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 kernelratios=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 anAbstractRNG
object or anInteger
seed to be used withXoshiro
if the JuliaVERSION
supports it. Otherwise, uses MersenneTwister`.
Transform Inputs
X
: A matrix or table of floats where each row is an observation from the datasety
: An abstract vector of labels (e.g., strings) that correspond to the observations inX
Transform Outputs
Xover
: A matrix or table that includes original data and the new observations due to oversampling. depending on whether the inputX
is a matrix or table respectivelyyover
: An abstract vector of labels corresponding toXover
Operations
transform(mach, X, y)
: resample the dataX
andy
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%)