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SMOTEN

Initiate a SMOTEN model with the given hyper-parameters.

SMOTEN

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

From MLJ, the type can be imported using

SMOTEN = @load SMOTEN pkg=Imbalance

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

SMOTEN implements the SMOTEN algorithm to correct for class imbalance as in N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTEN: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 2002.

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 = SMOTEN()

Hyperparameters

  • k=5: Number of nearest neighbors to consider in the SMOTEN algorithm. Should be within the range [1, n - 1], where n is the number of observations; otherwise set to the nearest of these two values.

  • 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 of integers or a table with element scitypes that subtype Finite. That is, for table inputs each column should have either OrderedFactor or Multiclass as the element scitype.
  • 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 SMOTEN, returning both the new and original observations

Example

using MLJ
using ScientificTypes
import Imbalance

## set probability of each class
class_probs = [0.5, 0.2, 0.3]                         
num_rows = 100
num_continuous_feats = 0
## want two categorical features with three and two possible values respectively
num_vals_per_category = [3, 2]

## generate a table and categorical vector accordingly
X, y = Imbalance.generate_imbalanced_data(num_rows, num_continuous_feats; 
                                class_probs, num_vals_per_category, rng=42)                      
julia> Imbalance.checkbalance(y)
1: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 19 (39.6%) 
2: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 33 (68.8%) 
0: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 48 (100.0%) 

julia> ScientificTypes.schema(X).scitypes
(Count, Count)

## coerce to a finite scitype (multiclass or ordered factor)
X = coerce(X, autotype(X, :few_to_finite))

## load SMOTEN
SMOTEN = @load SMOTEN pkg=Imbalance

## wrap the model in a machine
oversampler = SMOTEN(k=5, 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%)