Improve flake8 issues

Author: mandjevant

hpsklearn/components/feature_extraction/text.py

@@ -86,7 +86,7 @@ def _text_norm(name: str):
     Declaration search space 'norm' parameter
     """
     return hp.choice(name, ["l1", "l2"])
-   
+
 
 @validate(params=["analyzer"],
           validation_test=lambda param: not isinstance(param, str) or param in ["word", "char", "char_wb"],
@@ -147,7 +147,7 @@ def _name(msg):
     hp_space["min_df"] = _text_min_df(_name("min_df")) if min_df is None else min_df
     hp_space["norm"] = _text_norm(_name("norm")) if norm is None else norm
     hp_space["max_features"] = _text_max_features(_name("max_features")) \
-    if max_features is not None else max_features
+        if max_features is not None else max_features
 
     return scope.sklearn_TfidfVectorizer(**hp_space)
 
@@ -166,7 +166,7 @@ def hashing_vectorizer(name: str, norm: typing.Union[str, Apply] = None, **kwarg
     """
     def _name(msg):
         return f"{name}.hashing_vectorizer_{msg}"
-    
+
     hp_space = _text_hp_space(_name, **kwargs)
     hp_space["norm"] = _text_norm(_name("norm")) if norm is None else norm
 
@@ -191,11 +191,11 @@ def count_vectorizer(
     """
     def _name(msg):
         return f"{name}.count_vectorizer_{msg}"
-    
+
     hp_space = _text_hp_space(_name, **kwargs)
     hp_space["max_df"] = _text_max_df(_name("max_df")) if max_df is None else max_df
     hp_space["min_df"] = _text_min_df(_name("min_df")) if min_df is None else min_df
     hp_space["max_features"] = _text_max_features(_name("max_features")) \
-    if max_features is not None else max_features
-    
+        if max_features is not None else max_features
+
     return scope.sklearn_CountVectorizer(**hp_space)

hpsklearn/components/linear_model/_ridge.py

@@ -219,7 +219,7 @@ def _name(msg):
           validation_test=lambda param: not isinstance(param, str) or param == "balanced",
           msg="Invalid parameter '%s' with value '%s'. Value must be 'balanced'")
 def ridge_classifier_cv(name: str,
-                        class_weight: typing.Union[dict, str] = None, 
+                        class_weight: typing.Union[dict, str] = None,
                         store_cv_results: bool = False,
                         **kwargs):
     """

tests/test_components/test_feature_extraction/test_text.py

@@ -62,7 +62,7 @@ def test_hashing_vectorizer(self):
             max_evals=5,
         )
         model.fit(self.X_test, self.Y_test)
-    
+
     @TrialsExceptionHandler
     def test_count_vectorizer(self):
         """