- Added a new fixture to manage runtime report artifacts in a writable temp directory during E2E tests, addressing permission issues in local CI/dev environments.
- Updated `conftest.py` to include a requirements.txt file in the model packaging path for training activities.
- Refactored existing fixtures to use `pytest.fixture` instead of `pytest_asyncio.fixture` for better compatibility.
- Enhanced the `Reports` class to include a target alias for report metrics, ensuring compatibility with Evidently's reporting requirements.
- Introduced new test scenarios to validate the handling of missing and whitespace-only `date_column` inputs in the training workflow.
These changes improve the robustness of the E2E testing framework and enhance the clarity of model reporting metrics.
- Made `date_column` a required field in `TrainModelParams`, ensuring it must be present in the input data.
- Updated related documentation in `input-sample.md`, `README.md`, and various test scenarios to reflect the change in requirement.
- Adjusted the handling of `date_format` to default to `yyyy-MM-dd HH:mm:ss` if omitted, enhancing usability.
- Refined test scenarios to include new examples and ensure compliance with the updated parameter structure.
These changes improve the robustness of the model training workflow and clarify the expectations for input data.
- Updated `pyproject.toml` to include new linting rules for end-to-end tests.
- Modified `requirements-dev.txt` to add dependencies for E2E testing with `testcontainers` and `requests`.
- Refactored multiple JSON test scenario files to standardize structure, including new fields for `experiment_run_id`, `bucket_name`, and `file_name`.
- Improved model training parameters in `train_model_params.py` to use `experiment_name` directly.
- Adjusted `data_manager_repository.py` to utilize the updated `experiment_name` for logging.
These changes improve the organization and clarity of regression model tests and enhance the overall testing framework.
- 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.
- Imported SientiaModel to standardize the wrapper type in the Training class.
- Added conditional logging to capture training process details when a logger is provided, improving traceability during model training.
- 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.
- 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.
