Refactor debug logging in Gates activity for improved readability
- Reformatted the debug logging statement for input data in the Gates activity to enhance clarity and maintain consistency with previous logging improvements.
Update image tag in values.yaml and enhance debug logging in Gates and MLFlow activities
- Updated the image tag in values.yaml from '0.0.1' to '0.0.2'.
- Improved debug logging in the Gates activity to format input data and filters for better readability.
- Enhanced MLFlow activity logging to include formatted output for raw and transformed response data, ensuring consistent logging format.
Update requirements and enhance logging in Gates and MLFlow activities
- Updated the sientia-dataops-library and sientia-mlops-library dependencies in requirements.txt to the latest versions.
- Improved debug logging in the Gates activity to display a sample of input data and filters, enhancing clarity and reducing output size.
- Refactored MLFlow activity logging to utilize the create_sample_dict function for better visualization of nested data structures in logs.
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.
Remove Docker configuration files and refactor project structure
- Deleted docker-compose.yml and Dockerfile as part of the project restructuring.
- Updated README.md to reflect changes in project setup and configuration.
- Introduced a new __init__.py file in the laborious package to provide an overview of the system.
- Enhanced documentation across various modules, including metrics, activities, and workflows, to improve clarity and usability.
- Added comprehensive docstrings and comments to key classes and methods for better maintainability.
Implement prediction store policy handling in Gates activity
- Added a new method `get_prediction_store_policy` to validate and parse the prediction store policy.
- Updated `format_prediction` method to utilize the new policy handling, allowing for sorting of predictions based on the specified policy.
- Enhanced test coverage for the new policy handling, including various scenarios for valid and invalid policies.
- Removed the obsolete `coverage.sh` script.
Update Gates activity and format_and_export_prediction workflow to use DATETIME_FORMAT_WITH_TZ for consistent timestamp handling
- Modified Gates activity to correctly handle the maximum timestamp without formatting it prematurely.
- Updated format_and_export_prediction workflow to utilize DATETIME_FORMAT_WITH_TZ for timestamp conversion.
- Enhanced tests to ensure timestamp conversion is applied consistently across workflows.
Update requirements and refactor Gates activity for improved datetime handling
- Update sientia-dataops-library reference in requirements.txt to version 1.4.3.
- Refactor datetime handling in Gates activity to use a consistent format with timezone support, replacing direct datetime calls with a centralized now() function and DATETIME_FORMAT_WITH_TZ constant.
Refactor logging in Gates and MLFlow activities to use info level for key operations
- Updated logging statements in the Gates class to replace debug logs with info logs for input and output gate operations, enhancing visibility.
- Modified MLFlow class to use info logs for data transformation and prediction processes, improving clarity in the logging output.
- Adjusted OPC class to return the count of successfully written tags, providing better insight into data writing operations.
Update dependencies and refactor logging imports for observability
- Updated the sientia-dataops-library dependency version to 1.4.0 in requirements.txt.
- Changed image tag in values.yaml from 0.3.2 to 0.4.1.
- Refactored logging imports across multiple files to use the new observability module instead of the temporal.utils.logger.
- Updated retry policy imports in workflow files to reflect the new module structure.
Enhance BaseActivity initialization across multiple activities to include error counter
- Updated the initialization of the BaseActivity in Gates, MLFlow, and OPC classes to set the error counter to True, improving error tracking and handling capabilities.
Update dependencies and configuration for improved functionality
- Updated sientia-dataops-library version from 1.3.7 to 1.3.8 in requirements.txt.
- Changed GITHUB_BRANCH in values.yaml to reflect new testing focus: SIENTIAPDE-1169.
- Modified notification level in gates.py from WARNING to ERROR for better error handling.
pdate metrics tracking to include response time histogram
- Changed PREDICTION_RESPONSE_TIME_MONITOR from Gauge to Histogram for better response time analysis.
- Updated response time observation method in gates.py to utilize the new Histogram functionality.
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.
Update notification handler references to use CoreNotificationHandler and add MongoDB configuration to connectors. Update GITHUB_BRANCH in values.yaml for optimization tasks.
Refactor notification handling in Gates and OPC activities to use send_notification method with metadata integration, enhancing error reporting and traceability.
Refactor Gates and OpcRepository for improved logging and error handling. Removed debug statement in Gates and enhanced disconnect method in OpcRepository to log disconnection status and handle exceptions.
Refactor logging in MLFlow and Gates activities; remove print statement in FormatAndExportPrediction; update data handling in PredictionProcess; delete unused redis-feeder script.
Refactor tests for Postgres activities and improve error handling
- Updated test_postgres.py to enhance the testing of load_custom_query method, including cases for None data and date conversion.
- Refactored repeat_last_prediction tests to use mocks for SQLAlchemy session execution.
- Added tests for export_data_to_postgres method, covering both success and error scenarios.
- Improved the initialization tests for Activities class to ensure proper instantiation of dependencies.
- Enhanced test coverage for OPC repository connection validation.
- Updated tests for prediction workflows to streamline input handling and improve clarity.
- Introduced tests for connectors configuration to validate environment variable handling for MLFlow, OPC, and Postgres.
- Added tests for logger utility to ensure default settings are correctly applied.
Refactor and enhance the laborious workflow and utilities
- Removed outdated test file `test_predictions_batch.py` from workflows.
- Added `input_sample.json` for standardized input configuration.
- Introduced `connectors_config.py` to manage database and service configurations.
- Implemented a logging utility in `logger.py` for consistent logging across the application.
- Created `policies.py` to define retry policies for workflows.
- Developed comprehensive tests for `MLFlowRepository` in `test_model_repository.py`.
- Added extensive tests for `OpcRepository` in `test_opc_repository.py`.
- Updated `test_predictions_batch.py` to reflect new workflow structure and testing methodology.
Implement new get_last_timestamp method in Gates class, refactor MLFlow activity methods to return only transformed data, and update PredictionsBatch and PredictionProcess workflows to utilize Activities module. Add detailed docstrings for new methods and enhance test coverage for get_last_timestamp functionality.
Refactor activity methods and update requirements.txt to enhance functionality and remove deprecated filters. Added detailed docstrings for clarity and improved error handling in data processing workflows.
