forked from 626_privacy/tensorflow_privacy
105 lines
2.8 KiB
Python
105 lines
2.8 KiB
Python
# Copyright 2017 The 'Scalable Private Learning with PATE' Authors All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Plots LS(q).
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A script in support of the PATE2 paper. NOT PRESENTLY USED.
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The output is written to a specified directory as a pdf file.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import os
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import sys
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sys.path.append('..') # Main modules reside in the parent directory.
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from absl import app
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from absl import flags
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import matplotlib
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matplotlib.use('TkAgg')
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import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
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import numpy as np
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import smooth_sensitivity as pate_ss
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plt.style.use('ggplot')
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FLAGS = flags.FLAGS
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flags.DEFINE_string('figures_dir', '', 'Path where the output is written to.')
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def compute_ls_q(sigma, order, num_classes):
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def beta(q):
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return pate_ss._compute_rdp_gnmax(sigma, math.log(q), order)
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def bu(q):
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return pate_ss._compute_bu_gnmax(q, sigma, order)
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def bl(q):
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return pate_ss._compute_bl_gnmax(q, sigma, order)
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def delta_beta(q):
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if q == 0 or q > .8:
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return 0
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beta_q = beta(q)
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beta_bu_q = beta(bu(q))
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beta_bl_q = beta(bl(q))
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assert beta_bl_q <= beta_q <= beta_bu_q
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return beta_bu_q - beta_q # max(beta_bu_q - beta_q, beta_q - beta_bl_q)
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logq0 = pate_ss.compute_logq0_gnmax(sigma, order)
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logq1 = pate_ss._compute_logq1(sigma, order, num_classes)
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print(math.exp(logq1), math.exp(logq0))
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xs = np.linspace(0, .1, num=1000, endpoint=True)
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ys = [delta_beta(x) for x in xs]
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return xs, ys
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def main(argv):
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del argv # Unused.
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sigma = 20
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order = 20.
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num_classes = 10
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# sigma = 20
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# order = 25.
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# num_classes = 10
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x_axis, ys = compute_ls_q(sigma, order, num_classes)
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fig, ax = plt.subplots()
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fig.set_figheight(4.5)
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fig.set_figwidth(4.7)
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ax.plot(x_axis, ys, alpha=.8, linewidth=5)
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plt.xlabel('Number of queries answered', fontsize=16)
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plt.ylabel(r'Privacy cost $\varepsilon$ at $\delta=10^{-8}$', fontsize=16)
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ax.tick_params(labelsize=14)
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fout_name = os.path.join(FLAGS.figures_dir, 'ls_of_q.pdf')
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print('Saving the graph to ' + fout_name)
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plt.show()
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plt.close('all')
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if __name__ == '__main__':
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app.run(main)
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