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scikit-uplift

README.md

Scikit-Uplift

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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

Quick Start

Train and predict your 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
);
from sklift.viz import plot_uplift_curve
import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 1)
ax.set_title('Uplift curves')

plot_uplift_curve(
    y_test, uplift_slearner, trmnt_test,
    perfect=True, name='Slearner', ax=ax
);

plot_uplift_curve(
    y_test, uplift_revert, trmnt_test,
    perfect=False, name='Revert label', ax=ax
);
from sklift.viz import plot_uplift_by_percentile

plot_uplift_by_percentile(y_true=y_val, uplift=uplift_preds,
                          treatment=treat_val, kind='bar')

⭐User Guide

API sklift

This is the modules reference of scikit-uplift.