SIENTIAPDE-1430: Implement advanced model training capabilities and enhanced data preprocessing. This includes support for Polynomial Regression with configurable degree and interaction terms, flexible per-variable lag configurations, and new data filtering options by date range and removed intervals. Comprehensive business validations are now enforced for all parameters, and MLflow logging has been extended to capture these detailed configurations. Additionally, Reduced Coulomb Energy (RCE) metrics are added for drift detection, with a new changelog documenting all pipeline parameter updates.

tests/utils/models/test_train_model_result.py

@@ -14,8 +14,8 @@ def sample_params():
     """Create sample TrainModelParams for testing."""
     return TrainModelParams(
         variable_columns=['var1', 'var2'],
-        lag_train=5,
-        lag_val=3,
+        lag_train={'var1': 5, 'var2': 5},
+        lag_val={'var1': 3, 'var2': 3},
         target_variable='target',
         rem_static_win=True,
         low_lim={'var1': 0.0, 'var2': 0.0},
@@ -32,6 +32,14 @@ def sample_params():
         experiment_run_id=123,
         experiment_name='test-experiment',
         removed_intervals=[],
+        model_name='Linear Regression',
+        degree=1,
+        interaction_only=False,
+        nan_treatment='drop',
+        start_date=None,
+        end_date=None,
+        scaler_name='Standard Scaler',
+        support_filters={},
     )