[query] Use EncodedLiteral instead of Literal from python to scala#13814
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danking merged 40 commits intoOct 25, 2023
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danking
previously requested changes
Oct 13, 2023
| return genetics.Call(alleles, phased) | ||
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| def _convert_to_encoding(self, byte_writer, value): | ||
| pass # TODO |
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heh yeah this one is a bit complex
| ) | ||
| def test_literal_encoding(value): | ||
| lit = hl.literal(value) | ||
| round_trip = lit.dtype._from_encoding(lit.dtype._to_encoding(value)) |
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Should be mostly if not entirely passing now.
| byte_writer.write_int64(dim) | ||
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| if self.element_type in _numeric_types: | ||
| byte_writer.write_bytes(value.data) |
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Getting the element ordering right here makes me a little nervous. Should maybe have some more rigorous tests.
danking
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Oct 16, 2023
| interval = hl.Interval(start=start, end=end, includes_start=True, includes_end=False) | ||
| _assert_encoding_roundtrip(start) | ||
| _assert_encoding_roundtrip(end) | ||
| _assert_encoding_roundtrip(interval) |
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Each of these three tests is slightly different, but they all ultimately call the same method. What's the argument against just parameterizing _assert_encoding_roundtrip itself?
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I tried that at first, and looks like I can do this with Call, but I can't instantiate a Locus before init (because it tries to load a reference genome) so I can't include a Locus in the parametrize.
| {"foo", "bar", "baz"}, | ||
| {"a": 1, "b": 2}, | ||
| np.array([[1, 2], [3, 4], [5, 6]]), | ||
| np.array([[1, 2], [3, 4], [5, 6]]).T, |
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Pursuant to your comment above can we add:
- Test for 0d np.array
- Test for 1d np.array
- Test for 2d np.array
- Test for 4d np.array
- Transposition of all of the above
danking
previously approved these changes
Oct 18, 2023
| byte_writer.write_bytes(value.data) | ||
| else: | ||
| for elem in np.nditer(value, order='F'): | ||
| self.element_type._convert_to_encoding(byte_writer, elem) |
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I feel good about this now. You're using ENDArrayColumnMajor and the column ordering is Fortran, so ti all checks out.
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Oct 18, 2023
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The bugs about getting non integral values is because pandas is giving you a pandas NA.
| while i < length: | ||
| missing_byte = 0 | ||
| for j in range(min(8, length - i)): | ||
| if value[i + j] is None: |
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Suggested change
| if value[i + j] is None: | |
| if value[i + j] in (None, pd.NA): |
danking
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Oct 18, 2023
| while i < length: | ||
| missing_byte = 0 | ||
| for j in range(min(8, length - i)): | ||
| if value[keys[i + j]] is None: |
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same here for missing values from pandas.
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dismissing as theres been a non-trivial change
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Hmm. Seems like the ArraySorter still isn't quite right? I also saw some issues with sockets timing out but I don't know what to make of those yet. |
danking
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Oct 20, 2023
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something still seems off here b/c of the test failures
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| val sorter = new ArraySorter(EmitRegion(cb.emb, region), ab) | ||
| def lessThan(cb: EmitCodeBuilder, region: Value[Region], l: Value[_], r: Value[_]): Value[Boolean] = { | ||
| val lk = cb.memoize(sct.loadToSValue(cb, l).asBaseStruct.loadField(cb, 0)) |
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@patrick-schultz is a missing key value handled correctly by this code?
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See failure here: https://batch.hail.is/batches/8064082/jobs/83
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Given how we're generating these from Python, how could a key value ever be missing?
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I don't see anything obviously wrong. I can try checking out the branch and debugging.
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Thanks Patrick!
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Hmm. What's special about The other issue seems like it might be simpler, we're just setting a bad type on a field: |
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Ah we have the same ordering problem with sets as we have with dicts |
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We sure do |
danking
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Oct 25, 2023
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CHANGELOG: Pipelines that are memory-bound by copious use of
hl.literal, such asvds.filter_intervals, require substantially less memory.Closes #13757