def OperationDataGenerator(self, operation):
myFile = open(
'performance_testing/performance_datasets/' + self.operation_type +
'_data_performance.csv', 'w')
writer = csv.writer(myFile)
writer.writerows([["Features", "Seconds", "Percent_cons"]])
for cons in self.percent_cons:
for feat in self.features:
path = "models/synthetic/simple_betty_gen_models/" + str(
feat) + "/" + str(cons) + "-9.xml"
parser = XMLTransformation(path)
fm = parser.transform()
if self.operation_type in ("vc", "f"):
configuration = self.get_config(fm)
operation.set_configuration(configuration)
transform = FmToPysat(fm)
transform.transform()
if self.operation_type == "vp":
product = self.get_product(fm, transform)
operation.set_configuration(product)
t0 = time.time()
operation.execute(transform.destination_model)
t1 = time.time() - t0
writer.writerows([[feat, t1, cons]])
print(
"Generated data!\nNow you can see it with Seaborn Visualizer file")
def setUp(self):
xmlreader = XMLTransformation(
"models/fama_test_suite/relationships/allrelationships/allrelationships.xml"
)
fm = xmlreader.transform()
transform = FmToPysat(fm)
return fm, transform.transform()
def get_results(self, fm_model, case_results) -> tuple[Any, Any]:
transform = FmToPysat(fm_model)
transform.transform()
pysat_model = transform.destination_model
if self.operation_type == "p":
operation = Glucose3Products()
elif self.operation_type == "pn":
operation = Glucose3ProductsNumber()
case_results = len(case_results)
elif self.operation_type == "cf":
operation = Glucose3CoreFeatures()
if case_results:
cores = list(pysat_model.features.values())
else:
cores = []
for product in case_results:
cores = [feature for feature in product if feature in cores]
case_results = cores
elif self.operation_type == "df":
operation = Glucose3DeadFeatures()
deads = list(pysat_model.features.values())
for product in case_results:
deads = [
feature for feature in deads if feature not in product
]
case_results = deads
elif self.operation_type == "v":
operation = Glucose3Valid()
if case_results:
case_results = True
else:
case_results = False
elif self.operation_type == "fof":
operation = Glucose3FalseOptionalFeatures()
falses = []
if case_results:
cores = list(pysat_model.features.values())
else:
cores = []
for product in case_results:
cores = [feature for feature in product if feature in cores]
for relation in fm_model.relations:
names = [feat.name for feat in relation.children]
for core in cores:
relation_check = relation.is_optional() or relation.is_or(
) or relation.is_alternative()
if core in names and relation_check:
falses.append(core)
case_results = falses
else:
raise Exception("The operation type is not defined")
operation.execute(pysat_model)
operation_results = operation.get_result()
return operation_results, case_results
def setUp(self, model_path):
xmlreader = XMLTransformation(model_path)
fm = xmlreader.transform()
transform = FmToPysat(fm)
return transform.transform()
from famapy.metamodels.pysat_metamodel.operations.glucose3_products import Glucose3Products
from famapy.metamodels.pysat_metamodel.operations.glucose3_valid import Glucose3Valid
from famapy.metamodels.pysat_metamodel.transformations.fm_to_pysat import FmToPysat
# Parse a file
xmlreader = XMLTransformation("example.fama")
fm = xmlreader.transform()
# print the model
print(fm)
# Create a detination metamodel (Pysat for the record)
sat = PySATModel()
# Transform the first onto the second
transform = FmToPysat(fm, sat)
transform.transform()
# Create the operation
valid = Glucose3Valid()
# Execute the operation . TODO Investigate how t avoid that sat parameter
valid.execute(sat)
# Print the result
print("Is the model valid: " + str(valid.isValid()))
# Create the operation
products = Glucose3Products()
# Execute the operation . TODO Investigate how t avoid that sat parameter