Struct OneHotEncoder 

pub struct OneHotEncoder {
    pub columns: Vec<String>,
    pub categories: HashMap<String, Vec<String>>,
    /* private fields */
}

Expands each categorical column into multiple binary columns, one per distinct category.

Fields

columns: Vec<String>

categories: HashMap<String, Vec<String>>

Mapping from column name to list of distinct category values.

Implementations

impl OneHotEncoder

pub fn new(columns: Vec<String>) -> Self

Create a new OneHotEncoder for the specified columns.

pub async fn fit(&mut self, df: &DataFrame) -> FeatureFactoryResult<()>

Fit computes and stores distinct category values.

pub fn transform(&self, df: DataFrame) -> FeatureFactoryResult<DataFrame>

Transform applies one-hot encoding and returns a new DataFrame.

Trait Implementations

impl Transformer for OneHotEncoder

fn fit<'life0, 'life1, 'async_trait>( &'life0 mut self, df: &'life1 DataFrame, ) -> Pin<Box<dyn Future<Output = FeatureFactoryResult<()>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Fit the transformer given a DataFrame.

fn transform(&self, df: DataFrame) -> FeatureFactoryResult<DataFrame>

Transform the input DataFrame, returning a new DataFrame with the transformation applied.

fn is_stateful(&self) -> bool

Returns true if the transformer is stateful (i.e. requires a call to fit before transform can be called).