LECA.analyze.predict_conductivity_from_log_conductivity_objective

LECA.analyze.predict_conductivity_from_log_conductivity_objective(x_in: DataFrame, wf: WorkFlow, model: str, log: bool = False, objective: str = 'log conductivity', min_max: bool = False) → DataFrame

Predict the ionic conductivity for given electrolyte compositions at a given temperature. This function is to be used with single objective workflows.

Parameters:

  • x_in (pd.DataFrame) – DataFrame of input feature vectors, matching the DataFrame format for WorkFlow.

  • wf (WorkFlow) – LECA WorkFlow object containing trained models for the single objective function (generally “log conductivity”).

  • model (str) – String name of model to use for prediction.

  • log (bool) –

    If log=True return log_{10}(conductivity). If log=False return conductivity.

    Default value False

  • objective (str) –

    String name of the objective function for the trained models in the WorkFlow.

    Default value "log conductivity"

  • min_max (bool) –

    Whether to use the min_max method to estimate uncertainty, or MAPIE with conformity scores. min_max:True returns the standard deviations of the predictions from all the bootstrapped models for each point. min_max:False uses the MAPIE uncertainty estimation outlined in: Mapie jackknife+-AB This parameter is moot for GPR models.

    Default value False.

Returns:

DataFrame with the following columns structure of input features and conductivity predictions (and their one-sigma uncertainty). x_i here signifies each feature dimension in x_in.

inverse temperature

x_1

x_2

x…

conductivity

conducivity_std

Return type:

pd.DataFrame