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Examples.py
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Examples.py
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def random_deterministic_model_example():
from aalpy.utils import generate_random_deterministic_automata
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWMethodEqOracle
from aalpy.learning_algs import run_KV
model_type = 'mealy' # or 'moore', 'dfa'
# for random dfa's you can also define num_accepting_states
random_model = generate_random_deterministic_automata(automaton_type=model_type, num_states=100,
input_alphabet_size=3, output_alphabet_size=4)
sul = AutomatonSUL(random_model)
input_alphabet = random_model.get_input_alphabet()
# select any of the oracles
eq_oracle = RandomWMethodEqOracle(input_alphabet, sul, walks_per_state=10, walk_len=20)
learned_model = run_KV(input_alphabet, sul, eq_oracle, model_type)
assert learned_model == random_model
return learned_model
def angluin_seminal_example():
"""
Example automaton from Angluin's seminal paper.
:return: learned DFA
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_Lstar
from aalpy.utils import get_Angluin_dfa
dfa = get_Angluin_dfa()
alphabet = dfa.get_input_alphabet()
sul = AutomatonSUL(dfa)
eq_oracle = RandomWalkEqOracle(alphabet, sul, 500)
learned_dfa = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa',
cache_and_non_det_check=True, cex_processing=None, print_level=3)
assert learned_dfa == dfa
return learned_dfa
def tomita_example(tomita_number=3):
"""
Pass a tomita function to this example and learn it.
:param: function of the desired tomita grammar
:rtype: Dfa
:return DFA representing tomita grammar
"""
from aalpy.SULs import TomitaSUL
from aalpy.learning_algs import run_Lstar
from aalpy.oracles import StatePrefixEqOracle
tomita_sul = TomitaSUL(tomita_number)
alphabet = [0, 1]
state_origin_eq_oracle = StatePrefixEqOracle(alphabet, tomita_sul, walks_per_state=50, walk_len=10)
# or replace run_Lstar with run_KV
learned_dfa = run_Lstar(alphabet, tomita_sul, state_origin_eq_oracle, automaton_type='dfa', )
return learned_dfa
def regex_example(regex, alphabet):
"""
Learn a regular expression.
:param regex: regex to learn
:param alphabet: alphabet of the regex
:return: DFA representing the regex
"""
from aalpy.SULs import RegexSUL
from aalpy.oracles import StatePrefixEqOracle
from aalpy.learning_algs import run_Lstar
regex_sul = RegexSUL(regex)
eq_oracle = StatePrefixEqOracle(alphabet, regex_sul, walks_per_state=2000,
walk_len=15)
# or replace run_Lstar with run_KV
learned_regex = run_Lstar(alphabet, regex_sul, eq_oracle, automaton_type='dfa')
return learned_regex
def learn_date_validator():
from aalpy.base import SUL
from aalpy.utils import DateValidator
from aalpy.oracles import StatePrefixEqOracle
from aalpy.learning_algs import run_Lstar
class DateSUL(SUL):
"""
An example implementation of a system under learning that
can be used to learn the behavior of the date validator.
"""
def __init__(self):
super().__init__()
# DateValidator is a black-box class used for date string verification
# The format of the dates is %d/%m/%Y'
# Its method is_date_accepted returns True if date is accepted, False otherwise
self.dv = DateValidator()
self.string = ""
def pre(self):
# reset the string used for testing
self.string = ""
pass
def post(self):
pass
def step(self, letter):
# add the input to the current string
if letter is not None:
self.string += str(letter)
# test if the current sting is accepted
return self.dv.is_date_accepted(self.string)
# instantiate the SUL
sul = DateSUL()
# define the input alphabet
alphabet = list(range(0, 9)) + ['/']
# define a equivalence oracle
eq_oracle = StatePrefixEqOracle(alphabet, sul, walks_per_state=500, walk_len=15)
# run the learning algorithm
learned_model = run_Lstar(alphabet, sul, eq_oracle, automaton_type='dfa')
# visualize the automaton
learned_model.visualize()
def random_deterministic_example_with_provided_sequences():
from random import choice, randint
from aalpy.SULs import AutomatonSUL
from aalpy.learning_algs import run_Lstar
from aalpy.utils import generate_random_deterministic_automata
random_mealy = generate_random_deterministic_automata('mealy', num_states=10,
input_alphabet_size=4, output_alphabet_size=3)
input_alphabet = random_mealy.get_input_alphabet()
sul_mealy = AutomatonSUL(random_mealy)
# samples obtained form somewhere else
samples = []
for _ in range(1000):
inputs = tuple(choice(input_alphabet) for _ in range(randint(4, 12)))
outputs = sul_mealy.query(inputs)
input_output_pair = (inputs, outputs)
samples.append(input_output_pair)
from aalpy.oracles import RandomWalkEqOracle
random_walk_eq_oracle = RandomWalkEqOracle(input_alphabet, sul_mealy, 5000)
learned_mealy = run_Lstar(input_alphabet, sul_mealy, random_walk_eq_oracle, automaton_type='mealy',
samples=samples)
def big_input_alphabet_example():
"""
Small example where input alphabet can be huge and outputs are just true and false (DFA).
