All your remarks are valid, but what it really boils down to is that general
purpose persistence is hard. Given well-defined objects, good persistence
scheme can be developped, but than you have to worry about transition that code
as the data models of the objects evolve. Hard again.
Gael
On Tue, Jan 31, 2012 at 10:57:50PM +0100, Andreas wrote:
> That might be a stupid question, but what are the kinds of things that
> break the models?
> I imagine it would be things like renaming and removing attributes. What
> else is there?
>
> Having code that says "Attribute A was called B in
On Tue, Jan 31, 2012 at 20:44, Jacob VanderPlas
wrote:
> Hello,
> I've been working on applying Gaussian Processes to noisy input data.
> The scikit-learn docs are not especially helpful on this topic, but
> after reading through some of the references and scanning the code, I
> found that the key
On 1 February 2012 08:57, Andreas wrote:
> That might be a stupid question, but what are the kinds of things that
> break the models?
> I imagine it would be things like renaming and removing attributes. What
> else is there?
>
> Having code that says "Attribute A was called B in the last version
That might be a stupid question, but what are the kinds of things that
break the models?
I imagine it would be things like renaming and removing attributes. What
else is there?
Having code that says "Attribute A was called B in the last version and
C in the one before that"
seems not desirable
On 1 February 2012 06:19, Olivier Grisel wrote:
> 2012/1/31 Jeff Farris :
> > I'm currently using pickle to persist models (e.g. SVC). After
> upgrading
> > sklearn, these pickled models from a previous version of sklearn don't
> tend
> > to work and then I need to retrain. Is there some versi
Hello,
I've been working on applying Gaussian Processes to noisy input data.
The scikit-learn docs are not especially helpful on this topic, but
after reading through some of the references and scanning the code, I
found that the keyword 'nugget' in the initializer of GaussianProcess
does esse
2012/1/31 Jeff Farris :
> I'm currently using pickle to persist models (e.g. SVC). After upgrading
> sklearn, these pickled models from a previous version of sklearn don't tend
> to work and then I need to retrain. Is there some version independent way
> of saving models (e.g. libsvm model form
On Tue, Jan 31, 2012 at 02:08:21PM -0500, Jeff Farris wrote:
> I'm currently using pickle to persist models (e.g. SVC). After upgrading
> sklearn, these pickled models from a previous version of sklearn don't tend
> to work and then I need to retrain. Is there some version independent way
> of s
I'm currently using pickle to persist models (e.g. SVC). After upgrading
sklearn, these pickled models from a previous version of sklearn don't tend
to work and then I need to retrain. Is there some version independent way
of saving models (e.g. libsvm model format) or other recommendations on
Hi Olivier, all
After playing around some more, I may have a partial solution. But I
would appreciate it if you could help me check some assumptions.
First, I realized that my original PCA did not make much sense. What
I want to do is reduce the feature dimensions in my classification,
but keep
On Tue, Jan 31, 2012 at 05:05:53PM +0100, Lars Buitinck wrote:
> I don't have a NumPy 2 installation and I haven't followed its
> development closely. Could you open an issue for this?
https://github.com/scikit-learn/scikit-learn/issues/600
I'll do better: I'll try to fix the problem when I get t
2012/1/23 Gael Varoquaux :
> On Sat, Jan 21, 2012 at 03:49:24PM +0100, Lars Buitinck wrote:
>> This is very strange; I get no such error.
>
> I have numpy 2 (dev). The rules for strides in array creation have
> changed.
I don't have a NumPy 2 installation and I haven't followed its
development clo
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