From 62ea90ecf70e0f9fed9e7fede25fdc2034b232ed Mon Sep 17 00:00:00 2001
From: Manos <manos@delkappa.com>
Date: Wed, 29 Sep 2021 14:17:19 +0200
Subject: [PATCH] code: WIP

---
 code/expt/bgt_cmp_hue.py | 173 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 173 insertions(+)
 create mode 100644 code/expt/bgt_cmp_hue.py

diff --git a/code/expt/bgt_cmp_hue.py b/code/expt/bgt_cmp_hue.py
new file mode 100644
index 0000000..63714d2
--- /dev/null
+++ b/code/expt/bgt_cmp_hue.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+
+import sys
+sys.path.insert(1, '../lib')
+import argparse
+import ast
+from datetime import datetime
+from geopy.distance import distance
+import lmdk_bgt
+import lmdk_lib
+import math
+import numpy as np
+from matplotlib import pyplot as plt
+import time
+
+
+def main(args):
+  res_file = '/home/manos/Cloud/Data/HUE/Results.zip'
+  # User's consumption
+  seq = lmdk_lib.load_data(args, 'cons')
+  # Contacts for landmark's percentages for all users
+  lmdk_data = lmdk_lib.load_data(args, 'usrs_expt')
+  # The name of the dataset
+  d = 'HUE'
+  # The landmarks percentages
+  lmdks_pct = [0, 20, 40, 60, 80, 100]
+  # Landmarks' thresholds
+  lmdks_th = [10.0, .3, .23, .15, .13, 0]
+  # The privacy budget
+  epsilon = 1.0
+
+  # Number of methods
+  n = 3
+  # 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')
+
+  # 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')  # Set y axis label.
+  # plt.yscale('log')
+  plt.ylim(0, 1.4)
+  # 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_evt = np.zeros(len(lmdks_pct))
+  mae_usr = 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)
+
+    for _ in range(args.iter):
+      # Skip
+      rls_data_s, bgts_s = lmdk_bgt.skip_cont(seq, lmdks, epsilon)
+      # lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_s)
+      mae_s[i] += lmdk_bgt.mae_cont(rls_data_s)/args.iter
+
+      # Uniform
+      rls_data_u, bgts_u = lmdk_bgt.uniform_cont(seq, lmdks, epsilon)
+      # lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_u)
+      mae_u[i] += lmdk_bgt.mae_cont(rls_data_u)/args.iter
+
+      # Adaptive
+      rls_data_a, _, _ = lmdk_bgt.adaptive_cont(seq, lmdks, epsilon, .5, .5)
+      mae_a[i] += lmdk_bgt.mae_cont(rls_data_a)/args.iter
+
+      # Event
+      # Calculate once
+      if i == 0:
+        rls_data_evt, _ = lmdk_bgt.uniform_cont(seq, lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, 0), epsilon)
+      mae_evt[i] += lmdk_bgt.mae_cont(rls_data_evt)/args.iter
+      # User
+      # Calculate once
+      if i == 0:
+        rls_data_usr, _ = lmdk_bgt.uniform_cont(seq, lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, 100), epsilon)
+      mae_usr[i] += lmdk_bgt.mae_cont(rls_data_usr)/args.iter
+
+  plt.plot(
+    x_i,
+    mae_evt,
+    linewidth=lmdk_lib.line_width
+  )
+  plt.text(x_i[-1], mae_evt[-1], '        event')
+
+  plt.plot(
+    x_i,
+    mae_usr,
+    linewidth=lmdk_lib.line_width
+  )
+  plt.text(x_i[-1], mae_usr[-1], '        user')
+
+  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
+  )
+  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/HUE/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()