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code: WIP

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Manos Katsomallos
2021-10-06 13:14:28 +02:00
parent fbf2c15869
commit ca85703de8
4 changed files with 36 additions and 51 deletions
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23
code/expt/copenhagen-sel.py
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@ -8,6 +8,8 @@ from datetime import datetime
from geopy.distance import distance
import lmdk_bgt
import lmdk_lib
import lmdk_sel
import exp_mech
import math
import numpy as np
from matplotlib import pyplot as plt
@ -66,33 +68,20 @@ def main(args):
for _ in range(args.iter):
eps_sel = 0
if pct != 0 and pct != 100:
# Get landmarks timestamps in sequence
lmdks_seq = lmdk_lib.find_lmdks_seq(seq, lmdks)
# Turn landmarks to histogram
hist, h = lmdk_lib.get_hist(lmdk_lib.get_seq(1, len(seq)), lmdks_seq)
# Find all possible options
opts = lmdk_sel.get_opts_from_top_h(lmdk_lib.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_pareto(hist, opts, exp_mech.score, 1.0, eps_sel)
# Get actual landmarks values
lmdks = seq[lmdks_seq]
lmdks, eps_out = lmdk_sel.find_lmdks(seq, lmdks, epsilon)
# Skip
rls_data_s, bgts_s = lmdk_bgt.skip_cont(seq, lmdks, epsilon - eps_sel)
rls_data_s, bgts_s = lmdk_bgt.skip_cont(seq, lmdks, eps_out)
# 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 - eps_sel)
rls_data_u, bgts_u = lmdk_bgt.uniform_cont(seq, lmdks, eps_out)
# 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 - eps_sel, .5, .5)
rls_data_a, _, _ = lmdk_bgt.adaptive_cont(seq, lmdks, eps_out, .5, .5)
mae_a[i] += lmdk_bgt.mae_cont(rls_data_a)/args.iter
# Calculate once

23
code/expt/hue-sel.py
View File

@ -8,6 +8,8 @@ from datetime import datetime
from geopy.distance import distance
import lmdk_bgt
import lmdk_lib
import lmdk_sel
import exp_mech
import math
import numpy as np
from matplotlib import pyplot as plt
@ -62,32 +64,19 @@ def main(args):
for _ in range(args.iter):
eps_sel = 0
if pct != 0 and pct != 100:
# Get landmarks timestamps in sequence
lmdks_seq = lmdk_lib.find_lmdks_seq(seq, lmdks)
# Turn landmarks to histogram
hist, h = lmdk_lib.get_hist(lmdk_lib.get_seq(1, len(seq)), lmdks_seq)
# Find all possible options
opts = lmdk_sel.get_opts_from_top_h(lmdk_lib.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_pareto(hist, opts, exp_mech.score, 1.0, eps_sel)
# Get actual landmarks values
lmdks = seq[lmdks_seq]
lmdks, eps_out = lmdk_sel.find_lmdks(seq, lmdks, epsilon)
# Skip
rls_data_s, bgts_s = lmdk_bgt.skip_cons(seq, lmdks, epsilon - eps_sel)
rls_data_s, bgts_s = lmdk_bgt.skip_cons(seq, lmdks, eps_out)
# lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_s)
mae_s[i] += lmdk_bgt.mae_cons(seq, rls_data_s)/args.iter
# Uniform
rls_data_u, bgts_u = lmdk_bgt.uniform_cons(seq, lmdks, epsilon - eps_sel)
rls_data_u, bgts_u = lmdk_bgt.uniform_cons(seq, lmdks, eps_out)
mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter
# Adaptive
rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon - eps_sel, .5, .5)
rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, eps_out, .5, .5)
mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter
# Calculate once

23
code/expt/t-drive-sel.py
View File

@ -7,6 +7,8 @@ from datetime import datetime
from geopy.distance import distance
import lmdk_bgt
import lmdk_lib
import lmdk_sel
import exp_mech
import numpy as np
from matplotlib import pyplot as plt
import time
@ -85,31 +87,18 @@ def main(args):
for bgt in bgt_conf:
for _ in range(args.iter):
eps_sel = 0
if lmdk != 0 and lmdk != 100:
# Get landmarks timestamps in sequence
lmdks_seq = lmdk_lib.find_lmdks_seq(seq, lmdks)
# Turn landmarks to histogram
hist, h = lmdk_lib.get_hist(lmdk_lib.get_seq(1, len(seq)), lmdks_seq)
# Find all possible options
opts = lmdk_sel.get_opts_from_top_h(lmdk_lib.get_seq(1, len(seq)), lmdks_seq)
# Landmarks selection budget
eps_sel = bgt['epsilon']/(len(lmdks_seq) + 1)
# Get private landmarks timestamps
lmdks_seq, _ = exp_mech.exponential_pareto(hist, opts, exp_mech.score, 1.0, eps_sel)
# Get actual landmarks values
lmdks = seq[lmdks_seq]
lmdks, eps_out = lmdk_sel.find_lmdks(seq, lmdks, bgt['epsilon'])
# Skip
rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, bgt['epsilon'] - eps_sel)
rls_data_s, _ = lmdk_bgt.skip(seq, lmdks, eps_out)
mae_s[i] += lmdk_bgt.mae(seq, rls_data_s)/args.iter
# Uniform
rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, bgt['epsilon'] - eps_sel)
rls_data_u, _ = lmdk_bgt.uniform_r(seq, lmdks, eps_out)
mae_u[i] += lmdk_bgt.mae(seq, rls_data_u)/args.iter
# Adaptive
rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, bgt['epsilon'] - eps_sel, .5, .5)
rls_data_a, _, _ = lmdk_bgt.adaptive(seq, lmdks, eps_out, .5, .5)
mae_a[i] += lmdk_bgt.mae(seq, rls_data_a)/args.iter
# Event