GitHub

UnivariateDiscretizer

UnivariateDiscretizer

A model type for constructing a single variable discretizer, based on MLJModels.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

UnivariateDiscretizer = @load UnivariateDiscretizer pkg=MLJModels

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

Discretization converts a Continuous vector into an OrderedFactor vector. In particular, the output is a CategoricalVector (whose reference type is optimized).

The transformation is chosen so that the vector on which the transformer is fit has, in transformed form, an approximately uniform distribution of values. Specifically, if n_classes is the level of discretization, then 2*n_classes - 1 ordered quantiles are computed, the odd quantiles being used for transforming (discretization) and the even quantiles for inverse transforming.

Training data

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

mach = machine(model, x)

where

  • x: any abstract vector with Continuous element scitype; check scitype with scitype(x).

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

Hyper-parameters

  • n_classes: number of discrete classes in the output

Operations

  • transform(mach, xnew): discretize xnew according to the discretization learned when fitting mach
  • inverse_transform(mach, z): attempt to reconstruct from z a vector that transforms to give z

Fitted parameters

The fields of fitted_params(mach).fitesult include:

  • odd_quantiles: quantiles used for transforming (length is n_classes - 1)
  • even_quantiles: quantiles used for inverse transforming (length is n_classes)

Example

using MLJ
using Random
Random.seed!(123)

discretizer = UnivariateDiscretizer(n_classes=100)
mach = machine(discretizer, randn(1000))
fit!(mach)

julia> x = rand(5)
5-element Vector{Float64}:
 0.8585244609846809
 0.37541692370451396
 0.6767070590395461
 0.9208844241267105
 0.7064611415680901

julia> z = transform(mach, x)
5-element CategoricalArrays.CategoricalArray{UInt8,1,UInt8}:
 0x52
 0x42
 0x4d
 0x54
 0x4e

x_approx = inverse_transform(mach, z)
julia> x - x_approx
5-element Vector{Float64}:
 0.008224506144777322
 0.012731354778359405
 0.0056265330571125816
 0.005738175684445124
 0.006835652575801987