Args:
input_alphabet_size: size of input alphabet
automaton_depth: depth of alternating True/False paths in final automaton
Returns:
learned model
"""
from aalpy.base import SUL
from aalpy.learning_algs import run_Lstar
from aalpy.oracles import RandomWMethodEqOracle
input_alphabet_size = 1000
automaton_depth = 4
class alternatingSUL(SUL):
def __init__(self):
super().__init__()
self.counter = 0
def pre(self):
self.counter = 0
def post(self):
pass
def step(self, letter):
if letter is None:
return False
out = letter % 2
self.counter = min(self.counter + 1, automaton_depth)
if self.counter % 2 == 1:
return not out
return out
input_al = list(range(input_alphabet_size))
sul = alternatingSUL()
eq_oracle = RandomWMethodEqOracle(input_al, sul)
model = run_Lstar(input_al, sul, eq_oracle, 'dfa', cache_and_non_det_check=False)
return model
def random_onfsm_example(num_states, input_size, output_size, n_sampling):
"""
Generate and learn random ONFSM.
:param num_states: number of states of the randomly generated automaton
:param input_size: size of the input alphabet
:param output_size: size of the output alphabet
:param n_sampling: number of times each query will be repeated to ensure that all non-determinist outputs are
observed
:return: learned ONFSM
"""
from aalpy.SULs import AutomatonSUL
from aalpy.utils import generate_random_ONFSM
from aalpy.oracles import RandomWalkEqOracle, RandomWordEqOracle
from aalpy.learning_algs import run_non_det_Lstar
onfsm = generate_random_ONFSM(num_states=num_states, num_inputs=input_size, num_outputs=output_size)
alphabet = onfsm.get_input_alphabet()
sul = AutomatonSUL(onfsm)
eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=500, reset_prob=0.15, reset_after_cex=True)
eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=8, max_walk_len=20)
learned_model = run_non_det_Lstar(alphabet, sul, eq_oracle=eq_oracle, n_sampling=n_sampling, print_level=2)
return learned_model
def random_mdp_example():
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_stochastic_Lstar
from aalpy.utils import generate_random_mdp
mdp = generate_random_mdp(num_states=10, input_size=3, output_size=3)
input_alphabet = mdp.get_input_alphabet()
sul = AutomatonSUL(mdp)
eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=5000, reset_prob=0.11,
reset_after_cex=False)
learned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle,
min_rounds=5, max_rounds=50)
return learned_mdp
def learn_python_class():
"""
Learn a Mealy machine where inputs are methods and arguments of the class that serves as SUL.
