from af.model.algorithms.Datafly import Datafly
from af.model.algorithms.IncognitoK import IncognitoK
from af.model.algorithms.IncognitoL import IncognitoL
from af.model.reports.TransformationMetrics import TransformationMetrics
[docs]def create_sickness_db(number_of_records):
import af.utils.create_sickness_db as create_sickness_db
db_directory, db_name = create_sickness_db.get_directory_and_db_name()
create_sickness_db.create_db(db_directory, db_name, number_of_records)
[docs]def look_for_all_ocurrences():
look_for_all = raw_input("Buscar todas las ocurrencias posibles? [y/N]: ")
return True if look_for_all.lower() == "y" else False
[docs]def create_db_and_data_config_associated():
create_db = raw_input("Crear DB sickness? [y/N]: ")
if create_db.lower() == "y":
number_of_records = int(raw_input("Cantidad de registros random: "))
create_sickness_db(number_of_records)
print "\nCreando Data Config a partir de la base sicknesslarge.db"
from af.utils import create_full_data_config
dc = create_full_data_config.data_config
return dc
[docs]def af_test():
dc = create_db_and_data_config_associated()
algorithm = raw_input("\nAlgoritmo a usar (Datafly [1] / IncognitoK [2] / IncognitoL [3]): ")
k_value = int(raw_input("Valor de K: "))
if algorithm.lower() in ("1", "datafly", "datafly [1]"):
algorithm_instance = Datafly(dc, k=k_value)
elif algorithm.lower() in ("2", "incognitok", "incognitok [2]"):
algorithm_instance = IncognitoK(dc, k=k_value, optimized_processing=look_for_all_ocurrences())
elif algorithm.lower() in ("3", "incognitol", "incognitol [3]"):
l_value = int(raw_input("Valor de L: "))
algorithm_instance = IncognitoL(dc, k=k_value, l=l_value, optimized_processing=look_for_all_ocurrences())
else:
print "\nAlgoritmo seleccionado incorrecto"
exit(-1)
print "\n{0} anonimizando ...".format(algorithm_instance.__class__.__name__)
algorithm_instance.anonymize()
print "\nDone!"
print "\nDoing some metrics..."
additional_info = algorithm_instance.additional_anonymization_info
for info_value in additional_info.itervalues():
title, value = info_value
print "\n{0}: {1}".format(title, value)
tm = TransformationMetrics(dc)
print "\nComparison between equivalence classes (original, anonymized)"
for att_name, eq_comp in tm.qi_eq_classes_differences().iteritems():
print "{0} - {1}".format(att_name, eq_comp)
print "\nOutliers rows removed: {0}".format(tm.removed_outlier_rows())
print '\nNumber of equivalence classes generated: {0} '.format(tm.number_of_qi_eq_classes_generated())
see_classes = raw_input("\nVer clases de equivalencia generadas? [y/N]: ")
if see_classes.lower() == "y":
print '\nEquivalence classes generated: '
k = 1
for eq_class in tm.qi_eq_classes_generated():
msg = '\nEq class number {0} with {1} occurrences: ('.format(k, eq_class[0])
k += 1
eq_class = list(eq_class)
eq_class.pop(0)
for i in range(len(eq_class)):
msg += ' {0} : {1}'.format(tm.qi_attributes[i].name, eq_class[i])
msg += ')'
print msg
create_report = raw_input("\nCrear reporte? [Y/n]: ")
if create_report.lower() != "n":
from af.model.reports import create_basic_report
location = create_basic_report(tm)
print "Reporte creado en: %s" % location
if __name__ == "__main__":
af_test()