pep8 fix and bumping up the changelog note to the 0.17 release

rasbt · May 17, 2019 · 8108b0e · 8108b0e
1 parent 5433db0
commit 8108b0e
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diff --git a/docs/sources/CHANGELOG.md b/docs/sources/CHANGELOG.md
@@ -16,7 +16,7 @@ The CHANGELOG for the current development version is available at
 
 ##### New Features
 
-- -
+- Add optional `groups` parameter to `SequentialFeatureSelector` and `ExhaustiveFeatureSelector` `fit()` methods for forwarding to sklearn CV ([#537](https://github.com/rasbt/mlxtend/pull/537) via [arc12](https://github.com/qiaguhttps://github.com/arc12))
 
 ##### Changes
 
@@ -44,7 +44,6 @@ The CHANGELOG for the current development version is available at
 -  Now, the `StackingCVRegressor` also enables grid search over the `regressors` and even a single base regressor. When there are level-mixed parameters, `GridSearchCV` will try to replace hyperparameters in a top-down order (see the [documentation](http://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/) for examples details). ([#515](https://github.com/rasbt/mlxtend/pull/512) via [Qiang Gu](https://github.com/qiaguhttps://github.com/qiagu))
 - Adds a `verbose` parameter to `apriori` to show the current iteration number as well as the itemset size currently being sampled. ([#519](https://github.com/rasbt/mlxtend/pull/519)
 - Adds an optional `class_name` parameter to the confusion matrix function to display class names on the axis as tick marks. ([#487](https://github.com/rasbt/mlxtend/pull/487) via [sandpiturtle](https://github.com/qiaguhttps://github.com/sandpiturtle))
-- Add optional `groups` parameter to `SequentialFeatureSelector` and `ExhaustiveFeatureSelector` `fit()` methods for forwarding to sklearn CV
 
 ##### Changes
 

diff --git a/mlxtend/feature_selection/sequential_feature_selector.py b/mlxtend/feature_selection/sequential_feature_selector.py
@@ -295,8 +295,8 @@ def fit(self, X, y, custom_feature_names=None, groups=None, **fit_params):
         if not isinstance(self.k_features, int) and\
                 not isinstance(self.k_features, tuple)\
                 and not isinstance(self.k_features, str):
-                raise AttributeError('k_features must be a positive integer'
-                                     ', tuple, or string')
+            raise AttributeError('k_features must be a positive integer'
+                                 ', tuple, or string')
 
         if (isinstance(self.k_features, int) and (
                 self.k_features < 1 or self.k_features > X_.shape[1])):
@@ -355,7 +355,8 @@ def fit(self, X, y, custom_feature_names=None, groups=None, **fit_params):
                 k_to_select = min_k
             k_idx = tuple(range(X_.shape[1]))
             k = len(k_idx)
-            k_idx, k_score = _calc_score(self, X_, y, k_idx, groups=groups, **fit_params)
+            k_idx, k_score = _calc_score(self, X_, y, k_idx,
+                                         groups=groups, **fit_params)
             self.subsets_[k] = {
                 'feature_idx': k_idx,
                 'cv_scores': k_score,
@@ -480,7 +481,7 @@ def fit(self, X, y, custom_feature_names=None, groups=None, **fit_params):
                                           X)
                     raise KeyboardInterrupt
 
-        except KeyboardInterrupt as e:
+        except KeyboardInterrupt:
             self.interrupted_ = True
             sys.stderr.write('\nSTOPPING EARLY DUE TO KEYBOARD INTERRUPT...')
 
@@ -549,7 +550,8 @@ def _inclusion(self, orig_set, subset, X, y, ignore_feature=None,
                    all_cv_scores[best])
         return res
 
-    def _exclusion(self, feature_set, X, y, fixed_feature=None, groups=None, **fit_params):
+    def _exclusion(self, feature_set, X, y, fixed_feature=None,
+                   groups=None, **fit_params):
         n = len(feature_set)
         res = (None, None, None)
         if n > 1:
@@ -560,7 +562,8 @@ def _exclusion(self, feature_set, X, y, fixed_feature=None, groups=None, **fit_p
             n_jobs = min(self.n_jobs, features)
             parallel = Parallel(n_jobs=n_jobs, verbose=self.verbose,
                                 pre_dispatch=self.pre_dispatch)
-            work = parallel(delayed(_calc_score)(self, X, y, p, groups=groups, **fit_params)
+            work = parallel(delayed(_calc_score)(self, X, y, p,
+                                                 groups=groups, **fit_params)
                             for p in combinations(feature_set, r=n - 1)
                             if not fixed_feature or fixed_feature in set(p))