:return: Mealy machine
"""
# class
from aalpy.SULs import PyClassSUL, FunctionDecorator
from aalpy.oracles import StatePrefixEqOracle
from aalpy.learning_algs import run_Lstar
from aalpy.utils import MockMqttExample
mqtt = MockMqttExample
input_al = [FunctionDecorator(mqtt.connect), FunctionDecorator(mqtt.disconnect),
FunctionDecorator(mqtt.subscribe, 'topic'), FunctionDecorator(mqtt.unsubscribe, 'topic'),
FunctionDecorator(mqtt.publish, 'topic')]
sul = PyClassSUL(mqtt)
eq_oracle = StatePrefixEqOracle(input_al, sul, walks_per_state=20, walk_len=20)
mealy = run_Lstar(input_al, sul, eq_oracle=eq_oracle, automaton_type='mealy', cache_and_non_det_check=True)
mealy.visualize()
def mqtt_example():
from aalpy.base import SUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_Lstar
from aalpy.utils import MockMqttExample
class MQTT_SUL(SUL):
def __init__(self):
super().__init__()
self.mqtt = MockMqttExample()
def pre(self):
self.mqtt.state = 'CONCLOSED'
def post(self):
self.mqtt.topics.clear()
def step(self, letter):
if letter == 'connect':
return self.mqtt.connect()
elif letter == 'disconnect':
return self.mqtt.disconnect()
elif letter == 'publish':
return self.mqtt.publish(topic='test')
elif letter == 'subscribe':
return self.mqtt.subscribe(topic='test')
else:
return self.mqtt.unsubscribe(topic='test')
sul = MQTT_SUL()
input_al = ['connect', 'disconnect', 'publish', 'subscribe', 'unsubscribe']
eq_oracle = RandomWalkEqOracle(input_al, sul, num_steps=2000, reset_after_cex=True, reset_prob=0.15)
mealy = run_Lstar(input_al, sul, eq_oracle=eq_oracle, automaton_type='mealy', cache_and_non_det_check=True,
print_level=3)
mealy.visualize()
def onfsm_mealy_paper_example():
"""
Learning a ONFSM presented in 'Learning Finite State Models of Observable Nondeterministic Systems in a Testing
Context'.
:return: learned ONFSM
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWordEqOracle
from aalpy.learning_algs import run_non_det_Lstar
from aalpy.utils import get_benchmark_ONFSM
onfsm = get_benchmark_ONFSM()
alphabet = onfsm.get_input_alphabet()
sul = AutomatonSUL(onfsm)
eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=5, max_walk_len=12)
learned_onfsm = run_non_det_Lstar(alphabet, sul, eq_oracle, n_sampling=10, print_level=2)
return learned_onfsm
def multi_client_mqtt_example():
"""
Example from paper P'Learning Abstracted Non-deterministic Finite State Machines'.
https://link.springer.com/chapter/10.1007/978-3-030-64881-7_4
Returns:
learned automaton
"""
import random
from aalpy.base import SUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_abstracted_ONFSM_Lstar
from aalpy.SULs import AutomatonSUL
from aalpy.utils import load_automaton_from_file
class Multi_Client_MQTT_Mapper(SUL):
def __init__(self):
super().__init__()
five_clients_mqtt_mealy = load_automaton_from_file('DotModels/five_clients_mqtt_abstracted_onfsm.dot',
automaton_type='mealy')
self.five_client_mqtt = AutomatonSUL(five_clients_mqtt_mealy)
self.connected_clients = set()
self.subscribed_clients = set()
self.clients = ('c0', 'c1', 'c2', 'c3', 'c4')
def get_input_alphabet(self):
return ['connect', 'disconnect', 'subscribe', 'unsubscribe', 'publish']
def pre(self):
self.five_client_mqtt.pre()
def post(self):
self.five_client_mqtt.post()
self.connected_clients = set()
self.subscribed_clients = set()
def step(self, letter):
client = random.choice(self.clients)
inp = client + '_' + letter
concrete_output = self.five_client_mqtt.step(inp)
all_out = ''
if letter == 'connect':
if client not in self.connected_clients:
self.connected_clients.add(client)
elif client in self.connected_clients:
self.connected_clients.remove(client)
if client in self.subscribed_clients:
self.subscribed_clients.remove(client)
if len(self.subscribed_clients) == 0:
all_out = '_UNSUB_ALL'
elif letter == 'subscribe' and client in self.connected_clients:
self.subscribed_clients.add(client)
elif letter == 'disconnect' and client in self.connected_clients:
self.connected_clients.remove(client)
if client in self.subscribed_clients:
self.subscribed_clients.remove(client)
