I see. But in my case I have things like 

for i in xrange(0, num_experiments):
   estimators =  # some pipeline
  etc... 

so estimators should be "re-created" everytime. 

In any case, I recently changed my code and I am no longer using lists. 

With scalar numbers I think it is working and  I see the correct number all the 
time and from both __init__ and fit(). 

I'm not sure if sklearn is supposed to support lists as parameters. But for now 
I'm no longer using lists . 

Thank you, 


-----Original Message-----
From: Andy [mailto:t3k...@gmail.com] 
Sent: Tuesday, February 17, 2015 6:42 PM
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] custom regressor keeps failing


On 02/16/2015 03:58 PM, Vlad Niculae wrote:
>
>   
>> 1)      Except for the first iteration of grid search, all other times the 
>> lists I print from within __init__() function are None
> I’m not 100% sure but I think this is expected. You are only printing things 
> from `__init__`, but grid search might end up calling `set_params` on the 
> same object, rather than building a new one (depends on whether n_jobs > 1?). 
> Your estimator should support this.
This is what always happens, and that is the whole point of ``set_params``.
After all, you are passing an object, not a class, and you could have other 
attributes set.
It will always clone the estimator and do "set_params" on it, independent of 
n_jobs.

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