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@@ -88,12 +88,24 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram
#### Costly: Healthy → predicted sick : high precision score or low FP
## STEP 5:
Current results taken:
| model | stage | accuracy | f1_macro | f2_macro | recall_macro | precision_macro | f1_class0 | f1_class1 | f2_class0 | f2_class1 | recall_class0 | recall_class1 | precision_class0 | precision_class1 | TP | TN | FP | FN |
|-----------------------|-------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|-----|-------|----|----|
| CatBoost_balanced | train | 0.9843784049402589 | 0.8696686267343388 | 0.8824472728294012 | 0.8916952848998795 | 0.8508242781484853 | 0.9919396338322237 | 0.7473976196364541 | 0.9908276010500254 | 0.7740669446087769 | 0.9900881006639566 | 0.7933024691358025 | 0.9938004847319636 | 0.7078480715650071 | 789 | 26898 | 140 | 19 |
| CatBoost_balanced | test | 0.9802604802604803 | 0.8348421298822796 | 0.8461546793313885 | 0.8541662696976049 | 0.8176680164072361 | 0.9898162729658793 | 0.6798679867986799 | 0.988757446094471 | 0.703551912568306 | 0.9880528191154894 | 0.7202797202797203 | 0.991586032814472 | 0.64375 | 103 | 4714 | 57 | 40 |
| LGBM_KMEANS_SMOTE | train | 0.9883286128479746 | 0.8784419356817057 | 0.8436008106620193 | 0.8240767336379762 | 0.9582821430574249 | 0.9940169232360254 | 0.7628669481273861 | 0.9966698960611392 | 0.6905317252628993 | 0.9984466771524954 | 0.6497067901234568 | 0.9896275269971563 | 0.9269367591176938 | 775 | 27036 | 2 | 33 |
| LGBM_KMEANS_SMOTE | test | 0.9865689865689866 | 0.8543196878009516 | 0.8121616449258658 | 0.7895809912158687 | 0.9600745182511498 | 0.9931221342225928 | 0.7155172413793104 | 0.9964866786565728 | 0.6278366111951589 | 0.9987424020121568 | 0.5804195804195804 | 0.9875647668393782 | 0.9325842696629213 | 83 | 4765 | 6 | 60 |
## next steps:
```
✅ 1. Stratified K-fold only apply on train.
🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10
🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10
🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained)
🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained)
🗹 4. implement proposed methods of this article : https://1drv.ms/b/c/ab2a38fe5c318317/IQBEDsSFcYj6R6AMtOnh0X6DAZUlFqAYq19WT8nTeXomFwg
🗹 5. compare proposed model with SMOTE vs oversampling balancing method
```

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iter Passed Remaining
0 31 15495
1 56 13981
2 78 13029
3 102 12704
4 125 12422
5 149 12287
6 173 12237
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8 221 12080
9 244 11997
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500 498 11992 24
501 499 12015 0

