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ARROW-12431: [Python] Mask is inverted when creating FixedSizeBinaryArray #10199

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ARROW-12431: [Python] Mask is inverted when creating FixedSizeBinaryArray #10199

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e5656e6
Proof of concept
amol- Apr 29, 2021
6e5d952
Add comment
amol- Apr 29, 2021
f8ad0d5
More complete test to confirm VarLength binaries don't face the issue
amol- Apr 30, 2021
b2f936c
linter
amol- Apr 30, 2021
7777a17
lint cpp
amol- Apr 30, 2021
998ac4e
Adapt tests
amol- Apr 30, 2021
0d7672c
do not pass -1 for varlen binaries
amol- May 3, 2021
e06be2e
Iterate over data, not mask
amol- May 4, 2021
85993b9
Faster and simplified implementation
amol- May 5, 2021
8d6f1bc
Revert "Faster and simplified implementation"
amol- May 6, 2021
7cf6cd1
modify array behaviour
amol- May 6, 2021
d0602c7
Fix conversion of strided binary arrays
amol- May 28, 2021
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15 changes: 13 additions & 2 deletions cpp/src/arrow/python/numpy_to_arrow.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -594,9 +594,20 @@ Status NumPyConverter::Visit(const FixedSizeBinaryType& type) {

if (mask_ != nullptr) {
Ndarray1DIndexer<uint8_t> mask_values(mask_);
RETURN_NOT_OK(builder.AppendValues(data, length_, mask_values.data()));
RETURN_NOT_OK(builder.Reserve(length_));
for (int64_t i = 0; i < length_; ++i) {
if (mask_values[i]) {
RETURN_NOT_OK(builder.AppendNull());
} else {
RETURN_NOT_OK(builder.Append(data));
}
data += stride_;
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@pitrou pitrou May 12, 2021

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Does this solve the strided conversion case? If so, perhaps you can add a test for it?

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@amol- amol- May 14, 2021 •
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Sadly not, I expected it would, but I wrote some tests and it wasn't enough. That's why I made https://issues.apache.org/jira/browse/ARROW-12667 as a follow up issue. So that I can test it for all various types and make sure it works in all cases.

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@amol- amol- Jun 15, 2021

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Also added tests and fix for strided binary arrays (with and without mask)

}
} else {
RETURN_NOT_OK(builder.AppendValues(data, length_));
for (int64_t i = 0; i < length_; ++i) {
RETURN_NOT_OK(builder.Append(data));
data += stride_;
}
}

std::shared_ptr<Array> result;
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45 changes: 45 additions & 0 deletions python/pyarrow/tests/test_array.py
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Expand Up @@ -2714,6 +2714,51 @@ def test_array_masked():
assert arr.type == pa.int64()


def test_binary_array_masked():
# ARROW-12431
masked_basic = pa.array([b'\x05'], type=pa.binary(1),
mask=np.array([False]))
assert [b'\x05'] == masked_basic.to_pylist()

# Fixed Length Binary
masked = pa.array(np.array([b'\x05']), type=pa.binary(1),
mask=np.array([False]))
assert [b'\x05'] == masked.to_pylist()

masked_nulls = pa.array(np.array([b'\x05']), type=pa.binary(1),
mask=np.array([True]))
assert [None] == masked_nulls.to_pylist()

# Variable Length Binary
masked = pa.array(np.array([b'\x05']), type=pa.binary(),
mask=np.array([False]))
assert [b'\x05'] == masked.to_pylist()

masked_nulls = pa.array(np.array([b'\x05']), type=pa.binary(),
mask=np.array([True]))
assert [None] == masked_nulls.to_pylist()

# Fixed Length Binary, copy
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@pitrou pitrou May 12, 2021

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I don't understand what this is supposed to test. The fact that a copy is made is just an implementation detail.

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@amol- amol- May 14, 2021 •
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It verifies that the behaviour is the same that we get from variable length binary arrays, which do not reuse the numpy array memory. I don't think it's an implementation detail because it changes the user experience.

The fact that the underlying numpy array is shared or not changes the user experience as it means the user can't modify the original numpy array without indirectly modifying (probably unexpectedly) the Arrow array too.

which lead me to create https://issues.apache.org/jira/browse/ARROW-12666 because in some cases we reuse the numpy memory (all basic types) and in other cases we don't (the string, binary etc... types). The follow up ticket suggests to make that behaviour clear as numpy does by adding a copy=True/False argument to the pyarrow.array  function.

We can discuss further what's the best way to go in that dedicated ticket, here I wanted to make sure we were consistent with that happens when pa.binary() and pa.binary(N) are used. So the test verifies that if you modify the numpy array the arraw array doesn't change.

npa = np.array([b'aaa', b'bbb', b'ccc']*10)
arrow_array = pa.array(npa, type=pa.binary(3),
mask=np.array([False, False, False]*10))
npa[npa == b"bbb"] = b"XXX"
assert ([b'aaa', b'bbb', b'ccc']*10) == arrow_array.to_pylist()


def test_binary_array_strided():
# Masked
nparray = np.array([b"ab", b"cd", b"ef"])
arrow_array = pa.array(nparray[::2], pa.binary(2),
mask=np.array([False, False]))
assert [b"ab", b"ef"] == arrow_array.to_pylist()

# Unmasked
nparray = np.array([b"ab", b"cd", b"ef"])
arrow_array = pa.array(nparray[::2], pa.binary(2))
assert [b"ab", b"ef"] == arrow_array.to_pylist()


def test_array_invalid_mask_raises():
# ARROW-10742
cases = [
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2 changes: 1 addition & 1 deletion python/pyarrow/tests/test_pandas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1705,7 +1705,7 @@ def test_numpy_string_array_to_fixed_size_binary(self):
expected = pa.array(list(arr), type=pa.binary(3))
assert converted.equals(expected)

mask = np.array([True, False, True])
mask = np.array([False, True, False])
converted = pa.array(arr, type=pa.binary(3), mask=mask)
expected = pa.array([b'foo', None, b'baz'], type=pa.binary(3))
assert converted.equals(expected)
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