if len(self.subscribed_clients) == 0:
all_out = '_UNSUB_ALL'
elif letter == 'unsubscribe' and client in self.connected_clients:
if client in self.subscribed_clients:
self.subscribed_clients.remove(client)
if len(self.subscribed_clients) == 0:
all_out = '_ALL'
concrete_outputs = concrete_output.split('__')
abstract_outputs = set([e[3:] for e in concrete_outputs])
if 'Empty' in abstract_outputs:
abstract_outputs.remove('Empty')
if abstract_outputs == {'CONCLOSED'}:
if len(self.connected_clients) == 0:
all_out = '_ALL'
return 'CONCLOSED' + all_out
else:
if 'CONCLOSED' in abstract_outputs:
abstract_outputs.remove('CONCLOSED')
abstract_outputs = sorted(list(abstract_outputs))
output = '_'.join(abstract_outputs)
return '_'.join(set(output.split('_'))) + all_out
sul = Multi_Client_MQTT_Mapper()
alphabet = sul.get_input_alphabet()
eq_oracle = RandomWalkEqOracle(alphabet, sul, num_steps=5000, reset_prob=0.09, reset_after_cex=True)
abstraction_mapping = {
'CONCLOSED': 'CONCLOSED',
'CONCLOSED_UNSUB_ALL': 'CONCLOSED',
'CONCLOSED_ALL': 'CONCLOSED',
'UNSUBACK': 'UNSUBACK',
'UNSUBACK_ALL': 'UNSUBACK'
}
learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle, abstraction_mapping=abstraction_mapping,
n_sampling=200, print_level=3)
return learned_onfsm
def abstracted_onfsm_example():
"""
Learning an abstracted ONFSM. The original ONFSM has 9 states.
The learned abstracted ONFSM only has 3 states.
:return: learned abstracted ONFSM
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWordEqOracle
from aalpy.learning_algs import run_abstracted_ONFSM_Lstar
from aalpy.utils import get_ONFSM
onfsm = get_ONFSM()
alphabet = onfsm.get_input_alphabet()
sul = AutomatonSUL(onfsm)
eq_oracle = RandomWordEqOracle(alphabet, sul, num_walks=500, min_walk_len=4, max_walk_len=8, reset_after_cex=True)
abstraction_mapping = {0: 0, 'O': 0}
learned_onfsm = run_abstracted_ONFSM_Lstar(alphabet, sul, eq_oracle=eq_oracle,
abstraction_mapping=abstraction_mapping,
n_sampling=50, print_level=3)
return learned_onfsm
def faulty_coffee_machine_mdp_example(automaton_type='mdp'):
"""
Learning faulty coffee machine that can be found in Chapter 5 and Chapter 7 of Martin's Tappler PhD thesis.
:automaton_type either mdp or smm
:return learned MDP
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWalkEqOracle
from aalpy.learning_algs import run_stochastic_Lstar
from aalpy.utils import get_faulty_coffee_machine_MDP
mdp = get_faulty_coffee_machine_MDP()
input_alphabet = mdp.get_input_alphabet()
sul = AutomatonSUL(mdp)
eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=500, reset_prob=0.11,
reset_after_cex=False)
learned_mdp = run_stochastic_Lstar(input_alphabet, sul, automaton_type=automaton_type,
eq_oracle=eq_oracle, n_c=20, n_resample=100, min_rounds=3,
max_rounds=50, print_level=3, cex_processing='longest_prefix',
samples_cex_strategy='bfs')
return learned_mdp
def weird_coffee_machine_mdp_example():
"""
Learning faulty coffee machine that can be found in Chapter 5 and Chapter 7 of Martin's Tappler PhD thesis.
:return learned MDP
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWordEqOracle
from aalpy.learning_algs import run_stochastic_Lstar
from aalpy.utils import get_weird_coffee_machine_MDP
mdp = get_weird_coffee_machine_MDP()
input_alphabet = mdp.get_input_alphabet()
sul = AutomatonSUL(mdp)
eq_oracle = RandomWordEqOracle(input_alphabet, sul=sul, num_walks=2000, min_walk_len=4, max_walk_len=10,
reset_after_cex=True)
learned_mdp = run_stochastic_Lstar(input_alphabet, sul, eq_oracle, n_c=20, n_resample=1000, min_rounds=10,
max_rounds=500, strategy='normal', cex_processing=None,
samples_cex_strategy=None, automaton_type='smm')
return learned_mdp
def benchmark_stochastic_example(example, automaton_type='smm', n_c=20, n_resample=1000, min_rounds=10, max_rounds=500,
strategy='normal', cex_processing='longest_prefix', stopping_based_on_prop=None,
samples_cex_strategy=None):
"""
Learning the stochastic Mealy Machine(SMM) various benchmarking examples
found in Chapter 7 of Martin's Tappler PhD thesis.
