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docs: eval dual-axes
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mengting.jmt committed Nov 19, 2024
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150 changes: 150 additions & 0 deletions evaluations/datastes/chart/dual-axes/eval.json
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164 changes: 164 additions & 0 deletions evaluations/datastes/chart/dual-axes/metrics.json
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373 changes: 373 additions & 0 deletions evaluations/datastes/chart/dual-axes/test.json
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[
{
"type": "text2chart",
"question": "用双轴图展示某公司的年收入与员工满意度，2017年至2021年的收入分别为85.4亿、93.2亿、100.1亿、108.6亿、115.5亿，员工满意度（满分10分）分别为7.2、7.5、7.8、8.0、8.2。",
"answer": {
"type": "dual-axes",
"categories": ["2017", "2018", "2019", "2020", "2021"],
"title": "2017-2021年收入与员工满意度",
"axisXTitle": "年份",
"series": [
{
"type": "column",
"data": [85.4, 93.2, 100.1, 108.6, 115.5],
"axisYTitle": "年收入 (亿)"
},
{
"type": "line",
"data": [7.2, 7.5, 7.8, 8.0, 8.2],
"axisYTitle": "员工满意度"
}
]
}
},
{
"type": "text2chart",
"question": "用双轴图来表示一个城市的年度平均气温和降水量。从2016到2020年，平均气温分别是15°C、16°C、17°C、18°C、19°C；同期的年降水量为800mm、850mm、900mm、950mm、1000mm。",
"answer": {
"type": "dual-axes",
"categories": ["2016", "2017", "2018", "2019", "2020"],
"title": "2016-2020年城市气候数据",
"axisXTitle": "年份",
"series": [
{
"type": "line",
"data": [15, 16, 17, 18, 19],
"axisYTitle": "平均气温 (°C)"
},
{
"type": "column",
"data": [800, 850, 900, 950, 1000],
"axisYTitle": "降水量 (mm)"
}
]
}
},
{
"type": "text2chart",
"question": "利用双轴图展示一家电商平台的商品销量和退货率。在2019至2023年间，商品销量依次是120万件、130万件、140万件、150万件、160万件；相应的退货率则为2%、2.5%、3%、3.5%、4%。",
"answer": {
"type": "dual-axes",
"categories": ["2019", "2020", "2021", "2022", "2023"],
"title": "2019-2023电商平台销量及退货情况",
"axisXTitle": "年份",
"series": [
{
"type": "column",
"data": [120, 130, 140, 150, 160],
"axisYTitle": "销量 (万件)"
},
{
"type": "line",
"data": [0.02, 0.025, 0.03, 0.035, 0.04],
"axisYTitle": "退货率 (%)"
}
]
}
},
{
"type": "text2chart",
"question": "通过双轴图呈现某地区每年的人口增长数量以及经济增长率。2015年至2019年间，人口增量分别是12万、14万、16万、18万、20万；同期GDP增长率分别为3.5%、4.0%、4.5%、5.0%、5.5%。",
"answer": {
"type": "dual-axes",
"categories": ["2015", "2016", "2017", "2018", "2019"],
"title": "2015-2019人口增长与经济增长",
"axisXTitle": "年份",
"series": [
{
"type": "column",
"data": [12, 14, 16, 18, 20],
"axisYTitle": "人口增长 (万人)"
},
{
"type": "line",
"data": [0.035, 0.04, 0.045, 0.05, 0.055],
"axisYTitle": "GDP增长率 (%)"
}
]
}
},
{
"type": "text2chart",
"question": "采用双轴图形式描绘一国能源消费总量及其可再生能源占比的变化趋势。从2013到2017年，该国总能源消费量分别为50亿吨标准煤、52亿吨标准煤、54亿吨标准煤、56亿吨标准煤、58亿吨标准煤；可再生能源比例依次为10%、12%、14%、16%、18%。",
"answer": {
"type": "dual-axes",
"categories": ["2013", "2014", "2015", "2016", "2017"],
"title": "2013-2017能源消费与可再生能源占比",
"axisXTitle": "年份",
"series": [
{
"type": "column",
"data": [50, 52, 54, 56, 58],
"axisYTitle": "能源消费 (亿吨标准煤)"
},
{
"type": "line",
"data": [0.1, 0.12, 0.14, 0.16, 0.18],
"axisYTitle": "可再生能源占比 (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用双轴图可视化我的数据 [{ \"year\": 2019, \"sales\": 300, \"profit_margin\": 20 }, { \"year\": 2020, \"sales\": 400, \"profit_margin\": 22 }, { \"year\": 2021, \"sales\": 500, \"profit_margin\": 25 }]",
"answer": {
"type": "dual-axes",
"categories": ["2019", "2020", "2021"],
"title": "2019-2021 Sales and Profit Margin",
"axisXTitle": "Year",
"series": [
{
"type": "column",
"data": [300, 400, 500],
"axisYTitle": "Sales"
},
{
"type": "line",
"data": [20, 22, 25],
"axisYTitle": "Profit Margin (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用双轴图展示过去三年的气温和降水量数据 [{ \"month\": \"Jan\", \"temperature\": 5, \"rainfall\": 50 }, { \"month\": \"Feb\", \"temperature\": 7, \"rainfall\": 60 }, { \"month\": \"Mar\", \"temperature\": 10, \"rainfall\": 80 }]",
