Increase version to 0.2.0 (#74)

* Remove tensorflow dependency if not using keras model
* Remove xgboost dependency if not using xgboost model
* Documentation updates

Signed-off-by: abigailt <abigailt@il.ibm.com>

apt/risk/data_assessment/attack_strategy_utils.py

@@ -9,21 +9,20 @@ from apt.utils.datasets import ArrayDataset
 
 class AttackStrategyUtils(abc.ABC):
     """
-        Abstract base class for common utilities of various privacy attack strategies.
+    Abstract base class for common utilities of various privacy attack strategies.
     """
     pass
 
 
 class KNNAttackStrategyUtils(AttackStrategyUtils):
     """
-         Common utilities for attack strategy based on KNN distances.
+    Common utilities for attack strategy based on KNN distances.
+
+    :param use_batches: Use batches with a progress meter or not when finding KNNs for query set.
+    :param batch_size: if use_batches=True, the size of batch_size should be > 0.
     """
 
     def __init__(self, use_batches: bool = False, batch_size: int = 10) -> None:
-        """
-        :param use_batches: Use batches with a progress meter or not when finding KNNs for query set
-        :param batch_size: if use_batches=True, the size of batch_size should be > 0
-        """
         self.use_batches = use_batches
         self.batch_size = batch_size
         if use_batches:
@@ -31,11 +30,18 @@ class KNNAttackStrategyUtils(AttackStrategyUtils):
                 raise ValueError(f"When using batching batch_size should be > 0, and not {batch_size}")
 
     def fit(self, knn_learner: NearestNeighbors, dataset: ArrayDataset):
+        """
+        Fit the KNN learner.
+
+        :param knn_learner: The KNN model to fit.
+        :param dataset: The training set to fit the model on.
+        """
         knn_learner.fit(dataset.get_samples())
 
     def find_knn(self, knn_learner: NearestNeighbors, query_samples: ArrayDataset, distance_processor=None):
         """
         Nearest neighbor search function.
+
         :param query_samples: query samples, to which nearest neighbors are to be found
         :param knn_learner: unsupervised learner for implementing neighbor searches, after it was fitted
         :param distance_processor: function for processing the distance into another more relevant metric per sample.