- Websites https://watakandai.github.io/specless/
- Source code: https://github.com/watakandai/specless.git
- PyPI: https://pypi.org/project/specless/
curl -sSL https://install.python-poetry.org | python3 -# Clone locally
git clone https://github.com/watakandai/specless.git
cd specless
# Optional: Change Python version using pyenv
# pyenv install 3.9
# pyenv local 3.9
# Activate the virtual environment
python -m venv env
source env/bin/activate
# Install specless & dependencies
poetry installDone!
You can use the specless package in two ways: as a library, and as a CLI tool.
>>> import specless as sl # or load from specless.inference import TPOInference
>>> import pandas as pd
# Manually prepare a list of demonstrations
>>> demonstrations = [
... ["e1", "e2", "e3", "e4", "e5"], # trace 1
... ["e1", "e4", "e2", "e3", "e5"], # trace 2
... ["e1", "e2", "e4", "e3", "e5"], # trace 3
... ]
# Run the inference
>>> inference = sl.POInferenceAlgorithm()
>>> specification = inference.infer(demonstrations) # returns a Specification
# prints the specification
>>> print(specification) # doctest: +ELLIPSIS
PartialOrder...
# exports the specification to a file
>>> sl.save_graph(specification, filepath='spec')
# drawws the specification to a file
>>> sl.draw_graph(specification, filepath='spec')
<IPython.core.display.Image object>The environment is based on the OpenAI Gym library (or more specifically, PettingZoo)
>>> import gymnasium as gym
>>> import gym_minigrid # To load minigrid envs
>>> import specless as sl
# Instantiate an env
>>> env = gym.make("MiniGrid-Empty-5x5-v0")
>>> env = sl.LabelMiniGridWrapper(env, labelkey="label")
>>> env = sl.SelectStateDataWrapper(env, columns=["label"])
>>> # Collect Demonstrations
>>> demonstrations = sl.collect_demonstrations(
... env,
... only_success=False,
... add_timestamp=True,
... num=10,
... timeout=1000,
... )- Once the specification is obtained, synthesize a strategy:
>>> import gymnasium as gym
>>> import gym_minigrid # To load minigrid envs (e.g., MiniGrid-Empty-5x5-v0)
>>> import specless as sl
>> env = gym.make("MiniGrid-Empty-5x5-v0")
# Choose an engine
>> specparser = sl.LTLfParser(engine='ltlf2dfa')
# Translate a LTLf formula to specification class
>> specification = specparser.parse("G(a -> X b)")
# Set parameters at initialization
>> synthesizer = sl.TSPSynthesisAlgorithm()
# Run the synthesis Algorithm
>> strategy = synthesizer.synthesize(specification, env)
>> print(strategy)
>> sl.save_graph(strategy, filepath='./strategy')>> state, info = env.reset()
>> terminated, truncated = False, False
>> while not (terminated or truncated):
.. action = strategy.action(state) # Stategies make a decision given an observed state
.. (next_state,
.. reward,
.. terminated,
.. truncated,
.. info) = env.step(action) # PlanStrategy class is ******a** feedforward strategy.
.. # It precomputs a plan at each **step** and does not
.. # depend on the observed state.
.. state = next_state
>> env.close()With the click package, we exposed some functions as a command line tool.
demo2spec -f <path/to/file> -a tposynthesize -d <path/to/demo> OR -s <LTLf formula> AND -e <Gym env> AND -p <path/to/param>Use Dev Container.
If you want to contribute, set up your development environment as follows:
- Install Poetry
- Clone the repository:
git clone https://github.com/watakandai/specless.git && cd specless - Install the dependencies:
poetry shell && poetry install
To run all tests: tox
To run only the code tests: tox -e py38 (or py39, py310, py311)
To run doctests, tox -e doctest
To obtain test coverages : tox -e report
Locally, run make html inside the docs directory.
Once you are ready, make a pull request and the documentations are built automatically with GitHub Actions.
See .github/generate-documentation.yml.
Apache 2.0 License
Copyright 2023- KandaiWatanabe
- Load demonstration from a file
- Choose an inference algorithm
- MUST SUPPORT AUTOMATA INFERENCE
- Implement a CLI