Enhance MLFlow reference data handling and testing
- Updated the artifact handling in MLFlow to prioritize 'retrain_input.csv' over 'train_data.csv' when resolving reference data.
- Introduced new methods for resolving artifact names and locating downloaded CSV files.
- Modified the `get_reference_data` method to improve artifact resolution and error handling.
- Expanded unit tests to cover scenarios for missing artifacts and preference logic between retrain and train data CSVs.
Update requirements and enhance OPC communication handling
- Updated `requirements-local.txt` to use the latest versions of `sientia-dataops-library` (1.12.1) and `sientia-model-library` (0.10.0).
- Improved documentation in `opc-communication.md` to clarify the transition from async to sync implementation and added details on error classification and reconnection behavior.
- Refactored `init_opc` method in `opc.py` to enhance connection handling and logging, ensuring independent server initialization.
- Enhanced validation and writing methods in `opc.py` to provide better feedback and error handling for OPC server operations.
- Updated `opc_repository.py` to improve error payload construction and session management metrics.
SIENTIAPDE-1646 Enhance OPC integration and E2E testing framework
- Updated .gitignore to exclude '.cursor/*' for better file management.
- Added new marker in pyproject.toml for E2E tests using OPC.
- Introduced async OPC server fixtures and improved connection handling in conftest.py.
- Enhanced error handling in OPC activities and added metrics for session management in metrics.py.
- Updated E2E tests to cover new OPC scenarios, including session errors and reconnect handling.
- Refactored helper functions to improve prediction assertion logic in helpers.py.
- Documented new OPC scenarios in scenarios.md for clarity on expected outcomes.
Refactor OPC and OpcRepository classes to remove logger dependency and streamline logging calls
- Removed logger attribute from OPC and OpcRepository classes, replacing direct logger calls with class methods for logging.
- Updated write_data method signatures to eliminate logger parameter, simplifying the interface.
- Adjusted related tests to reflect changes in method signatures and logging behavior.
Implement new scheduling configurations for model retraining and drift analysis in input_sample.json
- Added three new schedule configurations: `minimal-retrain-test-runtime`, `drift-test-runtime`, and `simple-metrics-test-runtime`.
- Each configuration includes parameters such as model ID, workflow type, frequency, and specific queries for data retrieval.
- Enhanced the structure to support active status and updated timestamps for better tracking of schedule states.
SIENTIAPDE-1646 Add new scheduling configurations and remove outdated documentation
- Introduced new scheduling configurations for minimal retrain, drift analysis, and simple metrics in `input_sample.json`.
- Removed obsolete documentation files related to drift analysis and E2E test reports to streamline project resources.
- Updated E2E tests for minimal retrain to enhance reporting and error handling during model retraining processes.
SIENTIAPDE-1646 Update dependencies and refactor OPC integration
- Replaced `opcua` with `asyncua` in `requirements-local.txt` and `requirements.txt` to utilize the async capabilities.
- Updated `values.yaml` to change the GitHub branch from `feature/SIENTIAPDE-1646` to `release/SIENTIAPDE-1646`.
- Refactored `opc.py` and `opc_repository.py` to accommodate the new `asyncua` library, ensuring compatibility with the synchronous API.
- Adjusted tests in `test_opc_repository.py` to reflect changes in the client implementation and maintain functionality.
Update E2E test report and enhance drift analysis handling
- Updated the E2E test report metrics to reflect the latest test results, showing 47 collected tests with all passing.
- Removed outdated sections related to failed tests and their causes, streamlining the report.
- Implemented a regression fix in the drift analysis to handle empty merged frames, ensuring workflows skip export when no drift metrics are available.
- Enhanced the `insert_sample_data` and `insert_sample_prediction` functions to allow customizable timestamps for better test accuracy.
- Refactored E2E tests to improve clarity and maintainability, particularly in handling repeat scenarios with distinct timestamps.
Refactor ModelMetrics to utilize DriftAnalysis for drift detection
- Replaced ModelAnalysis with DriftAnalysis in the ModelMetrics class to enhance drift detection capabilities.
- Updated method signatures and documentation to reflect the changes in target_name and return values.
- Adjusted data handling to ensure compatibility with the new analysis methods and improved clarity in the drift metrics dataframe preparation.
Update README, requirements, and E2E tests for improved configuration and functionality
- Enhanced the README with updated model configuration examples, including the addition of an alias for production.
