Metadata-Version: 2.1
Name: atts
Version: 0.0.7
Summary: Train_test splitter with adversarial validation
Home-page: https://github.com/alikula314/att_split
Author: Muhammet Ali Kula
Author-email: alikula3.14@gmail.com
License: MIT
Keywords: adversarial validation train-test-split data-sceince machine-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
License-File: LICENSE.txt
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: seaborn
Requires-Dist: matplotlib
Requires-Dist: roc-auc-score
Requires-Dist: train-test-split
Requires-Dist: xgboost
Requires-Dist: cv
Requires-Dist: plotly
Requires-Dist: plotly.express


Auto_train_test_splitter
============================================================

ATTS (Auto Train Test Splitter) splits the given dataframe to train and test taking into account the Concept Drift. It does this with xgboost running in the background. 

Calculates roc_auc scores for the given test size values. Creates a dataframe with these values.
Provides easier change visibility with chart.

The code is Python 3

Installation
------------------------

Fast install:

::

    pip install atts

For a manual install get this package:

::

    wget https://github.com/alikula314/att_split/atts/archive/master.zip
    unzip master.zip
    rm master.zip
    cd atts-master

Install the package:

::

    python setup.py install    

Example on Red Wine Quality Dataset
--------------------------------------------------------------------------------

.. code:: python

    pip install atts
    from atts.auto_test_splitter import atts 

    # run module
    k = ATTS(df, "quality" , 0.05, 0.50, 0.05)

    # get auc_score - test_size dataframe
    k.test_sizes_df()
    
    #get the plot
    k.test_sizes_plot(800,400)


    
    


