修改--修改文档20250226

2025-02-26 11:07:23 +08:00 · 2025-02-26 11:07:23 +08:00 · 57d486b0c9
commit 57d486b0c9
parent 872e8dc9fe
37 changed files with 786 additions and 33 deletions
--- a/api/pycache/data_api.cpython-39.pyc
+++ b/api/pycache/data_api.cpython-39.pyc
--- a/dataset/dataset_processed/breast_cancer_20250226_103609/process_record__breast_cancer_20250226_103609.json
+++ b/dataset/dataset_processed/breast_cancer_20250226_103609/process_record__breast_cancer_20250226_103609.json
@ -0,0 +1,81 @@
 {
  "input_file": "dataset/dataset_raw/breast_cancer.csv",
  "timestamp": "2025-02-26T10:36:09.030139",
  "process_methods": [
    {
      "method_name": "SimpleImputer",
      "params": {
        "strategy": "mean",
        "missing_values": NaN
      }
    },
    {
      "method_name": "IsolationForest",
      "params": {
        "contamination": 0.1,
        "random_state": 42
      }
    },
    {
      "method_name": "StandardScaler",
      "params": {
        "with_mean": true,
        "with_std": true
      }
    }
  ],
  "feature_methods": [],
  "split_params": {
    "val_size": 0.3
  },
  "steps": [
    {
      "step": "load_data",
      "shape": [
        569,
        31
      ]
    },
    {
      "step": "cleaning",
      "method": "SimpleImputer",
      "params": {
        "strategy": "mean",
        "missing_values": NaN
      },
      "shape": [
        569,
        31
      ]
    },
    {
      "step": "cleaning",
      "method": "IsolationForest",
      "params": {
        "contamination": 0.1,
        "random_state": 42
      },
      "shape": [
        512,
        31
      ]
    },
    {
      "step": "cleaning",
      "method": "StandardScaler",
      "params": {
        "with_mean": true,
        "with_std": true
      },
      "shape": [
        512,
        31
      ]
    }
  ],
  "output_files": {
    "train": "dataset/dataset_processed/breast_cancer_20250226_103609/train_breast_cancer_20250226_103609.csv",
    "validation": "dataset/dataset_processed/breast_cancer_20250226_103609/val_breast_cancer_20250226_103609.csv",
    "test": ""
  }
 }
--- a/dataset/dataset_processed/breast_cancer_20250226_103609/train_breast_cancer_20250226_103609.csv
+++ b/dataset/dataset_processed/breast_cancer_20250226_103609/train_breast_cancer_20250226_103609.csv
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--- a/dataset/dataset_processed/breast_cancer_20250226_103609/val_breast_cancer_20250226_103609.csv
+++ b/dataset/dataset_processed/breast_cancer_20250226_103609/val_breast_cancer_20250226_103609.csv
@ -0,0 +1,155 @@
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 2.090672079339843,0.14854716283589428,2.0049145134132,2.2944769026668035,-1.1597926838383135,-0.24098932358844624,0.6608602517269285,1.0367109040811036,0.5771137746726085,-1.9491783881853895,1.1153518071010613,-0.9849600955935893,0.9343426120651895,1.490353156981809,-0.8978609358829549,-0.6706671367429933,-0.045899003893868764,0.2935541295692348,0.3239594744805103,-1.0900931250510844,1.7251844661128768,-0.39813258271827173,1.6190673211170337,1.8108443098769713,-1.2079347234569784,-0.6237842419329391,0.09144197917343362,0.5903363419504781,0.3352930577154924,-1.501419541190821,0
--- a/doc/安装文档.md
