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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 non-majority class will be oversampled to the size of the majority class times the float; the majority class counts are preserved. 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%)