# -*- coding: utf-8 -*-
import csv
import aggregate_insight as ag
import glob
if __name__ == "__main__":
k_list = [5, 10, 15, 20, 80, 100, 256]
for k in k_list:
file_ideal_topk = 'raw_results/heart.xlsx'
topk = ag.get_topk_aggregate(k, file_ideal_topk)
print("Ideal topk", topk)
percentage_list = [0,10,20,30]
for percent in percentage_list:
for i in range(100):
file_name = 'raw_results/db_' +str(percent) + 'rand_missing_attr' + str(i+1) + '.xlsx'
for file_view in glob.glob(file_name):
print(percent, file_name, k)
missing = ag.get_topk_aggregate(k, file_view)
#print("Missing topk: ", missing)
#print("RBO Standard to Ideal = {}".format(ag.rboresult(missing, topk)))
#print("Jaccard Standard to Ideal = {}".format(ag.jaccard_similarity(missing, topk)))
with open('results/missing_attributes_vs_ideal.csv', 'a', newline='') as f:
fields = [percent, k, ag.rboresult(missing, topk), ag.jaccard_similarity(missing, topk)]
writer = csv.writer(f)
writer.writerow(fields)
k, file_ideal_topk), ag.get_utility_score_ideal_topk(k, ideal_df)
print("Ideal topk", topk)
percentage_list = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]
for percent in percentage_list:
for i in range(100):
try:
file_name = 'raw_results/db_' + str(
percent) + 'rand_missing_a_m' + str(i + 1) + '.xlsx'
for file_view in glob.glob(file_name):
print(percent, file_name, k)
missing, cum_missing = ag.get_topk_aggregate(
k, file_view), ag.get_utility_score_missing_topk(
k, ideal_df, file_view)
#print("Missing topk: ", missing)
#print("RBO Standard to Ideal = {}".format(ag.rboresult(missing, topk)))
#print("Jaccard Standard to Ideal = {}".format(ag.jaccard_similarity(missing, topk)))
with open('results/div_missing_a_m_vs_ideal.csv',
'a',
newline='') as f:
fields = [
percent, k,
ag.rboresult(missing, topk),
ag.jaccard_similarity(missing, topk),
ag.cum_score(cum_missing, cum_topk)
]
writer = csv.writer(f)
writer.writerow(fields)
except:
pass # doing nothing on exception
print("done")
if __name__ == "__main__":
k_list = [5,6,7,8,9,10,11,12,13,15,20,30,40,50,60,70,78,144]
file_ideal_topk = 'raw_results/heart.xlsx'
ideal_df = ag.df_ideal_sim(file_ideal_topk)
for k in k_list:
topk, cum_topk = ag.get_sim_topk_aggregate(k, file_ideal_topk), ag.get_utility_score_ideal_topk_sim(k, ideal_df)
print("Ideal topk", topk)
percentage_list = [0,10,20,30,40,50,60,70,80,90]
for percent in percentage_list:
for i in range(100):
try:
file_name = 'raw_results/db_' +str(percent) + 'rand_missing_a_m' + str(i+1) + '.xlsx'
for file_view in glob.glob(file_name):
print(percent, file_name, k)
missing, cum_missing = ag.get_sim_topk_aggregate(k, file_view), ag.get_utility_score_missing_topk_sim(k, ideal_df, file_view)
#print("Missing topk: ", missing)
#print("Missing topk: ", missing)
#print("RBO Standard to Ideal = {}".format(ag.rboresult(missing, topk)))
#print("Jaccard Standard to Ideal = {}".format(ag.jaccard_similarity(missing, topk)))
with open('results/sim_missing_a_m_vs_ideal.csv', 'a', newline='') as f:
fields = [percent, k, ag.rboresult(missing, topk), ag.jaccard_similarity(missing, topk), ag.cum_score(cum_missing, cum_topk)]
writer = csv.writer(f)
writer.writerow(fields)
except:
pass # doing nothing on exception
print("done")