Enable Coefficients for `DiscreteSumConstraint` and `from_simplex` by Scienfitz · Pull Request #786 · emdgroup/baybe

Scienfitz · 2026-04-29T15:39:26Z

use-case motivated addition:

DiscreteSumConstraint gets a coefficients that works akin to whats been done in the continuous constraint
from_simplex gets a simplex_coefficients keyword that allows specifying coefficients for the simplex parameters. This is possible by changing the way the max/min incoming sums are assessed
I'm using matrix multiplication @ for from_simplex because we ensure by construciton that the incoming array is contiguous and does not have to be copied for a reshape and multiplication. This contiguousness is not guaranteed for data[params] in get_invalid in DiscreteSumConstraint so its more memory efficient to use per-column approaches in the assumption of a small countable amount of parameters (usually the case)
The restriction that values of simplex parameters must be positive has been lifted (no longer needed)

Unrelated optimization
I also replaced the inner loop of from_simplex to use numpy and not pandas. This is also motivated by memory and time efficiency. This commit can be dropped tho if undesired, but there are singificant time and mem savings:

Scenario	Rows	main time (s)	feature time (s)	Δ time	main mem (MB)	feature mem (MB)	Δ mem
4p × 11v	1,001	0.025	0.005	-81%	0.3	0.2	-48%
6p × 11v	8,008	0.068	0.005	-93%	4.1	1.6	-62%
8p × 11v	43,758	0.280	0.010	-96%	32.9	10.8	-67%
6p × 21v	230,230	0.904	0.019	-98%	137.3	46.2	-66%
6p × 21v boundary	53,130	0.961	0.020	-98%	137.3	45.5	-67%

(p = simplex parameters, v = values)

Copilot

Pull request overview

This PR extends BayBE’s discrete constraint and search space construction capabilities by adding weighted-sum support to DiscreteSumConstraint and enabling weighted simplex construction via SubspaceDiscrete.from_simplex(simplex_coefficients=...). It also refactors the hot loop in from_simplex to use NumPy arrays for improved performance and memory usage.

Changes:

Add coefficients to DiscreteSumConstraint (defaulting to all ones) and apply weighting in both pandas and polars evaluation paths.
Add keyword-only simplex_coefficients to SubspaceDiscrete.from_simplex (defaulting to all ones) and adjust pruning logic to work with weighted sums.
Expand test coverage for the new weighted behaviors and length-mismatch validation.

Reviewed changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
`baybe/constraints/discrete.py`	Adds `DiscreteSumConstraint.coefficients` with validation + weighted evaluation (pandas/polars).
`baybe/searchspace/discrete.py`	Adds `simplex_coefficients`, makes args keyword-only, and rewrites `from_simplex` construction loop using NumPy with weighted pruning.
`CHANGELOG.md`	Documents the new weighted features and the keyword-only breaking change for `from_simplex`.
`tests/validation/test_constraint_validation.py`	Adds validation test for `DiscreteSumConstraint` coefficients length mismatch.
`tests/hypothesis_strategies/constraints.py`	Updates Hypothesis discrete-constraint strategy generation to optionally include coefficients.
`tests/hypothesis_strategies/alternative_creation/test_searchspace.py`	Adds brute-force parity tests for weighted simplex generation and mismatch validation.
`tests/constraints/test_constraints_polars.py`	Adds parity test to ensure polars vs pandas agree for weighted sum constraints.
`tests/constraints/test_constraints_discrete.py`	Adds behavioral tests for weighted sum constraints in the discrete constraint suite.

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AVHopp

Main question is whether or not we still need the assumption on the non-negativity of parameter values

AVHopp

Only open point from my end is whether or not we want to allow negative values. I am happy with both possible solutions (either investigate and implement now or defer), and since this is captured by a comment, take my approve.

Follows the ContinuousLinearConstraint pattern: coefficients default to all-ones (preserving existing behavior), are validated for length parity with parameters, and the weighted sum is evaluated via a single numpy matrix-vector product to avoid intermediate DataFrame copies.

Reworks the signature to make all optional arguments keyword-only (via *). Adds simplex_coefficients for a weighted simplex sum constraint. The incremental early-pruning algorithm is generalised to handle negative coefficients correctly by computing per-parameter weighted min/max contributions rather than assuming monotonicity, and by keeping nonzero cardinality tracking separate (raw parameter values, coefficient-sign independent). The weighted row-sum uses a single numpy matrix-vector product to avoid intermediate DataFrame copies.

…plex_coefficients Weighted-sum filtering correctness (default and custom coefficients) added to the existing discrete constraint test file, parametrized across all-ones, scaled, negative, and equality operator cases. Simplex coefficient tests (brute-force equivalence, mixed-sign, boundary_only, and equivalence with from_product+DiscreteSumConstraint) added to the existing from_simplex test file. Validation error tests for length mismatch added to the constraint validation test file.