:param n_c: cutoff for a state to be considered complete
:param automaton_type: either smm (stochastic mealy machine) or mdp (Markov decision process)
:param n_resample: resampling size
:param example: One of ['first_grid', 'second_grid', 'shared_coin', 'slot_machine']
:param min_rounds: minimum number of learning rounds
:param max_rounds: maximum number of learning rounds
:param strategy: normal, classic or chi2
:param cex_processing: counterexample processing strategy
:stopping_based_on_prop: a tuple (path to properties, correct values, error bound)
:param samples_cex_strategy: strategy to sample cex in the trace tree
:return: learned SMM
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWalkEqOracle, RandomWordEqOracle
from aalpy.learning_algs import run_stochastic_Lstar
from aalpy.utils import load_automaton_from_file
# Specify the path to the dot file containing a MDP
mdp = load_automaton_from_file(f'./DotModels/MDPs/{example}.dot', automaton_type='mdp')
input_alphabet = mdp.get_input_alphabet()
sul = AutomatonSUL(mdp)
eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15,
reset_after_cex=True)
eq_oracle = RandomWalkEqOracle(input_alphabet, sul=sul, num_steps=2000, reset_prob=0.25,
reset_after_cex=True)
learned_mdp = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, n_c=n_c,
n_resample=n_resample, min_rounds=min_rounds, max_rounds=max_rounds,
automaton_type=automaton_type, strategy=strategy, cex_processing=cex_processing,
samples_cex_strategy=samples_cex_strategy, target_unambiguity=0.99,
property_based_stopping=stopping_based_on_prop)
return learned_mdp
def custom_stochastic_example(stochastic_machine, learning_type='smm', min_rounds=10, max_rounds=500):
"""
Learning custom SMM.
:param stochastic_machine: stochastic Mealy machine or MDP to learn
:param learning_type: 'smm' or 'mdp'
:param min_rounds: minimum number of learning rounds
:param max_rounds: maximum number of learning rounds
:return: learned model
"""
from aalpy.SULs import AutomatonSUL
from aalpy.oracles import RandomWordEqOracle
from aalpy.learning_algs import run_stochastic_Lstar
input_al = stochastic_machine.get_input_alphabet()
sul = AutomatonSUL(stochastic_machine)
eq_oracle = RandomWordEqOracle(alphabet=input_al, sul=sul, num_walks=1000, min_walk_len=10, max_walk_len=30,
reset_after_cex=True)
learned_model = run_stochastic_Lstar(input_al, sul, eq_oracle,
automaton_type=learning_type,
min_rounds=min_rounds,
max_rounds=max_rounds,
print_level=2)
return learned_model
def learn_stochastic_system_and_do_model_checking(example, automaton_type='smm', n_c=20, n_resample=1000, min_rounds=10,
max_rounds=500, strategy='normal', cex_processing='longest_prefix',
stopping_based_on_prop=None, samples_cex_strategy=None):
import aalpy.paths
from aalpy.automata import StochasticMealyMachine
from aalpy.utils import model_check_experiment, get_properties_file, get_correct_prop_values
from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion
aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat"
aalpy.paths.path_to_properties = "Benchmarking/prism_eval_props/"
learned_model = benchmark_stochastic_example(example, automaton_type, n_c, n_resample, min_rounds, max_rounds,
strategy,
cex_processing, stopping_based_on_prop, samples_cex_strategy)
if isinstance(learned_model, StochasticMealyMachine):
mdp = smm_to_mdp_conversion(learned_model)
else:
mdp = learned_model
values, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), mdp)
print('Value for each property:', [round(d * 100, 2) for d in values.values()])
print('Error for each property:', [round(d * 100, 2) for d in diff.values()])
def alergia_mdp_example():
from aalpy.SULs import AutomatonSUL
