It will be fixed in version 0.15, but that is not yet released. So you can
use the development version for now (see
http://scikit-learn.org/stable/developers/index.html#git-repo).
On 14 March 2014 11:15, Alexey Sorokin <[email protected]> wrote:
> Nice, thanks!
>
> What is the version number where this is fixed?
>
> Alexey.
>
>
> On Fri, Mar 14, 2014 at 12:03 AM, Joel Nothman <[email protected]>wrote:
>
>> Hi Alexey,
>>
>> That part of the code has since been rewritten, and whether intentional
>> or not, this is now fixed.
>>
>> - Joel
>>
>>
>>
>> On 14 March 2014 10:35, Alexey Sorokin <[email protected]>wrote:
>>
>>> Hello,
>>>
>>>
>>>
>>> I'm quite new to scikit-learn, so please forgive me if I'm not doing it
>>> right. I'm using version 0.14.
>>>
>>>
>>>
>>> I'm using LassoCV and passing my own vector of alphas. What seems to be
>>> happening is that somewhere inside the LinearModelCV.fit() function, the
>>> alphas vector is copied and then sorted in a descending order. However,
>>> when the optimal alpha index is found the alpha is then taken from the
>>> original vector!!! Is this a bug or a feature?
>>>
>>>
>>>
>>> Example:
>>>
>>> X = [[1, 2], [-1, 2], [1, -2], [-2, 0], [0, -2]]
>>>
>>> Y = [4, 5, 6, 7, 10]
>>>
>>> regr = linear_model.LassoCV(alphas = [0.0001, 0.001], fit_intercept =
>>> False)
>>>
>>> regr.fit(X, Y)
>>>
>>> print "lasso r2", regr.score(X, Y), "alpha", regr.alpha_
>>>
>>> regr = linear_model.LassoCV(alphas = [0.001, 0.0001], fit_intercept =
>>> False)
>>>
>>> regr.fit(X, Y)
>>>
>>> print "lasso r2", regr.score(X, Y), "alpha", regr.alpha_
>>>
>>> outputs
>>>
>>> lasso r2 -8.27498253267 alpha 0.0001
>>>
>>> lasso r2 -8.27498282478 alpha 0.001
>>>
>>>
>>>
>>> Thanks,
>>>
>>> Alexey Sorokin.
>>>
>>>
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>>
>>
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>
>
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> "Graph Databases" is the definitive new guide to graph databases and their
> applications. Written by three acclaimed leaders in the field,
> this first edition is now available. Download your free book today!
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