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Unverified Commit 6571d16d authored by Lukasz Kaiser's avatar Lukasz Kaiser Committed by GitHub
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Merge pull request #3544 from cshallue/master 

Add AstroNet to tensorflow/models
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research/astronet/third_party/robust_mean/LICENSE 0 → 100644
View file @ 6571d16d
Copyright (c) 2014, Wayne Landsman
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
research/astronet/third_party/robust_mean/robust_mean.py 0 → 100644
View file @ 6571d16d
"""Function for computing a robust mean estimate in the presence of outliers.
This is a modified Python implementation of this file:
https://idlastro.gsfc.nasa.gov/ftp/pro/robust/resistant_mean.pro
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def robust_mean(y, cut):
"""Computes a robust mean estimate in the presence of outliers.
Args:
y: 1D numpy array. Assumed to be normally distributed with outliers.
cut: Points more than this number of standard deviations from the median are
ignored.
Returns:
mean: A robust estimate of the mean of y.
mean_stddev: The standard deviation of the mean.
mask: Boolean array with the same length as y. Values corresponding to
outliers in y are False. All other values are True.
"""
# First, make a robust estimate of the standard deviation of y, assuming y is
# normally distributed. The conversion factor of 1.4826 takes the median
# absolute deviation to the standard deviation of a normal distribution.
# See, e.g. https://www.mathworks.com/help/stats/mad.html.
absdev = np.abs(y - np.median(y))
sigma = 1.4826 * np.median(absdev)
# If the previous estimate of the standard deviation using the median absolute
# deviation is zero, fall back to a robust estimate using the mean absolute
# deviation. This estimator has a different conversion factor of 1.253.
# See, e.g. https://www.mathworks.com/help/stats/mad.html.
if sigma < 1.0e-24:
sigma = 1.253 * np.mean(absdev)
# Identify outliers using our estimate of the standard deviation of y.
mask = absdev <= cut * sigma
# Now, recompute the standard deviation, using the sample standard deviation
# of non-outlier points.
sigma = np.std(y[mask])
# Compensate the estimate of sigma due to trimming away outliers. The
# following formula is an approximation, see
# http://w.astro.berkeley.edu/~johnjohn/idlprocs/robust_mean.pro.
sc = np.max([cut, 1.0])
if sc <= 4.5:
sigma /= (-0.15405 + 0.90723 * sc - 0.23584 * sc**2 + 0.020142 * sc**3)
# Identify outliers using our second estimate of the standard deviation of y.
mask = absdev <= cut * sigma
# Now, recompute the standard deviation, using the sample standard deviation
# with non-outlier points.
sigma = np.std(y[mask])
# Compensate the estimate of sigma due to trimming away outliers.
sc = np.max([cut, 1.0])
if sc <= 4.5:
sigma /= (-0.15405 + 0.90723 * sc - 0.23584 * sc**2 + 0.020142 * sc**3)
# Final estimate is the sample mean with outliers removed.
mean = np.mean(y[mask])
mean_stddev = sigma / np.sqrt(len(y) - 1.0)
return mean, mean_stddev, mask
research/astronet/third_party/robust_mean/robust_mean_test.py 0 → 100644
View file @ 6571d16d
"""Tests for robust_mean.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
import numpy as np
from third_party.robust_mean import robust_mean
from third_party.robust_mean.test_data import random_normal
class RobustMeanTest(absltest.TestCase):
def testRobustMean(self):
# To avoid non-determinism in the unit test, we use a pre-generated vector
# of length 1,000. Each entry is independently sampled from a random normal
# distribution with mean 2 and standard deviation 1. The maximum value of
# y is 6.075 (+4.075 sigma from the mean) and the minimum value is -1.54
# (-3.54 sigma from the mean).
y = np.array(random_normal.RANDOM_NORMAL)
self.assertAlmostEqual(np.mean(y), 2.00336615850485)
self.assertAlmostEqual(np.std(y), 1.01690907798)
