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RandomUndersampler

Initiate a random undersampling model with the given hyper-parameters.

RandomUndersampler

A model type for constructing a random undersampler, based on Imbalance.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

RandomUndersampler = @load RandomUndersampler pkg=Imbalance

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

RandomUndersampler implements naive undersampling by randomly removing existing observations.

Training data

In MLJ or MLJBase, wrap the model in a machine by mach = machine(model)

There is no need to provide any data here because the model is a static transformer.

Likewise, there is no need to fit!(mach).

For default values of the hyper-parameters, model can be constructed by model = RandomUndersampler()

Hyperparameters

  • ratios=1.0: A parameter that controls the amount of undersampling to be done for each class

    • Can be a float and in this case each class will be undersampled to the size of the minority class times the float. By default, all classes are undersampled to the size of the minority class
    • Can be a dictionary mapping each class label to the float ratio for that class

  • rng::Union{AbstractRNG, Integer}=default_rng(): Either an AbstractRNG object or an Integer seed to be used with Xoshiro if the Julia VERSION supports it. Otherwise, uses MersenneTwister`.

Transform Inputs

  • X: A matrix of real numbers or a table with element scitypes that subtype Union{Finite, Infinite}. Elements in nominal columns should subtype Finite (i.e., have scitype OrderedFactor or Multiclass) and elements in continuous columns should subtype Infinite (i.e., have scitype Count or Continuous).
  • y: An abstract vector of labels (e.g., strings) that correspond to the observations in X

Transform Outputs

  • X_under: A matrix or table that includes the data after undersampling depending on whether the input X is a matrix or table respectively
  • y_under: An abstract vector of labels corresponding to X_under

Operations

  • transform(mach, X, y): resample the data X and y using RandomUndersampler, returning both the new and original observations

Example

using MLJ
import Imbalance

## set probability of each class
class_probs = [0.5, 0.2, 0.3]                         
num_rows, num_continuous_feats = 100, 5
## generate a table and categorical vector accordingly
X, y = Imbalance.generate_imbalanced_data(num_rows, num_continuous_feats; 
                                class_probs, rng=42)   

julia> Imbalance.checkbalance(y; ref="minority")
 1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (100.0%) 
 2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 33 (173.7%) 
 0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48 (252.6%) 

## load RandomUndersampler
RandomUndersampler = @load RandomUndersampler pkg=Imbalance

## wrap the model in a machine
undersampler = RandomUndersampler(ratios=Dict(0=>1.0, 1=> 1.0, 2=>1.0), 
               rng=42)
mach = machine(undersampler)

## provide the data to transform (there is nothing to fit)
X_under, y_under = transform(mach, X, y)
                                      
julia> Imbalance.checkbalance(y_under; ref="minority")
0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (100.0%) 
2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (100.0%) 
1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (100.0%)