Fast Ndimensional aggregation functions with Numba
Project description
Numbagg: Fast Ndimensional aggregation functions with Numba
Fast, flexible Ndimensional array functions written with Numba and NumPy's generalized ufuncs.
Currently accelerated functions:
 Array functions:
allnan
,anynan
,count
,nanargmax
,nanargmin
,nanmax
,nanmean
,nanstd
,nanvar
,nanmin
,nansum
 Moving window functions:
move_exp_nanmean
,move_mean
,move_sum
Note: Only functions listed here (exposed in Numbagg's top level namespace) are supported as part of Numbagg's public API.
Easy to extend
Numbagg makes it easy to write, in pure Python/NumPy, flexible aggregation functions accelerated by Numba. All the hard work is done by Numba's JIT compiler and NumPy's gufunc machinery (as wrapped by Numba).
For example, here is how we wrote nansum
:
import numpy as np from numbagg.decorators import ndreduce @ndreduce def nansum(a): asum = 0.0 for ai in a.flat: if not np.isnan(ai): asum += ai return asum
You are welcome to experiment with Numbagg's decorator functions, but these are not public APIs (yet): we reserve the right to change them at any time.
We'd rather get your pull requests to add new functions into Numbagg directly!
Advantages over Bottleneck
 Way less code. Easier to add new functions. No adhoc templating system. No Cython!
 Fast functions still work for >3 dimensions.
axis
argument handles tuples of integers.
Most of the functions in Numbagg (including our test suite) are adapted from Bottleneck's battlehardened implementations. Still, Numbagg is experimental, and probably not yet ready for production.
Benchmarks
Initial benchmarks are quite encouraging. Numbagg/Numba has comparable (slightly better) performance than Bottleneck's handwritten C:
import numbagg import numpy as np import bottleneck x = np.random.RandomState(42).randn(1000, 1000) x[x < 1] = np.NaN # timings with numba=0.41.0 and bottleneck=1.2.1 In [2]: %timeit numbagg.nanmean(x) 1.8 ms ± 92.3 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) In [3]: %timeit numbagg.nanmean(x, axis=0) 3.63 ms ± 136 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [4]: %timeit numbagg.nanmean(x, axis=1) 1.81 ms ± 41 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [5]: %timeit bottleneck.nanmean(x) 2.22 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [6]: %timeit bottleneck.nanmean(x, axis=0) 4.45 ms ± 107 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) In [7]: %timeit bottleneck.nanmean(x, axis=1) 2.19 ms ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Our approach
Numbagg includes somewhat awkward workarounds for features missing from NumPy/Numba:
 It implements its own cache for functions wrapped by Numba's
guvectorize
, because that decorator is rather slow.  It does its own handling of array
transposes
to handle the
axis
argument, which we hope will eventually be directly supported by all NumPy gufuncs.  It uses some terrible hacks to hide the outofbound memory access necessary to write gufuncs that handle scalar values with Numba.
I hope that the need for most of these will eventually go away. In the meantime, expect Numbagg to be tightly coupled to Numba and NumPy release cycles.
License
3clause BSD. Includes portions of Bottleneck, which is distributed under a Simplified BSD license.
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