#!/usr/bin/env python3 import sys sys.path.insert(1, '../lib') import argparse import ast from datetime import datetime from geopy.distance import distance import lmdk_bgt import lmdk_lib import math import numpy as np from matplotlib import pyplot as plt import time def main(args): res_file = '/home/manos/Cloud/Data/HUE/Results.zip' # User's consumption seq = lmdk_lib.load_data(args, 'cons') # The name of the dataset d = 'HUE' # The landmarks percentages lmdks_pct = [0, 20, 40, 60, 80, 100] # Landmarks' thresholds lmdks_th = [0, .54, .68, .88, 1.12, 10] # The privacy budget epsilon = 1.0 # Number of methods n = 3 # Width of bars bar_width = 1/(n + 1) # The x axis x_i = np.arange(len(lmdks_pct)) x_margin = bar_width*(n/2 + 1) print('\n##############################', d, '\n') # Initialize plot lmdk_lib.plot_init() # The x axis plt.xticks(x_i, np.array(lmdks_pct, int)) plt.xlabel('Landmarks (%)') # Set x axis label. plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin) # The y axis plt.ylabel('Mean absolute error (kWh)') # Set y axis label. plt.yscale('log') plt.ylim(.1, 100000) # Bar offset x_offset = -(bar_width/2)*(n - 1) mae_u = np.zeros(len(lmdks_pct)) mae_s = np.zeros(len(lmdks_pct)) mae_a = np.zeros(len(lmdks_pct)) mae_evt = 0 mae_usr = 0 for i, pct in enumerate(lmdks_pct): # Find landmarks lmdks = seq[seq[:, 1] < lmdks_th[i]] for _ in range(args.iter): # Skip rls_data_s, bgts_s = lmdk_bgt.skip_cons(seq, lmdks, epsilon) # lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_s) mae_s[i] += lmdk_bgt.mae_cons(seq, rls_data_s)/args.iter # Uniform rls_data_u, bgts_u = lmdk_bgt.uniform_cons(seq, lmdks, epsilon) mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter # Adaptive rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon, .5, .5) mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter # Calculate once if pct == lmdks_pct[0]: # Event rls_data_evt, _ = lmdk_bgt.uniform_cons(seq, lmdks, epsilon) mae_evt += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter elif pct == lmdks_pct[-1]: # User rls_data_usr, _ = lmdk_bgt.uniform_cons(seq, lmdks, epsilon) mae_usr += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter plt.axhline( y = mae_evt, color = '#212121', linewidth=lmdk_lib.line_width ) plt.text(x_i[-1] + x_i[-1]*.14, mae_evt - mae_evt*.14, 'event') plt.axhline( y = mae_usr, color = '#616161', linewidth=lmdk_lib.line_width ) plt.text(x_i[-1] + x_i[-1]*.14, mae_usr - mae_usr*.14, 'user') plt.bar( x_i + x_offset, mae_s, bar_width, label='Skip', linewidth=lmdk_lib.line_width ) x_offset += bar_width plt.bar( x_i + x_offset, mae_u, bar_width, label='Uniform', linewidth=lmdk_lib.line_width ) x_offset += bar_width plt.bar( x_i + x_offset, mae_a, bar_width, label='Adaptive', linewidth=lmdk_lib.line_width ) x_offset += bar_width path = str('../../rslt/bgt_cmp/' + d) # Plot legend lmdk_lib.plot_legend() # Show plot # plt.show() # Save plot lmdk_lib.save_plot(path + '.pdf') print('[OK]', flush=True) def parse_args(): ''' Parse arguments. Optional: res - The results archive file. iter - The total iterations. ''' # Create argument parser. parser = argparse.ArgumentParser() # Mandatory arguments. # Optional arguments. parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip') parser.add_argument('-i', '--iter', help='The total iterations.', type=int, default=1) # Parse arguments. args = parser.parse_args() return args if __name__ == '__main__': try: start_time = time.time() main(parse_args()) end_time = time.time() print('##############################') print('Time elapsed: %s' % (time.strftime('%H:%M:%S', time.gmtime(end_time - start_time)))) print('##############################') except KeyboardInterrupt: print('Interrupted by user.') exit()