Update environment variables in values.yaml and enhance metric labels in metrics.py
- Changed POSTGRES_USER and POSTGRES_PASSWORD values in values.yaml for improved security.
- Added 'runtime' label to metrics in metrics.py for better environment identification.
- Updated CORE_LABELS to include 'runtime' for consistency across metrics.
- Modified type hint for data parameter in PredictionProcess to use a dictionary for better clarity.
- Adjusted tests to reflect changes in core labels and MinIO configuration.
Refactor MinioDataFramePayload usage across activities
- Updated instances of MinioDataFramePayload initialization in Gates, MLFlow, and Storage classes to use the new from_dict method for better data reconstruction from dictionaries.
- Enhanced the PredictionProcess workflow to utilize the updated payload handling.
- Added passthrough fixtures in tests to accommodate the new from_dict method for consistent testing behavior.
Remove code validation script and refactor imports in activities and workflows
- Deleted the `validate.sh` script, which was responsible for running code quality checks.
- Cleaned up import statements in `activities.py`, `gates.py`, `mlflow.py`, and `storage.py` by removing unused imports and organizing them.
- Refactored initialization methods in `MinioManager` and `MLFlow` classes for improved readability.
- Updated various workflows to ensure compatibility with the new structure and removed unnecessary comments.
- Enhanced test cases to accommodate changes in the activities and workflows, ensuring proper mocking of dependencies.
Implement MinIO Offload and Retention Features
- Added configuration options for MinIO retention hours and offload threshold in README.
- Introduced MinIO payload offloading for large DataFrame-derived payloads, storing them as parquet files.
- Updated activities to utilize MinIO for data loading and cleanup, including new methods for offloading and retention management.
- Refactored existing activities to integrate MinIO functionality, ensuring compatibility with previous workflows.
- Removed the legacy MinioRepository class, consolidating MinIO operations under a new manager structure.
- Updated requirements to use the latest version of the sientia-dataops-library.
Refactor PI Web API response handling in tests
- Updated test cases in test_api.py to handle response data as lists instead of dictionaries for consistency with the API's expected output format.
- Adjusted mock responses to reflect the new structure, ensuring tests accurately simulate API behavior.
- Enhanced clarity in test descriptions and improved overall test coverage for response processing scenarios.
Update sonar-project.properties to exclude all worker files from coverage and modify execution counts and timestamps in tests.ipynb. Add a new test for empty DataFrame handling in test_model_repository.py.
Refactor validation script and improve logging in API and model repository
- Updated validation script to include 'e2e/' directory in code formatting and linting checks.
- Enhanced error logging in API class to improve readability of error messages.
- Refactored debug logging in model repository for better structured output.
- Cleaned up import statements in various files for improved organization.
Enhance end-to-end tests for PredictionsBatch workflow scenarios
- Introduced mock repositories for PI Web API and OPC operations to improve test coverage.
- Updated test scenarios to handle partial write errors for PI Web API and OPC.
- Refactored existing tests to assert correct behavior under various error conditions.
- Enhanced logging and error handling in API and OPC activities to provide clearer feedback on failures.
- Removed outdated integration test file to streamline test suite.
SIENTIAPDE-1478
Enhance end-to-end tests for PredictionsBatch workflow
- Added new test scenarios for input and transform gates handling CONTINUE, STOP, and REPEAT policies.
- Implemented sample data insertion functions for testing various prediction outcomes.
- Updated existing tests to verify behavior under different input conditions and response validations.
- Refactored test structure for clarity and maintainability.
Update coverage source in pyproject.toml, add testcontainers for PostgreSQL in requirements-dev.txt, increment image tag and adjust probe delays in values.yaml, and refine condition checks in format_and_export_prediction.py and mlflow.py. Additionally, enhance test coverage in test_gates.py.
SIENTIAPDE-1478
Implement PI Web API response processing and metrics tracking
- Added a new method in the API class to process responses from the PI Web API, validating tag writes and emitting metrics for success and errors.
- Enhanced error handling for missing WebIds and tag names in responses, with appropriate logging and notifications.
- Updated tests to cover various scenarios for processing PI Web API responses, ensuring robust functionality and metrics emission.
- Refactored existing methods to integrate the new response processing logic, improving overall code clarity and maintainability.
Refactor Activities and API Integration for PI Web API
- Reintroduced the API import in the Activities class for proper integration.
- Cleaned up whitespace and formatting in the API class and related tests for improved readability.
- Updated test cases to ensure consistent formatting in error messages and configuration structures for PI Web API.
- Enhanced connectors_config.py with additional whitespace for better organization.
Enhance Activities and Prediction Workflows with PI Web API Integration
- Updated the Activities class to include API integration, allowing for configuration of PI Web API parameters.
- Modified prediction workflows to support output configuration for PI Web API, enabling data writing to the API.
- Refactored connectors_config.py by removing unused PostgreSQL and MongoDB configuration functions.
- Added tests to validate the new PI Web API functionality in activities and workflows, ensuring robust integration and functionality.
