Update Gates and FormatAndExportPrediction classes to standardize timestamp usage
- Changed 'last_timestamp' to 'timestamp' in the Gates class for consistency in input data handling.
- Updated the FormatAndExportPrediction class to reflect the same change in the output data structure.
Update Gates and FormatAndExportPrediction classes to use 'last_timestamp' for improved data handling
- Modified the Gates class to utilize 'last_timestamp' when only one row is present, ensuring accurate timestamp assignment.
- Updated the FormatAndExportPrediction class to replace 'timestamp' with 'last_timestamp' in the output data structure.
Update dependencies and refactor input filter handling for consistency
- Updated sientia-dataops-library dependency version from 1.10.3 to 1.10.4 in requirements.txt.
- Refactored input filter handling in the Gates class to read policy and config keys in a case-insensitive manner.
- Updated test cases to ensure consistency in filter key naming conventions across various scenarios.
Refactor metrics and API handling for improved consistency and clarity
- Removed the SIENTIA_CORE_LABELS constant and replaced it with CORE_LABELS for uniformity across metrics.
- Updated the API class to ensure operation_type is always included in core labels for PI Web API metrics.
- Simplified metric tag handling in the Gates class by consolidating common tags into a single core_tags dictionary.
- Enhanced the PredictionProcess class to improve error handling and variable naming for clarity.
SIENTIAPDE-1712 Add last_timestamp parameter to MLFlow and Gates activities for enhanced tracking
- Introduced last_timestamp parameter in the MLFlow and Gates classes to improve tracking of data processing times.
- Updated MinioDataFramePayload to handle last_timestamp, ensuring it defaults to the maximum timestamp from the dataframe if not provided.
Refactor imports in gates.py and mlflow.py for improved organization
- Removed unnecessary import statement in mlflow.py and re-added it in a more appropriate location.
- Cleaned up the workflow metadata assignment in gates.py for better readability.
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 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 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 sientia-dataops-library dependency version to 1.6.1 and refactor return types in Gates class methods for improved type clarity. Changed return types from dict[str, Any] to dict for better compatibility with downstream systems.
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.
Update requirements and enhance metrics and data handling
- Updated the sientia-dataops-library dependency version in requirements.txt to 1.5.3.
- Added new metrics for model analysis, including lag, count, and error count in metrics.py.
- Implemented a new method for formatting transformed data in gates.py.
- Enhanced MLFlowRepository with methods to load artifact dataframes and calculate model metrics, including drift and performance metrics.
- Updated the prediction process to handle transformed data and ensure proper execution of related activities in format_and_export_prediction.py and prediction_process.py.
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.
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.
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.
Enhance Gates and MLFlowRepository with new functionalities and improvements
- Added a new method `clean_tmp_files` in the Gates class to remove temporary files associated with model retraining.
- Updated MLFlowRepository methods to improve experiment handling, including dynamic parameter logging and model retrieval.
- Refactored model loading methods to streamline the process and enhance error handling.
- Improved logging for model operations and added support for model parameter retrieval.
- Adjusted minimal_retrain workflow to extend timeouts for activities and ensure proper model configuration handling.
Update model retraining and reporting functionality
- Changed the GITHUB_BRANCH value in values.yaml to reflect the latest adjustments for retraining the courier.
- Enhanced the Gates class with a new method `format_retrain_report` to format retraining report data according to storage policies.
- Refactored the MLFlow class to improve error handling during model retraining and return structured output.
- Updated the model_repository to utilize the latest MLFlow API for retrieving model versions and improved logging.
- Modified the minimal_retrain workflow to conditionally update the production model based on retraining success.
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.
