+1
Doug Coleman <doug.cole...@gmail.com> schrieb:
>I guess transforming it would be more in line with other classifiers.
>The
>design decision could be "You should only have to know about
>multi-output
>if you want to use it."
>
>
>On Thu, Nov 29, 2012 at 10:07 AM, Doug Coleman
><doug.cole...@gmail.com>wrote:
>
>> Going off of my unit tests in my branch for classes= in init:
>>
>> Single output:
>> clf = tree.DecisionTreeClassifier(classes=[1,2,3,4])
>> clf.fit([[10]], [0])
>> In [13]: clf.classes_
>> Out[13]: [array([0, 1, 2, 3, 4])]
>>
>> Multi-output:
>> clf = tree.DecisionTreeClassifier(classes=[[-1,1],[0,1,2,3,4]])
>> y_hat = clf.fit(X_multi, y_multi).predict(T)
>> In [9]: clf.classes_
>> Out[9]: [array([-1, 1]), array([0, 1, 2, 3, 4])]
>>
>>
>> So it's as you say and I understand why. Maybe we could have a method
>> ``supports_multi_output`` that returns a boolean so we know what
>shape the
>> classes_ are given some arbitrary clf? Or just introspect it?
>>
>> Doug
>>
>>
>> On Thu, Nov 29, 2012 at 9:57 AM, Gilles Louppe <g.lou...@gmail.com>
>wrote:
>>
>>> Hi,
>>>
>>> Yes, since decision trees handle multi-output problems, classes_[i]
>is
>>> an array containing the classes for the i-th output. Hence
>classes_[0]
>>> is the array you are looking for when `y` is 1D.
>>>
>>> I guess we could transform classes_ directly into that array if the
>>> decision tree is trained on a 1D-output, actually just like we
>already
>>> do at the prediction time.
>>>
>>> What do you think?
>>>
>>> Gilles
>>>
>>> On 29 November 2012 18:48, <amuel...@ais.uni-bonn.de> wrote:
>>> > the classes_ attribute is not present in all classifiers and not
>>> consistent,
>>> > as you noticed.
>>> > this is a known issue (see the issue tracker) and it would be
>great to
>>> > address this.
>>> > I am not sure about the decision trees in particular.
>>> >
>>> >
>>> >
>>> > Doug Coleman <doug.cole...@gmail.com> schrieb:
>>> >>
>>> >> Decision trees' classes are wrapped in another array for some
>reason. I
>>> >> was under the impression that I could just get ``clf.classes_``
>from
>>> any old
>>> >> classifier and it would be a nice list, but I guess I'm mistaken.
>It
>>> makes
>>> >> it hard to write utilities...is this an oversight or a bug, or by
>>> design? I
>>> >> haven't checked other classifiers.
>>> >>
>>> >> from sklearn.tree import DecisionTreeClassifier
>>> >> clf1 = DecisionTreeClassifier()
>>> >>
>>> >> In [104]: clf1.classes_
>>> >> Out[104]: [array([1, 2, 3])]
>>> >>
>>> >>
>>> >> from sklearn.linear_model import SGDClassifier
>>> >> clf2 = SGDClassifier()
>>> >> clf2.fit([[1],[2],[3]], [1,2,3])
>>> >>
>>> >> In [100]: clf2.classes_
>>> >> Out[100]: array([1, 2, 3])
>>> >>
>>> >>
>>> >>
>>> >>
>>> >> Thanks,
>>> >> Doug
>>> >>
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>>
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