Classic approaches of Uplift modelling in scikit-learn style in python
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.
Сomfortable and intuitive scikit-learn-like API;
More uplift metrics than 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;
Supporting any estimator compatible with scikit-learn (e.g. Xgboost, LightGBM, Catboost, etc.);
All approaches can be used in the sklearn.pipeline. See the example of usage on the Tutorials page;
Also metrics are compatible with the classes from sklearn.model_selection. See the example of usage on the Tutorials page;
Almost all implemented approaches solve classification and regression problems;
Nice and useful viz for analysing a performance model.
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
The full documentation is available at uplift-modeling.com.
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.
Train and predict uplift model
Use the intuitive python API to train uplift models with sklift.models.
# import approaches from sklift.models import SoloModel, ClassTransformation # import any estimator adheres to scikit-learn conventions. from lightgbm import LGBMClassifier # define models estimator = LGBMClassifier(n_estimators=10) # define metamodel slearner = SoloModel(estimator=estimator) # fit model slearner.fit( X=X_tr, y=y_tr, treatment=trmnt_tr, ) # predict uplift uplift_slearner = slearner.predict(X_val)
Evaluate your uplift model
Uplift model evaluation metrics are available in sklift.metrics.
# import metrics to evaluate your model from sklift.metrics import ( uplift_at_k, uplift_auc_score, qini_auc_score, weighted_average_uplift ) # Uplift@30% uplift_at_k = uplift_at_k(y_true=y_val, uplift=uplift_slearner, treatment=trmnt_val, strategy='overall', k=0.3) # Area Under Qini Curve qini_coef = qini_auc_score(y_true=y_val, uplift=uplift_slearner, treatment=trmnt_val) # Area Under Uplift Curve uplift_auc = uplift_auc_score(y_true=y_val, uplift=uplift_slearner, treatment=trmnt_val) # Weighted average uplift wau = weighted_average_uplift(y_true=y_val, uplift=uplift_slearner, treatment=trmnt_val)
Vizualize the results
Visualize performance metrics with sklift.viz.
from sklift.viz import plot_qini_curve import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) ax.set_title('Qini curves') plot_qini_curve( y_test, uplift_slearner, trmnt_test, perfect=True, name='Slearner', ax=ax ); plot_qini_curve( y_test, uplift_revert, trmnt_test, perfect=False, name='Revert label', ax=ax );
We welcome new contributors of all experience levels.
Please see our Contributing Guide for more details.
By participating in this project, you agree to abide by its Code of Conduct.
Thanks to all our contributors!
If you have any questions, please contact us at email@example.com
Papers and materials
- 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).
- 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.
- 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.
- Athey, Susan, and Imbens, Guido. 2015.
Machine learning methods for estimating heterogeneous causal effects. Preprint, arXiv:1504.01132. Google Scholar.
- Oscar Mesalles Naranjo. 2012.
Testing a New Metric for Uplift Models. Dissertation Presented for the Degree of MSc in Statistics and Operational Research.
- 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.
- Maciej Jaskowski and Szymon Jaroszewicz.
Uplift modeling for clinical trial data. ICML Workshop on Clinical Data Analysis, 2012.
- Lo, Victor. 2002.
The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations. 4. 78-86.
- Zhao, Yan & Fang, Xiao & Simchi-Levi, David. 2017.
Uplift Modeling with Multiple Treatments and General Response Types. 10.1137/1.9781611974973.66.
- 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.
- Devriendt, F., Guns, T., & Verbeke, W. 2020.
Learning to rank for uplift modeling. ArXiv, abs/2002.05897.
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