v0.1.0

The first stable release of ALF, under the Apache 2.0 license.

Published to PyPI as alf-core and alf-tools 0.1.0.

Added

• RandomAcquisition and UncertaintySampling acquisition functions, giving a random baseline and a pure-exploration strategy.
• Matbench dataset and Modality.MATERIALS, behind a new optional matbench/materials extra.
• SmilesMutationSearch and ElementSubstitutionSearch, search protocols that propose novel molecules and crystals each round.
• GuacaMolOracle, for scoring arbitrary SMILES in online loops.
• top_k on SingleMutantSearch, to seed mutants from more than just the single best sequence.
• BaseModel.embed() and Surrogate.embed(), giving embeddings a contract separate from featurise().

Changed

• CoreSet now calls embed() instead of featurise(). BaseModel.embed() raises NotImplementedError by default, so a custom model that relied on featurise() returning a 2-D embedding must now override embed() — return self.featurise(inputs) restores the previous behaviour.
• Tied labels in LabelledCandidates.get_top_k and sort now break by position, rather than in an unspecified order that varied with array size. Ties are routine, so benchmark numbers may shift and reference results may need regenerating.
• SingleMutantSearch now deduplicates its proposals, so the pool it returns can be smaller than before. It also rejects empty training sets and multi-output labels instead of silently mis-selecting.
• chemprop is capped below 2.3.0, and uv overrides raise the matminer, scipy, monty and scikit-learn floors workspace-wide.

Fixed

• one_hot_encode() pads variable-length batches instead of raising IndexError.
• BoTorchAcquisition featurises candidates, fixing GP models whose Candidate.data is not already numeric.

Full changelog: v0.1.0b0...v0.1.0