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UnivariateFillImputer

UnivariateFillImputer

A model type for constructing a single variable fill imputer, based on MLJTransforms.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

UnivariateFillImputer = @load UnivariateFillImputer pkg=MLJTransforms

Do model = UnivariateFillImputer() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in UnivariateFillImputer(continuous_fill=...).

Use this model to imputing missing values in a vector with a fixed value learned from the non-missing values of training vector.

For imputing missing values in tabular data, use FillImputer instead.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, x)

where

  • x: any abstract vector with element scitype Union{Missing, T} where T is a subtype of Continuous, Multiclass, OrderedFactor or Count; check scitype using scitype(x)

Train the machine using fit!(mach, rows=...).

Hyper-parameters

  • continuous_fill: function or other callable to determine value to be imputed in the case of Continuous (abstract float) data; default is to apply median after skipping missing values
  • count_fill: function or other callable to determine value to be imputed in the case of Count (integer) data; default is to apply rounded median after skipping missing values
  • finite_fill: function or other callable to determine value to be imputed in the case of Multiclass or OrderedFactor data (categorical vectors); default is to apply mode after skipping missing values

Operations

  • transform(mach, xnew): return xnew with missing values imputed with the fill values learned when fitting mach

Fitted parameters

The fields of fitted_params(mach) are:

  • filler: the fill value to be imputed in all new data

Examples

using MLJ
imputer = UnivariateFillImputer()

x_continuous = [1.0, 2.0, missing, 3.0]
x_multiclass = coerce(["y", "n", "y", missing, "y"], Multiclass)
x_count = [1, 1, 1, 2, missing, 3, 3]

mach = machine(imputer, x_continuous)
fit!(mach)

julia> fitted_params(mach)
(filler = 2.0,)

julia> transform(mach, [missing, missing, 101.0])
3-element Vector{Float64}:
 2.0
 2.0
 101.0

mach2 = machine(imputer, x_multiclass) |> fit!

julia> transform(mach2, x_multiclass)
5-element CategoricalArray{String,1,UInt32}:
 "y"
 "n"
 "y"
 "y"
 "y"

mach3 = machine(imputer, x_count) |> fit!

julia> transform(mach3, [missing, missing, 5])
3-element Vector{Int64}:
 2
 2
 5

For imputing tabular data, use FillImputer.