Firstly, a note that I've added that example to the doctest on my branch,
with some extensions to show selecting over parameter values and grouping
over named fields (e.g. identifying the 'C' with the best result per
'degree').
I think hyperopt's use of mongodb (an alternatively) sounds a lot like
2013/6/9 Joel Nothman :
> Again, it's probably over the top, but I think it's a useful interface
> (prototyped at
> https://github.com/jnothman/scikit-learn/tree/search_results):
>
from __future__ import print_function
from sklearn.grid_search import GridSearchCV
from sklearn.dataset
2013/6/9 Olivier Grisel :
>
>> Is this crazy, or does it do exactly what we want? or both? And how does it
>> not meet the needs of your wishlist, Olivier (except where the number of
>> folds differ)?
>
> Interesting but I am not sure I understand it all. Can you give an
> example of a typical seri
2013/6/9 Joel Nothman :
> Thanks, Olivier. Those are some interesting use-cases:
>
>> A- Fault tolerance and handling missing results caused by evaluation
>> errors
>
> I don't think this affects the output format, except where we can actually
> get partial results for a fold, or if we want to repo
Again, it's probably over the top, but I think it's a useful interface
(prototyped at https://github.com/jnothman/scikit-learn/tree/search_results
):
>>> from __future__ import print_function
>>> from sklearn.grid_search import GridSearchCV
>>> from sklearn.datasets import load_iris
>>> from sklea
On Sun, Jun 9, 2013 at 12:38 PM, Joel Nothman
wrote:
>
> This may be getting into crazy land, and certainly close to reimplementing
> Pandas for the 2d case, or recarrays with benefits, but: imagine we had a
> SearchResult object with:
> * attributes like fold_test_score, fold_train_score, fold_tr
Thanks, Olivier. Those are some interesting use-cases:
> A- Fault tolerance and handling missing results caused by evaluation
errors
I don't think this affects the output format, except where we can actually
get partial results for a fold, or if we want to report successful folds
and ignore other
But where it is the case, an index into the results (so that you can use
np.asarray(results)[grid.build_index()] in the desired manner) is possible.
https://github.com/scikit-learn/scikit-learn/pull/1842
On the other hand, as long as you can get an array of parameter values for
each parameter name
> It would be great if there was a way to access the parameter search
> results as a numpy ndarray, with one axis for each parameter and one
> additional axis for the cross-validation folds. This would make it easy
> to visualise the grid search results, compute the mean, median or
> variance for e
On 06/07/2013 03:13 PM, Romaniuk, Michal wrote:
> It would be great if there was a way to access the parameter search results
> as a numpy ndarray, with one axis for each parameter and one additional axis
> for the cross-validation folds. This would make it easy to visualise the grid
> search re
It would be great if there was a way to access the parameter search results as
a numpy ndarray, with one axis for each parameter and one additional axis for
the cross-validation folds. This would make it easy to visualise the grid
search results, compute the mean, median or variance for each gri
TL;DNR: parameter search results datastructure choice should
anticipate new use-cases
Thanks Joel for the detailed analysis.
I the current situation I think I my-self I like:
5. many attributes, each an array, on a custom results object
This makes it possible to write a `__repr__` method on tha
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