From 840bd95c635d3fb5cfd096c23921511ab80a1a7e Mon Sep 17 00:00:00 2001
From: Manos <manos@delkappa.com>
Date: Tue, 5 Oct 2021 04:37:13 +0200
Subject: [PATCH] code: Ready to experiment with lmdk_sel

---
 code/expt/expt_lmdk_sel.py |  29 ++-
 code/lib/exp_mech.py       | 141 ++++++++++++++
 code/lib/lmdk_sel.py       | 388 +++++++++++++++++++++++++++++++++++++
 3 files changed, 553 insertions(+), 5 deletions(-)
 create mode 100644 code/lib/exp_mech.py
 create mode 100644 code/lib/lmdk_sel.py

diff --git a/code/expt/expt_lmdk_sel.py b/code/expt/expt_lmdk_sel.py
index 8fb7261..a273d2a 100644
--- a/code/expt/expt_lmdk_sel.py
+++ b/code/expt/expt_lmdk_sel.py
@@ -1,5 +1,7 @@
 #!/usr/bin/env python3
 
+import sys
+sys.path.insert(1, '../lib')
 import argparse
 import lmdk_lib
 import lmdk_sel
@@ -36,7 +38,7 @@ def main(args):
     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.ylim(0, len(seq)/3)
+    # plt.ylim(0, len(seq)/3)
     # Bar offset
     x_offset = -(bar_width/2)*(len(epsilon) - 1)
     for e_i, e in enumerate(epsilon):
@@ -45,9 +47,26 @@ def main(args):
         for r in range(args.reps):
           lmdks = lmdk_lib.get_lmdks(seq, n, d)
           hist, h = lmdk_lib.get_hist(seq, lmdks)
-          opts = lmdk_sel.get_opts_from_top_h(seq, lmdks)
-          delta = 1.0
-          res, _ = exp_mech.exponential(hist, opts, exp_mech.score, delta, e)
+          res = np.zeros([len(hist)])
+          # Split sequence in parts of size h 
+          pt_idx = []
+          for idx in range(h, len(seq), h):
+            pt_idx.append(idx)
+          seq_pt = np.split(seq, pt_idx)
+          for pt_i, pt in enumerate(seq_pt):
+            # Find this part's landmarks
+            lmdks_pt = np.intersect1d(pt, lmdks)
+            # Find possible options for this part
+            opts = lmdk_sel.get_opts_from_top_h(pt, lmdks_pt)
+            # Turn part to histogram
+            hist_pt, _ = lmdk_lib.get_hist(pt, lmdks_pt)
+            # Get an option for this part
+            res_pt = np.array([])
+            if len(opts) > 0:
+              res_pt, _ = exp_mech.exponential(hist_pt, opts, exp_mech.score, 1.0, e)
+            # Merge options of all parts
+            res[pt_i] = np.sum(res_pt)
+          # Calculate MAE
           mae[n_i] += lmdk_lib.get_norm(hist, res)/args.reps
       # Plot bar for current epsilon
       plt.bar(
@@ -59,7 +78,7 @@ def main(args):
       )
       # Change offset for next bar
       x_offset += bar_width
-    path = str('/home/manos/Git/the-thing/code/expt_lmdk_sel/' + title)
+    path = str('../../rslt/lmdk_sel/' + title)
     # Plot legend
     lmdk_lib.plot_legend()
     # Show plot
diff --git a/code/lib/exp_mech.py b/code/lib/exp_mech.py
new file mode 100644
index 0000000..25c7ea5
--- /dev/null
+++ b/code/lib/exp_mech.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+
+import numpy as np
+import math
+import heapq
+import itertools
+import random
+import scipy.stats as stats
+import lmdk_lib
+from matplotlib import pyplot as plt
+import time
+
+
+'''
+  The scoring function.
+
+  Parameters:
+    data - The data.
+    option - The option to evaluate.
+  Returns:
+    The score for the option.
+'''
+def score(data, option):
+  return (option.sum() - data.sum())
+
+
+'''
+  The exponential mechanism.
+
+  Parameters:
+    x - The data.
+    R - The possible outputs.
+    u - The scoring function.
+    delta - The sensitivity of the scoring function.
