#Task 1a print(file, "Task", "1A") print(r_squared_table(variables, data, CRIME_TOTAL_COL) ) print() #\n #Task 1b print(file, "Task", "1B") model_1b = Model(variables, data, CRIME_TOTAL_COL, list(COMPLAINT_COLS) ) r2 = compute_r_squared(model_1b.beta, model_1b.pre_col, model_1b.dep_col) print(print_model (model_1b.pre_var, r2) ) print()#\n #Task 2 print(file, "Task", "2") print(get_highest_pair(variables, data, list(COMPLAINT_COLS), CRIME_TOTAL_COL) ) print() #\n #Task 3a print(file, "Task", "3a") sel_dict = forward_selection_r_squared(variables, data, \ list(COMPLAINT_COLS), CRIME_TOTAL_COL) print(output_sel(sel_dict)) print() #\n #Task 3b print(file, "Task", "3b") threshold_3b1 = 0.1 task_3b1 = select_best_k(sel_dict, threshold_3b1) print( "Threshold", threshold_3b1, print_model( task_3b1[Model.VARIABLES], \
if __name__ == "__main__": # Task 1a print(file, "Task", "1A") print(r_squared_table(variables, data, STOCKS)) print() # \n # Task 1b print(file, "Task", "1B") model_1b = Model(variables, data, STOCKS, list(COMPLAINT_COLS)) r2 = compute_r_squared(model_1b.beta, model_1b.pre_col, model_1b.dep_col) print(print_model(model_1b.pre_var, r2)) print() # \n # Task 2 print(file, "Task", "2") print(get_highest_pair(variables, data, list(COMPLAINT_COLS), STOCKS)) print() # \n # Task 3a print(file, "Task", "3a") sel_dict = forward_selection_r_squared(variables, data, list(COMPLAINT_COLS), CRIME_TOTAL_COL) print(output_sel(sel_dict)) print() # \n # Task 3b print(file, "Task", "3b") threshold_3b1 = 0.1 task_3b1 = select_best_k(sel_dict, threshold_3b1) print("Threshold", threshold_3b1, print_model(task_3b1[Model.VARIABLES], task_3b1[Model.R2])) threshold_3b2 = 0.01