "target": "终点" - } + { + "source": { + "data": [ + { "起点": "伦敦", "终点": "曼彻斯特" }, + { "起点": "曼彻斯特", "终点": "利兹" }, + { "起点": "利兹", "终点": "约克" }, + { "起点": "约克", "终点": "纽卡斯尔" }, + { "起点": "纽卡斯尔", "终点": "爱丁堡" }, + { "起点": "爱丁堡", "终点": "格拉斯哥" } + ] + }, + "target": { + "type": "network_graph", + "encode": { + "source": "起点", + "target": "终点" } + } + }, + { + "source": { + "data": [ + { "source": "Rome", "target": "Milan" }, + { "source": "Milan", "target": "Venice" }, + { "source": "Venice", "target": "Florence" }, + { "source": "Florence", "target": "Rome" }, + { "source": "Rome", "target": "Naples" }, + { "source": "Naples", "target": "Sicily" } + ] }, - { - "source": { - "data": [ - { "source": "Rome", "target": "Milan"}, - { "source": "Milan", "target": "Venice"}, - { "source": "Venice", "target": "Florence"}, - { "source": "Florence", "target": "Rome"}, - { "source": "Rome", "target": "Naples"}, - { "source": "Naples", "target": "Sicily"} - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "source", - "target": "target" - } + "target": { + "type": "network_graph", + "encode": { + "source": "source", + "target": "target" } + } + }, + { + "source": { + "data": [ + { "发源地": "长江", "流经": "武汉" }, + { "发源地": "长江", "流经": "南京" }, + { "发源地": "长江", "流经": "上海" }, + { "发源地": "黄河", "流经": "郑州" }, + { "发源地": "黄河", "流经": "济南" } + ] }, - { - "source": { - "data": [ - { "发源地": "长江", "流经": "武汉"}, - { "发源地": "长江", "流经": "南京"}, - { "发源地": "长江", "流经": "上海"}, - { "发源地": "黄河", "流经": "郑州"}, - { "发源地": "黄河", "流经": "济南"} - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "发源地", - "target": "流经" - } + "target": { + "type": "network_graph", + "encode": { + "source": "发源地", + "target": "流经" } + } + }, + { + "source": { + "data": [ + { "start": "New York", "end": "Chicago" }, + { "start": "Chicago", "end": "Los Angeles" }, + { "start": "Los Angeles", "end": "San Francisco" }, + { "start": "San Francisco", "end": "Las Vegas" }, + { "start": "Las Vegas", "end": "Seattle" } + ] }, - { - "source": { - "data": [ - { "start": "New York", "end": "Chicago"}, - { "start": "Chicago", "end": "Los Angeles"}, - { "start": "Los Angeles", "end": "San Francisco"}, - { "start": "San Francisco", "end": "Las Vegas"}, - { "start": "Las Vegas", "end": "Seattle"} - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "start", - "target": "end" - } + "target": { + "type": "network_graph", + "encode": { + "source": "start", + "target": "end" } + } + }, + { + "source": { + "data": [ + { "起始地": "东京", "目的地": "大阪", "交通工具": "新干线" }, + { "起始地": "大阪", "目的地": "京都", "交通工具": "新干线" }, + { "起始地": "京都", "目的地": "名古屋", "交通工具": "新干线" }, + { "起始地": "名古屋", "目的地": "东京", "交通工具": "新干线" } + ] }, - { - "source": { - "data": [ - { "起始地": "东京", "目的地": "大阪", "交通工具": "新干线" }, - { "起始地": "大阪", "目的地": "京都", "交通工具": "新干线" }, - { "起始地": "京都", "目的地": "名古屋", "交通工具": "新干线" }, - { "起始地": "名古屋", "目的地": "东京", "交通工具": "新干线" } - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "起始地", - "target": "目的地", - "label": "交通工具" - } + "target": { + "type": "network_graph", + "encode": { + "source": "起始地", + "target": "目的地", + "label": "交通工具" } + } + }, + { + "source": { + "data": [ + { "actor": "Leonardo DiCaprio", "co_actor": "Kate Winslet", "movie": "Titanic" }, + { "actor": "Leonardo DiCaprio", "co_actor": "Tom Hanks", "movie": "Catch Me If You Can" }, + { "actor": "Tom Hanks", "co_actor": "Meg Ryan", "movie": "You've Got Mail" }, + { "actor": "Tom Hanks", "co_actor": "Robin Wright", "movie": "Forrest Gump" } + ] }, - { - "source": { - "data": [ - { "actor": "Leonardo DiCaprio", "co_actor": "Kate Winslet", "movie": "Titanic" }, - { "actor": "Leonardo DiCaprio", "co_actor": "Tom Hanks", "movie": "Catch Me If You Can" }, - { "actor": "Tom Hanks", "co_actor": "Meg Ryan", "movie": "You've Got Mail" }, - { "actor": "Tom Hanks", "co_actor": "Robin Wright", "movie": "Forrest Gump" } - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "actor", - "target": "co_actor", - "label": "movie" - } + "target": { + "type": "network_graph", + "encode": { + "source": "actor", + "target": "co_actor", + "label": "movie" } + } + }, + { + "source": { + "data": [ + { "发送人": "张伟", "接收人": "李强", "邮件类型": "工作" }, + { "发送人": "李强", "接收人": "王芳", "邮件类型": "私人" }, + { "发送人": "王芳", "接收人": "张伟", "邮件类型": "工作" }, + { "发送人": "张伟", "接收人": "王芳", "邮件类型": "私人" } + ] }, - { - "source": { - "data": [ - { "发送人": "张伟", "接收人": "李强", "邮件类型": "工作" }, - { "发送人": "李强", "接收人": "王芳", "邮件类型": "私人" }, - { "发送人": "王芳", "接收人": "张伟", "邮件类型": "工作" }, - { "发送人": "张伟", "接收人": "王芳", "邮件类型": "私人" } - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "发送人", - "target": "接收人", - "label": "邮件类型" - } + "target": { + "type": "network_graph", + "encode": { + "source": "发送人", + "target": "接收人", + "label": "邮件类型" } + } + }, + { + "source": { + "data": [ + { "developer": "Microsoft", "product": "Windows" }, + { "developer": "Apple", "product": "macOS" }, + { "developer": "Google", "product": "Android" }, + { "developer": "Ubuntu", "product": "Linux" } + ] }, - { - "source": { - "data": [ - { "developer": "Microsoft", "product": "Windows"}, - { "developer": "Apple", "product": "macOS"}, - { "developer": "Google", "product": "Android"}, - { "developer": "Ubuntu", "product": "Linux"} - ] - }, - "target": { - "type": "network_graph", - "encode": { - "source": "developer", - "target": "product" - } + "target": { + "type": "network_graph", + "encode": { + "source": "developer", + "target": "product" } } + } ] diff --git a/evaluations/datastes/recommend/pie/01_base.json b/evaluations/datastes/recommend/pie/01_base.json index b2a3622..f3b5a69 100644 --- a/evaluations/datastes/recommend/pie/01_base.json +++ b/evaluations/datastes/recommend/pie/01_base.json @@ -18,10 +18,10 @@ { "type": "pie", "encode": { - "x": ["type"], - "y": ["value"] + "x": ["type"], + "y": ["value"] } - } + } ] }, { @@ -213,196 +213,196 @@ } ] }, - { - "source": { - "data": [ - { "文化活动": "电影", "人数": 60000 }, - { "文化活动": "音乐会", "人数": 20000 }, - { "文化活动": "艺术展览", "人数": 15000 }, - { "文化活动": "戏剧", "人数": 12000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["文化活动"], - "y": ["人数"] - } - } + { + "source": { + "data": [ + { "文化活动": "电影", "人数": 60000 }, + { "文化活动": "音乐会", "人数": 20000 }, + { "文化活动": "艺术展览", "人数": 15000 }, + { "文化活动": "戏剧", "人数": 12000 } ] }, - { - "source": { - "data": [ - { "产品类别": "电子产品", "销售额": 500000 }, - { "产品类别": "家具", "销售额": 150000 }, - { "产品类别": "服饰", "销售额": 100000 }, - { "产品类别": "食品", "销售额": 250000 }, - { "产品类别": "书籍", "销售额": 50000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["产品类别"], - "y": ["销售额"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["文化活动"], + "y": ["人数"] } + } + ] + }, + { + "source": { + "data": [ + { "产品类别": "电子产品", "销售额": 500000 }, + { "产品类别": "家具", "销售额": 150000 }, + { "产品类别": "服饰", "销售额": 100000 }, + { "产品类别": "食品", "销售额": 250000 }, + { "产品类别": "书籍", "销售额": 50000 } ] }, - { - "source": { - "data": [ - { "Payment Method": "Credit Card", "Amount": 75000 }, - { "Payment Method": "Cash", "Amount": 125000 }, - { "Payment Method": "Digital Wallet", "Amount": 52000 }, - { "Payment Method": "Bank Transfer", "Amount": 43000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["Payment Method"], - "y": ["Amount"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["产品类别"], + "y": ["销售额"] } + } + ] + }, + { + "source": { + "data": [ + { "Payment Method": "Credit Card", "Amount": 75000 }, + { "Payment Method": "Cash", "Amount": 125000 }, + { "Payment Method": "Digital Wallet", "Amount": 52000 }, + { "Payment Method": "Bank Transfer", "Amount": 43000 } ] }, - { - "source": { - "data": [ - { "Energy Source": "Solar", "Usage": 1500 }, - { "Energy Source": "Wind", "Usage": 800 }, - { "Energy Source": "Hydro", "Usage": 2000 }, - { "Energy Source": "Nuclear", "Usage": 2500 }, - { "Energy Source": "Coal", "Usage": 3000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["Energy Source"], - "y": ["Usage"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["Payment Method"], + "y": ["Amount"] } + } + ] + }, + { + "source": { + "data": [ + { "Energy Source": "Solar", "Usage": 1500 }, + { "Energy Source": "Wind", "Usage": 800 }, + { "Energy Source": "Hydro", "Usage": 2000 }, + { "Energy Source": "Nuclear", "Usage": 2500 }, + { "Energy Source": "Coal", "Usage": 3000 } ] }, - { - "source": { - "data": [ - { "社交媒体平台": "微信", "用户数": 800000 }, - { "社交媒体平台": "微博", "用户数": 300000 }, - { "社交媒体平台": "豆瓣", "用户数": 120000 }, - { "社交媒体平台": "知乎", "用户数": 130000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["社交媒体平台"], - "y": ["用户数"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["Energy Source"], + "y": ["Usage"] } + } + ] + }, + { + "source": { + "data": [ + { "社交媒体平台": "微信", "用户数": 800000 }, + { "社交媒体平台": "微博", "用户数": 300000 }, + { "社交媒体平台": "豆瓣", "用户数": 120000 }, + { "社交媒体平台": "知乎", "用户数": 130000 } ] }, - { - "source": { - "data": [ - { "Transport Mode": "Bicycle", "Count": 15000 }, - { "Transport Mode": "Bus", "Count": 60000 }, - { "Transport Mode": "Tram", "Count": 30000 }, - { "Transport Mode": "Train", "Count": 