multi-imbalance 0.0.4

Python package for tackling multiclass imbalance problems.

multi-imbalance

multi-imbalance is a python package tackling the problem of multi-class imbalanced datasets in machine learning.

Requirements

Tha package has been tested under python 3.7. Relies heavily on scikit-learn and typical scientific stack (numpy, scipy, pandas etc.).

Installation

Just type in

pip install multi-imbalance

Implemented algorithms

1. SOUP, MDO

2. ECOC

3. Roughly Balanced Bagging

4. SPIDER3 algorithm implementation for selective preprocessing of multi-class imbalanced data sets, according to article:

   Wojciechowski, S., Wilk, S., Stefanowski, J.: An Algorithm for Selective Preprocessing of Multi-class Imbalanced Data. Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017

Example usage

from multi_imbalance.resampling.mdo import MDO

# Mahalanbois Distance Oversampling
mdo = MDO(k=9, k1_frac=0, seed=0)

# read the data
X_train, y_train, X_test, y_test = ...

# preprocess
X_train_resampled, y_train_resampled = mdo.fit_transform(np.copy(X_train), np.copy(y_train))

# train the classifier on preprocessed data
clf_tree = DecisionTreeClassifier(random_state=0)
clf_tree.fit(X_train_resampled, y_train_resampled)

# make predictions
y_pred = clf_tree.predict(X_test)