# -*- coding: utf-8 -*- """ Created on Fri Aug 25 14:36:37 2023 @author: luis.pinos-ullauri """ import read_functions as rdf import math from scipy.special import expit import itertools import time import pandas as pd import numpy as np ######################## User Defined Functions ############################### ### Function that returns the overall fitness of the solution across all soft skill dimensions ### It checks whether the fitness amongst the different dimensions is compensatoryensated or not ### as well as which scoring function to use def fitness_func(ga_instance, solution, solution_idx): soft_skill_scores=[] if compensatory: fitness=0 else: fitness=1 for i in range(10):#soft skill id from 0 to 9 estimated_outcome=soft_skill_estimation_mean(solution,i) if score_function==1: score=linear(estimated_outcome,i) elif score_function==2: score=logistic(estimated_outcome,i) elif score_function==3: score=quadratic(estimated_outcome,i) if ga_instance is None: soft_skill_scores.append(score) if compensatory: fitness=fitness+score*(1/10) else: fitness=fitness*score if ga_instance is None: return fitness,soft_skill_scores return fitness ### Linear scoring function ### Fair scoring with no bonuses nor penalisations if the ### soft skill proficiency mean reaches or not the expected profile ### Scoring function rescaled so that it ranges from 0 to 1 def linear(estimated_skill,soft_skill_id): #function min value f_min=min_skill-desired_outcome[soft_skill_id] #function max value f_max=max_skill-desired_outcome[soft_skill_id] return (estimated_skill-desired_outcome[soft_skill_id]-f_min)/(f_max-f_min) ### Logistic scoring function ### Stricter scoring with penalisations and bonuses if the ### soft skill proficiency mean reaches or not the expcted profile ### Scoring function rescaled so that it ranges from 0 to 1 def logistic(estimated_skill,soft_skill_id): #crossing value with linear function at estimated_goal=goal_skill fcrossing=(desired_outcome[soft_skill_id]-min_skill)/(max_skill-min_skill) return (1)/(1+((1-fcrossing)/fcrossing)*pow(math.e,3*(desired_outcome[soft_skill_id]-estimated_skill))) ### Quadratic Root scoring function ### Less demanding scoring that allows an easier scoring if the ### soft skill proficiency mean reaches or not the expcted profile ### Scoring function rescaled so that it ranges from 0 to 1 def quadratic(estimated_skill,soft_skill_id): #crossing value with linear function at estimated_goal=goal_skill fcrossing=(desired_outcome[soft_skill_id]-min_skill)/(max_skill-min_skill) #function min value f_min=-1*pow(min_skill-desired_outcome[soft_skill_id],2) #function max value f_max=1*pow(max_skill-desired_outcome[soft_skill_id],2) if estimated_skill<=desired_outcome[soft_skill_id]: return -1*pow(estimated_skill-desired_outcome[soft_skill_id],2)/(-f_min)*fcrossing+fcrossing else: return 1*pow(estimated_skill-desired_outcome[soft_skill_id],2)/(f_max)*(1-fcrossing)+fcrossing ### Function that estimates the soft skill mean based on the ordinal logistic regression model ### It calculates the probability of each level and estimates the mean SUM x*P(X=x) def soft_skill_estimation_mean(thresholds,courses_effects,theta,solution,soft_skill_id): linear_combination=0 for i in range(len(solution)): if solution[i]!=0: linear_combination=linear_combination+courses_effects.iloc[soft_skill_id,0]+courses_effects.iloc[soft_skill_id,solution[i]] linear_combination=linear_combination+theta eta12=thresholds.iloc[soft_skill_id,0]-linear_combination eta23=thresholds.iloc[soft_skill_id,1]-linear_combination eta34=thresholds.iloc[soft_skill_id,2]-linear_combination p_1=expit(eta12) p_2=expit(eta23)-p_1 p_3=expit(eta34)-p_1-p_2 p_4=1-p_3-p_2-p_1 expected_outcome=1*p_1+2*p_2+3*p_3+4*p_4 return expected_outcome ################## End of User Defined Functions ############################# ### NEED FURTHER THOUGHTS INTO WHICH PLOT TO USE OR MAYBE JUST A TABLE ################## Brute Force Estimation compensatoryuting Time ###################### #Read real data set real_data=rdf.read_real_data() #Considering only stage 2 real_data_stage2=real_data.loc[real_data["stage"]==2] real_data_stage2=real_data_stage2.loc[real_data_stage2["N_courses_followed"]>5] #Domain 1: NU real_data_stage2=real_data_stage2.loc[real_data_stage2["domain_id"]==4] real_data_stage2=real_data_stage2.reset_index(drop=True) #Thresholds thresholds=rdf.get_thresholds() #Course Effects courses_effects=rdf.get_courses_effects() student_id=real_data_stage2.iloc[8,0] domain_id=real_data_stage2.iloc[8,26] N_courses_followed=real_data_stage2.iloc[8,25] min_skill=1#Minimum Soft skill proficiency max_skill=4#Maximum Soft skill proficiency #Compensatory boolean variable compensatory=True #Score function flag variable score_function=1#Linear #score_function=2#Logistic #Student Effect theta=rdf.get_student_random_effect(student_id) #Desired outcome desired_outcome=rdf.get_desired_standard(domain_id) #get possible courses possible_courses=rdf.get_courses_domain(domain_id) calculation_time=pd.DataFrame(np.zeros(shape=(1,15))) calculation_time.columns=['combination_index','c1','c2','c3','c4', 'c5','c6','c7','c8', 'c9','c10','c11','fitness','time(s)','Ncombs'] best_fitness=-1 best_solution=[] start_time = time.time() i=0 for solution in itertools.combinations(possible_courses, N_courses_followed): current_fitness=fitness_func(None,solution,0) if current_fitness>best_fitness: best_fitness=current_fitness best_solution=solution if i==0 or i%2000==0: end_time = time.time() best_solution=list(best_solution) best_solution.sort() elapsed_time = end_time - start_time combs=math.comb(len(possible_courses),len(solution)) calculation_time.loc[i,:]=[i,best_solution[0],best_solution[1], best_solution[2],best_solution[3],best_solution[4], best_solution[5],best_solution[6],best_solution[7], best_solution[8],best_solution[9],0, best_fitness,elapsed_time,combs] calculation_time.to_csv("./real_data/combinations_cal_time_bf_NU.csv") i+=1 ################# End of Brute Force Estimation compensatoryuting Time ################