+++ b/doc/安装文档.md
@ -2,28 +2,40 @@
 ## 1. 启动mlflow
    - mlflow server --host 10.0.0.202 --port 5000
    - 这里host要和config/config.yaml中配置的host一致, 不然连接不上
 ## 2. 运行 main.py
    - python main.py
    - 运行ip和端口在config/config.yaml中配置
    - 当前python环境名为trt
    - 在ip:port/docs可以看到可视化接口文档
 ## 3. 安装python环境
    - 使用conda创建一个新的python环境
    - pip 换源 清华源
        -- pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
    - pip install -r requirements.txt
-## 3. 测试接口
+
 ## 4. 测试接口
 注: 10.0.0.202为当前本机ip
 GET http://10.0.0.202:8992/health
-### 3.1 获取数据预处理方法列表
+### 4.1 获取数据预处理方法列表
 GET http://10.0.0.202:8992/data/preprocessing/methods
-### 3.2 获取预处理方法详情
+### 4.2 获取预处理方法详情
 GET http://10.0.0.202:8992/data/preprocessing/method/{method_name}
-### 3.3 获取特征工程方法列表
+### 4.3 获取特征工程方法列表
 GET http://10.0.0.202:8992/data/feature/methods
-### 3.4 获取特征工程方法详情
+### 4.4 获取特征工程方法详情
 GET http://10.0.0.202:8992/data/feature/method/{method_name}
-### 3.5 处理数据集
+### 4.5 处理数据集
 POST http://10.0.0.202:8992/data/process
 传递参数
@ -48,19 +60,19 @@ POST http://10.0.0.202:8992/data/process
        }
    }
-### 3.6 获取可用数据集列表
+### 4.6 获取可用数据集列表
 GET http://10.0.0.202:8992/data/datasets
-### 3.7 获取可用模型列表
+### 4.7 获取可用模型列表
 GET http://10.0.0.202:8992/model/available
-### 3.8 获取模型详情
+### 4.8 获取模型详情
 GET http://10.0.0.202:8992/model/available/{model_name}
-### 3.9 获取评价指标列表
+### 4.9 获取评价指标列表
 GET http://10.0.0.202:8992/model/metrics
-### 3.10 模型训练
+### 4.10 模型训练
 POST http://10.0.0.202:8992/model/train
 {
@ -77,16 +89,16 @@ POST http://10.0.0.202:8992/model/train
  "experiment_name": "test_post_1"
 }
-### 3.11 获取实验列表
+### 4.11 获取实验列表
 GET http://10.0.0.202:8992/model/experiments
-### 3.12 获取具体实验内容
+### 4.12 获取具体实验内容
 GET http://10.0.0.202:8992/model/experiment/{experiment_name}
-### 3.13 删除模型指定实验模型
+### 4.13 删除模型指定实验模型
 DELETE 
-### 3.14 模型预测
+### 4.14 模型预测
 POST http://10.0.0.202:8992/model/predict
    {
    "run_id": "33939ea6d8ce4d43a268f23f7361651e",
@ -98,11 +110,14 @@ POST http://10.0.0.202:8992/model/predict
    ]
    }
-### 3.15 查看系统资源使用情况
+### 4.15 查看系统资源使用情况
 GET http://10.0.0.202:8992/system/resources
-### 3.16 查看训练历史
+### 4.16 查看训练历史
 GET http://10.0.0.202:8992/system/history
-### 3.17 查看系统训练日志
+### 4.17 查看系统训练日志
-GET http://10.0.0.202:8992/system/logs
+GET http://10.0.0.202:8992/system/logs
 ## 5. 前端界面
    - 暂未完成
--- a/doc/接口文档._旧版md
+++ b/doc/接口文档._旧版md
--- a/doc/接口文档code.md
+++ b/doc/接口文档code.md
@ -135,15 +135,15 @@ Request:
 {
    "input_path": "dataset/dataset_raw/data.csv",
    "output_dir": "dataset/dataset_processed/",
-    "preprocessing": [
+    "process_methods": [
        {
-            "method": "missing_value_handler",
+            "method_name": "missing_value_handler",
            "params": {
                "strategy": "mean"
            }
        },
        {
-            "method": "standard_scaler",
+            "method_name": "standard_scaler",
            "params": {
                "with_mean": true,
                "with_std": true
@ -152,12 +152,13 @@ Request:
    ],
  "feature_methods":[
        {
-          "method" : "KBinsDiscretizer"
+          "method_name" : "KBinsDiscretizer"
          "params":{
              "n_bins": 5,
              "encode": onehot,
              ...
