def train_and_extract_tomita(tomita_grammar,
acc_stop=1.,
loss_stop=0.005,
load=False):
tomita_alphabet = ["0", "1"]
if not load:
rnn = train_RNN_on_tomita_grammar(tomita_grammar,
acc_stop=acc_stop,
loss_stop=loss_stop)
else:
rnn = train_RNN_on_tomita_grammar(tomita_grammar, train=False)
rnn.load(f"RNN_Models/tomita_{tomita_grammar}.model")
sul = RnnBinarySUL(rnn)
alphabet = tomita_alphabet
state_eq_oracle = StatePrefixEqOracle(alphabet,
sul,
walks_per_state=1000,
walk_len=5)
dfa = run_Lstar(alphabet=alphabet,
sul=sul,
eq_oracle=state_eq_oracle,
automaton_type='dfa',
cache_and_non_det_check=True)
save_automaton_to_file(dfa,
f'LearnedAutomata/learned_tomita{tomita_grammar}')
visualize_automaton(dfa)
def train_and_extract_bp(path="TrainingDataAndAutomata/balanced()_1.txt",
load=False):
bp_alphabet = list(string.ascii_lowercase + "()")
x, y = parse_data(path)
x_train, y_train, x_test, y_test = preprocess_binary_classification_data(
x, y, bp_alphabet)
# CHANGE PARAMETERS OF THE RNN if you want
rnn = RNNClassifier(bp_alphabet,
output_dim=2,
num_layers=2,
hidden_dim=50,
x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test,
batch_size=18,
nn_type="GRU")
data_index = path[-5]
if not load:
rnn.train(stop_acc=1., stop_epochs=3, verbose=True)
rnn.save(f"RNN_Models/balanced_parentheses{data_index}.rnn")
else:
rnn.load(f"RNN_Models/balanced_parentheses{data_index}.rnn")
sul = RnnBinarySUL(rnn)
alphabet = bp_alphabet
state_eq_oracle = TransitionFocusOracle(alphabet,
sul,
num_random_walks=500,
walk_len=30,
same_state_prob=0.3)
dfa = run_Lstar(alphabet=alphabet,
sul=sul,
eq_oracle=state_eq_oracle,
automaton_type='dfa',
cache_and_non_det_check=False,
max_learning_rounds=5)
save_automaton_to_file(
dfa, f'LearnedAutomata/balanced_parentheses{data_index}')
return dfa
def __str__(self):
"""
:return: A string representation of the automaton
"""
from aalpy.utils import save_automaton_to_file
return save_automaton_to_file(self,
path='learnedModel',
file_type='string')
visualize_automaton(learned_model, display_same_state_trans=True)
return learned_model
def learn_coffee_machine_mbd(visualize=False):
sul = FaultInjectedCoffeeMachineSUL()
alphabet = ['coin', 'button', 'coin_double_value', 'button_no_effect']
eq_oracle = RandomWMethodEqOracle(alphabet,
sul,
walks_per_state=5000,
walk_len=20)
learned_model = run_Lstar(alphabet,
sul,
eq_oracle,
automaton_type='mealy',
cache_and_non_det_check=False)
if visualize:
visualize_automaton(learned_model, display_same_state_trans=True)
return learned_model
if __name__ == '__main__':
model = learn_crossroad(False)
save_automaton_to_file(model, path='CrossroadModelFull')
#visualize_automaton(model, display_same_state_trans=True)
def save(self, file_path='LearnedModel'):
from aalpy.utils import save_automaton_to_file
save_automaton_to_file(self, path=file_path)
sul = RnnBinarySUL(rnn)
alphabet = tomita_alphabet
state_eq_oracle = StatePrefixEqOracle(alphabet,
sul,
walks_per_state=200,
walk_len=6)
dfa = run_Lstar(alphabet=alphabet,
sul=sul,
eq_oracle=state_eq_oracle,
automaton_type='dfa',
cache_and_non_det_check=True)
save_automaton_to_file(dfa, f'RNN_Models/tomita{3}')
visualize_automaton(dfa)
# train and extract balanced parentheses
bp_model = train_and_extract_bp(
path='TrainingDataAndAutomata/balanced()_2.txt', load=False)
print("Print extracted model")
print(bp_model)
# train and learn mealy machine example
coffee_machine_automaton = train_RNN_and_extract_FSM('coffee')
save_automaton_to_file(coffee_machine_automaton, 'CoffeeMachineModel')
mqtt_automaton = train_RNN_and_extract_FSM('mqtt')
save_automaton_to_file(coffee_machine_automaton, 'MqttModel')