class BDDModel:
"""
A Binary Decision Diagram (BDD) representation of the feature model given as a CNF formula.
It relies on the dd module: https://pypi.org/project/dd/
"""
AND = '&'
OR = '|'
NOT = '!'
def __init__(self, feature_model: FeatureModel, cnf_formula: str):
self.feature_model = feature_model
self.cnf = cnf_formula.replace('-', '')
self.variables = self._extract_variables(self.cnf)
self.bdd = BDD() # Instantiate a manager
self.declare_variables(self.variables) # Declare variables
self.expression = self.bdd.add_expr(self.cnf)
def _extract_variables(self, cnf_formula: str) -> list[str]:
variables = set()
for v in cnf_formula.split():
if BDDModel.AND not in v and BDDModel.OR not in v:
var = v.strip().replace('(', '').replace(')', '').replace(
BDDModel.NOT, '')
variables.add(var)
return list(variables)
def declare_variables(self, variables: list[str]):
for v in variables:
self.bdd.declare(v)
def serialize(self, filepath: str, filetype: str = 'png'):
self.bdd.dump(filename=filepath,
roots=[self.expression],
filetype=filetype)
def get_number_of_configurations(
self,
selected_features: list[Feature] = None,
deselected_features: list[Feature] = None) -> int:
if not selected_features:
expr = self.cnf
else:
expr = f' {BDDModel.AND} '.join(
[f.name for f in selected_features])
if deselected_features:
expr += f' {BDDModel.AND} ' + f' {BDDModel.AND} !'.join(
[f.name for f in deselected_features])
expr += f' {BDDModel.AND} ' + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(expr)
return self.bdd.count(u, nvars=len(self.variables))
def get_configurations(
self,
selected_features: list[Feature] = None,
deselected_features: list[Feature] = None
) -> list[FMConfiguration]:
if not selected_features:
expr = self.cnf
else:
expr = f' {BDDModel.AND} '.join(
[f.name for f in selected_features])
if deselected_features:
expr += f' {BDDModel.AND} ' + f' {BDDModel.AND} '.join(
['!' + f.name for f in deselected_features])
expr += f' {BDDModel.AND} ' + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(expr)
configs = []
for c in self.bdd.pick_iter(u, care_vars=self.variables):
elements = {
self.feature_model.get_feature_by_name(f): True
for f in c.keys() if c[f]
}
configs.append(FMConfiguration(elements))
return configs
class BDDModel:
"""
A Binary Decision Diagram (BDD) representation of the feature model given as a CNF formula.
It relies on the dd module: https://pypi.org/project/dd/
"""
AND = '&'
OR = '|'
NOT = '!'
def __init__(self, cnf_formula: str):
self.cnf = cnf_formula.replace('-', '')
self.variables = self._extract_variables(self.cnf)
self.bdd = BDD() # Instantiate a manager
self.declare_variables(self.variables) # Declare variables
self.expression = self.bdd.add_expr(self.cnf)
def _extract_variables(self, cnf_formula: str) -> list[str]:
variables = set()
for v in cnf_formula.split():
if BDDModel.AND not in v and BDDModel.OR not in v:
var = v.strip().replace('(', '').replace(')', '').replace(
BDDModel.NOT, '')
variables.add(var)
return list(variables)
def declare_variables(self, variables: list[str]):
for v in variables:
self.bdd.declare(v)
def serialize(self, filepath: str, filetype: str = 'png'):
self.bdd.dump(filename=filepath,
roots=[self.expression],
filetype=filetype)
def get_number_of_configurations(self,
features: list[Feature] = None) -> int:
if features is None:
return self.bdd.count(self.expression, nvars=len(self.variables))
expr = f' {BDDModel.AND} '.join([
f.name for f in features
]) + f' {BDDModel.AND} ' + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(expr)
return self.bdd.count(u, nvars=len(self.variables))
def get_configurations(self,
features: list[Feature] = None
) -> list[FMConfiguration]:
if features is None:
expr = self.cnf
else:
expr = f' {BDDModel.AND} '.join([
f.name for f in features
]) + f' {BDDModel.AND} ' + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(expr)
configs = []
for c in self.bdd.pick_iter(u, care_vars=self.variables):
configs.append(sorted([f for f in c.keys() if c[f]]))
return configs
def get_uniform_random_sample(self, size: int) -> list[list[str]]:
"""This generates all configurations."""
configs = self.get_configurations()
if size > len(configs):
size = len(configs)
return random.sample(configs, size)
def get_random_configuration(self) -> list[str]:
"""This follows the Knut algorithm, but needs to be optimized"""
solutions = self.bdd.count(self.expression, nvars=len(self.variables))
expr = ""
variables = list(self.variables)
while solutions > 1:
feature = random.choice(variables)
variables.remove(feature)
possible_expr = expr + f' {BDDModel.AND} '.join(
[feature]) + f' {BDDModel.AND} '
formula = possible_expr + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(formula)
solutions = self.bdd.count(u, nvars=len(self.variables))
if solutions <= 0:
possible_expr = expr + f' {BDDModel.AND} '.join(
['!' + feature]) + f' {BDDModel.AND} '
formula = possible_expr + '{x}'.format(x=self.expression)
u = self.bdd.add_expr(formula)
solutions = self.bdd.count(u, nvars=len(self.variables))
expr = possible_expr
config = self.bdd.pick(u)
return sorted([f for f in config.keys() if config[f]])
def get_sample_of_configurations(self, size: int) -> list[list[str]]:
"""
Bad implementation, we need to:
The original algorithm by Knuth is specified on BDDs very efficiently, as the probabilities required for all the possible SAT solutions are computed just once with a single BDD traversal,
and then reused every time a solution is generated.
"""
nof_configs = self.get_number_of_configurations()
if size > nof_configs:
size = nof_configs
sample = list()
while len(sample) < size:
config = self.get_random_configuration()
if config not in sample:
sample.append(config)
return sample