Configuration¶

Each pipeline layer accepts an optional config object. Defaults work well for most cases, but all parameters can be tuned.

Full Example¶

result, analyzer = surrox.run(
    problem=problem,
    dataframe=df,
    surrogate_config=surrox.TrainingConfig(
        n_trials=100,
        ensemble_size=7,
        cv_folds=10,
        default_coverage=0.95,
    ),
    optimizer_config=surrox.OptimizerConfig(
        population_size=200,
        n_generations=500,
    ),
    analysis_config=surrox.AnalysisConfig(
        shap_background_size=200,
        pdp_grid_resolution=100,
    ),
)

TrainingConfig¶

Controls surrogate model training: HPO budget, ensemble construction, and conformal calibration.

Parameter	Default	Description
`n_trials`	50	Optuna HPO trials per target column
`cv_folds`	5	Cross-validation folds
`ensemble_size`	5	Maximum models in the ensemble
`diversity_threshold`	0.95	Max correlation between ensemble members
`calibration_fraction`	0.2	Data fraction held out for conformal calibration
`default_coverage`	0.9	Conformal prediction interval coverage
`study_timeout_s`	300	Optuna study timeout in seconds
`min_r2`	0.7	Minimum R² quality threshold (None to disable)
`random_seed`	42	Random seed

See TrainingConfig for the full API.

OptimizerConfig¶

Controls the evolutionary optimization.

Parameter	Default	Description
`population_size`	100	Population size for the evolutionary algorithm
`n_generations`	200	Number of generations
`extrapolation_k`	5	k-NN neighbors for extrapolation detection
`extrapolation_threshold`	2.0	Distance threshold for extrapolation flag
`constraint_confidence`	0.95	Conformal confidence for constraint evaluation
`seed`	42	Random seed

See OptimizerConfig for the full API.

AnalysisConfig¶

Controls the post-optimization analysis.

Parameter	Default	Description
`shap_background_size`	100	Background samples for SHAP
`pdp_grid_resolution`	50	Grid points for PDP/ICE
`pdp_percentiles`	(0.05, 0.95)	Grid range percentile bounds
`monotonicity_check_resolution`	50	Grid resolution for monotonicity checks
`random_seed`	42	Random seed

See AnalysisConfig for the full API.