I don't understand the problem. The default DBSCAN will still have
algorithm='auto'.
On 6 August 2014 17:01, Maheshakya Wijewardena <[email protected]>
wrote:
> I too considered passing the estimator instance as a parameter to DBSCAN.
> If we want to use KDTree or BallTree, NearestNeighbor instances created
> with algorithm=kdtree or ball_tree can be passed. But Robert mentioned that
> it would fail the unit test cases- the base test that ensures that all
> BaseEstimators can be constructed requires that all class attributes be
> set-able directly from init parameters. This ensures that it can be used
> with pipelines.
>
> How would we deal with this?
>
>
>
>
> On Wed, Aug 6, 2014 at 11:22 AM, Joel Nothman <[email protected]>
> wrote:
>
>> It seems to me that the LSH forest is substituting for the `algorithm`
>> parameter, which selects between ball_tree, kd_tree and brute search for
>> nearest neighbour search. These are designed not to take additional
>> parameters.
>>
>> So you need to accept additional parameters. You could indeed create
>> another estimator like ApproximateNeighborsDBSCAN, but you'd need to do the
>> same for KNeighborsClassifier, RadiusNeighborsClassifier,
>> KNeighborsRegressor and RadiusNeighborsRegressor. That proliferation seems
>> out of hand.
>>
>> Instead, could we have an interface in which the `algorithm` parameter
>> could take any object supporting `fit(X)`, `query(X)` and
>> `query_radius(X)`, such as an LSHForest instance? Indeed you could also
>> make 'lsh' an available algorithm using reasonable parameters automatically
>> inferred from the data, but you certainly want the user to be able to
>> control the LSH parameters.
>>
>> (Note, currently BallTree, KDTree don't support fit(), and the index data
>> is passed into their constructors.)
>>
>> There is also the caveat that currently the BallTree and KDTree are
>> passed an effective_metric parameter in their constructors; not all metrics
>> are possible with a particular LSH implementation, and only euclidean is
>> currently supported. So the outer estimator (e.g. DBSCAN) could set an
>> effective_metric parameter on the algorithm object, or it could not.
>>
>> WDYT?
>>
>>
>> On 6 August 2014 15:33, Maheshakya Wijewardena <[email protected]>
>> wrote:
>>
>>> Hi,
>>>
>>> I'm trying to use LSH Forest approximate neighbor search method to
>>> obtain radius neighbors in DBSCAN. It adheres the API of sklearn.neighbors
>>> (at least radius_neighbors method at this moment). But LSH Forest itself
>>> has a set of parameters, so they need to be initialized.
>>>
>>> I'm thinking about passing an argumant to DBSCAN init method as
>>> `approximate_neighbors=True` (or something suitable) and have the LSH
>>> Forest parameters as well in DBSCAN init method.
>>>
>>> The other method Robert suggested to subclass from DBSCAN to use
>>> approximate neighbors.
>>>
>>> Once LSH Forest is initialized, it's just a matter of applying that in
>>> the place of `NearestNeighbors`. Are the above methods appropriate or is
>>> there better ways?
>>>
>>> PR to LSH Forest: https://github.com/scikit-learn/scikit-learn/pull/3304
>>>
>>> Best Regards,
>>> Maheshakya
>>>
>>> --
>>> Undergraduate,
>>> Department of Computer Science and Engineering,
>>> Faculty of Engineering.
>>> University of Moratuwa,
>>> Sri Lanka
>>>
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>>
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>
>
> --
> Undergraduate,
> Department of Computer Science and Engineering,
> Faculty of Engineering.
> University of Moratuwa,
> Sri Lanka
>
>
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