修改--修改文档
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@ -107,7 +107,7 @@ NASA Turbofan Engine Degradation Simulation数据集(通常称为C-MAPSS数据
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- 超过55%的变异性未被模型捕获,说明初始模型拟合效果不佳
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**参数优化**:
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使用GridSearchCV进行超参数调优,探索以下参数组合(共1296种组合):
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RandomForestRegressor使用GridSearchCV进行超参数调优,探索以下参数组合(共1296种组合):
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| 参数名称 | 可选值 |
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|----------|--------|
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@ -136,6 +136,13 @@ Request:
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"input_path": "dataset/dataset_raw/train_FD001.csv",
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"output_dir": "dataset/dataset_processed/",
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"process_methods": [
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{
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"method_name": "IsolationForest",
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"params": {
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"n_estimators": 100,
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"max_samples": "auto"
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}
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},
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{
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"method_name": "StandardScaler",
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"params": {
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@ -144,12 +151,10 @@ Request:
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}
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}
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],
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"feature_methods":[
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],
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"feature_methods": [],
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"split_params": {
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"train_size": 0.7,
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"val_size": 0.3
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"train_size": 0.8,
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"val_size": 0.2
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}
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}
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@ -467,14 +472,14 @@ Content-Type: application/json
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Request:
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{
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"train_path": "/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/train_FD001_20250407_174421/train_train_FD001_20250407_174421.csv",
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"val_path": "/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/train_FD001_20250407_174421/val_train_FD001_20250407_174421.csv",
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"algorithm": "RandomForestRegressor",
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"task_type": "regression",
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"train_path": "dataset/dataset_processed/tpb_20250409_174922/train_tpb_20250409_174922.csv",
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"val_path": "dataset/dataset_processed/tpb_20250409_174922/val_tpb_20250409_174922.csv",
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"algorithm": "MLPClassifier",
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"task_type": "classification",
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"parameters": {
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},
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"experiment_name": "test_FD001_1"
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"experiment_name": "test_tpb_1"
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}
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Response:
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@ -731,29 +736,52 @@ Content-Type: application/json
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Request:
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{
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run_id": "bd3697dc238c4d1587e0f4f319d04448",
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"data_path": "/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/train_FD001_20250409_105906",
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"output_dir": null,
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"experiment_name": "test_FDD001_1",
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"method": "GridSearchCV",
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"parameters": {
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"max_depth": [3, 5, 7],
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"n_estimators": [50, 100, 200],
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"min_samples_split": [2, 5, 10]
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"param_grid": { //一定要加参数网格
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"n_estimators": [
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50,
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100,
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200
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],
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"max_depth": [
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null,
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10,
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20,
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30
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]
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},
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"scoring": "r2" // 评价指标,不同任务选择不同指标.
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},
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"data_path": "dataset/dataset_processed/test_optimize/train.csv",
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"output_dir": None,
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"experiment_name": "测试模型优化方法"
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"run_id": "ef413c6fd25f4042a793d4c452af3e1b"
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}
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Response:
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{
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"status": "success",
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"optimization": {
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"status": "success",
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"message": "优化任务已执行",
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"run_id": "903ab190690e496aa74e6aaefac8a7cb", # 新的运行id
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"optimized_model_id": "bd3697dc238c4d1587e0f4f319d04448" # 被优化模型的运行id
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}
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"status": "success",
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"message": "优化任务已执行",
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"optimization": {
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"id": "opt_20250410_cbc1d9",
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"run_id": "2c9d4363e55b4530a6881633ade5f4e3",
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"experiment_id": "670583721104894053",
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"task_id": "opt_20250410_cbc1d9",
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"method": "GridSearchCV",
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"original_model": "regression",
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"best_params": { //最佳参数
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"max_depth": 10,
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"n_estimators": 200
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},
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"best_score": 0.4481105476031095,
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"cv_results_file": "optimized_models/opt_20250410_cbc1d9/cv_results.csv",
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"optimized_model_id": "ef413c6fd25f4042a793d4c452af3e1b",
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"execution_time": "0:01:51.399587",
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"status": "completed",
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"timestamp": "2025-04-10T10:20:09.709085"
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}
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}
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Error Response:
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