| Name | wlplan JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | WLPlan: Relational Features for PDDL Planning |
| upload_time | 2024-11-01 10:09:31 |
| maintainer | None |
| docs_url | None |
| author | Dillon Z. Chen |
| requires_python | >=3.10 |
| license | MIT License |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
WLPlan
======
[](https://pypi.org/project/wlplan/)
[](LICENSE)
WLPlan is a package for generating embeddings of PDDL planning problems for machine learning tasks. It supports both classical and numeric planning problems.
<p align="center">
<img src="image.png" alt="WLPlan" title="WLPlan"/>
</p>
## Installation
### Python Interface
The Python interface can be installed simply with
pip install wlplan
The PyPI release only supports `python>=3.10`. Alternatively, you can also install the package from the source code with the `install.sh` script.
### C++ Interface
The C++ interface can be installed in your project by running
./cmake_build.py <path/to/installation>
and adding the following to the root CMakeLists.txt file of your project
list(APPEND CMAKE_PREFIX_PATH "<path/to/installation>")
find_package(wlplan)
...
target_link_libraries(<your_project> PRIVATE wlplan)
## Graph Representations
The graph representations of planning tasks implemented thus far are
| Name | WLPlan shorthand | Reference |
| -------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| Instance Learning Graph (ILG) | `ilg` | D. Chen, F. Trevizan, S. Thiébaux: Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. ICAPS 2024 |
| Numeric Instance Learning Graph (νILG) | `nilg` | D. Chen, S. Thiébaux: Graph Learning for Numeric Planning. NeurIPS 2024 |
## Feature Generators
The feature generators implemented thus far are
| Name | WLPlan shorthand | Reference |
| --------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| Weisfeiler Leman (WL)/colour refinement | `wl` | D. Chen, F. Trevizan, S. Thiébaux: Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. ICAPS 2024 |
| 2-WL | `kwl2` | J. Cai, M. Fürer, N. Immerman: An optimal lower bound on the number of variables for graph identification. Combinatorica (1992) |
| Local 2-WL (2-LWL) | `lwl2` | C. Morris, K. Kersting, P. Mutzel: Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs. ICDM 2017 |
| continuous-categorical WL (ccWL) | `ccwl` | D. Chen, S. Thiébaux: Graph Learning for Numeric Planning. NeurIPS 2024 |
| individualised WL (iWL) | `iwl` | coming soon |
| normalised iWL (niWL) | `niwl` | coming soon |
## Usage
Examples for how to use the package include this self-contained [notebook](docs/examples/blocksworld.ipynb) and [test](tests/test_train_eval_blocks.py), as well as the [GOOSE](https://github.com/DillonZChen/goose) framework.
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"description": "WLPlan\n======\n\n[](https://pypi.org/project/wlplan/)\n[](LICENSE)\n\nWLPlan is a package for generating embeddings of PDDL planning problems for machine learning tasks. It supports both classical and numeric planning problems.\n\n<p align=\"center\">\n <img src=\"image.png\" alt=\"WLPlan\" title=\"WLPlan\"/>\n</p>\n\n## Installation\n### Python Interface\nThe Python interface can be installed simply with\n\n pip install wlplan\n\nThe PyPI release only supports `python>=3.10`. Alternatively, you can also install the package from the source code with the `install.sh` script.\n\n### C++ Interface\nThe C++ interface can be installed in your project by running\n\n ./cmake_build.py <path/to/installation>\n\nand adding the following to the root CMakeLists.txt file of your project\n\n list(APPEND CMAKE_PREFIX_PATH \"<path/to/installation>\")\n find_package(wlplan)\n ...\n target_link_libraries(<your_project> PRIVATE wlplan)\n\n## Graph Representations\nThe graph representations of planning tasks implemented thus far are \n| Name | WLPlan shorthand | Reference |\n| -------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------- |\n| Instance Learning Graph (ILG) | `ilg` | D. Chen, F. Trevizan, S. Thi\u00e9baux: Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. ICAPS 2024 |\n| Numeric Instance Learning Graph (\u03bdILG) | `nilg` | D. Chen, S. Thi\u00e9baux: Graph Learning for Numeric Planning. NeurIPS 2024 |\n\n## Feature Generators\nThe feature generators implemented thus far are\n| Name | WLPlan shorthand | Reference |\n| --------------------------------------- | ---------------- | --------------------------------------------------------------------------------------------------------------------------------- |\n| Weisfeiler Leman (WL)/colour refinement | `wl` | D. Chen, F. Trevizan, S. Thi\u00e9baux: Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. ICAPS 2024 |\n| 2-WL | `kwl2` | J. Cai, M. F\u00fcrer, N. Immerman: An optimal lower bound on the number of variables for graph identification. Combinatorica (1992) |\n| Local 2-WL (2-LWL) | `lwl2` | C. Morris, K. Kersting, P. Mutzel: Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs. ICDM 2017 |\n| continuous-categorical WL (ccWL) | `ccwl` | D. Chen, S. Thi\u00e9baux: Graph Learning for Numeric Planning. NeurIPS 2024 |\n| individualised WL (iWL) | `iwl` | coming soon |\n| normalised iWL (niWL) | `niwl` | coming soon |\n\n## Usage\nExamples for how to use the package include this self-contained [notebook](docs/examples/blocksworld.ipynb) and [test](tests/test_train_eval_blocks.py), as well as the [GOOSE](https://github.com/DillonZChen/goose) framework.\n",
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