ClusterUndersampler
Initiate a cluster undersampling model with the given hyper-parameters.
ClusterUndersampler
A model type for constructing a cluster undersampler, based on Imbalance.jl, and implementing the MLJ model interface.
From MLJ, the type can be imported using
ClusterUndersampler = @load ClusterUndersampler pkg=Imbalance
Do model = ClusterUndersampler()
to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in ClusterUndersampler(mode=...)
.
ClusterUndersampler
implements clustering undersampling as presented in Wei-Chao, L., Chih-Fong, T., Ya-Han, H., & Jing-Shang, J. (2017). Clustering-based undersampling in class-imbalanced data. Information Sciences, 409–410, 17–26. with K-means as the clustering algorithm.
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 with model = ClusterUndersampler()
.
Hyperparameters
mode::AbstractString="nearest
: If"center"
then the undersampled data will consist of the centriods of
each cluster found; if `"nearest"` then it will consist of the nearest neighbor of each centroid.
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
maxiter::Integer=100
: Maximum number of iterations to run K-meansrng::Integer=42
: Random number generator seed. Must be an integer.
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
X_under
: A matrix or table that includes the data after undersampling depending on whether the inputX
is a matrix or table respectivelyy_under
: An abstract vector of labels corresponding toX_under
Operations
transform(mach, X, y)
: resample the dataX
andy
using ClusterUndersampler, returning the undersampled versions
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 cluster_undersampling
ClusterUndersampler = @load ClusterUndersampler pkg=Imbalance
## wrap the model in a machine
undersampler = ClusterUndersampler(mode="nearest",
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%)