the-last-thing/code/lib/lmdk_sel.py

#!/usr/bin/env python3

import itertools
from lmdk_lib import *
import exp_mech
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 find_lmdks(seq, lmdks, epsilon):
  '''
    Add dummy landmarks to original landmarks.

    Parameters:
      seq     - All of the data points.
      lmdks   - The original landmarks.
      epsilon - The available privacy budget.

    Returns:
      lmdks_new - The new landmarks.
      The remaining privacy budget. 
  '''
  # The new landmarks
  lmdks_new = lmdks
  # The privacy budget to use
  eps_sel = 0
  if len(lmdks) > 0 and len(seq) != len(lmdks):
    # Get landmarks timestamps in sequence
    lmdks_seq = find_lmdks_seq(seq, lmdks)
    # Turn landmarks to histogram
    hist, h = get_hist(get_seq(1, len(seq)), lmdks_seq)
    # Find all possible options
    opts = get_opts_from_top_h(get_seq(1, len(seq)), lmdks_seq)
    # Landmarks selection budget
    eps_sel = epsilon/(len(lmdks_seq) + 1)
    # Get private landmarks timestamps
    lmdks_seq, _ = exp_mech.exponential(hist, opts, exp_mech.score, 1.0, eps_sel)
    # Get actual landmarks values
    lmdks_new = seq[lmdks_seq]
  return lmdks_new, epsilon - eps_sel

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()