+    epsilon - The privacy budget.
+  Returns:
+    res - A randomly sampled output.
+    pr - The PDF of all possible outputs.
+'''
+def exponential(x, R, u, delta, epsilon):
+  # Calculate the score for each element of R
+  scores = [u(x, r) for r in R]
+  # Normalize the scores between 0 and 1
+  # (the higher, the better the utility)
+  scores = 1 - (scores - np.min(scores))/(np.max(scores) - np.min(scores))
+
+  # Calculate the probability for each element, based on its score
+  pr = [np.exp(epsilon*score/(2*delta)) for score in scores]
+
+  # Normalize the probabilities so that they sum to 1
+  pr = pr/np.linalg.norm(pr, ord = 1)
+
+  # Debugging
+  # print(R[np.argmax(pr)], pr.max(), scores[np.argmax(pr)])
+  # print(R[np.argmin(pr)], pr.min(), scores[np.argmin(pr)])
+  # print(abs(pr.max() - pr.min()), abs(scores[np.argmax(pr)] - scores[np.argmin(pr)]))
+
+  # Choose an element from R based on the probabilities
+  if len(pr) > 0:
+    return R[np.random.choice(range(len(R)), 1, p = pr)[0]], pr
+  else:
+    return np.array([]), pr
+
+
+def main():
+  start, end = 1.0, 10.0
+  scale = 1.0
+  locs = [2.0, 4.0, 8.0]
+  k = len(locs)
+
+  # dists = [truncnorm(start, end, loc, scale) for loc in locs]
+  dists = [stats.laplace(loc, scale) for loc in locs]
+
+  mix = lmdk_lib.MixtureModel(dists)
+
+  delta = 1.0
+  epsilon = 10.0
+
+  combos = list(itertools.combinations(range(int(start), int(end) + 1), k))
+
+  res, pr = exponential(mix, combos, score, delta, epsilon)
+
+  plt.rc('font', family='serif')
+  plt.rc('font', size=10)
+  plt.rc('text', usetex=True)
+
+  # Plot the options' probabilities.
+  # pr.sort()
+  # n = np.arange(1.0, len(pr) + 1.0, 1.0)
+  # plt.plot(n,\
+  #          pr,\
+  #          label=r'$\textrm{Pr}$',\
+  #          color='blue')
+  # # Configure the plot.
+  # plt.axis([1.0, len(pr), 0.0, max(pr)])  # Set plot box.
+  # plt.legend(loc='best', frameon=False)  # Set plot legend.
+  # plt.grid(axis='y', alpha=1.0)  # Add grid on y axis.
+  # plt.xlabel('Options')  # Set x axis label.
+  # plt.ylabel('Likelihood')  # Set y axis label.
+  # plt.show()  # Show the plot in a new window.
+
+  x = np.arange(start, end, 0.01)
+  plt.plot(x,\
+          mix.pdf(x),\
+          label=r'$\textrm{Mix}$',\
+          color='red')
+
+  # print(mix.sample(start, end, 10))
+
+  # Test MixtureModel's sample function
+  # t = 1000
+  # c = np.array(int(end)*[0])
+  # for i in range(t):
+  #   c[mix.sample(start, end, 1)[0] - 1] += 1
+
+  # plt.plot(range(int(start), int(end) + 1),\
+  #          c/t,\
+  #          label=r'$\textrm{Test}$',\
+  #          color='blue')
+
+  # Configure the plot.
+  plt.axis([start, end, 0.0, 1.0])  # Set plot box.
+  plt.legend(loc='best', frameon=False)  # Set plot legend.
+  plt.grid(axis='y', alpha=1.0)  # Add grid on y axis.
+  plt.xlabel('Timestamp')  # Set x axis label.
+  plt.ylabel('Likelihood')  # Set y axis label.
+
+  plt.show()  # Show the plot in a new window.
+
+
+if __name__ == '__main__':
+  try:
+    start_time = time.time()
+    main()
+    end_time = time.time()
+    print('##############################')
+    print('Time   : %.4fs' % (end_time - start_time))
+    print('##############################')
+  except KeyboardInterrupt:
+    print('Interrupted by user.')
