Format code with yapf, black, autopep8 and isort

Author: deepsource-autofix[bot]

semisupervised/CoTraining.py

@@ -87,8 +87,7 @@ def __init__(
             self.h1, self.h2 = configs
         except ValueError:
             raise AttributeError(
-                "Classifiers and/or params were not correctly passed."
-            )
+                "Classifiers and/or params were not correctly passed.")
 
     def fit(self, samples, y):
         """

semisupervised/DemocraticCoLearning.py

@@ -95,8 +95,7 @@ def __init__(
             self.h1, self.h2, self.h3 = configs
         except ValueError:
             raise AttributeError(
-                "Classifiers and/or params were not correctly passed."
-            )
+                "Classifiers and/or params were not correctly passed.")
 
     def fit(self, samples, y):
         """

semisupervised/TriTraining.py

@@ -88,8 +88,7 @@ def __init__(
             self.hj, self.hk, self.hi = configs
         except ValueError:
             raise AttributeError(
-                "Classifiers and/or params were not correctly passed."
-            )
+                "Classifiers and/or params were not correctly passed.")
 
         self.random_state = (
             random_state

semisupervised/ensemble/_RESSEL.py

@@ -176,8 +176,8 @@ def _validate_params(self, base_estimator, labeled, unlabeled):
             )
         if not isinstance(unlabeled, pd.DataFrame):
             raise AttributeError(
-                "Unlabeled samples object needs to be a Pandas DataFrame. "
-                "Not a ", type(unlabeled),
+                "Unlabeled samples object needs to be a Pandas DataFrame. " "Not a ",
+                type(unlabeled),
             )
         if labeled.shape[1] != unlabeled.shape[1] + 1:
             raise ValueError(
@@ -221,7 +221,8 @@ def _robust_self_training(self, iteration, l_i, u_i, oob_i, d_class_i):
 
         y_pred = self.ensemble[iteration].predict(oob_i.iloc[:, :-1])
         best_error_i = f1_score(
-            y_true=np.ravel(oob_i.iloc[:, -1:]), y_pred=y_pred,
+            y_true=np.ravel(oob_i.iloc[:, -1:]),
+            y_pred=y_pred,
             average="weighted",
         )
         best_c_i = self.ensemble[iteration]
@@ -270,7 +271,8 @@ def _robust_self_training(self, iteration, l_i, u_i, oob_i, d_class_i):
 
             y_pred = self.ensemble[iteration].predict(oob_i.iloc[:, :-1])
             current_error_i = f1_score(
-                y_true=np.ravel(oob_i.iloc[:, -1:]), y_pred=y_pred,
+                y_true=np.ravel(oob_i.iloc[:, -1:]),
+                y_pred=y_pred,
                 average="weighted",
             )