@Maheshakya, will you be able to do work in the near future on speeding up
the ascending phase instead? Or should another contributor take that baton?
Not only does it seem to be a major contributor to runtime, but it is
independent of metric and hashing mechanism (within binary hashes), and
hence the most fundamental component of LSHForest.
On 30 July 2015 at 22:28, Joel Nothman <joel.noth...@gmail.com> wrote:
> (sorry, I should have said the first b layers, not 2**b layers, producing
> a memoization of 2**b offsets)
>
> On 30 July 2015 at 22:22, Joel Nothman <joel.noth...@gmail.com> wrote:
>
>> One approach to fixing the ascending phase would ensure that
>> _find_matching_indices is only searching over parts of the tree that have
>> not yet been explored, while currently it searches over the entire index at
>> each depth.
>>
>> My preferred, but more experimental, solution is to memoize where the
>> first 2**b layers of the tree begin and end in the index, for small b. So
>> if our index stored:
>> [[0, 0, 0, 1, 1, 0, 0, 0],
>> [0, 0, 1, 0, 1, 0, 1, 0],
>> [0, 0, 1, 0, 1, 0, 1, 0],
>> [0, 1, 0, 0, 0, 0, 0, 0],
>> [0, 1, 0, 1, 1, 0, 0, 0],
>> [0, 1, 1, 0, 0, 1, 1, 0],
>> [1, 0, 0, 0, 0, 1, 0, 1],
>> [1, 0, 0, 1, 0, 1, 0, 1],
>> [1, 1, 0, 0, 0, 0, 0, 0],
>> [1, 1, 1, 1, 1, 0, 0, 0]]
>> and b=2, we'd memoize offsets for prefixes of size 2:
>> [0, # 00
>> 3, # 01
>> 6, # 10
>> 8, # 11
>> ]
>>
>> Given a query like [0, 1, 1, 0, 0, 0, 0, 0], it's easy to shift down to
>> leave the first b bits [0, 1] remaining, and look them up in the array just
>> defined to identify a much narrower search space [3, 6) matching that
>> prefix in the overall index.
>>
>> Indeed, given the min_hash_match constraint, not having this sort of
>> thing for b >= min_hash_match seems wasteful.
>>
>> This provides us O(1) access to the top layers of the tree when
>> ascending, and makes the searchsorted calls run in log(n / (2 ** b)) time
>> rather than log(n). It is also much more like traditional LSH. However, it
>> complexifies the code, as we now have to consider two strategies for
>> descent/ascent.
>>
>>
>>
>> On 30 July 2015 at 21:46, Joel Nothman <joel.noth...@gmail.com> wrote:
>>
>>> What makes you think this is the main bottleneck? While it is not an
>>> insignificant consumer of time, I really doubt this is what's making
>>> scikit-learn's LSH implementation severely underperform with respect to
>>> other implementations.
>>>
>>> We need to profile. In order to do that, we need some sensible
>>> parameters that users might actually want, e.g. number of features for
>>> {dense, sparse} cases, index size, target 10NN precision and recall
>>> (selecting corresponding n_estimators and n_candidates). Ideally we'd
>>> consider real-world datasets. And of course, these should be sensible for
>>> whichever metric we're operating over, and whether we're doing KNN or
>>> Radius searches.
>>>
>>> I don't know if it's realistic, but I've profiled the following
>>> bench_plot_approximate_neighbors settings:
>>>
>>> Building NearestNeighbors for 100000 samples in 100 dimensions
>>> LSHF parameters: n_estimators = 15, n_candidates = 100
>>> Building LSHForest for 100000 samples in 100 dimensions
>>> Done in 1.492s
>>> Average time for lshf neighbor queries: 0.005s
>>> Average time for exact neighbor queries: 0.002s
>>> Average Accuracy : 0.88
>>> Speed up: 0.5x
>>>
>>> Of 4.77s total time spent in LSHForest.kneighbors for a 1000-query
>>> matrix, we have:
>>>
>>> - 0.03 spent in _query (hashing and descending)
>>> - 0.91 spent in _compute_distances (exact distance calculation)
>>> - 3.80 remaining in _get_candidates (ascending phase), almost all of
>>> which is spent in _find_matching_indices
>>>
>>> Cutting exact distance calculation to 0s will still not get this faster
>>> than the exact approach. Of course, your mileage may vary, but this
>>> suggests to me you're barking up the wrong tree (no pun intended).
>>>
>>> On 30 July 2015 at 19:43, Maheshakya Wijewardena <pmaheshak...@gmail.com
>>> > wrote:
>>>
>>>> Hi,
>>>>
>>>> I've started to look into the matter of improving performance of
>>>> LSHForest. As we have discussed sometime before(in fact, quite a long
>>>> time), main concern is to Cythonize distance calculations. Currently, this
>>>> done by iteratively moving over all the query vectors when `kneighbors`
>>>> method is called for a set of query vectors. It has been identified that
>>>> iterating over each query with Python loops is a huge overhead. I have
>>>> implemented a few Cython hacks to demonstrate the distance calculation in
>>>> LSHForest and I was able to get an approximate speedup 10x compared to
>>>> current distance calculation with a Python loop. However, I came across
>>>> some blockers while trying to do this and need some clarifications.
>>>>
>>>> What I need to know is, do we use a mechanism to release GIL when we
>>>> want to parallelize. One of my observations is `pairwise_distance` uses all
>>>> the cores even when I don't specify `n_jobs` parameter which is 1 in
>>>> default. Is this an expected behavior?
>>>>
>>>> If I want to release GIL, can I use OpenMP module in Cython? Or is that
>>>> a task of Joblib?
>>>> Any input on this is highly appreciated.
>>>>
>>>> Best regards,
>>>> --
>>>>
>>>> *Maheshakya Wijewardena,Undergraduate,*
>>>> *Department of Computer Science and Engineering,*
>>>> *Faculty of Engineering.*
>>>> *University of Moratuwa,*
>>>> *Sri Lanka*
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
>>>>
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>>>> Scikit-learn-general@lists.sourceforge.net
>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>>
>>>>
>>>
>>
>
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