All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
Release of pyTigerGraph version 1.1.
- TensorFlow support for homogeneous GNNs via the Spektral library.
- Heterogeneous Graph Dataloading support for DGL.
- Support of lists of strings in dataloaders.
- Fixed KeyError when creating a data loader on a graph where PrimaryIdAsAttribute is False.
- Error catch if Kafka dataloader doesn't run in async mode.
- Refresh schema during dataloader instantiation and featurizer attribute addition.
- Reduce connection instantiation time.
- Reinstall query if it is disabled.
- Confirm Kafka topic is created before subscription.
- More efficient use of Kafka resources.
- Allow multiple consumers on the same data.
- Improved deprecation warnings.
Release of pyTigerGraph version 1.0, in conjunction with version 1.0 of the link:https://docs.tigergraph.com/ml-workbench/current/overview/[TigerGraph Machine Learning Workbench].
- Kafka authentication support for ML Workbench enterprise users.
- Custom query support for Featurizer, allowing developers to generate their own graph-based features as well as use our link:https://docs.tigergraph.com/graph-ml/current/intro/[built-in Graph Data Science algorithms].
- Additional testing of GDS functionality
- More demos and tutorials for TigerGraph ML Workbench, found link:https://github.com/TigerGraph-DevLabs/mlworkbench-docs[here].
- Various bug fixes.
We are excited to announce the pyTigerGraph v0.9 release! This release adds many new features for graph machine learning and graph data science, a refactoring of core code, and more robust testing. Additionally, we have officially “graduated” it to an official TigerGraph product. This means brand-new documentation, a new GitHub repository, and future feature enhancements. While becoming an official product, we are committed to keeping pyTigerGraph true to its roots as an open-source project. Check out the contributing page and GitHub issues if you want to help with pyTigerGraph’s development.
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Feature: Include Graph Data Science Capability
- Many new capabilities added for graph data science and graph machine learning. Highlights include data loaders for training Graph Neural Networks in DGL and PyTorch Geometric, a "featurizer" to generate graph-based features for machine learning, and utilities to support those activities.
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Documentation: We have moved the documentation to the official TigerGraph Documentation site and updated many of the contents with type hints and more descriptive parameter explanations.
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Testing: There is now well-defined testing for every function in the package. A more defined testing framework is coming soon.
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Code Structure: A major refactor of the codebase was performed. No breaking changes were made to accomplish this.
- Fix : added safeChar method to fix URL encoding
- Fix : removed the localhost to 127.0.0.1 translation
- Fix : SSL issue with Rest++ for self-signed certs
- Fix : Updates for pyTigerDriver bounding
- Feature : added the checks to debug
- Fix : added USE GRAPH cookie
- runInstalledQuery(usePost=True) will post params as body
- Made SSL Port configurable to grab SSL cert from different port in case of firewall on 443
- Fix : (more) runInstalledQuery() params
- Fix : (more) runInstalledQuery() params processing bugs
- Fix : (more) runInstalledQuery() params processing bugs
- Fix : runInstalledQuery() params processing
- Add Path finding endpoint
- Add Full schema retrieval
- Fix GSQL client
- Fix parseQueryOutput
- Code cleanup
- Remove urllib as dependency
- Fix space in query param issue #22
- SSL Cert support on REST requests
- Fix getVertexDataframeById()
- Fix GSQL versioning issue
- Fix GSQL Bug
- Fix GSQL getVer() bug
- Fix initialization of gsql bug
- Fix initialization of gsql bug
- Fix bug in gsqlInit()
- Add getVertexSet()
- Move GSQL functionality to main package
- Main functionality exists and is in relatively stable
- Minor bug fixes