50000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["Transport Mode"], - "y": ["Count"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["社交媒体平台"], + "y": ["用户数"] } + } + ] + }, + { + "source": { + "data": [ + { "Transport Mode": "Bicycle", "Count": 15000 }, + { "Transport Mode": "Bus", "Count": 60000 }, + { "Transport Mode": "Tram", "Count": 30000 }, + { "Transport Mode": "Train", "Count": 50000 } ] }, - { - "source": { - "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 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["水果"], - "y": ["数量"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["Transport Mode"], + "y": ["Count"] } + } + ] + }, + { + "source": { + "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 } ] }, - { - "source": { - "data": [ - { "Beverage": "Coffee", "Volume": 120000 }, - { "Beverage": "Tea", "Volume": 80000 }, - { "Beverage": "Juice", "Volume": 40000 }, - { "Beverage": "Soda", "Volume": 30000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["Beverage"], - "y": ["Volume"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["水果"], + "y": ["数量"] } + } + ] + }, + { + "source": { + "data": [ + { "Beverage": "Coffee", "Volume": 120000 }, + { "Beverage": "Tea", "Volume": 80000 }, + { "Beverage": "Juice", "Volume": 40000 }, + { "Beverage": "Soda", "Volume": 30000 } ] }, - { - "source": { - "data": [ - { "宠物类别": "猫", "数量": 5000 }, - { "宠物类别": "狗", "数量": 8000 }, - { "宠物类别": "鸟", "数量": 2000 }, - { "宠物类别": "鱼", "数量": 4000 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["宠物类别"], - "y": ["数量"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["Beverage"], + "y": ["Volume"] } + } + ] + }, + { + "source": { + "data": [ + { "宠物类别": "猫", "数量": 5000 }, + { "宠物类别": "狗", "数量": 8000 }, + { "宠物类别": "鸟", "数量": 2000 }, + { "宠物类别": "鱼", "数量": 4000 } ] }, - { - "source": { - "data": [ - { "Cuisine Type": "Italian", "Number of Restaurants": 120 }, - { "Cuisine Type": "Chinese", "Number of Restaurants": 300 }, - { "Cuisine Type": "Mexican", "Number of Restaurants": 150 }, - { "Cuisine Type": "Indian", "Number of Restaurants": 100 } - ] - }, - "target": [ - { - "type": "pie", - "encode": { - "x": ["Cuisine Type"], - "y": ["Number of Restaurants"] - } + "target": [ + { + "type": "pie", + "encode": { + "x": ["宠物类别"], + "y": ["数量"] } + } + ] + }, + { + "source": { + "data": [ + { "Cuisine Type": "Italian", "Number of Restaurants": 120 }, + { "Cuisine Type": "Chinese", "Number of Restaurants": 300 }, + { "Cuisine Type": "Mexican", "Number of Restaurants": 150 }, + { "Cuisine Type": "Indian", "Number of Restaurants": 100 } ] - } + }, + "target": [ + { + "type": "pie", + "encode": { + "x": ["Cuisine Type"], + "y": ["Number of Restaurants"] + } + } + ] + } ] diff --git a/evaluations/datastes/recommend/radar/01_single_item.json b/evaluations/datastes/recommend/radar/01_single_item.json index 18131ba..6bb86b3 100644 --- a/evaluations/datastes/recommend/radar/01_single_item.json +++ b/evaluations/datastes/recommend/radar/01_single_item.json @@ -5,7 +5,7 @@ { "Development": 60, "Marketing": 50, - "Users": 40, + "Users": 40, "Test": 60, "Language": 70, "Technology": 50, @@ -15,241 +15,363 @@ } ] }, - "target": [{ - "type": "radar", - "encode": { - "y": ["Development", "Marketing", "Users", "Test", "Language", "Technology", "Support", "Sales", "UX"] + "target": [ + { + "type": "radar", + "encode": { + "y": [ + "Development", + "Marketing", + "Users", + "Test", + "Language", + "Technology", + "Support", + "Sales", + "UX" + ] + } } - }] + ] }, - { - "source": { - "data": [ - { - "开发": 70, - "市场": 65, - "用户": 40, - "测试": 75, - "语言": 85, - "技术": 60, - "支持": 45, - "销售": 55, - "用户体验": 70 - } - ] - }, - "target": [{ + { + "source": { + "data": [ + { + "开发": 70, + "市场": 65, + "用户": 40, + "测试": 75, + "语言": 85, + "技术": 60, + "支持": 45, + "销售": 55, + "用户体验": 70 + } + ] + }, + "target": [ + { "type": "radar", "encode": { "y": ["开发", "市场", "用户", "测试", "语言", "技术", "支持", "销售", "用户体验"] } - }] + } + ] + }, + { + "source": { + "data": [ + { + "Development": 80, + "Marketing": 70, + "Customers": 60, + "Testing": 90, + "Communication": 75, + "Innovation": 85, + "Service": 55, + "Revenue": 65, + "Design": 80 + } + ] }, - { - "source": { - "data": [ - { - "Development": 80, - "Marketing": 70, - "Customers": 60, - "Testing": 90, - "Communication": 75, - "Innovation": 85, - "Service": 55, - "Revenue": 65, - "Design": 80 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["Development", "Marketing", "Customers", "Testing", "Communication", "Innovation", "Service", "Revenue", "Design"] + "y": [ + "Development", + "Marketing", + "Customers", + "Testing", + "Communication", + "Innovation", + "Service", + "Revenue", + "Design" + ] + } + } + ] + }, + { + "source": { + "data": [ + { + "运营": 60, + "市场分析": 70, + "用户粘性": 50, + "迭代": 80, + "沟通": 65, + "创造力": 75, + "客户服务": 80, + "利润": 55, + "视觉设计": 90 } - }] + ] }, - { - "source": { - "data": [ - { - "运营": 60, - "市场分析": 70, - "用户粘性": 50, - "迭代": 80, - "沟通": 65, - "创造力": 75, - "客户服务": 80, - "利润": 55, - "视觉设计": 90 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["运营", "市场分析", "用户粘性", "迭代", "沟通", "创造力", "客户服务", "利润", "视觉设计"] + "y": [ + "运营", + "市场分析", + "用户粘性", + "迭代", + "沟通", + "创造力", + "客户服务", + "利润", + "视觉设计" + ] } - }] + } + ] + }, + { + "source": { + "data": [ + { + "Engineering": 75, + "Promotion": 65, + "Client Base": 55, + "Auditing": 65, + "Diversity": 80, + "Efficiency": 70, + "Help Desk": 60, + "Growth": 85, + "Aesthetics": 75 + } + ] }, - { - "source": { - "data": [ - { - "Engineering": 75, - "Promotion": 65, - "Client Base": 55, - "Auditing": 65, - "Diversity": 80, - "Efficiency": 70, - "Help Desk": 60, - "Growth": 85, - "Aesthetics": 75 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["Engineering", "Promotion", "Client Base", "Auditing", "Diversity", "Efficiency", "Help Desk", "Growth", "Aesthetics"] + "y": [ + "Engineering", + "Promotion", + "Client Base", + "Auditing", + "Diversity", + "Efficiency", + "Help Desk", + "Growth", + "Aesthetics" + ] + } + } + ] + }, + { + "source": { + "data": [ + { + "战略": 85, + "广告": 60, + "客户反馈": 70, + "质量控制": 90, + "多样性": 65, + "产品研发": 75, + "技术支持": 80, + "盈利": 60, + "界面设计": 85 } - }] + ] }, - { - "source": { - "data": [ - { - "战略": 85, - "广告": 60, - "客户反馈": 70, - "质量控制": 90, - "多样性": 65, - "产品研发": 75, - "技术支持": 80, - "盈利": 60, - "界面设计": 85 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["战略", "广告", "客户反馈", "质量控制", "多样性", "产品研发", "技术支持", "盈利", "界面设计"] + "y": [ + "战略", + "广告", + "客户反馈", + "质量控制", + "多样性", + "产品研发", + "技术支持", + "盈利", + "界面设计" + ] } - }] + } + ] + }, + { + "source": { + "data": [ + { + "Strategy": 90, + "Social Media": 75, + "Satisfaction": 85, + "Verification": 80, + "Culture": 70, + "Project Development": 80, + "Online Support": 60, + "Finance": 70, + "Art Direction": 95 + } + ] }, - { - "source": { - "data": [ - { - "Strategy": 90, - "Social Media": 75, - "Satisfaction": 85, - "Verification": 80, - "Culture": 70, - "Project Development": 80, - "Online Support": 60, - "Finance": 70, - "Art Direction": 95 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["Strategy", "Social Media", "Satisfaction", "Verification", "Culture", "Project Development", "Online Support", "Finance", "Art Direction"] + "y": [ + "Strategy", + "Social Media", + "Satisfaction", + "Verification", + "Culture", + "Project Development", + "Online Support", + "Finance", + "Art Direction" + ] } - }] + } + ] + }, + { + "source": { + "data": [ + { + "产品设计": 65, + "传播": 70, + "客户关系": 60, + "测试验证": 85, + "文化": 75, + "项目管理": 90, + "技术服务": 85, + "财务指标": 65, + "艺术表现": 80 + } + ] }, - { - "source": { - "data": [ - { - "产品设计": 65, - "传播": 70, - "客户关系": 60, - "测试验证": 85, - "文化": 75, - "项目管理": 90, - "技术服务": 85, - "财务指标": 65, - "艺术表现": 80 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["产品设计", "传播", "客户关系", "测试验证", "文化", "项目管理", "技术服务", "财务指标", "艺术表现"] + "y": [ + "产品设计", + "传播", + "客户关系", + "测试验证", + "文化", + "项目管理", + "技术服务", + "财务指标", + "艺术表现" + ] } - }] + } + ] + }, + { + "source": { + "data": [ + { + "Leadership": 95, + "Digital Marketing": 85, + "User Base": 75, + "Risk Management": 90, + "Ethics": 80, + "Innovation Strategy": 85, + "Customer Assistance": 75, + "Profitability": 90, + "Visual Craft": 80 + } + ] }, - { - "source": { - "data": [ - { - "Leadership": 95, - "Digital Marketing": 85, - "User Base": 75, - "Risk Management": 90, - "Ethics": 80, - "Innovation Strategy": 85, - "Customer Assistance": 75, - "Profitability": 90, - "Visual Craft": 80 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["Leadership", "Digital Marketing", "User Base", "Risk Management", "Ethics", "Innovation Strategy", "Customer Assistance", "Profitability", "Visual Craft"] + "y": [ + "Leadership", + "Digital Marketing", + "User Base", + "Risk Management", + "Ethics", + "Innovation Strategy", + "Customer Assistance", + "Profitability", + "Visual Craft" + ] + } + } + ] + }, + { + "source": { + "data": [ + { + "领导力": 85, + "社交推广": 75, + "用户基础": 65, + "风险控制": 70, + "道德标准": 90, + "创新策略": 80, + "客户援助": 70, + "盈利能力": 85, + "视觉功夫": 75 } - }] + ] }, - { - "source": { - "data": [ - { - "领导力": 85, - "社交推广": 75, - "用户基础": 65, - "风险控制": 70, - "道德标准": 90, - "创新策略": 80, - "客户援助": 70, - "盈利能力": 85, - "视觉功夫": 75 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["领导力", "社交推广", "用户基础", "风险控制", "道德标准", "创新策略", "客户援助", "盈利能力", "视觉功夫"] + "y": [ + "领导力", + "社交推广", + "用户基础", + "风险控制", + "道德标准", + "创新策略", + "客户援助", + "盈利能力", + "视觉功夫" + ] + } + } + ] + }, + { + "source": { + "data": [ + { + "Data Analysis": 80, + "Public Relations": 85, + "Customer Retention": 90, + "Regulatory Compliance": 75, + "Inclusivity": 70, + "Tech Evolution": 95, + "Help Line": 70, + "Economics": 60, + "Creative Design": 85 } - }] + ] }, - { - "source": { - "data": [ - { - "Data Analysis": 80, - "Public Relations": 85, - "Customer Retention": 90, - "Regulatory Compliance": 75, - "Inclusivity": 70, - "Tech Evolution": 95, - "Help Line": 70, - "Economics": 60, - "Creative Design": 85 - } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { - "y": ["Data Analysis", "Public Relations", "Customer Retention", "Regulatory Compliance", "Inclusivity", "Tech Evolution", "Help Line", "Economics", "Creative Design"] + "y": [ + "Data Analysis", + "Public Relations", + "Customer Retention", + "Regulatory Compliance", + "Inclusivity", + "Tech Evolution", + "Help Line", + "Economics", + "Creative Design" + ] } - }] - } + } + ] + } ] diff --git a/evaluations/datastes/recommend/radar/02_measure_dimension.json b/evaluations/datastes/recommend/radar/02_measure_dimension.json index 05fc3cf..79c78b8 100644 --- a/evaluations/datastes/recommend/radar/02_measure_dimension.json +++ b/evaluations/datastes/recommend/radar/02_measure_dimension.json @@ -14,13 +14,15 @@ { "skill": "UX", "score": 50 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -32,13 +34,15 @@ { "skill": "创新", "score": 78 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -50,13 +54,15 @@ { "skill": "Creativity", "score": 80 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -68,13 +74,15 @@ { "skill": "财务管理", "score": 60 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -86,13 +94,15 @@ { "skill": "Financial Management", "score": 73 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -104,13 +114,15 @@ { "ability": "冲突解决", "score": 82 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["ability"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["ability"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -122,13 +134,15 @@ { "能力": "Conflict Resolution", "表现": 84 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["能力"], - "y": ["表现"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["能力"], + "y": ["表现"] + } } - }] + ] }, { "source": { @@ -140,13 +154,15 @@ { "aspect": "品牌认知", "score": 83 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["aspect"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["aspect"], + "y": ["score"] + } } - }] + ] }, { "source": { @@ -158,13 +174,15 @@ { "评分维度": "Brand Awareness", "打分": 79 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["评分维度"], - "y": ["打分"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["评分维度"], + "y": ["打分"] + } } - }] + ] }, { "source": { @@ -176,13 +194,15 @@ { "特性": "易用性", "分数": 85 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["特性"], - "y": ["分数"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["特性"], + "y": ["分数"] + } } - }] + ] }, { "source": { @@ -194,12 +214,14 @@ { "attribute": "Ease of Use", "score": 86 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["attribute"], - "y": ["score"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["attribute"], + "y": ["score"] + } } - }] + ] } ] diff --git a/evaluations/datastes/recommend/radar/03_split.json b/evaluations/datastes/recommend/radar/03_split.json index a26de77..1eec54e 100644 --- a/evaluations/datastes/recommend/radar/03_split.json +++ b/evaluations/datastes/recommend/radar/03_split.json @@ -24,253 +24,275 @@ { "skill": "UX", "gender": "男", "score": 60 } ] }, - "target": [{ - "type": "radar", - "encode": { - "x": ["skill"], - "y": ["score"], - "s": ["gender"] + "target": [ + { + "type": "radar", + "encode": { + "x": ["skill"], + "y": ["score"], + "s": ["gender"] + } } - }] + ] }, - { - "source": { - "data": [ - { "指标": "生产效率", "类别": "甲", "分数": 80 }, - { "指标": "生产效率", "类别": "乙", "分数": 85 }, - { "指标": "产品质量", "类别": "甲", "分数": 90 }, - { "指标": "产品质量", "类别": "乙", "分数": 88 }, - { "指标": "创新能力", "类别": "甲", "分数": 86 }, - { "指标": "创新能力", "类别": "乙", "分数": 82 }, - { "指标": "市场份额", "类别": "甲", "分数": 75 }, - { "指标": "市场份额", "类别": "乙", "分数": 78 }, - { "指标": "客户满意度", "类别": "甲", "分数": 92 }, - { "指标": "客户满意度", "类别": "乙", "分数": 89 } - ] - }, - "target": [{ + { + "source": { + "data": [ + { "指标": "生产效率", "类别": "甲", "分数": 80 }, + { "指标": "生产效率", "类别": "乙", "分数": 85 }, + { "指标": "产品质量", "类别": "甲", "分数": 90 }, + { "指标": "产品质量", "类别": "乙", "分数": 88 }, + { "指标": "创新能力", "类别": "甲", "分数": 86 }, + { "指标": "创新能力", "类别": "乙", "分数": 82 }, + { "指标": "市场份额", "类别": "甲", "分数": 75 }, + { "指标": "市场份额", "类别": "乙", "分数": 78 }, + { "指标": "客户满意度", "类别": "甲", "分数": 92 }, + { "指标": "客户满意度", "类别": "乙", "分数": 89 } + ] + }, + "target": [ + { "type": "radar", "encode": { "x": ["指标"], "y": ["分数"], "s": ["类别"] } - }] + } + ] + }, + { + "source": { + "data": [ + { "attribute": "Communication", "team": "Alpha", "score": 75 }, + { "attribute": "Communication", "team": "Beta", "score": 80 }, + { "attribute": "Teamwork", "team": "Alpha", "score": 82 }, + { "attribute": "Teamwork", "team": "Beta", "score": 78 }, + { "attribute": "Problem Solving", "team": "Alpha", "score": 88 }, + { "attribute": "Problem Solving", "team": "Beta", "score": 84 }, + { "attribute": "Project Management", "team": "Alpha", "score": 79 }, + { "attribute": "Project Management", "team": "Beta", "score": 77 }, + { "attribute": "Technical Skills", "team": "Alpha", "score": 85 }, + { "attribute": "Technical Skills", "team": "Beta", "score": 83 } + ] }, - { - "source": { - "data": [ - { "attribute": "Communication", "team": "Alpha", "score": 75 }, - { "attribute": "Communication", "team": "Beta", "score": 80 }, - { "attribute": "Teamwork", "team": "Alpha", "score": 82 }, - { "attribute": "Teamwork", "team": "Beta", "score": 78 }, - { "attribute": "Problem Solving", "team": "Alpha", "score": 88 }, - { "attribute": "Problem Solving", "team": "Beta", "score": 84 }, - { "attribute": "Project Management", "team": "Alpha", "score": 79 }, - { "attribute": "Project Management", "team": "Beta", "score": 77 }, - { "attribute": "Technical Skills", "team": "Alpha", "score": 85 }, - { "attribute": "Technical Skills", "team": "Beta", "score": 83 } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { "x": ["attribute"], "y": ["score"], "s": ["team"] } - }] + } + ] + }, + { + "source": { + "data": [ + { "维度": "性价比", "商品": "A", "评分": 90 }, + { "维度": "性价比", "商品": "B", "评分": 85 }, + { "维度": "外观设计", "商品": "A", "评分": 88 }, + { "维度": "外观设计", "商品": "B", "评分": 82 }, + { "维度": "功能性", "商品": "A", "评分": 84 }, + { "维度": "功能性", "商品": "B", "评分": 86 }, + { "维度": "耐用性", "商品": "A", "评分": 87 }, + { "维度": "耐用性", "商品": "B", "评分": 80 }, + { "维度": "环保性", "商品": "A", "评分": 82 }, + { "维度": "环保性", "商品": "B", "评分": 79 } + ] }, - { - "source": { - "data": [ - { "维度": "性价比", "商品": "A", "评分": 90 }, - { "维度": "性价比", "商品": "B", "评分": 85 }, - { "维度": "外观设计", "商品": "A", "评分": 88 }, - { "维度": "外观设计", "商品": "B", "评分": 82 }, - { "维度": "功能性", "商品": "A", "评分": 84 }, - { "维度": "功能性", "商品": "B", "评分": 86 }, - { "维度": "耐用性", "商品": "A", "评分": 87 }, - { "维度": "耐用性", "商品": "B", "评分": 80 }, - { "维度": "环保性", "商品": "A", "评分": 82 }, - { "维度": "环保性", "商品": "B", "评分": 79 } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { "x": ["维度"], "y": ["评分"], "s": ["商品"] } - }] + } + ] + }, + { + "source": { + "data": [ + { "aspect": "Efficiency", "department": "Sales", "score": 78 }, + { "aspect": "Efficiency", "department": "Marketing", "score": 76 }, + { "aspect": "Creativity", "department": "Sales", "score": 82 }, + { "aspect": "Creativity", "department": "Marketing", "score": 85 }, + { "aspect": "Reliability", "department": "Sales", "score": 80 }, + { "aspect": "Reliability", "department": "Marketing", "score": 81 }, + { "aspect": "Customer Service", "department": "Sales", "score": 89 }, + { "aspect": "Customer Service", "department": "Marketing", "score": 90 }, + { "aspect": "Adaptability", "department": "Sales", "score": 77 }, + { "aspect": "Adaptability", "department": "Marketing", "score": 79 } + ] }, - { - "source": { - "data": [ - { "aspect": "Efficiency", "department": "Sales", "score": 78 }, - { "aspect": "Efficiency", "department": "Marketing", "score": 76 }, - { "aspect": "Creativity", "department": "Sales", "score": 82 }, - { "aspect": "Creativity", "department": "Marketing", "score": 85 }, - { "aspect": "Reliability", "department": "Sales", "score": 80 }, - { "aspect": "Reliability", "department": "Marketing", "score": 81 }, - { "aspect": "Customer Service", "department": "Sales", "score": 89 }, - { "aspect": "Customer Service", "department": "Marketing", "score": 90 }, - { "aspect": "Adaptability", "department": "Sales", "score": 77 }, - { "aspect": "Adaptability", "department": "Marketing", "score": 79 } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { "x": ["aspect"], "y": ["score"], "s": ["department"] } - }] + } + ] + }, + { + "source": { + "data": [ + { "属性": "服务质量", "类别": "一类", "得分": 78 }, + { "属性": "服务质量", "类别": "二类", "得分": 82 }, + { "属性": "价格优势", "类别": "一类", "得分": 80 }, + { "属性": "价格优势", "类别": "二类", "得分": 77 }, + { "属性": "市场反馈", "类别": "一类", "得分": 85 }, + { "属性": "市场反馈", "类别": "二类", "得分": 88 }, + { "属性": "售后服务", "类别": "一类", "得分": 79 }, + { "属性": "售后服务", "类别": "二类", "得分": 81 }, + { "属性": "品牌影响力", "类别": "一类", "得分": 84 }, + { "属性": "品牌影响力", "类别": "二类", "得分": 83 } + ] }, - { - "source": { - "data": [ - { "属性": "服务质量", "类别": "一类", "得分": 78 }, - { "属性": "服务质量", "类别": "二类", "得分": 82 }, - { "属性": "价格优势", "类别": "一类", "得分": 80 }, - { "属性": "价格优势", "类别": "二类", "得分": 77 }, - { "属性": "市场反馈", "类别": "一类", "得分": 85 }, - { "属性": "市场反馈", "类别": "二类", "得分": 88 }, - { "属性": "售后服务", "类别": "一类", "得分": 79 }, - { "属性": "售后服务", "类别": "二类", "得分": 81 }, - { "属性": "品牌影响力", "类别": "一类", "得分": 84 }, - { "属性": "品牌影响力", "类别": "二类", "得分": 83 } - ] - }, - "target": [{ + "target": [ + { "type": "radar", "encode": { "x": ["属性"], "y": ["得分"], "s": ["类别"] } - }] + } + ] + }, + { + "source": { + "data": [ + { "factor": "Quality", "brand": "X", "score": 88 }, + { "factor": "Quality", "brand": "Y", "score": 86 }, + { "factor": "Design", "brand": "X", "score": 84 }, + { "factor": "Design", "brand": "Y", "score": 82 }, + { "factor": "Durability", "brand": "X", "score": 87 }, + { "factor": "Durability", "brand": "Y", "score": 85 }, + { "factor": 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["CropYield"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["Rainfall"], + "y": ["CropYield"] + } + } + ] + }, + { + "source": { + "data": [ + { "车速": 40, "燃油效率": 15 }, + { "车速": 60, "燃油效率": 16 }, + { "车速": 80, "燃油效率": 14 }, + { "车速": 100, "燃油效率": 12 }, + { "车速": 120, "燃油效率": 10 }, + { "车速": 140, "燃油效率": 8 } + ] + }, + "target": [ { - "source": { - "data": [ - {"车速": 40, "燃油效率": 15}, - {"车速": 60, "燃油效率": 16}, - {"车速": 80, "燃油效率": 14}, - {"车速": 100, "燃油效率": 12}, - {"车速": 120, "燃油效率": 10}, - {"车速": 140, "燃油效率": 8} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["车速"], - "y": ["燃油效率"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["车速"], + "y": ["燃油效率"] + } + } + ] + }, + { + "source": { + "data": [ + { "HoursWorked": 20, "WeeklyIncome": 200 }, + { "HoursWorked": 25, "WeeklyIncome": 250 }, + { "HoursWorked": 30, "WeeklyIncome": 300 }, + { "HoursWorked": 35, "WeeklyIncome": 350 }, + { "HoursWorked": 40, "WeeklyIncome": 400 }, + { "HoursWorked": 45, "WeeklyIncome": 450 } + ] + }, + "target": [ { - "source": { - "data": [ - {"HoursWorked": 20, "WeeklyIncome": 200}, - {"HoursWorked": 25, "WeeklyIncome": 250}, - {"HoursWorked": 30, "WeeklyIncome": 300}, - {"HoursWorked": 35, "WeeklyIncome": 350}, - {"HoursWorked": 40, "WeeklyIncome": 400}, - {"HoursWorked": 45, "WeeklyIncome": 450} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["HoursWorked"], - "y": ["WeeklyIncome"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["HoursWorked"], + "y": ["WeeklyIncome"] + } + } + ] + }, + { + "source": { + "data": [ + { "咖啡摄入量": 1, "工作效率": 60 }, + { "咖啡摄入量": 2, "工作效率": 65 }, + { "咖啡摄入量": 3, "工作效率": 70 }, + { "咖啡摄入量": 4, "工作效率": 68 }, + { "咖啡摄入量": 5, "工作效率": 66 }, + { "咖啡摄入量": 6, "工作效率": 63 } + ] + }, + "target": [ { - "source": { - "data": [ - {"咖啡摄入量": 1, "工作效率": 60}, - {"咖啡摄入量": 2, "工作效率": 65}, - {"咖啡摄入量": 3, "工作效率": 70}, - {"咖啡摄入量": 4, "工作效率": 68}, - {"咖啡摄入量": 5, "工作效率": 66}, - {"咖啡摄入量": 6, "工作效率": 63} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["咖啡摄入量"], - "y": ["工作效率"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["咖啡摄入量"], + "y": ["工作效率"] + } + } + ] + }, + { + "source": { + "data": [ + { "Age": 25, "MonthlySavings": 100 }, + { "Age": 30, "MonthlySavings": 200 }, + { "Age": 35, "MonthlySavings": 300 }, + { "Age": 40, "MonthlySavings": 400 }, + { "Age": 45, "MonthlySavings": 350 }, + { "Age": 50, "MonthlySavings": 320 } + ] + }, + "target": [ { - "source": { - "data": [ - {"Age": 25, "MonthlySavings": 100}, - {"Age": 30, "MonthlySavings": 200}, - {"Age": 35, "MonthlySavings": 300}, - {"Age": 40, "MonthlySavings": 400}, - {"Age": 45, "MonthlySavings": 350}, - {"Age": 50, "MonthlySavings": 320} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["Age"], - "y": ["MonthlySavings"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["Age"], + "y": ["MonthlySavings"] + } + } + ] + }, + { + "source": { + "data": [ + { "演讲时间": 5, "观众满意度": 6 }, + { "演讲时间": 10, "观众满意度": 7 }, + { "演讲时间": 15, "观众满意度": 8 }, + { "演讲时间": 30, "观众满意度": 7 }, + { "演讲时间": 45, "观众满意度": 5 }, + { "演讲时间": 60, "观众满意度": 4 } + ] + }, + "target": [ { - "source": { - "data": [ - {"演讲时间": 5, "观众满意度": 6}, - {"演讲时间": 10, "观众满意度": 7}, - {"演讲时间": 15, "观众满意度": 8}, - {"演讲时间": 30, "观众满意度": 7}, - {"演讲时间": 45, "观众满意度": 5}, - {"演讲时间": 60, "观众满意度": 4} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["演讲时间"], - "y": ["观众满意度"] - } - } - ] - }, + "type": "scatter", + "encode": { + "x": ["演讲时间"], + "y": ["观众满意度"] + } + } + ] + }, + { + "source": { + "data": [ + { "AdvertisingBudget": 500, "NewCustomers": 50 }, + { "AdvertisingBudget": 1000, "NewCustomers": 100 }, + { "AdvertisingBudget": 1500, "NewCustomers": 140 }, + { "AdvertisingBudget": 2000, "NewCustomers": 180 }, + { "AdvertisingBudget": 2500, "NewCustomers": 210 }, + { "AdvertisingBudget": 3000, "NewCustomers": 245 } + ] + }, + "target": [ { - "source": { - "data": [ - {"AdvertisingBudget": 500, "NewCustomers": 50}, - {"AdvertisingBudget": 1000, "NewCustomers": 100}, - {"AdvertisingBudget": 1500, "NewCustomers": 140}, - {"AdvertisingBudget": 2000, "NewCustomers": 180}, - {"AdvertisingBudget": 2500, "NewCustomers": 210}, - {"AdvertisingBudget": 3000, "NewCustomers": 245} - ] - }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["AdvertisingBudget"], - "y": ["NewCustomers"] - } - } - ] + "type": "scatter", + "encode": { + "x": ["AdvertisingBudget"], + "y": ["NewCustomers"] + } } + ] + } ] diff --git a/evaluations/datastes/recommend/scatter/02_split.json b/evaluations/datastes/recommend/scatter/02_split.json index f2f25e6..187a24e 100644 --- a/evaluations/datastes/recommend/scatter/02_split.json +++ b/evaluations/datastes/recommend/scatter/02_split.json @@ -1,91 +1,91 @@ [ { "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}, - {"Series":"IV", "X":8.0, "Y":6.58}, - {"Series":"IV", "X":8.0, "Y":5.76}, - {"Series":"IV", "X":8.0, "Y":7.71}, - {"Series":"IV", "X":8.0, "Y":8.84}, - {"Series":"IV", "X":8.0, "Y":8.47}, - {"Series":"IV", "X":8.0, "Y":7.04}, - {"Series":"IV", "X":8.0, "Y":5.25}, - {"Series":"IV", "X":19.0, "Y":12.50} - ] + "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": 8.1 }, + { "Series": "II", "X": 6.0, "Y": 6.13 }, + { "Series": "II", "X": 4.0, "Y": 3.1 }, + { "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 }, + { "Series": "IV", "X": 8.0, "Y": 6.58 }, + { "Series": "IV", "X": 8.0, "Y": 5.76 }, + { "Series": "IV", "X": 8.0, "Y": 7.71 }, + { "Series": "IV", "X": 8.0, "Y": 8.84 }, + { "Series": "IV", "X": 8.0, "Y": 8.47 }, + { "Series": "IV", "X": 8.0, "Y": 7.04 }, + { "Series": "IV", "X": 8.0, "Y": 5.25 }, + { "Series": "IV", "X": 19.0, "Y": 12.5 } + ] }, "target": [ - { - "type": "scatter", - "encode": { - "x": ["X"], - "x2": ["Y"], - "s": ["Series"] - } + { + "type": "scatter", + "encode": { + "x": ["X"], + "x2": ["Y"], + "s": ["Series"] } - ] -}, -{ - "source": { - "data": [ - {"食品": "A", "价格": 15, "销量": 200}, - {"食品": "B", "价格": 18, "销量": 250}, - {"食品": "C", "价格": 20, "销量": 300}, - {"食品": "D", "价格": 22, "销量": 320}, - {"食品": "E", "价格": 25, "销量": 340} + } ] }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["价格"], - "y": ["销量"] + { + "source": { + "data": [ + { "食品": "A", "价格": 15, "销量": 200 }, + { "食品": "B", "价格": 18, "销量": 250 }, + { "食品": "C", "价格": 20, "销量": 300 }, + { "食品": "D", "价格": 22, "销量": 320 }, + { "食品": "E", "价格": 25, "销量": 340 } + ] + }, + "target": [ + { + "type": "scatter", + "encode": { + "x": ["价格"], + "y": ["销量"] + } } - } - ] -}, -{ - "source": { - "data": [ - {"城市": "A", "人口": 500000, "绿化率": 30}, - {"城市": "B", "人口": 1000000, "绿化率": 25}, - {"城市": "C", "人口": 1500000, "绿化率": 20}, - {"城市": "D", "人口": 2000000, "绿化率": 18}, - {"城市": "E", "人口": 2500000, "绿化率": 15} ] }, - "target": [ - { - "type": "scatter", - "encode": { - "x": ["人口"], - "y": ["绿化率"] + { + "source": { + "data": [ + { "城市": "A", "人口": 500000, "绿化率": 30 }, + { "城市": "B", "人口": 1000000, "绿化率": 25 }, + { "城市": "C", "人口": 1500000, "绿化率": 20 }, + { "城市": "D", "人口": 2000000, "绿化率": 18 }, + { "城市": "E", "人口": 2500000, "绿化率": 15 } + ] + }, + "target": [ + { + "type": "scatter", + "encode": { + "x": ["人口"], + "y": ["绿化率"] + } } - } - ] -}, + ] + }, { "source": { "metas": [ @@ -103,11 +103,11 @@ } ], "data": [ - {"Category": "A", "Age": 25, "Income": 30000}, - {"Category": "A", "Age": 30, "Income": 40000}, - {"Category": "B", "Age": 35, "Income": 50000}, - {"Category": "B", "Age": 