Format code with yapf, black, autopep8 and isort

Author: deepsource-autofix[bot]

instance_selection/_RNN.py

@@ -48,8 +48,8 @@ def filter(self, samples, y):
         samples, y = cnn.filter(samples, y)
         samp_set = transform(samples, y)
 
-        data = deque(list(samp_set['data']))
-        target = deque(list(samp_set['target']))
+        data = deque(list(samp_set["data"]))
+        target = deque(list(samp_set["target"]))
 
         for instance in zip(samp_set.data, samp_set.target):
             (sample, class_sample) = instance

semisupervised/CoTraining.py

@@ -86,8 +86,9 @@ def __init__(
         try:
             self.h1, self.h2 = configs
         except ValueError:
-            raise AttributeError("Classifiers and/or params were not "
-                                 "correctly passed.")
+            raise AttributeError(
+                "Classifiers and/or params were not " "correctly passed."
+            )
 
     def fit(self, samples, y):
         """

semisupervised/DemocraticCoLearning.py

@@ -94,8 +94,9 @@ def __init__(
         try:
             self.h1, self.h2, self.h3 = configs
         except ValueError:
-            raise AttributeError("Classifiers and/or params were not "
-                                 "correctly passed.")
+            raise AttributeError(
+                "Classifiers and/or params were not " "correctly passed."
+            )
 
     def fit(self, samples, y):
         """

semisupervised/TriTraining.py

@@ -87,8 +87,9 @@ def __init__(
         try:
             self.hj, self.hk, self.hi = configs
         except ValueError:
-            raise AttributeError("Classifiers and/or params were not "
-                                 "correctly passed.")
+            raise AttributeError(
+                "Classifiers and/or params were not " "correctly passed."
+            )
 
         self.random_state = (
             random_state