dataframe-learn-2.4.1.0: Interpretable, expression-returning machine learning for the dataframe ecosystem.
Safe HaskellNone
LanguageHaskell2010

DataFrame.Transform

Description

Fitted column transforms as a composable monoid. A Transform is a list of named output expressions; s <> t means "apply s, then t", fusing t's references to s's outputs by simultaneous substitution. applyTransform runs one against a frame; compileThrough folds a transform into a model's prediction expression so the result is a single expression over the raw inputs.

Every right-hand side must be row-wise (no aggregation/window), and within one transform each expression reads the original frame.

Synopsis

Documentation

newtype Transform Source #

A fitted transform: named output columns derived from the input frame.

Constructors

Transform 

Instances

Instances details
Monoid Transform Source # 
Instance details

Defined in DataFrame.Transform

Semigroup Transform Source # 
Instance details

Defined in DataFrame.Transform

applyTransform :: Transform -> DataFrame -> DataFrame Source #

Apply a transform to a frame (deriving its outputs in order).

compileThrough :: Columnable a => Transform -> Expr a -> Expr a Source #

Fold a preprocessing transform into a model's prediction expression, yielding one expression over the transform's input columns.

data ScalerModel Source #

A fitted standardizer: per-column means and standard deviations.

Constructors

ScalerModel 

Fields

Instances

Instances details
Show ScalerModel Source # 
Instance details

Defined in DataFrame.Transform

Eq ScalerModel Source # 
Instance details

Defined in DataFrame.Transform

standardScaler :: [Text] -> DataFrame -> ScalerModel Source #

Fit a standard scaler over the named columns.

scalerTransform :: ScalerModel -> Transform Source #

The scaler as a Transform: (col - μ) / σ per column.