docs: fix README signatures, add viz API page, complete study API docs#102
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jc-macdonald merged 3 commits intomainfrom Apr 17, 2026
Merged
docs: fix README signatures, add viz API page, complete study API docs#102jc-macdonald merged 3 commits intomainfrom
jc-macdonald merged 3 commits intomainfrom
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- Fix 4 incorrect call signatures in README quick-start and API overview: study.front(phase), study.front_hypervolume(phase, ref), hypervolume(front, ref_point, dirs), screen(n_trajectories=) - Create docs/api/viz.md with mkdocstrings directives for plot_front, plot_parallel, plot_scores, plot_calibration - Add weighted_sum_filter and feasibility_filter to docs/api/study.md - Add Visualization row to docs/index.md overview table - Update Study row in index.md to list all 3 filter functions - Add Visualization entry to mkdocs.yml nav
Bayesian example: - Add energy score and MAE observables (5 metrics total) - Add Constraint + feasibility_filter demo (coverage_95 >= 0.90) - Add weighted_sum_filter demo - Add IGD+ quality indicator alongside hypervolume - Add Sobol grid generation (build_grid method='sobol') - Add Study workflow with summary() and stack() - Add Phase.scorer override in multi-fidelity section CSTR example: - Add Sobol screening (screen method='sobol') - Add Constraint definitions (min_conversion, max_energy) - Add progress callback + parallel execution (n_jobs=2) - Add adaptive phase (Phase grid='adaptive' with optuna) - Add feasibility_filter phase demo - Add IGD+ and Study.summary() to results
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This pull request significantly expands the documentation and examples for trade-study workflows, with a particular focus on multi-objective Bayesian optimization, feasibility constraints, new filtering methods, and improved metrics. It introduces new API documentation, adds comprehensive guide sections and usage demonstrations, and updates the example scripts to showcase advanced features like feasibility filtering, weighted-sum filtering, and new quality indicators.
Documentation and Guide Enhancements:
weighted_sum_filter,feasibility_filter, and visualization utilities. [1] [2] [3]Example Script Improvements (
examples/bayesian_study.py):Constraint,feasibility_filter,weighted_sum_filter,hypervolume, andigd_plus. The script now includes functions to showcase each of these features with realistic outputs and explanations. [1] [2] [3] [4] [5]API and Output Consistency:
study.front,study.front_hypervolume, andscreen, reflecting changes in their arguments and output formats. [1] [2] [3]New Features and Demonstrations:
These changes collectively provide much richer guidance and tooling for users performing advanced multi-objective optimization and analysis with the package.