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-request@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)
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Message: 1 Date: Tue, 21 Jun 2016 19:18:12 -0700 From: Jonathan Taylor <jonathan.taylor@stanford.edu> To: scikit-learn@python.org Subject: [scikit-learn] isotonic regression weird behavior? Message-ID: < CANmCCuSkekfbjH0d450_Cm5V3JYVXsSgEyGtj3pNmA6Ek8CssQ@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