numbagg 0.5.1

Fast N-dimensional aggregation functions with Numba

Numbagg: Fast N-dimensional aggregation functions with Numba

Fast, flexible N-dimensional 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_exp_nansum, move_exp_nanvar, 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 ad-hoc 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 battle-hardened 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 hand-written 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)

Benchmarks vs. pandas

Here are the current benchmark results relative to pandas for the rolling exponential functions:

Function	n	numbagg	pandas	%change
move_exp_nanmean	1000	77.6μs	360μs	-78%
move_exp_nanmean	100000	6.85ms	18.8ms	-63%
move_exp_nanmean	10000000	793ms	1.96s	-59%
move_exp_nansum	1000	92.3μs	335μs	-72%
move_exp_nansum	100000	10.9ms	11.3ms	-3%
move_exp_nansum	10000000	1.02s	1.22s	-16%
move_exp_nanvar	1000	74.3μs	360μs	-79%
move_exp_nanvar	100000	6.63ms	15.3ms	-56%
move_exp_nanvar	10000000	1.06s	1.86s	-43%

Benchmarks were run on a Mac M1 in September 2023 on numbagg's HEAD and pandas 2.1.1.

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 out-of-bound 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

3-clause BSD. Includes portions of Bottleneck, which is distributed under a Simplified BSD license.