Fix notebook + typing

Signed-off-by: abigailt <[email protected]>

Author: abigailgold

art/metrics/privacy/membership_leakage.py

@@ -30,7 +30,7 @@
 from art.utils import check_and_transform_label_format, is_probability_array
 
 if TYPE_CHECKING:
-    from art.estimators.classification.classifier import Classifier
+    from art.utils import CLASSIFIER_TYPE
 
 
 class ComparisonType(Enum):
@@ -43,8 +43,8 @@ class ComparisonType(Enum):
 
 
 def PDTP(  # pylint: disable=C0103
-    target_estimator: "Classifier",
-    extra_estimator: "Classifier",
+    target_estimator: "CLASSIFIER_TYPE",
+    extra_estimator: "CLASSIFIER_TYPE",
     x: np.ndarray,
     y: np.ndarray,
     indexes: Optional[np.ndarray] = None,
@@ -148,7 +148,7 @@ def PDTP(  # pylint: disable=C0103
 
 
 def SHAPr(  # pylint: disable=C0103
-    target_estimator: "Classifier",
+    target_estimator: "CLASSIFIER_TYPE",
     x_train: np.ndarray,
     y_train: np.ndarray,
     x_test: np.ndarray,

notebooks/README.md

@@ -104,6 +104,12 @@ demonstrates a MembershipInferenceBlackBox membership inference attack using sha
 [label_only_membership_inference.ipynb](label_only_membership_inference.ipynb) [[on nbviewer](https://nbviewer.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/label_only_membership_inference.ipynb)]
 demonstrates a LabelOnlyDecisionBoundary membership inference attack on a PyTorch classifier for the MNIST dataset.
 
+## Metrics
+
+[privacy_metric.ipynb](privacy_metric.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/privacy_metric.ipynb)] 
+demonstrates how to apply both the PDTP and the SHAPr privacy metrics to random forest and decision tree classifiers
+trained on the nursery dataset.
+
 ## Classifiers
 
 [classifier_blackbox.ipynb](classifier_blackbox.ipynb) [[on nbviewer](https://nbviewer.jupyter.org/github/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/classifier_blackbox.ipynb)] demonstrates BlackBoxClassifier, the most general and