>
> I have been experimenting with the above code. I have noticed the
> following things:
>
>
> 1. If we set algorithm = 'brute' the algorithm does not enter the
> function tan, i.e., putting a breakpoint at the print statement does not
> stop execution on it during the fit method. It does however use this
> function when using kneighbors method. I think one cannot use the user
> defined metric with 'brute'.
>
> This sounds like a bug – can you open an issue?
>
> 1. On the other hand if we set the algorithm = 'ball_tree' the
> execution does go through the tan function during the fit method. But if
> you see the values of x and y at this time it will be different from the
> values of x and y that you entered. Clearly, the ball_tree algorithm is
> doing some weird stuff. I don't think it is using the defined metric tan
> for making the tree.
>
> Probably related to the bug fixed here:
https://github.com/scikit-learn/scikit-learn/pull/6288
>
>
>
> --
> sp
>
> On Thu, Jan 14, 2016 at 2:42 AM, Sebastian Raschka <se.rasc...@gmail.com>
>> wrote:
>>
>>> I guess I got it now! This behavior (see below) is indeed a bit strange:
>>>
>>> from sklearn.neighbors import NearestNeighbors
>>> import numpy as np
>>>
>>> X = np.array([[1.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 0.0], [1.0, 1.0,
>>> 1.0, 1.0]])
>>>
>>> def tan(x, y):
>>> print(y)
>>> return 1
>>>
>>> nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree',
>>> metric=tan).fit(X)
>>> distances, indices = nbrs.kneighbors(X)
>>>
>>> [ 0.51786272 0.53042315 0.87815766 0.90239616 0.34253599 0.98631925
>>> 0.29768794 0.36593595 0.28956526 0.24720931]
>>> [ 1. 0. 1. 1.]
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>> [ 0.66666667 0.33333333 1. 0.66666667]
>>> [ 1. 0. 1. 1.]
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>> [ 0.66666667 0.33333333 1. 0.66666667]
>>> [ 1. 0. 1. 1.]
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>> [ 0.66666667 0.33333333 1. 0.66666667]
>>> [ 1. 0. 1. 1.]
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>>
>>>
>>> It seems to be due to the partitioning via the ball tree algorithm; I am
>>> not sure if this is intended. It would be nice to get some feedback on this
>>> ...
>>>
>>> Switching to "brute" seems to return the expected results:
>>>
>>> from sklearn.neighbors import NearestNeighbors
>>> import numpy as np
>>>
>>> X = np.array([[1.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 0.0], [1.0, 1.0,
>>> 1.0, 1.0]])
>>>
>>> def tan(x, y):
>>> print(y)
>>> return 1
>>>
>>> nbrs = NearestNeighbors(n_neighbors=1, algorithm='brute',
>>> metric=tan).fit(X)
>>> distances, indices = nbrs.kneighbors(X)
>>>
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>> [ 1. 1. 1. 1.]
>>> [ 1. 0. 1. 1.]
>>> [ 0. 0. 1. 0.]
>>> [ 1. 1. 1. 1.]
>>>
>>>
>>>
>>>
>
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