PyStruct uses
minimum_spanning_tree
check_random_state
gen_even_slices
shuffle
check_arrays (this one is just for a backport of train_test_split, which
I probably don't need any more)
Cheers,
Andy
On 09/15/2014 03:40 PM, Mathieu Blondel wrote:
lightning is using the following utils:
- check_random_state
- safe_sparse_dot
- shuffle
- safe_mask
- sklearn.utils.testing.*
The latter is not big deal but I like importing assertions from the
same place.
On a second thought, importing all public utils in __init__.py might
quickly become messy.
I like Andy's propsal of having no imports at all in __init__.py.
I suggest we move everything in __init__.py to a suitable submodule.
For the utils which we make private, I suggest we make a deprecation
cycle for safety.
A lot of utils will likely become private so we could use a decorator
@deprecated_util to automate the task.
Mathieu
On Sat, Sep 13, 2014 at 11:22 AM, Mathieu Blondel
<math...@mblondel.org <mailto:math...@mblondel.org>> wrote:
We should survey what other packages use. I'll have a look at what
lightning uses later.
Mathieu
On Sat, Sep 13, 2014 at 2:23 AM, Andy <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
+1 of cleaning up __init__.py (maybe no implementations at all?)
+1 for making private methods start with underscore (which
will break everything ^^)
Also we need to add utils to the References then.
No idea how to decide what should be public and what not, though.
On 09/08/2014 04:01 PM, Mathieu Blondel wrote:
Maintaining backward compatibility for a subset of the utils
only means that from now on we will have to decide whether an
util deserves to be public or not. While we are at it, I
would rather make it explicit and use an underscore prefix
for private utils and no prefix for public utils.
This can be combined with your proposal of importing all
public utils in__init__.py but __init__.py will have to be
cleaned up as it's kind of messy right now. One concern is
that the usefulness of an util is very subjective. I would
like also to argue that utils can be important for the
consistency across scikit-learn compatible projects. For
example, the utils.validation module can be useful for doing
validation in a consistent manner in all projects.
Mathieu
On Mon, Sep 8, 2014 at 10:00 PM, Gael Varoquaux
<gael.varoqu...@normalesup.org
<mailto:gael.varoqu...@normalesup.org>> wrote:
Hi people,
So far we have had no policy of backward compatibility in
sklearn/utils.
However, some of the utilities there are very useful for
packages that
want to extend scikit-learn's functionality, such as
seqlearn,
sklearn-theano, nilearn...
The latest set of changes in the validation utilities
have brought in a
great cleanup of these utilities. However as a result it
has also broken
nilearn. This isn't a big deal, as nilearn isn't
released, however I
think that we need to think about our backward
compatibility strategy in
sklearn/utils.
Here is my proposal: we need to apply standard
deprecation cycle for
everything in sklearn/utils/__init__.py and all the rest
is off limits.
What do people think?
Gaël
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