User guide

SMOTE algorithm, as well as any other over-sampling method based on the SMOTE mechanism, generates synthetic samples along line segments that join minority class instances. Geometric SMOTE (G-SMOTE) is an enhancement of the SMOTE data generation mechanism. G-SMOTE generates synthetic samples in a geometric region of the input space, around each selected minority instance. The GeometricSMOTE class can be used with multiple classes as well as binary classes classification. It uses a one-vs-rest approach by selecting each targeted class and computing the necessary statistics against the rest of the data set which are grouped in a single class.

Initially, we generate multi-class imbalanced data represented by the input data X and targets y:

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_classes=3, weights=[0.10, 0.10, 0.80], random_state=0, n_informative=10)
>>> print(sorted(Counter(y).items()))
[(0, 10), (1, 10), (2, 80)]

We can use GeometricSMOTE to resample the data:

>>> from gsmote import GeometricSMOTE
>>> geometric_smote = GeometricSMOTE()
>>> X_resampled, y_resampled = geometric_smote.fit_resample(X, y)
>>> from collections import Counter
>>> print(sorted(Counter(y_resampled).items()))
[(0, 80), (1, 80), (2, 80)]

The augmented data set can be used instead of the original data set to train a classifier:

>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier()
>>> clf.fit(X_resampled, y_resampled)

GeometricSMOTE can be used also in a machine learning pipeline:

from imblearn.pipeline import make_pipeline
pipeline = make_pipeline(GeometricSMOTE(), DecisionTreeClassifier())
pipeline.fit(X, y)