create a summary string for privacy risk scores

tensorflow_privacy/privacy/membership_inference_attack/data_structures.py

@@ -559,15 +559,37 @@ class SingleRiskScoreResult:
           recall_list.append(true_positive_normalized)
     return np.array(meaningful_threshold_list), np.array(precision_list), np.array(recall_list)
   
-  def print_results(self, threshold_list=np.array([1,0.9,0.8,0.7,0.6,0.5])):
+  def collect_results(self, threshold_list=np.array([1,0.9,0.8,0.7,0.6,0.5])):
     """ The privacy risk score (from 0 to 1) represents each sample's probability of being in the training set.
     Here, we choose a list of threshold values from 0.5 (uncertain of training or test) to 1 (100% certain of training)
     to compute corresponding attack precision and recall.
     """
     meaningful_threshold_list, precision_list, recall_list = self.attack_with_varied_thresholds(threshold_list)
+    summary = []
+    summary.append('\nPrivacy risk score analysis over slice: \"%s\"' %
+                   str(self.slice_spec))
     for i in range(len(meaningful_threshold_list)):
-      print(f"with {meaningful_threshold_list[i]} as the threshold on privacy risk score, the precision-recall pair is {(precision_list[i], recall_list[i])}")
-    return
+      summary.append('  with %.5f as the threshold on privacy risk score, the precision-recall pair is (%.5f, %.5f)' %
+                     (meaningful_threshold_list[i], precision_list[i], recall_list[i]))
+    return summary
+
+
+@dataclass
+class RiskScoreResults:
+  """Privacy risk score results from multiple data slices.
+  """
+
+  risk_score_results: Iterable[SingleRiskScoreResult]
+    
+  def summary(self):
+    """ return the summary of privacy risk score analysis on all given data slices
+    """
+    summary = []
+    for single_result in self.risk_score_results:
+      single_summary = single_result.collect_results()
+      for line in single_summary:
+        summary.append(line)
+    return '\n'.join(summary)
 
 
 @dataclass

tensorflow_privacy/privacy/membership_inference_attack/membership_inference_attack.py

@@ -35,6 +35,7 @@ from tensorflow_privacy.privacy.membership_inference_attack.data_structures impo
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleSliceSpec
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SingleRiskScoreResult
+from tensorflow_privacy.privacy.membership_inference_attack.data_structures import RiskScoreResults
 from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_single_slice_specs
 from tensorflow_privacy.privacy.membership_inference_attack.dataset_slicing import get_slice
 
@@ -223,7 +224,7 @@ def run_seq2seq_attack(attack_input: Seq2SeqAttackInputData,
 
 
 def _compute_privacy_risk_score(attack_input: AttackInputData,
-                                num_bins: int = 15):
+                                num_bins: int = 15) -> SingleRiskScoreResult:
   """compute each individual point's likelihood of being a member (https://arxiv.org/abs/2003.10595)
   Args:
     attack_input: input data for compute privacy risk scores
@@ -272,6 +273,34 @@ def _compute_privacy_risk_score(attack_input: AttackInputData,
                                test_risk_scores=test_risk_scores)
 
 
+def privacy_risk_score_analysis(attack_input: AttackInputData,
+                                slicing_spec: SlicingSpec = None) -> RiskScoreResults:
+    
+  """Perform privacy risk score analysis on all given slice types
+
+  Args:
+    attack_input: input data for compute privacy risk scores
+    slicing_spec: specifies attack_input slices
+
+  Returns:
+    the privacy risk score results.
+  """
+  attack_input.validate()
+  risk_score_results = []
+
+  if slicing_spec is None:
+    slicing_spec = SlicingSpec(entire_dataset=True)
+  num_classes = None
+  if slicing_spec.by_class:
+    num_classes = attack_input.num_classes
+  input_slice_specs = get_single_slice_specs(slicing_spec, num_classes)
+  for single_slice_spec in input_slice_specs:
+    attack_input_slice = get_slice(attack_input, single_slice_spec)
+    risk_score_results.append(_compute_privacy_risk_score(attack_input_slice))
+    
+  return RiskScoreResults(risk_score_results=risk_score_results)
+
+
 def _compute_missing_privacy_report_metadata(
     metadata: PrivacyReportMetadata,
     attack_input: AttackInputData) -> PrivacyReportMetadata: