Final test fixes

Signed-off-by: Álvaro Bacca Peña <[email protected]>

Author: c4ts0up

Dockerfile

@@ -9,7 +9,7 @@ RUN pip3 install tensorflow==2.9.1 keras==2.9.0 numpy==1.22.4 scipy==1.8.1 matpl
                  resampy==0.3.1 ffmpeg-python==0.2.0 cma==3.2.2 pandas==1.4.3 h5py==3.7.0 tensorflow-addons==0.17.1 \
                  torch==1.12.0 torchaudio==0.12.0 torchvision==0.13.0 catboost==1.0.6 GPy==1.10.0 \
                  lightgbm==3.3.2 xgboost==1.6.1 kornia==0.6.6 lief==0.12.1 pytest==7.1.2 pytest-pep8==1.0.6 \
-                 pytest-mock==3.8.2 requests==2.28.1
+                 pytest-mock==3.8.2 requests==2.28.1 umap-learn==0.5.7
 
 RUN apt-get -y install ffmpeg libavcodec-extra vim git
 

art/attacks/poisoning/perturbations/__init__.py

@@ -7,7 +7,3 @@
     add_single_bd,
     insert_image,
 )
-
-from .network_perturbations import (
-    create_flip_perturbation
-)

art/defences/detector/poison/clustering_centroid_analysis.py

@@ -244,8 +244,6 @@ def get_clusters(self) -> np.array:
 
         return result
 
-    # TODO: MAP THE ENCODINGS
-    # NP ARGMAX IN THE LAST LAYER
     def __init__(
             self,
             classifier: "CLASSIFIER_TYPE",
@@ -254,7 +252,7 @@ def __init__(
             benign_indices: np.array,
             final_feature_layer_name: str,
             misclassification_threshold: float,
-            reducer = UMAP(n_neighbors=5, min_dist=0, random_state=42),
+            reducer = UMAP(n_neighbors=5, min_dist=0),
             clusterer = DBSCAN(eps=0.8, min_samples=20)
     ):
         """
@@ -472,8 +470,7 @@ def detect_poison(self, **kwargs) -> (dict, list[int]):
 
         logging.info("Evaluating cluster misclassification...")
         for cluster_label, mr in misclassification_rates.items():
-            # FIXME: changed the misclassification threshold
-            if mr >= self.misclassification_threshold:
+            if mr >= 1 - self.misclassification_threshold:
                 cluster_indices = np.where(self.class_cluster_labels == cluster_label)[0]
                 self.is_clean[cluster_indices] = 0
                 logging.info(f"Cluster k={cluster_label} i={self.cluster_class_mapping[cluster_label]} considered poison ({misclassification_rates[cluster_label]} >= {1 - self.misclassification_threshold})")

notebooks/poisoning_defense_clustering_centroid_analysis.ipynb

@@ -21,6 +21,7 @@ opencv-python
 sortedcontainers==2.4.0
 h5py==3.13.0
 multiprocess>=0.70.12
+umap-learn==0.5.7
 
 # frameworks
 

requirements_test.txt

@@ -25,6 +25,9 @@ then
     pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/detector/poison/test_spectral_signature_defense.py --framework=$framework --durations=20 --durations-min=0
     if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/detector/poison/test_spectral_signature_defense.py tests"; fi
 
+    pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/detector/poison/test_clustering_centroid_analysis.py --framework=$framework --durations=20 --durations-min=0
+    if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/detector/poison/test_clustering_centroid_analysis.py tests"; fi
+
     pytest --cov-report=xml --cov=art --cov-append  -q -vv tests/defences/preprocessor --framework=$framework --durations=20 --durations-min=0
     if [[ $? -ne 0 ]]; then exit_code=1; echo "Failed defences/preprocessor tests"; fi
 
@@ -161,6 +164,7 @@ else
 
     declare -a art=("tests/test_data_generators.py" \
                     "tests/test_optimizers.py" \
+                    "tests/test_performance_monitor.py" \
                     "tests/test_utils.py" \
                     "tests/test_visualization.py" )
 

run_tests.sh

@@ -953,7 +953,7 @@ def test_detect_poison_all_benign(self):
             y_train=self.y_train,
             benign_indices=self.benign_indices,
             final_feature_layer_name='hidden_layer',
-            misclassification_threshold=0.1
+            misclassification_threshold=0.1 # 1.0 - 0.1 = 0.9
         )
 
         # Mock the _calculate_misclassification_rate method to return low rates (all benign)
@@ -981,7 +981,7 @@ def test_detect_poison_with_poisoned_samples_as_outliers(self):
             y_train=self.y_train,
             benign_indices=self.benign_indices,
             final_feature_layer_name='hidden_layer',
-            misclassification_threshold=0.1
+            misclassification_threshold=0.1 # 1.0 - 0.1 = 0.9
         )
 
         # Mock the _calculate_misclassification_rate method to return low rates (all benign)