feat: integrate PluginStore and MinIO repository into model manager activities

- Added PluginStore integration for model management.
- Replaced StorageRepository with MinIORepository in Activities, Cleanup, and Training classes.
- Updated training logic to handle validation files and improved data management.
- Enhanced configuration for MinIO and PluginStore in connectors.
- Removed deprecated model repository and storage repository files.
- Updated environment variable handling for new configurations.

model_manager/utils/models/train_model_params.py

@@ -33,7 +33,8 @@ class TrainModelParams:
         use_scaler (bool): Whether to use a scaler for data normalization.
         include_ar (bool): Whether to include autoregressive variables.
         bucket_name (str): Name of the MinIO bucket containing training data.
-        file_name (str): Name of the file in the MinIO bucket.
+        file_name (str): Name of the training file in the MinIO bucket.
+        validation_file_name (str | None): Optional name of the validation file in the same MinIO bucket as the training file.
         line_separator (str): Line separator used in the CSV file.
         decimal_separator (str): Decimal separator used in the CSV file.
         date_column (str | None): Name of the date/time column. If set with date_format, the column is parsed as datetime.
@@ -66,6 +67,7 @@ class TrainModelParams:
     include_ar: bool
     bucket_name: str
     file_name: str
+    validation_file_name: str | None
     line_separator: str
     decimal_separator: str
     date_column: str | None
@@ -122,6 +124,9 @@ class TrainModelParams:
             include_ar=cls._check_none(data.get('include_ar'), bool, 'include_ar'),
             bucket_name=cls._check_none(data.get('bucket_name'), str, 'bucket_name'),
             file_name=cls._check_none(data.get('file_name'), str, 'file_name'),
+            validation_file_name=cls._check_type(
+                data.get('validation_file_name'), str, 'validation_file_name'
+            ),
             line_separator=cls._check_none(data.get('line_separator'), str, 'line_separator'),
             decimal_separator=cls._check_none(
                 data.get('decimal_separator'), str, 'decimal_separator'
@@ -220,6 +225,7 @@ class TrainModelParams:
         self._validate_limits()
         self._validate_required_strings()
         self._validate_date_format()
+        self._validate_validation_file_name()
 
     def _validate_numeric_ranges(self) -> None:
         """Validate numeric parameters are within acceptable ranges."""
@@ -335,3 +341,21 @@ class TrainModelParams:
         """Validate date_format is one of the allowed frontend formats when set."""
         if self.date_format:
             validate_frontend_date_format(self.date_format)
+
+    def _validate_validation_file_name(self) -> None:
+        """
+        Validate that validation_file_name, when provided, is not empty or whitespace.
+
+        This field is optional; when present it must point to a valid object key in the
+        same MinIO bucket specified by bucket_name.
+        """
+        if self.validation_file_name is None:
+            return
+
+        if not isinstance(self.validation_file_name, str):
+            raise TypeError(
+                f'validation_file_name must be a string, got {type(self.validation_file_name).__name__}'
+            )
+
+        if not self.validation_file_name.strip():
+            raise ValueError('validation_file_name cannot be empty or whitespace')

model_manager/utils/models/train_model_result.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 
 import pandas as pd
 
-from model_manager.sientia.models import DataPreprocessor, LinearRegressionModel
 from model_manager.utils.models.train_model_params import TrainModelParams
 
 
@@ -12,18 +11,15 @@ class TrainModelResult:
     A data container for storing the results of a machine learning training process.
 
     This dataclass encapsulates all outputs from the training pipeline, including
-    the trained model, datasets, evaluation metrics, and paths to generated artifacts.
+    the prepared datasets, evaluation metrics, and paths to generated artifacts.
     It is used to pass results between activities in the training workflow.
 
     Attributes:
         params (TrainModelParams): The parameters used to train the model.
-        process_data (DataPreprocessor): The data preprocessor object used to process the input data.
         x_train (pd.DataFrame): The training dataset features.
         x_test (pd.DataFrame): The testing dataset features.
         y_train (pd.DataFrame): The training dataset target values.
         y_test (pd.DataFrame): The testing dataset target values.
-        regr (LinearRegressionModel): The trained linear regression model.
-        scaler_dict (dict): A dictionary containing the scalers used to scale the features and target values.
         y_pred (pd.Series | None): The predicted target values for the testing dataset. Default is None.
         y_train_pred (pd.Series | None): The predicted target values for the training dataset. Default is None.
         mse_val (float | None): The Mean Squared Error (MSE) of the predictions. Default is None.
@@ -35,17 +31,13 @@ class TrainModelResult:
         report_path (str | None): The path to the generated HTML report file. Default is None.
         train_data_path (str | None): The path to the training dataset CSV file. Default is None.
         test_data_path (str | None): The path to the testing dataset CSV file. Default is None.
-        run_dir (str | None): The path to the run directory containing all artifacts. Default is None.
     """
 
     params: TrainModelParams
-    process_data: DataPreprocessor
     x_train: pd.DataFrame
     x_test: pd.DataFrame
     y_train: pd.Series
     y_test: pd.Series
-    regr: LinearRegressionModel
-    scaler_dict: dict
     y_pred: pd.Series | None = None
     y_train_pred: pd.Series | None = None
     mse_val: float | None = None
@@ -57,4 +49,3 @@ class TrainModelResult:
     report_path: str | None = None
     train_data_path: str | None = None
     test_data_path: str | None = None
-    run_dir: str | None = None