#!/usr/bin/env python3 import sys sys.path.insert(1, '../lib') import argparse import lmdk_lib import lmdk_sel import exp_mech import numpy as np import os from matplotlib import pyplot as plt import time def main(args): # Privacy goal epsilon = 1.0 # Number of timestamps seq = lmdk_lib.get_seq(1, args.time) # Distribution type dist_type = np.array(range(0, 4)) # Number of landmarks lmdk_n = np.array(range(int(.2*args.time), args.time, int(args.time/5))) # Initialize plot lmdk_lib.plot_init() # Width of bars bar_width = 1/(len(dist_type) + 1) # The x axis x_i = np.arange(len(lmdk_n)) x_margin = bar_width*(len(dist_type)/2 + 1) plt.xticks(x_i, ((lmdk_n/len(seq))*100).astype(int)) plt.xlabel('Landmarks (%)') # Set x axis label. plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin) # The y axis # plt.yscale('log') plt.ylabel('Euclidean distance') # Set y axis label. # plt.ylabel('Wasserstein distance') # Set y axis label. # Bar offset x_offset = -(bar_width/2)*(len(dist_type) - 1) for d_i, d in enumerate(dist_type): # Set label label = lmdk_lib.dist_type_to_str(d) if d_i == 1: label = 'Skewed' # Logging title = label + ' landmark distribution' print('(%d/%d) %s... ' %(d_i + 1, len(dist_type), title), end='', flush=True) mae = np.zeros(len(lmdk_n)) for n_i, n in enumerate(lmdk_n): for r in range(args.reps): lmdks = lmdk_lib.get_lmdks(seq, n, d) hist, h = lmdk_lib.get_hist(seq, lmdks) opts = lmdk_sel.get_opts_from_top_h(seq, lmdks) delta = 1.0 res, _ = exp_mech.exponential(hist, opts, exp_mech.score, delta, epsilon) mae[n_i] += lmdk_lib.get_norm(hist, res)/args.reps # Euclidean # mae[n_i] += lmdk_lib.get_emd(hist, res)/args.reps # Wasserstein print('[OK]', flush=True) # Plot bar for current distribution plt.bar( x_i + x_offset, mae, bar_width, label=label, linewidth=lmdk_lib.line_width ) # Change offset for next bar x_offset += bar_width path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-norm') # path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-emd') # Plot legend lmdk_lib.plot_legend() # Show plot # plt.show() # Save plot lmdk_lib.save_plot(path + '.pdf') ''' Parse arguments. Optional: reps - The number of repetitions. time - The time limit of the sequence. ''' def parse_args(): # Create argument parser. parser = argparse.ArgumentParser() # Mandatory arguments. # Optional arguments. parser.add_argument('-r', '--reps', help='The number of repetitions.', type=int, default=1) parser.add_argument('-t', '--time', help='The time limit of the sequence.', type=int, default=100) # 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()