On 10/22/2013 09:46 PM, ChungHung Liu wrote:
> I read following links
>
>
> http://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features
>
> http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html
>
> It seems that I should
On 10/22/2013 09:40 PM, Ralf Gunter wrote:
>
> However, I'm new to all of this and so have a related but potentially
> dumb question.
Don't worry about it ;)
I think there are two parts to the answer:
GridSearchCV has a parameter "refit" which is True by default, which
means that after taining,
I read following links
http://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features
http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html
It seems that I should use DictVectorizer, but
http://www.mail-archive.com
2013/10/22 Andreas Mueller
> I think the answer is that the results of RMSE are (somewhat
> counterintuitively) always negative when reporting the results of
> GridSearchCV.
> The "greater_is_better" actually just flips the sign.
> The reason it does that is that GridSearchCV always tries to max
I think the answer is that the results of RMSE are (somewhat
counterintuitively) always negative when reporting the results of
GridSearchCV.
The "greater_is_better" actually just flips the sign.
The reason it does that is that GridSearchCV always tries to maximize
the score.
I am not sure this
Hi Ralf.
Sorry I'm tired I didn't see the attachment, sorry.
Andy
On 10/22/2013 08:48 PM, Ralf Gunter wrote:
Hi Andreas,
I'm not sure what you mean by "more comprehensive"; the gist on the
first message should reproduce the problem -- if not, then it might be
something on my local configurat
Hi Andreas,
I'm not sure what you mean by "more comprehensive"; the gist on the first
message should reproduce the problem -- if not, then it might be something
on my local configuration (python, numpy, etc). The script is exactly the
same one I'm using in "production", just with a much bigger dat
Hi Ralf.
Can you give a more comprehensive gist maybe? https://gist.github.com/
My first intuition would be that you are in fact using the r2 score, not
the MSE, when outputting these numbers.
Cheers,
Andy
On 10/22/2013 07:20 PM, Ralf Gunter wrote:
Hello,
I'm testing a few regression algor
On 10/22/2013 03:54 PM, Ankit Agrawal wrote:
> Hi Jim,
>
> What Joe said is correct when you want to label/classify images,
> since classifying images by trying to find similarity of the test image with
> the training images on pixel level would not work even if there is some
> ordinary
I would also suggest the book "computer vision" by Richard Szeliski.
For you classification problem it really depends on what you want as
output and what the statistics of the data are.
If I understand you correctly, you want a prediction for each label. If
your images are somewhat natural, the
Hello,
I'm testing a few regression algorithms to map ndarrays of eigenvalues to
floats, using StratifieldKFolds + GridSearchCV for cross-validation &
hyperparameter estimation using some code borrowed from [1]. Although
GridSearchCV appears to be working as advertised (i.e. the
"best_estimator_"
Hi Jim,
What Joe said is correct when you want to label/classify
images, since classifying images by trying to find similarity of the
test image with the training images on pixel level would not work even
if there is some ordinary geometric transform like scaling or rotation
or Intensity c
Dear all,
I promised some time ago to write a guideline for using C integer
types in Cython code. Here's a start; currently on the wiki instead of
in a PR because of the rough state.
https://github.com/scikit-learn/scikit-learn/wiki/C-integer-types:-the-missing-manual
Regards,
Lars
On 10/22/2013 3:32 PM, Joseph Jacobs wrote:
The best book I have come across for image processing/vision + machine
learning is one by Simon Prince. You can download the book from his
website (http://computervisionmodels.com/). Chapter 13 gives a good
intro to feature extraction.
OK, great --
On 10/22/2013 3:32 PM, Joseph Jacobs wrote:
The best book I have come across for image processing/vision + machine
learning is one by Simon Prince. You can download the book from his
website (http://computervisionmodels.com/). Chapter 13 gives a good
intro to feature extraction.
Joe
On 22 Oc
The best book I have come across for image processing/vision + machine learning
is one by Simon Prince. You can download the book from his website
(http://computervisionmodels.com/). Chapter 13 gives a good intro to feature
extraction.
Joe
On 22 Oct 2013, at 22:27, jim vickroy wrote:
> On 10/
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification pixel-wise
would not be ideal (please correct me if I am wrong). I think the
better way would be to perform some sort of feature extraction on the
image (eg. SIFT, SURF, HOG, LBP a
Hey Jim,
From my (non-expert) perspective, performing classification pixel-wise would
not be ideal (please correct me if I am wrong). I think the better way would be
to perform some sort of feature extraction on the image (eg. SIFT, SURF, HOG,
LBP and many, many more...checkout scikit-image or
Hi,
Apologies if this is an inappropriate question for this forum.
I have a collection of (1024x1024) mono-chromatic images in which each
pixel is to be labeled as 1 of several categories (e.g., 10).
Furthermore, each mono-chromatic image was captured through several
filters (e.g., 5).
My
I stumbled across an implementation of this a while back which used numba -
maybe it will be helpful for comparison?
http://nbviewer.ipython.org/3407544
Kyle
On Tue, Oct 22, 2013 at 10:35 AM, Gmail wrote:
> Okay very cool! That gives me a plan of attack going forward. I'll try
> it on the
Okay very cool! That gives me a plan of attack going forward. I'll try it on
the digits to start.
Thank you guys for all of the guidance!
Sent from my iPhone
> On Oct 22, 2013, at 12:06 AM, Olivier Grisel wrote:
>
> I would rather not add a new model class if there is no way to
> demonstra
Hm, maybe I'm doing something wrong but I'm still getting the error:
ValueError: operands could not be broadcast together with shapes (3) (6)
I am using 0.14.1.
Full stacktrace:
Traceback (most recent call last):
File "GB_problem.py", line 46, in
main()
File "GB_problem.py", line 43, in
Ok, below is the adaptor that will work. The code requires that the output
of predict is 2d.
Thanks for the test-case.
best,
Peter
class Adaptor(object):
def __init__(self, est):
self.est = est
def predict(self, X):
return self.est.predict_proba(X)[:, np.newaxis]
de
Right, I thought you were using the multi-class loss function.
Please send me a testcase so that I can investigate the issue.
thanks,
Peter
2013/10/22 Attila Balogh
> Hi Peter,
>
> thanks for your answer. I have tried this before also, and the problem is
> that in this case I get
> ValueErro
Hi Peter,
thanks for your answer. I have tried this before also, and the problem is
that in this case I get
ValueError: operands could not be broadcast together with shapes (74)
(148), because the y array is raveled and it has shape (74,2).
Do you need a self containing testcase which reproduces
Hi Attila,
please use the following adaptor::
def __init__(self, est):
self.est = est
def predict(self, X):
return self.est.predict_proba(X)
def fit(self, X, y):
self.est.fit(X, y)
The one in the stackoverflow question returns an array of shape
(n_samples,) bu
Hi all,
first of all thanks for all the developers for working on scikit-learn, it
is a wonderful library.
I am struggling for a while now with the following problem:
Trying to use GBR with LR as a BaseEstimator, and I'm getting the following
error:
File "main.py", line 110, in main
score =
I would rather not add a new model class if there is no way to
demonstrate that they can solve a non-synthetic task in an example.
I would rather not have the scikit-learn code base turn into a museum
of useless algorithms.
So +1 for inclusion of a SOM model if it can lead to interesting
results
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