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Add an invertible sdm.processing.Asinh processor for numerical values, with sinh as its inverse.
Description of the change
The stateless transform compresses numerical magnitudes while retaining their signs and leaves other column types unchanged. It composes with fitted transforms such as standardization in an invertible recipe.
User-provided context: “can we also move asinh as a processor?” This AI-generated draft exposes that requested reusable transform for human review.
Validation
Manual checks match the forward transform exactly to PyTorch's asinh and round-trip through the inverse within floating-point tolerance on CPU and CUDA with float32, float16, and float64. Checks include negative values, zero, NaNs, infinities, and preservation of dtype, device, and shape.
✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.75%. Comparing base (82f9125) to head (b1f0162).
✅ All tests successful. No failed tests found.
Added an Asinh numerical processor that transforms values using the inverse hyperbolic sine and supports converting them back.
The change adds an Asinh numerical processor. It applies inverse hyperbolic sine during transformation and hyperbolic sine during inverse transformation. The processor is exported from both sdm.processing.numerical and sdm.processing.
Changes: Asinh Processor
Layer / File(s)
Summary
Implement and export Asinh sdm/processing/numerical/asinh.py, sdm/processing/numerical/__init__.py, sdm/processing/__init__.py
Adds the processor and exports it from both package levels.
The new Asinh processor works for floating-point numerical data. If a table is built directly with an integer numerical block, large integers can silently lose precision and the inverse will not restore them. Rejecting integer input is a small fix that can be made before or shortly after merge.
Pre-merge checks: 5
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Status
Explanation
Title check
The title clearly and concisely identifies the main change: adding an invertible Asinh processor.
Description check
The description directly explains the Asinh processor, its inverse, intended numerical behavior, composition with fitted transforms, and validation checks.
Docstring Coverage
No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
No eligible functions found in the changed files. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 0 functions across 3 files.
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Follow-up Comments
Reject integer numerical input before applying asinh.
15-15: Minor · Data Integrity & Integration · Quick win
Reject integer numerical input before applying asinh.
TableTensor can contain an integer numerical block when constructed directly. Tensor.asinh() promotes that input to floating point. When promotion uses float32, distinct values such as 16777216 and 16777217 can become identical before the inverse runs. This silently breaks value restoration.
The repository does not require invertible processors to restore the original dtype, so the concern is limited to integer value loss.
Suggested fix
def _transform(self, table: TableTensor) -> TableTensor:
+ if not table.numerical.is_floating_point():+ raise TypeError("Asinh requires floating-point numerical data")
return table.replace_blocks(numerical=table.numerical.asinh())
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Review comment at @sdm/processing/numerical/asinh.py at line 15:
In _transform, reject numerical blocks that are not floating point before
calling asinh, raising a TypeError; preserve the existing transformation for
floating-point inputs.
Reviewing files that changed from the base of the PR and between 82f9125 and 2dfde08.
Files selected for processing: 3
sdm/processing/__init__.py
sdm/processing/numerical/__init__.py
sdm/processing/numerical/asinh.py
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Summary
Add an invertible
sdm.processing.Asinhprocessor for numerical values, withsinhas its inverse.Description of the change
The stateless transform compresses numerical magnitudes while retaining their signs and leaves other column types unchanged. It composes with fitted transforms such as standardization in an invertible recipe.
User-provided context: “can we also move asinh as a processor?” This AI-generated draft exposes that requested reusable transform for human review.
Validation
Manual checks match the forward transform exactly to PyTorch's
asinhand round-trip through the inverse within floating-point tolerance on CPU and CUDA with float32, float16, and float64. Checks include negative values, zero, NaNs, infinities, and preservation of dtype, device, and shape.