+    exit()
diff --git a/code/lib/lmdk_sel.py b/code/lib/lmdk_sel.py
new file mode 100644
index 0000000..2685844
--- /dev/null
+++ b/code/lib/lmdk_sel.py
@@ -0,0 +1,388 @@
+#!/usr/bin/env python3
+
+import itertools
+from lmdk_lib import *
+import numpy as np
+import random
+import time
+
+
+'''
+  Print all the points.
+
+  Parameters:
+    seq - The point sequence.
+    combs - All the possible point combinations for a specified size.
+    lmdks - The landmarks.
+  Returns:
+    Nothing.
+'''
+def print_rslt(seq, combs, lmdks):
+  eval_sum = .0
+  for idx, c in enumerate(combs):
+    rslt = str(idx + 1) + ':\t'
+    for i in seq:
+      # Selected
+      if i in c:
+        rslt += '(' + str(i) + ')\t'
+      # Landmark
+      elif i in lmdks:
+        rslt += '*' + str(i) + '*\t'
+      # Not selected
+      else:
+        rslt += ' ' + str(i) + ' \t'
+    dists = get_rel_dists(seq, list(c), lmdks)
+    eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+    eval_cur = eval_seq(dists)
+    eval_sum += eval_cur
+    # print(rslt, '\t', dists, '\t', sum(dists), '\t', eval_cur)
+    print(rslt, eval_cur)
+  eval_avg = eval_sum/len(combs)
+  print('Average STD (difference with original): %.4f (%.2f%%)' %(eval_avg, 100*(eval_avg - eval_orig)/eval_orig))
+
+
+'''
+  Print the difference with the original.
+
+  Parameters:
+    seq - The point sequence.
+    combs - All the possible point combinations for a specified size.
+    lmdks - The landmarks.
+  Returns:
+    The difference with the original.
+'''
+def print_diff(seq, combs, lmdks):
+  eval_sum = .0
+  for c in combs:
+    dists = get_rel_dists(seq, list(c), lmdks)
+    eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+    eval_cur = eval_seq(dists)
+    eval_sum += eval_cur
+  eval_avg = eval_sum/len(combs)
+  diff = 100*(eval_avg - eval_orig)/eval_orig
+  print('Average STD (difference with original): %.4f (%.2f%%)' %(eval_avg, diff))
+  return diff
+
+
+'''
+  Finds the optimal set of regular points.
+
+  Parameters:
+    seq - The point sequence.
+    lmdks - The landmarks.
+  Returns:
+    The optimal option.
+
+  Requirements:
+    n = Regular points
+    r = The size of a combination
+    Time  - O(C(n, r) + 2^C(n, r))
+    Space - O(r*C(n, r))
+'''
+def get_opts_optim(seq, lmdks):
+  # Evaluate the original
+  eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+
+  # Get all possible options
+  opts = get_opts(seq, lmdks)
+
+  # Evaluate options
+  # Track the minimum (best) evaluation
+  diff_min = float('inf')
+
+  # Track the optimal sequence (the one with the best evaluation)
+  optim = []
+  for opt in opts:
+    eval_sum = 0
+    for o in opt:
+      eval_sum += eval_seq(get_rel_dists(seq, o, lmdks))
+    # Compare with current optimal
+    diff_cur = abs(eval_sum/len(opt) - eval_orig)
+    if diff_cur < diff_min:
+      diff_min = diff_cur
+      optim = list(opt)
+
+  return optim
+
+
+'''
+  Finds a set of regular points from top (less) to bottom (many)
+  (seems to perform better than the bottom-to-top approach).
+
+  Parameters:
+    seq - The point sequence.
+    lmdks - The landmarks.
+  Returns:
+    The resulting set of options.
+
+  Requirements:
+    For all possible sets of regular points n such that n = len(seq) - len(lmdks).
+    The result is a set of options for every possible value of n and for all possible combinations for each n.
