2014-09-08 7:46 GMT-07:00 Yaroslav Halchenko <[email protected]>: > > It is a bit early to say about Debian servers conclusively -- I have just > uploaded to Debian proper, so they have been rebuilt across > architectures: > > https://buildd.debian.org/status/package.php?p=scikit-learn&suite=unstable > and armel seems to whine a bit: > > ====================================================================== > FAIL: sklearn.feature_extraction.tests.test_text.test_tfidf_no_smoothing > ---------------------------------------------------------------------- > Traceback (most recent call last): > File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest > self.test(*self.arg) > File > "/«PKGBUILDDIR»/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/tests/test_text.py", > line 361, in test_tfidf_no_smoothing > tr.fit_transform, X).toarray() > File > "/«PKGBUILDDIR»/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/utils/testing.py", > line 211, in assert_warns_message > % func.__name__) > AssertionError: No warning raised when calling fit_transform > > ====================================================================== > FAIL: sklearn.metrics.tests.test_metrics.test_precision_recall_curve_toydata > ---------------------------------------------------------------------- > Traceback (most recent call last): > File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest > self.test(*self.arg) > File > "/«PKGBUILDDIR»/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/metrics/tests/test_metrics.py", > line 1224, in test_precision_recall_curve_toydata > assert_raises(Exception, precision_recall_curve, y_true, y_score) > AssertionError: Exception not raised > > ---------------------------------------------------------------------- > Ran 2753 tests in 1756.468s
Interesting. It's great to have people test scikit-learn on the ARM platform. What is the armel architecture? how it compares to arm64 and armhf? Why the does arm64 build does not work for scikit-learn? Is there a missing dependency on that platform? Apparently those two errors on armel are caused by the fact that numpy does not raise an error when dividing by zero on this platform. I created an issue here: https://github.com/scikit-learn/scikit-learn/issues/3649 It might be a bug in numpy or a fundamental limitation on armel. In the latter case we should update our code to take that into account. Do you know how to detect that we are running on armel from Python? e.g. what are the values of: from platform import processor, machine, architecture print(processor()) print(machine()) print(architecture()) -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
