[FEA] Dask-cuDF Resample

[beckernick]

Today, Dask's resample codepath converts the resampling frequencies into DateOffsets before downstream processing. This is convenient, but currently prevents using resample with Dask-cuDF, as cuDF does not provide support for DateOffsets in the Grouper API.

To enable CPU/GPU compatibility in Dask, it might be nice to explore whether it's necessary in Dask to convert resampling frequencies into DateOffsets. If it's not necessary, we might be able to identify an alternative that is compatible with cuDF's existing resample functionality.

import cudf
​
index = cudf.date_range(start="2001-01-01", periods=10, freq="1T")
sr = cudf.Series(range(10), index=index)
​
sr.resample(cudf.DateOffset(minutes=10)).sum()
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Input In [83], in <module>
      3 index = cudf.date_range(start="2001-01-01", periods=10, freq="1T")
      4 sr = cudf.Series(range(10), index=index)
----> 6 sr.resample(cudf.DateOffset(minutes=10)).sum()

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/indexed_frame.py:1238, in IndexedFrame.resample(self, rule, axis, closed, label, convention, kind, loffset, base, on, level, origin, offset)
   1223     raise NotImplementedError(
   1224         "The following arguments are not "
   1225         "currently supported by resample:\n\n"
   (...)
   1232         "- offset"
   1233     )
   1234 by = cudf.Grouper(
   1235     key=on, freq=rule, closed=closed, label=label, level=level
   1236 )
   1237 return (
-> 1238     cudf.core.resample.SeriesResampler(self, by=by)
   1239     if isinstance(self, cudf.Series)
   1240     else cudf.core.resample.DataFrameResampler(self, by=by)
   1241 )

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/resample.py:37, in _Resampler.__init__(self, obj, by, axis, kind)
     36 def __init__(self, obj, by, axis=None, kind=None):
---> 37     by = _ResampleGrouping(obj, by)
     38     super().__init__(obj, by=by)

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1488, in _Grouping.__init__(self, obj, by, level)
   1485 # Need to keep track of named key columns
   1486 # to support `as_index=False` correctly
   1487 self._named_columns = []
-> 1488 self._handle_by_or_level(by, level)
   1490 if len(obj) and not len(self._key_columns):
   1491     raise ValueError("No group keys passed")

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1513, in _Grouping._handle_by_or_level(self, by, level)
   1511     self._handle_mapping(by)
   1512 elif isinstance(by, Grouper):
-> 1513     self._handle_grouper(by)
   1514 else:
   1515     try:

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1577, in _Grouping._handle_grouper(self, by)
   1575 def _handle_grouper(self, by):
   1576     if by.freq:
-> 1577         self._handle_frequency_grouper(by)
   1578     elif by.key:
   1579         self._handle_label(by.key)

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/resample.py:107, in _ResampleGrouping._handle_frequency_grouper(self, by)
    104 closed = by.closed
    106 if isinstance(freq, (cudf.DateOffset, pd.DateOffset)):
--> 107     raise NotImplementedError(
    108         "Resampling by DateOffset objects is not yet supported."
    109     )
    110 if not isinstance(freq, str):
    111     raise TypeError(
    112         f"Unsupported type for freq: {type(freq).__name__}"
    113     )

NotImplementedError: Resampling by DateOffset objects is not yet supported.

sr.groupby(cudf.Grouper(cudf.DateOffset(minutes=10))).mean()
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [94], in <module>
----> 1 sr.groupby(cudf.Grouper(cudf.DateOffset(minutes=10))).mean()

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/series.py:3265, in Series.groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)
   3257 if by is None and level is None:
   3258     raise TypeError(
   3259         "groupby() requires either by or level to be specified."
   3260     )
   3262 return (
   3263     cudf.core.resample.SeriesResampler(self, by=by)
   3264     if isinstance(by, cudf.Grouper) and by.freq
-> 3265     else SeriesGroupBy(
   3266         self, by=by, level=level, dropna=dropna, sort=sort
   3267     )
   3268 )

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:82, in GroupBy.__init__(self, obj, by, level, sort, as_index, dropna)
     80     self.grouping = by
     81 else:
---> 82     self.grouping = _Grouping(obj, by, level)

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1488, in _Grouping.__init__(self, obj, by, level)
   1485 # Need to keep track of named key columns
   1486 # to support `as_index=False` correctly
   1487 self._named_columns = []
-> 1488 self._handle_by_or_level(by, level)
   1490 if len(obj) and not len(self._key_columns):
   1491     raise ValueError("No group keys passed")

