import numpy as np
import nose.tools as nt
from sklearn.isotonic import isotonic_regression
def test_isotonic_ymin_ymax():
X = np.array([1.26, 1.31,-0.57, 0.30, -0.70,
-0.17, -1.59, 1.05, 1.39, 1.90,
0.20, 0.03, -0.08, 0.44, 0.01,
-0.37, -0.89, -0.37, -1.32, 0.18])
X_iso = isotonic_regression(X, y_min=0., y_max=0.1)
nt.assert_true(np.all((X_iso <= 0.1) * (X_iso >= 0.)))
test_isotonic_ymin_ymax()
On Wed, Jun 22, 2016 at 12:27 AM, <scikit-learn-requ...@python.org> wrote:
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> Today's Topics:
>
> 1. isotonic regression weird behavior? (Jonathan Taylor)
> 2. Re: isotonic regression weird behavior? (Jonathan Taylor)
> 3. Re: isotonic regression weird behavior? (Jonathan Taylor)
> 4. Re: isotonic regression weird behavior? (Gael Varoquaux)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 21 Jun 2016 19:18:12 -0700
> From: Jonathan Taylor <jonathan.tay...@stanford.edu>
> To: scikit-learn@python.org
> Subject: [scikit-learn] isotonic regression weird behavior?
> Message-ID:
> <
> canmccuskekfbjh0d450_cm5v3jyvxssgeygtj3pnma6ek8c...@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Was trying to fit isotonic regression with non-trivial y_min and y_max:
>
> In [*17*]: X
>
> Out[*17*]:
>
> array([ 1.26336413, 1.31853693, -0.57200917, 0.3072928 , -0.70686507,
>
> -0.17614937, -1.59943059, 1.05908504, 1.3958263 , 1.90580318,
>
> 0.20992272, 0.02836316, -0.08092235, 0.44438247, 0.01791253,
>
> -0.3771914 , -0.89577538, -0.37726249, -1.32687569, 0.18013201])
>
>
> In [*18*]: iso.isotonic_regression(X, y_min=0, y_max=0.1)
>
> Out[*18*]:
>
> array([-0.00826919, -0.00826919, -0.00826919, -0.00826919, -0.00826919,
>
> -0.00826919, -0.00826919, 0.10449344, 0.10449344, 0.10449344,
>
> 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344,
>
> 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344])
>
>
> The solution does not satisfy the bounds that each entry should be in
> [0,0.1]
>
>
>
> --
> Jonathan Taylor
> Dept. of Statistics
> Sequoia Hall, 137
> 390 Serra Mall
> Stanford, CA 94305
> Tel: 650.723.9230
> Fax: 650.725.8977
> Web: http://www-stat.stanford.edu/~jtaylo
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL: <
> http://mail.python.org/pipermail/scikit-learn/attachments/20160621/178cef23/attachment-0001.html
> >
>
> ------------------------------
>
> Message: 2
> Date: Tue, 21 Jun 2016 19:19:42 -0700
> From: Jonathan Taylor <jonathan.tay...@stanford.edu>
> To: scikit-learn@python.org
> Subject: Re: [scikit-learn] isotonic regression weird behavior?
> Message-ID:
> <CANmCCuSJH45Z=ZUj+gVzsBAS0CG5iaUXTinfVVnEy4+DLA=
> 4...@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Should have included:
>
> In [*22*]: iso
>
> Out[*22*]: <module 'sklearn.isotonic' from
>
> '/Users/jonathantaylor/anaconda/envs/py27/lib/python2.7/site-packages/sklearn/isotonic.pyc'>
>
> On Tue, Jun 21, 2016 at 7:18 PM, Jonathan Taylor <
> jonathan.tay...@stanford.edu> wrote:
>
> > Was trying to fit isotonic regression with non-trivial y_min and y_max:
> >
> > In [*17*]: X
> >
> > Out[*17*]:
> >
> > array([ 1.26336413, 1.31853693, -0.57200917, 0.3072928 , -0.70686507,
> >
> > -0.17614937, -1.59943059, 1.05908504, 1.3958263 , 1.90580318,
> >
> > 0.20992272, 0.02836316, -0.08092235, 0.44438247, 0.01791253,
> >
> > -0.3771914 , -0.89577538, -0.37726249, -1.32687569, 0.18013201])
> >
> >
> > In [*18*]: iso.isotonic_regression(X, y_min=0, y_max=0.1)
> >
> > Out[*18*]:
> >
> > array([-0.00826919, -0.00826919, -0.00826919, -0.00826919, -0.00826919,
> >
> > -0.00826919, -0.00826919, 0.10449344, 0.10449344, 0.10449344,
> >
> > 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344,
> >
> > 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344])
> >
> >
> > The solution does not satisfy the bounds that each entry should be in
> > [0,0.1]
> >
> >
> >
> > --
> > Jonathan Taylor
> > Dept. of Statistics
> > Sequoia Hall, 137
> > 390 Serra Mall
> > Stanford, CA 94305
> > Tel: 650.723.9230
> > Fax: 650.725.8977
> > Web: http://www-stat.stanford.edu/~jtaylo
> >
>
>
>
> --
> Jonathan Taylor
> Dept. of Statistics
> Sequoia Hall, 137
> 390 Serra Mall
> Stanford, CA 94305
> Tel: 650.723.9230
> Fax: 650.725.8977
> Web: http://www-stat.stanford.edu/~jtaylo
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL: <
> http://mail.python.org/pipermail/scikit-learn/attachments/20160621/2e999af0/attachment-0001.html
> >
>
> ------------------------------
>
> Message: 3
> Date: Tue, 21 Jun 2016 19:21:59 -0700
> From: Jonathan Taylor <jonathan.tay...@stanford.edu>
> To: scikit-learn@python.org
> Subject: Re: [scikit-learn] isotonic regression weird behavior?
