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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:

  1. ADASYN
  2. ICOTE (Immune Centroids Oversampling)
  3. MTDF (Mega-Trend Difussion Function)
  4. MWMOTE (Majority Weighted Minority Oversampling Technique)
  5. SMOTE (Synthetic Minority Oversampling Technique)
  6. SMOTENC (Synthetic Minority Over-sampling Technique for Nominal and Continuous)
  7. SMOTETOMEK (Synthetic Minority Oversampling Technique + Tomek links for undersampling)
  8. SMOTEENN (Synthetic Minority Oversampling Technique + ENN for undersampling)
  9. SCUT (SMOTE and Clustered Undersampling Technique)
  10. SLS (Safe-Level-Synthetic Minority Over-Sampling TEchnique)
  11. 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

Project details


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Files for crucio, version 0.1.4
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