one_hot

OneHot(feature_categories, *, check_for_missing_categories=False)

Bases: Transform

Transforms integer features into a set of binary one-hot features.

Transforms integer features into a set of binary one-hot features. The original integer features are dropped and are not part of the resulting data frame.

As an example consider the following data

feature
0
1
2
1
5

When the one-hot transformation is applied, the result is as follows

feature_0	feature_1	feature_2	feature_5
1	0	0	0
0	1	0	0
0	0	1	0
0	1	0	0
0	0	0	1

Initializes the One-Hot transform.

Parameters:

Name	Type	Description	Default
feature_categories	dict[str, list[Any]]	Dictionary of features and a list of categorical values to encode for each.	required
check_for_missing_categories	bool	If set to true, a check is performed to see if all values belong to a category. If an unknown value is found which does not belong to any category, a NoMatchingCategoryError is thrown. To perform this check, the dataframe must be materialised, resulting in a potential performance decrease. Therefore it defaults to false.	False

Source code in src/flowcean/transforms/one_hot.py

def __init__(
    self,
    feature_categories: dict[str, list[Any]],
    *,
    check_for_missing_categories: bool = False,
) -> None:
    """Initializes the One-Hot transform.

    Args:
        feature_categories: Dictionary of features and a list of
            categorical values to encode for each.
        check_for_missing_categories: If set to true, a check is performed
            to see if all values belong to a category. If an unknown value
            is found which does not belong to any category, a
            NoMatchingCategoryError is thrown. To perform this check, the
            dataframe must be materialised, resulting in a potential
            performance decrease. Therefore it defaults to false.
    """
    self.feature_category_mapping = {
        feature: {f"{feature}_{value}": value for value in values}
        for feature, values in feature_categories.items()
    }
    self.check_for_missing_categories = check_for_missing_categories

apply(data)

Transform data with this one hot transformation.

Transform data with this one hot transformation and return the resulting dataframe.

Parameters:

Name	Type	Description	Default
data	DataFrame	The data to transform.	required

Returns:

Type	Description
DataFrame	The transformed data.

Source code in src/flowcean/transforms/one_hot.py

@override
def apply(
    self,
    data: pl.DataFrame,
) -> pl.DataFrame:
    """Transform data with this one hot transformation.

    Transform data with this one hot transformation and return the
    resulting dataframe.

    Args:
        data: The data to transform.

    Returns:
        The transformed data.
    """
    if len(self.feature_category_mapping) == 0:
        raise NoCategoriesError
    for (
        feature,
        category_mappings,
    ) in self.feature_category_mapping.items():
        data = data.with_columns(
            [
                pl.col(feature).eq(value).cast(pl.Int64).alias(name)
                for name, value in category_mappings.items()
            ]
        ).drop(feature)

        if self.check_for_missing_categories and (
            not data.select(
                [
                    pl.col(name).cast(pl.Boolean)
                    for name in category_mappings
                ]
            )
            .select(pl.any_horizontal(pl.all()).all())
            .item(0, 0)
        ):
            raise NoMatchingCategoryError
    return data

from_dataframe(data, features, *, check_for_missing_categories=False) classmethod

Creates a new one-hot transformation based on sample data.

Parameters:

Name	Type	Description	Default
data	DataFrame	A dataframe containing sample data for determining the categories of the transform.	required
features	Iterable[str]	Name of the features for which the one hot transformation will determine the categories.	required
check_for_missing_categories	bool	If set to true, a check is performed to see if all values belong to a category. If an unknown value is found which does not belong to any category, a NoMatchingCategoryError is thrown. To perform this check, the dataframe must be materialised, resulting in a potential performance decrease. Therefore it defaults to false.	False

Source code in src/flowcean/transforms/one_hot.py

@classmethod
def from_dataframe(
    cls,
    data: pl.DataFrame,
    features: Iterable[str],
    *,
    check_for_missing_categories: bool = False,
) -> Self:
    """Creates a new one-hot transformation based on sample data.

    Args:
        data: A dataframe containing sample data for determining the
            categories of the transform.
        features: Name of the features for which the one hot transformation
            will determine the categories.
        check_for_missing_categories: If set to true, a check is performed
            to see if all values belong to a category. If an unknown value
            is found which does not belong to any category, a
            NoMatchingCategoryError is thrown. To perform this check, the
            dataframe must be materialised, resulting in a potential
            performance decrease. Therefore it defaults to false.
    """
    # Derive categories from the data frame
    feature_categories: dict[str, list[Any]] = {}
    for feature in features:
        if data.schema[feature].is_float():
            logger.warning(
                (
                    "Feature %s is of type float. Applying a one-hot",
                    "transform to it may produce undesired results.",
                    "Check your datatypes and transforms.",
                ),
                feature,
            )
        feature_categories[feature] = (
            data.select(pl.col(feature).unique()).to_series().to_list()
        )
    return cls(
        feature_categories,
        check_for_missing_categories=check_for_missing_categories,
    )