copenhagen: New experiment
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#!/usr/bin/env python3
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import sys
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sys.path.insert(1, '../lib')
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import argparse
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import ast
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from datetime import datetime
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from geopy.distance import distance
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import lmdk_bgt
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import lmdk_lib
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import math
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import numpy as np
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from matplotlib import pyplot as plt
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import time
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def main(args):
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res_file = '/home/manos/Cloud/Data/HUE/Results.zip'
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# User's consumption
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seq = lmdk_lib.load_data(args, 'cons')
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# The name of the dataset
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d = 'HUE'
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# The landmarks percentages
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lmdks_pct = [0, 20, 40, 60, 80, 100]
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# Landmarks' thresholds
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lmdks_th = [0, .54, .68, .88, 1.12, 10]
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# The privacy budget
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epsilon = 10.0
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# Number of methods
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n = 3
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# Width of bars
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bar_width = 1/(n + 1)
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# The x axis
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x_i = np.arange(len(lmdks_pct))
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x_margin = bar_width*(n/2 + 1)
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print('\n##############################', d, '\n')
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# Initialize plot
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lmdk_lib.plot_init()
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# The x axis
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plt.xticks(x_i, np.array(lmdks_pct, int))
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plt.xlabel('Landmarks (%)') # Set x axis label.
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plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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# The y axis
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plt.ylabel('Mean absolute error (kWh)') # Set y axis label.
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plt.yscale('log')
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# plt.ylim(.01, 10000)
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# Bar offset
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x_offset = -(bar_width/2)*(n - 1)
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mae_u = np.zeros(len(lmdks_pct))
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mae_s = np.zeros(len(lmdks_pct))
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mae_a = np.zeros(len(lmdks_pct))
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mae_evt = 0
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mae_usr = 0
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for i, pct in enumerate(lmdks_pct):
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# Find landmarks
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lmdks = seq[seq[:, 1] < lmdks_th[i]]
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for _ in range(args.iter):
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# Skip
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rls_data_s, bgts_s = lmdk_bgt.skip_cons(seq, lmdks, epsilon)
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# lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_s)
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mae_s[i] += lmdk_bgt.mae_cons(seq, rls_data_s)/args.iter
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# Uniform
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rls_data_u, bgts_u = lmdk_bgt.uniform_cons(seq, lmdks, epsilon)
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mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter
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# Adaptive
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rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon, .5, .5)
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mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter
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# Calculate once
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# Event
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if i == 0:
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rls_data_evt, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[0]], epsilon)
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mae_evt += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter
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# User
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if i == 0:
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rls_data_usr, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[len(lmdks_th)-1]], epsilon)
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mae_usr += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter
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plt.axhline(
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y = mae_evt,
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color = '#212121',
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linewidth=lmdk_lib.line_width
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)
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plt.text(x_i[-1] + x_i[-1]*.14, mae_evt - mae_evt*.14, 'event')
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plt.axhline(
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y = mae_usr,
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color = '#616161',
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linewidth=lmdk_lib.line_width
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)
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plt.text(x_i[-1] + x_i[-1]*.14, mae_usr - mae_usr*.14, 'user')
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plt.bar(
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x_i + x_offset,
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mae_s,
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bar_width,
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label='Skip',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_u,
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bar_width,
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label='Uniform',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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plt.bar(
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x_i + x_offset,
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mae_a,
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bar_width,
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label='Adaptive',
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linewidth=lmdk_lib.line_width
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)
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x_offset += bar_width
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path = str('../../rslt/bgt_cmp/' + d)
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# Plot legend
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lmdk_lib.plot_legend()
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# Show plot
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# plt.show()
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# Save plot
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lmdk_lib.save_plot(path + '.pdf')
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print('[OK]', flush=True)
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def parse_args():
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'''
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Parse arguments.
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Optional:
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res - The results archive file.
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iter - The total iterations.
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'''
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# Create argument parser.
