That seems real bad. Can you please open an issue on github?
On 08/18/2015 01:24 PM, Othman Soufan wrote:
Hi Andreas,
Indeed, I was not yet registered with the mail-list.
The sklearn version I have installed is 0.16.1
I did not get an error when inputing 1d X and what I receive back are
predictions as many as the length of this 1d list.
For example:
>>> from sklearn import datasets
>>> from sklearn.multiclass import OneVsOneClassifier
>>> from sklearn.svm import LinearSVC
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> OneVsOneClassifier(LinearSVC(random_state=0)).fit(X, y).predict(X[1,:])
Out[*1*]: array([0, 1, 1, 1])
And by replacing X[1,:] to be X[1:2,:] which in terms of values are
the same:
>>> OneVsOneClassifier(LinearSVC(random_state=0)).fit(X, y).predict(X[*1:2*,:])
Out[*2*]: array([0]) # Proper output
Regards,
Othman Soufan
PhD Candidate
Mathematical and Computer Sciences and Engineering
King Abdullah University of Science and Technology
Thuwal 23955-6900
KAUST Mail Box # 2620
Kingdom of Saudi Arabia
Tel.: (+966) 506134003
On Tue, Aug 18, 2015 at 6:54 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
Hi.
I just replied to the thread above, maybe you weren't subscribed
to the ml yet.
Did you get an error when inputting a 1d X?
Which version of scikit-learn are you on?
X should really always be 2d. Unfortunately that is currently
inconsistent, and will be fixed soon.
So yes, that will be fixed, but it would be great to know the
exact behavior you encountered,
and the version.
Thanks,
Andy
On 08/18/2015 11:50 AM, Othman Soufan wrote:
Greetings Guys,
I came through the contributed implementation to multiclass.py in
Scikit-learn. I just have a suggestion for you to consider the
case when only one testing sample is passed to decision_function
"Decision function for the OneVsOneClassifier". As for the
current implementation, an undesirable output comes since
n_samples =X.shape[0] will take a number larger than one when X
is only a single list vector with some values. I may suggest you
check the shape of X before parsing it in a particular way, or
update the documentation to advise the user on a suggested way to
get the prediction for one testing sample.
In a sense, it is true to say that usually, there is a testing
set of many samples but in a specific case of mine, it was
preferable to predict sample by sample. I overcome this by using
X[0:1,:] instead of X[0,:] where X is a testing set of several
samples.
Regards,
Othman Soufan
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