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Commit 5bf96823 authored by Stavros Mitsis's avatar Stavros Mitsis
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Commit test

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{"num_hidden_layers": 3, "hidden_layer_size": 512, "learning_rate": 0.005, "batch_size": 64, "threshold": 0.25} {"num_hidden_layers": 1, "hidden_layer_size": 48, "learning_rate": 0.01, "batch_size": 64, "threshold": 0.25}
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{"age": {"mean": 37.26280350948786, "std": 21.842276857225034}, "latest_creatinine_value": {"mean": 166.31846085832825, "std": 94.44752591026013}, "median_previous": {"mean": 134.41235802217233, "std": 46.13848554494386}, "mean_previous": {"mean": 134.4250456736426, "std": 46.02537337310225}, "std_dev_previous": {"mean": 10.737323990705239, "std": 7.306492442412928}, "abs_percentage_diff": {"mean": 0.2897293206129817, "std": 0.40866939525398815}}
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...@@ -457,7 +457,7 @@ def objective(trial, data, device): ...@@ -457,7 +457,7 @@ def objective(trial, data, device):
# Suggest hyperparameters # Suggest hyperparameters
num_hidden_layers = trial.suggest_int("num_hidden_layers", 1, 5) num_hidden_layers = trial.suggest_int("num_hidden_layers", 1, 5)
hidden_layer_size = trial.suggest_categorical( hidden_layer_size = trial.suggest_categorical(
"hidden_layer_size", [32, 64, 128, 256, 512, 1024] "hidden_layer_size", [32, 48,64, 96,128,184, 256, 512, 1024]
) )
learning_rate = trial.suggest_categorical( learning_rate = trial.suggest_categorical(
"learning_rate", [0.001, 0.005, 0.01, 0.02, 0.05] "learning_rate", [0.001, 0.005, 0.01, 0.02, 0.05]
...@@ -484,7 +484,7 @@ def objective(trial, data, device): ...@@ -484,7 +484,7 @@ def objective(trial, data, device):
return avg_f3 return avg_f3
def tune_hyperparameters(data: pd.DataFrame, n_trials=10): def tune_hyperparameters(data: pd.DataFrame, n_trials=30):
""" """
Run Optuna hyperparameter tuning on `data`, maximizing F3 via cross-validation. Run Optuna hyperparameter tuning on `data`, maximizing F3 via cross-validation.
......
...@@ -27,7 +27,7 @@ def hyper_parameter_tuning(): ...@@ -27,7 +27,7 @@ def hyper_parameter_tuning():
processor = DataProcessor() processor = DataProcessor()
data = processor.preprocess(['training.csv'], save_constants=True) data = processor.preprocess(['training.csv'], save_constants=True)
balanced_data = processor.handle_class_imbalance(data) balanced_data = processor.handle_class_imbalance(data)
best_trial = tune_hyperparameters(balanced_data, n_trials=10) best_trial = tune_hyperparameters(balanced_data, n_trials=100)
return best_trial return best_trial
...@@ -135,6 +135,7 @@ def main(): ...@@ -135,6 +135,7 @@ def main():
aki_label = 'y' if pred == 1 else 'n' aki_label = 'y' if pred == 1 else 'n'
writer.writerow([aki_label]) writer.writerow([aki_label])
# [I 2025-01-21 11:12:32,171] Trial 9 finished with value: 0.9955269684057108 and parameters: {'num_hidden_layers': 1, 'hidden_layer_size': 512, 'learning_rate': 0.05, 'batch_size': 1024, 'threshold': 0.625}. Best is trial 3 with value: 0.9984615847421683.
if __name__ == '__main__': if __name__ == '__main__':
""" """
...@@ -145,7 +146,7 @@ if __name__ == '__main__': ...@@ -145,7 +146,7 @@ if __name__ == '__main__':
3) Train final model on combined CSVs 3) Train final model on combined CSVs
4) Or run main() for final predictions 4) Or run main() for final predictions
""" """
#hyper_parameter_tuning() hyper_parameter_tuning()
#train_model_and_test() train_model_and_test()
train_final_model() train_final_model()
#main() main()
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