Update sientia-mlops-library dependency to version 0.40.6 and refactor get_prediction_data method calls for improved readability in model_repository.py and test_model_repository.py.
Enhance prediction data retrieval in MLFlowRepository by adding predict_flavor parameter
- Updated get_prediction_data method to accept a predict_flavor argument, allowing for different prediction model handling.
- Adjusted calls to get_prediction_data throughout the codebase to include the new parameter.
- Added new test cases to validate behavior for different predict_flavor values, ensuring robust functionality.
Update sientia-mlops-library dependency to version 0.40.5 and fix SQL interval formatting in Drift and SimpleMetrics workflows for improved query accuracy.
Enhance data handling and export processes in Laborious workflows
- Updated `gates.py` to improve data quality validation, filtering, and formatting operations, including enhanced metrics recording.
- Refined `mlflow.py` to better manage model transformations and reference data retrieval from MLflow Model Registry.
- Enhanced `format_and_export_prediction.py` to support separate export of transformed data, improving flexibility in data handling.
- Added comprehensive test coverage for new functionalities, including transformed data formatting and retrain report generation.
- Improved documentation in `README.md` to reflect changes in activities and workflows, ensuring clarity on data processing and export paths.
SIENTIAPDE-1273
Enhance security analysis and SQL injection handling
- Added skip for potential SQL injection false positives in Bandit configuration.
- Updated validate.sh to use the pyproject.toml configuration for Bandit security analysis.
- Refactored code to replace ensure_dataframe utility with direct DataFrame usage in multiple activities, improving clarity and reducing dependencies.
- Removed the deprecated dataframe_utils module to streamline the codebase.
Update version and enhance metrics calculation in Laborious system
- Updated image tag in values.yaml from 1.1.0 to 1.1.1.
- Modified GITHUB_BRANCH environment variable for consistency.
- Added a new method `calculate_simple_metrics` in model_metrics.py to compute various model performance metrics including RMSE, MSE, MAE, and R2.
- Integrated the new metrics calculation into the worker setup, allowing for concurrent processing of simple metrics.
- Updated tests to cover the new metrics calculation functionality, ensuring comprehensive validation of the implementation.
Refactor Drift class to improve target name handling
- Extracted target name from model configuration in the Drift class for better clarity and maintainability.
- Updated test cases to reflect changes in input data structure, ensuring consistency in target name retrieval.
Enhance MLFlowRepository and Activities classes with new methods and metrics
- Added `check_artifact_exists` method to MLFlowRepository for verifying artifact presence in the MLflow Model Registry.
- Implemented `get_prediction_data` method in MLFlowRepository to retrieve prediction data from models.
- Updated Activities class to integrate ModelMetrics for improved metrics handling.
- Enhanced tests for artifact existence checks and prediction data retrieval, ensuring robust coverage for new functionalities.
- Updated various workflows to include `transform_table_name` in input data for better data handling.
Update logging in MLFlowRepository and OpcRepository to use unified logging methods
- Refactored logging calls in MLFlowRepository to replace `self.logger.info` and `self.logger.debug` with `self.info` and `self.debug` for consistency.
- Updated connection logging in OpcRepository to format the connection message properly.
- Adjusted test cases to reflect changes in logging behavior and ensure proper assertions.
Enhance metrics handling in Gates and MLFlow classes
- Added checks for `None` response times before emitting OPC writing metrics in the Gates class to prevent unnecessary metric emissions.
- Updated the MLFlow class to conditionally sort and drop duplicates based on the presence of the 'created_at' column, ensuring robustness in data processing.
- Adjusted corresponding tests to validate the new behavior in both classes.
Refactor monitoring and metrics integration across various components
- Removed coverage options from `pyproject.toml`.
- Updated prediction metrics in `README.md` to replace `pipeline_name` with `workflow_name`.
- Upgraded `sientia-dataops-library` dependency version in `requirements-light.txt` and `requirements.txt`.
- Enhanced metrics handling in `laborious` activities, including `Activities`, `Gates`, `MLFlow`, and `OPC`, to utilize a new `MetricsController`.
- Refactored metric emission methods to improve clarity and consistency across the codebase.
- Updated tests to reflect changes in metrics handling and ensure proper functionality.
Refactor workflow names to include 'subworkflow' prefix
- Updated the workflow definitions and calls in the predictions_batch, format_and_export_prediction, and prediction_process files to use the 'subworkflow' prefix for consistency.
- Adjusted corresponding test cases to reflect these changes in workflow execution calls.
Refactor OPC Test for Output Management
- Added spacing for improved readability in the test_manage_output_tags_failed and test_manage_output_tags_do_nothing functions.
- Reformatted the parameters in the call to manage_output_tags for better clarity and organization.
Enhance OPC Testing for Output Management
- Refactored the test_manage_output_tags_failed function to use parameterization for improved test coverage of different side effects.
- Added a new test_manage_output_tags_do_nothing to verify behavior when an invalid key is provided in the configuration, ensuring that no write operations are performed in this case.
Refactor OPC Class and Enhance Testing for Output Management
- Removed the unused 'success' parameter from the manage_output_tags method in the OPC class to streamline its signature.
- Commented out the error handling logic in the OpcRepository for clarity and future reference.
- Added new tests for manage_output_tags to validate successful and failed write operations, ensuring accurate assertions for output data and metrics.
Refactor OPC Write Method and Comment Out Error Handling Logic
- Reformatted the `write_opc_data` method for improved readability by adjusting its signature.
- Commented out the error handling logic in `OpcRepository` related to disconnection due to multiple errors, preserving the original functionality for future reference.
- Updated tests to reflect the commented-out error handling, ensuring clarity in the testing process.
Refactor OPC Write Test to Separate Output Data and Metrics
- Updated the test for the OPC write operation to separately assert output data and OPC metrics for improved clarity and accuracy in testing.
- Changed the variable names to reflect the new structure of the returned values from the write operation.
Update test_format_and_export_prediction.py to use prediction_data for workflow execution
- Replaced the mock return value with prediction_data in the test_run_none_path_flag function to ensure accurate testing of the workflow's data handling.
Enhance OPC Metrics Handling and Refactor Write Operations
- Updated the OPC class to return response times for write operations, improving metrics tracking.
- Refactored the Gates activity to incorporate OPC metrics into the metrics writing process.
- Adjusted the manage_output_tags method in OpcRepository to return response times for each tag written.
- Modified tests to validate the new metrics structure and ensure correct behavior of the updated methods.
Refactor error handling in MLFlowRepository and update tests
- Improved error message formatting in MLFlowRepository for better readability.
- Updated test assertions to ensure correct calls to MLflow methods during experiment retrieval and creation.
Refactor OPC handling by removing pod_id from initialization and updating logging format
- Removed pod_id parameter from OPC class and repository initialization to streamline connection management.
- Updated logging statements for improved readability during disconnection attempts and error handling.
Refactor tests and update model_repository.py for clarity and consistency
- Added a blank line in model_repository.py for improved readability.
- Adjusted formatting in test_mlflow.py to streamline assertions.
- Updated ensure_bucket_exists method tests in test_minio_repository.py to reflect the new return value of None instead of True.
Enhance validation script and refactor code in various modules
- Updated the validation script to include automatic code formatting and linting fixes using Ruff.
- Removed the `clean_tmp_files` method from the Gates class to streamline functionality.
- Simplified conditional checks in the OpcRepository for better clarity and error handling.
- Added model ID to the minimal retrain workflow for improved tracking.
- Introduced new test cases for error handling in MLFlow and storage operations, ensuring robustness in repository interactions.
Update .gitignore and refactor metrics.py for improved logging and consistency
- Added coverage.xml to .gitignore to prevent tracking of coverage reports.
- Refactored metric labels in metrics.py for consistency in string formatting and improved readability.
- Enhanced logging messages in various activities to ensure uniformity in message formatting.
Add placeholder class 'Any' in test_model_repository.py and update invalid_cases to use it
- Introduced a new placeholder class 'Any' to be used in test cases.
- Updated the 'invalid_cases' list to replace integer keys with instances of the 'Any' class, enhancing test coverage for key types.
Refactor model configuration handling in MLFlow and workflows
- Replaced 'model_retention' with 'model_config' to encapsulate retention settings and improve consistency across various components.
- Updated test cases to reflect changes in argument structure, ensuring compatibility with the new model configuration format.
- Added 'prediction_store_policy' to input data handling in workflows for enhanced configuration management.
Update test cases in model repository and prediction process
- Replaced string data with MagicMock in test_transform_success and test_transform_error to improve test isolation.
- Updated the predict method calls in test_predict_success and test_predict_error to reflect changes in argument structure.
- Added 'prediction_store_policy' to the test_run configuration in test_prediction_process for consistency with recent updates.
Refactor datetime index handling in MLFlow and MLFlowRepository
- Moved the detect_and_parse_datetime_index method from MLFlow to MLFlowRepository for better organization and reusability.
- Updated the method to include enhanced logging and error handling for invalid datetime formats.
- Adjusted the transform method in MLFlowRepository to utilize the new datetime index parsing logic.
- Added unit tests for both valid and invalid datetime index cases to ensure robustness.
Add placeholder class 'Any' in test_model_repository.py and update invalid_cases to use it
- Introduced a new placeholder class 'Any' to be used in test cases.
- Updated the 'invalid_cases' list to replace integer keys with instances of the 'Any' class, enhancing test coverage for key types.
Refactor model configuration handling in MLFlow and workflows
- Replaced 'model_retention' with 'model_config' to encapsulate retention settings and improve consistency across various components.
- Updated test cases to reflect changes in argument structure, ensuring compatibility with the new model configuration format.
- Added 'prediction_store_policy' to input data handling in workflows for enhanced configuration management.
Update test cases in model repository and prediction process
- Replaced string data with MagicMock in test_transform_success and test_transform_error to improve test isolation.
- Updated the predict method calls in test_predict_success and test_predict_error to reflect changes in argument structure.
- Added 'prediction_store_policy' to the test_run configuration in test_prediction_process for consistency with recent updates.
