- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact management.
- Introduced PROJECT_BASE_PATH constant for consistent project directory reference.
- Updated Reports class to require template_path for loading HTML templates.
- Modified DataManagerRepository to pass the new template_path when generating reports.
- Removed exception handling in load_html_from_file for cleaner code.
- Added debug print statements in the Reports class to log output directory, report path, and base path for better traceability during report generation.
- Added target_name parameter to Reports class for improved report context.
- Updated inject_content function to ensure proper handling of HTML sections.
- Modified DataManagerRepository to join predictions with training and validation data for accurate report generation.
- Deleted the sientia/models.py file, which contained the LinearRegressionModel and related functionality.
- Moved the frontend date format validation logic to train_model_params.py, ensuring a single source of truth for date formats.
- Modified `.env.example` to set local defaults for PostgreSQL, MLflow, and MinIO configurations.
- Added MongoDB configuration parameters to the environment setup.
- Updated `README.md` to reflect changes in workflow input parameters and task queue naming conventions.
- Removed the `ModelServing` class to streamline the codebase, as it was deemed unnecessary.
- Adjusted `connectors_config.py` to align with new environment variable names and improve clarity.
- Updated tests to reflect changes in configuration handling and removed tests related to the deleted `ModelServing` class.
- Introduced a new activity to load model metadata from the model store.
- Refactored training logic to utilize new model metadata and improved parameter handling.
- Updated the `TrainModelParams` class to include additional fields for model configuration.
- Replaced deprecated utility functions with a custom train-test split implementation.
- Removed unused utility functions and cleaned up the data manager repository.
- Adjusted experiment tracking to include model-specific metadata in notifications.
