fix: 自动机器学习添加算法描述

react-ui/src/pages/AutoML/components/AutoMLBasic/index.tsx

@@ -7,10 +7,21 @@ import {
   autoMLTaskTypeOptions,
 } from '@/enums';
 import { useComputingResource } from '@/hooks/useComputingResource';
+import {
+  classificationAlgorithms,
+  featureAlgorithms,
+  regressorAlgorithms,
+} from '@/pages/AutoML/components/CreateForm/utils';
 import { AutoMLData } from '@/pages/AutoML/types';
 import { type NodeStatus } from '@/types';
 import { parseJsonText } from '@/utils';
-import { formatBoolean, formatDataset, formatDate, formatEnum } from '@/utils/format';
+import {
+  formatBoolean,
+  formatDataset,
+  formatDate,
+  formatEnum,
+  type EnumOptions,
+} from '@/utils/format';
 import classNames from 'classnames';
 import { useMemo } from 'react';
 import ExperimentRunBasic from '../ExperimentRunBasic';
@@ -21,6 +32,7 @@ const formatOptimizeMode = (value: boolean) => {
   return value ? '越大越好' : '越小越好';
 };
 
+// 格式化权重
 const formatMetricsWeight = (value: string) => {
   if (!value) {
     return '--';
@@ -34,6 +46,20 @@ const formatMetricsWeight = (value: string) => {
     .join('\n');
 };
 
+// 格式化算法
+const formatAlgorithm = (algorithms: EnumOptions[]) => {
+  return (value: string) => {
+    if (!value) {
+      return '--';
+    }
+    const list = value
+      .split(',')
+      .filter((v) => v !== '')
+      .map((v) => v.trim());
+    return list.map((v) => formatEnum(algorithms)(v)).join('，');
+  };
+};
+
 type AutoMLBasicProps = {
   info?: AutoMLData;
   className?: string;
@@ -96,10 +122,12 @@ function AutoMLBasic({
         {
           label: '特征预处理算法',
           value: info.include_feature_preprocessor,
+          format: formatAlgorithm(featureAlgorithms),
         },
         {
           label: '排除的特征预处理算法',
           value: info.exclude_feature_preprocessor,
+          format: formatAlgorithm(featureAlgorithms),
         },
         {
           label: info.task_type === AutoMLTaskType.Regression ? '回归算法' : '分类算法',
@@ -107,6 +135,11 @@ function AutoMLBasic({
             info.task_type === AutoMLTaskType.Regression
               ? info.include_regressor
               : info.include_classifier,
+          format: formatAlgorithm(
+            info.task_type === AutoMLTaskType.Regression
+              ? regressorAlgorithms
+              : classificationAlgorithms,
+          ),
         },
         {
           label: info.task_type === AutoMLTaskType.Regression ? '排除的回归算法' : '排除的分类算法',
@@ -114,6 +147,11 @@ function AutoMLBasic({
             info.task_type === AutoMLTaskType.Regression
               ? info.exclude_regressor
               : info.exclude_classifier,
+          format: formatAlgorithm(
+            info.task_type === AutoMLTaskType.Regression
+              ? regressorAlgorithms
+              : classificationAlgorithms,
+          ),
         },
         {
           label: '集成方式',

react-ui/src/pages/AutoML/components/CreateForm/ExecuteConfig.tsx

@@ -8,70 +8,7 @@ import {
   autoMLTaskTypeOptions,
 } from '@/enums';
 import { Col, Form, InputNumber, Radio, Row, Select, Switch } from 'antd';
-
-// 分类算法
-const classificationAlgorithms = [
-  'adaboost',
-  'bernoulli_nb',
-  'decision_tree',
-  'extra_trees',
-  'gaussian_nb',
-  'gradient_boosting',
-  'k_nearest_neighbors',
-  'lda',
-  'liblinear_svc',
-  'libsvm_svc',
-  'tablenet',
-  'mlp',
-  'multinomial_nb',
-  'passive_aggressive',
-  'qda',
-  'random_forest',
-  'sgd',
-  'LightGBMClassification',
-  'XGBoostClassification',
-  'StackingClassification',
-].map((name) => ({ label: name, value: name }));
-
-// 回归算法
-const regressorAlgorithms = [
-  'adaboost',
-  'ard_regression',
-  'decision_tree',
-  'extra_trees',
-  'gaussian_process',
-  'gradient_boosting',
-  'k_nearest_neighbors',
-  'liblinear_svr',
-  'libsvm_svr',
-  'mlp',
-  'random_forest',
-  'sgd',
-  'LightGBMRegression',
-  'XGBoostRegression',
-].map((name) => ({ label: name, value: name }));
-
-// 特征预处理算法
-const featureAlgorithms = [
-  'densifier',
-  'extra_trees_preproc_for_classification',
-  'extra_trees_preproc_for_regression',
-  'fast_ica',
-  'feature_agglomeration',
-  'kernel_pca',
-  'kitchen_sinks',
-  'liblinear_svc_preprocessor',
-  'no_preprocessing',
-  'nystroem_sampler',
-  'pca',
-  'polynomial',
-  'random_trees_embedding',
-  'select_percentile_classification',
-  'select_percentile_regression',
-  'select_rates_classification',
-  'select_rates_regression',
-  'truncatedSVD',
-].map((name) => ({ label: name, value: name }));
+import { classificationAlgorithms, featureAlgorithms, regressorAlgorithms } from './utils';
 
