You can also modify that line in sklearn/externals/joblib/pool.py in your
local copy of scikit-learn to include an additional condition:
and a.dtype.kind != 'O'
On 19 August 2014 16:55, Joel Nothman wrote:
> Oh well. I'm not a very experienced monkey-patcher. There may be a better
> way to do i
Oh well. I'm not a very experienced monkey-patcher. There may be a better
way to do it (make sure you apply the monkey patch before importing any
other scikit-learn modules).
On 19 August 2014 16:52, Anders Aagaard wrote:
> It does work with 1 job.
>
> I tried your monkey patch:
> # joblib.Para
It does work with 1 job.
I tried your monkey patch:
# joblib.Parallel
functools.partial(,
max_nbytes=None)
I still get the same error though.
On Tue, Aug 19, 2014 at 8:19 AM, Joel Nothman
wrote:
> I suspect this is a bug in joblib, and that you won't get it with
> n_jobs=1. Joblib employs me
I suspect this is a bug in joblib, and that you won't get it with n_jobs=1.
Joblib employs memmap for inter-process communication if the array is
larger than a fized size:
https://github.com/joblib/joblib/blob/master/joblib/pool.py#L203. It seems
it needs another criterion to check ensure that the
Hello, I am wrapping up my final blogpost and I want to say that this was
an awesome summer of code! It has been a great feeling for me to wake up
daily and contribute to a a software project that is valuable to me.
http://hamzehgsBest,
oc.blogspot.com/2014/08/google-summer-of-code-2014-final-summ
If I understand your question correctly, the answer is yes!
If you want a clearer response, you might clarify what the alternative
hypothesis is to your suggestion.
On 19 August 2014 03:13, ZORAIDA HIDALGO SANCHEZ <
[email protected]> wrote:
> I am using TdidfTransformer on
Hi
I've got a reasonably large dataset I'm trying to do a gridsearch on. If I
feed in a subset of it it works fine, but if I feed in the entire file it
dies with : "Array can't be memory-mapped: Python objects in dtype.". Now I
realize what that's telling me, but I seem to remember building pipeli
On Aug 18, 2014, at 12:15 PM, Olivier Grisel wrote:
> > since it would make the "estimate" and "error" calculation more convenient,
> > right?
>
> I don't understand what you mean "estimate" by "error". Both the model
> parameters, its individual predictions and its cross-validation scores or
On Mon, Aug 18, 2014 at 12:43 PM, Olivier Grisel
wrote:
> 2014-08-18 18:28 GMT+02:00 :
> >
> >
> >
> > On Mon, Aug 18, 2014 at 12:15 PM, Olivier Grisel <
> [email protected]>
> > wrote:
> >>
> >> Le 18 août 2014 16:16, "Sebastian Raschka" a
> écrit
> >> :
> >>
> >>
> >> >
> >> >
> >> > O
I am using TdidfTransformer on documents that I need to classify. In order
to evaluate the model, I need to apply the whole
pipeline(TdidfTransformer, Classifier) to the test dataset. On the
training step, I am using a cross-validation and in each iteration I am
applying tdidf.fit_transform/transfo
2014-08-18 18:28 GMT+02:00 :
>
>
>
> On Mon, Aug 18, 2014 at 12:15 PM, Olivier Grisel
> wrote:
>>
>> Le 18 août 2014 16:16, "Sebastian Raschka" a écrit
>> :
>>
>>
>> >
>> >
>> > On Aug 18, 2014, at 3:46 AM, Olivier Grisel
>> > wrote:
>> >
>> > > But the sklearn.cross_validation.Bootstrap curren
On Mon, Aug 18, 2014 at 12:15 PM, Olivier Grisel
wrote:
> Le 18 août 2014 16:16, "Sebastian Raschka" a écrit
> :
>
> >
> >
> > On Aug 18, 2014, at 3:46 AM, Olivier Grisel
> wrote:
> >
> > > But the sklearn.cross_validation.Bootstrap currently implemented in
> sklearn is a cross validation itera
Le 18 août 2014 16:16, "Sebastian Raschka" a écrit :
>
>
> On Aug 18, 2014, at 3:46 AM, Olivier Grisel
wrote:
>
> > But the sklearn.cross_validation.Bootstrap currently implemented in
sklearn is a cross validation iterator, not a generic resampling method to
estimate variance or confidence interv
On Aug 18, 2014, at 3:46 AM, Olivier Grisel wrote:
> But the sklearn.cross_validation.Bootstrap currently implemented in sklearn
> is a cross validation iterator, not a generic resampling method to estimate
> variance or confidence intervals. Don't be mislead by the name. If we chose
> to dep
2014-08-17 7:26 GMT+02:00 Amey :
> Zero-one classification Loss as : 0.0426
>
> I would like somebody to help me interpret this in terms of benchmarks :
> http://yann.lecun.com/exdb/mnist/
>
> What is the test error % metric given on the link corresponding to the
> metrics I have computed?
It's ze
thanks, very informative.
On Mon, Aug 18, 2014 at 1:08 PM, Olivier Grisel
wrote:
> Le 18 août 2014 09:57, "Arman Eshaghi" a écrit :
>
> >
> > thanks for the discussion. Could you please what the right way of using
> boostraping for confidence interval calculation (or other statistics) would
>
Le 18 août 2014 09:57, "Arman Eshaghi" a écrit :
>
> thanks for the discussion. Could you please what the right way of using
boostraping for confidence interval calculation (or other statistics) would
be? I mean what would you do to get, as olivier said a "generic resampling
method to estimate var
thanks for the discussion. Could you please what the right way of using
boostraping for confidence interval calculation (or other statistics) would
be? I mean what would you do to get, as olivier said a "generic resampling
method to estimate variance or confidence intervals"? I'm under the
impressi
But the sklearn.cross_validation.Bootstrap currently implemented in sklearn
is a cross validation iterator, not a generic resampling method to estimate
variance or confidence intervals. Don't be mislead by the name. If we chose
to deprecate and then remove this class, it's precisely because it caus
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