SIENTIAPDE-1321: Formatted files

.gitignore

@@ -145,6 +145,7 @@ celerybeat.pid
 env/
 venv/
 ENV/
+venv_311/
 env.bak/
 venv.bak/
 

model_manager/utils/repository/model_repository.py

@@ -224,7 +224,7 @@ class ModelRepository:
             self.model_serving.log_artifact(data.report_path)
             self.model_serving.log_artifact(data.train_data_path)
             self.model_serving.log_artifact(data.test_data_path)
-            
+
             # Log equation artifact if available
             if data.equation_path and path.exists(data.equation_path):
                 self.model_serving.log_artifact(data.equation_path)

model_manager/utils/repository/training_repository.py

@@ -175,10 +175,10 @@ class TrainingRepository:
         )
 
         tmr.r2_val = round(r2(tmr.y_test.astype(np.float64), tmr.y_pred.astype(np.float64)), 2)
-        
+
         # Extract model equation
         tmr.equation = self._extract_model_equation(tmr.regr, params)
-        
+
         self.logger.info(
             f'Model metrics calculated successfully - experiment run id: {params.experiment_run_id}'
         )
@@ -254,18 +254,20 @@ class TrainingRepository:
             ar_var=params.target_variable if params.include_ar else None,
         )
 
-    def _extract_model_equation(self, regr: LinearRegressionModel, params: TrainModelParams) -> dict:
+    def _extract_model_equation(
+        self, regr: LinearRegressionModel, params: TrainModelParams
+    ) -> dict:
         """
         Extract the linear regression equation coefficients and create equation metadata.
-        
+
         This method extracts the coefficients and intercept from the trained model
         and creates a structured dictionary containing the equation information
         for serialization as JSON artifact.
-        
+
         Args:
             regr: Trained LinearRegressionModel object
             params: Training parameters containing variable information
-            
+
         Returns:
             dict: Equation metadata containing:
                 - target_variable: Name of the target variable
@@ -276,25 +278,27 @@ class TrainingRepository:
         """
         coefficients = regr.regr.coef_
         intercept = regr.regr.intercept_
-        
+
         # Create coefficients dictionary
         coefficients_dict = {}
         for i, var in enumerate(params.variable_columns):
             coefficients_dict[var] = float(coefficients[i])
-        
+
         # Create equation string
-        equation_parts = [f"{coef:.6f} * {var}" for var, coef in coefficients_dict.items()]
+        equation_parts = [f'{coef:.6f} * {var}' for var, coef in coefficients_dict.items()]
-        equation_string = f"{params.target_variable} = {intercept:.6f} + " + " + ".join(equation_parts)
+        equation_string = f'{params.target_variable} = {intercept:.6f} + ' + ' + '.join(
-        
+            equation_parts
+        )
+
         # Create LaTeX equation
-        latex_parts = [f"{coef:.6f} \\cdot {var}" for var, coef in coefficients_dict.items()]
+        latex_parts = [f'{coef:.6f} \\cdot {var}' for var, coef in coefficients_dict.items()]
-        latex_equation = f"{params.target_variable} = {intercept:.6f} + " + " + ".join(latex_parts)
+        latex_equation = f'{params.target_variable} = {intercept:.6f} + ' + ' + '.join(latex_parts)
-        
+
         return {
             'target_variable': params.target_variable,
             'coefficients': coefficients_dict,
             'intercept': float(intercept),
             'equation_string': equation_string,
             'latex_equation': latex_equation,
-            'model_type': 'Linear Regression'
+            'model_type': 'Linear Regression',
         }