def test_hash(self):
ar1 = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
ar2 = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
test_set = {ar1, ar2}
self.assertEqual(1, len(test_set))
def test_create_from_response_payload(self):
ar1 = AnonymizeResult._from_response(self.test_dataset,
self.test_risk_profile,
self.raw_metrics, 'ANONYMOUS')
ar2 = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
self.assertEqual(ar1, ar2)
def test_equaltiy(self):
ar1 = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
ar2 = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
self.assertEqual(ar1, ar2)
ar2._dataset = Dataset([["data", "data2"]])
self.assertNotEqual(ar1, ar2)
def _anonymize_result(self, response):
"""
Creates the result to be delivered back to the caller
:param response:
:return:
"""
json_string = response.text
response_dict = json.loads(json_string)
attributes = self._attributes(response_dict)
dataset = Dataset(response_dict["anonymizeResult"]["data"], attributes)
risk_profile = RiskProfile(response_dict["riskProfile"])
anon_status = response_dict["anonymizeResult"]["anonymizationStatus"]
anonymization_metrics = response_dict["anonymizeResult"]["metrics"]
return AnonymizeResult._from_response(dataset, risk_profile,
anonymization_metrics,
anon_status)
def setUp(self):
self.test_data = [['id', 'name'], ['0', 'Viktor'], ['1', 'Jerry']]
self.test_attribute_type_mapping = {
'id': AttributeType.IDENTIFYING,
'name': AttributeType.QUASIIDENTIFYING
}
self.test_dataset = Dataset(self.test_data,
self.test_attribute_type_mapping)
self.risk_profile_response = {
"reIdentificationRisk": {
"measures": {
"measure_value":
"[%]",
"Prosecutor_attacker_success_rate":
"98.72",
"records_affected_by_highest_prosecutor_risk":
"97.46000000000001",
"sample_uniques":
"97.46000000000001",
"estimated_prosecutor_risk":
"100.0",
"population_model":
"PITMAN",
"highest_journalist_risk":
"100.0",
"records_affected_by_lowest_risk":
"0.06",
"estimated_marketer_risk":
"98.72000000000001",
"Journalist_attacker_success_rate":
"98.72",
"highest_prosecutor_risk":
"100.0",
"estimated_journalist_risk":
"100.0",
"lowest_risk":
"33.33333333333333",
"Marketer_attacker_success_rate":
"98.72",
"average_prosecutor_risk":
"98.72000000000001",
"records_affected_by_highest_journalist_risk":
"97.46000000000001",
"population_uniques":
"39.64593493418713",
"quasi_identifiers": [
"Innvandrerbakgrunn", "Ytelse", "Innsatsgruppe",
"Ledighetsstatus"
]
}
},
"distributionOfRisk": {
"riskIntervalList": [{
"interval": "]50,100]",
"recordsWithRiskWithinInteval": 0.9746,
"recordsWithMaxmalRiskWithinInterval": 1.0
}, {
"interval":
"]33.4,50]",
"recordsWithRiskWithinInteval":
0.0248,
"recordsWithMaxmalRiskWithinInterval":
0.0254
}, {
"interval":
"]25,33.4]",
"recordsWithRiskWithinInteval":
0.0006,
"recordsWithMaxmalRiskWithinInterval":
0.0006
}, {
"interval": "]20,25]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]16.7,20]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]14.3,16.7]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]12.5,14.3]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]10,12.5]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]9,10]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]8,9]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]7,8]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]6,7]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]5,6]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]4,5]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]3,4]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]2,3]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]1,2]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]0.1,1]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]0.01,0.1]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]0.001,0.01]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]0.0001,0.001]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]1e-5,0.0001]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]1e-6,1e-5]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}, {
"interval": "]0,1e-6]",
"recordsWithRiskWithinInteval": 0.0,
"recordsWithMaxmalRiskWithinInterval": 0.0
}]
}
}
self.test_riskprofile = RiskProfile(self.risk_profile_response)
self.test_anonymize_result = AnonymizeResult(self.test_data,
self.test_riskprofile,
self.test_metrics)
def test_anonymization_metrics(self):
ar = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
anonymization_metrics = ar.anonymization_metrics
self.assertIsNotNone(anonymization_metrics)
self.assertEqual(self.test_anon_metrics, anonymization_metrics)
def test_risk_profile(self):
ar = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
risk_profile = ar.risk_profile
self.assertIsNotNone(risk_profile)
self.assertEqual(self.test_risk_profile, risk_profile)
def test_dataset(self):
ar = AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')
dataset = ar.dataset
self.assertIsNotNone(dataset)
self.assertEqual(self.test_dataset, dataset)
def test_init(self):
AnonymizeResult(self.test_dataset, self.test_risk_profile,
self.test_anon_metrics, 'ANONYMOUS')