+    Time  - O(n^2)
+    Space - O(n^2)
+'''
+def get_opts_from_top(seq, lmdks):
+  # Evaluate the original
+  eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+
+  opts = []
+  lmdks_cur = np.array(lmdks)
+  while not np.array_equal(lmdks_cur, seq):
+    # Find the combinations for one more point
+    reg = get_reg(seq, lmdks_cur)
+
+    # Track the minimum (best) evaluation
+    diff_min = float('inf')
+
+    point = ()
+    for r in reg:
+      # Evaluate current
+      eval_cur = eval_seq(get_rel_dists(seq, r, lmdks_cur))
+
+      # Compare evaluations
+      if abs(eval_cur - eval_orig) <= diff_min:
+        diff_min = abs(eval_cur - eval_orig)
+        point = r
+
+    # Save new point to landmarks
+    lmdks_cur = np.append(lmdks_cur, point)
+    lmdks_cur.sort()
+
+    # Add new option
+    opts.append(np.setdiff1d(lmdks_cur, lmdks))
+
+  return opts
+
+def get_opts_from_top_h(seq, lmdks):
+  # Create histogram
+  hist, h = get_hist(seq, lmdks)
+  # Keep track of points
+  hist_cur = np.copy(hist)
+  # The options to be returned
+  hist_opts = []
+  # Keep adding points until the maximum is reached
+  while np.sum(hist_cur) < max(seq):
+    # Track the minimum (best) evaluation
+    diff_min = float('inf')
+    # The candidate option
+    hist_cand = np.copy(hist_cur)
+    # Check every possibility
+    for i, h_i in enumerate(hist_cur):
+      # Can we add one more point?
+      if h_i + 1 <= h:
+        hist_tmp = np.copy(hist_cur)
+        hist_tmp[i] += 1
+        # Find difference from original
+        diff_cur = get_norm(hist, hist_tmp)
+        # Remember if it is the best that you've seen
+        if diff_cur < diff_min:
+          diff_min = diff_cur
+          hist_cand = np.copy(hist_tmp)
+    # Update current histogram
+    hist_cur = np.copy(hist_cand)
+    # Add current best to options
+    hist_opts.append(hist_cand)
+  # Return options
+  return hist_opts
+
+
+def get_non_opts_from_top(seq, lmdks):
+  # Evaluate the original
+  eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+
+  non_opts = []
+  lmdks_cur = np.array(lmdks)
+  while not np.array_equal(lmdks_cur, seq):
+    # Find the combinations for one more point
+    reg = get_reg(seq, lmdks_cur)
+
+    # Track the maximum (worst) evaluation
+    diff_max = .0
+
+    point = ()
+    for r in reg:
+      # Evaluate current
+      eval_cur = eval_seq(get_rel_dists(seq, r, lmdks_cur))
+
+      # Compare evaluations
+      if abs(eval_cur - eval_orig) >= diff_max:
+        diff_max = abs(eval_cur - eval_orig)
+        point = r
+
+    # Save new point to landmarks
+    lmdks_cur = np.append(lmdks_cur, point)
+    lmdks_cur.sort()
+
+    # Add new option
+    non_opts.append(np.setdiff1d(lmdks_cur, lmdks))
+
+  return non_opts
+
+
+'''
+  Finds a set of regular points from bottom (many) to top (less)
+  (seems to perform worse than the top-to-bottom approach).
+
+  Parameters:
+    seq - The point sequence.
+    lmdks - The landmarks.
+  Returns:
+    The resulting set of options.
+
+  Requirements:
+    For all possible sets of regular points n such that n = len(seq) - len(lmdks).
+    The result is a set of options for every possible value of n and for all possible combinations for each n.
