Update liveness and readiness probe initial delays in values.yaml; change GITHUB_BRANCH to feature/SIENTIAPDE-1712; refactor MinioRepository initialization in activities.py
Enhance README and Implement Drift Detection and Metrics Workflows
- Added new sections in README for Drift Workflow and Simple Metrics Workflow, detailing their execution flows and functionalities.
- Introduced `drift.py` for data drift detection, comparing current data against reference datasets.
- Added `simple_metrics.py` for calculating regression metrics (RMSE, MSE, MAE, R²).
- Updated `values.yaml` to include configuration for MinIO retention hours and offload threshold.
- Refactored `minio_dataframe_payload.py` to use the new offload threshold environment variable.
- Adjusted tests to reflect changes in environment variable handling for MinIO offload threshold.
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.
Enhance API class with optional operation_type in get_core_labels method
- Added a new method get_core_labels to the API class that generates core labels for metrics.
- Introduced an optional operation_type parameter to control the inclusion of the operation_type key in the returned labels.
- Maintained compatibility with the base implementation while providing flexibility for metrics without the operation_type label.
Enhance API class with default headers for JSON requests
- Added a headers configuration to the API class to specify default Content-Type, Accept, x-requested-with, and User-Agent for JSON requests.
Update sientia-dataops-library dependency version from 1.8.1 to 1.8.2 in requirements.txt and modify API class to accept a list of response data for PI Web API integration.
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.
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.
Enhance MLFlow logging and add skip_transform option in MLFlowRepository
- Updated logging in mlflow.py to output processed input data as CSV.
- Introduced skip_transform parameter in MLFlowRepository to conditionally bypass data transformation.
- Improved logging in model_repository.py to display data in a more structured format (to_dict) for predictions and transformations.
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.
Update environment variables in values.yaml and refactor worker.py for improved worker preparation
- Removed KAFKA_BOOTSTRAP_SERVERS from environment variables in values.yaml.
- Added PYPI_SERVER environment variable for library distribution.
- Refactored worker.py to replace resource tuner and poller behavior with a new prepare_worker function, streamlining worker initialization and enhancing code clarity.
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.
Enhance README and Codebase with PI Web API Integration
- Updated README.md to include details about PI Web API integration, including configuration and export capabilities.
- Modified Activities class to incorporate PI Web API export operations and error handling.
- Added new API class for handling PI Web API interactions, including writing prediction and confidence data.
- Updated prediction workflows to support PI Web API output configuration.
- Enhanced worker and sub-workflows to include PI Web API in task queues and export processes.
- Improved documentation and error handling for PI Web API connections and configurations.
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.
Refactor import statements in worker.py for improved organization
- Moved the import of the os module to the appropriate section, enhancing clarity and consistency in the import structure.
Refactor imports in opc.py to improve code clarity
- Moved the import of Hashable from collections.abc to the appropriate section, enhancing readability and organization of imports.
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.
Enhance resource management and configuration in Laborious worker
- Updated `values.yaml` to define resource limits and requests for better performance tuning.
- Modified environment variables in `worker.py` to support resource-based scaling and improved task queue management.
- Introduced new functions for creating resource tuners and poller behaviors, enhancing scalability and efficiency in handling workloads.
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.
Refactor data size calculation in ModelMetrics class to use shape method for improved accuracy. Changed from using len(target_data) to data.shape[0] for better clarity in data handling.
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.
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.
Refactor return type of get_reference_data method in MLFlow class to improve type specificity. Changed from a list of dictionaries with Hashable keys to a generic list of dictionaries for enhanced clarity in data handling.
Refactor return types in ModelMetrics class methods to enhance type clarity. Updated calculate_drift and calculate_simple_metrics methods to return lists of dictionaries instead of generic lists, improving type specificity for better data handling.
Refactor return type of calculate_drift method in ModelMetrics class to improve compatibility. Changed from a list of dictionaries to a generic list for enhanced flexibility in data handling.
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.
Refactor ModelMetrics return format and correct import name in worker module
- Changed the return format of the metrics data in ModelMetrics from a dictionary to a list for improved usability.
- Corrected the import statement for SimpleMetrics in the worker module to ensure consistency and clarity.
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 OPC data handling in opc_repository.py to improve code clarity. Updated commented-out code for SourceTimestamp to include NOSONAR annotations, ensuring better code quality checks.
