def run_active_Alergia(data, sul, sampler, n_iter):
"""
Active version of IOAlergia algorithm. Based on intermediate hypothesis sampling on the system is performed.
Sampled data is added to the learning data and more accurate model is learned.
Proposed in "Aichernig and Tappler, Probabilistic Black-Box Reachability Checking"
Args:
data: initial learning data, in form [[O, (I,O), (I,O)...] ,...] where O is outputs and I input.
sul: system under learning which is basis for sampling
sampler: instance of Sampler class
n_iter: number of iterations of active learning
Returns:
learned MDP
"""
model = None
for i in range(n_iter):
print(f'Active Alergia Iteration: {i}')
model = run_Alergia(data, automaton_type='mdp')
new_samples = sampler.sample(sul, model)
data.extend(new_samples)
return model
def alergia_smm_example():
from aalpy.SULs import StochasticMealySUL
from random import randint, choice
from aalpy.learning_algs import run_Alergia
from aalpy.utils import visualize_automaton, generate_random_smm
smm = generate_random_smm(5, 2, 5)
visualize_automaton(smm, path='Original')
sul = StochasticMealySUL(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=0.005, print_info=True)
visualize_automaton(model)
return model
def run_active_Alergia(data,
sul,
sampler,
n_iter,
eps=0.005,
compatibility_checker=None,
automaton_type='mdp',
print_info=True):
"""
Active version of IOAlergia algorithm. Based on intermediate hypothesis sampling on the system is performed.
Sampled data is added to the learning data and more accurate model is learned.
Proposed in "Aichernig and Tappler, Probabilistic Black-Box Reachability Checking"
Args:
data: initial learning data, in form [[O, (I,O), (I,O)...] ,...] where O is outputs and I input.
sul: system under learning which is basis for sampling
sampler: instance of Sampler class
n_iter: number of iterations of active learning
eps: epsilon value if the default checker is used. Look in run_Alergia for description
compatibility_checker: passed to run_Alergia, check there for description
automaton_type: either 'mdp' or 'smm' (Markov decision process or Stochastic Mealy Machine)
print_info: print current learning iteration
Returns:
learned MDP
"""
model = None
for i in range(n_iter):
if print_info:
print(f'Active Alergia Iteration: {i}')
model = run_Alergia(data,
automaton_type='mdp',
eps=eps,
compatibility_checker=compatibility_checker)
new_samples = sampler.sample(sul, model)
data.extend(new_samples)
return model
def alergia_mc_example():
from os import remove
from aalpy.SULs import McSUL
from random import randint
from aalpy.learning_algs import run_Alergia
from aalpy.utils import visualize_automaton, generate_random_markov_chain
from aalpy.utils import CharacterTokenizer
mc = generate_random_markov_chain(10)
visualize_automaton(mc, path='Original')
sul = McSUL(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):
str_len = randint(4, 12)
seq = [f'{sul.pre()}']
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=0.005, print_info=True)
# print(model)
visualize_automaton(model)
remove('mcData.txt')
return model
mdp_sul = MdpSUL(original_mdp)
for _ in range(1):
data = []
for _ in range(num_traces):
sample = [mdp_sul.pre()]
for _ in range(random.randint(10, 50)):
i = random.choice(input_alphabet)
o = mdp_sul.step(i)
sample.append((i, o))
data.append(sample)
mdp_sul.post()
learned_mdp = run_Alergia(data, automaton_type='mdp')
for s in data:
s.pop(0)
learned_smm = run_Alergia(data, automaton_type='smm')
smm_2_mdp = smm_to_mdp_conversion(learned_smm)
mdp_results, mdp_err = model_check_experiment(get_properties_file(exp_name),
get_correct_prop_values(exp_name), learned_mdp)
smm_results, smm_err = model_check_experiment(get_properties_file(exp_name),
get_correct_prop_values(exp_name), smm_2_mdp)
print(learned_mdp.size, learned_smm.size, smm_2_mdp.size)
print(f'-------{exp_name}---------')