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parser = argparse.ArgumentParser()
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# Mandatory arguments.
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# Optional arguments.
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parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip')
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parser.add_argument('-i', '--iter', help='The total iterations.', type=int, default=1)
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# Parse arguments.
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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try:
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start_time = time.time()
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main(parse_args())
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end_time = time.time()
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print('##############################')
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print('Time : %.4fs' % (end_time - start_time))
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print('##############################')
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except KeyboardInterrupt:
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print('Interrupted by user.')
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exit()
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@ -19,11 +19,11 @@ def main(args):
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# Contacts for all users
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cont_data = lmdk_lib.load_data(args, 'cont')
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# Contacts for landmark's percentages for all users
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lmdk_data = lmdk_lib.load_data(args, 'usrs_expt')
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lmdk_data = lmdk_lib.load_data(args, 'usrs_data')
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# The name of the dataset
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d = 'Copenhagen'
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# The user's id
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uid = '623'
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uid = '449'
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# The landmarks percentages
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lmdks_pct = [0, 20, 40, 60, 80, 100]
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# The privacy budget
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@ -39,7 +39,7 @@ def main(args):
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print('\n##############################', d, '\n')
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# Get user's contacts sequence
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seq = cont_data[cont_data[:, 1] == float(uid)]
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seq = cont_data[cont_data[:, 1] == float(uid)][:1000]
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# Initialize plot
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lmdk_lib.plot_init()
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@ -50,15 +50,15 @@ def main(args):
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# The y axis
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plt.ylabel('Mean absolute error') # Set y axis label.
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# plt.yscale('log')
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plt.ylim(0, 1.4)
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# plt.ylim(0, 1.4)
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# Bar offset
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x_offset = -(bar_width/2)*(n - 1)
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mae_u = np.zeros(len(lmdks_pct))
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mae_s = np.zeros(len(lmdks_pct))
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mae_a = np.zeros(len(lmdks_pct))
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mae_evt = np.zeros(len(lmdks_pct))
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mae_usr = np.zeros(len(lmdks_pct))
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mae_evt = 0
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mae_usr = 0
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for i, pct in enumerate(lmdks_pct):
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# Find landmarks
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@ -79,30 +79,28 @@ def main(args):
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rls_data_a, _, _ = lmdk_bgt.adaptive_cont(seq, lmdks, epsilon, .5, .5)
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mae_a[i] += lmdk_bgt.mae_cont(rls_data_a)/args.iter
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# Event
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# Calculate once
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if i == 0:
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# Event
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rls_data_evt, _ = lmdk_bgt.uniform_cont(seq, lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, 0), epsilon)
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mae_evt[i] += lmdk_bgt.mae_cont(rls_data_evt)/args.iter
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# User
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# Calculate once
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if i == 0:
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mae_evt += lmdk_bgt.mae_cont(rls_data_evt)/args.iter
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# User
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rls_data_usr, _ = lmdk_bgt.uniform_cont(seq, lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, 100), epsilon)
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mae_usr[i] += lmdk_bgt.mae_cont(rls_data_usr)/args.iter
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mae_usr += lmdk_bgt.mae_cont(rls_data_usr)/args.iter
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plt.plot(
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x_i,
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mae_evt,
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plt.axhline(
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y = mae_evt,
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color = '#212121',
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linewidth=lmdk_lib.line_width
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)
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plt.text(x_i[-1], mae_evt[-1], ' event')
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plt.text(x_i[-1] + x_i[-1]*.14, mae_evt - mae_evt*.14, 'event')
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plt.plot(
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x_i,
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mae_usr,
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plt.axhline(
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y = mae_usr,
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color = '#616161',
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linewidth=lmdk_lib.line_width
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)
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plt.text(x_i[-1], mae_usr[-1], ' user')
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plt.text(x_i[-1] + x_i[-1]*.14, mae_usr - mae_usr*.14, 'user')
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plt.bar(
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x_i + x_offset,
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