uplift-analysis 0.0.3

A Python library providing analysis utilities for uplift modeling techniques.

uplift-analysis

uplift-analysis is a Python library that contains implementations of methods and utilities, which can serve use cases requiring the analysis of uplift modeling techniques.
The implemented modules include scoring utilities, analysis strategy, and relevant visualization methods.

This library works for Python 3.7 and higher.

Installation

This library is distributed on PyPi and can be installed using pip.

$ pip install uplift-analysis 

The command above will automatically install all the required dependencies. Please visit the installation page for more details.

Getting started

Check out the comprehensive tutorial for a complete walk-through of the library.

import pandas as pd
from uplift_analysis import data, evaluation

eval_set = data.EvalSet(df=pd.DataFrame({
    'observed_action': treatments,
    'responses': responses,
    'score': scores,
    'proposed_action': recommended_treatments
}))

evaluator = evaluation.Evaluator()
eval_res, summary = evaluator.eval_and_show(eval_set, specify=['uplift'],
                                            show_random=True, num_random_rep=4)

Documentation

For more information, refer to the accompanying blogpost "Analyzing Uplift Models", the package's complete documentation, and the walkthrough tutorials.

Info for developers

The source code of the project is available on GitHub.

$ git clone https://github.com/PlaytikaOSS/uplift-analysis.git

You can install the library and the dependencies with one of the following commands:

$ pip install .                        # install library + dependencies
$ pip install ".[develop]"             # install library + dependencies + developer-dependencies
$ pip install -r requirements.txt      # install dependencies
$ pip install -r requirements-dev.txt  # install developer-dependencies

For creating the "pip-installable" *.whl file, run the following command (at the root of the repository):

$ python -m build

For creating the HTML documentation of the project, run the following commands:

$ cd docs
$ make clean
$ make html

Run tests

Tests can be executed with pytest running the following commands:

$ cd tests
$ pytest                                      # run all tests
$ pytest test_testmodule.py                   # run all tests within a module
$ pytest test_testmodule.py -k test_testname  # run only 1 test

License

MIT License