feat!: require spotoptim 1.0 (sequential) + sf2-safe 21, drop n_jobs_spotoptim#207
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…spotoptim spotoptim 1.0 is sequential-only: n_jobs / eval_batch_size were removed and passing them now raises TypeError. spotforecast2-safe 21.0.0 removed the dead n_jobs_spotoptim config field in lockstep. This change makes sf2 compatible with both and retires all parallel-SpotOptim scaffolding. Changes: - Bump pins: spotoptim[torch]>=1.0.0,<2 and spotforecast2-safe>=21.0.0,<22. spotoptim 1.0 made torch/tensorboard optional ([torch] extra); pin the extra so the TensorBoard tuning pass-through keeps working (was always available via spotoptim's old hard torch dependency). - Declare xgboost explicitly: it is imported at module top-level in spotforecast2.tasks.task_entsoe and the xgb forecaster model, previously satisfied transitively via spotoptim's dependency tree. - SpotOptimStrategy: stop forwarding config.n_jobs_spotoptim into SpotOptim; keep the TensorBoard kwargs pass-through. - spotoptim_search: remove the multiprocessing.Manager shared counter, the parallel-eval detection, the worker-side result-recovery block, and the config_counter/config_counter_lock objective parameters. The trial bar is now unconditional on show_progress; the "config k/N" label counts completed configs. Backtesting-level n_jobs (skforecast) is unchanged. - Tests: delete test_spotoptim_parallel.py; drop the shared-counter / parallel label tests; convert the TensorBoard pass-through test to a sequential run. - Docs: spotoptim_intro narrative updated to sequential-only. BREAKING CHANGE: spotforecast2 now requires spotoptim>=1.0 and spotforecast2-safe>=21. The n_jobs_spotoptim config field is gone and SpotOptim tuning is sequential-only; backtesting parallelism (n_jobs) is unaffected. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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🎉 This PR is included in version 7.0.0-rc.1 🎉 The release is available on GitHub release Your semantic-release bot 📦🚀 |
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🎉 This PR is included in version 7.0.0 🎉 The release is available on GitHub release Your semantic-release bot 📦🚀 |
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Summary
Makes spotforecast2 compatible with the breaking spotoptim 1.0 (sequential-only, lean deps) and spotforecast2-safe 21.0.0 (which removed the dead
n_jobs_spotoptimconfig field), and retires all parallel-SpotOptim scaffolding.Part of the coordinated migration: sf2-safe 21.0.0 (released) → sf2 7.0.0 (this PR) → bart26k-lecture.
Changes
spotoptim[torch]>=1.0.0,<2,spotforecast2-safe>=21.0.0,<22. spotoptim 1.0 moved torch/tensorboard to its[torch]extra — pinned so the TensorBoard tuning pass-through keeps working (it was always present via spotoptim's old hard torch dep).xgboostexplicitly: imported at module top-level intasks.task_entsoeand the xgb forecaster model; previously satisfied transitively via spotoptim. The slimming exposed it as an undeclared direct dependency.SpotOptimStrategy: stop forwardingconfig.n_jobs_spotoptimintoSpotOptim; TensorBoard kwargs pass-through kept.spotoptim_search: remove themultiprocessing.Managershared counter, parallel-eval detection, worker-side result-recovery block, and theconfig_counter/config_counter_lockobjective params. Trial bar is now unconditional onshow_progress; the "config k/N" label counts completed configs. Backtesting-leveln_jobs(skforecast) is unchanged.test_spotoptim_parallel.py; drop the shared-counter / parallel-label tests; convert the TensorBoard pass-through test to a sequential run.spotoptim_intronarrative updated to sequential-only.Verification
ruff checkclean; no SpotOptim-leveln_jobsreaches the constructor.docs/quartodoc_build.py+quartodoc interlinksregenerate cleanly.Breaking change
sf2 now requires
spotoptim>=1.0andspotforecast2-safe>=21. Then_jobs_spotoptimconfig field is gone and SpotOptim tuning is sequential-only; backtesting parallelism is unaffected. → semantic-release major (7.0.0).🤖 Generated with Claude Code