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1513, in _Grouping._handle_by_or_level(self, by, level)
   1511     self._handle_mapping(by)
   1512 elif isinstance(by, Grouper):
-> 1513     self._handle_grouper(by)
   1514 else:
   1515     try:

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1579, in _Grouping._handle_grouper(self, by)
   1577     self._handle_frequency_grouper(by)
   1578 elif by.key:
-> 1579     self._handle_label(by.key)
   1580 else:
   1581     self._handle_level(by.level)

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/groupby/groupby.py:1571, in _Grouping._handle_label(self, by)
   1570 def _handle_label(self, by):
-> 1571     self._key_columns.append(self._obj._data[by])
   1572     self.names.append(by)
   1573     self._named_columns.append(by)

File ~/conda/envs/rapids-22.04/lib/python3.8/site-packages/cudf/core/column_accessor.py:156, in ColumnAccessor.__getitem__(self, key)
    155 def __getitem__(self, key: Any) -> ColumnBase:
--> 156     return self._data[key]

KeyError: <DateOffset: minutes=10>

Env:

conda list | grep "rapids\|dask" # packages in environment at /home/nicholasb/conda/envs/rapids-22.04: cucim 22.04.00a220201 cuda_11_py38_g0861858_17 rapidsai-nightly cudf 22.04.00a220201 cuda_11_py38_g2c6b0dac61_95 rapidsai-nightly cudf_kafka 22.04.00a220201 py38_g2c6b0dac61_95 rapidsai-nightly cugraph 22.04.00a220201 cuda11_py38_g2b950598_32 rapidsai-nightly cuml 22.04.00a220201 cuda11_py38_ga70044cf2_39 rapidsai-nightly cusignal 22.04.00a220201 py39_gc620d82_7 rapidsai-nightly cuspatial 22.04.00a220201 py38_ge00d63f_9 rapidsai-nightly custreamz 22.04.00a220201 py38_g2c6b0dac61_95 rapidsai-nightly cuxfilter 22.04.00a220201 py38_g17de7c0_7 rapidsai-nightly dask 2022.1.0 pyhd8ed1ab_0 conda-forge dask-core 2022.1.0 pyhd8ed1ab_0 conda-forge dask-cuda 22.04.00a220201 py38_8 rapidsai-nightly dask-cudf 22.04.00a220201 cuda_11_py38_g2c6b0dac61_95 rapidsai-nightly libcucim 22.04.00a220201 cuda11_g0861858_17 rapidsai-nightly libcudf 22.04.00a220201 cuda11_g2c6b0dac61_95 rapidsai-nightly libcudf_kafka 22.04.00a220201 g2c6b0dac61_95 rapidsai-nightly libcugraph 22.04.00a220201 cuda11_g2b950598_32 rapidsai-nightly libcugraph_etl 22.04.00a220201 cuda11_g2b950598_32 rapidsai-nightly libcuml 22.04.00a220201 cuda11_ga70044cf2_39 rapidsai-nightly libcumlprims 22.04.00a220121 cuda11_g130a9d4_8 rapidsai-nightly libcuspatial 22.04.00a220201 cuda11_ge00d63f_9 rapidsai-nightly librmm 22.04.00a220201 cuda11_g81d523a_15 rapidsai-nightly libxgboost 1.5.0dev.rapidsai22.04 cuda11.2_0 rapidsai-nightly ptxcompiler 0.2.0 py38h98f4b32_0 rapidsai-nightly py-xgboost 1.5.0dev.rapidsai22.04 cuda11.2py38_0 rapidsai-nightly pylibcugraph 22.04.00a220201 cuda11_py38_g2b950598_32 rapidsai-nightly rapids 22.04.00a220201 cuda11_py38_g1db237c_48 rapidsai-nightly rapids-xgboost 22.04.00a220201 cuda11_py38_g1db237c_48 rapidsai-nightly rmm 22.04.00a220201 cuda11_py38_g81d523a_15_has_cma rapidsai-nightly ucx 1.12.0+gd367332 cuda11.2_0 rapidsai-nightly ucx-proc 1.0.0 gpu rapidsai-nightly ucx-py 0.25.00a220201 py38_gd367332_4 rapidsai-nightly xgboost 1.5.0dev.rapidsai22.04 cuda11.2py38_0 rapidsai-nightly

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