diff --git a/model_selection/hyperparam_tuning.py b/model_selection/hyperparam_tuning.py index 042ab119763a8d6a1f8b5eca795fec1baf601c0e..e688b7cb4f9d0e3789dcb91374c1faa20e67e281 100644 --- a/model_selection/hyperparam_tuning.py +++ b/model_selection/hyperparam_tuning.py @@ -143,7 +143,7 @@ if __name__ == "__main__": # Store each df as a sheet in an excel file sheets_dict = {} for i, group in enumerate(['pre']): - for j, method in enumerate(['over_']): #['', '', 'over_', 'under_'] + for j, method in enumerate(['under_']): #['', '', 'over_', 'under_'] # Get dataset based on group and method X = data_dic['X_train_' + method + group] y = data_dic['y_train_' + method + group] @@ -153,7 +153,7 @@ if __name__ == "__main__": # 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[2]}-{model_name}") + print(f"{group}-{method_names[3]}-{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 +162,11 @@ 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[j]}" + sheet_name = f"{group}_{method_names[3]}" sheets_dict[sheet_name] = hyperparam_df # Write results to Excel file - with pd.ExcelWriter('./output/hyperparam_pre_OVER.xlsx') as writer: + with pd.ExcelWriter('./output/hyperparam_pre_UNDER.xlsx') as writer: for sheet_name, data in sheets_dict.items(): data.to_excel(writer, sheet_name=sheet_name) diff --git a/model_selection/output/hyperparam_pre_UNDER.xlsx b/model_selection/output/hyperparam_pre_UNDER.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..555c86765aa5f80a4a5ab92f37637701bf548a03 Binary files /dev/null and b/model_selection/output/hyperparam_pre_UNDER.xlsx differ