Metadata-Version: 1.1
Name: crucio
Version: 0.1.1
Summary: 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.
Home-page: https://github.com/ScienceKot/crucio.git
Author: YOUR NAME
Author-email: vpapaluta06@gmail.com
License: MIT
Download-URL: https://github.com/ScienceKot/crucio/archive/v1.0.tar.gz
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
        
        
        
Keywords: ml,machine learning,imbalanced learning,class balancing,python
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
