LECA.prep.combine_cut

LECA.prep.combine_cut(data: DataFrame, objective_funcs: str | List[str], features: str | List[str], max_samples: int = 5) → DataFrame

Takes the mean value of the given objective functions for measurements where every declared feature is identical and also records the standard deviations. Measurements for identical input features will be combined into a single entry.

Parameters:

  • data (DataFrame) – Dataframe of experimental measurements

  • objective_funcs (Union[str, List[str]]) – Objective function or list of objective functions

  • features (Union[str, List[str]]) – Feature or list of features

  • max_samples (int) –

    Max number of samples to use to calculate the standard deviation of the mean. We take only the first n values and slice the rest away (i.e. not randomized).

    By default 5.

Returns:

DataFrame with the combined mean values for the objective functions, their standard deviations and the declared features. The resulting DataFrame will then only have row entries with unique input features. An additional count column is also added to record the number of repeated measurements for that feature set.

Return type:

DataFrame