 // 分类指标
 export const classificationMetrics = [

react-ui/src/pages/AutoML/components/CreateForm/utils.ts

@@ -0,0 +1,78 @@
+// 分类算法
+export const classificationAlgorithms = [
+  { label: 'adaboost (自适应提升算法)', value: 'adaboost' },
+  { label: 'bernoulli_nb (伯努利朴素贝叶斯)', value: 'bernoulli_nb' },
+  { label: 'decision_tree (决策树)', value: 'decision_tree' },
+  { label: 'extra_trees (极端随机树)', value: 'extra_trees' },
+  { label: 'gaussian_nb (高斯朴素贝叶斯)', value: 'gaussian_nb' },
+  { label: 'gradient_boosting (梯度提升)', value: 'gradient_boosting' },
+  { label: 'k_nearest_neighbors (k近邻)', value: 'k_nearest_neighbors' },
+  { label: 'lda (线性判别分析)', value: 'lda' },
+  { label: 'liblinear_svc (liblinear支持向量分类)', value: 'liblinear_svc' },
+  { label: 'libsvm_svc (libsvm支持向量分类)', value: 'libsvm_svc' },
+  { label: 'mlp (多层感知器)', value: 'mlp' },
+  { label: 'multinomial_nb (多项式朴素贝叶斯)', value: 'multinomial_nb' },
+  { label: 'passive_aggressive (被动攻击算法)', value: 'passive_aggressive' },
+  { label: 'qda (二次判别式分析)', value: 'qda' },
+  { label: 'random_forest (随机森林)', value: 'random_forest' },
+  { label: 'sgd (随机梯度下降)', value: 'sgd' },
+  { label: 'tablenet (表格网络)', value: 'tablenet' },
+  { label: 'LightGBMClassification (轻量梯度提升机分类)', value: 'LightGBMClassification' },
+  { label: 'XGBoostClassification (极端梯度提升机分类)', value: 'XGBoostClassification' },
+  { label: 'StackingClassification (堆叠泛化)', value: 'StackingClassification' },
+];
+
+// 回归算法
+export const regressorAlgorithms = [
+  { label: 'adaboost (自适应提升算法)', value: 'adaboost' },
+  { label: 'ard_regression (自动相关性确定回归)', value: 'ard_regression' },
+  { label: 'decision_tree (决策树)', value: 'decision_tree' },
+  { label: 'extra_trees (极端随机树)', value: 'extra_trees' },
+  { label: 'gaussian_process (高斯过程回归)', value: 'gaussian_process' },
+  { label: 'gradient_boosting (梯度提升)', value: 'gradient_boosting' },
+  { label: 'k_nearest_neighbors (梯度提升)', value: 'k_nearest_neighbors' },
+  { label: 'liblinear_svr (liblinear支持向量回归)', value: 'liblinear_svr' },
+  { label: 'libsvm_svr (libsvm支持向量回归)', value: 'libsvm_svr' },
+  { label: 'mlp (多层感知器)', value: 'mlp' },
+  { label: 'random_forest (随机森林)', value: 'random_forest' },
+  { label: 'sgd (随机梯度下降)', value: 'sgd' },
+  { label: 'LightGBMRegression (轻量梯度提升机回归)', value: 'LightGBMRegression' },
+  { label: 'XGBoostRegression (极端梯度提升机回归)', value: 'XGBoostRegression' },
+];
+
+// 特征预处理算法
+export const featureAlgorithms = [
+  { label: 'densifier (数据增稠)', value: 'densifier' },
+  {
+    label: 'extra_trees_preproc_for_classification (分类任务极端随机树)',
+    value: 'extra_trees_preproc_for_classification',
+  },
+  {
+    label: 'extra_trees_preproc_for_regression (回归任务极端随机树)',
+    value: 'extra_trees_preproc_for_regression',
+  },
+  { label: 'fast_ica (快速独立成分分析)', value: 'fast_ica' },
+  { label: 'feature_agglomeration (特征聚合)', value: 'feature_agglomeration' },
+  { label: 'kernel_pca (核主成分分析)', value: 'kernel_pca' },
+  { label: 'kitchen_sinks (随机特征映射)', value: 'kitchen_sinks' },
+  { label: 'liblinear_svc_preprocessor (线性svc预处理器)', value: 'liblinear_svc_preprocessor' },
+  { label: 'no_preprocessing (无预处理)', value: 'no_preprocessing' },
+  { label: 'nystroem_sampler (尼斯特罗姆采样器)', value: 'nystroem_sampler' },
+  { label: 'pca (主成分分析)', value: 'pca' },
+  { label: 'polynomial (多项式特征扩展)', value: 'polynomial' },
+  { label: 'random_trees_embedding (随机森林特征嵌入)', value: 'random_trees_embedding' },
+  {
+    label: 'select_percentile_classification (基于百分位的分类特征选择)',
+    value: 'select_percentile_classification',
+  },
+  {
+    label: 'select_percentile_regression (基于百分位的回归特征选择)',
+    value: 'select_percentile_regression',
+  },
+  {
+    label: 'select_rates_classification (基于比率的分类特征选择)',
+    value: 'select_rates_classification',
+  },
+  { label: 'select_rates_regression (基于比率的回归特征选择)', value: 'select_rates_regression' },
+  { label: 'truncatedSVD (截断奇异值分解)', value: 'truncatedSVD' },
+];