code: Update

code/expt/hue-sel.py

@@ -27,7 +27,7 @@ def main(args):
   # Landmarks' thresholds
   lmdks_th = [0, .54, .68, .88, 1.12, 10]
   # The privacy budget
-  epsilon = 10.0
+  epsilon = 1.0
 
   # Number of methods
   n = 3
@@ -48,7 +48,7 @@ def main(args):
   # The y axis
   plt.ylabel('Mean absolute error (kWh)')  # Set y axis label.
   plt.yscale('log')
-  plt.ylim(.01, 1000)
+  plt.ylim(.1, 10000)
   # Bar offset
   x_offset = -(bar_width/2)*(n - 1)
 

code/expt/hue.py

@@ -25,7 +25,7 @@ def main(args):
   # Landmarks' thresholds
   lmdks_th = [0, .54, .68, .88, 1.12, 10]
   # The privacy budget
-  epsilon = 10.0
+  epsilon = 1.0
 
   # Number of methods
   n = 3
@@ -46,7 +46,7 @@ def main(args):
   # The y axis
   plt.ylabel('Mean absolute error (kWh)')  # Set y axis label.
   plt.yscale('log')
-  plt.ylim(.01, 1000)
+  plt.ylim(.1, 10000)
   # Bar offset
   x_offset = -(bar_width/2)*(n - 1)
 

code/lib/lmdk_bgt.py

@@ -477,7 +477,7 @@ def adaptive_cons(seq, lmdks, epsilon, inc_rt, dec_rt):
   skipped = 0
   for i, p in enumerate(seq):
     # Check if current point is a landmark
-    is_landmark = any((lmdks[:]==p).all(1))
+    is_landmark = any(np.equal(lmdks, p).all(1))
     if is_landmark:
       lmdk_cur += 1
     if lmdk_lib.should_sample(samp_rt) or i == 0:
@@ -584,7 +584,7 @@ def skip_cons(seq, lmdks, epsilon):
   rls_data = [None]*len(seq)
   for i, p in enumerate(seq):
     # Check if current point is a landmark
-    is_landmark = any((lmdks[:]==p).all(1))
+    is_landmark = any(np.equal(lmdks, p).all(1))
     # Add noise
     o = [p[0], lmdk_lib.add_laplace_noise(p[1], 1, bgts[i])]
     if is_landmark:

code/parse_t-drive.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+
+import argparse
+import csv
+from datetime import datetime
+from geopy.distance import distance
+import io
+import lmdk_lib
+import numpy as np
+import os
+import matplotlib.pyplot as plt
+import sys
+import time
+import zipfile
+
+
+'''
+############ Stats ###########
+
+Trajectories: 10357
+  Length
+    Total   : 17662984
+    Minimum : 
+    Maximum : 
+
+Landmarks   : 212816
+  Length
+    Total   : 6851102 (27.54%)
+    Minimum : 1
+    Maximum : 6896
+
+##############################
+'''
+
+
+# https://cloud.delkappa.com/s/2Rs2wjS8zmt5bAE
+# T-drive data format
+# User ID
+uid = 0
+# Coordinates
+lat, lng = 3, 2
+# Timestamp
+tim = 1
+# Timestamp format
+tim_fmt = "%Y-%m-%d %H:%M:%S"
+
+
+def main(args):
+  # Get users' landmarks from previous parsing
+  usrs_lmdks = lmdk_lib.load_data(args, 'usrs_lmdks')
+  if usrs_lmdks.size == 0:
+    # Get users' data from previous parsing
+    usrs_data = lmdk_lib.load_data(args, 'usrs_data')
+    if usrs_data.size == 0:
+      usrs_data = np.empty((0,4), np.float32)
+      # Parse users' data
+      try:
+        print('Extracting %s... ' %(os.path.abspath(args.arc)), end='', flush=True)
+        with zipfile.ZipFile(args.arc, 'r') as arc:
+          print('[OK]')
+          # Get the list of users
+          print('Getting users... ', end='', flush=True)
+          # List of users
+          usrs = list(info.filename.split('/')[2].split('.')[0] for info in arc.infolist() if '.txt' in info.filename)
+          # Sort users numerically
+          usrs.sort(key=int)
+          print('[OK]')
+          for usr in usrs:
+            points = []
+            traj_file = 'release/taxi_log_2008_by_id/' + usr + '.txt'
+            print('[%d%% (%s/%d)] Loading data from %s... ' %((int(usr)/len(usrs))*100, usr, len(usrs), traj_file), end='', flush=True)
+            with io.TextIOWrapper(arc.open(traj_file), newline='\n') as dat:
+              try:
+                # df = pd.read_csv(dat, sep=',', index_col=None, names=data_cols)
+                traj = csv.reader(dat, delimiter=',')
+                for p in traj:
+                  points += [[p[uid], p[lat], p[lng], datetime.strptime(p[tim], tim_fmt).timestamp()]]
+                print('[OK]')
+              except Exception as e:
+                print('[Error: %s]' %(e))
+            if points:
+              usrs_data = np.append(usrs_data, np.asarray(points, dtype=np.float32), axis=0)
+        # Save to results
+        lmdk_lib.save_data(args, usrs_data, 'usrs_data')
+      except Exception as e:
+        print('[Error: %s]' %(e))
+    # Find users' landmarks
+    usrs_lmdks = lmdk_lib.find_lmdks(usrs_data, args)
+    # Save to results
+    lmdk_lib.save_data(args, usrs_lmdks, 'usrs_lmdks')
+  # Landmarks' stats
+  lmdk_lib.lmdks_stats(args, usrs_lmdks)
+
+
+'''
+  Parse arguments.
+
+  Optional:
+    arc  - The archive file.
+    dist - The coordinates distance threshold in meters.
+    per  - The timestaps period threshold in mimutes.
+    res  - The results zip file.
+'''
+def parse_args():
+  # Create argument parser.
+  parser = argparse.ArgumentParser()
+
+  # Mandatory arguments.
+
+  # Optional arguments.
+  parser.add_argument('-a', '--arc', help='The data archive file.', type=str, default='/home/manos/Cloud/Data/T-drive/Data.zip')
+  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')
+
+  # 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()