Looking at the code, fit_intercept=False will unfortunately not prevent
_randomized_lasso from centering the data. I think this should be
considered an inconsistency (whether it be the reason or not for the
differences you observe)
Michael
On Tuesday, July 8, 2014, Michael Eickenberg <michael.eickenb...@gmail.com>
wrote:
> The RandomizedLasso object fits an intercept by default, ie it subtracts
> the means of the columns of your design X. This can make it rank deficient.
> You can try setting fit_intercept to false, but there may very likely be
> other processing steps that make the two differ.
>
> Michael
>
> On Tuesday, July 8, 2014, Luca Puggini <lucapug...@gmail.com
> <javascript:_e(%7B%7D,'cvml','lucapug...@gmail.com');>> wrote:
>
>> I did not but according to the theory all the variables should be selected
>> with probability one if p<n.
>>
>> This happens in lasso_stability_path but not in randomizedlasso
>>
>> The randomness should not have effect in this case
>>
>> Luca
>>
>>
>> Did you fix the random number generator using the keyword random_state= ?
>> Otherwise this may vary statistically.
>>
>> Michael
>>
>>
>> On Tue, Jul 8, 2014 at 6:11 PM, Luca Puggini <lucapuggio@...> wrote:
>>
>> > Hi,
>> > RandomizedLasso and lasso_stability path should return the same results if
>> > used on the same data. This does not happen when the number of variables
>> > is smaller than the number of samples
>> > (at least this is the situation that I have tried).
>> > Accoring to the theory the correct result should be the one returned by
>> > lasso_stability_path i.e. all the score equal to 1 in case p<n
>> >
>> > alphas, scores = lasso_stability_path(x, y, scaling=0.3)
>> > r = RandomizedLasso(alpha=alphas, scaling=0.3).fit(x,y)
>> >
>> >
>> > Let me know.
>> > Thanks!
>>
>>
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