          }
          "columns": ["feature1", "feature2"]
        }
    ],
    "split_params": {
@ -481,17 +482,17 @@ Content-Type: application/json
 Request:
 {
-    "model": "xgboost",
+  "train_path": "/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/train_breast_cancer_20250224_170615.csv",
-    "dataset": {
+  "val_path": "/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/val_breast_cancer_20250224_170615.csv",
-        "train": "dataset/dataset_processed/train.csv",
+  "algorithm": "XGBClassifier",
-        "val": "dataset/dataset_processed/val.csv",
+  "task_type": "classification",
-        "test": "dataset/dataset_processed/test.csv"
+  "parameters": {
-    },
+        "n_estimators": 100,
-    "parameters": {
+        "learning_rate": 0.1,
        "max_depth": 6,
-        "learning_rate": 0.1
+        "random_state": 42
-    },
+  },
-    "metrics": ["accuracy", "f1"]
+  "experiment_name": "test_post_1"
 }
 Response:
--- a/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/MLmodel
+++ b/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/MLmodel
@ -0,0 +1,20 @@
 artifact_path: model
 flavors:
  python_function:
    env:
      conda: conda.yaml
      virtualenv: python_env.yaml
    loader_module: mlflow.sklearn
    model_path: model.pkl
    predict_fn: predict
    python_version: 3.9.19
  sklearn:
    code: null
    pickled_model: model.pkl
    serialization_format: cloudpickle
    sklearn_version: 1.5.2
 mlflow_version: 2.20.1
 model_size_bytes: 106353
 model_uuid: 48424fc5417d4a8699a02d0b5f2490d9
 run_id: e5648871c15c48d98466f1ceb942d882
 utc_time_created: '2025-02-26 02:36:31.331022'
--- a/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/conda.yaml
+++ b/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/conda.yaml
@ -0,0 +1,15 @@
 channels:
 - conda-forge
 dependencies:
 - python=3.9.19
 - pip<=24.0
 - pip:
  - mlflow==2.20.1
  - cloudpickle==3.1.0
  - numpy==1.26.4
  - pandas==2.2.2
  - psutil==6.0.0
  - scikit-learn==1.5.2
  - scipy==1.13.1
  - xgboost==2.1.4
 name: mlflow-env
--- a/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/model.pkl
+++ b/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/model.pkl
--- a/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/python_env.yaml
+++ b/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/python_env.yaml
@ -0,0 +1,7 @@
 python: 3.9.19
 build_dependencies:
 - pip==24.0
 - setuptools==60.2.0
 - wheel==0.43.0
 dependencies:
 - -r requirements.txt
--- a/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/requirements.txt
+++ b/mlartifacts/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts/model/requirements.txt
@ -0,0 +1,8 @@
 mlflow==2.20.1
 cloudpickle==3.1.0
 numpy==1.26.4
 pandas==2.2.2
 psutil==6.0.0
 scikit-learn==1.5.2
 scipy==1.13.1
 xgboost==2.1.4
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/meta.yaml
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/meta.yaml
@ -0,0 +1,15 @@
 artifact_uri: mlflow-artifacts:/433321862082712659/e5648871c15c48d98466f1ceb942d882/artifacts
 end_time: 1740537394182
 entry_point_name: ''
 experiment_id: '433321862082712659'
 lifecycle_stage: active
 run_id: e5648871c15c48d98466f1ceb942d882
 run_name: merciful-stoat-306
 run_uuid: e5648871c15c48d98466f1ceb942d882
 source_name: ''
 source_type: 4
 source_version: ''
 start_time: 1740537390889
 status: 3
 tags: []
 user_id: admin-root
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/accuracy
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/accuracy
@ -0,0 +1 @@
 1740537391309 0.9902912621359223 0
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/f1
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/f1
@ -0,0 +1 @@
 1740537391323 0.990328791886068 0
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/precision
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/precision
@ -0,0 +1 @@
 1740537391315 0.9905768132495717 0
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/recall
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/recall
@ -0,0 +1 @@
 1740537391319 0.9902912621359223 0
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/roc_auc
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/metrics/roc_auc
@ -0,0 +1 @@
 1740537391327 0.9928571428571429 0
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/advantages
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/advantages
@ -0,0 +1 @@
 ['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/algorithm
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/algorithm
@ -0,0 +1 @@
 XGBClassifier
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/dataset_train
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/dataset_train
@ -0,0 +1 @@
 /home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/train_breast_cancer_20250224_170615.csv
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/dataset_val
