Additional review comments - moved result to score base class

Signed-off-by: Maya Anderson <mayaa@il.ibm.com>

apt/risk/data_assessment/dataset_attack.py

@@ -37,8 +37,7 @@ class DatasetAttack(abc.ABC):
         :param original_data_non_members: A container for the holdout original samples and labels,
             only samples are used in the assessment
         :param synthetic_data: A container for the synthetic samples and labels, only samples are used in the assessment
-        :param config: Configuration parameters to guide the assessment process such as which attack
-               frameworks to use
+        :param config: Configuration parameters to guide the assessment process
         :param dataset_name: A name to identify the dataset under attack, optional
         :param attack_strategy_utils: Utils for use with the attack strategy, optional
         """

apt/risk/data_assessment/dataset_attack_membership_knn_probabilities.py

@@ -13,7 +13,7 @@ from sklearn.neighbors import NearestNeighbors
 from apt.risk.data_assessment.attack_strategy_utils import KNNAttackStrategyUtils
 from apt.risk.data_assessment.dataset_attack import DatasetAttackMembership, Config
 from apt.risk.data_assessment.dataset_attack_result import DatasetAttackScore, DatasetAttackResultMembership, \
-    DatasetAttackScoreWithResult, DEFAULT_DATASET_NAME
+    DEFAULT_DATASET_NAME
 from apt.utils.datasets import ArrayDataset
 
 
@@ -41,7 +41,7 @@ class DatasetAttackConfigMembershipKnnProbabilities(Config):
 
 
 @dataclass
-class DatasetAttackScoreMembershipKnnProbabilities(DatasetAttackScoreWithResult):
+class DatasetAttackScoreMembershipKnnProbabilities(DatasetAttackScore):
     """DatasetAttackMembershipKnnProbabilities privacy score.
     Attributes
     ----------
@@ -50,8 +50,8 @@ class DatasetAttackScoreMembershipKnnProbabilities(DatasetAttackScoreWithResult)
     average_precision_score: the proportion of predicted members that are correctly members
     assessment_type : assessment type is 'MembershipKnnProbabilities', to be used in reports
     """
-    roc_auc_score: float
-    average_precision_score: float
+    roc_auc_score: float = -1.0
+    average_precision_score: float = -1.0
     assessment_type: str = 'MembershipKnnProbabilities'
 
 

apt/risk/data_assessment/dataset_attack_result.py

@@ -1,23 +1,20 @@
 from dataclasses import dataclass, field
+from typing import Optional
 
 import numpy as np
 
 DEFAULT_DATASET_NAME = "dataset"
 
 
-@dataclass
-class DatasetAttackScore:
-    dataset_name: str
-
-
 @dataclass
 class DatasetAttackResult:
     pass
 
 
-@dataclass(repr=False)
-class DatasetAttackScoreWithResult(DatasetAttackScore):
-    result: DatasetAttackResult = field(repr=False)
+@dataclass
+class DatasetAttackScore:
+    dataset_name: str
+    result: Optional[DatasetAttackResult] = None
 
 
 @dataclass

apt/risk/data_assessment/dataset_attack_whole_dataset_knn_distance.py

@@ -46,7 +46,7 @@ class DatasetAttackScoreWholeDatasetKnnDistance(DatasetAttackScore):
             A value of 0.5 or close to it means good privacy.
     assessment_type : assessment type is 'WholeDatasetKnnDistance', to be used in reports
     """
-    share: float
+    share: float = -1.0
     assessment_type: str = 'WholeDatasetKnnDistance'