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| --- | ||
| title: getSpecificInsight | ||
| order: 3 | ||
| --- | ||
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| <embed src='@/docs/common/style.md'></embed> | ||
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| Extract insight of a specified type from the data and corresponding visualization specs. | ||
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| ## **getSpecificInsight** | ||
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| <i>(props: SpecificInsightProps): SpecificInsightResult</i> | ||
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| * ***SpecificInsightProps*** is the same as `InsightExtractorProps`. See [InsightExtractorProps API](./insight-patterns-extractor.en.md) for more details. | ||
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| * ***SpecificInsightResult*** output | ||
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| | Properties | Type | Description | Example| | ||
| | ----| ---- | ---- | -----| | ||
| | subspace | `Subspace` | The subspace of the data subject | `[{ dimension: 'Year', value: '2000' }]`(subspace: Year = 2000) | | ||
| | dimensions | `string[]` | The dimensions of the data subject | `['country']` | | ||
| | measures | `Measure[]` | The measures of the data subject | `[{ field: 'life_expect', method: 'MEAN' }]` | | ||
| | data | `Datum[]` | data | `[{ country: 'China', life_expect: 61 }]` | | ||
| | patterns | `PatternInfo[]` | The collection of patterns in the data | `[{ type: 'outlier', significance: 0.98, dimension: 'country', measure: 'life_expect', index: 5, x: 'china', y: '43' }, ...]` | | ||
| | visualizationSpecs | `InsightVisualizationSpec[]` | The insight visualization scheme, including chart type, title, insight description, and chart configuration (based on G2Spec) | `[{ type: 'column_chart', caption: string, narrativeSpec: string[] \| IPhrase[][], chartSpec: G2Spec }]` | | ||
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| ### Usage | ||
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| * Extract time series outlier insights from the data. | ||
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| ```ts | ||
| import { getSpecificInsight } from '@antv/ava'; | ||
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| getSpecificInsight({ | ||
| data, | ||
| measures: [{ fieldName: 'life_expect', method: 'MEAN' }], | ||
| dimensions: [{ fieldName: 'date' }], | ||
| insightType: 'time_series_outlier', | ||
| }); | ||
| ``` | ||
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| * Custom algorithm parameters | ||
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| ```ts | ||
| import { getSpecificInsight } from '@antv/ava'; | ||
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| const insightResult = getSpecificInsight({ | ||
| data, | ||
| measures: [{ fieldName: 'life_expect', method: 'MEAN' }], | ||
| dimensions: [{ fieldName: 'date' }], | ||
| insightType: 'time_series_outlier', | ||
| options: { | ||
| // Filter out not significant insights | ||
| filterInsight: true, | ||
| // Verify whether the input meets the algorithm requirements | ||
| dataValidation: true, | ||
| // Adjust the significance test threshold | ||
| algorithmParameter: { | ||
| outlier: { | ||
| threshold: 0.05, | ||
| }, | ||
| }, | ||
| } | ||
| }); | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,66 @@ | ||
| --- | ||
| title: getSpecificInsight | ||
| order: 3 | ||
| --- | ||
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| <embed src='@/docs/common/style.md'></embed> | ||
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| 从数据中提取的指定类型的洞察和对应的可视化spec。 | ||
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| ## **getSpecificInsight** | ||
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| <i>(props: SpecificInsightProps): SpecificInsightResult</i> | ||
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| * ***SpecificInsightProps*** 和`InsightExtractorProps`相同，详见 [InsightExtractorProps API](./insight-patterns-extractor.zh.md)。 | ||
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| * ***SpecificInsightResult*** 输出内容 | ||
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| | 属性 | 类型 | 描述 | 示例 | | ||
| | ----| ---- | ---- | -----| | ||
| | subspace | `Subspace` | 该洞察数据主体的子空间信息（Subspace）。 | `[{ dimension: 'Year', value: '2000' }]`(子空间为 Year = 2000) | | ||
| | dimensions | `string[]` | 该洞察数据主体的维度, 并且作为下一轮子空间划分的分组维度。 | `[fieldName: 'country']` | | ||
| | measures | `Measure[]` | 该洞察数据主体的计算指标。 | `[{ field: 'life_expect', method: 'MEAN' }]` | | ||
| | data | `Datum[]` | 该洞察数据主体的相关数据。 | `[{ country: 'China', life_expect: 61 }]` | | ||
| | patterns | `PatternInfo[]` | 该洞察数据主体上的模式集合 | `[{ type: 'outlier', significance: 0.98, dimension: 'country', measure: 'life_expect', index: 5, x: 'china', y: '43' }, ...]` | | ||
| | visualizationSpecs | `visualizationSpec[]` | 该洞察的可视化方案，包含图表类型、标题、洞察描述以及图表配置（基于G2Plot） | `[{ type: 'column_chart', caption: string, narrativeSpec: string[] \| IPhrase[][], chartSpec: G2PlotConfig }]` | | ||
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| ### 用例 | ||
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| * 指定从数据中提取时序异常类型的洞察结果。 | ||
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| ```ts | ||
| import { getSpecificInsight } from '@antv/ava'; | ||
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| getSpecificInsight({ | ||
| data, | ||
| measures: [{ fieldName: 'life_expect', method: 'MEAN' }], | ||
| dimensions: [{ fieldName: 'date' }], | ||
| insightType: 'time_series_outlier', | ||
| }); | ||
| ``` | ||
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| * 自定义算法参数 | ||
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| ```ts | ||
| import { getSpecificInsight } from '@antv/ava'; | ||
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| const insightResult = getSpecificInsight({ | ||
| data, | ||
| measures: [{ fieldName: 'life_expect', method: 'MEAN' }], | ||
| dimensions: [{ fieldName: 'date' }], | ||
| insightType: 'time_series_outlier', | ||
| options: { | ||
| // 筛选显著的洞察结果 | ||
| filterInsight: true, | ||
| // 进行数据校验 | ||
| dataValidation: true, | ||
| // 调整显著性检验的相关参数 | ||
| algorithmParameter: { | ||
| outlier: { | ||
| method: 'IQR', | ||
| iqrK: 1.5, | ||
| }, | ||
| }, | ||
| } | ||
| }); | ||
| ``` |
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