diff --git a/sslearn/wrapper/_co.py b/sslearn/wrapper/_co.py
index 0e7c2eb..a01d67d 100644
--- a/sslearn/wrapper/_co.py
+++ b/sslearn/wrapper/_co.py
@@ -1102,7 +1102,6 @@ class CoTrainingByCommittee(BaseCoTraining):
     Pisa, 2008, pp. 563-572,  [10.1109/ICDMW.2008.27](https://doi.org/10.1109/ICDMW.2008.27)
     """
 
-
     def __init__(
         self,
         ensemble_estimator=BaggingClassifier(),
diff --git a/sslearn/wrapper/_self.py b/sslearn/wrapper/_self.py
index f58d5eb..e18799d 100644
--- a/sslearn/wrapper/_self.py
+++ b/sslearn/wrapper/_self.py
@@ -197,7 +197,7 @@ def __init__(
         poolsize : float, optional
             Max number of unlabel instances candidates to pseudolabel, by default 0.25
         rejection_threshold : float, optional
-            significance level, by default 0.1
+            significance level, by default 0.05
         graph_neighbors : int, optional
             Number of neighbors for each sample., by default 1
         random_state : int, RandomState instance, optional
diff --git a/test/test_wrapper.py b/test/test_wrapper.py
index 85d7b11..a14092e 100644
--- a/test/test_wrapper.py
+++ b/test/test_wrapper.py
@@ -16,7 +16,7 @@
 from sslearn.model_selection import artificial_ssl_dataset
 from sslearn.wrapper import (
     CoTraining, CoForest, CoTrainingByCommittee, DemocraticCoLearning, Rasco, RelRasco,
-    SelfTraining, Setred, TriTraining, WiWTriTraining, DeTriTraining
+    SelfTraining, Setred, TriTraining, DeTriTraining    
 )
 
 X, y = read_csv(os.path.join(os.path.dirname(os.path.realpath(__file__)), "example_files", "abalone.csv"), format="pandas")
@@ -238,40 +238,40 @@ def test_all_label(self):
 groups = np.array(groups)
 groups = groups[:X.shape[0]]
 
-class TestWiWTriTraining:
+# class TestWiWTriTraining:
     
-    def test_basic(self):
-        clf = WiWTriTraining(base_estimator=DecisionTreeClassifier())
-        clf.fit(X, y, instance_group=groups)
-        clf.predict(X, instance_group=groups)
-        clf.predict_proba(X)
-
-        clf = WiWTriTraining(DecisionTreeClassifier())
-        clf.fit(X2, y2, instance_group=groups)
-        clf.predict(X2, instance_group=groups)
-        clf.predict_proba(X2)
-
-    def test_multiple(self):
-        clf = WiWTriTraining(base_estimator=[DecisionTreeClassifier(max_depth=1), DecisionTreeClassifier(max_depth=2), DecisionTreeClassifier(max_depth=3)])
-        clf.fit(X, y, instance_group=groups)
-        clf.predict(X, instance_group=groups)
-        clf.predict_proba(X)
+#     def test_basic(self):
+#         clf = WiWTriTraining(base_estimator=DecisionTreeClassifier())
+#         clf.fit(X, y, instance_group=groups)
+#         clf.predict(X, instance_group=groups)
+#         clf.predict_proba(X)
+
+#         clf = WiWTriTraining(DecisionTreeClassifier())
+#         clf.fit(X2, y2, instance_group=groups)
+#         clf.predict(X2, instance_group=groups)
+#         clf.predict_proba(X2)
+
+#     def test_multiple(self):
+#         clf = WiWTriTraining(base_estimator=[DecisionTreeClassifier(max_depth=1), DecisionTreeClassifier(max_depth=2), DecisionTreeClassifier(max_depth=3)])
+#         clf.fit(X, y, instance_group=groups)
+#         clf.predict(X, instance_group=groups)
+#         clf.predict_proba(X)
     
-    def test_random_state(self):
-        for i in range(10):
-            clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
-            clf.fit(X, y, instance_group=groups)
-            y1 = clf.predict(X, instance_group=groups)
-
-            clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
-            clf.fit(X, y, instance_group=groups)
-            y2 = clf.predict(X, instance_group=groups)
-
-            assert np.all(y1 == y2)
-
-    def test_all_label(self):
-        clf = WiWTriTraining(base_estimator=KNeighborsClassifier())
-        clf.fit(X, y, instance_group=groups)
-        clf.predict(X, instance_group=groups)
-        clf.predict_proba(X)
+#     def test_random_state(self):
+#         for i in range(10):
+#             clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
+#             clf.fit(X, y, instance_group=groups)
+#             y1 = clf.predict(X, instance_group=groups)
+
+#             clf = WiWTriTraining(base_estimator=KNeighborsClassifier(), random_state=i)
+#             clf.fit(X, y, instance_group=groups)
+#             y2 = clf.predict(X, instance_group=groups)
+
+#             assert np.all(y1 == y2)
+
+#     def test_all_label(self):
+#         clf = WiWTriTraining(base_estimator=KNeighborsClassifier())
+#         clf.fit(X, y, instance_group=groups)
+#         clf.predict(X, instance_group=groups)
+#         clf.predict_proba(X)