Hi Joel,
Sorry for the late reply. That solved the problem. Thanks for the tip,
I did not notice the parameter should be prefixed (which is kind of
obvious).
Thanks a lot,
José
José Guilherme
On Tue, Jun 9, 2015 at 1:38 PM, Joel Nothman wrote:
> Until sample_weight is directly supported in Pi
Until sample_weight is directly supported in Pipeline, you need to prefix
`sample_weight` by the step name with '__'. So for Pipeline([('a', A()),
('b', B())] use fit_params={'a__sample_weight': sample_weight,
'b__sample_weight': sample_weight} or similar.
HTH
On 10 June 2015 at 03:57, José Guilh
Hi Andy,
Thanks for your reply. The full traceback is below, weights.shape and
the training data shape are:
(773,)
(773, 82)
I weas using a ExtraTreeClassifier but the same thing happens with an
SVC. It doesn't seem to be an estimator-specific issue.
"""
Traceback (most recent call last):
Fi
Hi Jose.
That should work.
Can you provide the full traceback?
Also can you provide weights.shape?
Andy
On 06/08/2015 08:49 PM, José Guilherme Camargo de Souza wrote:
> Hi all,
>
> I am having a different issue when trying to use sample_weights with
> RandomizedSearchCV:
>
> weights = np.array(ca
Hi all,
I am having a different issue when trying to use sample_weights with
RandomizedSearchCV:
weights = np.array(calculate_weighting(y_train))
search = RandomizedSearchCV(estimator, param_dist, n_iter=n_iter,
scoring="accuracy",
n_jobs=-1, iid=True, cv=
Dear Joel,
Yes. After updating the version of Scikit-learn to 0.15b2 the problem was
solved.
Thanks,
Hamed
On Tue, Jul 8, 2014 at 2:51 PM, Joel Nothman wrote:
> This shouldn't be the case, though it's not altogether well-documented.
> According to
> https://github.com/scikit-learn/scikit-lea
This shouldn't be the case, though it's not altogether well-documented.
According to
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_validation.py#L1225,
if the fit_params value has the same length as the samples, it should be
similarly indexed.
So this would be a bug ... if
It looks like fit_params are passed wholesale to the classifier being fit -
this means the sample weights will be a different size than the fold of (X,
y) fed to the classifier (since the weights aren't getting KFolded...).
Unfortunately I do not see a way to accomodate for this currently -
sample_
Dear all,
I am using Scikit-Learn library and I want to weight all training samples
(according to unbalanced data). According to the tutorial and what I found
in the web, I should use this method:
search = RandomizedSearchCV(estimator, param_distributions,
n_iter=args.iterations, scoring=mae_scor
