aalpy 0.1.1

An active automata learning library

AALpy - An Active Automata Learning Library

AALpy is a light-weight active automata learning library written in pure Python. By implementing a single method and a few lines of configuration, you can start learning automata.

Whether you work with regular languages, or you want to learn models of reactive systems, AALpy supports a wide range of modelling formalisms including deterministic, non-deterministic, and stochastic automata. You can use it to learn deterministic finite automata, Moore machines, and Mealy machines of deterministic systems. If the system that you want to learn shows non-deterministic or stochastic behavior, you can use AALpy to learn observable nondeterministic finite-state machines, Markov decision processes, or stochastic transducers.

AALpy enables efficient learning by providing a large array of equivalence oracles, implementing various conformance testing strategies. Learning is mostly based on Angluin's L* algorithm, for which AALpy supports a selection of optimizations, including efficient counterexample processing. Finally, support for learning abstracted nondeterministic Mealy machines enables efficient learning of system models with large input space.

If AALpy misses a feature that you need, you can easily extend it.

Installation

Use the package manager pip to install AALpy.

pip install aalpy

Ensure that you have Graphviz installed and added to your path if you want to visualize models.

For manual installation clone the master and install the following dependency.

pip install pydot

Documentation and Wiki

If you are interested in automata learning or want to understand the automata learning process in more detail, please check out our Wiki. On Wiki, you will find more detailed examples on how to use ALLpy.

• https://github.com/emuskardin/AALpy/wiki

For the official documentation of all classes and methods check out:

• https://emuskardin.github.io/AALpy/

Furthermore, there you will find everything from an introduction to automata learning to the discussion of some advanced topics.

In Examples.py, many examples demonstrating all ALLpy functionality are presented.

Usage

All automata learning procedures follow this high-level approach:

• Define input alphabet
• Define system under learning (SUL)
• Define equivalence oracle
• Run learning algorithm with input alphabet

The following snippet demonstrates a short example in which automaton is either loaded or randomly generated and then learned.

from aalpy.utils import load_automaton_from_file, save_automaton_to_file, visualize_automaton, generate_random_dfa
from aalpy.SULs import DfaSUL
from aalpy.oracles import RandomWalkEqOracle, StatePrefixEqOracle
from aalpy.learning_algs import run_Lstar

# load an automaton
automaton = load_automaton_from_file('path_to_the_file.dot')
# or randomly generate one
random_dfa = generate_random_dfa(alphabet=[1,2,3,4,5],num_states=2000, num_accepting_states=200)
# get input alphabet of the automaton
alphabet = random_dfa.get_input_alphabet()
# create a SUL instance for the automaton/system under learning
sul = DfaSUL(random_dfa)

# define the equivalence oracle
eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09)
eq_oracle_2 = StatePrefixEqOracle(alphabet, sul, walks_per_state=20, walk_len=10)

# start learning
learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa')

# save automaton to file and visualize it
save_automaton_to_file(learned_dfa, path='Learned_Automaton', file_type='dot')
visualize_automaton(learned_dfa)

To make experiments reproducible, simply define a random seed at the beginning of your program.

from random import seed
seed(2) # all experiments will be reproducible

For more examples and instructions check out the Wiki and Examples.py.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. In case of any questions or possible bugs, please open issues.

Contributors

• Edi Muskardin
• Martin Tappler

License