Intercept the ConvergenceWarning from sklearn in some cases.

examples/model_security/model_attacks/white_box/mnist_attack_pgd.py

@@ -74,10 +74,10 @@ def test_projected_gradient_descent_method():
     # attacking
     loss = SoftmaxCrossEntropyWithLogits(sparse=True)
     attack = ProjectedGradientDescent(net, eps=0.3, loss_fn=loss)
-    start_time = time.clock()
+    start_time = time.process_time()
     adv_data = attack.batch_generate(np.concatenate(test_images),
                                      true_labels, batch_size=32)
-    stop_time = time.clock()
+    stop_time = time.process_time()
     np.save('./adv_data', adv_data)
     pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()
     # rescale predict confidences into (0, 1).

mindarmour/privacy/evaluation/attacker.py

@@ -14,12 +14,15 @@
 """
 Attacker of Membership Inference.
 """
+import warnings
+
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.neural_network import MLPClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import GridSearchCV
 from sklearn.model_selection import RandomizedSearchCV
+from sklearn.exceptions import ConvergenceWarning
 
 from mindarmour.utils.logger import LogUtil
 
@@ -141,15 +144,16 @@ def get_attack_model(features, labels, config, n_jobs=-1):
         >>> attack_model = get_attack_model(features, labels, config)
     """
     method = str.lower(config["method"])
-
-    if method == "knn":
-        return _attack_knn(features, labels, config["params"], n_jobs)
-    if method == "lr":
-        return _attack_lr(features, labels, config["params"], n_jobs)
-    if method == "mlp":
-        return _attack_mlpc(features, labels, config["params"], n_jobs)
-    if method == "rf":
-        return _attack_rf(features, labels, config["params"], n_jobs)
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', category=ConvergenceWarning)
+        if method == "knn":
+            return _attack_knn(features, labels, config["params"], n_jobs)
+        if method == "lr":
+            return _attack_lr(features, labels, config["params"], n_jobs)
+        if method == "mlp":
+            return _attack_mlpc(features, labels, config["params"], n_jobs)
+        if method == "rf":
+            return _attack_rf(features, labels, config["params"], n_jobs)
 
     msg = "Method {} is not supported.".format(config["method"])
     LOGGER.error(TAG, msg)