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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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115 changes: 73 additions & 42 deletions python/dask_cudf/dask_cudf/groupby.py
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Open in desktop
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 @@ -60,23 +73,24 @@ def mean(self, split_every=None, split_out=1):
as_index=self.as_index,
)

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},
split_every=split_every,
split_out=split_out,
dropna=self.dropna,
sep=self.sep,
sort=self.sort,
as_index=self.as_index,
)

def aggregate(self, arg, split_every=None, split_out=1):
if arg == "size":
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 @@ -129,33 +143,62 @@ def mean(self, split_every=None, split_out=1):
as_index=self.as_index,
)[self._slice]

def std(self, split_every=None, split_out=1):
return groupby_agg(
self.obj,
self.index,
{self._slice: "std"},
split_every=split_every,
split_out=split_out,
dropna=self.dropna,
sep=self.sep,
sort=self.sort,
as_index=self.as_index,
)[self._slice]

def var(self, split_every=None, split_out=1):
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I added in overrides for var and std because it seems like trying to use upstream Dask's implementations here fails:

python/dask_cudf/dask_cudf/tests/test_groupby.py ............F
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> traceback >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

aggregation = 'std'

    @pytest.mark.parametrize("aggregation", SUPPORTED_AGGS)
    def test_groupby_basic_series(aggregation):
        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 = getattr(gdf.groupby("x").x, aggregation)()
>       b = getattr(ddf.groupby("x").x, aggregation)().compute()

python/dask_cudf/dask_cudf/tests/test_groupby.py:61: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../dask/dask/dataframe/groupby.py:1445: in std
    v = self.var(ddof, split_every=split_every, split_out=split_out)
../dask/dask/dataframe/groupby.py:1439: in var
    result = result[self._slice]
../dask/dask/dataframe/core.py:4062: in __getitem__
    meta = self._meta[_extract_meta(key)]
../compose/etc/conda/cuda_11.2/envs/rapids/lib/python3.8/contextlib.py:75: in inner
    return func(*args, **kwds)
python/cudf/cudf/core/dataframe.py:1007: in __getitem__
    return self._get_columns_by_label(arg, downcast=True)
python/cudf/cudf/core/dataframe.py:1863: in _get_columns_by_label
    new_data = super()._get_columns_by_label(labels, downcast)
python/cudf/cudf/core/frame.py:507: in _get_columns_by_label
    return self._data.select_by_label(labels)
python/cudf/cudf/core/column_accessor.py:344: in select_by_label
    return self._select_by_label_grouped(key)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = ColumnAccessor(multiindex=False, level_names=[None])
y: float64, key = 'x'

    def _select_by_label_grouped(self, key: Any) -> ColumnAccessor:
>       result = self._grouped_data[key]
E       KeyError: 'x'

python/cudf/cudf/core/column_accessor.py:406: KeyError

Instead of adding these functions in individually, is it possible / would it make sense to add them in programmatically based on SUPPORTED_AGGS?

return groupby_agg(
self.obj,
self.index,
{self._slice: "var"},
split_every=split_every,
split_out=split_out,
dropna=self.dropna,
sep=self.sep,
sort=self.sort,
as_index=self.as_index,
)[self._slice]

def collect(self, split_every=None, split_out=1):
return groupby_agg(
self.obj,
self.index,
{self._slice: "collect"},
split_every=split_every,
split_out=split_out,
dropna=self.dropna,
sep=self.sep,
sort=self.sort,
as_index=self.as_index,
)[self._slice]

def aggregate(self, arg, split_every=None, split_out=1):
if arg == "size":
return self.size()
arg = _redirect_aggs(arg)

_supported = {
"count",
"mean",
"std",
"var",
"sum",
"min",
"max",
"collect",
"first",
"last",
}
if not isinstance(arg, dict):
arg = {self._slice: arg}

if (
isinstance(self.obj, DaskDataFrame)
and isinstance(self.index, (str, list))
and _is_supported({self._slice: arg}, _supported)
and _is_supported(arg, SUPPORTED_AGGS)
):
return groupby_agg(
self.obj,
self.index,
{self._slice: arg},
arg,
split_every=split_every,
split_out=split_out,
dropna=self.dropna,
Expand Down Expand Up @@ -201,21 +244,9 @@ 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 @@ -475,7 +506,7 @@ 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
46 changes: 13 additions & 33 deletions python/dask_cudf/dask_cudf/tests/test_groupby.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,11 +11,12 @@
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"])
def test_groupby_basic_aggs(aggregation):
@pytest.mark.parametrize("aggregation", SUPPORTED_AGGS)
@pytest.mark.parametrize("series", [False, True])
def test_groupby_basic(series, aggregation):
pdf = pd.DataFrame(
{
"x": np.random.randint(0, 5, size=10000),
Expand All @@ -24,19 +25,23 @@ def test_groupby_basic_aggs(aggregation):
)

gdf = cudf.DataFrame.from_pandas(pdf)
gdf_grouped = gdf.groupby("x")
ddf_grouped = dask_cudf.from_cudf(gdf, npartitions=5).groupby("x")

ddf = dask_cudf.from_cudf(gdf, npartitions=5)
if series:
gdf_grouped = gdf_grouped.x
ddf_grouped = ddf_grouped.x

a = getattr(gdf.groupby("x"), aggregation)()
b = getattr(ddf.groupby("x"), aggregation)().compute()
a = getattr(gdf_grouped, aggregation)()
b = getattr(ddf_grouped, aggregation)().compute()

if aggregation == "count":
dd.assert_eq(a, b, check_dtype=False)
else:
dd.assert_eq(a, b)

a = gdf.groupby("x").agg({"x": aggregation})
b = ddf.groupby("x").agg({"x": aggregation}).compute()
a = gdf_grouped.agg({"x": aggregation})
b = ddf_grouped.agg({"x": aggregation}).compute()

if aggregation == "count":
dd.assert_eq(a, b, check_dtype=False)
Expand Down Expand Up @@ -117,31 +122,6 @@ 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