pyphi.optimization.OptimizationResult#

class pyphi.optimization.OptimizationResult(best_params, best_objective, best_substrate, best_sia, trajectory, bounds, seed, goal, quantity_name, settings, config_snapshot, n_evaluations, n_unreachable)[source]#

Bases: Displayable, Serializable

The outcome of an optimize() run.

Variables:

  • best_params (NDArray) – The winning parameter vector.

  • best_objective (float) – Its objective value.

  • best_substrate (Substrate) – builder(best_params).

  • best_sia (SystemIrreducibilityAnalysis) – The analysis at the best point — its selection identity and margins say how robust the winning substrate is.

  • trajectory (pd.DataFrame) – One row per evaluation: eval, generation, one column per parameter dimension (p0, p1, …), objective, reachable, the selection identity (partition, cause_state, effect_state) and the selection margins (partition_margin, cause_state_margin, effect_state_margin).

  • bounds (list[tuple[float, float]]) – The search box.

  • seed (int) – The seed driving the run.

  • goal (str) – "maximize" or "minimize".

  • quantity_name (str) – The optimized quantity’s name, or "<callable>".

  • settings (dict) – Backend and budget: backend, popsize, maxiter, tol.

  • config_snapshot (dict) – PyPhi configuration at run time (precision, formalism).

  • n_evaluations (int) – Total candidate evaluations.

  • n_unreachable (int) – How many candidates had a dynamically unreachable state.

Parameters: