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cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
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[BUG] dask_cudf pivot_table function is broken: TypeError: StringIndex object is not iterable. #14935

Closed pvnick closed 1 week ago

pvnick commented 8 months ago

Describe the bug Pivot_table fails on a dask_cudf dataframe due to an unimplemented Index iteration function:

Steps/Code to reproduce bug

ddf = dask_cudf.from_cudf(cudf.DataFrame(
    data={
        "A": ["foo", "bar", "bar"],
        "B": ["one", "two", "one"],
        "C": [1, 2, 3]
    }
), npartitions=1)
ddf = ddf.categorize("B")
ddf.pivot_table(index="A", columns="B", values="C")

Error:

TypeError                                 Traceback (most recent call last)
Cell In[3], line 9
      1 ddf = dask_cudf.from_cudf(cudf.DataFrame(
      2     data={
      3         "A": ["foo", "bar", "bar"],
   (...)
      6     }
      7 ), npartitions=1)
      8 ddf = ddf.categorize("B")
----> 9 ddf.pivot_table(index="A", columns="B", values="C")

File lib/python3.10/site-packages/dask/dataframe/core.py:6373, in DataFrame.pivot_table(self, index, columns, values, aggfunc)
   6352 """
   6353 Create a spreadsheet-style pivot table as a DataFrame. Target ``columns``
   6354 must have category dtype to infer result's ``columns``.
   (...)
   6369 table : DataFrame
   6370 """
   6371 from dask.dataframe.reshape import pivot_table
-> 6373 return pivot_table(
   6374     self, index=index, columns=columns, values=values, aggfunc=aggfunc
   6375 )

File lib/python3.10/site-packages/dask/dataframe/reshape.py:233, in pivot_table(df, index, columns, values, aggfunc)
    226     raise ValueError(
    227         "aggfunc must be either " + ", ".join(f"'{x}'" for x in available_aggfuncs)
    228     )
    230 # _emulate can't work for empty data
    231 # the result must have CategoricalIndex columns
--> 233 columns_contents = pd.CategoricalIndex(df[columns].cat.categories, name=columns)
    234 if is_scalar(values):
    235     new_columns = columns_contents

File lib/python3.10/site-packages/pandas/core/indexes/category.py:234, in CategoricalIndex.__new__(cls, data, categories, ordered, dtype, copy, name)
    231 if is_scalar(data):
    232     raise cls._scalar_data_error(data)
--> 234 data = Categorical(
    235     data, categories=categories, ordered=ordered, dtype=dtype, copy=copy
    236 )
    238 return cls._simple_new(data, name=name)

File lib/python3.10/site-packages/pandas/core/arrays/categorical.py:410, in Categorical.__init__(self, values, categories, ordered, dtype, fastpath, copy)
    408         dtype = CategoricalDtype(values.categories, dtype.ordered)
    409 elif not isinstance(values, (ABCIndex, ABCSeries, ExtensionArray)):
--> 410     values = com.convert_to_list_like(values)
    411     if isinstance(values, list) and len(values) == 0:
    412         # By convention, empty lists result in object dtype:
    413         values = np.array([], dtype=object)

File lib/python3.10/site-packages/pandas/core/common.py:541, in convert_to_list_like(values)
    539     return values
    540 elif isinstance(values, abc.Iterable) and not isinstance(values, str):
--> 541     return list(values)
    543 return [values]

File lib/python3.10/site-packages/cudf/utils/utils.py:242, in NotIterable.__iter__(self)
    235 def __iter__(self):
    236     """
    237     Iteration is unsupported.
    238 
    239     See :ref:`iteration <pandas-comparison/iteration>` for more
    240     information.
    241     """
--> 242     raise TypeError(
    243         f"{self.__class__.__name__} object is not iterable. "
    244         f"Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` "
    245         f"if you wish to iterate over the values."
    246     )

TypeError: StringIndex object is not iterable. Consider using `.to_arrow()`, `.to_pandas()` or `.values_host` if you wish to iterate over the values.

Expected behavior Pivot_table succeeds as documented.

Environment overview (please complete the following information) Installed cuDF using pip, using the stable release:

pip install \
    --extra-index-url=https://pypi.nvidia.com \
    cudf-cu12==23.12.* dask-cudf-cu12==23.12.* cuml-cu12==23.12.* \
    cugraph-cu12==23.12.* cuspatial-cu12==23.12.* cuproj-cu12==23.12.* \
    cuxfilter-cu12==23.12.* cucim-cu12==23.12.* pylibraft-cu12==23.12.* \
    raft-dask-cu12==23.12.*

Environment details

<details><summary>Click here to see environment details</summary><pre>

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     | N/A   28C    P0              54W / 400W |      4MiB / 81920MiB |      0%      Default |
     |                                         |                      |             Disabled |
     +-----------------------------------------+----------------------+----------------------+

     +---------------------------------------------------------------------------------------+
     | Processes:                                                                            |
     |  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
     |        ID   ID                                                             Usage      |
     |=======================================================================================|
     |    4   N/A  N/A   2669759      C   python3                                     570MiB |
     |    5   N/A  N/A   1903237      C   pmemd.cuda_SPFP                             762MiB |
     |    6   N/A  N/A   1446394      C   python                                    76510MiB |
     +---------------------------------------------------------------------------------------+

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./print_env.sh: /apps/jupyter/6.5.4/bin/cmake: /apps/jupyter/6.5.4/bin/python3.11: bad interpreter: No such file or directory

     ***g++***
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     Copyright (C) 2018 Free Software Foundation, Inc.
     This is free software; see the source for copying conditions.  There is NO
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     ***nvcc***
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     /blue/ptighe-rapidsai/pvnick/rapids-test/rapids-test/bin/python
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[notice] A new release of pip is available: 23.0.1 -> 23.3.2
[notice] To update, run: pip install --upgrade pip

</pre></details>
beckernick commented 7 months ago

Hi @pvnick , thanks for the report. We'll investigate and follow up on this issue.

bdice commented 6 months ago

Related: https://github.com/rapidsai/cudf/issues/15179

bdice commented 6 months ago

For context, the decision to disallow iteration over GPU objects is intentional -- it keeps users from accidentally triggering many host-device transfers (e.g. in a for loop) that are highly inefficient. This is problematic in some cases when column names are part of an object on the GPU that needs to be iterated over. The solution to this will likely require some code change in dask-cudf to convert the StringIndex into a type that is supported on the host.

wence- commented 6 months ago

While it is inefficient to iterate row-wise over the dataframe, it's pretty difficult to adapt all of dask-dataframe to do something different based on cudf/pandas. Note we can't really do this in dask-cudf without monkey-patching and/or reimplementing dask.dataframe.pivot_table.

I'm not sure the iteration is that inefficient, if we implemented it as (for a stringindex)

def __iter__(self):
    return iter(self.to_pandas())

There's only one device-to-host copy

vyasr commented 4 months ago

I am leaning towards the same view as Lawrence here. We've had these disabled code paths for a long time, and while I understand the rationale I think at this point I'm OK with relaxing this behavior. Especially in light of cudf.pandas or dask integration, disabling a code path in a way that breaks those weights seems less favorable than it may once have.

bdice commented 4 months ago

I’m okay with that proposal. My comments above were primarily to establish historical context — I am alright with changing the behavior to solve compatibility issues.