- Removed the `requirements-light.txt` file and updated `requirements-local.txt` and `requirements.txt` to replace `asyncua` with `opcua`.
- Refactored E2E test scenarios to utilize scenario input files for better maintainability and clarity.
- Improved test coverage for MinIO offload functionality and added new helper functions for loading scenario inputs.
- Updated `values.yaml` to reflect new global configurations and environment variables for the laborious worker.
Refactor MLFlow retraining logic to always use retrain method
- Removed the conditional logic for full retraining, ensuring the `retrain` method is always called.
- Updated the documentation in the `retrain_model` method to reflect the changes in retraining flow.
- Adjusted tests to verify that the `retrain` method is invoked correctly, while ensuring `train` is not called when the full retrain flag is set.
Update dependencies and refactor MLFlow activities
- Replaced direct GitHub dependencies in `requirements.txt` with specific versioned packages for `sientia_do` and `sientia_model`.
- Refactored imports in `activities.py` to streamline the code structure.
- Enhanced the `MLFlow` class in `mlflow.py` by introducing a method to resolve model aliases, improving flexibility in model lookups.
- Simplified shutdown logic in `worker.py` for better readability.
- Added new tests for MLFlow activities and improved existing test coverage for data handling and model retraining processes.
Enhance environment configuration and update dependencies
- Added new environment variables for PluginStore and MLflow configuration in `.env.example`, including `RUNTIME`, `STORE_BASE_URL`, `STORE_OWNER`, `STORE_REPO`, `STORE_BRANCH`, `STORE_USERNAME`, `STORE_PASSWORD`, `STORE_CACHE_TTL_SECONDS`, `PYPI_SERVER`, `PYPI_USERNAME`, and `PYPI_PASSWORD`.
- Updated `git-requirements-mapping.txt` to reflect changes in repository names.
- Modified `requirements-light.txt` and `requirements.txt` to upgrade `sientia-dataops-library` to version 1.12.0 and `sientia-mlops-library` to version 0.8.1.
- Updated `values.yaml` to include new environment variables for worker runtime and PluginStore configuration.
- Refactored E2E tests to utilize new MLflow repository stubs and PluginStore mocks for improved testing accuracy.
SIENTIAPDE-1712 Enhance logging across various classes by adding logger parameters and improving debug statements. This update includes adjustments in Gates, MLFlow, ModelMetrics, and MLFlowRepository classes for better traceability and observability during operations.
SIENTIAPDE-1712 Enhance logging in Gates, MLFlow, and Storage classes by integrating logger parameter for improved traceability. This update allows for better monitoring of operations and data handling across these components.
SIENTIAPDE-1712 Refactor debug logging for DataFrames across multiple classes. Introduced a new method to log DataFrame content conditionally based on row count in Gates, MLFlow, ModelMetrics, and MLFlowRepository classes, improving debugging capabilities while managing log output effectively.
SIENTIAPDE-1712 Implement debug logging for DataFrames in MLFlow and MLFlowRepository classes. Added a method to log DataFrame content conditionally based on row count, enhancing debugging capabilities while preventing excessive log output.
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.
Enhance E2E testing with MinIO support and update documentation
- Updated `requirements-dev.txt` to include MinIO support in testcontainers.
- Added a new fixture for MinIO container setup in `conftest.py` to facilitate E2E tests involving S3-compatible storage.
- Introduced a new test fixture for activities using a real MinIO container in `conftest.py`.
- Updated E2E test scenarios and documentation to reflect the integration of MinIO for offload uploads and clarified error handling in workflows.
- Refactored existing tests to improve clarity and maintainability.
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.
SIENTIAPDE-1712 Add timestamp column to processed data in MLFlow and improve dataframe validation in MinioDataFramePayload
- Added a 'timestamp' column to the processed data in the MLFlow class for better tracking of data entries.
- Updated the validation check in MinioDataFramePayload to handle None values for the dataframe more explicitly.
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 `query_to_minio` method from Storage class and update worker activities to eliminate its usage. This change streamlines the codebase by removing unused functionality related to MinIO queries.
Update liveness and readiness probe initial delays in values.yaml; change GITHUB_BRANCH to feature/SIENTIAPDE-1712; refactor MinioRepository initialization in activities.py
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.
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.
