修改--修改模型管理方法的返回参数

doc/接口文档code.md

@@ -395,6 +395,32 @@ Response:
 }
 ```
 ### 2.5 获取MLFlow中保存的实验
+```http
+GET /api/experiments
+
+Response:
+{
+    "status": "success",
+    "experiments": [
+        {
+            "experiment_id": "656341556838275234",
+            "name": "breast_cancer_classification_2",
+            "artifact_location": "mlruns/656341556838275234",
+            "lifecycle_stage": "active",
+            "creation_time": "2025-02-19T08:43:02",
+            "last_update_time": "2025-02-19T14:30:00",
+            "tags": {
+                "mlflow.note.content": "乳腺癌分类实验",
+                "mlflow.user": "admin"
+            },
+            "runs_count": 5
+        }
+    ],
+    "total_count": 1,
+    "page": 1,
+    "page_size": 10
+}
+```
 
 ### 2.6 获取已经训练好的模型列表
 ```http

example_model_manager.py

@@ -31,6 +31,9 @@ if result['status'] == 'success':
 
         print(f"训练开始时间: {model['training_start_time']}")
         print(f"训练结束时间: {model['training_end_time']}")
+        print("模型参数:")
+        for k, v in model['parameters'].items():
+            print(f" {k}: {v}")
         print("评估指标:")
         for metric_name, metric_value in model['metrics'].items():
             print(f"  {metric_name}: {metric_value:.4f}")

example_model_trainer.py

@@ -30,18 +30,19 @@ model_config = {
 
 # 训练模型,  删除训练实验时要删除 mlruns/.trash/  回收站里的文件
 # 模型文件 直接在 mlruns/文件夹下
-result = trainer.train_model(
+for i in range(8, 20):
-    {
+    result = trainer.train_model(
-        'features': X_train,
+        {
-        'labels': y_train
+            'features': X_train,
-    },
+            'labels': y_train
-    {
+        },
-        'features': X_val,
+        {
-        'labels': y_val
+            'features': X_val,
-    },
+            'labels': y_val
-    model_config,
+        },
-    'breast_cancer_classification_2'
+        model_config,
-)
+        f'breast_cancer_classification_{i}'
+    )
 
 # 打印结果
 print("\n训练结果:")

mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/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: 96534
+model_uuid: 3145221114ff4b51abdaf0ae7c28dd36
+run_id: 569fccd21c0e4c429752a87e0b6d725b
+utc_time_created: '2025-02-19 09:08:51.667593'

mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/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

mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/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

mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/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

mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/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: 96534
+model_uuid: ddfeb220cad04f56bc973e63c1681050
+run_id: 435ab004649d42ef8fa67b6f9b2a1263
+utc_time_created: '2025-02-19 09:10:38.183644'

mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/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

mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/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

mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/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

mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/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: 96534
+model_uuid: 34a25f14ca4a460a8c60c307dd39a7f6
+run_id: 7283286bf5464a298c33888867e2d576
+utc_time_created: '2025-02-19 09:10:27.607253'

mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/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

mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/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

mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/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

mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/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: 96534
+model_uuid: 77d5512c01184da8b52766ed8dc916b3
+run_id: bd3697dc238c4d1587e0f4f319d04448
+utc_time_created: '2025-02-19 09:10:22.302072'

mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/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

mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/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

mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/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

mlartifacts/231104280109669409/27ca4b5f64ab4809b82724be5e020e0e/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: 96534
+model_uuid: 778ac1ede03746c8a746af7a8ae3426a
+run_id: 27ca4b5f64ab4809b82724be5e020e0e
+utc_time_created: '2025-02-19 09:10:30.262090'

mlartifacts/231104280109669409/27ca4b5f64ab4809b82724be5e020e0e/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

mlartifacts/231104280109669409/27ca4b5f64ab4809b82724be5e020e0e/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

mlartifacts/231104280109669409/27ca4b5f64ab4809b82724be5e020e0e/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

mlartifacts/295184801263243463/c56783cefbc94584885b3001dae9629e/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: 96534
+model_uuid: 4c07de21f54a4115ade4e4f9e18d900c
+run_id: c56783cefbc94584885b3001dae9629e
+utc_time_created: '2025-02-19 09:10:17.082104'

mlartifacts/295184801263243463/c56783cefbc94584885b3001dae9629e/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

mlartifacts/295184801263243463/c56783cefbc94584885b3001dae9629e/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

mlartifacts/295184801263243463/c56783cefbc94584885b3001dae9629e/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

mlartifacts/350700170188734171/334a8f38e6c34b7cb67c74c9ca87a93d/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: 96534
+model_uuid: 6140bfbc853747dc9c2ba4ff689ef943
+run_id: 334a8f38e6c34b7cb67c74c9ca87a93d
+utc_time_created: '2025-02-19 09:10:11.226764'

mlartifacts/350700170188734171/334a8f38e6c34b7cb67c74c9ca87a93d/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

mlartifacts/350700170188734171/334a8f38e6c34b7cb67c74c9ca87a93d/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

mlartifacts/350700170188734171/334a8f38e6c34b7cb67c74c9ca87a93d/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

mlartifacts/351528204479233310/5d0fcd8c75854d5a8119c0a308434db3/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: 96534
+model_uuid: d7d38c744f724e748079a5c2213de58e
+run_id: 5d0fcd8c75854d5a8119c0a308434db3
+utc_time_created: '2025-02-19 09:10:14.532512'

mlartifacts/351528204479233310/5d0fcd8c75854d5a8119c0a308434db3/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

mlartifacts/351528204479233310/5d0fcd8c75854d5a8119c0a308434db3/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

mlartifacts/351528204479233310/5d0fcd8c75854d5a8119c0a308434db3/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

mlartifacts/433321862082712659/33939ea6d8ce4d43a268f23f7361651e/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: 96534
+model_uuid: 9623688d7e2849f7b9e885534acb08f8
+run_id: 33939ea6d8ce4d43a268f23f7361651e
+utc_time_created: '2025-02-19 09:07:33.554658'

mlartifacts/433321862082712659/33939ea6d8ce4d43a268f23f7361651e/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

mlartifacts/433321862082712659/33939ea6d8ce4d43a268f23f7361651e/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

mlartifacts/433321862082712659/33939ea6d8ce4d43a268f23f7361651e/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

mlartifacts/443109976989808567/a2172f74c8e54ab0a1ba1db0e5eebb19/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
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