scikit-uplift

scikit-uplift is a Python module for classic approaches for uplift modelling built on top of scikit-learn.

Uplift prediction aims to estimate the causal impact of a treatment at the individual level.

More about uplift modelling problem read in russian on habr.com.

Features:

• Comfortable and intuitive style of modelling like scikit-learn;
• Applying any estimator adheres to scikit-learn conventions;
• Almost all implemented approaches solve both the problem of classification and regression;
• A lot of metrics (Such as Area Under Uplift Curve or Area Under Qini Curve) are implemented to evaluate your uplift model.

Installation

Install the package by the following command from PyPI:

pip install scikit-uplift

Or install from source:

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

Documentation

The full documentation is available at scikit-uplift.readthedocs.io.

Or you can build the documentation locally using Sphinx 1.4 or later:

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, RU) for details.

Train and predict uplift model

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

# define approach
sm = SoloModel(CatBoostClassifier(verbose=100, random_state=777))
# fit model
sm = sm.fit(X_train, y_train, treat_train, estimator_fit_params={{'plot': True})

# predict uplift
uplift_sm = sm.predict(X_val)

Evaluate your uplift model

# import metrics to evaluate your model
from sklift.metrics import auqc, auuc, uplift_at_k

# Uplift@30%
sm_uplift_at_k = uplift_at_k(y_true=y_val, uplift=uplift_sm, treatment=treat_val, k=0.3)
# Area Under Qini Curve
sm_auqc = auqc(y_true=y_val, uplift=uplift_sm, treatment=treat_val)
# Area Under Uplift Curve
sm_auuc = auuc(y_true=y_val, uplift=uplift_sm, treatment=treat_val)

Vizualize the results

# import vizualisation tools
from sklift.viz import plot_uplift_preds, plot_uplift_qini_curves

# get conditional predictions (probabilities) of performing a target action
# with interaction for each object
sm_trmnt_preds = sm.trmnt_preds_
# get conditional predictions (probabilities) of performing a target action
# without interaction for each object
sm_ctrl_preds = sm.ctrl_preds_

# draw probability distributions and their difference (uplift)
plot_uplift_preds(trmnt_preds=sm_trmnt_preds, ctrl_preds=sm_ctrl_preds);
# draw Uplift and Qini curves
plot_uplift_qini_curves(y_true=y_val, uplift=uplift_sm, treatment=treat_val);

Development

We welcome new contributors of all experience levels.

Important links

• Official source code repo: https://github.com/maks-sh/scikit-uplift/
• Issue tracker: https://github.com/maks-sh/scikit-uplift/issues

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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.

Tags

EN: uplift modeling, uplift modelling, causal inference, causal effect, causality, individual treatment effect, true lift, net lift