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.
Refactor data export in Drift, SimpleMetrics, and FormatAndExportPrediction workflows to improve data handling. Changed the export format from a list of dictionaries to direct objects for enhanced clarity and compatibility with downstream systems.
Refactor data export in MinimalRetrain workflow to improve structure. Changed the export format from a list of dictionaries to a direct report object for enhanced clarity and 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 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 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.
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.
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.
Enhance MinIO integration and update environment configurations
- Added MinIO configuration parameters to .env.example and values.yaml for improved storage management.
- Updated requirements.txt to include necessary libraries for MinIO support.
- Refactored Activities class to utilize Storage for MinIO interactions.
- Enhanced MLFlow class to integrate MinIO for data retrieval during model retraining.
- Introduced build_minio_config function to streamline MinIO configuration setup.
- Updated minimal_retrain workflow to support data storage in MinIO.
Update model retraining and logging enhancements
- Changed the GITHUB_BRANCH value in values.yaml to 'main' for consistency.
- Refactored MLFlow class to improve timestamp handling and error messaging during model retraining.
- Enhanced MLFlowRepository methods to include metadata logging and improved model version retrieval.
- Updated minimal_retrain workflow to support extended timeout for activities and include model configuration in input data.
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.
Update prediction_store_policy handling in workflows
- Added 'prediction_store_policy' to the input data handling in PredictionsBatch, ensuring a default value of 'lts:1' is used when not provided.
- Modified FormatAndExportPrediction to directly use 'prediction_store_policy' from input_data, removing the default fallback.
- Updated PredictionProcess to include 'prediction_store_policy' in the output data structure, ensuring consistency across workflows.
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.
Refactor connectors_config.py and conditional_filters.py for improved configuration management and data filtering
- Updated PostgreSQL and MLFlow configuration functions to enhance default values and environment variable handling.
- Simplified OPC server configuration logic and improved MongoDB connection string construction.
- Refactored conditional filters to streamline null value checks and empty data validation, removing unnecessary comments and examples for clarity.
- Removed extensive module docstrings to enhance code readability.
Remove all module docstrings and the versioning information from the Laborious package, activities, utils, and workflows. This cleanup enhances code readability and reduces unnecessary comments in the codebase.
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 dependencies and enhance datetime handling
- Bump sientia-dataops-library version in requirements.txt to 1.4.4.
- Update image tag in values.yaml to 0.4.4.
- Add 'datetime_columns' to metadata in MinimalRetrain and PredictionsBatch workflows for improved data handling.
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 values for SIENTIAPDE-1193
- Bump sientia-dataops-library version from 1.4.1 to 1.4.2 in requirements.txt.
- Change GITHUB_BRANCH in values.yaml to reflect the new task SIENTIAPDE-1193 regarding datetime writing in temporal.
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.
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 dependencies and enhance ML model retraining functionality
- Updated sientia-dataops-library version in requirements.txt from 1.3.3 to 1.3.4.
- Incremented image tag in values.yaml from 0.2.4 to 0.2.5 and added a new environment variable MONGODB_TTL_INDEX_HOURS.
- Introduced new methods in MLFlowRepository for model retraining and production model updates, including error handling and logging.
- Added retrain_model and update_production_model activities in mlflow.py to support model management workflows.
- Modified MongoDB connection settings in connectors_config.py for improved security and configuration flexibility.
Refactor OPC connection handling to improve error notifications and update GITHUB_BRANCH in values.yaml for MongoDB integration. Change requirements.txt to point to local dataops library path.
Refactor logging in MLFlow and Gates activities; remove print statement in FormatAndExportPrediction; update data handling in PredictionProcess; delete unused redis-feeder script.
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.