> Message-ID:
> <
> canmccuqy33tx884_7owwyz1nac8tpgqhjozcytuxssspxbs...@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Sorry, docstring is also a bit funny.
>
> Is the problem it is trying to solve have an __equality__ constraint for
> y_min, y_max or __inequality__ constraint for y_min / y_max?
>
> Either way the produced solution does not satisfy such a constraint...
>
> On Tue, Jun 21, 2016 at 7:19 PM, Jonathan Taylor <
> jonathan.tay...@stanford.edu> wrote:
>
> > Should have included:
> >
> > In [*22*]: iso
> >
> > Out[*22*]: <module 'sklearn.isotonic' from
> >
> '/Users/jonathantaylor/anaconda/envs/py27/lib/python2.7/site-packages/sklearn/isotonic.pyc'>
> >
> > On Tue, Jun 21, 2016 at 7:18 PM, Jonathan Taylor <
> > jonathan.tay...@stanford.edu> wrote:
> >
> >> Was trying to fit isotonic regression with non-trivial y_min and y_max:
> >>
> >> In [*17*]: X
> >>
> >> Out[*17*]:
> >>
> >> array([ 1.26336413, 1.31853693, -0.57200917, 0.3072928 , -0.70686507,
> >>
> >> -0.17614937, -1.59943059, 1.05908504, 1.3958263 , 1.90580318,
> >>
> >> 0.20992272, 0.02836316, -0.08092235, 0.44438247, 0.01791253,
> >>
> >> -0.3771914 , -0.89577538, -0.37726249, -1.32687569, 0.18013201])
> >>
> >>
> >> In [*18*]: iso.isotonic_regression(X, y_min=0, y_max=0.1)
> >>
> >> Out[*18*]:
> >>
> >> array([-0.00826919, -0.00826919, -0.00826919, -0.00826919, -0.00826919,
> >>
> >> -0.00826919, -0.00826919, 0.10449344, 0.10449344, 0.10449344,
> >>
> >> 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344,
> >>
> >> 0.10449344, 0.10449344, 0.10449344, 0.10449344, 0.10449344])
> >>
> >>
> >> The solution does not satisfy the bounds that each entry should be in
> >> [0,0.1]
> >>
> >>
> >>
> >> --
> >> Jonathan Taylor
> >> Dept. of Statistics
> >> Sequoia Hall, 137
> >> 390 Serra Mall
> >> Stanford, CA 94305
> >> Tel: 650.723.9230
> >> Fax: 650.725.8977
> >> Web: http://www-stat.stanford.edu/~jtaylo
> >>
> >
> >
> >
> > --
> > Jonathan Taylor
> > Dept. of Statistics
> > Sequoia Hall, 137
> > 390 Serra Mall
> > Stanford, CA 94305
> > Tel: 650.723.9230
> > Fax: 650.725.8977
> > Web: http://www-stat.stanford.edu/~jtaylo
> >
>
>
>
> --
> Jonathan Taylor
> Dept. of Statistics
> Sequoia Hall, 137
> 390 Serra Mall
> Stanford, CA 94305
> Tel: 650.723.9230
> Fax: 650.725.8977
> Web: http://www-stat.stanford.edu/~jtaylo
> -------------- next part --------------
> An HTML attachment was scrubbed...
> URL: <
> http://mail.python.org/pipermail/scikit-learn/attachments/20160621/f7c40e1c/attachment-0001.html
> >
>
> ------------------------------
>
> Message: 4
> Date: Wed, 22 Jun 2016 09:27:06 +0200
> From: Gael Varoquaux <gael.varoqu...@normalesup.org>
> To: Scikit-learn user and developer mailing list
> <scikit-learn@python.org>
> Subject: Re: [scikit-learn] isotonic regression weird behavior?
> Message-ID: <20160622072706.gf1018...@phare.normalesup.org>
> Content-Type: text/plain; charset=us-ascii
>
> Looks like a bug indeed. Could you please put a small code snippet to
> enable us to reproduce.
>
>
>
> ------------------------------
>
> Subject: Digest Footer
>
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> ------------------------------
>
> End of scikit-learn Digest, Vol 3, Issue 37
> *******************************************
>
--
Jonathan Taylor
Dept. of Statistics
Sequoia Hall, 137
390 Serra Mall
Stanford, CA 94305
Tel: 650.723.9230
Fax: 650.725.8977
Web: http://www-stat.stanford.edu/~jtaylo
import numpy as np
import nose.tools as nt
from sklearn.isotonic import isotonic_regression
def test_isotonic_ymin_ymax():
X = np.array([1.26, 1.31,-0.57, 0.30, -0.70,
-0.17, -1.59, 1.05, 1.39, 1.90,
0.20, 0.03, -0.08, 0.44, 0.01,
-0.37, -0.89, -0.37, -1.32, 0.18])
X_iso = isotonic_regression(X, y_min=0., y_max=0.1)
nt.assert_true(np.all((X_iso <= 0.1) * (X_iso >= 0.)))
test_isotonic_ymin_ymax()
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