I was removing the class 'O' (other) from labels as given in the
python-crfsuite example [1]. It is interesting that the same code with one less
label works in original example, but not with my data. Anyway, I could fix the
issue by keeping all the labels. Thanks a lot Andreas!
Cheers
[1] function bio_classification_report () -
http://nbviewer.ipython.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL%202002.ipynb
Date: Mon, 13 Apr 2015 18:27:20 -0400
From: t3k...@gmail.com
To: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] Help: Getting ValueError
@precision_recall_fscore_support
I think this is because your y_true_combined has five classes, while
your labels and target names only have four classes.
On 04/13/2015 11:29 AM, namma igloo
wrote:
Hi,
I'm new to sklearn and trying out python-crfsuit example
[1] on my own data and following the example code, I'm getting
-
@precision_recall_fscore_support method: ValueError: too
many boolean indices
I must note that this method is called by
[classification_report] function which I'm calling with
following signature:
classification_report(
y_true_combined,
y_pred_combined,
labels = [class_indices[cls] for cls in tagset],
target_names = tagset
)
I do not get any error if I omit the optional parameters
'labels' and 'target_names'. So I am not sure if the issue is
because of values of those two parameters or somewhere else.
I have pasted here [2] the values of all four variables
supplied to classification_report.
I'll appreciate in any help in troubleshooting this issue.
[1]
http://nbviewer.ipython.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL%202002.ipynb
[2] http://pastebin.com/qaq7Kf1u
Thanks
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