#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
