Fix formatting in test_ensemble_vote_classifier

Author: sachinn854

docs/sources/user_guide/feature_selection/ExhaustiveFeatureSelector.ipynb

@@ -1113,8 +1113,7 @@
     "from sklearn.linear_model import LogisticRegression\n",
     "from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS\n",
     "\n",
-    "lr = LogisticRegression( solver='newton-cg', \n",
-    "                        random_state=123)\n",
+    "lr = LogisticRegression( solver='newton-cg',random_state=123)\n",
     "\n",
     "efs1 = EFS(estimator=lr, \n",
     "           min_features=2,\n",

docs/sources/user_guide/plotting/plot_decision_regions.ipynb

@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,8 +160,7 @@
     "import numpy as np\n",
     "\n",
     "# Initializing Classifiers\n",
-    "clf1 = LogisticRegression(random_state=1,\n",
-    "                          solver='newton-cg')\n",
+    "clf1 = LogisticRegression(random_state=1, solver='newton-cg')\n",
     "clf2 = RandomForestClassifier(random_state=1, n_estimators=100)\n",
     "clf3 = GaussianNB()\n",
     "clf4 = SVC(gamma='auto')\n",

mlxtend/classifier/perceptron.py

@@ -85,7 +85,7 @@ def _fit(self, X, y, init_params=True):
                 update = self.eta * (y_data[idx] - self._to_classlabels(X[idx]))
                 self.w_ += (update * X[idx]).reshape(self.w_.shape)
                 self.b_ += update
-                errors += int(update != 0.0)
+                errors += int(np.any(update != 0.0))
 
             if self.print_progress:
                 self._print_progress(iteration=i + 1, n_iter=self.epochs, cost=errors)

mlxtend/classifier/tests/test_ensemble_vote_classifier.py

@@ -133,8 +133,7 @@ def test_no_weight_support_with_no_weight():
 
 
 def test_1model_labels():
-    clf = LogisticRegression(solver="newton-cg", random_state=123
-    )
+    clf = LogisticRegression(solver="newton-cg", random_state=123)
     ens_clf_1 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=None)
     ens_clf_2 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=[1.0])
 
@@ -147,8 +146,7 @@ def test_1model_labels():
 
 
 def test_1model_probas():
-    clf = LogisticRegression(solver="newton-cg", random_state=123
-    )
+    clf = LogisticRegression(solver="newton-cg", random_state=123)
     ens_clf_1 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=None)
     ens_clf_2 = EnsembleVoteClassifier(clfs=[clf], voting="soft", weights=[1.0])
 

mlxtend/feature_selection/tests/test_exhaustive_feature_selector.py

@@ -382,7 +382,7 @@ def _fit(self, X, y, init_params=True):
                     update = self.eta * (y_data[idx] - self._to_classlabels(X[idx]))
                     self.w_ += (update * X[idx]).reshape(self.w_.shape)
                     self.b_ += update
-                    errors += int(update != 0.0)
+                    errors += int(np.any(update != 0.0))
 
                 if self.print_progress:
                     self._print_progress(

mlxtend/feature_selection/tests/test_sequential_feature_selector.py

@@ -526,7 +526,7 @@ def _fit(self, X, y, init_params=True):
                     update = self.eta * (y_data[idx] - self._to_classlabels(X[idx]))
                     self.w_ += (update * X[idx]).reshape(self.w_.shape)
                     self.b_ += update
-                    errors += int(update != 0.0)
+                    errors += int(np.any(update != 0.0))
 
                 if self.print_progress:
                     self.print_progress(