Metadata-Version: 2.1
Name: crucio
Version: 0.1.91
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/SigmoidAI/crucio
Author: Păpăluță Vasile, Stojoc Vladimir
Author-email: vladimir.stojoc@gmail.com
License: MIT
Download-URL: https://github.com/ScienceKot/crucio/archive/v1.0.tar.gz
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
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: statistics


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 kydavra 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:
```python
from crucio import SMOTE
```

class names are written above.Next create a object of this algorithm (I will use SMOTE method as an example).
```python
method = SMOTE()
```

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. Small tip, balance only the training set, not full one.

```python
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 vladimir.stojoc@gmail.com


