the-last-thing/code/expt/bgt_cmp_copenhagen.py

#!/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 numpy as np
from matplotlib import pyplot as plt
import time


def main(args):
  res_file = '/home/manos/Cloud/Data/Copenhagen/Results.zip'
  # Contacts for all users
  cont_data = lmdk_lib.load_data(args, 'cont')
  # Contacts for landmark's percentages for all users
  lmdk_data = lmdk_lib.load_data(args, 'usrs_expt')
  # The name of the dataset
  d = 'Copenhagen'
  # The user's id
  uid = '623'
  # The landmarks percentages
  lmdks_pct = [0, 20, 40, 60, 80, 100]
  # The privacy budget
  epsilon = 1.0

  # Number of methods
  n = 6
  # 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')
  # Get user's contacts sequence
  seq = cont_data[cont_data[:, 1] == float(uid)]

  # Initialize plot
  lmdk_lib.plot_init()
  # The x axis
  plt.xticks(x_i, np.array(lmdks_pct, int))
  plt.xlabel('Landmarks percentage')  # 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, 100000000)
  # 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_r = np.zeros(len(lmdks_pct))
  mae_d = np.zeros(len(lmdks_pct))
  mae_i = np.zeros(len(lmdks_pct))
  for i, pct in enumerate(lmdks_pct):
    # Find landmarks
    lmdks = lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, pct)

    print(pct, np.shape(lmdks)[0]/np.shape(seq)[0])

    # for _ in range(args.iter):
    #   # Skip
    #   rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, epsilon)
    #   mae_s[i] += lmdk_bgt.mae(seq, rls_data_s)/args.iter

    #   # Uniform
    #   rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, epsilon)
    #   mae_u[i] += lmdk_bgt.mae(seq, rls_data_u)/args.iter

    #   # Adaptive
    #   rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, epsilon, .5, .5)
    #   mae_a[i] += lmdk_bgt.mae(seq, rls_data_a)/args.iter

    #   # Sample
    #   rls_data_r, _, _ = lmdk_bgt.sample(seq, lmdks, epsilon)
    #   mae_r[i] += lmdk_bgt.mae(seq, rls_data_r)/args.iter

    #   # Discount
    #   rls_data_d, _, _ = lmdk_bgt.discount(seq, lmdks, epsilon)
    #   mae_d[i] += lmdk_bgt.mae(seq, rls_data_d)/args.iter

    #   # Incremental
    #   rls_data_i, _, _ = lmdk_bgt.incremental(seq, lmdks, epsilon, .5)
    #   mae_i[i] += lmdk_bgt.mae(seq, rls_data_i)/args.iter

  # plt.bar(
  #   x_i + x_offset,
  #   mae_s,
  #   bar_width,
  #   label='Skip',
  #   linewidth=lmdk_lib.line_width
  # )
  # x_offset += bar_width
  # # Plot bars
  # 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
  # plt.bar(
  #   x_i + x_offset,
  #   mae_r,
  #   bar_width,
  #   label='Sample',
  #   linewidth=lmdk_lib.line_width
  # )
  # x_offset += bar_width
  # plt.bar(
  #   x_i + x_offset,
  #   mae_d,
  #   bar_width,
  #   label='Discount',
  #   linewidth=lmdk_lib.line_width
  # )
  # x_offset += bar_width
  # plt.bar(
  #   x_i + x_offset,
  #   mae_i,
  #   bar_width,
  #   label='Incremental',
  #   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/Copenhagen/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   : %.4fs' % (end_time - start_time))
    print('##############################')
  except KeyboardInterrupt:
    print('Interrupted by user.')
    exit()