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/dataset_val
@ -0,0 +1 @@
 /home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/val_breast_cancer_20250224_170615.csv
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/disadvantages
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/disadvantages
@ -0,0 +1 @@
 ['参数较多，调优较复杂。']
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/learning_rate
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/learning_rate
@ -0,0 +1 @@
 0.1
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/max_depth
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/max_depth
@ -0,0 +1 @@
 6
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/n_estimators
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/n_estimators
@ -0,0 +1 @@
 100
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/principle
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/principle
@ -0,0 +1 @@
 XGBoost（Extreme Gradient Boosting）是一种基于梯度提升树（GBDT）的改进算法，具有更强的正则化和并行处理能力。
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/random_state
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/random_state
@ -0,0 +1 @@
 42
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/task_type
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/params/task_type
@ -0,0 +1 @@
 classification
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.log-model.history
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.log-model.history
@ -0,0 +1 @@
 [{"run_id": "e5648871c15c48d98466f1ceb942d882", "artifact_path": "model", "utc_time_created": "2025-02-26 02:36:31.331022", "model_uuid": "48424fc5417d4a8699a02d0b5f2490d9", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.5.2", "serialization_format": "cloudpickle", "code": null}}}]
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.runName
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.runName
@ -0,0 +1 @@
 merciful-stoat-306
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.git.commit
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.git.commit
@ -0,0 +1 @@
 9fd425173c3b33d9f309215502efb49cf03b5b82
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.name
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.name
@ -0,0 +1 @@
 /home/admin-root/haotian/MLPlatform/example_model_manager.py
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.type
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.source.type
@ -0,0 +1 @@
 LOCAL
--- a/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.user
+++ b/mlruns/433321862082712659/e5648871c15c48d98466f1ceb942d882/tags/mlflow.user
@ -0,0 +1 @@
 admin-root
--- a/readme.md
+++ b/readme.md
@ -0,0 +1,39 @@
 # 机器学习平台
 ## 1. 目录结构
    MLPlatform/
    ├── api/                    # API接口层
    │   ├── __init__.py
    │   ├── data_api.py        # 数据处理相关接口
    │   ├── model_api.py       # 模型相关接口
    │   └── system_api.py      # 系统监控相关接口
    ├── function/              # 功能实现层
    │   ├── data_manager.py  # 数据处理类
    │   ├── model_manager.py   # 模型管理类
    │   ├── system_monitor.py  # 系统监控类
    │   └── utils/            # 工具函数
    ├── config/               # 配置文件
    │   └── config.yaml      # 系统配置
    ├── dataset/             # 数据集
    │   ├── dataset_raw/     # 原始数据
    │   └── dataset_processed/ # 处理后数据
    ├── .log/                # 日志文件
    ├── doc/                 # 文档
    └── main.py             # 主程序入口
    - 具体接口详见doc/接口文档code.md
 ## 2. 项目功能
    - 数据管理
        - 数据处理
        - 特征工程
        - 方法介绍
    - 模型管理
        - 模型介绍
        - 模型训练
        - 模型预测
        - 模型评估
    - 系统管理
        - 资源监控
        - 日志管理
        - 配置管理
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,16 @@
 catboost==1.2.7
 fastapi==0.115.8
 GPUtil==1.4.0
 lightgbm==4.6.0
 mlflow==2.20.2
 numpy==2.2.3
 pandas==2.2.3
 psutil==7.0.0
 pydantic==2.10.6
 pytest==8.3.4
 PyYAML==6.0.2
 scikit_learn==1.6.1
 scipy==1.15.2
 torch==2.6.0
 uvicorn==0.34.0
 xgboost==2.1.4
		`@ -0,0 +1 @@`
							`['计算效率高，支持并行计算。', '具有内置的缺失值处理能力。']`
		`@ -0,0 +1 @@`
							`/home/admin-root/haotian/MLPlatform/dataset/dataset_processed/breast_cancer_20250224_170615/train_breast_cancer_20250224_170615.csv`
		`@ -0,0 +1 @@`
							`XGBoost（Extreme Gradient Boosting）是一种基于梯度提升树（GBDT）的改进算法，具有更强的正则化和并行处理能力。`
		`@ -0,0 +1 @@`
							[{"run_id": "e5648871c15c48d98466f1ceb942d882", "artifact_path": "model", "utc_time_created": "2025-02-26 02:36:31.331022", "model_uuid": "48424fc5417d4a8699a02d0b5f2490d9", "flavors": {"python_function": {"model_path": "model.pkl", "predict_fn": "predict", "loader_module": "mlflow.sklearn", "python_version": "3.9.19", "env": {"conda": "conda.yaml", "virtualenv": "python_env.yaml"}}, "sklearn": {"pickled_model": "model.pkl", "sklearn_version": "1.5.2", "serialization_format": "cloudpickle", "code": null}}}]
		`@ -0,0 +1 @@`
							`/home/admin-root/haotian/MLPlatform/example_model_manager.py`