Struct DatetimeSubtraction 

pub struct DatetimeSubtraction {
    pub new_features: Vec<(String, String, String, TimeUnit)>,
}

Computes time differences between two datetime columns. new_features is a list of tuples: (new_feature_name, left_column, right_column, time_unit). Transform validates that each left and right column exists and is of a datetime type.

Fields

new_features: Vec<(String, String, String, TimeUnit)>

Implementations

impl DatetimeSubtraction

pub fn new(new_features: Vec<(String, String, String, TimeUnit)>) -> 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 left and right column exists and is a datetime type, then adds a new column for each specified time difference.

Trait Implementations

impl Transformer for DatetimeSubtraction

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).