- 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.
- Introduced experiment_name parameter in TrainModelResult to enhance tracking of training experiments.
- Updated the Training class to utilize run_name and experiment_name for improved MLflow run management.
- Implemented _set_timezone_on_index method to ensure DataFrame indices are set to UTC if not already timezone-aware.
- Updated training and validation DataFrame processing to include timezone configuration for improved data consistency.
- Added debug logging for data preparation, transformation, and prediction steps in the Training class to improve traceability.
- Updated compute_regression_metrics method to include metadata for better debugging and validation of index alignment between true and predicted values.
- Updated image tag in values.yaml from "1.0.0" to "1.0.2" for the latest version.
- Modified STORE_BASE_URL to include port 3000 for proper service access.
- Changed import from MinioRepository to MinioRepositorySync for synchronization support.
- Updated import from Postgres to PostgresSync to enhance experiment tracking capabilities.
- Renamed TRAIN_TASK_QUEUE from "train_model-single-queue" to "train_model-basic-queue" for clarity.
- 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.
- Changed project name in values.yaml from "sientia-dataops-model-manager" to "sientia-model-manager".
- Added new environment variables for GitHub repository and branch configuration.
- Refactored cleanup paths to use a centralized REPORTS_TEMP_DIR constant for consistency.
- Updated runtime configurations and adjusted volume mounts for better resource management.
- Enabled SSH access for the model manager and disabled Grafana dashboard creation.
- Updated tests to reflect changes in directory paths and environment variable usage.
- Replaced synchronous MinIO repository calls with asynchronous counterparts in the Training class for improved performance.
- Enhanced logging throughout the training process to provide better insights into model metadata loading, parameter validation, and training execution.
- Updated the train_test_split function to enforce DataFrame input type, ensuring consistency in data handling.
- Removed the deprecated model_repository.py file to streamline the codebase.
- Adjusted cleanup schedule logic to improve error handling and logging during schedule reconciliation.
- Updated tests to reflect changes in the training workflow and repository interactions.
- 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.
- Updated `.env.example` to include new environment variables for MinIO and PyPI configuration.
- Refactored `create_cleanup_schedule` to utilize runtime-specific task queues and improve schedule reconciliation logic.
- Enhanced `Activities` class to require a default bucket in MinIO configuration.
- Adjusted `requirements.txt` to specify version for `evidently`.
- Updated tests to reflect changes in schedule creation and configuration handling.
- 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.
- Updated `Activities` class to improve garbage collection handling.
- Enhanced error messaging in `ExperimentTracking` for better clarity on update failures.
- Refactored `Training` class to streamline exception handling and improve type hints.
- Introduced new methods in `TrainModelParams` for better handling of experiment run IDs and model metadata.
- Added functionality to extract model equations in `DataManagerRepository` for linear regression models.
- Added `evidently` to requirements for improved model evaluation.
- Introduced `TrainModelResult` class with a `to_dict` method for better result handling.
- Updated `train_model` method to return a comprehensive training result, including run details.
- Enhanced `cleanup_run_directory` method in `DataManagerRepository` for improved resource management.
- Adjusted type hints in `TrainModel` for clarity and consistency.
- Added functionality to store run name and ID in the training results.
- Implemented report generation in the `DataManagerRepository`, including methods to create a run directory and generate comprehensive reports.
- Updated `TrainModelResult` to include `run_id` and `run_dir` attributes for better tracking of training sessions.
- Eliminated MinIO repository dependencies from the `Activities`, `Cleanup`, and `Training` classes.
- Updated the `cleanup_resources` method to focus on removing local temporary directories instead of handling MinIO file deletions.
- Adjusted the `TrainModel` class to pass the run directory for cleanup, enhancing resource management in the training workflow.
- Updated the `Training` class to raise `ModelTrainingError` on training failures for better error management.
- Enhanced the `run` method in `TrainModel` to return training results and ensure proper resource cleanup, including validation files.
- Refactored exception handling to prevent silent failures during resource cleanup and experiment run updates.
- Adjusted type hints for improved clarity and consistency in method signatures.
- 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.
- 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.
This parameter allows customizing the threshold (1-1000) used when rem_static_win is enabled, defaulting to 1 if null.
Updates include parameter definition, business rule validation, repository logic for passing the threshold, documentation in README.md and PIPELINE_PARAMS_CHANGELOG.md, and new unit and integration tests.
