v0.4.2

What's Changed

• Add a clickable demo preview to the README by @yangjun42 in #9
• Fit the full scheduling demo in the README preview by @yangjun42 in #10
• Clean up the README demo preview and improve resolution by @yangjun42 in #11
• Fix class coverage, CUDA RNG and configuration boundaries by @yangjun42 in #12

Full Changelog: v0.4.1...v0.4.2

v0.4.2rc1

What's Changed

• Add a clickable demo preview to the README by @yangjun42 in #9
• Fit the full scheduling demo in the README preview by @yangjun42 in #10
• Clean up the README demo preview and improve resolution by @yangjun42 in #11

Full Changelog: v0.4.1...v0.4.2rc1

v0.4.1

What's Changed

• Fix schedule finalization and Torch evaluation; add interactive demo by @yangjun42 in #8

Full Changelog: v0.4.0...v0.4.1

v0.4.1rc1

Full Changelog: v0.4.0...v0.4.1rc1

v0.4.0

What's Changed

• Clarify copyright attribution and package project notices by @yangjun42 in #5
• Simplify repository layout and focus project documentation by @yangjun42 in #6
• Support custom retraining costs and historical model state by @yangjun42 in #7

Full Changelog: v0.3.1...v0.4.0

v0.4.0rc1

What's Changed

• Clarify copyright attribution and package project notices by @yangjun42 in #5
• Simplify repository layout and focus project documentation by @yangjun42 in #6

Full Changelog: v0.3.1...v0.4.0rc1

v0.3.1

RetrainOpt 0.3.1 fixes input validation and strengthens the tests for approximate planning and online continuation.

• Reject fractional/non-finite class labels and malformed Prototype features before they can produce misleading losses. Preserve valid integer-valued labels and NumPy sample rows.
• Validate the actual model IDs and boolean observation flags returned by custom evaluators.
• Exercise real GP fitting and prediction of unobserved candidates in the BoTorch integration test.
• Verify continuation from positive and negative accumulated staleness against uninterrupted online execution, including actions, costs and model state.
• Clarify Base's label-independent decision rule, the controller's measurement requirements, and the existing approximate-search and initialization conventions.

The public planning and online APIs are unchanged. Inputs that were silently coerced or accepted incorrectly may now raise ValueError; see the migration guide.

python -m pip install --upgrade retrainopt

Validation includes the core/optional-backend matrices, minimum direct dependencies, strict documentation builds, reproducible wheel and source builds, and anonymous index installation with artifact checksum verification.

Full changelog · Changes since v0.3.0

v0.3.1rc1

What's Changed

• Make README retraining overview more approachable by @yangjun42 in #3

Full Changelog: v0.3.0...v0.3.1rc1

v0.3.0

What's Changed

• Add online retraining policies and publish RetrainOpt 0.3 by @yangjun42 in #2

Full Changelog: v0.2.0...v0.3.0

v0.3.0rc1

Full Changelog: v0.2.0...v0.3.0rc1