from random import randint, choice
from aalpy.learning_algs import run_Alergia
from aalpy.utils import generate_random_mdp
mdp = generate_random_mdp(5, 2, 3)
initial_output = mdp.initial_state.output
sul = AutomatonSUL(mdp)
inputs = mdp.get_input_alphabet()
data = []
for _ in range(100000):
str_len = randint(5, 12)
# add the initial output
sul.pre()
seq = [initial_output]
for _ in range(str_len):
i = choice(inputs)
o = sul.step(i)
seq.append((i, o))
sul.post()
data.append(seq)
# run alergia with the data and automaton_type set to 'mdp' to True to learn a MDP
model = run_Alergia(data, automaton_type='mdp', eps=0.05, print_info=True)
model.visualize()
def alergia_smm_example():
from aalpy.SULs import AutomatonSUL
from random import randint, choice
from aalpy.learning_algs import run_Alergia
from aalpy.utils import generate_random_smm
smm = generate_random_smm(5, 2, 2)
sul = AutomatonSUL(smm)
inputs = smm.get_input_alphabet()
data = []
for _ in range(100000):
str_len = randint(5, 15)
sul.pre()
seq = []
for _ in range(str_len):
i = choice(inputs)
o = sul.step(i)
seq.append((i, o))
sul.post()
data.append(seq)
# run alergia with the data and automaton_type set to 'mdp' to True to learn a MDP
model = run_Alergia(data, automaton_type='smm', eps=0.05, print_info=True)
model.visualize()
return model
def alergia_mc_example_with_loaded_data():
from os import remove
from aalpy.SULs import AutomatonSUL
from random import randint
from aalpy.learning_algs import run_Alergia
from aalpy.utils import generate_random_markov_chain
from aalpy.utils import CharacterTokenizer
mc = generate_random_markov_chain(10)
initial_output = mc.initial_state.output
mc.visualize('Original')
sul = AutomatonSUL(mc)
# note that this example shows writing to file just to show how tokenizer is used...
# this step can ofc be skipped and lists passed to alergia
data = []
for _ in range(20000):
sul.pre()
str_len = randint(4, 12)
seq = [f'{initial_output}']
for _ in range(str_len):
o = sul.step()
seq.append(f'{o}')
sul.post()
data.append(''.join(seq))
with open('mcData.txt', 'w') as file:
for seq in data:
file.write(f'{seq}\n')
file.close()
# create tokenizer
tokenizer = CharacterTokenizer()
# parse data
data = tokenizer.tokenize_data('mcData.txt')
# run alergia with the data and automaton_type set to 'mc' to learn a Markov Chain
model = run_Alergia(data, automaton_type='mc', eps=0.05, print_info=True)
# print(model)
model.visualize()
remove('mcData.txt')
return model
def jAlergiaExample():
from aalpy.learning_algs import run_JAlergia
# if you need more heap space check
model = run_JAlergia(path_to_data_file='jAlergia/exampleMdpData.txt', automaton_type='mdp', eps=0.05,
path_to_jAlergia_jar='jAlergia/alergia.jar')
# # alternatively pass the data in following format
# mc_data = [[1,2,3,4,5], [1,2,3,4,2,1], [1,3,5,2,3]]
# mdp_data = [[1,2,3,1,2], [1,3,6,4,2]]
# model = run_JAlergia(path_to_data_file=mc_data, automaton_type='mdp', eps=0.05,
# path_to_jAlergia_jar='jAlergia/alergia.jar', optimize_for='memory')
model.visualize()
return model
def active_alergia_example(example='first_grid'):
from random import choice, randint
from aalpy.SULs import AutomatonSUL
from aalpy.utils import load_automaton_from_file
from aalpy.learning_algs import run_active_Alergia
from aalpy.learning_algs.stochastic_passive.ActiveAleriga import RandomWordSampler
mdp = load_automaton_from_file(f'./DotModels/MDPs/{example}.dot', automaton_type='mdp')
input_alphabet = mdp.get_input_alphabet()
sul = AutomatonSUL(mdp)
data = []
for _ in range(50000):
input_query = tuple(choice(input_alphabet) for _ in range(randint(6, 14)))
outputs = sul.query(input_query)
