#!/usr/bin/env python3 import sys sys.path.insert(1, '../lib') import argparse from datetime import datetime from geopy.distance import distance import lmdk_bgt import lmdk_lib import lmdk_sel import exp_mech import numpy as np from matplotlib import pyplot as plt import time def main(args): # The data files data_files = { 'T-drive': '/home/manos/Cloud/Data/T-drive/Results.zip', } # Data related info data_info = { 'T-drive': { 'uid': 2, 'lmdks': { 0: {'dist': 0, 'per': 1000}, # 0.0% 20: {'dist': 2095, 'per': 30}, # 19.6% 40: {'dist': 2790, 'per': 30}, # 40.2% 60: {'dist': 3590, 'per': 30}, # 59.9% 80: {'dist': 4825, 'per': 30}, # 79.4% 100: {'dist': 10350, 'per': 30} # 100.0% } } } # The data sets data_sets = {} # Load data sets for df in data_files: args.res = data_files[df] data_sets[df] = lmdk_lib.load_data(args, 'usrs_data') # Geo-I configuration # epsilon = level/radius # Radius is in meters bgt_conf = [ {'epsilon': 1}, ] # Number of methods n = 3 # Width of bars bar_width = 1/(n + 1) # The x axis x_i = np.arange(len(list(data_info.values())[0]['lmdks'])) x_margin = bar_width*(n/2 + 1) for d in data_sets: print('\n##############################', d, '\n') args.res = data_files[d] data = data_sets[d] # Truncate trajectory according to arguments seq = data[data[:,0]==data_info[d]['uid'], :][:args.time] # Initialize plot lmdk_lib.plot_init() # The x axis plt.xticks(x_i, np.array([key for key in data_info[d]['lmdks']]).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.ylabel('Mean absolute error (m)') # Set y axis label. plt.yscale('log') plt.ylim(1, 1000000) # Bar offset x_offset = -(bar_width/2)*(n - 1) mae_u = np.zeros(len(data_info[d]['lmdks'])) mae_u_sel= np.zeros(len(data_info[d]['lmdks'])) mae_s = np.zeros(len(data_info[d]['lmdks'])) mae_s_sel = np.zeros(len(data_info[d]['lmdks'])) mae_a = np.zeros(len(data_info[d]['lmdks'])) mae_a_sel = np.zeros(len(data_info[d]['lmdks'])) mae_evt = 0 mae_usr = 0 for i, lmdk in enumerate(data_info[d]['lmdks']): # Find landmarks args.dist = data_info[d]['lmdks'][lmdk]['dist'] args.per = data_info[d]['lmdks'][lmdk]['per'] lmdks = lmdk_lib.find_lmdks(seq, args)[:args.time] for bgt in bgt_conf: for _ in range(args.iter): lmdks_sel, eps_out = lmdk_sel.find_lmdks(seq, lmdks, bgt['epsilon']) # Skip rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, eps_out) mae_s[i] += (lmdk_bgt.mae(seq, rls_data_s)/args.iter)*100 rls_data_s_sel, _ = lmdk_bgt.skip(seq, lmdks_sel, eps_out) mae_s_sel[i] += (lmdk_bgt.mae(seq, rls_data_s_sel)/args.iter)*100 # Uniform rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, eps_out) mae_u[i] += (lmdk_bgt.mae(seq, rls_data_u)/args.iter)*100 rls_data_u_sel, _ = lmdk_bgt.uniform_r(seq, lmdks_sel, eps_out) mae_u_sel[i] += (lmdk_bgt.mae(seq, rls_data_u_sel)/args.iter)*100 # Adaptive rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, eps_out, .5, .5) mae_a[i] += (lmdk_bgt.mae(seq, rls_data_a)/args.iter)*100 rls_data_a_sel, _, _ = lmdk_bgt.adaptive(seq, lmdks_sel, eps_out, .5, .5) mae_a_sel[i] += (lmdk_bgt.mae(seq, rls_data_a_sel)/args.iter)*100 # Calculate once if lmdk == min(data_info[d]['lmdks']): # Event rls_data_evt, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon']) mae_evt += lmdk_bgt.mae(seq, rls_data_evt)/args.iter elif lmdk == max(data_info[d]['lmdks']): # User rls_data_usr, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon']) mae_usr += lmdk_bgt.mae(seq, rls_data_usr)/args.iter # Plot lines 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') # Plot bars plt.bar( x_i + x_offset, mae_s_sel, bar_width, label='Skip', linewidth=lmdk_lib.line_width ) plt.bar( x_i + x_offset, mae_s, bar_width, color='none', linestyle='dashed', edgecolor='#bdbdbd', linewidth=lmdk_lib.line_width ) x_offset += bar_width plt.bar( x_i + x_offset, mae_u_sel, bar_width, label='Uniform', linewidth=lmdk_lib.line_width ) plt.bar( x_i + x_offset, mae_u, bar_width, color='none', linestyle='dashed', edgecolor='#bdbdbd', linewidth=lmdk_lib.line_width ) x_offset += bar_width plt.bar( x_i + x_offset, mae_a_sel, bar_width, label='Adaptive', linewidth=lmdk_lib.line_width ) plt.bar( x_i + x_offset, mae_a, bar_width, color='none', linestyle='dashed', edgecolor='#bdbdbd', 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 + '-sel-cmp.pdf') print('[OK]', flush=True) def parse_args(): ''' Parse arguments. Optional: dist - The coordinates distance threshold in meters. per - The timestaps period threshold in mimutes. time - The total timestamps. iter - The total iterations. ''' # Create argument parser. parser = argparse.ArgumentParser() # Mandatory arguments. # Optional arguments. parser.add_argument('-l', '--dist', help='The coordinates distance threshold in meters.', type=int, default=200) parser.add_argument('-p', '--per', help='The timestaps period threshold in mimutes.', type=int, default=30) parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/T-drive/Results.zip') parser.add_argument('-t', '--time', help='The total timestamps.', type=int, default=1000) 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()