API Reference
Gryffin
- class gryffin.Gryffin(config_file=None, config_dict=None, known_constraints=None, frac_feas=None, silent=False)[source]
- __init__(config_file=None, config_dict=None, known_constraints=None, frac_feas=None, silent=False)[source]
Initialize Gryffin from a config dict or file.
A config file or dict must be provided. If both a config file and a config dict are provided, the file will be ignored.
- Parameters
config_file (
Optional
[str
]) – Gryffin config filepathconfig_dict (
Optional
[Dict
]) – Gryffin config dictknown_constraints (
Optional
[Callable
[[Dict
],bool
]]) – Function imposing contraints on the Gryffin search space.frac_feas – Feasability fraction TODO: Not sure what this is for
silent (
bool
) – Suppress all standard output. If True, theverbosity
settings inconfig
will be overwritten. Default is False.
- build_surrogate(observations=None)[source]
Builds surrogate models of Gryffin without proposing any new experiments.
- Parameters
observations (
Optional
[List
]) – List of dictionaries with the previous observations- Return type
None
- get_descriptor_summary_feasibility()[source]
Retrieve a summary of the descriptor relavances for the feasibility surrogate
- get_descriptor_summary_regression()[source]
Retrieve a summary of descriptor relavances for the regression surrogate
- get_feasibility_surrogate(params, threshold=None)[source]
Retrieve the feasibility surrogate model.
- Parameters
params (
Union
[List
,DataFrame
]) – List of dicts with input parameters to evaluate. Alternatively it can also be a pandas DataFrame where each column name corresponds to one of the input parameters in Gryffin.threshold (
Optional
[float
]) – Threshold used to classify whether a set of parameters is feasible or not. IfNone
, the probability of feasibility is returned instead of a binary True/False (feasible/infeasible) output. Default is None.
- Return y_pred
Surrogate model evaluated at the locations defined in params.
- Return type
List
- get_kernel_density_estimate(params, separate_kwn_ukwn=False)[source]
Retrieve the feasibility surrogate model.
- Parameters
params (
Union
[List
,DataFrame
]) – List of dicts with input parameters to evaluate. Alternatively it can also be a pandas DataFrame where each column name corresponds to one of the input parameters in Gryffin.separate_kwn_ukwn (
bool
) – Return the density for all samples, or separate the density for feasible/infeasible samples.
- Return y_pred
Kernel density estimates.
- Return type
List
- get_regression_surrogate(params)[source]
Retrieve the surrogate model.
- Parameters
params (
Union
[List
,DataFrame
]) – List of dicts with input parameters to evaluate. Alternatively it can also be a pandas DataFrame where each column name corresponds to one of the input parameters in Gryffin.- Return y_pred
Surrigate model evaluated at the locations defined in params.
- recommend(observations=None, sampling_strategies=None, num_batches=None, as_array=False)[source]
Recommends the next set(s) of parameters based on the provided observations.
- Parameters
observations (
Optional
[List
]) – List of dictionaries with the previous observations.sampling_strategies (
Optional
[List
]) – List of the chosen sampling strategies. When providing this argument, the config settingstrategies
will be ignored.num_batches (
Optional
[int
]) – Number of parameter batches requested. When providing this argument, the config settingbatches
will be ignored.as_array (
bool
) – Whether to return suggested samples as numpy arrays instead of a list of dictionaries. Default is False.
- Return params
List of dictionaries with the suggested parameters.
- Return type
List