# format data in [O, (I, O), (I, O)...]
formatted_io = [outputs.pop(0)]
for i, o in zip(input_query, outputs):
formatted_io.append((i, o))
data.append(formatted_io)
sampler = RandomWordSampler(num_walks=1000, min_walk_len=8, max_walk_len=20)
model = run_active_Alergia(data, sul, sampler, n_iter=10)
print(model)
def rpni_example():
data = [(('a', 'a', 'a'), True),
(('a', 'a', 'b', 'a'), True),
(('b', 'b', 'a'), True),
(('b', 'b', 'a', 'b', 'a'), True),
(('a',), False),
(('b', 'b'), False),
(('a', 'a', 'b'), False),
(('a', 'b', 'a'), False)]
from aalpy.learning_algs import run_RPNI
model = run_RPNI(data, automaton_type='dfa')
model.visualize()
def rpni_check_model_example():
import random
from aalpy.SULs import AutomatonSUL
from aalpy.learning_algs import run_RPNI
from aalpy.oracles import StatePrefixEqOracle
from aalpy.utils import generate_random_moore_machine, generate_random_dfa
model = generate_random_dfa(num_states=5, alphabet=[1, 2, 3], num_accepting_states=2)
model = generate_random_moore_machine(num_states=5, input_alphabet=[1, 2, 3], output_alphabet=['a', 'b'])
input_al = model.get_input_alphabet()
num_sequences = 1000
data = []
for _ in range(num_sequences):
seq_len = random.randint(1, 20)
random_seq = random.choices(input_al, k=seq_len)
output = model.compute_output_seq(model.initial_state, random_seq)[-1]
data.append((random_seq, output))
rpni_model = run_RPNI(data, automaton_type='moore', print_info=True)
rpni_model.make_input_complete('sink_state')
sul = AutomatonSUL(model)
eq_oracle_2 = StatePrefixEqOracle(input_al, sul, walks_per_state=100)
cex = eq_oracle_2.find_cex(rpni_model)
# or simply do
# if rpni_model != model
if cex is None:
print("Could not find a counterexample between the RPNI-model and the original model.")
else:
print('Counterexample found. Either RPNI data was incomplete, or there is a bug in RPNI algorithm :o ')
def rpni_mealy_example():
import random
from aalpy.learning_algs import run_RPNI
from aalpy.utils import generate_random_deterministic_automata
from aalpy.utils.HelperFunctions import all_prefixes
# make reproducible
random.seed(1)
model = generate_random_deterministic_automata(automaton_type='mealy', num_states=5,
input_alphabet_size=3, output_alphabet_size=4)
# model = load_automaton_from_file('DotModels/Bluetooth/bluetooth_model.dot', automaton_type='mealy')
input_al = model.get_input_alphabet()
num_sequences = 1000
data = []
for _ in range(num_sequences):
seq_len = random.randint(1, 10)
random_seq = random.choices(input_al, k=seq_len)
# make sure that all prefixes all included in the dataset
for prefix in all_prefixes(random_seq):
output = model.compute_output_seq(model.initial_state, prefix)[-1]
data.append((prefix, output))
rpni_model = run_RPNI(data, automaton_type='mealy', print_info=True)
return rpni_model
def random_active_rpni_example():
import random
from aalpy.learning_algs import run_active_RPNI
from aalpy.learning_algs.deterministic_passive.active_RPNI import RandomWordSampler
from aalpy.utils import generate_random_deterministic_automata
from aalpy.utils.HelperFunctions import all_prefixes
from aalpy.SULs import AutomatonSUL
model = generate_random_deterministic_automata('mealy', num_states=50,
input_alphabet_size=3, output_alphabet_size=5)
input_al = model.get_input_alphabet()
num_sequences = 100
data = []
for _ in range(num_sequences):
seq_len = random.randint(1, 20)
random_seq = random.choices(input_al, k=seq_len)
# make sure that all prefixes all included in the dataset
for prefix in all_prefixes(random_seq):
output = model.compute_output_seq(model.initial_state, prefix)[-1]
data.append((prefix, output))
sampler = RandomWordSampler(500, 5, 25)
sul = AutomatonSUL(model)
active_rpni_model = run_active_RPNI(data, sul, sampler=sampler, n_iter=5,
automaton_type='mealy', print_info=True)
return active_rpni_model
def compare_stochastic_and_non_deterministic_learning(example='first_grid'):
import aalpy.paths
