Hmm... and on windows 7 ~ (do you want specific build details?)
The ExtraForestClassifier with 10 trees (the default) crashes and gives
this:
http://i.imgur.com/7hLyM.png
.. don't think this happened on ubuntu 12.10....?
On 3 December 2012 00:41, Ronnie Ghose <[email protected]> wrote:
> hmm interesting it seems that using only low level svms it worked this
> time.. but broke on LabelSpreading. Also please uncomment the higher degree
> SVM's I think those are the ones that tend to crash. let me test again
> also. ... and I forgot to use ones with the poly kernel ... I think that's
> the problem
>
> ---------------
>
> working on DecisionTreeClassifier
> DecisionTreeClassifier difference: [ 0.01] percent diff: [ 0.11890606]
> working on SVM:sigmoid:3
> SVM:sigmoid:3 difference: [ 0.41] percent diff: [ 4.87514863]
> working on SVM:rbf
> SVM:rbf difference: [ 0.41] percent diff: [ 4.87514863]
> working on SVM:sigmoid:4
> SVM:sigmoid:4 difference: [ 0.41] percent diff: [ 4.87514863]
> working on LASSO:0.1
> LASSO:0.1 difference: 0.247515523301 percent diff: 2.94310967064
> working on NaiveBayes
> NaiveBayes difference: [ 0.11] percent diff: [ 1.30796671]
> working on DecisionTreeRegressor
> DecisionTreeRegressor difference: [ 0.01] percent diff: [ 0.11890606]
> working on LabelSpreading:rbf
>
> Traceback (most recent call last):
> File
> "C:\Users\Shomiron\Documents\GitHub\Sandbox\Misc\Allthemodels\sim_data.py",
> line 85, in <module>
> serial()
> File
> "C:\Users\Shomiron\Documents\GitHub\Sandbox\Misc\Allthemodels\sim_data.py",
> line 81, in serial
> e.fit(X,Y)
> File
> "C:\Python27\lib\site-packages\sklearn\semi_supervised\label_propagation.py",
> line 211, in fit
> graph_matrix = self._build_graph()
> File
> "C:\Python27\lib\site-packages\sklearn\semi_supervised\label_propagation.py",
> line 383, in _build_graph
> affinity_matrix = self._get_kernel(self.X_)
> File
> "C:\Python27\lib\site-packages\sklearn\semi_supervised\label_propagation.py",
> line 115, in _get_kernel
> return rbf_kernel(X, X, gamma=self.gamma)
> File "C:\Python27\lib\site-packages\sklearn\metrics\pairwise.py", line
> 347, in rbf_kernel
> K = euclidean_distances(X, Y, squared=True)
> File "C:\Python27\lib\site-packages\sklearn\metrics\pairwise.py", line
> 174, in euclidean_distances
> distances = safe_sparse_dot(X, Y.T, dense_output=True)
> File "C:\Python27\lib\site-packages\sklearn\utils\extmath.py", line 78,
> in safe_sparse_dot
> return np.dot(a, b)
> MemoryError
> >>>
>
>
> On 3 December 2012 00:36, Ronnie Ghose <[email protected]> wrote:
>
>> oh hmm so if anyone wants to suggest additions to the script i'm all for
>> it. it's a horrible mess :) and i didn't have any success in running the
>> classifiers in parallel. ironically due to what i thought was an error
>> caused by it (which i now find to be the svm). ... besides things like
>> feature scaling ~ this is just a test one.
>>
>> also anyone know any way to speed it up :P ? cross validations assume
>> you're running multiple in different threads? .... no?
>>
>> run it overnight, you should see a seg fault on one of the svms when you
>> wake up
>>
>> version: 12.1
>>
>> https://github.com/RONNCC/Sandbox/tree/master/Misc/Allthemodels just run
>> sim_data.py
>>
>> -- backupish ->
>>
>> Script: http://pastie.org/5465841
>> data: ok so I don't know how to make this better so here's the whole
>> thing, the subsets don't always generate the error and as i try more it of
>> course grows gigantic ._.
>>
>>
>> thanks
>>
>>
>>
>>
>>
>> On 2 December 2012 04:22, Mathieu Blondel <[email protected]> wrote:
>>
>>>
>>> On Sun, Dec 2, 2012 at 5:44 PM, Ronnie Ghose <[email protected]>wrote:
>>>
>>>> So somehow while running an SVM model i'm getting a nice seg fault :(.
>>>>
>>>> http://pastie.org/5465679 is the last 50 lines of the strace file.....
>>>>
>>>> in specific it seems to be SVM with poly setting and degree=10. I'm
>>>> running it in 20000 numbers... then I tried to lower it to 1000 and still
>>>> got seg fault..
>>>>
>>>
>>> To help us reproduce your problem, it would be nice to give us:
>>> - the version of scikit-learn you're using
>>> - the script you're using
>>> - the data you're using (ideally a minimalistic subset that triggers the
>>> bug)
>>>
>>> Thanks,
>>> Mathieu
>>>
>>>
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>>
>
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