Re: [Numpy-discussion] Matrix Class

On Sat, Feb 14, 2015 at 12:36 PM, wrote:

On Sat, Feb 14, 2015 at 12:05 PM, cjw wrote:

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On 14-Feb-15 11:35 AM, josef.pktd@gmail.com wrote:

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On Wed, Feb 11, 2015 at 4:18 PM, Ryan Nelson wrote:

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Colin,

I currently use Py3.4 and Numpy 1.9.1. However, I built a quick test conda environment with Python2.7 and Numpy 1.7.0, and I get the same:

############ Python 2.7.9 |Continuum Analytics, Inc.| (default, Dec 18 2014,

16:57:52)

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[MSC v .1500 64 bit (AMD64)] Type "copyright", "credits" or "license" for more information.

IPython 2.3.1 -- An enhanced Interactive Python. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://binstar.org ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details.

In [1]: import numpy as np

In [2]: np.__version__ Out[2]: '1.7.0'

In [3]: np.mat([4,'5',6]) Out[3]: matrix([['4', '5', '6']], dtype='|S1')

In [4]: np.mat([4,'5',6], dtype=int) Out[4]: matrix([[4, 5, 6]]) ###############

As to your comment about coordinating with Statsmodels, you should see the links in the thread that Alan posted: http://permalink.gmane.org/gmane.comp.python.numeric.general/56516 http://permalink.gmane.org/gmane.comp.python.numeric.general/56517 Josef's comments at the time seem to echo the issues the devs (and others) have with the matrix class. Maybe things have changed with Statsmodels.

Not changed, we have a strict policy against using np.matrix.

generic efficient versions for linear operators, kronecker or sparse block matrix styly operations would be useful, but I would use array semantics, similar to using dot or linalg functions on ndarrays.

Josef (long reply canceled because I'm writing too much that might only be of tangential interest or has been in some of the matrix discussion before.)

Josef,

Many thanks. I have gained the impression that there is some antipathy to np.matrix, perhaps this is because, as others have suggested, the array doesn't provide an appropriate framework.

It's not directly antipathy, it's cost-benefit analysis.

np.matrix has few advantages, but makes reading and maintaining code much more difficult. Having to watch out for multiplication `*` is a lot of extra work.

Checking shapes and fixing bugs with unexpected dtypes is also a lot of work, but we have large benefits. For a long time the policy in statsmodels was to keep pandas out of the core of functions (i.e. out of the actual calculations) and restrict it to inputs and returns. However, pandas is becoming more popular and can do some things much better than plain numpy, so it is slowly moving inside some of our core calculations. It's still an easy source of bugs, but we do gain something.

Any bits of Pandas that might be good for numpy/scipy to steal? <snip> Chuck