40, "Income": 60000}, - {"Category": "C", "Age": 45, "Income": 70000} + { "Category": "A", "Age": 25, "Income": 30000 }, + { "Category": "A", "Age": 30, "Income": 40000 }, + { "Category": "B", "Age": 35, "Income": 50000 }, + { "Category": "B", "Age": 40, "Income": 60000 }, + { "Category": "C", "Age": 45, "Income": 70000 } ] }, "target": [ @@ -138,11 +138,11 @@ } ], "data": [ - {"商品类型": "Item1", "Weight": 5, "Price": 100}, - {"商品类型": "Item2", "Weight": 10, "Price": 200}, - {"商品类型": "Item3", "Weight": 15, "Price": 250}, - {"商品类型": "Item4", "Weight": 20, "Price": 300}, - {"商品类型": "Item5", "Weight": 25, "Price": 400} + { "商品类型": "Item1", "Weight": 5, "Price": 100 }, + { "商品类型": "Item2", "Weight": 10, "Price": 200 }, + { "商品类型": "Item3", "Weight": 15, "Price": 250 }, + { "商品类型": "Item4", "Weight": 20, "Price": 300 }, + { "商品类型": "Item5", "Weight": 25, "Price": 400 } ] }, "target": [ @@ -173,11 +173,11 @@ } ], "data": [ - {"城市": "城市A", "人口": 1000000, "公园数量": 50}, - {"城市": "城市B", "人口": 2000000, "公园数量": 80}, - {"城市": "城市C", "人口": 3000000, "公园数量": 100}, - {"城市": "城市D", "人口": 4000000, "公园数量": 150}, - {"城市": "城市E", "人口": 5000000, "公园数量": 200} + { "城市": "城市A", "人口": 1000000, "公园数量": 50 }, + { "城市": "城市B", "人口": 2000000, "公园数量": 80 }, + { "城市": "城市C", "人口": 3000000, "公园数量": 100 }, + { "城市": "城市D", "人口": 4000000, "公园数量": 150 }, + { "城市": "城市E", "人口": 5000000, "公园数量": 200 } ] }, "target": [ @@ -208,11 +208,11 @@ } ], "data": [ - {"国家": "Country1", "GDP": 5000, "人均收入": 30000}, - {"国家": "Country2", "GDP": 10000, "人均收入": 35000}, - {"国家": "Country3", "GDP": 15000, "人均收入": 40000}, - {"国家": "Country4", "GDP": 20000, "人均收入": 45000}, - {"国家": "Country5", "GDP": 25000, "人均收入": 50000} + { "国家": "Country1", "GDP": 5000, "人均收入": 30000 }, + { "国家": "Country2", "GDP": 10000, "人均收入": 35000 }, + { "国家": "Country3", "GDP": 15000, "人均收入": 40000 }, + { "国家": "Country4", "GDP": 20000, "人均收入": 45000 }, + { "国家": "Country5", "GDP": 25000, "人均收入": 50000 } ] }, "target": [ @@ -243,11 +243,11 @@ } ], "data": [ - {"项目": "Project1", "时间投入": 10, "任务完成数量": 100}, - {"项目": "Project2", "时间投入": 20, "任务完成数量": 200}, - {"项目": "Project3", "时间投入": 30, "任务完成数量": 300}, - {"项目": "Project4", "时间投入": 40, "任务完成数量": 350}, - {"项目": "Project5", "时间投入": 50, "任务完成数量": 450} + { "项目": "Project1", "时间投入": 10, "任务完成数量": 100 }, + { "项目": "Project2", "时间投入": 20, "任务完成数量": 200 }, + { "项目": "Project3", "时间投入": 30, "任务完成数量": 300 }, + { "项目": "Project4", "时间投入": 40, "任务完成数量": 350 }, + { "项目": "Project5", "时间投入": 50, "任务完成数量": 450 } ] }, "target": [ @@ -278,11 +278,11 @@ } ], "data": [ - {"公司": "企业A", "员工人数": 50, "营业额": 500000}, - {"公司": "企业B", "员工人数": 100, "营业额": 1000000}, - {"公司": "企业C", "员工人数": 200, "营业额": 2000000}, - {"公司": "企业D", "员工人数": 300, "营业额": 3000000}, - {"公司": "企业E", "员工人数": 400, "营业额": 4000000} + { "公司": "企业A", "员工人数": 50, "营业额": 500000 }, + { "公司": "企业B", "员工人数": 100, "营业额": 1000000 }, + { "公司": "企业C", "员工人数": 200, "营业额": 2000000 }, + { "公司": "企业D", "员工人数": 300, "营业额": 3000000 }, + { "公司": "企业E", "员工人数": 400, "营业额": 4000000 } ] }, "target": [ @@ -313,11 +313,11 @@ } ], "data": [ - {"Subject": "Math", "Hours": 5, "Scores": 80}, - {"Subject": "Science", "Hours": 10, "Scores": 85}, - {"Subject": "History", "Hours": 15, "Scores": 90}, - {"Subject": "Art", "Hours": 20, "Scores": 95}, - {"Subject": "PE", "Hours": 25, "Scores": 100} + { "Subject": "Math", "Hours": 5, "Scores": 80 }, + { "Subject": "Science", "Hours": 10, "Scores": 85 }, + { "Subject": "History", "Hours": 15, "Scores": 90 }, + { "Subject": "Art", "Hours": 20, "Scores": 95 }, + { "Subject": "PE", "Hours": 25, "Scores": 100 } ] }, "target": [ @@ -334,11 +334,11 @@ { "source": { "data": [ - {"创意类型": "新", "访问次数": 500, "购买量": 50}, - {"创意类型": "新", "访问次数": 1000, "购买量": 100}, - {"创意类型": "旧", "访问次数": 1500, "购买量": 150}, - {"创意类型": "旧", "访问次数": 2000, "购买量": 200}, - {"创意类型": "旧", "访问次数": 2500, "购买量": 250} + { "创意类型": "新", "访问次数": 500, "购买量": 50 }, + { "创意类型": "新", "访问次数": 1000, "购买量": 100 }, + { "创意类型": "旧", "访问次数": 1500, "购买量": 150 }, + { "创意类型": "旧", "访问次数": 2000, "购买量": 200 }, + { "创意类型": "旧", "访问次数": 2500, "购买量": 250 } ] }, "target": [ @@ -369,11 +369,11 @@ } ], "data": [ - {"性别": "男", "用户满意度": 60, "销售量": 500}, - {"性别": "男", "用户满意度": 70, "销售量": 600}, - {"性别": "女", "用户满意度": 80, "销售量": 700}, - {"性别": "女", "用户满意度": 90, "销售量": 800}, - {"性别": "男", "用户满意度": 100, "销售量": 900} + { "性别": "男", "用户满意度": 60, "销售量": 500 }, + { "性别": "男", "用户满意度": 70, "销售量": 600 }, + { "性别": "女", "用户满意度": 80, "销售量": 700 }, + { "性别": "女", "用户满意度": 90, "销售量": 800 }, + { "性别": "男", "用户满意度": 100, "销售量": 900 } ] }, "target": [ @@ -390,11 +390,11 @@ { "source": { "data": [ - {"Programming Language": "Python", "Popularity": 90, "Number of Users": 500000}, - {"Programming Language": "Java", "Popularity": 85, "Number of Users": 450000}, - {"Programming Language": "C++", "Popularity": 80, "Number of Users": 400000}, - {"Programming Language": "JavaScript", "Popularity": 95, "Number of Users": 600000}, - {"Programming Language": "Ruby", "Popularity": 70, "Number of Users": 350000} + { "Programming Language": "Python", "Popularity": 90, "Number of Users": 500000 }, + { "Programming Language": "Java", "Popularity": 85, "Number of Users": 450000 }, + { "Programming Language": "C++", "Popularity": 80, "Number of Users": 400000 }, + { "Programming Language": "JavaScript", "Popularity": 95, "Number of Users": 600000 }, + { "Programming Language": "Ruby", "Popularity": 70, "Number of Users": 350000 } ] }, "target": [ diff --git a/evaluations/datastes/recommend/scatter/03_split_and_size.json b/evaluations/datastes/recommend/scatter/03_split_and_size.json index 4fadbd2..35d33e1 100644 --- a/evaluations/datastes/recommend/scatter/03_split_and_size.json +++ b/evaluations/datastes/recommend/scatter/03_split_and_size.json @@ -2,44 +2,47 @@ { "source": { "data": [ - {"使用率": 75, "平均点击次数": 300, "业务分类": "电商", "平均耗时": 120}, - {"使用率": 85, "平均点击次数": 500, "业务分类": "社交媒体", "平均耗时": 95}, - {"使用率": 60, "平均点击次数": 250, "业务分类": "金融", "平均耗时": 200}, - {"使用率": 90, "平均点击次数": 600, "业务分类": "教育", "平均耗时": 80}, - {"使用率": 70, "平均点击次数": 400, "业务分类": "健康", "平均耗时": 150}, - {"使用率": 80, "平均点击次数": 550, "业务分类": "旅游", "平均耗时": 100}, - {"使用率": 65, "平均点击次数": 350, "业务分类": "娱乐", "平均耗时": 130}, - {"使用率": 95, "平均点击次数": 700, "业务分类": "科技", "平均耗时": 90}, - {"使用率": 72, "平均点击次数": 420, "业务分类": "物流", "平均耗时": 160}, - {"使用率": 88, "平均点击次数": 580, "业务分类": "新闻", "平均耗时": 110} + { "使用率": 75, "平均点击次数": 300, "业务分类": "电商", "平均耗时": 120 }, + { "使用率": 85, "平均点击次数": 500, "业务分类": "社交媒体", "平均耗时": 95 }, + { "使用率": 60, "平均点击次数": 250, "业务分类": "金融", "平均耗时": 200 }, + { "使用率": 90, "平均点击次数": 600, "业务分类": "教育", "平均耗时": 80 }, + { "使用率": 70, "平均点击次数": 400, "业务分类": "健康", "平均耗时": 150 }, + { "使用率": 80, "平均点击次数": 550, "业务分类": "旅游", "平均耗时": 100 }, + { "使用率": 65, "平均点击次数": 350, "业务分类": "娱乐", "平均耗时": 130 }, + { "使用率": 95, "平均点击次数": 700, "业务分类": "科技", "平均耗时": 90 }, + { "使用率": 72, "平均点击次数": 420, "业务分类": "物流", "平均耗时": 160 }, + { "使用率": 88, "平均点击次数": 580, "业务分类": "新闻", "平均耗时": 110 } ] }, - "target": [{ - "type": "scatter", - "encode": { - "x": ["使用率"], - "y": ["平均点击次数"], - "s": ["业务分类"], - "size": "平均耗时" + "target": [ + { + "type": "scatter", + "encode": { + "x": ["使用率"], + "y": ["平均点击次数"], + "s": ["业务分类"], + "size": "平均耗时" + } } - }] + ] }, - { - "source": { - "data": [ - {"GDP": 1500, "Inflation Rate": 3.0, "Country": "USA", "Population": 320}, - {"GDP": 900, "Inflation Rate": 4.2, "Country": "UK", "Population": 65}, - {"GDP": 800, "Inflation Rate": 2.5, "Country": "Japan", "Population": 127}, - {"GDP": 600, "Inflation Rate": 1.8, "Country": "Germany", "Population": 82}, - {"GDP": 700, "Inflation Rate": 2.2, "Country": "India", "Population": 1380}, - {"GDP": 550, "Inflation Rate": 3.5, "Country": "Brazil", "Population": 212}, - {"GDP": 730, "Inflation Rate": 2.7, "Country": "France", "Population": 67}, - {"GDP": 470, "Inflation Rate": 3.9, "Country": "Italy", "Population": 60}, - {"GDP": 910, "Inflation Rate": 2.8, "Country": "China", "Population": 1420}, - {"GDP": 670, "Inflation Rate": 2.6, "Country": "Canada", "Population": 37} - ] - }, - "target": [{ + { + "source": { + "data": [ + { "GDP": 1500, "Inflation Rate": 3.0, "Country": "USA", "Population": 320 }, + { "GDP": 900, "Inflation Rate": 4.2, "Country": "UK", "Population": 65 }, + { "GDP": 800, "Inflation Rate": 2.5, "Country": "Japan", "Population": 127 }, + { "GDP": 600, "Inflation Rate": 1.8, "Country": "Germany", "Population": 82 }, + { "GDP": 700, "Inflation Rate": 2.2, "Country": "India", "Population": 1380 }, + { "GDP": 550, "Inflation Rate": 3.5, "Country": "Brazil", "Population": 212 }, + { "GDP": 730, "Inflation Rate": 2.7, "Country": "France", "Population": 67 }, + { "GDP": 470, "Inflation Rate": 3.9, "Country": "Italy", "Population": 60 }, + { "GDP": 910, "Inflation Rate": 2.8, "Country": "China", "Population": 1420 }, + { "GDP": 670, "Inflation Rate": 2.6, "Country": "Canada", "Population": 37 } + ] + }, + "target": [ + { "type": "scatter", "encode": { "x": ["GDP"], @@ -47,24 +50,26 @@ "s": ["Country"], "size": "Population" } - }] + } + ] + }, + { + "source": { + "data": [ + { "房价": 20000, "租金回报率": 4.5, "城市": "北京", "人口": 2100 }, + { "房价": 18000, "租金回报率": 3.8, "城市": "上海", "人口": 2400 }, + { "房价": 17000, "租金回报率": 4.0, "城市": "深圳", "人口": 1300 }, + { "房价": 16000, "租金回报率": 4.2, "城市": "广州", "人口": 1500 }, + { "房价": 15000, "租金回报率": 3.5, "城市": "成都", "人口": 1600 }, + { "房价": 14000, "租金回报率": 4.8, "城市": "重庆", "人口": 1800 }, + { "房价": 13000, "租金回报率": 3.6, "城市": "武汉", "人口": 1100 }, + { "房价": 12000, "租金回报率": 4.1, "城市": "西安", "人口": 900 }, + { "房价": 11000, "租金回报率": 4.4, "城市": "杭州", "人口": 1050 }, + { "房价": 10000, "租金回报率": 3.9, "城市": "天津", "人口": 1300 } + ] }, - { - "source": { - "data": [ - {"房价": 20000, "租金回报率": 4.5, "城市": "北京", "人口": 2100}, - {"房价": 18000, "租金回报率": 3.8, "城市": "上海", "人口": 2400}, - {"房价": 17000, "租金回报率": 4.0, "城市": "深圳", "人口": 1300}, - {"房价": 16000, "租金回报率": 4.2, "城市": "广州", "人口": 1500}, - {"房价": 15000, "租金回报率": 3.5, "城市": "成都", "人口": 1600}, - {"房价": 14000, "租金回报率": 4.8, "城市": "重庆", "人口": 1800}, - {"房价": 13000, "租金回报率": 3.6, "城市": "武汉", "人口": 1100}, - {"房价": 12000, "租金回报率": 4.1, "城市": "西安", "人口": 900}, - {"房价": 11000, "租金回报率": 4.4, "城市": "杭州", "人口": 1050}, - {"房价": 10000, "租金回报率": 3.9, "城市": "天津", "人口": 1300} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["房价"], @@ -72,24 +77,26 @@ "s": ["城市"], "size": "人口" } - }] + } + ] + }, + { + "source": { + "data": [ + { "Mileage": 27, "Gas Price": 3.2, "Brand": "Toyota", "Age": 5 }, + { "Mileage": 22, "Gas Price": 3.5, "Brand": "Ford", "Age": 8 }, + { "Mileage": 30, "Gas Price": 3.0, "Brand": "Honda", "Age": 6 }, + { "Mileage": 25, "Gas Price": 2.9, "Brand": "Chevrolet", "Age": 7 }, + { "Mileage": 24, "Gas Price": 3.1, "Brand": "Hyundai", "Age": 4 }, + { "Mileage": 28, "Gas Price": 3.3, "Brand": "Mazda", "Age": 5 }, + { "Mileage": 26, "Gas Price": 2.8, "Brand": "BMW", "Age": 8 }, + { "Mileage": 29, "Gas Price": 3.4, "Brand": "Nissan", "Age": 6 }, + { "Mileage": 23, "Gas Price": 3.6, "Brand": "Kia", "Age": 5 }, + { "Mileage": 21, "Gas Price": 3.2, "Brand": "Volkswagen", "Age": 7 } + ] }, - { - "source": { - "data": [ - {"Mileage": 27, "Gas Price": 3.2, "Brand": "Toyota", "Age": 5}, - {"Mileage": 22, "Gas Price": 3.5, "Brand": "Ford", "Age": 8}, - {"Mileage": 30, "Gas Price": 3.0, "Brand": "Honda", "Age": 6}, - {"Mileage": 25, "Gas Price": 2.9, "Brand": "Chevrolet", "Age": 7}, - {"Mileage": 24, "Gas Price": 3.1, "Brand": "Hyundai", "Age": 4}, - {"Mileage": 28, "Gas Price": 3.3, "Brand": "Mazda", "Age": 5}, - {"Mileage": 26, "Gas Price": 2.8, "Brand": "BMW", "Age": 8}, - {"Mileage": 29, "Gas Price": 3.4, "Brand": "Nissan", "Age": 6}, - {"Mileage": 23, "Gas Price": 3.6, "Brand": "Kia", "Age": 5}, - {"Mileage": 21, "Gas Price": 3.2, "Brand": "Volkswagen", "Age": 7} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["Mileage"], @@ -97,24 +104,26 @@ "s": ["Brand"], "size": "Age" } - }] + } + ] + }, + { + "source": { + "data": [ + { "平均温度": 22, "降雨量": 100, "城市": "南京", "人口": 840 }, + { "平均温度": 25, "降雨量": 80, "城市": "福州", "人口": 620 }, + { "平均温度": 30, "降雨量": 60, "城市": "广州", "人口": 1500 }, + { "平均温度": 28, "降雨量": 90, "城市": "长沙", "人口": 760 }, + { "平均温度": 24, "降雨量": 110, "城市": "苏州", "人口": 1040 }, + { "平均温度": 20, "降雨量": 130, "城市": "南京", "人口": 840 }, + { "平均温度": 32, "降雨量": 70, "城市": "海口", "人口": 230 }, + { "平均温度": 27, "降雨量": 85, "城市": "南昌", "人口": 550 }, + { "平均温度": 26, "降雨量": 95, "城市": "昆明", "人口": 640 }, + { "平均温度": 23, "降雨量": 120, "城市": "重庆", "人口": 1800 } + ] }, - { - "source": { - "data": [ - {"平均温度": 22, "降雨量": 100, "城市": "南京", "人口": 840}, - {"平均温度": 25, "降雨量": 80, "城市": "福州", "人口": 620}, - {"平均温度": 30, "降雨量": 60, "城市": "广州", "人口": 1500}, - {"平均温度": 28, "降雨量": 90, "城市": "长沙", "人口": 760}, - {"平均温度": 24, "降雨量": 110, "城市": "苏州", "人口": 1040}, - {"平均温度": 20, "降雨量": 130, "城市": "南京", "人口": 840}, - {"平均温度": 32, "降雨量": 70, "城市": "海口", "人口": 230}, - {"平均温度": 27, "降雨量": 85, "城市": "南昌", "人口": 550}, - {"平均温度": 26, "降雨量": 95, "城市": "昆明", "人口": 640}, - {"平均温度": 23, "降雨量": 120, "城市": "重庆", "人口": 1800} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["平均温度"], @@ -122,24 +131,26 @@ "s": ["城市"], "size": "人口" } - }] + } + ] + }, + { + "source": { + "data": [ + { "Salary": 8000, "Experience": 5, "Industry": "Technology", "Age": 27 }, + { "Salary": 6000, "Experience": 8, "Industry": "Finance", "Age": 32 }, + { "Salary": 5500, "Experience": 4, "Industry": "Healthcare", "Age": 29 }, + { "Salary": 7000, "Experience": 7, "Industry": "Education", "Age": 35 }, + { "Salary": 4800, "Experience": 3, "Industry": "Retail", "Age": 24 }, + { "Salary": 7500, "Experience": 6, "Industry": "Real Estate", "Age": 30 }, + { "Salary": 5200, "Experience": 5, "Industry": "Marketing", "Age": 28 }, + { "Salary": 6900, "Experience": 9, "Industry": "Engineering", "Age": 40 }, + { "Salary": 5700, "Experience": 4, "Industry": "Design", "Age": 26 }, + { "Salary": 6200, "Experience": 8, "Industry": "Manufacturing", "Age": 33 } + ] }, - { - "source": { - "data": [ - {"Salary": 8000, "Experience": 5, "Industry": "Technology", "Age": 27}, - {"Salary": 6000, "Experience": 8, "Industry": "Finance", "Age": 32}, - {"Salary": 5500, "Experience": 4, "Industry": "Healthcare", "Age": 29}, - {"Salary": 7000, "Experience": 7, "Industry": "Education", "Age": 35}, - {"Salary": 4800, "Experience": 3, "Industry": "Retail", "Age": 24}, - {"Salary": 7500, "Experience": 6, "Industry": "Real Estate", "Age": 30}, - {"Salary": 5200, "Experience": 5, "Industry": "Marketing", "Age": 28}, - {"Salary": 6900, "Experience": 9, "Industry": "Engineering", "Age": 40}, - {"Salary": 5700, "Experience": 4, "Industry": "Design", "Age": 26}, - {"Salary": 6200, "Experience": 8, "Industry": "Manufacturing", "Age": 33} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["Salary"], @@ -147,24 +158,26 @@ "s": ["Industry"], "size": "Age" } - }] + } + ] + }, + { + "source": { + "data": [ + { "评论数": 500, "评级": 4.2, "产品类别": "电子产品", "销售量": 15000 }, + { "评论数": 400, "评级": 4.0, "产品类别": "服装", "销售量": 12000 }, + { "评论数": 320, "评级": 4.1, "产品类别": "家庭用品", "销售量": 13000 }, + { "评论数": 400, "评级": 3.8, "产品类别": "运动器材", "销售量": 11000 }, + { "评论数": 250, "评级": 4.3, "产品类别": "珠宝", "销售量": 5000 }, + { "评论数": 300, "评级": 3.9, "产品类别": "化妆品", "销售量": 9000 }, + { "评论数": 450, "评级": 4.0, "产品类别": "玩具", "销售量": 8000 }, + { "评论数": 280, "评级": 4.4, "产品类别": "家具", "销售量": 7500 }, + { "评论数": 370, "评级": 3.7, "产品类别": "图书", "销售量": 14000 }, + { "评论数": 520, "评级": 4.1, "产品类别": "食品", "销售量": 15000 } + ] }, - { - "source": { - "data": [ - {"评论数": 500, "评级": 4.2, "产品类别": "电子产品", "销售量": 15000}, - {"评论数": 400, "评级": 4.0, "产品类别": "服装", "销售量": 12000}, - {"评论数": 320, "评级": 4.1, "产品类别": "家庭用品", "销售量": 13000}, - {"评论数": 400, "评级": 3.8, "产品类别": "运动器材", "销售量": 11000}, - {"评论数": 250, "评级": 4.3, "产品类别": "珠宝", "销售量": 5000}, - {"评论数": 300, "评级": 3.9, "产品类别": "化妆品", "销售量": 9000}, - {"评论数": 450, "评级": 4.0, "产品类别": "玩具", "销售量": 8000}, - {"评论数": 280, "评级": 4.4, "产品类别": "家具", "销售量": 7500}, - {"评论数": 370, "评级": 3.7, "产品类别": "图书", "销售量": 14000}, - {"评论数": 520, "评级": 4.1, "产品类别": "食品", "销售量": 15000} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["评论数"], @@ -172,24 +185,26 @@ "s": ["产品类别"], "size": "销售量" } - }] + } + ] + }, + { + "source": { + "data": [ + { "出勤率": 98, "通过率": 89, "课程": "数学", "班级人数": 30 }, + { "出勤率": 92, "通过率": 84, "课程": "英语", "班级人数": 28 }, + { "出勤率": 95, "通过率": 91, "课程": "化学", "班级人数": 32 }, + { "出勤率": 90, "通过率": 80, "课程": "物理", "班级人数": 26 }, + { "出勤率": 88, "通过率": 85, "课程": "生物", "班级人数": 29 }, + { "出勤率": 93, "通过率": 87, "课程": "历史", "班级人数": 34 }, + { "出勤率": 91, "通过率": 83, "课程": "地理", "班级人数": 30 }, + { "出勤率": 94, "通过率": 90, "课程": "图画", "班级人数": 24 }, + { "出勤率": 89, "通过率": 86, "课程": "音乐", "班级人数": 20 }, + { "出勤率": 97, "通过率": 92, "课程": "计算机", "班级人数": 40 } + ] }, - { - "source": { - "data": [ - {"出勤率": 98, "通过率": 89, "课程": "数学", "班级人数": 30}, - {"出勤率": 92, "通过率": 84, "课程": "英语", "班级人数": 28}, - {"出勤率": 95, "通过率": 91, "课程": "化学", "班级人数": 32}, - {"出勤率": 90, "通过率": 80, "课程": "物理", "班级人数": 26}, - {"出勤率": 88, "通过率": 85, "课程": "生物", "班级人数": 29}, - {"出勤率": 93, "通过率": 87, "课程": "历史", "班级人数": 34}, - {"出勤率": 91, "通过率": 83, "课程": "地理", "班级人数": 30}, - {"出勤率": 94, "通过率": 90, "课程": "图画", "班级人数": 24}, - {"出勤率": 89, "通过率": 86, "课程": "音乐", "班级人数": 20}, - {"出勤率": 97, "通过率": 92, "课程": "计算机", "班级人数": 40} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["出勤率"], @@ -197,24 +212,51 @@ "s": ["课程"], "size": "班级人数" } - }] + } + ] + }, + { + "source": { + "data": [ + { "Average Speed": 80, "Fuel Consumption": 8, "Transport Type": "Car", "Distance": 200 }, + { "Average Speed": 70, "Fuel Consumption": 12, "Transport Type": "Truck", "Distance": 300 }, + { + "Average Speed": 60, + "Fuel Consumption": 5, + "Transport Type": "Motorcycle", + "Distance": 100 + }, + { "Average Speed": 40, "Fuel Consumption": 0, "Transport Type": "Bicycle", "Distance": 20 }, + { + "Average Speed": 120, + "Fuel Consumption": 15, + "Transport Type": "Airplane", + "Distance": 1000 + }, + { "Average Speed": 30, "Fuel Consumption": 0, "Transport Type": "Foot", "Distance": 5 }, + { + "Average Speed": 50, + "Fuel Consumption": 3, + "Transport Type": "Electric Scooter", + "Distance": 50 + }, + { + "Average