Crucio is a python sci-kit learn inspired package for class imbalance. It use some classic methods for class balancing taking as parameters a data frame and the target column.
Project description
Crucio is a python sci-kit learn inspired package for class imbalance. It use some classic methods for class balancing taking as parameters a data frame and the target column.
This version of crucio has the next methods of feature selection:
ADASYN
ICOTE (Immune Centroids Oversampling)
MTDF (Mega-Trend Difussion Function)
MWMOTE (Majority Weighted Minority Oversampling Technique)
SMOTE (Synthetic Minority Oversampling Technique)
SMOTENC (Synthetic Minority Over-sampling Technique for Nominal and Continuous)
SMOTETOMEK (Synthetic Minority Oversampling Technique + Tomek links for undersampling)
SMOTEENN (Synthetic Minority Oversampling Technique + ENN for undersampling)
SCUT (SMOTE and Clustered Undersampling Technique)
SLS (Safe-Level-Synthetic Minority Over-Sampling TEchnique)
TKRKNN (Top-K ReverseKNN)
All these methods takes the pandas Data Frame and y column to balance on.
How to use crucio
To use balancer from crucio you should just import the balancer from crucio in the following framework:
`from crucio import <class name>`
class names are written above in parantheses.
Next create a object of this algorithm (I will use ADASYN method as an example).
`method = ADASYN()`
To balance the dataset on the target column use the ‘balance’ function, using as parameters the pandas Data Frame and the column that you want to balance.
`new_dataframe = method.balance(df, 'target')`
Returned value is a new data frame with the target column balanced.
With love from Sigmoid.
We are open for feedback. Please send your impression to vpapaluta06@gmail.com
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