Sort all imports

shell.nix

@@ -8,5 +8,6 @@ mkShell {
     python38Packages.pandas
     python38Packages.scikitlearn
     python38Packages.seaborn
+    python38Packages.isort
   ];
 }

src/preprocessing.py

@@ -1,6 +1,6 @@
 from pandas import read_csv
-from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import KFold
+from sklearn.preprocessing import LabelEncoder
 
 
 def replace_values(df):

src/processing.py

@@ -1,16 +1,16 @@
-from numpy import mean, arange
+from sys import argv
+
+from matplotlib.pyplot import *
+from numpy import arange, mean
+from pandas import DataFrame, cut
+from seaborn import countplot, heatmap, set_style, set_theme
 from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve
 from sklearn.naive_bayes import GaussianNB
-from sklearn.neural_network import MLPClassifier
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.neural_network import MLPClassifier
 from sklearn.preprocessing import scale
 from sklearn.svm import LinearSVC
 from sklearn.tree import DecisionTreeClassifier
-from seaborn import set_theme, set_style, heatmap, countplot
-from matplotlib.pyplot import *
-from pandas import DataFrame, cut
-
-from sys import argv
 
 from preprocessing import parse_data, split_k_sets
 
@@ -110,8 +110,8 @@ def plot_attributes_correlation(data, target):
 
 def plot_all_figures(results, data, target):
     set_theme()
-    # plot_roc_auc_curve(results=results)
-    # plot_confusion_matrix(results=results)
+    plot_roc_auc_curve(results=results)
+    plot_confusion_matrix(results=results)
     plot_attributes_correlation(data=data, target=target)