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Modify mean chunk functions to return dicts rather than arrays #4513

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merged 2 commits into from Feb 25, 2019
+19 −18
Merged

Modify mean chunk functions to return dicts rather than arrays #4513

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54ce73e
Modify mean chunk functions to return dicts rather than arrays
mrocklin Feb 20, 2019
b2f3394
remove excess sum
mrocklin Feb 21, 2019
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38 changes: 20 additions & 18 deletions dask/array/reductions.py
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Expand Up @@ -22,7 +22,7 @@
from ..compatibility import getargspec, builtins
from ..base import tokenize
from ..highlevelgraph import HighLevelGraph
from ..utils import ignoring, funcname, Dispatch
from ..utils import ignoring, funcname, Dispatch, deepmap
from .. import config

# Generic functions to support chunks of different types
Expand Down Expand Up @@ -331,26 +331,27 @@ def nannumel(x, **kwargs):
def mean_chunk(x, sum=chunk.sum, numel=numel, dtype='f8', **kwargs):
n = numel(x, dtype=dtype, **kwargs)
total = sum(x, dtype=dtype, **kwargs)
empty = empty_lookup.dispatch(type(n))
result = empty(n.shape, dtype=[('total', total.dtype), ('n', n.dtype)])
result['n'] = n
result['total'] = total
return result
return {'n': n, 'total': total}


def mean_combine(pair, sum=chunk.sum, numel=numel, dtype='f8', **kwargs):
n = sum(pair['n'], **kwargs)
total = sum(pair['total'], **kwargs)
empty = empty_lookup.dispatch(type(n))
result = empty(n.shape, dtype=pair.dtype)
result['n'] = n
result['total'] = total
return result
def mean_combine(pairs, sum=chunk.sum, numel=numel, dtype='f8', axis=None, **kwargs):
if not isinstance(pairs, list):
pairs = [pairs]
ns = deepmap(lambda pair: pair['n'], pairs)
totals = deepmap(lambda pair: pair['total'], pairs)
n = _concatenate2(ns, axes=axis).sum(axis=axis, **kwargs)
total = _concatenate2(totals, axes=axis).sum(axis=axis, **kwargs)
return {'n': n, 'total': total}


def mean_agg(pair, dtype='f8', **kwargs):
return divide(pair['total'].sum(dtype=dtype, **kwargs),
pair['n'].sum(dtype=dtype, **kwargs), dtype=dtype)
def mean_agg(pairs, dtype='f8', axis=None, **kwargs):
ns = deepmap(lambda pair: pair['n'], pairs)
totals = deepmap(lambda pair: pair['total'], pairs)
n = _concatenate2(ns, axes=axis).sum(axis=axis, **kwargs)
total = _concatenate2(totals, axes=axis).sum(axis=axis, **kwargs)

return divide(total.sum(dtype=dtype, **kwargs),
n.sum(dtype=dtype, **kwargs), dtype=dtype)
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Found the mistake, you're calling .sum() twice for both total and n. Get rid of one and you should be good to go!



@wraps(chunk.mean)
Expand All @@ -361,7 +362,7 @@ def mean(a, axis=None, dtype=None, keepdims=False, split_every=None, out=None):
dt = getattr(np.mean(np.empty(shape=(1,), dtype=a.dtype)), 'dtype', object)
return reduction(a, mean_chunk, mean_agg, axis=axis, keepdims=keepdims,
dtype=dt, split_every=split_every, combine=mean_combine,
out=out)
out=out, concatenate=False)


def nanmean(a, axis=None, dtype=None, keepdims=False, split_every=None,
Expand All @@ -373,6 +374,7 @@ def nanmean(a, axis=None, dtype=None, keepdims=False, split_every=None,
return reduction(a, partial(mean_chunk, sum=chunk.nansum, numel=nannumel),
mean_agg, axis=axis, keepdims=keepdims, dtype=dt,
split_every=split_every, out=out,
concatenate=False,
combine=partial(mean_combine, sum=chunk.nansum, numel=nannumel))


Expand Down