"answer": {
"type": "dual-axes",
"categories": ["Jan", "Feb", "Mar"],
"title": "Monthly Temperature and Rainfall (Q1)",
"axisXTitle": "Month",
"series": [
{
"type": "line",
"data": [5, 7, 10],
"axisYTitle": "Temperature (°C)"
},
{
"type": "column",
"data": [50, 60, 80],
"axisYTitle": "Rainfall (mm)"
}
]
}
},
{
"type": "data2chart",
"question": "用双轴图显示用户增长与活跃度 [{ \"quarter\": \"Q1\", \"user_growth\": 500, \"activity_rate\": 60 }, { \"quarter\": \"Q2\", \"user_growth\": 700, \"activity_rate\": 65 }, { \"quarter\": \"Q3\", \"user_growth\": 900, \"activity_rate\": 70 }]",
"answer": {
"type": "dual-axes",
"categories": ["Q1", "Q2", "Q3"],
"title": "User Growth and Activity Rate by Quarter",
"axisXTitle": "Quarter",
"series": [
{
"type": "column",
"data": [500, 700, 900],
"axisYTitle": "User Growth"
},
{
"type": "line",
"data": [60, 65, 70],
"axisYTitle": "Activity Rate (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用双轴图表示产品销售量与退货率 [{ \"product\": \"A\", \"sales_volume\": 200, \"return_rate\": 5 }, { \"product\": \"B\", \"sales_volume\": 300, \"return_rate\": 7 }, { \"product\": \"C\", \"sales_volume\": 400, \"return_rate\": 9 }]",
"answer": {
"type": "dual-axes",
"categories": ["A", "B", "C"],
"title": "Product Sales Volume and Return Rate",
"axisXTitle": "Product",
"series": [
{
"type": "column",
"data": [200, 300, 400],
"axisYTitle": "Sales Volume"
},
{
"type": "line",
"data": [5, 7, 9],
"axisYTitle": "Return Rate (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用双轴图绘制公司收入与支出情况 [{ \"year\": 2018, \"income\": 1000, \"expenses\": 800 }, { \"year\": 2019, \"income\": 1200, \"expenses\": 900 }, { \"year\": 2020, \"income\": 1500, \"expenses\": 1000 }]",
"answer": {
"type": "dual-axes",
"categories": ["2018", "2019", "2020"],
"title": "Annual Income and Expenses",
"axisXTitle": "Year",
"series": [
{
"type": "column",
"data": [1000, 1200, 1500],
"axisYTitle": "Income"
},
{
"type": "line",
"data": [800, 900, 1000],
"axisYTitle": "Expenses"
}
]
}
},
{
"type": "data2chart",
"question": "用组合图可视化我的数据 [{ \"year\": 2017, \"revenue\": 800, \"profit_margin\": 15 }, { \"year\": 2018, \"revenue\": 900, \"profit_margin\": 18 }, { \"year\": 2019, \"revenue\": 1000, \"profit_margin\": 20 }]",
"answer": {
"type": "dual-axes",
"categories": ["2017", "2018", "2019"],
"title": "2017-2019 Revenue and Profit Margin",
"axisXTitle": "Year",
"series": [
{
"type": "column",
"data": [800, 900, 1000],
"axisYTitle": "Revenue"
},
{
"type": "line",
"data": [15, 18, 20],
"axisYTitle": "Profit Margin (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用组合图展示过去三年的网站访问量和转化率 [{ \"month\": \"Apr\", \"visits\": 10000, \"conversion_rate\": 2.5 }, { \"month\": \"May\", \"visits\": 12000, \"conversion_rate\": 3.0 }, { \"month\": \"Jun\", \"visits\": 15000, \"conversion_rate\": 3.5 }]",
"answer": {
"type": "dual-axes",
"categories": ["Apr", "May", "Jun"],
"title": "Monthly Website Visits and Conversion Rate (Q2)",
"axisXTitle": "Month",
"series": [
{
"type": "column",
"data": [10000, 12000, 15000],
"axisYTitle": "Visits"
},
{
"type": "line",
"data": [2.5, 3.0, 3.5],
"axisYTitle": "Conversion Rate (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用组合图表示不同地区的销售额和市场份额 [{ \"region\": \"North\", \"sales\": 5000, \"market_share\": 20 }, { \"region\": \"South\", \"sales\": 7000, \"market_share\": 25 }, { \"region\": \"East\", \"sales\": 6000, \"market_share\": 22 }]",
"answer": {
"type": "dual-axes",
"categories": ["North", "South", "East"],
"title": "Regional Sales and Market Share",
"axisXTitle": "Region",
"series": [
{
"type": "column",
"data": [5000, 7000, 6000],
"axisYTitle": "Sales"
},
{
"type": "line",
"data": [20, 25, 22],
"axisYTitle": "Market Share (%)"
}
]
}
},
{
"type": "data2chart",
"question": "用组合图可视化过去50天的股票价格和交易量 [{\"day\":1,\"price\":100,\"volume\":10000},{\"day\":2,\"price\":102,\"volume\":12000},{\"day\":3,\"price\":101,\"volume\":11000},{\"day\":4,\"price\":103,\"volume\":13000},{\"day\":5,\"price\":104,\"volume\":14000},{\"day\":6,\"price\":105,\"volume\":15000},{\"day\":7,\"price\":106,\"volume\":16000},{\"day\":8,\"price\":107,\"volume\":17000},{\"day\":9,\"price\":108,\"volume\":18000},{\"day\":10,\"price\":109,\"volume\":19000},{\"day\":11,\"price\":110,\"volume\":20000},{\"day\":12,\"price\":111,\"volume\":21000},{\"day\":13,\"price\":112,\"volume\":22000},{\"day\":14,\"price\":113,\"volume\":23000},{\"day\":15,\"price\":114,\"volume\":24000},{\"day\":16,\"price\":115,\"volume\":25000},{\"day\":17,\"price\":116,\"volume\":26000},{\"day\":18,\"price\":117,\"volume\":27000},{\"day\":19,\"price\":118,\"volume\":28000},{\"day\":20,\"price\":119,\"volume\":29000},{\"day\":21,\"price\":120,\"volume\":30000},{\"day\":22,\"price\":121,\"volume\":31000},{\"day\":23,\"price\":122,\"volume\":32000},{\"day\":24,\"price\":123,\"volume\":33000},{\"day\":25,\"price\":124,\"volume\":34000},{\"day\":26,\"price\":125,\"volume\":35000},{\"day\":27,\"price\":126,\"volume\":36000},{\"day\":28,\"price\":127,\"volume\":37000},{\"day\":29,\"price\":128,\"volume\":38000},{\"day\":30,\"price\":129,\"volume\":39000},{\"day\":31,\"price\":130,\"volume\":40000},{\"day\":32,\"price\":131,\"volume\":41000},{\"day\":33,\"price\":132,\"volume\":42000},{\"day\":34,\"price\":133,\"volume\":43000},{\"day\":35,\"price\":134,\"volume\":44000},{\"day\":36,\"price\":135,\"volume\":45000},{\"day\":37,\"price\":136,\"volume\":46000},{\"day\":38,\"price\":137,\"volume\":47000},{\"day\":39,\"price\":138,\"volume\":48000},{\"day\":40,\"price\":139,\"volume\":49000},{\"day\":41,\"price\":140,\"volume\":50000},{\"day\":42,\"price\":141,\"volume\":51000},{\"day\":43,\"price\":142,\"volume\":52000},{\"day\":44,\"price\":143,\"volume\":53000},{\"day\":45,\"price\":144,\"volume\":54000},{\"day\":46,\"price\":145,\"volume\":55000},{\"day\":47,\"price\":146,\"volume\":56000},{\"day\":48,\"price\":147,\"volume\":57000},{\"day\":49,\"price\":148,\"volume\":58000},{\"day\":50,\"price\":149,\"volume\":59000}]",
"answer": {
"type": "dual-axes",
"categories": [
"Day 1",
"Day 2",
"Day 3",
"Day 4",
"Day 5",
"Day 6",
"Day 7",
"Day 8",
"Day 9",
"Day 10",
"Day 11",
"Day 12",
"Day 13",
"Day 14",
"Day 15",
"Day 16",
"Day 17",
"Day 18",
"Day 19",
"Day 20",
"Day 21",
"Day 22",
"Day 23",
"Day 24",
"Day 25",
"Day 26",
"Day 27",
"Day 28",
"Day 29",
"Day 30",
"Day 31",
"Day 32",
"Day 33",
"Day 34",
"Day 35",
"Day 36",
"Day 37",
"Day 38",
"Day 39",
"Day 40",
"Day 41",
"Day 42",
"Day 43",
"Day 44",
"Day 45",
"Day 46",
"Day 47",
"Day 48",
"Day 49",
"Day 50"
],
"title": "Stock Price and Trading Volume Over 50 Days",
"axisXTitle": "Day",
"series": [
{
"type": "line",
"data": [
100, 102, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149
],
"axisYTitle": "Stock Price"
},
{
"type": "column",
"data": [
10000, 12000, 11000, 13000, 14000, 15000, 16000, 17000, 18000,
19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000,
28000, 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000,
37000, 38000, 39000, 40000, 41000, 42000, 43000, 44000, 45000,
46000, 47000, 48000, 49000, 50000, 51000, 52000, 53000, 54000,
55000, 56000, 57000, 58000, 59000
],
"axisYTitle": "Trading Volume"
}
]
}
}
]
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