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generate_boxplot_info.py
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generate_boxplot_info.py
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"""
Script to aggregate the results from an experiment.
Input: source folder path, e.g.
python3 python3 evaluate.py blazer_login_unsafe/fuzzer-out-
"""
import sys
import csv
import statistics
import math
import numpy
import re
from numpy import mean
from numpy import std
from scipy.stats import ttest_ind
from scipy.stats import mannwhitneyu
from scipy.stats import wilcoxon
from scipy.stats import sem
from scipy.stats import t
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# do not change this parameters
START_INDEX = 1
# significance level
alpha = 0.05
# apache_wss4j_KGreedy, 5 seeds, 30 runs
#
# #files=4779
# rep=1
#
# timeGreedy=1.3817693E7
# timeKDynamic=2.2148869E7
#
# avgTimeGreedy=2891.3356350700983
# avgTimeKDynamic=4634.6241891609125
if __name__ == '__main__':
n = 4779
greedy=[1931988.0, 18939.0, 22570.0, 20125.0, 24831.0, 18802.0, 24132.0, 21841.0, 27532.0, 21131.0, 41215.0, 17370.0, 22226.0, 16397.0, 29356.0, 53767.0, 23151.0, 21013.0, 24589.0, 26129.0, 36026.0, 25430.0, 31739.0, 28756.0, 54069.0, 25369.0, 21636.0, 26361.0, 40054.0, 21516.0, 32338.0, 23863.0, 24697.0, 25401.0, 22442.0, 26857.0, 21509.0, 20139.0, 44706.0, 26577.0, 24719.0, 25844.0, 23450.0, 53193.0, 26164.0, 24772.0, 25844.0, 18000.0, 17613.0, 23297.0, 29955.0, 29179.0, 19962.0, 23954.0, 25384.0, 23405.0, 27130.0, 23894.0, 47257.0, 24703.0, 23814.0, 30405.0, 25702.0, 26884.0, 55045.0, 23916.0, 23315.0, 23171.0, 24362.0, 26396.0, 25360.0, 24094.0, 21896.0, 22063.0, 23166.0, 29520.0, 26957.0, 25581.0, 28979.0, 25899.0, 52146.0, 28448.0, 22059.0, 23480.0, 24739.0, 46463.0, 24025.0, 18319.0, 19393.0, 19515.0, 21760.0, 19757.0, 19171.0, 22593.0, 20358.0, 20198.0, 20613.0, 20283.0, 18042.0, 19148.0, 21471.0, 65024.0, 21886.0, 90007.0, 7874.0, 13201.0, 11000.0, 13808.0, 14233.0, 10314.0, 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2373.0, 2262.0, 3198.0, 2525.0, 3395.0, 5619.0, 3434.0, 2545.0, 5736.0, 2530.0, 2462.0, 2135.0, 3819.0, 2197.0, 2153.0, 2448.0, 2134.0, 2050.0, 2082.0, 1896.0, 2127.0, 2255.0, 2142.0, 1775.0, 3127.0, 2659.0, 2956.0, 3300.0, 3741.0, 3008.0, 2344.0, 2696.0, 2271.0, 2142.0, 2926.0, 8750.0, 3122.0, 2306.0, 3271.0, 3564.0, 4799.0, 4257.0, 3871.0, 3306.0, 2570.0, 2124.0, 2213.0, 3012.0, 3086.0, 2168.0, 4242.0, 2755.0, 2914.0, 3512.0, 6397.0, 2907.0, 2224.0, 3059.0, 3370.0, 3728.0, 1865.0, 3595.0, 3822.0, 1722.0, 2459.0, 1649.0, 1423.0, 1396.0, 1437.0, 1593.0, 2833.0, 3932.0, 2127.0, 4865.0]
data1 = greedy
data2 = dynamic
if len(data1) != len(data2) or len(data1) != n:
print("Wrong number of elements!")
exit()
print("n=" + str(len(data1)))
print('data1: mean=%.2f stdv=%.2f' % (mean(data1), std(data1)))
print('data2 mean=%.2f stdv=%.2f' % (mean(data2), std(data2)))
print()
print("Student's t-test:")
stat, p = ttest_ind(data1, data2)
print('Statistics=%.2f, p=%.2f' % (stat, p))
if p > alpha:
print('Same distributions (fail to reject H0)')
else:
print('Different distributions (reject H0)')
print()
print("Mann-Whitney U Test")
stat, p = mannwhitneyu(data1, data2)
print('Statistics=%.2f, p=%.2f' % (stat, p))
if p > alpha:
print('Same distributions (fail to reject H0)')
else:
print('Different distributions (reject H0)')
print()
print("> Wilcoxon Signed-Rank Test")
stat, p = wilcoxon(data1, data2)
print('Statistics=%.2f, p=%.2f' % (stat, p))
if p > alpha:
print('Same distributions (fail to reject H0)')
else:
print('Different distributions (reject H0)')
print()
## Boxplot
data = [data1, data2]
fig, ax = plt.subplots()
ax.set_title('Time Comparison (n=4779 files)')
ax.set_ylabel('time in nanosec')
ax.set_xlabel('Clustering Algorithms')
bp = ax.boxplot(data, showfliers=False)
plt.xticks([1, 2], ['Greedy', 'KDynamic'])
t = [item.get_ydata() for item in bp['whiskers']]
print(t)
print("Greedy:")
print("lower_whisker=" + str(bp['whiskers'][0].get_ydata()[1]))
print("lower_quartile=" + str(bp['boxes'][0].get_ydata()[1]))
print("median=" + str(bp['medians'][0].get_ydata()[1]))
print("upper_quartile=" + str(bp['boxes'][0].get_ydata()[2]))
print("upper_whisker=" + str(bp['whiskers'][1].get_ydata()[1]))
print()
print("KDynamic:")
print("lower_whisker=" + str(bp['whiskers'][2].get_ydata()[1]))
print("lower_quartile=" + str(bp['boxes'][1].get_ydata()[1]))
print("median=" + str(bp['medians'][1].get_ydata()[1]))
print("upper_quartile=" + str(bp['boxes'][1].get_ydata()[2]))
print("upper_whisker=" + str(bp['whiskers'][3].get_ydata()[1]))
print()
# dict1['lower_quartile'] = bp['whiskers'][i].get_ydata()[1]
# dict1['median'] = bp['medians'][i].get_ydata()[1]
# dict1['upper_quartile'] = bp['boxes'][i].get_ydata()[2]
# dict1['upper_whisker'] = bp['whiskers'][(i*2)+1].get_ydata()[1]
#plt.show()