… sum The previous approach (to_numpy() @ np.asarray(coefficients)) consolidates all referenced columns into a contiguous (N, k) array before computing the dot product. When the constraint parameters are non-adjacent columns in the DataFrame this forces a full (N, k) memory copy regardless. For the typical use case of sum constraints (k < 10 parameters), a column-by-column accumulation avoids this: each data[p].to_numpy() is a zero-copy view of a single contiguous column, the scalar multiply produces one (N,) temporary, and the built-in sum accumulates in-place. No (N, k) consolidation allocation is needed. Also removes the now-unused numpy import.

Replaces the pandas-based inner loop (pd.merge cross-join, pd.DataFrame, df.drop inplace) with raw numpy operations (np.repeat + np.tile + np.column_stack for cross-joins, boolean indexing for pruning). The DataFrame is created once at the end. This avoids per-iteration pandas overhead (index management, BlockManager, merge machinery) and reduces peak memory by eliminating duplicate DataFrame+numpy representations.

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

…implex

Scienfitz · 2026-07-07T14:11:54Z

benchmark results for reference

Scenario	Rows	old time (s)	new time (s)	Δ time	old mem (MB)	new mem (MB)	Δ mem
4p × 11v	1,001	0.025	0.005	-81%	0.3	0.2	-48%
6p × 11v	8,008	0.068	0.005	-93%	4.1	1.6	-62%
8p × 11v	43,758	0.280	0.010	-96%	32.9	10.8	-67%
6p × 21v	230,230	0.904	0.019	-98%	137.3	46.2	-66%
6p × 21v boundary	53,130	0.961	0.020	-98%	137.3	45.5	-67%

(p = simplex parameters, v = values)

Scienfitz self-assigned this Apr 29, 2026

Scienfitz added enhancement Expand / change existing functionality new feature New functionality labels Apr 29, 2026

Scienfitz force-pushed the feature/sum_constraint_coefficients branch from f8b3f17 to cce898a Compare May 7, 2026 11:20

Scienfitz marked this pull request as ready for review May 7, 2026 11:48

Scienfitz requested review from AVHopp and AdrianSosic as code owners May 7, 2026 11:48

Copilot AI review requested due to automatic review settings May 7, 2026 11:48

Copilot started reviewing on behalf of Scienfitz May 7, 2026 11:49 View session

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Scienfitz force-pushed the feature/sum_constraint_coefficients branch from 466cf21 to a232085 Compare May 7, 2026 12:29

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Scienfitz added this to the 0.16.0 milestone Jun 2, 2026

Scienfitz force-pushed the feature/sum_constraint_coefficients branch 2 times, most recently from 3cc4c94 to df93b36 Compare June 3, 2026 16:56

AdrianSosic requested changes Jun 8, 2026

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Scienfitz force-pushed the feature/sum_constraint_coefficients branch from d9f4faa to 2ce4a29 Compare June 10, 2026 17:51

AVHopp approved these changes Jun 17, 2026

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Scienfitz force-pushed the feature/sum_constraint_coefficients branch 3 times, most recently from 65bb3d4 to 2e0b9ca Compare July 6, 2026 13:11

AdrianSosic approved these changes Jul 7, 2026

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Scienfitz added 6 commits July 7, 2026 16:07

Use any() for non-negativity check in from_simplex

19d3df0

Scienfitz and others added 16 commits July 7, 2026 16:07

Fix mypy error

8cf2438

Improve validation in from_simplex

9aaecfa

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

Improve deserialization validation

4cec5f8

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

Forbid all-zero coefficients in linear/sum constraints

cde860c

Forbid individual zero coefficients in linear/sum constraints

69dd7bb

Add tests for non-zero coefficient validation

6146bcf

Unify coefficient validation tests for both constraint types

e3faa40

Merge simplex coefficient tests into three-way comparison

1cb4810

Consolidate polars sum constraint tests

0961e1b

Improve docstring

c4d3c8f

Use active settings dtype

3a4c4d9

Expand Raises section

c4c4bf4

Use broadcasting to avoid materializing intermediate arrays in from_s…

5bb5651

…implex

Allow negative parameter values in from_simplex

e27743d

Update CHANGELOG

8b72c5c

Simplify from_simplex loop by seeding with empty configuration

0884e25

Scienfitz force-pushed the feature/sum_constraint_coefficients branch from cea19b7 to 0884e25 Compare July 7, 2026 14:07

Scienfitz merged commit d8bb2a8 into main Jul 7, 2026
23 of 24 checks passed

Scienfitz deleted the feature/sum_constraint_coefficients branch July 7, 2026 15:18

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Scienfitz commented Jul 7, 2026

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Scienfitz commented Apr 29, 2026 •

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