SpotOptim.SpotOptimState

SpotOptim.SpotOptimState(
    X_=None,
    y_=None,
    y_mo=None,
    best_x_=None,
    best_y_=None,
    n_iter_=0,
    counter=0,
    success_rate=0.0,
    success_counter=0,
    _success_history=list(),
    _zero_success_count=0,
    mean_X=None,
    mean_y=None,
    var_y=None,
    min_mean_X=None,
    min_mean_y=None,
    min_var_y=None,
    min_X=None,
    min_y=None,
    restarts_results_=list(),
    _restarts_without_improvement=0,
    _best_y_before_restart=None,
    _early_stopped=False,
)

Mutable state of the optimization process.

Attributes

Name	Type	Description
X_	np.ndarray	Input data.
y_	np.ndarray	Output data.
y_mo	np.ndarray	Multi-objective output data.
best_x_	np.ndarray	Best input data.
best_y_	float	Best output data.
n_iter_	int	Number of iterations.
counter	int	Counter.
success_rate	float	Success rate.
success_counter	int	Success counter.
_success_history	List	History of success.
_zero_success_count	int	Count of zero success.
mean_X	np.ndarray	Mean of input data.
mean_y	np.ndarray	Mean of output data.
var_y	np.ndarray	Variance of output data.
min_mean_X	np.ndarray	Minimum of mean input data.
min_mean_y	float	Minimum of mean output data.
min_var_y	float	Minimum of mean variance of output data.
min_X	np.ndarray	Minimum of input data.
min_y	float	Minimum of output data.
restarts_results_	List	History of restarts.
_restarts_without_improvement	int	Count of consecutive restarts that did not improve the best objective value. Consumed by the max_restarts patience-based early-stopping rule.
_best_y_before_restart	Optional[float]	Snapshot of the best objective value observed before the most recent restart, used to detect whether the latest restart improved on the incumbent.
_early_stopped	bool	Flag set to True when the patience-based early-stopping rule terminates the run.