SIENTIAPDE-1478 Update tests.ipynb to enhance data loading and visualization
- Refactored data loading functions to improve clarity and efficiency.
- Updated plotting functionality to include additional data series and improved axis handling.
- Adjusted execution counts and output formatting for consistency with new data structures.
SIENTIAPDE-1478 Update tests.ipynb with new execution counts, modify output values and timestamps, and enhance plotting functionality
- Adjusted execution counts for code cells to maintain consistency.
- Updated output values and timestamps in test results to reflect new data.
- Enhanced plotting functionality by adding a second subplot for additional data visualization.
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.
SIENTIAPDE-1478 Update tests.ipynb execution counts, modify timestamps, and enhance API class for PI Web API integration
- Adjusted execution counts in tests.ipynb for consistency.
- Updated timestamps in test outputs to reflect new data.
- Refactored API class to streamline data writing to PI Web API by removing redundant endpoint handling.
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.
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- Removed outdated integration test file to streamline test suite.
Update dependencies and refactor data handling in various modules
- Updated sientia-dataops-library dependency version from 1.5.3 to 1.5.4 in requirements files.
- Updated sientia-mlops-library dependency version from 0.39.0 to 0.40.2 in requirements files.
- Refactored return types in Gates, MLFlow, and ModelMetrics classes to return dictionaries instead of DataFrames for improved compatibility with downstream systems.
- Removed the temporal_codec module as it is no longer needed for DataFrame serialization.
- Adjusted data handling in the Drift workflow to ensure proper data structure is maintained.
Refactor data handling in various modules to ensure DataFrame consistency
- Replaced direct DataFrame instantiation with `ensure_dataframe` utility in Gates, MLFlow, OPC, and ModelMetrics classes to standardize data handling.
- Updated return types in several asynchronous methods to return DataFrames instead of dictionaries for improved usability.
- Adjusted data export processes in workflows to convert DataFrames to dictionaries with `to_dict(orient='records')` for compatibility with downstream systems.
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.
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.
SIENTIAPDE-1214: Enhance MLFlow and tests with datetime index handling and logging improvements
- Added a new method in MLFlow to detect and parse datetime indices in DataFrames, ensuring proper format and raising errors for invalid types.
- Updated prediction workflows to utilize the new datetime index handling, improving data integrity during transformations.
- Enhanced logging in model_repository to include detailed data outputs for better traceability.
- Adjusted timeout settings in prediction workflows for improved execution time management.
- Updated tests.ipynb to include additional checks for index types and outputs for better validation of functionality.
SIENTIAPDE-1214: Refactor MLFlow and model repository methods to use model_config dictionary
- Updated MLFlow class methods to accept model_config instead of model_retention for improved flexibility.
- Modified model_repository methods to handle model_config, extracting necessary parameters for transformation and prediction.
- Adjusted predictions_batch and prediction_process workflows to utilize model_config for better configuration management.
- Commented out the previous sientia-mlops-library dependency in requirements.txt for clarity.
Update tests.ipynb and gates.py for enhanced functionality and clarity
- Adjusted execution count in tests.ipynb for reproducibility.
- Modified DataFrame creation in tests.ipynb to dynamically set size and update timestamp frequency.
- Added a new filter function for empty data in gates.py to improve data handling.
Enhance tests.ipynb and gates.py for improved logging and output clarity
- Set execution count to null in tests.ipynb for reproducibility.
- Added stdout output to tests.ipynb for better visibility of results.
- Updated DataFrame display logic in tests.ipynb to include additional print statements for length and size.
- Improved debug logging in gates.py to log prediction data as a string for better traceability.
- Changed info log in gates.py to use len() instead of size for consistency.
Update tests.ipynb and values.yaml for improved functionality and versioning
- Updated execution count in tests.ipynb for reproducibility.
- Modified DataFrame creation in tests.ipynb to include a timestamp column and save to CSV.
- Changed image tag in values.yaml from "0.4.4" to "0.4.5" for versioning.
- Updated GITHUB_BRANCH in values.yaml to reflect the latest branch adjustments.
SIENTIAPDE-1174 Update tests.ipynb to normalize counter and rollout values, and fix MLFlowRepository initialization error
- Changed execution_count for a cell to null for consistency.
- Updated normalization logic for counter and rollout values to scale between -100 and 100.
- Fixed TypeError in MLFlowRepository initialization by ensuring the logger argument is provided.
Update dependencies, modify replica count, and implement metrics tracking
- Updated sientia-dataops-library version from 1.3.5 to 1.3.7 in requirements.txt.
- Changed replicaCount in values.yaml from 5 to 3 and incremented image tag from 0.2.7 to 0.3.1.
- Added Prometheus metrics tracking in gates.py and worker.py, including a new write_metrics method.
- Configured Prometheus service and ServiceMonitor in values.yaml for metrics collection.
