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covid_analysis
Commit 05aa2417 authored by Joaquin Torres's avatar Joaquin Torres
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ready to retune

parent d3a20982
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Showing with 16 additions and 17 deletions
model_selection/hyperparam_tuning.py
View file @ 05aa2417
......@@ -79,7 +79,7 @@ if __name__ == "__main__":
"AB" : AdaBoostClassifier(algorithm='SAMME'),
"XGB": XGBClassifier(),
"LR" : LogisticRegression(max_iter=1000),
"SVM" : SVC(max_iter=1000),
"SVM" : SVC(probability=True, max_iter=1000),
"MLP" : MLPClassifier(max_iter=500)
# "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet')
}
......@@ -90,7 +90,7 @@ if __name__ == "__main__":
"Bagging" : BaggingClassifier(estimator= DecisionTreeClassifier(class_weight='balanced')),
"AB" : AdaBoostClassifier(estimator= DecisionTreeClassifier(class_weight='balanced'), algorithm='SAMME'),
"LR" : LogisticRegression(max_iter=1000, class_weight='balanced'),
"SVM" : SVC(max_iter = 1000, class_weight='balanced'),
"SVM" : SVC(probability=True, max_iter = 1000, class_weight='balanced'),
# "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet', class_weight='balanced'),
# "XGB": XGBClassifier(), # <-
# "MLP" : MLPClassifier(max_iter=500) # <-
......@@ -142,18 +142,17 @@ if __name__ == "__main__":
# --------------------------------------------------------------------------------------------------------
# Store each df as a sheet in an excel file
sheets_dict = {}
for i, group in enumerate(['post']):
for j, method in enumerate(['']): #['', '', 'over_', 'under_']
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_CS if j == 1 else models_simple
models = models_CS
models = models_CS if j == 1 else models_simple
# Save results: params and best score for each of the mdodels of this method and group
hyperparam_df = pd.DataFrame(index=list(models.keys()), columns=['Parameters','Score'])
for model_name, model in models.items():
print(f"{group}-{method_names[1]}-{model_name}")
print(f"{group}-{method_names[j]}-{model_name}")
# Find optimal hyperparams for curr model
params = hyperparameters[model_name]
search = RandomizedSearchCV(model, param_distributions=params, cv=cv, n_jobs=8, scoring='precision')
......@@ -162,11 +161,11 @@ if __name__ == "__main__":
hyperparam_df.at[model_name,'Score']=round(search.best_score_,4)
# Store the DataFrame in the dictionary with a unique key for each sheet
sheet_name = f"{group}_{method_names[1]}"
sheet_name = f"{group}_{method_names[j]}"
sheets_dict[sheet_name] = hyperparam_df
# Write results to Excel file
with pd.ExcelWriter('./output/hyperparam_post_ORIG_CS.xlsx') as writer:
with pd.ExcelWriter('./output/hyperparamers_pre_and_post') as writer:
for sheet_name, data in sheets_dict.items():
data.to_excel(writer, sheet_name=sheet_name)
......
model_selection/test_models.py
View file @ 05aa2417
......@@ -49,7 +49,7 @@ def get_tuned_models(group_id, method_id):
"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}),
#"SVM" : SVC(**{'C': 0.9872682949695772, 'kernel': 'linear', 'max_iter':1000, 'probability': True}),
"MLP" : MLPClassifier(**{'activation': 'identity', 'hidden_layer_sizes': 122, 'learning_rate': 'invscaling', 'max_iter':500})
}
# 1.2) Trained with original dataset and cost-sensitive learning
......@@ -60,7 +60,7 @@ def get_tuned_models(group_id, method_id):
"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, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
"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'}),
#"SVM": SVC(**{'C': 1.5550524351360953, 'kernel': 'linear', 'max_iter': 1000, 'class_weight': 'balanced', 'probability': True}),
}
# 1.3) Trained with oversampled training dataset
elif method_id == 2:
......@@ -71,7 +71,7 @@ def get_tuned_models(group_id, method_id):
"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}),
#"SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000, 'probability': True}),
"MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
}
# 1.4) Trained with undersampled training dataset
......@@ -83,7 +83,7 @@ def get_tuned_models(group_id, method_id):
"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}),
#"SVM" : SVC(**{'C': 1.1996501173654208, 'kernel': 'poly', 'max_iter':1000, 'probability': True}),
"MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 131, 'learning_rate': 'constant', 'max_iter':500})
}
# 2. POST
......@@ -97,7 +97,7 @@ def get_tuned_models(group_id, method_id):
"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}),
#"SVM" : SVC(**{'C': 1.9890638540240584, 'kernel': 'linear', 'max_iter':1000, 'probability': True}),
"MLP" : MLPClassifier(**{'activation': 'logistic', 'hidden_layer_sizes': 112, 'learning_rate': 'constant', 'max_iter':500})
}
# 2.2) Trained with original dataset and cost-sensitive learning
......@@ -108,7 +108,7 @@ def get_tuned_models(group_id, method_id):
"Bagging": BaggingClassifier(**{'max_features': 1.0, 'max_samples': 0.8, 'n_estimators': 11, 'warm_start': True, 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
"AB": AdaBoostClassifier(**{'learning_rate': 1.7102248217141944, 'n_estimators': 108, 'algorithm': 'SAMME', 'estimator': DecisionTreeClassifier(class_weight='balanced')}),
"LR": LogisticRegression(**{'solver': 'lbfgs', 'penalty': None, 'max_iter': 1000, 'class_weight': 'balanced'}),
#"SVM": SVC(**{'C': 1.1313840454519628, 'kernel': 'sigmoid', 'max_iter': 1000, 'class_weight': 'balanced'})
#"SVM": SVC(**{'C': 1.1313840454519628, 'kernel': 'sigmoid', 'max_iter': 1000, 'class_weight': 'balanced', 'probability': True})
}
# 2.3) Trained with oversampled training dataset
elif method_id == 2:
......@@ -119,7 +119,7 @@ def get_tuned_models(group_id, method_id):
# "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}),
# "SVM" : SVC(**{'C': 1.676419306008229, 'kernel': 'poly', 'max_iter':1000, 'probability': True}),
# "MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 116, 'learning_rate': 'invscaling', 'max_iter':500})
}
# 2.4) Trained with undersampled training dataset
......@@ -131,7 +131,7 @@ def get_tuned_models(group_id, method_id):
"AB" : AdaBoostClassifier(**{'learning_rate': 1.836659462701278, 'n_estimators': 138, 'algorithm': 'SAMME'}),
"XGB": XGBClassifier(**{'learning_rate': 0.2517946893282251, 'max_depth': 4, 'n_estimators': 646}),
"LR" : LogisticRegression(**{'solver': 'lbfgs', 'penalty': 'l2', 'max_iter': 1000}),
#"SVM" : SVC(**{'C': 1.8414678085000697, 'kernel': 'linear', 'max_iter':1000}),
#"SVM" : SVC(**{'C': 1.8414678085000697, 'kernel': 'linear', 'max_iter':1000, 'probability': True}),
"MLP" : MLPClassifier(**{'activation': 'relu', 'hidden_layer_sizes': 76, 'learning_rate': 'constant', 'max_iter':500})
}
return tuned_models
......
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