126 lines
3.7 KiB
Python
126 lines
3.7 KiB
Python
#!/usr/bin/env python3
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import sys
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sys.path.insert(1, '../lib')
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import argparse
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import lmdk_lib
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import lmdk_sel
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import exp_mech
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import numpy as np
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import os
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from matplotlib import pyplot as plt
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import time
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def main(args):
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# Privacy goal
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epsilon = [.01, .1, 1.0, 10.0, 100.0]
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# Number of timestamps
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seq = lmdk_lib.get_seq(1, args.time)
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# Distribution type
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dist_type = np.array(range(-1, 4))
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# Number of landmarks
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lmdk_n = np.array(range(int(.2*args.time), args.time, int(args.time/5)))
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# Width of bars
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bar_width = 1/(len(epsilon) + 1)
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# The x axis
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x_i = np.arange(len(lmdk_n))
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x_margin = bar_width*(len(epsilon)/2 + 1)
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for d_i, d in enumerate(dist_type):
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# Logging
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title = lmdk_lib.dist_type_to_str(d) + ' landmark distribution'
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print('(%d/%d) %s... ' %(d_i + 1, len(dist_type), title), end='', flush=True)
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# Initialize plot
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lmdk_lib.plot_init()
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# The x axis
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plt.xticks(x_i, ((lmdk_n/len(seq))*100).astype(int))
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plt.xlabel('Landmarks (%)') # Set x axis label.
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plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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# The y axis
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plt.ylabel('Mean absolute error') # Set y axis label.
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# plt.ylim(0, len(seq)/3)
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# Bar offset
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x_offset = -(bar_width/2)*(len(epsilon) - 1)
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for e_i, e in enumerate(epsilon):
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mae = np.zeros(len(lmdk_n))
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for n_i, n in enumerate(lmdk_n):
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for r in range(args.reps):
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lmdks = lmdk_lib.get_lmdks(seq, n, d)
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hist, h = lmdk_lib.get_hist(seq, lmdks)
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res = np.zeros([len(hist)])
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# Split sequence in parts of size h
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pt_idx = []
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for idx in range(h, len(seq), h):
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pt_idx.append(idx)
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seq_pt = np.split(seq, pt_idx)
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for pt_i, pt in enumerate(seq_pt):
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# Find this part's landmarks
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lmdks_pt = np.intersect1d(pt, lmdks)
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# Find possible options for this part
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opts = lmdk_sel.get_opts_from_top_h(pt, lmdks_pt)
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# Turn part to histogram
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hist_pt, _ = lmdk_lib.get_hist(pt, lmdks_pt)
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# Get an option for this part
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if len(opts) > 1:
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res_pt, _ = exp_mech.exponential(hist_pt, opts, exp_mech.score, 1.0, e)
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elif len(opts) > 0:
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res_pt = opts[0]
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# Merge options of all parts
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res[pt_i] = np.sum(res_pt)
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# Calculate MAE
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mae[n_i] += lmdk_lib.get_norm(hist, res)/args.reps
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# Plot bar for current epsilon
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plt.bar(
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x_i + x_offset,
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mae,
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bar_width,
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label=u'\u03B5 = ' + str("{:.0e}".format(e)),
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linewidth=lmdk_lib.line_width
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)
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# Change offset for next bar
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x_offset += bar_width
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path = str('../../rslt/lmdk_sel/' + title)
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# Plot legend
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lmdk_lib.plot_legend()
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# Show plot
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# plt.show()
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# Save plot
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lmdk_lib.save_plot(path + '.pdf')
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print('[OK]', flush=True)
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'''
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Parse arguments.
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Optional:
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reps - The number of repetitions.
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time - The time limit of the sequence.
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'''
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def parse_args():
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# Create argument parser.
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parser = argparse.ArgumentParser()
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# Mandatory arguments.
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# Optional arguments.
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parser.add_argument('-r', '--reps', help='The number of repetitions.', type=int, default=1)
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parser.add_argument('-t', '--time', help='The time limit of the sequence.', type=int, default=100)
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# Parse arguments.
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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try:
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start_time = time.time()
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main(parse_args())
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end_time = time.time()
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print('##############################')
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print('Time elapsed: %s' % (time.strftime('%H:%M:%S', time.gmtime(end_time - start_time))))
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print('##############################')
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except KeyboardInterrupt:
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print('Interrupted by user.')
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exit()
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