Speed": 100, + "Fuel Consumption": 10, + "Transport Type": "Train", + "Distance": 400 + }, + { "Average Speed": 90, "Fuel Consumption": 11, "Transport Type": "Bus", "Distance": 150 }, + { + "Average Speed": 110, + "Fuel Consumption": 6, + "Transport Type": "Helicopter", + "Distance": 600 + } + ] }, - { - "source": { - "data": [ - {"Average Speed": 80, "Fuel Consumption": 8, "Transport Type": "Car", "Distance": 200}, - {"Average Speed": 70, "Fuel Consumption": 12, "Transport Type": "Truck", "Distance": 300}, - {"Average Speed": 60, "Fuel Consumption": 5, "Transport Type": "Motorcycle", "Distance": 100}, - {"Average Speed": 40, "Fuel Consumption": 0, "Transport Type": "Bicycle", "Distance": 20}, - {"Average Speed": 120, "Fuel Consumption": 15, "Transport Type": "Airplane", "Distance": 1000}, - {"Average Speed": 30, "Fuel Consumption": 0, "Transport Type": "Foot", "Distance": 5}, - {"Average Speed": 50, "Fuel Consumption": 3, "Transport Type": "Electric Scooter", "Distance": 50}, - {"Average Speed": 100, "Fuel Consumption": 10, "Transport Type": "Train", "Distance": 400}, - {"Average Speed": 90, "Fuel Consumption": 11, "Transport Type": "Bus", "Distance": 150}, - {"Average Speed": 110, "Fuel Consumption": 6, "Transport Type": "Helicopter", "Distance": 600} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["Average Speed"], @@ -222,24 +264,76 @@ "s": ["Transport Type"], "size": "Distance" } - }] + } + ] + }, + { + "source": { + "data": [ + { + "Internet Usage": 70, + "Social Media Activity": 600, + "Age Group": "18-24", + "Population Proportion": 15 + }, + { + "Internet Usage": 65, + "Social Media Activity": 400, + "Age Group": "25-34", + "Population Proportion": 18 + }, + { + "Internet Usage": 55, + "Social Media Activity": 300, + "Age Group": "35-44", + "Population Proportion": 20 + }, + { + "Internet Usage": 50, + "Social Media Activity": 200, + "Age Group": "45-54", + "Population Proportion": 17 + }, + { + "Internet Usage": 40, + "Social Media Activity": 150, + "Age Group": "55-64", + "Population Proportion": 15 + }, + { + "Internet Usage": 30, + "Social Media Activity": 80, + "Age Group": "65+", + "Population Proportion": 15 + }, + { + "Internet Usage": 85, + "Social Media Activity": 700, + "Age Group": "16-17", + "Population Proportion": 5 + }, + { + "Internet Usage": 78, + "Social Media Activity": 550, + "Age Group": "18-19", + "Population Proportion": 10 + }, + { + "Internet Usage": 60, + "Social Media Activity": 350, + "Age Group": "20-21", + "Population Proportion": 15 + }, + { + "Internet Usage": 57, + "Social Media Activity": 250, + "Age Group": "22-23", + "Population Proportion": 12 + } + ] }, - { - "source": { - "data": [ - {"Internet Usage": 70, "Social Media Activity": 600, "Age Group": "18-24", "Population Proportion": 15}, - {"Internet Usage": 65, "Social Media Activity": 400, "Age Group": "25-34", "Population Proportion": 18}, - {"Internet Usage": 55, "Social Media Activity": 300, "Age Group": "35-44", "Population Proportion": 20}, - {"Internet Usage": 50, "Social Media Activity": 200, "Age Group": "45-54", "Population Proportion": 17}, - {"Internet Usage": 40, "Social Media Activity": 150, "Age Group": "55-64", "Population Proportion": 15}, - {"Internet Usage": 30, "Social Media Activity": 80, "Age Group": "65+", "Population Proportion": 15}, - {"Internet Usage": 85, "Social Media Activity": 700, "Age Group": "16-17", "Population Proportion": 5}, - {"Internet Usage": 78, "Social Media Activity": 550, "Age Group": "18-19", "Population Proportion": 10}, - {"Internet Usage": 60, "Social Media Activity": 350, "Age Group": "20-21", "Population Proportion": 15}, - {"Internet Usage": 57, "Social Media Activity": 250, "Age Group": "22-23", "Population Proportion": 12} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["Internet Usage"], @@ -247,24 +341,26 @@ "s": ["Age Group"], "size": "Population Proportion" } - }] + } + ] + }, + { + "source": { + "data": [ + { "员工满意度": 4.2, "年停工率": 5, "部门": "研发", "员工人数": 200 }, + { "员工满意度": 3.8, "年停工率": 8, "部门": "生产", "员工人数": 350 }, + { "员工满意度": 4.5, "年停工率": 3, "部门": "销售", "员工人数": 150 }, + { "员工满意度": 4.0, "年停工率": 6, "部门": "人力资源", "员工人数": 80 }, + { "员工满意度": 3.9, "年停工率": 7, "部门": "财务", "员工人数": 100 }, + { "员工满意度": 4.1, "年停工率": 4, "部门": "市场", "员工人数": 120 }, + { "员工满意度": 3.7, "年停工率": 9, "部门": "物流", "员工人数": 200 }, + { "员工满意度": 4.3, "年停工率": 5, "部门": "客服", "员工人数": 60 }, + { "员工满意度": 4.4, "年停工率": 2, "部门": "IT", "员工人数": 90 }, + { "员工满意度": 3.5, "年停工率": 10, "部门": "采购", "员工人数": 70 } + ] }, - { - "source": { - "data": [ - {"员工满意度": 4.2, "年停工率": 5, "部门": "研发", "员工人数": 200}, - {"员工满意度": 3.8, "年停工率": 8, "部门": "生产", "员工人数": 350}, - {"员工满意度": 4.5, "年停工率": 3, "部门": "销售", "员工人数": 150}, - {"员工满意度": 4.0, "年停工率": 6, "部门": "人力资源", "员工人数": 80}, - {"员工满意度": 3.9, "年停工率": 7, "部门": "财务", "员工人数": 100}, - {"员工满意度": 4.1, "年停工率": 4, "部门": "市场", "员工人数": 120}, - {"员工满意度": 3.7, "年停工率": 9, "部门": "物流", "员工人数": 200}, - {"员工满意度": 4.3, "年停工率": 5, "部门": "客服", "员工人数": 60}, - {"员工满意度": 4.4, "年停工率": 2, "部门": "IT", "员工人数": 90}, - {"员工满意度": 3.5, "年停工率": 10, "部门": "采购", "员工人数": 70} - ] - }, - "target": [{ + "target": [ + { "type": "scatter", "encode": { "x": ["员工满意度"], @@ -272,6 +368,7 @@ "s": ["部门"], "size": "员工人数" } - }] - } + } + ] + } ] diff --git a/evaluations/datastes/recommend/treemap/01_base.json b/evaluations/datastes/recommend/treemap/01_base.json index ee4e9c4..42f9ab5 100644 --- a/evaluations/datastes/recommend/treemap/01_base.json +++ b/evaluations/datastes/recommend/treemap/01_base.json @@ -2,11 +2,11 @@ { "source": { "data": [ - {"地区": "南方-东南", "销售额": 60}, - {"地区": "南方-华南", "销售额": 40}, - {"地区": "南方-西南", "销售额": 20}, - {"地区": "北方-华北", "销售额": 50}, - {"地区": "北方-东北", "销售额": 30} + { "地区": "南方-东南", "销售额": 60 }, + { "地区": "南方-华南", "销售额": 40 }, + { "地区": "南方-西南", "销售额": 20 }, + { "地区": "北方-华北", "销售额": 50 }, + { "地区": "北方-东北", "销售额": 30 } ] }, "target": [ @@ -18,15 +18,15 @@ { "source": { "data": [ - {"Region": "USA-North", "GDP": 15000}, - {"Region": "USA-South", "GDP": 12000}, - {"Region": "USA-East", "GDP": 10000}, - {"Region": "USA-West", "GDP": 14000}, - {"Region": "Europe-Western", "GDP": 300}, - {"Region": "Europe-Eastern", "GDP": 200}, - {"Region": "Europe-Northern", "GDP": 100}, - {"Region": "Europe-Southern", "GDP": 150}, - {"Region": "Asia", "GDP": 85000} + { "Region": "USA-North", "GDP": 15000 }, + { "Region": "USA-South", "GDP": 12000 }, + { "Region": "USA-East", "GDP": 10000 }, + { "Region": "USA-West", "GDP": 14000 }, + { "Region": "Europe-Western", "GDP": 300 }, + { "Region": "Europe-Eastern", "GDP": 200 }, + { "Region": "Europe-Northern", "GDP": 100 }, + { "Region": "Europe-Southern", "GDP": 150 }, + { "Region": "Asia", "GDP": 85000 } ] }, "target": [ @@ -38,14 +38,14 @@ { "source": { "data": [ - {"行业": "科技", "投资金额": 200}, - {"行业": "金融", "投资金额": 150}, - {"行业": "教育", "投资金额": 50}, - {"行业": "医疗", "投资金额": 100}, - {"行业": "电子产品", "投资金额": 30}, - {"行业": "家电", "投资金额": 20}, - {"行业": "家具", "投资金额": 10}, - {"行业": "服装", "投资金额": 40} + { "行业": "科技", "投资金额": 200 }, + { "行业": "金融", "投资金额": 150 }, + { "行业": "教育", "投资金额": 50 }, + { "行业": "医疗", "投资金额": 100 }, + { "行业": "电子产品", "投资金额": 30 }, + { "行业": "家电", "投资金额": 20 }, + { "行业": "家具", "投资金额": 10 }, + { "行业": "服装", "投资金额": 40 } ] }, "target": [ @@ -57,14 +57,14 @@ { "source": { "data": [ - {"Department": "Research", "Budget": 120}, - {"Department": "Development", "Budget": 80}, - {"Department": "Marketing", "Budget": 50}, - {"Department": "Sales", "Budget": 100}, - {"Department": "Technology", "Budget": 25}, - {"Department": "Finance", "Budget": 15}, - {"Department": "Healthcare", "Budget": 20}, - {"Department": "Retail", "Budget": 10} + { "Department": "Research", "Budget": 120 }, + { "Department": "Development", "Budget": 80 }, + { "Department": "Marketing", "Budget": 50 }, + { "Department": "Sales", "Budget": 100 }, + { "Department": "Technology", "Budget": 25 }, + { "Department": "Finance", "Budget": 15 }, + { "Department": "Healthcare", "Budget": 20 }, + { "Department": "Retail", "Budget": 10 } ] }, "target": [ @@ -73,110 +73,110 @@ } ] }, - { - "source": { - "data": [ - {"地区": "华东-上海", "销售额": 100}, - {"地区": "华东-江苏", "销售额": 120}, - {"地区": "华东-浙江", "销售额": 110}, - {"地区": "华北-北京", "销售额": 130}, - {"地区": "华北-天津", "销售额": 90} - ] - }, - "target": [ - { - "type": "treemap" - } + { + "source": { + "data": [ + { "地区": "华东-上海", "销售额": 100 }, + { "地区": "华东-江苏", "销售额": 120 }, + { "地区": "华东-浙江", "销售额": 110 }, + { "地区": "华北-北京", "销售额": 130 }, + { "地区": "华北-天津", "销售额": 90 } ] }, - { - "source": { - "data": [ - {"Region": "Africa/Northern", "Population": 20000}, - {"Region": "Africa/Western", "Population": 15000}, - {"Region": "Africa/Eastern", "Population": 18000}, - {"Region": "Africa/Southern", "Population": 10000}, - {"Region": "Africa/Central", "Population": 12000}, - {"Continent": "Asia", "Population": 4500}, - {"Continent": "Africa", "Population": 1300}, - {"Continent": "Europe", "Population": 750}, - {"Continent": "America", "Population": 1000} - ] - }, - "target": [ - { - "type": "treemap" - } + "target": [ + { + "type": "treemap" + } + ] + }, + { + "source": { + "data": [ + { "Region": "Africa/Northern", "Population": 20000 }, + { "Region": "Africa/Western", "Population": 15000 }, + { "Region": "Africa/Eastern", "Population": 18000 }, + { "Region": "Africa/Southern", "Population": 10000 }, + { "Region": "Africa/Central", "Population": 12000 }, + { "Continent": "Asia", "Population": 4500 }, + { "Continent": "Africa", "Population": 1300 }, + { "Continent": "Europe", "Population": 750 }, + { "Continent": "America", "Population": 1000 } ] }, - { - "source": { - "data": [ - {"Region": "Australia-New South Wales", "Tourism Revenue": 8000}, - {"Region": "Australia-Victoria", "Tourism Revenue": 7500}, - {"Region": "Australia-Queensland", "Tourism Revenue": 6800}, - {"Region": "Australia-Western Australia", "Tourism Revenue": 4000} - ] - }, - "target": [ - { - "type": "treemap" - } + "target": [ + { + "type": "treemap" + } + ] + }, + { + "source": { + "data": [ + { "Region": "Australia-New South Wales", "Tourism Revenue": 8000 }, + { "Region": "Australia-Victoria", "Tourism Revenue": 7500 }, + { "Region": "Australia-Queensland", "Tourism Revenue": 6800 }, + { "Region": "Australia-Western Australia", "Tourism Revenue": 4000 } ] }, - { - "source": { - "data": [ - {"地区": "中东-伊朗", "石油产量": 350}, - {"地区": "中东-伊拉克", "石油产量": 300}, - {"地区": "中东-沙特", "石油产量": 500}, - {"地区": "中东-阿联酋", "石油产量": 200} - ] - }, - "target": [ - { - "type": "treemap" - } + "target": [ + { + "type": "treemap" + } + ] + }, + { + "source": { + "data": [ + { "地区": "中东-伊朗", "石油产量": 350 }, + { "地区": "中东-伊拉克", "石油产量": 300 }, + { "地区": "中东-沙特", "石油产量": 500 }, + { "地区": "中东-阿联酋", "石油产量": 200 } ] }, - { - "source": { - "data": [ - {"Region": "Latin America-Brazil", "Internet Users": 150000}, - {"Region": "Latin America-Mexico", "Internet Users": 120000}, - {"Region": "Latin America-Argentina", "Internet Users": 80000}, - {"Region": "Latin America-Chile", "Internet Users": 60000}, - {"Region": "南亚-印度", "Internet Users": 1400000}, - {"Region": "南亚-巴基斯坦", "Internet Users": 220000}, - {"Region": "南亚-孟加拉", "Internet Users": 160000}, - {"Region": "南亚-尼泊尔", "Internet Users": 30000} - ] - }, - "target": [ - { - "type": "treemap" - } + "target": [ + { + "type": "treemap" + } + ] + }, + { + "source": { + "data": [ + { "Region": "Latin America-Brazil", "Internet Users": 150000 }, + { "Region": "Latin America-Mexico", "Internet Users": 120000 }, + { "Region": "Latin America-Argentina", "Internet Users": 80000 }, + { "Region": "Latin America-Chile", "Internet Users": 60000 }, + { "Region": "南亚-印度", "Internet Users": 1400000 }, + { "Region": "南亚-巴基斯坦", "Internet Users": 220000 }, + { "Region": "南亚-孟加拉", "Internet Users": 160000 }, + { "Region": "南亚-尼泊尔", "Internet Users": 30000 } ] }, - { - "source": { - "data": [ - {"地区": "Europe-Germany", "科技支出": 700000}, - {"地区": "Europe-France", "科技支出": 600000}, - {"地区": "Europe-Italy", "科技支出": 500000}, - {"地区": "Europe-UK", "科技支出": 550000}, - {"地区": "北美-美国", "科技支出": 500000}, - {"地区": "北美-加拿大", "科技支出": 80000}, - {"地区": "北美-墨西哥", "科技支出": 20000}, - {"地区": "Oceania-New Zealand", "科技支出": 10000}, - {"地区": "Oceania-Fiji", "科技支出": 500}, - {"地区": "Oceania-Samoa", "科技支出": 400} - ] - }, - "target": [ - { - "type": "treemap" - } + "target": [ + { + "type": "treemap" + } + ] + }, + { + "source": { + "data": [ + { "地区": "Europe-Germany", "科技支出": 700000 }, + { "地区": "Europe-France", "科技支出": 600000 }, + { "地区": "Europe-Italy", "科技支出": 500000 }, + { "地区": "Europe-UK", "科技支出": 550000 }, + { "地区": "北美-美国", "科技支出": 500000 }, + { "地区": "北美-加拿大", "科技支出": 80000 }, + { "地区": "北美-墨西哥", "科技支出": 20000 }, + { "地区": "Oceania-New Zealand", "科技支出": 10000 }, + { "地区": "Oceania-Fiji", "科技支出": 500 }, + { "地区": "Oceania-Samoa", "科技支出": 400 } ] - } + }, + "target": [ + { + "type": "treemap" + } + ] + } ] diff --git a/evaluations/datastes/recommend/word-cloud/01_base.json b/evaluations/datastes/recommend/word-cloud/01_base.json index 591f894..dbaa475 100644 --- a/evaluations/datastes/recommend/word-cloud/01_base.json +++ b/evaluations/datastes/recommend/word-cloud/01_base.json @@ -1016,7 +1016,7 @@ { "frequency": 6, "keyword": "数据可视化" } ] }, - "target": [ + "target": [ { "type": "wordCloud", "encode": { @@ -1377,5 +1377,5 @@ } ] } - ] + ] ] diff --git a/evaluations/scripts/eval/eval-recommend.js b/evaluations/scripts/eval/eval-recommend.js index 15af8d9..404f16b 100644 --- a/evaluations/scripts/eval/eval-recommend.js +++ b/evaluations/scripts/eval/eval-recommend.js @@ -1,6 +1,6 @@ +import _ from 'lodash'; import { evaluateChartEncodes } from '../helpers/evaluate-chart-encode.js'; import { readDataset, writeDataset } from '../helpers/read-dataset.js'; -import _ from 'lodash' export const evaluateChartRecommend = async () => { const evalDatasetPath = `datastes/recommend/eval.json`; @@ -8,42 +8,44 @@ export const evaluateChartRecommend = async () => { console.log('datasets count: ', testDataset.length); console.log('Beginning eval datasets...'); const misMap = new Map(); - const scoredData = testDataset.map(data => { - const target = data.target?.[0] ?? data.target; - const gen = data.generation?.[0] ?? data.generation; - if(!gen || !target) { - // 数据缺失 - misMap.set('missing', (misMap.get('missing') ?? 0) + 1); - return; - } - const chartTypeScore = gen.type === target.type ? 1 : 0; - const encodeScore = evaluateChartEncodes(gen.encode, target.encode); - if (!chartTypeScore) { - const key = `${target.type}_to_${gen.type}`; - if (misMap.has(key)) { - misMap.set(key, misMap.get(key) + 1); - } else { - misMap.set(key, 1); + const scoredData = testDataset + .map((data) => { + const target = data.target?.[0] ?? data.target; + const gen = data.generation?.[0] ?? data.generation; + if (!gen || !target) { + // 数据缺失 + misMap.set('missing', (misMap.get('missing') ?? 0) + 1); + return; + } + const chartTypeScore = gen.type === target.type ? 1 : 0; + const encodeScore = evaluateChartEncodes(gen.encode, target.encode); + if (!chartTypeScore) { + const key = `${target.type}_to_${gen.type}`; + if (misMap.has(key)) { + misMap.set(key, misMap.get(key) + 1); + } else { + misMap.set(key, 1); + } } - } - return { - ...data, - correctness: chartTypeScore, - encodeScore, - } - }).filter(data => data); + return { + ...data, + correctness: chartTypeScore, + encodeScore, + }; + }) + .filter((data) => data); // save evaluate result await writeDataset(`datastes/recommend/metrics.json`, scoredData); // output metrics let score = 0; let chartTypeScore = 0; let encodeScore = 0; - let chartTypeScoreMap = {} - scoredData.forEach(data => { + let chartTypeScoreMap = {}; + scoredData.forEach((data) => { chartTypeScore += data.correctness; encodeScore += data.encodeScore; const target = data.target?.[0] ?? data.target; - const chartType = target?.type + const chartType = target?.type; chartTypeScoreMap[chartType] = { chartTypeScore: (chartTypeScoreMap[chartType]?.chartTypeScore ?? 0) + data.correctness, encodeScore: (chartTypeScoreMap[chartType]?.encodeScore ?? 0) + data.encodeScore, @@ -53,17 +55,17 @@ export const evaluateChartRecommend = async () => { score /= testDataset.length; chartTypeScore /= testDataset.length; encodeScore /= testDataset.length; - chartTypeScoreMap = _.mapValues(chartTypeScoreMap, ((score) => ({ - chartTypeScore: score.chartTypeScore/score.count, - encodeScore: score.encodeScore/score.count, - count: score.count - }))) - console.log('scoredData.length', scoredData.length, 'datasets count: ', testDataset.length) - console.log('chart type recommend accuracy:', chartTypeScore) - console.log('chart encode score:', encodeScore) + chartTypeScoreMap = _.mapValues(chartTypeScoreMap, (score) => ({ + chartTypeScore: score.chartTypeScore / score.count, + encodeScore: score.encodeScore / score.count, + count: score.count, + })); + console.log('scoredData.length', scoredData.length, 'datasets count: ', testDataset.length); + console.log('chart type recommend accuracy:', chartTypeScore); + console.log('chart encode score:', encodeScore); console.log('misclassified:', misMap); - console.log('chartTypeScoreMap', chartTypeScoreMap) -} + console.log('chartTypeScoreMap', chartTypeScoreMap); +}; evaluateChartRecommend().catch((error) => { console.error('Error evaluating chart recommendation:', error); diff --git a/evaluations/scripts/helpers/evaluate-chart-encode.js b/evaluations/scripts/helpers/evaluate-chart-encode.js index 1541f65..34828aa 100644 --- a/evaluations/scripts/helpers/evaluate-chart-encode.js +++ b/evaluations/scripts/helpers/evaluate-chart-encode.js @@ -43,7 +43,7 @@ export const evaluateChartEncodes = (gen, ref) => { }); return v.length ? _.sum(v) / v.length : gen == ref; } catch (e) { - console.log('error', e) + console.log('error', e); return 0; } }; diff --git a/prompts/chart-recommend.md b/prompts/chart-recommend.md index 5116510..286b3f5 100644 --- a/prompts/chart-recommend.md +++ b/prompts/chart-recommend.md @@ -14,6 +14,7 @@ 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 @@ -21,26 +22,31 @@ - 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"}]} ```