From cbf63f2085612e5bd6cce6d30aef1d9d33796ff5 Mon Sep 17 00:00:00 2001
From: joaquintb <joaquintobrw@gmail.com>
Date: Fri, 10 May 2024 12:24:57 +0200
Subject: [PATCH] Completed description of tuned models for PRE from excel
 files

---
 model_selection/output/hyperparam_post.xlsx   | Bin 12265 -> 13712 bytes
 model_selection/output/hyperparam_pre_CS.xlsx | Bin 9253 -> 10300 bytes
 .../output/hyperparam_pre_ORIG.xlsx           | Bin 9047 -> 10409 bytes
 .../output/hyperparam_pre_OVER.xlsx           | Bin 9400 -> 10424 bytes
 .../output/hyperparam_pre_UNDER.xlsx          | Bin 9040 -> 10431 bytes
 model_selection/test_models.py                | 123 ++++++------------
 6 files changed, 43 insertions(+), 80 deletions(-)

diff --git a/model_selection/output/hyperparam_post.xlsx b/model_selection/output/hyperparam_post.xlsx
index 66037db22b1306121004c8c8ce6a1414f41aa28b..5d04496f151ed7200fe8a27aa23fcf6af97fbbdd 100644
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diff --git a/model_selection/output/hyperparam_pre_CS.xlsx b/model_selection/output/hyperparam_pre_CS.xlsx
index becfef04dcf1022c725b2e56a8dcb333d78973cb..853249595517a431427e2d2fc56d0285153bfad3 100644
GIT binary patch
delta 3288
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delta 2294
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diff --git a/model_selection/output/hyperparam_pre_ORIG.xlsx b/model_selection/output/hyperparam_pre_ORIG.xlsx
index acc975e69af393ad24254e68718c521cdc0f291c..61a62453027e2608a4a1e6ccc5e61a6ad287af5a 100644
GIT binary patch
delta 3842
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YNqylM1P_#!20#Nqof{`XzTb=f0sP(27ytkO

delta 2496
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diff --git a/model_selection/output/hyperparam_pre_OVER.xlsx b/model_selection/output/hyperparam_pre_OVER.xlsx
index 88bc7d49f604fb1992b5d5c2dfede07081c70c56..0c0bb36e93477902f43a173a3efb4532329e0391 100644
GIT binary patch
delta 3325
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diff --git a/model_selection/output/hyperparam_pre_UNDER.xlsx b/model_selection/output/hyperparam_pre_UNDER.xlsx
index 81b3c90c316fe8adc3587295a057325f9bd76db2..0e169ed177f2742d6aa38d3a84ddb5f6bc87e153 100644
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diff --git a/model_selection/test_models.py b/model_selection/test_models.py
index de36a19..3eda93c 100644
--- a/model_selection/test_models.py
+++ b/model_selection/test_models.py
@@ -43,109 +43,72 @@ def get_tuned_models(group_id, method_id):
         # 1.1) Trained with original dataset
         if method_id == 0:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 117}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 23, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.9189147333140566, 'n_estimators': 131, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.22870029177880222, 'max_depth': 8, 'n_estimators': 909}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 0.9872682949695772, 'kernel': 'linear', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'identity', 'hidden_layer_sizes': 122, 'learning_rate': 'invscaling', 'max_iter':500})
             }
         # 1.2) Trained with original dataset and cost-sensitive learning
         elif method_id == 1:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'log_loss', 'class_weight': 'balanced'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 118, 'class_weight': 'balanced'}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 15, 'warm_start': False}, estimator= DecisionTreeClassifier(class_weight='balanced')),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 0.8159074545140872, 'n_estimators': 121}, estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),  
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None}, max_iter=1000, class_weight='balanced'), 
+            "SVM" : SVC(**{'C': 1.5550524351360953, 'kernel': 'linear'}, max_iter = 1000, class_weight='balanced'), 
             }
         # 1.3) Trained with oversampled training dataset
         elif method_id == 2:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
+            "DT" : DecisionTreeClassifier(**{'splitter': 'random', 'max_features': 'sqrt', 'criterion': 'log_loss'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'gini', 'max_features': 'sqrt', 'n_estimators': 135}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 26, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.6590924545876917, 'n_estimators': 141, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.26946295284728783, 'max_depth': 7, 'n_estimators': 893}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
             }
-        # 1. 4) Trained with undersampled training dataset
+        # 1.4) Trained with undersampled training dataset
         elif method_id == 3:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'sqrt', 'criterion': 'gini'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 104}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 1.0, 'n_estimators': 38, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.6996764264041269, 'n_estimators': 93, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.26480707899668926, 'max_depth': 7, 'n_estimators': 959}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.1996501173654208, 'kernel': 'poly', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 131, 'learning_rate': 'constant', 'max_iter':500})
             }
     # 2. POST
     else:
         # 2.1) Trained with original dataset
         if method_id == 0:
             tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
+            "DT" : DecisionTreeClassifier(**{'splitter': 'best', 'max_features': 'log2', 'criterion': 'gini'}), 
+            "RF" : RandomForestClassifier(**{'criterion': 'entropy', 'max_features': 'sqrt', 'n_estimators': 213}), 
+            "Bagging" : BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 32, 'warm_start': True}),
+            "AB" : AdaBoostClassifier(**{'learning_rate': 1.7806904141367559, 'n_estimators': 66, 'algorithm': 'SAMME'}), 
+            "XGB": XGBClassifier(**{'learning_rate': 0.21889089898592098, 'max_depth': 6, 'n_estimators': 856}),
+            "LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000}), 
+            "SVM" : SVC(**{'C': 1.9890638540240584, 'kernel': 'linear', 'max_iter':1000}), 
+            "MLP" : MLPClassifier(**{'activation': 'logistic', 'hidden_layer_sizes': 112, 'learning_rate': 'constant', 'max_iter':500})
             }
         # 2.2) Trained with original dataset and cost-sensitive learning
         elif method_id == 1:
-            tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
-            }
+            ...
         # 2.3) Trained with oversampled training dataset
         elif method_id == 2:
-            tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
-            }
+            ...
         # 2.4) Trained with undersampled training dataset
         elif method_id == 3:
-            tuned_models = {
-            "DT" : DecisionTreeClassifier(), 
-            "RF" : RandomForestClassifier(), 
-            "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)
-            }
+            ...
     return tuned_models
 # --------------------------------------------------------------------------------------------------------
 
@@ -201,8 +164,8 @@ if __name__ == "__main__":
         'FN':FN_scorer, 
         'FP':FP_scorer, 
         'TP':TP_scorer
+        } 
         # AUROC and AUPRC (plot?)
-        }
     method_names = {
         0: "ORIG",
         1: "ORIG_CW",
-- 
2.24.1