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code: Ready for copenhagen

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This commit is contained in:
Manos Katsomallos 2021-09-29 03:12:24 +02:00
parent f5a6b317ac
commit 0fa215558a
2 changed files with 118 additions and 83 deletions
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146
code/expt/bgt_cmp_copenhagen.py
View File

@ -3,10 +3,12 @@
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
@ -28,7 +30,7 @@ def main(args):
epsilon = 1.0
# Number of methods
n = 6
n = 3
# Width of bars
bar_width = 1/(n + 1)
# The x axis
@ -46,111 +48,89 @@ def main(args):
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 (m)') # Set y axis label.
plt.yscale('log')
plt.ylim(1, 100000000)
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_r = np.zeros(len(lmdks_pct))
mae_d = np.zeros(len(lmdks_pct))
mae_i = 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_tim(lmdk_data, seq, uid, pct)
lmdks = lmdk_lib.find_lmdks_cont(lmdk_data, seq, uid, pct)
print(pct, np.shape(lmdks)[0]/np.shape(seq)[0])
for _ in range(args.iter):
# Skip
rls_data_s, _ = lmdk_bgt.skip_cont(seq, lmdks, epsilon)
mae_s[i] += lmdk_bgt.mae_cont(rls_data_s)/args.iter
# for _ in range(args.iter):
# # Skip
# rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, epsilon)
# mae_s[i] += lmdk_bgt.mae(seq, rls_data_s)/args.iter
# Uniform
rls_data_u, _ = lmdk_bgt.uniform_cont(seq, lmdks, epsilon)
mae_u[i] += lmdk_bgt.mae_cont(rls_data_u)/args.iter
# # Uniform
# rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, epsilon)
# mae_u[i] += lmdk_bgt.mae(seq, 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
# # Adaptive
# rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, epsilon, .5, .5)
# mae_a[i] += lmdk_bgt.mae(seq, rls_data_a)/args.iter
# Event
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
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
# # Sample
# rls_data_r, _, _ = lmdk_bgt.sample(seq, lmdks, epsilon)
# mae_r[i] += lmdk_bgt.mae(seq, rls_data_r)/args.iter
plt.plot(
x_i,
mae_evt,
linewidth=lmdk_lib.line_width
)
# # Discount
# rls_data_d, _, _ = lmdk_bgt.discount(seq, lmdks, epsilon)
# mae_d[i] += lmdk_bgt.mae(seq, rls_data_d)/args.iter
plt.plot(
x_i,
mae_usr,
linewidth=lmdk_lib.line_width
)
# # Incremental
# rls_data_i, _, _ = lmdk_bgt.incremental(seq, lmdks, epsilon, .5)
# mae_i[i] += lmdk_bgt.mae(seq, rls_data_i)/args.iter
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
# plt.bar(
# x_i + x_offset,
# mae_s,
# bar_width,
# label='Skip',
# linewidth=lmdk_lib.line_width
# )
# x_offset += bar_width
# # Plot bars
# 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
# plt.bar(
# x_i + x_offset,
# mae_r,
# bar_width,
# label='Sample',
# linewidth=lmdk_lib.line_width
# )
# x_offset += bar_width
# plt.bar(
# x_i + x_offset,
# mae_d,
# bar_width,
# label='Discount',
# linewidth=lmdk_lib.line_width
# )
# x_offset += bar_width
# plt.bar(
# x_i + x_offset,
# mae_i,
# bar_width,
# label='Incremental',
# linewidth=lmdk_lib.line_width
# )
# x_offset += bar_width
# path = str('rslt/bgt_cmp/' + d)
# # Plot legend
# lmdk_lib.plot_legend()
path = str('rslt/bgt_cmp/' + d)
# Plot legend
lmdk_lib.plot_legend()
# # Show plot
# # plt.show()
# plt.show()
# # Save plot
# lmdk_lib.save_plot(path + '.pdf')
print('[OK]', flush=True)
def parse_args():
'''
Parse arguments.

55
code/lib/lmdk_bgt.py
View File

@ -343,6 +343,61 @@ def adaptive(seq, lmdks, epsilon, inc_rt, dec_rt):
return rls_data, bgts, skipped
def adaptive_cont(seq, lmdks, epsilon, inc_rt, dec_rt):
'''
Adaptive budget allocation.
Parameters:
seq - The point sequence.
lmdks - The landmarks.
epsilon - The available privacy budget.
inc_rt - Sampling rate increase rate.
dec_rt - Sampling rate decrease rate.
Returns:
rls_data - The perturbed data.
bgts - The privacy budget allocation.
skipped - The number of skipped releases.
'''
# Uniform budget allocation
bgts = uniform(seq, lmdks, epsilon)
# Released
rls_data = [None]*len(seq)
# The sampling rate
samp_rt = 1
# Track landmarks
lmdk_cur = 0
# Track skipped releases
skipped = 0
for i, p in enumerate(seq):
# Check if current point is a landmark
r = p[2] in lmdks
if r:
lmdk_cur += 1
if lmdk_lib.should_sample(samp_rt) or i == 0:
# Add noise to original data
o = lmdk_lib.randomized_response(r, bgts[i])
rls_data[i] = [r, o]
# Adjust sampling rate
if i > 0:
if rls_data[i - 1][1] == o:
# Decrease
samp_rt -= samp_rt*dec_rt
else:
# Increase
samp_rt += (1 - samp_rt)*inc_rt
else:
skipped += 1
# Skip current release and approximate with previous
rls_data[i] = rls_data[i - 1]
if r:
# Allocate the current budget to the following releases uniformly
for j in range(i + 1, len(seq)):
bgts[j] += bgts[i]/(len(lmdks) - lmdk_cur + 1)
# No budget was spent
bgts[i] = 0
return rls_data, bgts, skipped
def skip(seq, lmdks, epsilon):
'''
Skip landmarks.