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ROSE

Initiate a ROSE model with the given hyper-parameters.

ROSE

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

From MLJ, the type can be imported using

ROSE = @load ROSE pkg=Imbalance

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

ROSE implements the ROSE (Random Oversampling Examples) algorithm to correct for class imbalance as in G Menardi, N. Torelli, “Training and assessing classification rules with imbalanced data,” Data Mining and Knowledge Discovery, 28(1), pp.92-122, 2014.

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 = ROSE()

Hyperparameters

  • s::float: A parameter that proportionally controls the bandwidth of the Gaussian kernel

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

    • Can be a float and in this case each class will be oversampled to the size of the majority class times the float. By default, all classes are oversampled to the size of the majority 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 or table of floats where each row is an observation from the dataset
  • y: An abstract vector of labels (e.g., strings) that correspond to the observations in X

Transform Outputs

  • Xover: A matrix or table that includes original data and the new observations due to oversampling. depending on whether the input X is a matrix or table respectively
  • yover: An abstract vector of labels corresponding to Xover

Operations

  • transform(mach, X, y): resample the data X and y using ROSE, 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)
1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (39.6%) 
2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 33 (68.8%) 
0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48 (100.0%) 

## load ROSE
ROSE = @load ROSE pkg=Imbalance

## wrap the model in a machine
oversampler = ROSE(s=0.3, ratios=Dict(0=>1.0, 1=> 0.9, 2=>0.8), rng=42)
mach = machine(oversampler)

## provide the data to transform (there is nothing to fit)
Xover, yover = transform(mach, X, y)

julia> Imbalance.checkbalance(yover)
2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 38 (79.2%) 
1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 43 (89.6%) 
0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48 (100.0%)