5
lgbm_vs_cat_kmeans_smote_k10_results.csv Normal file
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@@ -0,0 +1,5 @@
model,stage,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f1_class1,f2_class0,f2_class1,recall_class0,recall_class1,precision_class0,precision_class1,TP,TN,FP,FN
CatBoost_balanced,train,0.9843784049402589,0.8696686267343388,0.8824472728294012,0.8916952848998795,0.8508242781484853,0.9919396338322237,0.7473976196364541,0.9908276010500254,0.7740669446087769,0.9900881006639566,0.7933024691358025,0.9938004847319636,0.7078480715650071,789,26898,140,19
CatBoost_balanced,test,0.9802604802604803,0.8348421298822796,0.8461546793313885,0.8541662696976049,0.8176680164072361,0.9898162729658793,0.6798679867986799,0.988757446094471,0.703551912568306,0.9880528191154894,0.7202797202797203,0.991586032814472,0.64375,103,4714,57,40
LGBM_KMEANS_SMOTE,train,0.9883286128479746,0.8784419356817057,0.8436008106620193,0.8240767336379762,0.9582821430574249,0.9940169232360254,0.7628669481273861,0.9966698960611392,0.6905317252628993,0.9984466771524954,0.6497067901234568,0.9896275269971563,0.9269367591176938,775,27036,2,33
LGBM_KMEANS_SMOTE,test,0.9865689865689866,0.8543196878009516,0.8121616449258658,0.7895809912158687,0.9600745182511498,0.9931221342225928,0.7155172413793104,0.9964866786565728,0.6278366111951589,0.9987424020121568,0.5804195804195804,0.9875647668393782,0.9325842696629213,83,4765,6,60
1 model stage accuracy f1_macro f2_macro recall_macro precision_macro f1_class0 f1_class1 f2_class0 f2_class1 recall_class0 recall_class1 precision_class0 precision_class1 TP TN FP FN
2 CatBoost_balanced train 0.9843784049402589 0.8696686267343388 0.8824472728294012 0.8916952848998795 0.8508242781484853 0.9919396338322237 0.7473976196364541 0.9908276010500254 0.7740669446087769 0.9900881006639566 0.7933024691358025 0.9938004847319636 0.7078480715650071 789 26898 140 19
3 CatBoost_balanced test 0.9802604802604803 0.8348421298822796 0.8461546793313885 0.8541662696976049 0.8176680164072361 0.9898162729658793 0.6798679867986799 0.988757446094471 0.703551912568306 0.9880528191154894 0.7202797202797203 0.991586032814472 0.64375 103 4714 57 40
4 LGBM_KMEANS_SMOTE train 0.9883286128479746 0.8784419356817057 0.8436008106620193 0.8240767336379762 0.9582821430574249 0.9940169232360254 0.7628669481273861 0.9966698960611392 0.6905317252628993 0.9984466771524954 0.6497067901234568 0.9896275269971563 0.9269367591176938 775 27036 2 33
5 LGBM_KMEANS_SMOTE test 0.9865689865689866 0.8543196878009516 0.8121616449258658 0.7895809912158687 0.9600745182511498 0.9931221342225928 0.7155172413793104 0.9964866786565728 0.6278366111951589 0.9987424020121568 0.5804195804195804 0.9875647668393782 0.9325842696629213 83 4765 6 60

