Update workflows for TensorFlow/Keras 2.14

Signed-off-by: Beat Buesser <[email protected]>

art/attacks/poisoning/poisoning_attack_svm.py

@@ -49,7 +49,8 @@ class PoisoningAttackSVM(PoisoningAttackWhiteBox):
         "y_train",
         "x_val",
         "y_val",
-        "max_iter" "verbose",
+        "max_iter",
+        "verbose",
     ]
     _estimator_requirements = (ScikitlearnSVC,)
 

art/defences/detector/poison/clustering_analyzer.py

@@ -227,12 +227,12 @@ def analyze_by_relative_size(
             if np.size(sizes) > 2:
                 raise ValueError(" RelativeSizeAnalyzer does not support more than two clusters.")
             percentages = np.round(sizes / float(np.sum(sizes)), r_size)
-            poison_clusters = np.where(percentages < size_threshold)[0]
-            clean_clusters = np.where(percentages >= size_threshold)[0]
+            poison_clusters = np.where(percentages < size_threshold)
+            clean_clusters = np.where(percentages >= size_threshold)
 
-            for p_id in poison_clusters:
+            for p_id in poison_clusters[0]:
                 summary_poison_clusters[i][p_id] = 1
-            for c_id in clean_clusters:
+            for c_id in clean_clusters[0]:
                 summary_poison_clusters[i][c_id] = 0
 
             assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
@@ -251,7 +251,7 @@ def analyze_by_relative_size(
             report["Class_" + str(i)] = report_class
 
         report["suspicious_clusters"] = report["suspicious_clusters"] + np.sum(summary_poison_clusters).item()
-        return np.asarray(all_assigned_clean), summary_poison_clusters, report
+        return np.asarray(all_assigned_clean, dtype=object), summary_poison_clusters, report
 
     def analyze_by_silhouette_score(
         self,
@@ -309,8 +309,8 @@ def analyze_by_silhouette_score(
             if np.size(bins) > 2:
                 raise ValueError("Analyzer does not support more than two clusters.")
             percentages = np.round(bins / float(np.sum(bins)), r_size)
-            poison_clusters = np.where(percentages < size_threshold)[0]
-            clean_clusters = np.where(percentages >= size_threshold)[0]
+            poison_clusters = np.where(percentages < size_threshold)
+            clean_clusters = np.where(percentages >= size_threshold)
 
             # Generate report for class
             silhouette_avg = round(silhouette_score(activations, clusters), r_silhouette)
@@ -324,26 +324,26 @@ def analyze_by_silhouette_score(
                 # Relative size of the clusters is suspicious
                 if silhouette_avg > silhouette_threshold:
                     # In this case the cluster is considered poisonous
-                    clean_clusters = np.where(percentages < size_threshold)[0]
+                    clean_clusters = np.where(percentages < size_threshold)
                     logger.info("computed silhouette score: %s", silhouette_avg)
                     dict_i.update(suspicious=True)
                 else:
-                    poison_clusters = np.array([[]])
-                    clean_clusters = np.where(percentages >= 0)[0]
+                    poison_clusters = [[]]
+                    clean_clusters = np.where(percentages >= 0)
                     dict_i.update(suspicious=False)
             else:
                 # If relative size of the clusters is Not suspicious, we conclude it's not suspicious.
                 dict_i.update(suspicious=False)
 
             report_class: Dict[str, Dict[str, bool]] = {"class_" + str(i): dict_i}
 
-            for p_id in poison_clusters:
+            for p_id in poison_clusters[0]:
                 summary_poison_clusters[i][p_id] = 1
-            for c_id in clean_clusters:
+            for c_id in clean_clusters[0]:
                 summary_poison_clusters[i][c_id] = 0
 
             assigned_clean = self.assign_class(clusters, clean_clusters, poison_clusters)
             all_assigned_clean.append(assigned_clean)
             report.update(report_class)
 