# High cut. No points rejected, so the mean should be the sample mean, and
# the mean standard deviation should be the sample standard deviation
# divided by sqrt(1000 - 1).
mean, mean_stddev, mask = robust_mean.robust_mean(y, cut=5)
self.assertAlmostEqual(mean, 2.00336615850485)
self.assertAlmostEqual(mean_stddev, 0.032173579)
self.assertLen(mask, 1000)
self.assertEqual(np.sum(mask), 1000)
# Cut of 3 standard deviations.
mean, mean_stddev, mask = robust_mean.robust_mean(y, cut=3)
self.assertAlmostEqual(mean, 2.0059050070632178)
self.assertAlmostEqual(mean_stddev, 0.03197075302321066)
# There are exactly 3 points in the sample less than 1 or greater than 5.
# These have indices 12, 220, 344.
self.assertLen(mask, 1000)
self.assertEqual(np.sum(mask), 997)
self.assertFalse(np.any(mask[[12, 220, 344]]))
# Add outliers. This corrupts the sample mean to 2.082.
mean, mean_stddev, mask = robust_mean.robust_mean(
y=np.concatenate([y, [10] * 10]), cut=5)
self.assertAlmostEqual(mean, 2.0033661585048681)
self.assertAlmostEqual(mean_stddev, 0.032013749413590531)
self.assertLen(mask, 1010)
self.assertEqual(np.sum(mask), 1000)
self.assertFalse(np.any(mask[1000:1010]))
# Add an outlier. This corrupts the mean to 1.002.
mean, mean_stddev, mask = robust_mean.robust_mean(
y=np.concatenate([y, [-1000]]), cut=5)
self.assertAlmostEqual(mean, 2.0033661585048681)
self.assertAlmostEqual(mean_stddev, 0.032157488597211903)
self.assertLen(mask, 1001)
self.assertEqual(np.sum(mask), 1000)
self.assertFalse(mask[1000])
if __name__ == "__main__":
absltest.main()
research/astronet/third_party/robust_mean/test_data/random_normal.py 0 → 100644
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"""This file contains 1,000 points of a random normal distribution.
The mean of the distribution is 2, and the standard deviation is 1.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
RANDOM_NORMAL = [
0.692741320869,
1.948556207658,
-0.140158117639,
2.680322906859,
1.876671492867,
2.885286232509,
1.482222151802,
2.234349266246,
2.437427989583,
1.573053624952,
2.169198249367,
2.791023059619,
-1.053798286951,
1.796497126664,
3.806070621390,
1.744055208958,
3.474399140181,
1.564447665560,
1.143107137921,
1.618376255615,
2.615632139609,
1.413239777404,
1.047237320108,
3.190489536636,
2.918428435434,
1.268789896280,
0.931181066003,
3.797790627792,
0.493025834330,
1.866146169585,
0.949927834893,
1.439666857958,
2.705521702500,
1.815406073907,
1.570503841718,
1.834429337005,
2.903916580263,
-0.110549195467,
2.065338922749,
1.119498053048,
0.427627428035,
3.025052175045,
2.645448868784,
1.442644951218,
0.774681298962,
2.247561418494,
1.743438974941,
1.184440017832,
1.643691193885,
1.947748675186,
2.178309991836,
2.815355272672,
2.207620544168,
2.077889048169,
2.915504366132,
2.440862146850,
2.804729838623,
0.534712595625,
1.956491042766,
2.230542009671,
2.186536281651,
3.694129968231,
3.313526598170,
2.170240599444,
2.793531289796,
1.454464312809,
1.197463589804,
0.713332299712,
1.180965411999,
2.180022174106,
2.861107927091,
1.795223865106,
1.730056153040,
1.431404424890,
1.839372935334,
1.271871740741,
3.773103777671,
1.026069424885,
2.006070770486,
1.276836142291,
1.414098998873,
1.749117068374,
2.040006827147,
1.815581326626,
2.892666735522,
3.093934769003,
2.129166907135,
1.260521633663,
3.259431640120,
1.879415647487,
1.368769201985,
2.236653714367,
2.293120875655,
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