Re: [Numpy-discussion] slicing with boolean in numpy master

On Wed, Jun 26, 2013 at 1:56 PM, wrote:

On Wed, Jun 26, 2013 at 1:16 PM, Sebastian Berg wrote:

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On Wed, 2013-06-26 at 12:52 -0400, josef.pktd@gmail.com wrote:

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On Wed, Jun 26, 2013 at 12:01 PM, Sebastian Berg wrote:

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On Wed, 2013-06-26 at 11:30 -0400, josef.pktd@gmail.com wrote:

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Is there a change in the behavior of boolean slicing in current master?

Yes, but I think this is probably a bug in statsmodel. I would expect you should be using "..." and not ":" here, because ":" requires the dimension to actually exist, and I *expect* that your mask actually has the same dimensionality as the array itself.

I.e.:

x = np.arange(8).reshape(4,4) mask = np.ones_like(x) x[mask,:] # should NOT work, but this was buggy before current master.

Why should this not work?

that's fine, I didn't see that mask is 2d

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How do you select rows that don't have nans in them?

mask = np.isfinite(x).all(1) x[mask, :]

or columns with switched axis.

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x[mask[:, None]] array([ 1., 1., 1., 1.]) ???

I assume you wanted to write x[:, mask] there. Since boolean masks do *not* broadcast, instead they eat away as many dimensions as they have.

Maybe these examples will help explain why the new behaviour is correct:

x = np.random.random((3,3)) mask = np.ones((3,3), dtype=np.bool_)

# Check slices: x[:,:] # OK, result 2-d x[:,:,:] # too many indices.

# replace first dimension with the mask: x[mask[:,0], :] # OK, result 2-d

Good, if this still works, then I go hunting in our code for the "too many indices"

Was easy to find given the clues, even without being able to get the error messages form numpy > 1.7.1 Thanks, Josef

Thanks for the clarification,

Josef

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x[mask[:,0], :, :] # too many indices.

# replace *both* slices with a (single) mask: x[mask] # OK, result 1-d (i.e. there nothing more then the mask) x[mask, :] # too many indices! But it still works in 1.7.

# In fact we can make this absurd: x[mask, :, :, :, :, :] # Too many slices even without the mask!

The last case used to work in pre-master due to a bug.

- Sebastian

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(I have to check the usage in statsmodels, but I thought this is standard.)

Josef

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- Sebastian

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If not I have to find another candidate in numpy master.

(py27d) E:\Josef\testing\tox\py27d\Scripts>python Python 2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information.

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>> import numpy as np >> np.__version__ '1.7.1' >> x = np.ones((5,3)) >> mask = np.arange(5) < 4 >> x[mask, :] array([[ 1., 1., 1.], [ 1., 1., 1.], [ 1., 1., 1.], [ 1., 1., 1.]])

We get errors like the following when running the statsmodels tests with a current or recent numpy master, but not with numpy 1.7.1

====================================================================== ERROR: Failure: IndexError (too many indices) ---------------------------------------------------------------------- Traceback (most recent call last): File "/Users/tom/python2.7/lib/python2.7/site-packages/nose/loader.py", line 518, in makeTest return self._makeTest(obj, parent) File "/Users/tom/python2.7/lib/python2.7/site-packages/nose/loader.py", line 577, in _makeTest return MethodTestCase(obj) File "/Users/tom/python2.7/lib/python2.7/site-packages/nose/case.py", line 345, in __init__ self.inst = self.cls() File "/Users/tom/python2.7/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-macosx-10.8-x86_64.egg/statsmodels/emplike/tests/test_aft.py", line 19, in __init__ super(Test_AFTModel, self).__init__() File "/Users/tom/python2.7/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-macosx-10.8-x86_64.egg/statsmodels/emplike/tests/test_aft.py", line 12, in __init__ self.mod1 = sm.emplike.emplikeAFT(endog, exog, data.censors) File "/Users/tom/python2.7/lib/python2.7/site-packages/statsmodels-0.5.0-py2.7-macosx-10.8-x86_64.egg/statsmodels/emplike/aft_el.py", line 248, in __init__ self.uncens_endog = self.endog[np.bool_(self.censors), :].\ IndexError: too many indices

Thanks,

Josef _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

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