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Implement a 1D adaptive sampling learner backed by BTreeMap/BTreeSet internals, matching the semantics of Python's adaptive.Learner1D. - Core Learner1D with tell/tell_pending/tell_many/ask/run methods - LossManager priority queue with O(1) interval lookup + sorted access - Built-in loss functions: Default, Uniform, Resolution, Curvature, Triangle, AbsMinLog, and Python callback support - 44 comprehensive Python tests covering all functionality - Python benchmark script for performance measurement
- Replace .expect() with error handling in python_callback_loss (CRITICAL) - Store out-of-bounds data separately to prevent neighbor query pollution (HIGH) - Keep x_scale constant as bounds span, not derived from data (HIGH) - Expose loss(real) parameter in PyO3 binding (MEDIUM) - Fix empty learner linspace to include right endpoint (MEDIUM) - Add interval-based tiebreaking in ask() loss comparison (MEDIUM) - Use iterator .any() for has_missing_bounds instead of Vec allocation (LOW) - Avoid cloning in rebuild_scale via free function (LOW) - Add 12 new tests covering OOB tells, callback exceptions, loss(real=False)
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Native Rust Learner1D module: BTreeMap-based, 12-15× faster than Python adaptive.Learner1D for typical workloads. 6 built-in losses + Python callback fallback.
run()method keeps the adaptive loop in Rust (only user f(x) crosses PyO3). 56 Python tests + 4 Rust tests pass. ~1,840 LOC total. 3 commits: feat, fix (review findings), refactor (cleanup pass −281 lines).