Yes, but what is interesting is why is it not showing the warning for other labels? Why only 3?<br><br>Thanks,<br>Geethmala<br><br><div class="gmail_quote">On Tue, Feb 2, 2010 at 2:19 PM, Matthias Ekman <span dir="ltr"><<a href="mailto:Matthias.Ekman@nf.mpg.de">Matthias.Ekman@nf.mpg.de</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Hi,<br>
<br>
i assume you already tracked the problem, right? ;-) ... since there a<br>
no samples (of class 3) in chunk, 0, 4, 5... exactly what<br>
<div class="im"><br>
>>>> Classifier LinearCSVMC(kernel_type='linear', svm_impl='C_SVC')<br>
>>>> wasn't trained to classify labels Set([3.0]) present in testing<br>
>>>> dataset. Make sure that you have not mixed order/names of the<br>
>>>> arguments anywhere<br>
<br>
</div>says.<br>
<br>
cheers,<br>
<font color="#888888"> Matthias<br>
</font><div><div></div><div class="h5"><br>
Geethmala wrote:<br>
> Here you go,<br>
><br>
> Dataset / float32 153 x 40656<br>
> uniq: 8 chunks 5 labels<br>
> stats: mean=-0.0255406 std=0.994264 var=0.98856 min=-6.94855 max=6.71638<br>
><br>
> Counts of labels in each chunk:<br>
> chunks\labels 1.0 2.0 3.0 4.0 5.0<br>
> --- --- --- --- ---<br>
> 0.0 3 6 0 9 6<br>
> 1.0 3 0 6 3 6<br>
> 2.0 3 6 3 3 3<br>
> 3.0 0 6 9 0 6<br>
> 4.0 6 0 0 3 3<br>
> 5.0 6 3 0 9 0<br>
> 6.0 6 6 3 6 0<br>
> 7.0 3 3 9 0 6<br>
><br>
> Summary per label across chunks<br>
> label mean std min max #chunks<br>
> 1 3.75 1.98 0 6 7<br>
> 2 3.75 2.49 0 6 6<br>
> 3 3.75 3.6 0 9 5<br>
> 4 4.12 3.33 0 9 6<br>
> 5 3.75 2.49 0 6 6<br>
><br>
> Summary per chunk across labels<br>
> chunk mean std min max #labels<br>
> 0 4.8 3.06 0 9 4<br>
> 1 3.6 2.24 0 6 4<br>
> 2 3.6 1.2 3 6 5<br>
> 3 4.2 3.6 0 9 3<br>
> 4 2.4 2.24 0 6 3<br>
> 5 3.6 3.5 0 9 3<br>
> 6 4.2 2.4 0 6 4<br>
> 7 4.2 3.06 0 9 4<br>
><br>
><br>
> Thanks,<br>
> Geethmala<br>
><br>
> On Tue, Feb 2, 2010 at 2:11 PM, Matthias Ekman <<a href="mailto:Matthias.Ekman@nf.mpg.de">Matthias.Ekman@nf.mpg.de</a>>wrote:<br>
><br>
>> Hi,<br>
>><br>
>> could you please post:<br>
>> print ds.summary()<br>
>><br>
>> .. just to make sure, that there are samples belonging to class 3 :)<br>
>><br>
>><br>
>> Matthias<br>
>><br>
>> Geethmala wrote:<br>
>>> No, I don't have a mix of them. They are all integer values.<br>
>>><br>
>>> Thanks,<br>
>>> Geethmala<br>
>>><br>
>>> On Tue, Feb 2, 2010 at 1:59 PM, Yaroslav Halchenko <<br>
>> <a href="mailto:debian@onerussian.com">debian@onerussian.com</a>>wrote:<br>
>>>> it means that<br>
>>>><br>
>>>> Classifier LinearCSVMC(kernel_type='linear', svm_impl='C_SVC')<br>
>>>> wasn't trained to classify labels Set([3.0]) present in testing<br>
>>>> dataset. Make sure that you have not mixed order/names of the<br>
>>>> arguments anywhere<br>
>>>><br>
>>>> Also test if you don't have a mix of float and int labels in your<br>
>>>> datasets (as I remember you are manually composing those).<br>
>>>><br>
>>>><br>
>>>> On Tue, 02 Feb 2010, Geethmala wrote:<br>
>>>><br>
>>>>> Hi,<br>
>>>>> I get the following warning when I run LinearCSVMC.<br>
>>>>> WARNING: Classifier LinearCSVMC(kernel_type='linear',<br>
>>>> svm_impl='C_SVC')<br>
>>>>> wasn't trained to classify labels Set([3.0]) present in testing<br>
>>>>> dataset. Make sure that you have not mixed order/names of the<br>
>>>> arguments<br>
>>>>> anywhere<br>
>>>>> What does this warning mean?<br>
>>>>> Thanks,<br>
>>>>> Geethmala<br>
>>>>> _______________________________________________<br>
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>>>> .-.<br>
>>>> =------------------------------ /v\ ----------------------------=<br>
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>>>> Linux User ^^-^^ [175555]<br>
>>>><br>
>>>><br>
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