Exception when running LabelAmbiguity check

[ItayGabbay]

To reproduce:
https://www.kaggle.com/itay94/notebookf8c78e84d7

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
/tmp/ipykernel_34/2864323761.py in <module>
      1 from deepchecks.checks import LabelAmbiguity
      2 
----> 3 LabelAmbiguity().run(ds_train)

/opt/conda/lib/python3.7/site-packages/deepchecks/base/check.py in wrapped(*args, **kwargs)
    275     @wraps(func)
    276     def wrapped(*args, **kwargs):
--> 277         result = func(*args, **kwargs)
    278         if not isinstance(result, CheckResult):
    279             raise DeepchecksValueError(f'Check {class_instance.name()} expected to return CheckResult bot got: '

/opt/conda/lib/python3.7/site-packages/deepchecks/checks/integrity/label_ambiguity.py in run(self, dataset, model)
     64 
     65         group_unique_data = dataset.data.groupby(dataset.features, dropna=False)
---> 66         group_unique_labels = group_unique_data.nunique()[label_col]
     67 
     68         num_ambiguous = 0

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in nunique(self, dropna)
   1803         obj = self._obj_with_exclusions
   1804         results = self._apply_to_column_groupbys(
-> 1805             lambda sgb: sgb.nunique(dropna), obj=obj
   1806         )
   1807 

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _apply_to_column_groupbys(self, func, obj)
   1709         columns = obj.columns
   1710         results = [
-> 1711             func(col_groupby) for _, col_groupby in self._iterate_column_groupbys(obj)
   1712         ]
   1713 

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in <listcomp>(.0)
   1709         columns = obj.columns
   1710         results = [
-> 1711             func(col_groupby) for _, col_groupby in self._iterate_column_groupbys(obj)
   1712         ]
   1713 

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in <lambda>(sgb)
   1803         obj = self._obj_with_exclusions
   1804         results = self._apply_to_column_groupbys(
-> 1805             lambda sgb: sgb.nunique(dropna), obj=obj
   1806         )
   1807 

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in nunique(self, dropna)
    671 
    672         result = self.obj._constructor(res, index=ri, name=self.obj.name)
--> 673         return self._reindex_output(result, fill_value=0)
    674 
    675     @doc(Series.describe)

/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _reindex_output(self, output, fill_value)
   3163         levels_list = [ping.group_index for ping in groupings]
   3164         index, _ = MultiIndex.from_product(
-> 3165             levels_list, names=self.grouper.names
   3166         ).sortlevel()
   3167 

/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/multi.py in from_product(cls, iterables, sortorder, names)
    618 
    619         # codes are all ndarrays, so cartesian_product is lossless
--> 620         codes = cartesian_product(codes)
    621         return cls(levels, codes, sortorder=sortorder, names=names)
    622 

/opt/conda/lib/python3.7/site-packages/pandas/core/reshape/util.py in cartesian_product(X)
     52         b = np.zeros_like(cumprodX)
     53 
---> 54     return [tile_compat(np.repeat(x, b[i]), np.product(a[i])) for i, x in enumerate(X)]
     55 
     56 

/opt/conda/lib/python3.7/site-packages/pandas/core/reshape/util.py in <listcomp>(.0)
     52         b = np.zeros_like(cumprodX)
     53 
---> 54     return [tile_compat(np.repeat(x, b[i]), np.product(a[i])) for i, x in enumerate(X)]
     55 
     56 

<__array_function__ internals> in repeat(*args, **kwargs)

/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py in repeat(a, repeats, axis)
    477 
    478     """
--> 479     return _wrapfunc(a, 'repeat', repeats, axis=axis)
    480 
    481 

/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
     56 
     57     try:
---> 58         return bound(*args, **kwds)
     59     except TypeError:
     60         # A TypeError occurs if the object does have such a method in its

MemoryError: Unable to allocate 45.3 PiB for an array with shape (25499357367644160,) and data type int16

[ItayGabbay]

Pandas has a bug with categorical features with many unique values. Please see:

pandas-dev/pandas#45128