Metadata-Version: 2.1
Name: scikit-uplift
Version: 0.3.2
Summary: Classic approaches of Uplift modelling in scikit-learn style in python
Home-page: https://github.com/maks-sh/scikit-uplift
Maintainer: Maksim Shevchenko
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/x-rst
Requires-Dist: scikit-learn (>=0.21.0)
Requires-Dist: numpy (>=1.16)
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: requests
Provides-Extra: test
Requires-Dist: pytest ; extra == 'test'

.. -*- mode: rst -*-

|Python3|_ |PyPi|_ |Docs|_ |License|_

.. |Python3| image:: https://img.shields.io/badge/python-3-blue.svg
.. _Python3: https://badge.fury.io/py/scikit-uplift

.. |PyPi| image:: https://badge.fury.io/py/scikit-uplift.svg
.. _PyPi: https://badge.fury.io/py/scikit-uplift

.. |Docs| image:: https://readthedocs.org/projects/scikit-uplift/badge/?version=latest
.. _Docs: https://www.uplift-modeling.com/en/latest/

.. |License| image:: https://img.shields.io/badge/license-MIT-green
.. _License: https://github.com/maks-sh/scikit-uplift/blob/master/LICENSE

.. |Open In Colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
.. _Open In Colab1: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb

.. |Open In Colab2| image:: https://colab.research.google.com/assets/colab-badge.svg
.. _Open In Colab2: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb

.. |Open In Colab3| image:: https://colab.research.google.com/assets/colab-badge.svg
.. _Open In Colab3: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb

.. |Open In Colab4| image:: https://colab.research.google.com/assets/colab-badge.svg
.. _Open In Colab4: https://colab.research.google.com/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb

.. _uplift-modeling.com: https://www.uplift-modeling.com/en/latest/

.. image:: https://raw.githubusercontent.com/maks-sh/scikit-uplift/dev/docs/_static/sklift-github-logo.png
    :align: center
    :alt: scikit-uplift: uplift modeling in scikit-learn style in python


scikit-uplift
===============

**scikit-uplift (sklift)** is an uplift modeling python package that provides fast sklearn-style models implementation, evaluation metrics and visualization tools.

Uplift modeling estimates a causal effect of treatment and uses it to effectively target customers that are most likely to respond to a marketing campaign.

**Use cases for uplift modeling:**

* Target customers in the marketing campaign. Quite useful in promotion of some popular product where there is a big part of customers who make a target action by themself without any influence. By modeling uplift you can find customers who are likely to make the target action (for instance, install an app) only when treated (for instance, received a push).

* Combine a churn model and an uplift model to offer some bonus to a group of customers who are likely to churn.

* Select a tiny group of customers in the campaign where a price per customer is high.

Read more about uplift modeling problem in `User Guide <https://www.uplift-modeling.com/en/latest/user_guide/index.html>`__.

Articles in russian on habr.com: `Part 1 <https://habr.com/ru/company/ru_mts/blog/485980/>`__ ,
`Part 2 <https://habr.com/ru/company/ru_mts/blog/485976/>`__
and `Part 3 <https://habr.com/ru/company/ru_mts/blog/538934/>`__.

**Features**:

* Сomfortable and intuitive scikit-learn-like API;

* Applying any estimator compatible with scikit-learn (e.g. Xgboost, LightGBM, Catboost, etc.);

* All approaches can be used in sklearn.pipeline (see example (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_EN.ipynb>`__ |Open In Colab3|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/pipeline_usage_RU.ipynb>`__ |Open In Colab4|_));

* Almost all implemented approaches solve classification and regression problem;

* More uplift metrics that you have ever seen in one place! Include brilliants like  *Area Under Uplift Curve* (AUUC) or *Area Under Qini Curve* (Qini coefficient) with ideal cases;

* Nice and useful viz for analyzing a performance model.

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

**Install** the package by the following command from PyPI:

.. code-block:: bash

    pip install scikit-uplift

Or install from source:

.. code-block:: bash

    git clone https://github.com/maks-sh/scikit-uplift.git
    cd scikit-uplift
    python setup.py install

Documentation
--------------

The full documentation is available at `uplift-modeling.com`_.

Or you can build the documentation locally using `Sphinx <http://sphinx-doc.org/>`_ 1.4 or later:

.. code-block:: bash

    cd docs
    pip install -r requirements.txt
    make html

And if you now point your browser to ``_build/html/index.html``, you should see a documentation site.

Quick Start
-----------

See the **RetailHero tutorial notebook** (`EN <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero_EN.ipynb>`__ |Open In Colab1|_, `RU <https://nbviewer.jupyter.org/github/maks-sh/scikit-uplift/blob/master/notebooks/RetailHero.ipynb>`__ |Open In Colab2|_) for details.

**Train and predict uplift model**

Use the intuitive python API to train uplift models with `sklift.models  <https://www.uplift-modeling.com/en/latest/api/models/index.html>`__.

.. code-block:: python

    # import approaches
    from sklift.models import SoloModel, ClassTransformation, TwoModels
    # import any estimator adheres to scikit-learn conventions.
    from catboost import CatBoostClassifier


    # define models
    treatment_model = CatBoostClassifier(iterations=50, thread_count=3,
                                         random_state=42, silent=True)
    control_model = CatBoostClassifier(iterations=50, thread_count=3,
                                       random_state=42, silent=True)

    # define approach
    tm = TwoModels(treatment_model, control_model, method='vanilla')
    # fit model
    tm = tm.fit(X_train, y_train, treat_train)

    # predict uplift
    uplift_preds = tm.predict(X_val)

**Evaluate your uplift model**

Uplift model evaluation metrics are available in `sklift.metrics  <https://www.uplift-modeling.com/en/latest/api/metrics/index.html>`__.

