Notebooks¶
The repository ships explanatory notebooks (notebooks/) that narrate the
why of the methodological decisions, show the data and its distributions, and
produce analysis plots. Run them with uv run jupyter lab from the project root.
| # | Notebook | Use case | Capabilities |
|---|---|---|---|
| 01 | 01_screening_factores |
Identify the influential factors among 7 candidates in 8 runs | plackett_burman, main_effects, half_normal_plot, fold |
| 02 | 02_superficie_respuesta |
Optimize the yield of a 3-factor process | box_behnken, central_composite, fit_linear_model |
| 03 | 03_diseno_optimo |
D/A/I-optimal design in an irregular region with a fixed budget | optimal_design (KL + Fedorov), d_criterion |
| 04 | 04_quimica_optimizacion_reaccion |
[Chemistry] Optimize a reaction end-to-end, benchmarked against ground truth and random sampling | fractional_factorial, box_behnken, evaluate, alias_matrix, fds_plot |
| 05 | 05_optimizacion_diseno_optimo |
[Optimization] A D-optimal design beats random sampling as a surrogate in a constrained region | optimal_design, efficiencies, fds_plot |
| 06 | 06_machine_learning_tuning |
[ML] Hyperparameter tuning: DoE beats random search at equal budget and says which knobs matter | definitive_screening, main_effects, box_behnken |
| 07 | 07_quantum_ml_feature_map |
[Quantum ML] Screen a quantum feature map / kernel with minimal circuit evaluations (each = QPU time) | definitive_screening, evaluate, central_composite |
| 08 | 08_asesor_casos |
[Gallery] The recommend_design advisor across 6 cases side by side |
recommend_design, Recommendation |
| 09 | 09_analisis_bloques_mixed |
[Analysis] Fixed blocks, HC3 SE, lack-of-fit and REML mixed models | attach_blocks, fit_linear_model, lack_of_fit, fit_mixed_model |
| 10 | 10_sequential_augment |
[Sequential] Augment a weak design and decide if extra runs are worth it | propose_next_runs, augment_design, compare_designs |
Notebooks 04–07 share the build → evaluate → benchmark pattern: each uses a known ground-truth function to measure how well the DoE recovers the optimum, and compares it against a baseline (random / grid / random search). They are the empirical argument for why doekit is useful where each experiment is expensive.
Notebooks are versioned with their executed outputs, so they read without running anything. Regenerate with: