feat: enhance configuration and error handling in project setup

- Added new ignore rule for Ruff to allow temporary paths in tests.
- Introduced MyPy overrides for specific modules to ignore errors.
- Refactored `Cleanup` and `ExperimentTracking` classes to remove async keywords from methods, improving consistency in method signatures.
- Updated `Training` class methods to handle synchronous operations, enhancing performance and clarity.
- Adjusted `requirements.txt` to remove unnecessary Git dependency, streamlining project setup.

model_manager/utils/models/train_model_params.py

@@ -62,8 +62,8 @@ class TrainModelParams:
 
     # New Parameters
     val_file_name: str | None
-    data_model_kwargs: dict | None # Removed params used in DataPreprocessor here
-    model_kwargs: dict | None # Removed params used in Linear Regression Model here
+    data_model_kwargs: dict | None  # Removed params used in DataPreprocessor here
+    model_kwargs: dict | None  # Removed params used in Linear Regression Model here
     opt_params: dict | None
     model_type: str
     model_id: str | None
@@ -102,12 +102,16 @@ class TrainModelParams:
         model_name = cls._check_none(data.get('model_name'), str, 'model_name')
 
         return cls(
-            variable_columns=cls._check_none(data.get('variable_columns'), list, 'variable_columns'),
+            variable_columns=cls._check_none(
+                data.get('variable_columns'), list, 'variable_columns'
+            ),
             target_variable=cls._check_none(data.get('target_variable'), str, 'target_variable'),
             bucket_name=cls._check_none(data.get('bucket_name'), str, 'bucket_name'),
             file_name=cls._check_none(data.get('file_name'), str, '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'),
+            decimal_separator=cls._check_none(
+                data.get('decimal_separator'), str, 'decimal_separator'
+            ),
             date_column=data.get('date_column'),
             date_format=data.get('date_format'),
             train_size=cls._check_none(data.get('train_size'), int, 'train_size'),
@@ -117,12 +121,13 @@ class TrainModelParams:
             model_name=model_name,
             experiment_name=model_name + '_experiment',
             val_file_name=data.get('val_file_name'),
-            data_model_kwargs=cls._check_none(data.get('data_model_kwargs'), dict, 'data_model_kwargs'),
+            data_model_kwargs=cls._check_none(
+                data.get('data_model_kwargs'), dict, 'data_model_kwargs'
+            ),
             model_kwargs=cls._check_none(data.get('model_kwargs'), dict, 'model_kwargs'),
             opt_params=cls._check_none(data.get('opt_params'), dict, 'opt_params'),
             model_type=cls._check_none(data.get('model_type'), str, 'model_type'),
             model_id=data.get('model_id'),
-            
             model_metadata=cls._parse_optional_model_metadata(data.get('model_metadata')),
         )
 
@@ -233,9 +238,7 @@ class TrainModelParams:
             return None
         if isinstance(value, dict):
             return value
-        raise TypeError(
-            f'model_metadata must be a dict or None, but got {type(value).__name__}.'
-        )
+        raise TypeError(f'model_metadata must be a dict or None, but got {type(value).__name__}.')
 
     def validate_business_rules(self) -> None:
         """
@@ -261,21 +264,20 @@ class TrainModelParams:
 
         if not self.variable_columns:
             raise ValueError('variable_columns cannot be empty')
-    
 
     def _validate_model_params(self) -> None:
         """Validate model-related parameters."""
         if not self.model_metadata:
             raise ValueError('model_metadata is required')
-        
-        schemas = self.model_metadata.get('schemas', {}).get("components", {}).get("schemas")
+
+        schemas = self.model_metadata.get('schemas', {}).get('components', {}).get('schemas')
 
         if not schemas:
             return
 
-        data_model_schema = schemas.get("data_model")
-        model_schema = schemas.get("model")
-        opt_params_schema = schemas.get("opt_params")
+        data_model_schema = schemas.get('data_model')
+        model_schema = schemas.get('model')
+        opt_params_schema = schemas.get('opt_params')
 
         if data_model_schema:
             self._validate_model_param(data_model_schema, self.data_model_kwargs)
@@ -283,8 +285,6 @@ class TrainModelParams:
             self._validate_model_param(model_schema, self.model_kwargs)
         if opt_params_schema:
             self._validate_model_param(opt_params_schema, self.opt_params)
-        
-        
 
     def _validate_model_param(self, schema: dict[str, Any], value: Any) -> None:
         """Validate model parameter against schema."""
@@ -292,9 +292,7 @@ class TrainModelParams:
             validator = Draft202012Validator(schema)
             validator.validate(value)
         except ValidationError as e:
-            raise ValueError(f'Model parameters validation failed: {e.message}')
-        except Exception as e:
-            raise ValueError(f'Unexpected error: {e}')
+            raise ValueError(f'Model parameters validation failed: {e.message}') from e
 
     def _validate_required_strings(self) -> None:
         """Validate required string fields are not empty."""
@@ -313,4 +311,4 @@ class TrainModelParams:
     def _validate_date_format(self) -> None:
         """Validate date_format is one of the allowed frontend formats when set."""
         if self.date_format:
-            validate_frontend_date_format(self.date_format)
+            validate_frontend_date_format(self.date_format)

model_manager/utils/models/train_model_result.py

@@ -1,5 +1,4 @@
 from dataclasses import dataclass
-from typing import Any
 
 import pandas as pd