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Column equality testing fixes #10011

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merged 10 commits into from
Feb 8, 2022
Merged

Column equality testing fixes #10011

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a77f861
special case column assert
brandon-b-miller Jan 11, 2022
812577b
updates
brandon-b-miller Jan 11, 2022
4562f75
Merge branch 'branch-22.02' into fix-assert-frame-eq-dtype-kwarg
brandon-b-miller Jan 11, 2022
5517835
fix other issue
brandon-b-miller Jan 11, 2022
f088adf
Merge branch 'branch-22.02' into fix-assert-frame-eq-dtype-kwarg
brandon-b-miller Jan 25, 2022
042a4ed
reorganize logic
brandon-b-miller Jan 25, 2022
7c1b40d
Merge branch 'branch-22.04' into fix-assert-frame-eq-dtype-kwarg
brandon-b-miller Feb 1, 2022
2644898
address reviews, add tests
brandon-b-miller Feb 4, 2022
599253d
Update python/cudf/cudf/testing/testing.py
brandon-b-miller Feb 7, 2022
a44b97f
minor update
brandon-b-miller Feb 7, 2022
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70 changes: 49 additions & 21 deletions python/cudf/cudf/testing/testing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,12 @@
import pandas as pd

import cudf
from cudf.api.types import is_categorical_dtype, is_numeric_dtype
from cudf._lib.unary import is_nan
from cudf.api.types import (
is_categorical_dtype,
is_numeric_dtype,
is_string_dtype,
)
from cudf.core._compat import PANDAS_GE_110


Expand Down Expand Up @@ -201,31 +206,49 @@ def assert_column_equal(
):
left = left.astype(left.categories.dtype)
right = right.astype(right.categories.dtype)

columns_equal = False
try:
columns_equal = (
(
cp.all(left.isnull().values == right.isnull().values)
and cp.allclose(
if left.size == right.size == 0:
columns_equal = True
elif not (
(is_string_dtype(left) and is_numeric_dtype(right))
or (is_numeric_dtype(left) and is_string_dtype(right))
):
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Not sure if I follow these checks. Where the handler if the input falls in these dtypes?

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@vyasr vyasr Jan 20, 2022 •
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Yeah this seems odd. Are we just generally looking to avoid checking this for mismatched dtypes?

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If one column is string and the other is numeric, we can assume the columns are not equal. (1 != '1'). These lines check to make sure exactly one of the columns is string and the other is numeric, in which case we avoid the entire try/except block and therefore avoid any opportunity for columns_equal to be set to True. We should end up on line 243 from there.

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Is this only an issue for string types, or do we need to worry about other types as well? I see categoricals are handled above, what about list/struct dtypes?

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I looked into what happens here and it would appear that when list or struct are involved we fall into the left.equals(right) check at the very end and end up with a TypeError, except in the case that we're comparing list to list or struct to struct. That should probably not happen.

I see the point I think you are making though: there's a certain set of dtypes (beyond just string) where, even if check_dtype=False, we know up front that it's a 100% mismatch between non-null elements simply because a struct can't compare as equal to a "not struct". I think these dtypes are:

  • String
  • List
  • Struct
  • Interval
  • Decimal

I suppose the only edge case here would be that we should arguably return True even for that subset of dtypes if the columns are fully null.

If this seems like the correct logic I am happy to go back and wire it up as such here.

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Yep that all sounds good. For the fully null case, I think whether or not we return True should be determined by check_dtypes, but if someone has a different opinion I'm open to a different result.

try:
# nulls must be in the same places for all dtypes
nulls_equal = cp.all(left.isnull().values == right.isnull().values)
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if not check_exact and is_numeric_dtype(left):
# non-null values must be the same
values_equal = cp.allclose(
left[left.isnull().unary_operator("not")].values,
right[right.isnull().unary_operator("not")].values,
)
)
if not check_exact and is_numeric_dtype(left)
else left.equals(right)
)
except TypeError as e:
if str(e) != "Categoricals can only compare with the same type":
raise e
if is_categorical_dtype(left) and is_categorical_dtype(right):
left = left.astype(left.categories.dtype)
right = right.astype(right.categories.dtype)
if not left.dtype.kind == right.dtype.kind == "f":
columns_equal = nulls_equal and values_equal
else:
# nans must be the same for float types
nans_equal = cp.all(
is_nan(left).values == is_nan(right).values
)
columns_equal = nulls_equal and values_equal and nans_equal
else:
columns_equal = left.equals(right)
except TypeError as e:
if str(e) != "Categoricals can only compare with the same type":
raise e
if is_categorical_dtype(left) and is_categorical_dtype(right):
left = left.astype(left.categories.dtype)
right = right.astype(right.categories.dtype)
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if not columns_equal:
msg1 = f"{left.values_host}"
msg2 = f"{right.values_host}"
ldata = [val for val in left.to_pandas(nullable=True)]
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Side question - I've wondered how reliable nullable=True is given pandas support for nullable float dtype is still non-public?

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It seems like they're being fairly forward with these as of 1.2.0

rdata = [val for val in right.to_pandas(nullable=True)]
msg1 = f"{ldata}"
msg2 = f"{rdata}"
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try:
diff = left.apply_boolean_mask(left != right).size
diff = 0
for i in range(left.size):
if not null_safe_scalar_equals(left[i], right[i]):
diff += 1
diff = diff * 100.0 / left.size
except BaseException:
diff = 100.0
Expand All @@ -234,6 +257,12 @@ def assert_column_equal(
)


def null_safe_scalar_equals(left, right):
if left in {cudf.NA, np.nan} or right in {cudf.NA, np.nan}:
return left is right
return left == right
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def assert_index_equal(
left,
right,
Expand Down Expand Up @@ -358,7 +387,6 @@ def assert_index_equal(
obj=mul_obj,
)
return

assert_column_equal(
left._columns[0],
right._columns[0],
Expand Down