aalpy.paths.path_to_prism = "C:/Program Files/prism-4.6/bin/prism.bat"
aalpy.paths.path_to_properties = "Benchmarking/prism_eval_props/"
from aalpy.SULs import AutomatonSUL
from aalpy.automata import StochasticMealyMachine
from aalpy.automata.StochasticMealyMachine import smm_to_mdp_conversion
from aalpy.learning_algs import run_stochastic_Lstar, run_non_det_Lstar
from aalpy.utils import load_automaton_from_file, model_check_experiment, get_properties_file, \
get_correct_prop_values
from aalpy.oracles import RandomWordEqOracle
mdp = load_automaton_from_file(f'./DotModels/MDPs/first_grid.dot', automaton_type='mdp')
input_alphabet = mdp.get_input_alphabet()
# Stochastic Learning
print("Stochastic Learning")
sul = AutomatonSUL(mdp)
eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15,
reset_after_cex=True)
stochastic_learned_model = run_stochastic_Lstar(input_alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul,
automaton_type='smm', target_unambiguity=0.99)
# Non Deterministic Learning
print("Non-deterministic Learning")
sul = AutomatonSUL(mdp)
eq_oracle = RandomWordEqOracle(input_alphabet, sul, num_walks=100, min_walk_len=5, max_walk_len=15,
reset_after_cex=True)
non_det_model = run_non_det_Lstar(alphabet=input_alphabet, eq_oracle=eq_oracle, sul=sul, n_sampling=5,
stochastic=True, print_level=2)
for model_type, model in [('Stochastic Learning', stochastic_learned_model),
('Non-deterministic learning', non_det_model)]:
if isinstance(model, StochasticMealyMachine):
mdp = smm_to_mdp_conversion(model)
else:
mdp = model
values, diff = model_check_experiment(get_properties_file(example), get_correct_prop_values(example), mdp)
print(model_type)
print('Error for each property:', [round(d * 100, 2) for d in diff.values()])
def learning_context_free_grammar_example():
from aalpy.automata import SevpaAlphabet
from aalpy.learning_algs import run_KV
from aalpy.oracles import RandomWordEqOracle
from aalpy.utils.BenchmarkSULs import get_balanced_string_sul
call_return_map = {'(': ')', '[': ']'}
sevpa_alphabet = SevpaAlphabet([], list(call_return_map.keys()), list(call_return_map.values()))
# bounded deterministic approximation
balanced_string_sul = get_balanced_string_sul(call_return_map, allow_empty_string=False)
eq_oracle = RandomWordEqOracle(sevpa_alphabet.get_merged_alphabet(), balanced_string_sul, num_walks=1000,
min_walk_len=5, max_walk_len=30)
learned_deterministic_approximation = run_KV(sevpa_alphabet.get_merged_alphabet(),
balanced_string_sul, eq_oracle, automaton_type='dfa',
max_learning_rounds=20)
balanced_string_sul = get_balanced_string_sul(call_return_map, allow_empty_string=False)
eq_oracle = RandomWordEqOracle(sevpa_alphabet.get_merged_alphabet(), balanced_string_sul, num_walks=1000,
min_walk_len=5, max_walk_len=30)
learned_model = run_KV(sevpa_alphabet, balanced_string_sul, eq_oracle, automaton_type='vpa')
learned_model.visualize()
def arithmetic_expression_sevpa_learning():
from aalpy.base import SUL
from aalpy.automata import SevpaAlphabet
from aalpy.oracles import RandomWordEqOracle
from aalpy.learning_algs import run_KV
import warnings
import ast
warnings.filterwarnings("ignore")
class ArithmeticSUL(SUL):
def __init__(self):
super().__init__()
self.string_under_test = ''
def pre(self):
self.string_under_test = ''
def post(self):
pass
def step(self, letter):
if letter:
self.string_under_test += ' ' + letter if len(self.string_under_test) > 0 else letter
try:
# Parse the expression using ast
parsed_expr = ast.parse(self.string_under_test, mode='eval')
# Check if the parsed expression is a valid arithmetic expression
is_valid = all(isinstance(node, (ast.Expression, ast.BinOp, ast.UnaryOp, ast.Num, ast.Name, ast.Load))
or isinstance(node, ast.operator) or isinstance(node, ast.expr_context)
or (isinstance(node, ast.BinOp) and isinstance(node.op, ast.operator))
for node in ast.walk(parsed_expr))
return is_valid
except SyntaxError:
return False