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/activities/training.py

@@ -283,17 +283,17 @@ class Training(SientiaMonitoring):
             self.info(f'Starting MLflow run for {train_params.model_type}', metadata)
             with self.mlflow_repository.start_run(
                 model_name=train_params.model_name,
-                run_name=None,
+                run_name=train_result.run_name,
-                experiment_name=f'{train_params.model_name}_experiment',
+                experiment_name=train_result.experiment_name,
                 tags=None,
                 metadata=metadata,
             ) as run_info:
-                train_result.run_name = run_info.run_name
                 train_result.run_id = run_info.run_id
                 self._persist_training_artifacts(train_result, train_params, wrapper, metadata)
 
             return {
                 'run_name': train_result.run_name,
+                'experiment_name': train_result.experiment_name,
                 'run_id': train_result.run_id,
                 'run_dir': train_result.run_dir,
             }

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):