fixed minor issues, renaming

explicability/shap_vals.py

+# 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, accuracy_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
+# --------------------------------------------------------------------------------------------------------
\ No newline at end of file

models/hyperparam_tuning.py → model_selection/hyperparam_tuning.py

@@ -86,10 +86,10 @@ if __name__ == "__main__":
     models_1 = {"DT" : DecisionTreeClassifier(), 
             "RF" : RandomForestClassifier(), 
             "Bagging" : BaggingClassifier(),
-            "AB" : AdaBoostClassifier(), 
+            "AB" : AdaBoostClassifier(algorithm='SAMME'), 
             "XGB": XGBClassifier(),
             "LR" : LogisticRegression(max_iter=1000), 
-            "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet'), 
+            # "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet'), 
             "SVM" : SVC(probability=True), 
             "MLP" : MLPClassifier(max_iter=500)
             }
@@ -98,12 +98,12 @@ if __name__ == "__main__":
     models_2 = {"DT" : DecisionTreeClassifier(class_weight='balanced'), 
             "RF" : RandomForestClassifier(class_weight='balanced'), 
             "Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),
-            "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),  
+            "AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),  
             # "XGB": XGBClassifier(), # <-
             "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'), 
-            "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet', class_weight='balanced'), 
+            # "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet', class_weight='balanced'), 
             "SVM" : SVC(probability=True, class_weight='balanced'), 
-            # "MLP" : MLPClassifier(max_iter=500)
+            # "MLP" : MLPClassifier(max_iter=500) # <-
             }
 
     # Hyperparameter tuning setup
@@ -124,9 +124,9 @@ if __name__ == "__main__":
         "XGB": {'n_estimators': randint(100, 1000), 
                 'max_depth': randint(3, 10), 
                 'learning_rate': uniform(0.01, 0.3)},
-        "LR": {'penalty': ['l1', 'l2', None], 
+        "LR": {'penalty': ['l1', 'l2', 'elasticnet', None], 
             'solver': ['lbfgs', 'sag', 'saga']},
-        "EL": {'solver': ['lbfgs', 'sag', 'saga']},
+        # "ElNet": {'solver': ['lbfgs', 'sag', 'saga']},
         "SVM": {'C': uniform(0.8, 1.2), 
                 'kernel': ['linear', 'poly', 'rbf', 'sigmoid']},
         "MLP": {'activation': ['identity', 'logistic', 'tanh', 'relu'], 

models/eval_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
-# --------------------------------------------------------------------------------------------------------
-
-def read_data():
-    import numpy as np
-
-    # 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)
-
-    data_dic = {
-        "X_test_pre": X_test_pre,
-        "y_test_pre": y_test_pre,
-        "X_test_post": X_test_post,
-        "y_test_post": y_test_post,
-        "X_train_pre": X_train_pre,
-        "y_train_pre": y_train_pre,
-        "X_train_post": X_train_post,
-        "y_train_post": y_train_post,
-        "X_train_over_pre": X_train_over_pre,
-        "y_train_over_pre": y_train_over_pre,
-        "X_train_over_post": X_train_over_post,
-        "y_train_over_post": y_train_over_post,
-        "X_train_under_pre": X_train_under_pre,
-        "y_train_under_pre": y_train_under_pre,
-        "X_train_under_post": X_train_under_post,
-        "y_train_under_post": y_train_under_post,
-    }
-
-    return data_dic
-
-if __name__ == "__main__":
-
-    # Reading training data
-    data_dic = read_data()
-
-    # Defining the models to train
-    # --------------------------------------------------------------------------------------------------------
-    # 1. No class weight
-    models_1 = {#"DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(n_estimators=50), 
-            # "Bagging" : BaggingClassifier(),
-            # "AB" : AdaBoostClassifier(), 
-            # "XGB": XGBClassifier(),
-            # "LR" : LogisticRegression(max_iter=1000), 
-            # "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet'), 
-            # "SVM" : SVC(probability=True), 
-            # "MLP" : MLPClassifier(max_iter=500),
-            }
-    
-    # 2. Class weight 
-    models_2 = {#"DT" : DecisionTreeClassifier(class_weight='balanced'), 
-            "RF" : RandomForestClassifier(n_estimators=50, class_weight='balanced'), 
-            # "Bagging" : BaggingClassifier(), # <-
-            # "AB" : AdaBoostClassifier(),  # <-
-            # "XGB": XGBClassifier(), # <-
-            # "LR" : LogisticRegression(max_iter=1000, class_weight='balanced'), 
-            # "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet', class_weight='balanced'), 
-            # "SVM" : SVC(probability=True, class_weight='balanced'), 
-            # "MLP" : MLPClassifier(max_iter=500), # <-
-            }
-    # --------------------------------------------------------------------------------------------------------
-
-    # Setup
-    # --------------------------------------------------------------------------------------------------------
-    # Scorings to use for model evaluation
-    scorings = {'recall':make_scorer(recall_score), 'precision':make_scorer(precision_score)}
-    # Defining cross-validation protocol
-    cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=1)
-    result_cols = [f"{model}_{metric}" for model in models_1.keys() for metric in ['PREC', 'REC']]
-    method_names = {
-        0: "ORIG",
-        1: "ORIG_CW",
-        2: "OVER",
-        3: "UNDER"
-    }
-    # --------------------------------------------------------------------------------------------------------
-
-    # Evaluating performance through cross validation and exporting results
-    # --------------------------------------------------------------------------------------------------------
-    # Store each df as a sheet in an excel file
-    sheets_dict = {}
-    for i, group in enumerate(['pre', 'post']):
-        for j, method in enumerate(['', '', 'over_', 'under_']):
-            # Get dataset based on group and method
-            X = data_dic['X_train_' + method + group]
-            y = data_dic['y_train_' + method + group]
-            # Use group of models with class weight if needed
-            models = models_2 if j == 2 else models_1 
-            # Save results in dataframe (10 columns since 10-fold cv)
-            res_df = pd.DataFrame(columns=range(1,11), index=result_cols)
-            for model_name, model in models.items():
-                cv_scores = cross_validate(model, X, y, scoring=scorings, cv=cv, return_train_score=True, n_jobs=1)
-                res_df.loc[model_name + '_PREC'] = list(np.around(np.array(cv_scores["test_precision"]),4))
-                res_df.loc[model_name + '_REC'] = list(np.around(np.array(cv_scores["test_recall"]),4))
-            
-            # Store the DataFrame in the dictionary with a unique key for each sheet
-            sheet_name = f"{group}_{method_names[j]}"
-            sheets_dict[sheet_name] = res_df
-    # Write results to Excel file
-    with pd.ExcelWriter('./training_models/output/cross_val_res.xlsx') as writer:
-        for sheet_name, data in sheets_dict.items():
-            data.to_excel(writer, sheet_name=sheet_name)
-    # --------------------------------------------------------------------------------------------------------
-    
-                

models/shap.py

-# Libraries
-# --------------------------------------------------------------------------------------------------------
-import shap
-import numpy as np
-# --------------------------------------------------------------------------------------------------------
-
-# Load test data
-X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
-
-print(list(X_test_pre.columns.values))
\ No newline at end of file