Completed commments

model_selection/test_models.py

+# Testing Tuned Models
+# Author: Joaquín Torres Bravo
 """
     Evaluating optimized models with test data
 """
 
 # Libraries
 # --------------------------------------------------------------------------------------------------------
+# Basics
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+# Models
 from xgboost import XGBClassifier
-from sklearn.metrics import confusion_matrix
-from sklearn.metrics import f1_score, make_scorer, precision_score, recall_score, accuracy_score, roc_auc_score, average_precision_score
 from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
 from sklearn.neural_network import MLPClassifier
 from sklearn.svm import SVC
 from sklearn.linear_model import  LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
+# Metrics
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import f1_score, make_scorer, precision_score, recall_score, accuracy_score, roc_auc_score, average_precision_score
 from sklearn.metrics import RocCurveDisplay, roc_curve
 from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve
-import matplotlib.pyplot as plt
 from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+# Misc
 import ast # String to dictionary
-import seaborn as sns
-from mpl_toolkits.axes_grid1 import make_axes_locatable 
+from mpl_toolkits.axes_grid1 import make_axes_locatable # Custom color bar for confusion matrices
 # --------------------------------------------------------------------------------------------------------
 
 # Reading data