loaded data and defined models

training_models/train_models.py

+"""
+    Selecting best models through cross validation and hyperparameter tunning 
+    for each method: 
+        1. Original training dataset
+        2. Original training dataset - Cost sensitive
+        3. Oversampling
+        4. Undersampling
+"""
+
+# Libraries
+# --------------------------------------------------------------------------------------------------------
+import pandas as pd
+import numpy as np
+from xgboost import XGBClassifier
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import f1_score, make_scorer, precision_score, recall_score
+from sklearn.model_selection import StratifiedKFold, cross_validate
+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
+# --------------------------------------------------------------------------------------------------------
+
+
 if __name__ == "__main__":
-    print("Hello World!")
+
\ No newline at end of file
+    # Reading training data
+    # --------------------------------------------------------------------------------------------------------
+    # Load test data
+    X_test_pre = np.load('gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
+    y_test_pre = np.load('gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True)
+    X_test_post = np.load('gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True)
+    y_test_post = np.load('gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True)
+
+    # Load ORIGINAL training data
+    X_train_pre = np.load('gen_train_data/data/output/pre/X_train_pre.npy', allow_pickle=True)
+    y_train_pre = np.load('gen_train_data/data/output/pre/y_train_pre.npy', allow_pickle=True)
+    X_train_post = np.load('gen_train_data/data/output/post/X_train_post.npy', allow_pickle=True)
+    y_train_post = np.load('gen_train_data/data/output/post/y_train_post.npy', allow_pickle=True)
+
+    # Load oversampled training data
+    X_train_over_pre = np.load('gen_train_data/data/output/pre/X_train_over_pre.npy', allow_pickle=True)
+    y_train_over_pre = np.load('gen_train_data/data/output/pre/y_train_over_pre.npy', allow_pickle=True)
+    X_train_over_post = np.load('gen_train_data/data/output/post/X_train_over_post.npy', allow_pickle=True)
+    y_train_over_post = np.load('gen_train_data/data/output/post/y_train_over_post.npy', allow_pickle=True)
+
+    # Load undersampled training data
+    X_train_under_pre = np.load('gen_train_data/data/output/pre/X_train_under_pre.npy', allow_pickle=True)
+    y_train_under_pre = np.load('gen_train_data/data/output/pre/y_train_under_pre.npy', allow_pickle=True)
+    X_train_under_post = np.load('gen_train_data/data/output/post/X_train_under_post.npy', allow_pickle=True)
+    y_train_under_post = np.load('gen_train_data/data/output/post/y_train_under_post.npy', allow_pickle=True)
+    # --------------------------------------------------------------------------------------------------------
+
+    # Defining the models to train
+    # --------------------------------------------------------------------------------------------------------
+    # 1. No class weight
+    models_1 = {"DT" : DecisionTreeClassifier(), 
+            "RF" : RandomForestClassifier(), 
+            "Bagging" : BaggingClassifier(),
+            "AB" : AdaBoostClassifier(), 
+            "XGB": XGBClassifier(),
+            "LR" : LogisticRegression(), 
+            "ElNet" : LogisticRegression(penalty='elasticnet'), 
+            "SVM" : SVC(), 
+            "MLP" : MLPClassifier(),
+            }
+    
+    # 2. Class weight 
+    models_2 = {"DT" : DecisionTreeClassifier(class_weight='balanced'), 
+            "RF" : RandomForestClassifier(class_weight='balanced'), 
+            "Bagging" : BaggingClassifier(), # <-
+            "AB" : AdaBoostClassifier(),  # <-
+            "XGB": XGBClassifier(), # <-
+            "LR" : LogisticRegression(class_weight='balanced'), 
+            "ElNet" : LogisticRegression(penalty='elasticnet', class_weight='balanced'), 
+            "SVM" : SVC(class_weight='balanced'), 
+            "MLP" : MLPClassifier(), # <-
+            }
+    # --------------------------------------------------------------------------------------------------------