diff --git a/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py b/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
index c992dc8..32a22c7 100644
--- a/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
+++ b/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
@@ -82,6 +82,25 @@ class SlicingSpec:
   # examples will be generated.
   by_classification_correctness: bool = False
 
+  def __str__(self):
+    """Only keeps the True values."""
+    result = ['SlicingSpec(']
+    if self.entire_dataset:
+      result.append(' Entire dataset,')
+    if self.by_class:
+      if isinstance(self.by_class, Iterable):
+        result.append(' Into classes %s,' % self.by_class)
+      elif isinstance(self.by_class, int):
+        result.append(' Up to class %d,' % self.by_class)
+      else:
+        result.append(' By classes,')
+    if self.by_percentiles:
+      result.append(' By percentiles,')
+    if self.by_classification_correctness:
+      result.append(' By classification correctness,')
+    result.append(')')
+    return '\n'.join(result)
+
 
 class AttackType(enum.Enum):
   """An enum define attack types."""
@@ -96,8 +115,8 @@ class AttackType(enum.Enum):
     """Returns whether this type of attack requires training a model."""
     return self != AttackType.THRESHOLD_ATTACK
 
-  # Return LOGISTIC_REGRESSION instead of AttackType.LOGISTIC_REGRESSION
   def __str__(self):
+    """Returns LOGISTIC_REGRESSION instead of AttackType.LOGISTIC_REGRESSION."""
     return '%s' % self.name
 
 
@@ -220,6 +239,23 @@ class AttackInputData:
     _is_array_one_dimensional(self.labels_train, 'labels_train')
     _is_array_one_dimensional(self.labels_test, 'labels_test')
 
+  def __str__(self):
+    """Return the shapes of variables that are not None."""
+    result = ['AttackInputData(']
+    _append_array_shape(self.loss_train, 'loss_train', result)
+    _append_array_shape(self.loss_test, 'loss_test', result)
+    _append_array_shape(self.logits_train, 'logits_train', result)
+    _append_array_shape(self.logits_test, 'logits_test', result)
+    _append_array_shape(self.labels_train, 'labels_train', result)
+    _append_array_shape(self.labels_test, 'labels_test', result)
+    result.append(')')
+    return '\n'.join(result)
+
+
+def _append_array_shape(arr: np.array, arr_name: str, result):
+  if arr is not None:
+    result.append(' %s with shape: %s,' % (arr_name, arr.shape))
+
 
 @dataclass
 class RocCurve:
@@ -251,6 +287,14 @@ class RocCurve:
     """
     return max(np.abs(self.tpr - self.fpr))
 
+  def __str__(self):
+    """Returns AUC and advantage metrics."""
+    return '\n'.join([
+        'RocCurve(',
+        '  AUC: %f.02' % self.get_auc(),
+        '  Attacker advantage: %f.02' % self.get_attacker_advantage(), ')'
+    ])
+
 
 @dataclass
 class SingleAttackResult:
@@ -272,6 +316,16 @@ class SingleAttackResult:
   def get_auc(self):
     return self.roc_curve.get_auc()
 
+  def __str__(self):
+    """Returns SliceSpec, AttackType, AUC and advantage metrics."""
+    return '\n'.join([
+        'SingleAttackResult(',
+        '  SliceSpec: %s' % str(self.slice_spec),
+        '  AttackType: %s' % str(self.attack_type),
+        '  AUC: %f.02' % self.get_auc(),
+        '  Attacker advantage: %f.02' % self.get_attacker_advantage(), ')'
+    ])
+
 
 @dataclass
 class AttackResults: