pyproteonet.imputation.sklearn.iterative_impute

pyproteonet.imputation.sklearn.iterative_impute(dataset: Dataset, molecule: str, column: str, min_value: float | None = None, max_value: float | None = None, result_column: str | None = None, **kwargs) → Series

Apply the scikit-learn iterative imputer to a dataset that estimates each feature from all the others.

Parameters:

  • dataset (Dataset) – Dataset to impute.

  • molecule (str) – Molecule type to impute (e.g. protein, peptide etc.).

  • column (str) – Name of the value column to impute.

  • min_value (Optional[float], optional) – Minimum value to impute. If not given set to minimum non-missing value. Defaults to None.

  • max_value (Optional[float], optional) – Maximum value to impute. If not given set to maximum non-missing value. Defaults to None.

  • result_column (Optional[str], optional) – If given, name of the value column to store the imputed values in. Defaults to None.

  • transpose (bool, optional) – Whether to transpose the matrix before imputation. Defaults to False.

Returns:

The imputed values.

Return type:

pd.Series