feat: add experiment_name to TrainModelResult and update training logic

- Introduced experiment_name parameter in TrainModelResult to enhance tracking of training experiments.
- Updated the Training class to utilize run_name and experiment_name for improved MLflow run management.

model_manager/utils/models/train_model_result.py

@@ -44,6 +44,7 @@ class TrainModelResult:
     equation: dict | None = None
     equation_path: str | None = None
     run_name: str | None = None
+    experiment_name: str | None = None
     run_id: str | None = None
     report_path: str | None = None
     train_data_path: str | None = None

model_manager/utils/repository/data_manager_repository.py

@@ -20,6 +20,7 @@ from os import makedirs, path
 from shutil import rmtree
 from typing import Any
 
+from mlflow.entities import experiment
 import numpy as np
 import pandas as pd
 from sientia_do.observability.logger import Logger
@@ -200,9 +201,10 @@ class DataManagerRepository(SientiaMonitoring):
             metadata,
         )
 
-        run_name = f'train_model_{params.model_type}_{params.model_name}_{params.experiment_run_id}'
+        experiment_name = f'train_model_{params.model_type}_{params.model_name}_{params.experiment_run_id}'
+        run_name = f'{experiment_name}_{datetime.now().strftime("%Y%m%d_%H%M%S")}'
 
-        return TrainModelResult(params=params, train_data=train_data, val_data=val_data, run_name=run_name)
+        return TrainModelResult(params=params, train_data=train_data, val_data=val_data, run_name=run_name, experiment_name=experiment_name)
 
     def _as_series(self, pred: pd.DataFrame | pd.Series) -> pd.Series:
         if isinstance(pred, pd.Series):