Hi Peter
It is most probably 0.11.I'm not so sure, It can be older version too.
Regards
Nikit Saraf
On Fri, Jul 6, 2012 at 1:39 AM, Peter Prettenhofer <
[email protected]> wrote:
> Nikit,
> which version of sklearn do you use? master or 0.11?
>
> best,
> Peter
>
> 2012/7/5 Nikit Saraf <[email protected]>:
> > Hi Peter
> >
> > Thanks for the reply.
> >
> > dtype of Y_1 is 'string64' and its shape is (12137,1)
> > dtype of X_1 is 'float64" and its shape is (12137,100)
> >
> > And here is the example of 10 cases http://paste.ubuntu.com/1077028/
> >
> > It would be great if you could point out where I'm going wrong.Thank you
> so
> > much for the help.
> >
> > Regards
> > Nikit Saraf
> >
> >
> > On Fri, Jul 6, 2012 at 1:16 AM, Peter Prettenhofer
> > <[email protected]> wrote:
> >>
> >> Hi Nikit,
> >>
> >> thanks for reporting - I added a test case for symbolic class labels
> >> and it works ok (class labels get mapped to internal class ids prior
> >> to fitting; see gradient_boosting.py:629:631) - I think the source of
> >> the error is something different.
> >>
> >> Can you check the dtype and shape of ``Y_1``?
> >>
> >> It would be great if you could send me a minimal example to reproduce
> the
> >> error.
> >>
> >> thanks,
> >> Peter
> >>
> >> 2012/7/5 Nikit Saraf <[email protected]>:
> >> > I was trying to train a Character Recognition Model with the help of
> >> > GradientBoostingClassifier. When i tried to run, it gave me the
> >> > following
> >> > error :-
> >> >
> >> > Traceback (most recent call last):
> >> > File "charRecog.py", line 23, in <module>
> >> > clf = GradientBoostingClassifier().fit(X_1,Y_1)
> >> > File
> >> >
> >> >
> "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
> >> > line 633, in fit
> >> > return super(GradientBoostingClassifier, self).fit(X, y)
> >> > File
> >> >
> >> >
> "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
> >> > line 439, in fit
> >> > self.init.fit(X, y)
> >> > File
> >> >
> >> >
> "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
> >> > line 85, in fit
> >> > class_counts = np.bincount(y)
> >> > ValueError: object too deep for desired array
> >> >
> >> > Seeing the error, i realised it is the bincount() function which was
> >> > causing
> >> > the error as it counts only the no. of occurrences of non-negative
> >> > integers
> >> > in the array. But my labels are all strings such as 'A','e','3' etc.
> >> > This
> >> > concludes that the GradientBoostingClassifier would work only with the
> >> > non-negative integer labels and hence would not be a universal
> >> > classifier.
> >> > So, I believe, to fix the bug, the function bincount() should be
> >> > replaced
> >> > with another function which does similar work but also universal.
> >> >
> >> > Please correct me if I'm wrong, as I am fairly new to Machine
> Learning.
> >> >
> >> > Regards
> >> > Nikit Saraf
> >> >
> >> >
> >> >
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> >>
> >> --
> >> Peter Prettenhofer
> >>
> >>
> >>
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>
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