Metadata-Version: 2.1
Name: auto_feat_selection
Version: 0.0.8
Summary: Auto Feature Selection and Evaluation using Bregman Divergence & ItakuraSaitoSelector
Home-page: https://github.com/rupak-roy
Author: rupak_bob_roy
Author-email: rupak2013bob1@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/rupak-roy
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE



# Dependencies 
### Make sure you have install the sklearn  
### sklearn version == 1.0.1
### Feature Selection using Bregman divergence & Itakura Saito

## What is Bregman Divergence & Itakura Saito
In statistics, divergence is a function that finds and measures differences using
the distance between two probability distributions.
Bregman divergence is one of many divergences. It can be calculated with the squared Euclidean distance:

###Steps to apply auto_feat_selection

#import
```
from auto_feat_select_rupakbob import auto_feat_selection
```

#grid_feat_search(dataframe,'taget_column_name', max_divergence(default = 0,max = 10)accepted divergence with the target column) 
```
index 0 = BregmanDivergenceSelector, index 1 = ItakuraSaitoSelector
cols_BregmanDivergenceSelector = auto_feat_selection.grid_feat_search(df,'target',5)[0]
cols_ItakuraSaitoSelector = auto_feat_selection.grid_feat_search(df,'target',5)[1]
```

### Evaluate if the selected features improves the model

###Currently supports Logistic Regression base model with goal to evaluate feature performance

#evaluate_grid_feat_search(dataframe,'taget_column_name')
```
auto_feat_selection.evaluate_grid_feat_search(df,cols_BregmanDivergenceSelector,target ='target')
auto_feat_selection.evaluate_grid_feat_search(df,cols_ItakuraSaitoSelector,target ='target')

```


