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Make sure all dask-cudf supported aggs are handled in _tree_node_agg #9487

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merged 6 commits into from
Nov 9, 2021
Merged

Make sure all dask-cudf supported aggs are handled in _tree_node_agg #9487

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68fa35e
Make sure all supported aggs are handled in _tree_node_agg
charlesbluca Oct 21, 2021
db2960d
Make supported aggs a tuple to guarantee ordering
charlesbluca Oct 21, 2021
9fcc50d
Merge remote-tracking branch 'upstream/branch-21.12' into dask-fix-tr…
charlesbluca Nov 2, 2021
6659e13
Add tests for dask-cudf series groupby
charlesbluca Nov 2, 2021
d7fed2e
Add back mean methods
charlesbluca Nov 4, 2021
73bfc4d
Consolidate basic frame/series groupby tests
charlesbluca Nov 9, 2021
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73 changes: 31 additions & 42 deletions python/dask_cudf/dask_cudf/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,19 @@

import cudf

SUPPORTED_AGGS = (
"count",
"mean",
"std",
"var",
"sum",
"min",
"max",
"collect",
"first",
"last",
)
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class CudfDataFrameGroupBy(DataFrameGroupBy):
def __init__(self, *args, **kwargs):
Expand Down Expand Up @@ -47,6 +60,19 @@ def __getitem__(self, key):
g._meta = g._meta[key]
return g

def collect(self, split_every=None, split_out=1):
return groupby_agg(
self.obj,
self.index,
{c: "collect" for c in self.obj.columns if c not in self.index},
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split_every=split_every,
split_out=split_out,
dropna=self.dropna,
sep=self.sep,
sort=self.sort,
as_index=self.as_index,
)

def mean(self, split_every=None, split_out=1):
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return groupby_agg(
self.obj,
Expand All @@ -65,18 +91,6 @@ def aggregate(self, arg, split_every=None, split_out=1):
return self.size()
arg = _redirect_aggs(arg)

_supported = {
"count",
"mean",
"std",
"var",
"sum",
"min",
"max",
"collect",
"first",
"last",
}
if (
isinstance(self.obj, DaskDataFrame)
and (
Expand All @@ -86,7 +100,7 @@ def aggregate(self, arg, split_every=None, split_out=1):
and all(isinstance(x, str) for x in self.index)
)
)
and _is_supported(arg, _supported)
and _is_supported(arg, SUPPORTED_AGGS)
):
if isinstance(self._meta.grouping.keys, cudf.MultiIndex):
keys = self._meta.grouping.keys.names
Expand Down Expand Up @@ -134,23 +148,10 @@ def aggregate(self, arg, split_every=None, split_out=1):
return self.size()
arg = _redirect_aggs(arg)

_supported = {
"count",
"mean",
"std",
"var",
"sum",
"min",
"max",
"collect",
"first",
"last",
}

if (
isinstance(self.obj, DaskDataFrame)
and isinstance(self.index, (str, list))
and _is_supported({self._slice: arg}, _supported)
and _is_supported({self._slice: arg}, SUPPORTED_AGGS)
):
return groupby_agg(
self.obj,
Expand Down Expand Up @@ -201,21 +202,9 @@ def groupby_agg(
"""
# Assert that aggregations are supported
aggs = _redirect_aggs(aggs_in)
_supported = {
"count",
"mean",
"std",
"var",
"sum",
"min",
"max",
"collect",
"first",
"last",
}
if not _is_supported(aggs, _supported):
if not _is_supported(aggs, SUPPORTED_AGGS):
raise ValueError(
f"Supported aggs include {_supported} for groupby_agg API. "
f"Supported aggs include {SUPPORTED_AGGS} for groupby_agg API. "
f"Aggregations must be specified with dict or list syntax."
)

Expand Down Expand Up @@ -478,7 +467,7 @@ def _tree_node_agg(dfs, gb_cols, split_out, dropna, sort, sep):
agg = col.split(sep)[-1]
if agg in ("count", "sum"):
agg_dict[col] = ["sum"]
elif agg in ("min", "max", "collect"):
elif agg in SUPPORTED_AGGS:
agg_dict[col] = [agg]
else:
raise ValueError(f"Unexpected aggregation: {agg}")
Expand Down
29 changes: 2 additions & 27 deletions python/dask_cudf/dask_cudf/tests/test_groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,10 @@
from cudf.core._compat import PANDAS_GE_120

import dask_cudf
from dask_cudf.groupby import _is_supported
from dask_cudf.groupby import SUPPORTED_AGGS, _is_supported


@pytest.mark.parametrize("aggregation", ["sum", "mean", "count", "min", "max"])
@pytest.mark.parametrize("aggregation", SUPPORTED_AGGS)
def test_groupby_basic_aggs(aggregation):
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pdf = pd.DataFrame(
{
Expand Down Expand Up @@ -117,31 +117,6 @@ def test_groupby_std(func):
dd.assert_eq(a, b)


@pytest.mark.parametrize(
"func",
[
lambda df: df.groupby("x").agg({"y": "collect"}),
lambda df: df.groupby("x").y.agg("collect"),
],
)
def test_groupby_collect(func):
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pdf = pd.DataFrame(
{
"x": np.random.randint(0, 5, size=10000),
"y": np.random.normal(size=10000),
}
)

gdf = cudf.DataFrame.from_pandas(pdf)

ddf = dask_cudf.from_cudf(gdf, npartitions=5)

a = func(gdf).to_pandas()
b = func(ddf).compute().to_pandas()

dd.assert_eq(a, b)


# reason gotattr in cudf
@pytest.mark.parametrize(
"func",
Expand Down