import sys sys.path.append('XGBoost_Prediction_Model/') import warnings warnings.filterwarnings("ignore") import Predict import torch import numpy as np import os from os.path import isfile, isdir, join from Magazine_Optimization import * mypath = 'XGBoost_Prediction_Model/Magazine_Optimization_Demo/Magazines' results = {} #Target Magazine folders Target = [join(mypath, 'M1'), join(mypath, 'M3')] for Tg in Target: dir_list_target = [] for sub_f in os.listdir(Tg): if isdir(join(Tg, sub_f)): sub_path_temp = join(Tg, sub_f) if (sub_f.split('_')[0]) == 'Jpg': dir_list_target = os.listdir(sub_path_temp) dir_list_target.sort() for i in range(len(dir_list_target)): dir_list_target[i] = join(sub_path_temp,dir_list_target[i]) else: Slots_target = torch.load(join(sub_path_temp,'Slots')).astype('int32') # Sizes_target = torch.load(join(sub_path_temp,'surfaces')) # Product_Groups_target = torch.load(join(sub_path_temp,'Prod_Cat')) Textboxes_target = torch.load(join(sub_path_temp,'Textboxes')) Obj_and_Topics_target = torch.load(join(sub_path_temp,'Obj_and_Topics')) for f in os.listdir(mypath): if isdir(join(mypath, f)) and f != 'M1' and f != 'M3': print('Currently processing Target Magazine '+Tg+' with Ad Magazine '+f+'......') path_temp_ad = join(mypath, f) dir_list_ad = [] for sub_f in os.listdir(path_temp_ad): if isdir(join(path_temp_ad, sub_f)): sub_path_temp = join(path_temp_ad, sub_f) if (sub_f.split('_')[0]) == 'Jpg': dir_list_ad = os.listdir(sub_path_temp) dir_list_ad.sort() for i in range(len(dir_list_ad)): dir_list_ad[i] = join(sub_path_temp,dir_list_ad[i]) else: Slots_ad = torch.load(join(sub_path_temp,'Slots')).astype('int32') Sizes_ad = torch.load(join(sub_path_temp,'surfaces')) Product_Groups_ad = torch.load(join(sub_path_temp,'Prod_Cat')) Textboxes_ad = torch.load(join(sub_path_temp,'Textboxes')) Obj_and_Topics_ad = torch.load(join(sub_path_temp,'Obj_and_Topics')) result = Preference_Matrix_different_magazine(dir_list_target, dir_list_ad, Slots_target, Slots_ad, Product_Groups_ad, Sizes_ad, Textboxes_Target=Textboxes_target, Textboxes_Ad=Textboxes_ad, Obj_and_Topics_Target=Obj_and_Topics_target, Obj_and_Topics_Ad=Obj_and_Topics_ad) if result is not None: Ad_Gaze, Brand_Gaze, Double_Page_Ad_Attention, Double_Page_Brand_Attention, Assign_ids_ad, Assign_ids_target = result #Assignement Problem workers = [] jobs = [] N = np.max(Ad_Gaze.shape) M_small = np.min(Ad_Gaze.shape) for i in range(N): workers.append(i+1) jobs.append(i+1) zeros_aux = np.zeros((N,N)) zeros_aux[:Ad_Gaze.shape[0],:] = Ad_Gaze Ad_Gaze = zeros_aux zeros_aux = np.zeros((N,N)) zeros_aux[:Brand_Gaze.shape[0],:] = Brand_Gaze Brand_Gaze = zeros_aux max_ad_attention = np.max(Ad_Gaze) max_brand_attention = np.max(Brand_Gaze) Ad_Gaze_cost = max_ad_attention - Ad_Gaze Brand_Gaze_cost = max_brand_attention - Brand_Gaze Prob_solved_Ad = Assignment_Problem(Ad_Gaze_cost, workers, jobs) Prob_solved_Brand = Assignment_Problem(Brand_Gaze_cost, workers, jobs) # Print the variables optimized value print('If based on maximizing Overall Ad Attention: ') strategy_AG = '' BG_under_AG_assignment = 0 for v in Prob_solved_Ad.variables(): if v.varValue == 1: curr = (v.name).split('_') BG_under_AG_assignment += Brand_Gaze_cost[int(curr[1])-1,int(curr[2])-1] if int(curr[1]) <= M_small: temp = curr[0]+' Ad '+str(Assign_ids_ad[int(curr[1])-1])+' to Counterpage '+str(Assign_ids_target[int(curr[2])-1]) strategy_AG += temp+'; ' print(temp) # The optimised objective function value is printed to the screen m_ad = N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention) print("Maximized Ad Attention = ", m_ad, " sec.") print("Maximized Average Ad attention on each Ad = ", (N*max_ad_attention - value(Prob_solved_Ad.objective) + sum(Double_Page_Ad_Attention))/(N + len(Double_Page_Ad_Attention)), " sec.") print() # Print the variables optimized value print('If based on maximizing Overall Brand Attention: ') strategy_BG = '' for v in Prob_solved_Brand.variables(): if v.varValue == 1: curr = (v.name).split('_') if int(curr[1]) <= M_small: temp = curr[0]+' Ad '+str(Assign_ids_ad[int(curr[1])-1])+' to Counterpage '+str(Assign_ids_target[int(curr[2])-1]) strategy_BG += temp+'; ' print(temp) # The optimised objective function value is printed to the screen m_brand = N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention) BG_under_AG_assignment = N*max_brand_attention - BG_under_AG_assignment + sum(Double_Page_Brand_Attention) print("Maximized Brand Attention = ", m_brand, " sec.") print("New Brand Gaze under AG assignment = ", BG_under_AG_assignment, " sec.") print("Maximized Average Brand attention on each Ad = ", (N*max_brand_attention - value(Prob_solved_Brand.objective) + sum(Double_Page_Brand_Attention))/(N + len(Double_Page_Brand_Attention)), " sec.") print('End of Magazine '+f+'......') results[Tg+' '+f] = {'AG':[strategy_AG,m_ad,np.trace(Ad_Gaze)], 'BG':[strategy_BG,m_brand,np.trace(Brand_Gaze),BG_under_AG_assignment]} print() print() else: print("Ads cannot be fully assigned!") print() print('Summary: ') for f in list(results.keys()): print('Magazine '+f+': ') dict_curr = results[f] print('Ad Gaze: ') print('Strategy: '+dict_curr['AG'][0]) print('max Attention: ',dict_curr['AG'][1]) print('------------------------') print('Brand Gaze: ') print('Strategy: '+dict_curr['BG'][0]) print('max Attention: ',dict_curr['BG'][1]) print('------------------------') print()