code: Experimenting with T-drive

code/expt/t-drive.py

@@ -0,0 +1,197 @@
+#!/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):
+  # 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, 100000000)
+    # Bar offset
+    x_offset = -(bar_width/2)*(n - 1)
+
+    mae_u = np.zeros(len(data_info[d]['lmdks']))
+    mae_s = np.zeros(len(data_info[d]['lmdks']))
+    mae_a = 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):
+          # Skip
+          rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, bgt['epsilon'])
+          mae_s[i] += lmdk_bgt.mae(seq, rls_data_s)/args.iter
+
+          # Uniform
+          rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon'])
+          mae_u[i] += lmdk_bgt.mae(seq, rls_data_u)/args.iter
+
+          # Adaptive
+          rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, bgt['epsilon'], .5, .5)
+          mae_a[i] += lmdk_bgt.mae(seq, rls_data_a)/args.iter
+
+          # Event
+          if lmdk == 0:
+            rls_data_evt, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon'])
+            mae_evt += lmdk_bgt.mae(seq, rls_data_evt)/args.iter
+          # User
+          if lmdk == 100:
+            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,
+      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
+    )
+
+    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:
+      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   : %.4fs' % (end_time - start_time))
+    print('##############################')
+  except KeyboardInterrupt:
+    print('Interrupted by user.')
+    exit()