For each proportion setting, I still have the same number of objects. To me, the package seems to balance the dataset as if proportion = 1, regardless of which value I assign to proportion.

The code is given below.

from sklearn import datasets
import smote_variants as sv
from imblearn.datasets import make_imbalance

resample for proportion = 0.5

oversampler1= sv.MulticlassOversampling(sv.SMOTE_Cosine(proportion=0.5,random_state=42))

resample for proportion = 3

oversampler2= sv.MulticlassOversampling(sv.SMOTE_Cosine(proportion=3,random_state=42))
#resample for proportion = 1
oversampler3= sv.MulticlassOversampling(sv.SMOTE_Cosine(proportion=1,random_state=42))

iris dataset

iris = datasets.load_iris()
X = iris.data
y = iris.target

#make iris dataset imbalanced
X,y = make_imbalance(X, y,
sampling_strategy={0: 5, 1: 20, 2: 50},
random_state=42)

X1, y1 = oversampler1.sample(X, y)

X2, y2 = oversampler2.sample(X, y)

X3, y3 = oversampler3.sample(X, y)

#check the number of samples
print(len(X1))

print(len(X2))

print(len(X3))