497
runner.py
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@@ -1,14 +1,14 @@
import pandas
from catboost import CatBoostClassifier
from imblearn.ensemble import BalancedRandomForestClassifier
# from imblearn.ensemble import BalancedRandomForestClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
# from sklearn.ensemble import RandomForestClassifier
# from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
# from xgboost import XGBClassifier
from custom_models.LGBMFocalWrapper import LGBMFocalWrapper
# from custom_models.LGBMFocalWrapper import LGBMFocalWrapper
from train import test_model, train_model_with_kfold
data_frame = pandas.read_csv("./data/Ketamin_icp_cleaned.csv")
@@ -28,33 +28,266 @@ pos = sum(y_train == 1)
scale_pos = neg / pos if pos > 0 else 1.0
models = [
# {
# "name": "LGBM_FOCAL_LOSS",
# "model": LGBMFocalWrapper(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "LGBM_SMOTE",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "smote",
# },
# {
# "name": "LGBM_KMEANS_SMOTE",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "LGBM_SVM_SMOTE",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "svm",
# },
# {
# "name": "LGBM_BORDERLINE_SMOTE",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "borderline",
# },
# {
# "name": "LGBM_ADASYN_SMOTE",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "adasyn",
# },
# {
# "name": "LGBM_Balanced",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# class_weight="balanced",
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": False,
# },
# {
# "name": "LGBM_DART",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# boosting_type="dart",
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "LGBM_GOSS",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# boosting_type="goss",
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "LGBM_RF",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# boosting_type="rf",
# subsample=0.8,
# colsample_bytree=0.8,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "LGBM_scale_pos_weight",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# scale_pos_weight=scale_pos,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": False,
# },
# {
# "name": "LGBM_is_unbalance",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# is_unbalance=True,
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": False,
# },
# {
# "name": "LGBM_DART",
# "model": LGBMClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=-1,
# subsample=0.8,
# colsample_bytree=0.8,
# boosting_type="dart",
# random_state=42,
# verbose=-1,
# n_jobs=-1,
# ),
# "smote": True,
# "smote_method": "kmeans",
# },
# {
# "name": "XGB_scale_pos_weight",
# "model": XGBClassifier(
# n_estimators=500,
# learning_rate=0.05,
# max_depth=6,
# scale_pos_weight=scale_pos,
# random_state=42,
# n_jobs=-1,
# use_label_encoder=False,
# eval_metric="logloss",
# ),
# "smote": False,
# },
# {
# "name": "CatBoost_balanced",
# "model": CatBoostClassifier(
# iterations=500,
# learning_rate=0.05,
# depth=6,
# class_weights=[1, scale_pos],
# random_state=42,
# verbose=0,
# ),
# "smote": False,
# },
# {
# "name": "RandomForest_balanced",
# "model": RandomForestClassifier(
# n_estimators=500,
# max_depth=None,
# class_weight="balanced",
# random_state=42,
# n_jobs=-1,
# ),
# "smote": False,
# },
# {
# "name": "BalancedRandomForest",
# "model": BalancedRandomForestClassifier(
# n_estimators=500,
# max_depth=None,
# random_state=42,
# n_jobs=-1,
# ),
# "smote": False,
# },
# {
# "name": "LogisticRegression_balanced",
# "model": LogisticRegression(
# max_iter=1000,
# class_weight="balanced",
# solver="liblinear",
# random_state=42,
# ),
# "smote": False,
# },
{
"name": "LGBM_FOCAL_LOSS",
"model": LGBMFocalWrapper(
n_estimators=500,
"name": "CatBoost_balanced",
"model": CatBoostClassifier(
iterations=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
depth=6,
class_weights=[1, scale_pos],
random_state=42,
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "LGBM_SMOTE",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
verbose=-1,
verbose=0,
n_jobs=-1,
),
"smote": True,
"smote_method": "smote",
"smote": False,
},
{
"name": "LGBM_KMEANS_SMOTE",
@@ -70,212 +303,8 @@ models = [
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "LGBM_SVM_SMOTE",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "svm",
},
{
"name": "LGBM_BORDERLINE_SMOTE",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "borderline",
},
{
"name": "LGBM_ADASYN_SMOTE",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "adasyn",
},
{
"name": "LGBM_Balanced",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
class_weight="balanced",
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": False,
},
{
"name": "LGBM_DART",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
boosting_type="dart",
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "LGBM_GOSS",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
boosting_type="goss",
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "LGBM_RF",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
boosting_type="rf",
subsample=0.8,
colsample_bytree=0.8,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "LGBM_scale_pos_weight",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
scale_pos_weight=scale_pos,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": False,
},
{
"name": "LGBM_is_unbalance",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
is_unbalance=True,
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": False,
},
{
"name": "LGBM_DART",
"model": LGBMClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=-1,
subsample=0.8,
colsample_bytree=0.8,
boosting_type="dart",
random_state=42,
verbose=-1,
n_jobs=-1,
),
"smote": True,
"smote_method": "kmeans",
},
{
"name": "XGB_scale_pos_weight",
"model": XGBClassifier(
n_estimators=500,
learning_rate=0.05,
max_depth=6,
scale_pos_weight=scale_pos,
random_state=42,
n_jobs=-1,
use_label_encoder=False,
eval_metric="logloss",
),
"smote": False,
},
{
"name": "CatBoost_balanced",
"model": CatBoostClassifier(
iterations=500,
learning_rate=0.05,
depth=6,
class_weights=[1, scale_pos],
random_state=42,
verbose=0,
),
"smote": False,
},
{
"name": "RandomForest_balanced",
"model": RandomForestClassifier(
n_estimators=500,
max_depth=None,
class_weight="balanced",
random_state=42,
n_jobs=-1,
),
"smote": False,
},
{
"name": "BalancedRandomForest",
"model": BalancedRandomForestClassifier(
n_estimators=500,
max_depth=None,
random_state=42,
n_jobs=-1,
),
"smote": False,
},
{
"name": "LogisticRegression_balanced",
"model": LogisticRegression(
max_iter=1000,
class_weight="balanced",
solver="liblinear",
random_state=42,
),
"smote": False,
},
}
]
@@ -308,7 +337,7 @@ for m in models:
)
results_df = pandas.DataFrame(results_to_save)
csv_file = "lightgbm_results.csv"
csv_file = "lgbm_vs_cat_kmeans_smote_k10_results.csv"
try:
results_df.to_csv(csv_file, mode="a", index=False, header=False)

2
train.py
View File

@@ -43,7 +43,7 @@ def train_model_with_kfold(
if smote:
if smote_method.lower() == "kmeans":
sampler = KMeansSMOTE(
k_neighbors=5,
k_neighbors=10,
cluster_balance_threshold=0.1,
random_state=random_state,
)