Struct ArbitraryDiscretizer 

pub struct ArbitraryDiscretizer {
    pub columns: Vec<String>,
    pub intervals: HashMap<String, Vec<(f64, f64, String)>>,
}

Discretizes a column into arbitrary intervals defined by the user.

Fields

columns: Vec<String>

intervals: HashMap<String, Vec<(f64, f64, String)>>

Implementations

impl ArbitraryDiscretizer

pub fn new( columns: Vec<String>, intervals: HashMap<String, Vec<(f64, f64, String)>>, ) -> Self

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

Stateless transformer: fit does nothing.

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

Transform validates that each target column is numeric and that the intervals are valid, then applies the interval mapping.

Trait Implementations

impl Transformer for ArbitraryDiscretizer

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).