.. code-block:: python

    # import metrics to evaluate your model
    from sklift.metrics import (
        uplift_at_k, uplift_auc_score, qini_auc_score, weighted_average_uplift
    )


    # Uplift@30%
    tm_uplift_at_k = uplift_at_k(y_true=y_val, uplift=uplift_preds, treatment=treat_val,
                                 strategy='overall', k=0.3)

    # Area Under Qini Curve
    tm_qini_auc = qini_auc_score(y_true=y_val, uplift=uplift_preds, treatment=treat_val)

    # Area Under Uplift Curve
    tm_uplift_auc = uplift_auc_score(y_true=y_val, uplift=uplift_preds, treatment=treat_val)

    # Weighted average uplift
    tm_wau = weighted_average_uplift(y_true=y_val, uplift=uplift_preds,  treatment=treat_val)

**Vizualize the results**

Visualize performance metrics with `sklift.viz  <https://www.uplift-modeling.com/en/latest/api/viz/index.html>`__.

.. code-block:: python

    # import vizualisation tools
    from sklift.viz import plot_qini_curve

    plot_qini_curve(y_true=y_val, uplift=uplift_preds, treatment=treat_val, negative_effect=True)

.. image:: docs/_static/images/Readme_qini_curve.png
    :width: 514px
    :height: 400px
    :alt: Example of model's qini curve, perfect qini curve and random qini curve

Development
-----------

We welcome new contributors of all experience levels.

- Please see our `Contributing Guide <https://www.uplift-modeling.com/en/latest/contributing.html>`_ for more details.
- By participating in this project, you agree to abide by its `Code of Conduct <https://github.com/maks-sh/scikit-uplift/blob/master/.github/CODE_OF_CONDUCT.md>`__.

If you have any questions, please contact us at team@uplift-modeling.com

Important links
~~~~~~~~~~~~~~~

- Official source code repo: https://github.com/maks-sh/scikit-uplift/
- Issue tracker: https://github.com/maks-sh/scikit-uplift/issues
- Documentation: https://www.uplift-modeling.com/en/latest/
- User Guide: https://www.uplift-modeling.com/en/latest/user_guide/index.html
- Contributing guide: https://www.uplift-modeling.com/en/latest/contributing.html
- Release History: https://www.uplift-modeling.com/en/latest/changelog.html

===============

Papers and materials
---------------------
1. Gutierrez, P., & Gérardy, J. Y.
	Causal Inference and Uplift Modelling: A Review of the Literature.
	In International Conference on Predictive Applications and APIs (pp. 1-13).

2. Artem Betlei, Criteo Research; Eustache Diemert, Criteo Research; Massih-Reza Amini, Univ. Grenoble Alpes
	Dependent and Shared Data Representations improve Uplift Prediction in Imbalanced Treatment Conditions
	FAIM'18 Workshop on CausalML.

3. Eustache Diemert, Artem Betlei, Christophe Renaudin, and Massih-Reza Amini. 2018.
    A Large Scale Benchmark for Uplift Modeling.
    In Proceedings of AdKDD & TargetAd (ADKDD’18). ACM, New York, NY, USA, 6 pages.

4. Athey, Susan, and Imbens, Guido. 2015.
    Machine learning methods for estimating heterogeneous causal effects.
    Preprint, arXiv:1504.01132. Google Scholar.

5. Oscar Mesalles Naranjo. 2012.
    Testing a New Metric for Uplift Models.
    Dissertation Presented for the Degree of MSc in Statistics and Operational Research.

6. Kane, K., V. S. Y. Lo, and J. Zheng. 2014.
    Mining for the Truly Responsive Customers and Prospects Using True-Lift Modeling:
    Comparison of New and Existing Methods.
    Journal of Marketing Analytics 2 (4): 218–238.

7. Maciej Jaskowski and Szymon Jaroszewicz.
    Uplift modeling for clinical trial data.
    ICML Workshop on Clinical Data Analysis, 2012.

8. Lo, Victor. 2002.
    The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing.
    SIGKDD Explorations. 4. 78-86.

9. Zhao, Yan & Fang, Xiao & Simchi-Levi, David. 2017.
    Uplift Modeling with Multiple Treatments and General Response Types. 10.1137/1.9781611974973.66.

10. Nicholas J Radcliffe. 2007.
	Using control groups to target on predicted lift: Building and assessing uplift model. Direct Marketing Analytics Journal, (3):14–21, 2007.

11. Devriendt, F., Guns, T., & Verbeke, W. 2020.
	Learning to rank for uplift modeling. ArXiv, abs/2002.05897.

===============

Tags
~~~~~~~~~~~~~~~
**EN**: uplift modeling, uplift modelling, causal inference, causal effect, causality, individual treatment effect, true lift, net lift, incremental modeling

**RU**: аплифт моделирование, Uplift модель

**ZH**: 隆起建模,因果推断,因果效应,因果关系,个人治疗效应,真正的电梯,净电梯