-        return np.asarray(all_assigned_clean), summary_poison_clusters, report
+        return np.asarray(all_assigned_clean, dtype=object), summary_poison_clusters, report

tests/defences/detector/poison/test_activation_defence.py

@@ -118,7 +118,7 @@ def test_output_clusters(self):
             clusters_by_class, _ = self.defence.cluster_activations(nb_clusters=nb_clusters)
 
             # Verify expected number of classes
-            self.assertEqual(np.shape(clusters_by_class)[0], n_classes)
+            self.assertEqual(len(clusters_by_class), n_classes)
             # Check we get the expected number of clusters:
             found_clusters = len(np.unique(clusters_by_class[0]))
             self.assertEqual(found_clusters, nb_clusters)

tests/defences/detector/poison/test_ground_truth_evaluator.py

@@ -64,9 +64,9 @@ def test_analyze_correct_all_clean(self):
         # print(json_object)
         for i in range(self.n_classes):
             res_class_i = json_object["class_" + str(i)]
-            self.assertEqual(res_class_i["TruePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TruePositive"]["rate"], -1)
             self.assertEqual(res_class_i["TrueNegative"]["rate"], 100)
-            self.assertEqual(res_class_i["FalseNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], -1)
             self.assertEqual(res_class_i["FalsePositive"]["rate"], 0)
 
             self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)
@@ -99,9 +99,9 @@ def test_analyze_correct_all_poison(self):
         for i in range(self.n_classes):
             res_class_i = json_object["class_" + str(i)]
             self.assertEqual(res_class_i["TruePositive"]["rate"], 100)
-            self.assertEqual(res_class_i["TrueNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], -1)
             self.assertEqual(res_class_i["FalseNegative"]["rate"], 0)
-            self.assertEqual(res_class_i["FalsePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], -1)
 
             self.assertEqual(res_class_i["TruePositive"]["numerator"], self.n_dp)
             self.assertEqual(res_class_i["TruePositive"]["denominator"], self.n_dp)
@@ -166,9 +166,9 @@ def test_analyze_fully_misclassified(self):
         for i in range(self.n_classes):
             res_class_i = json_object["class_" + str(i)]
             self.assertEqual(res_class_i["TruePositive"]["rate"], 0)
-            self.assertEqual(res_class_i["TrueNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TrueNegative"]["rate"], -1)
             self.assertEqual(res_class_i["FalseNegative"]["rate"], 100)
-            self.assertEqual(res_class_i["FalsePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalsePositive"]["rate"], -1)
 
             self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)
             self.assertEqual(res_class_i["TruePositive"]["denominator"], self.n_dp)
@@ -200,9 +200,9 @@ def test_analyze_fully_misclassified_rev(self):
         pprint.pprint(json_object)
         for i in range(self.n_classes):
             res_class_i = json_object["class_" + str(i)]
-            self.assertEqual(res_class_i["TruePositive"]["rate"], "N/A")
+            self.assertEqual(res_class_i["TruePositive"]["rate"], -1)
             self.assertEqual(res_class_i["TrueNegative"]["rate"], 0)
-            self.assertEqual(res_class_i["FalseNegative"]["rate"], "N/A")
+            self.assertEqual(res_class_i["FalseNegative"]["rate"], -1)
             self.assertEqual(res_class_i["FalsePositive"]["rate"], 100)
 
             self.assertEqual(res_class_i["TruePositive"]["numerator"], 0)

tests/estimators/classification/test_deeplearning_common.py

@@ -154,7 +154,7 @@ def test_loss_functions(
         art_warning(e)
 
 
-@pytest.mark.skip_framework("non_dl_frameworks", "huggingface", "tensorflow2")
+@pytest.mark.skip_framework("non_dl_frameworks", "huggingface", "tensorflow2", "kerastf")
 def test_pickle(art_warning, image_dl_estimator, image_dl_estimator_defended, tmp_path):
     try:
         full_path = os.path.join(tmp_path, "my_classifier.p")