+    Time  - O(n^2)
+    Space - O(n^2)
+'''
+def get_opts_from_bottom(seq, lmdks):
+  # Evaluate the original
+  eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+
+  # Start with all regular points as landmarks
+  lmdks_cur = np.array(get_reg(seq, lmdks))
+
+  opts = [lmdks_cur]
+  while lmdks_cur.size != 1:
+    # Track the minimum (best) evaluation
+    diff_min = float('inf')
+
+    point = ()
+    for lmdk in lmdks_cur:
+      # Evaluate current by removing one point
+      eval_cur = eval_seq(get_rel_dists(seq, [], np.setdiff1d(lmdks_cur, lmdk)))
+
+      # Compare evaluations
+      if abs(eval_cur - eval_orig) <= diff_min:
+        diff_min = abs(eval_cur - eval_orig)
+        point = lmdk
+
+    # Remove point from landmarks
+    lmdks_cur = np.setdiff1d(lmdks_cur, point)
+
+    # Add new option
+    opts.append(np.setdiff1d(lmdks_cur, lmdks))
+
+  return opts
+
+
+def get_non_opts_from_bottom(seq, lmdks):
+  # Evaluate the original
+  eval_orig = eval_seq(get_rel_dists(seq, [], lmdks))
+
+  # Start with all regular points as landmarks
+  lmdks_cur = np.array(get_reg(seq, lmdks))
+
+  non_opts = [lmdks_cur]
+  while lmdks_cur.size != 1:
+    # Track the maximum (worst) evaluation
+    diff_max = .0
+
+    point = ()
+    for lmdk in lmdks_cur:
+      # Evaluate current by removing one point
+      eval_cur = eval_seq(get_rel_dists(seq, [], np.setdiff1d(lmdks_cur, lmdk)))
+
+      # Compare evaluations
+      if abs(eval_cur - eval_orig) >= diff_max:
+        diff_max = abs(eval_cur - eval_orig)
+        point = lmdk
+
+    # Remove point from landmarks
+    lmdks_cur = np.setdiff1d(lmdks_cur, point)
+
+    # Add new option
+    non_opts.append(np.setdiff1d(lmdks_cur, lmdks))
+
+  return non_opts
+
+
+def test():
+  # Start and end points of the sequence
+  # # Nonrandom
+  # start = 1
+  # end = 10
+  # Random
+  start = 1
+  end = random.randint(start + 1, 100)
+
+  # Landmarks
+  # # Nonrandom
+  # lmdks = np.array([1, 3, 5, 8])
+  # Random
+  size = random.randint(start, end - 1)
+  lmdks = np.array(random.sample(range(start, end), size))
+  lmdks.sort()
+
+  # Print the parameters
+  print('Start    : %d\n'
+        'End      : %d\n'
+        'Size     : %d\n'
+        'Landmarks: %s'
+        %(start, end, len(lmdks), str(lmdks)))
+
+  # Get the point sequence
+  seq = get_seq(start, end)
+
+  # Almost optimal solution
+  # print('\nOptimal...')
+  # t = time.time()
+
+  # opts_optim = get_opts_optim(seq, lmdks)
+
+  # print('Time:', time.time() - t, 'secs\n')
+
+  # print_rslt(seq, opts_optim, lmdks)
+
+
+  # Top to bottom approach
+  print('\nTop to bottom heuristic...')
+  t = time.time()
+
+  opts = get_opts_from_top(seq, lmdks)
+
+  print('Time:', time.time() - t, 'secs')
+
+  # print_rslt(seq, opts, lmdks)
+  diff_opt = print_diff(seq, opts, lmdks)
+
+  print('Non optimal version...')
+  non_opts = get_non_opts_from_top(seq, lmdks)
+  diff_non_opt = print_diff(seq, non_opts, lmdks)
+  print('Non optimal is %.2f%% different ([+]: worse | [-]: better).' %(100*(diff_non_opt - diff_opt)/diff_opt))
+
+
+  # Bottom to top approach
+  # Seems to perform worse
+  print('\nBottom to top heuristic...')
+  t = time.time()
+
+  opts = get_opts_from_bottom(seq, lmdks)
+
+  print('Time:', time.time() - t, 'secs')
+
+  # print_rslt(seq, opts, lmdks)
+  diff_opt = print_diff(seq, opts, lmdks)
+
+  print('Non optimal version...')
+  non_opts = get_non_opts_from_bottom(seq, lmdks)
+  diff_non_opt = print_diff(seq, non_opts, lmdks)
+  print('Non optimal is %.2f%% different ([+]: worse | [-]: better).' %(100*(diff_non_opt - diff_opt)/diff_opt))
+
+
+  # # Debugging
+  # # Number of desired actual and dummy landmarks
+  # k = len(lmdks) + 5
+  # # Number of dummy landmarks
+  # n = k - len(lmdks)
+  # combs = get_combs(reg, n)
+  # print_rslt(seq, combs, lmdks)
+  # exit()
+
+
+if __name__ == '__main__':
+  try:
+    test()
+  except KeyboardInterrupt:
+    print('Interrupted by user.')
+    exit()