[MRG] Fix tests on numpy master by lesteve · Pull Request #8355 · scikit-learn/scikit-learn

lesteve · 2017-02-14T13:44:22Z

numpy.apply_along_axis has changed behaviour when the function passed
in returns a 2d array. Very likely related to numpy/numpy#8441.

The first build in master I could find with the failure on numpy dev (from 2 days ago):
https://travis-ci.org/scikit-learn/scikit-learn/builds/200889357

import numpy as np
result = np.apply_along_axis(lambda X: np.array([[X[0]]]), 1, np.arange(6).reshape(3, -1))
print(result.shape)

outputs (3, 1, 1) on numpy master and (3, 1) on numpy 1.12.

A scikit-learn snippet closely related to the test failure:

from sklearn.gaussian_process import kernels
kernel = kernels.PairwiseKernel()
print(kernel.diag(np.arange(6).reshape(3, -1)).shape)

outputs (3, 1) on numpy master and (3,) on numpy 1.12.

codecov · 2017-02-14T14:16:50Z

Codecov Report

Merging #8355 into master will not change coverage.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master    #8355   +/-   ##
=======================================
  Coverage   94.75%   94.75%           
=======================================
  Files         342      342           
  Lines       60801    60801           
=======================================
  Hits        57609    57609           
  Misses       3192     3192

Impacted Files	Coverage Δ
sklearn/gaussian_process/kernels.py	`91.54% <100%> (ø)`	✅

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lesteve · 2017-02-14T16:44:13Z

The Travis build on this PR with numpy master is green as you can see from:
https://travis-ci.org/scikit-learn/scikit-learn/builds/201521405

jnothman · 2017-02-15T04:59:07Z

sklearn/gaussian_process/kernels.py

        """
        # We have to fall back to slow way of computing diagonal
-        return np.apply_along_axis(self, 1, X)[:, 0]
+        return np.apply_along_axis(lambda arr: self(arr).ravel(), 1, X)[:, 0]


I think this is written in a confusing way, if self(arr) is returning an array of shape (1,1).

Shouldn't either of the following do the same?

np.apply_along_axis(lambda arr: self(arr)[0, 0], 1, X) np.apply_along_axis(self, 1, X).ravel()

I think this is written in a confusing way, if self(arr) is returning an array of shape (1,1).

Yeah I was not sure what was the best way to write it. Would you rather have

np.apply_along_axis(lambda arr: self(arr)[0, 0], 1, X)

Shouldn't either of the following do the same?

It does in numpy 1.12 but not in numpy master as I tried to highlight in the description.

Actually my bad you need to use:the following in numpy master:

np.apply_along_axis(lambda arr: self(arr)[0], 1, X)

jnothman · 2017-02-15T08:20:18Z

Ravelling the output doesn't wok without the lambda?

…

On 15 February 2017 at 17:49, Loïc Estève ***@***.***> wrote: ***@***.**** commented on this pull request. ------------------------------ In sklearn/gaussian_process/kernels.py <#8355>: > @@ -1852,7 +1852,7 @@ def diag(self, X): Diagonal of kernel k(X, X) """ # We have to fall back to slow way of computing diagonal - return np.apply_along_axis(self, 1, X)[:, 0] + return np.apply_along_axis(lambda arr: self(arr).ravel(), 1, X)[:, 0] Actually my bad you need to use:the following in numpy master: np.apply_along_axis(lambda arr: self(arr)[0], 1, X) — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#8355>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAEz67UlTmgNOoezB-kMR_G3f53yW-0xks5rcp_dgaJpZM4MAfSB> .

lesteve · 2017-02-15T08:57:56Z

Ravelling the output doesn't wok without the lambda?

Ah sorry I read your comment too quickly above, this does work in numpy master indeed:

np.apply_along_axis(self, 1, X).ravel()

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

jnothman · 2017-02-15T12:22:28Z

LGTM

lesteve · 2017-02-15T12:23:04Z

Thanks!

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

* Fix tests on numpy master (#7946) Until now we were in a edge case on assert_array_equal * Fix tests on numpy master (#8355) numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array * [MRG] Updated plot_stock_market.py to use Google Finance (#9010) * DOC updated plot_stock_market.py to use Google Finance The implementations is intentionally very basic not to distract the users from the example. Specifically unlike ``quotes_historical_yahoo_ochl`` it does not cache downloaded data. I also had to remove some symbols because the have no data on Google for the specified date interval. These are WBA, LMT, KFT and MTU. Closes #8899 * DOC removed plot_stock_market.py from expected failing examples * Addressed review comments * Addressed another pass of review comments * [MRG] Remove DeprecationWarnings in examples due to using floats instead of ints (#8040)

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

lesteve force-pushed the fix-tests-on-numpy-master branch from 5bf780c to 8fb95cb Compare February 14, 2017 14:16

jnothman reviewed Feb 15, 2017

View reviewed changes

lesteve force-pushed the fix-tests-on-numpy-master branch from 8fb95cb to e9777a1 Compare February 15, 2017 06:50

Fix tests on numpy master

3578d4b

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

lesteve force-pushed the fix-tests-on-numpy-master branch from e9777a1 to 3578d4b Compare February 15, 2017 08:59

jnothman merged commit b89fcd3 into scikit-learn:master Feb 15, 2017

lesteve deleted the fix-tests-on-numpy-master branch February 15, 2017 12:22

sergeyf pushed a commit to sergeyf/scikit-learn that referenced this pull request Feb 28, 2017

Fix tests on numpy master (scikit-learn#8355)

aba9cdf

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

Przemo10 mentioned this pull request Mar 17, 2017

update fork (#1) #8606

Closed

lesteve mentioned this pull request Jun 10, 2017

Test failures with NumPy 1.13 #9094

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jakirkham mentioned this pull request Jun 12, 2017

Add NumPy 1.13 conda-forge/scikit-learn-feedstock#47

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Sundrique pushed a commit to Sundrique/scikit-learn that referenced this pull request Jun 14, 2017

Fix tests on numpy master (scikit-learn#8355)

243be04

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

jakirkham pushed a commit to jakirkham/scikit-learn that referenced this pull request Jun 15, 2017

Fix tests on numpy master (scikit-learn#8355)

eb850e2

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

jakirkham mentioned this pull request Jun 15, 2017

Backport NumPy 1.13.0 fixes to 0.18.X #9137

Merged

NelleV pushed a commit to NelleV/scikit-learn that referenced this pull request Aug 11, 2017

Fix tests on numpy master (scikit-learn#8355)

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numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

paulha pushed a commit to paulha/scikit-learn that referenced this pull request Aug 19, 2017

Fix tests on numpy master (scikit-learn#8355)

de5bcc2

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

maskani-moh pushed a commit to maskani-moh/scikit-learn that referenced this pull request Nov 15, 2017

Fix tests on numpy master (scikit-learn#8355)

7980aa5

numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

lemonlaug pushed a commit to lemonlaug/scikit-learn that referenced this pull request Jan 6, 2021

Fix tests on numpy master (scikit-learn#8355)

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numpy.apply_along_axis has changed behaviour when the function passed in returns a 2d array

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[MRG] Fix tests on numpy master#8355

[MRG] Fix tests on numpy master#8355
jnothman merged 1 commit intoscikit-learn:masterfrom
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