Quick StartΒΆ

See the RetailHero tutorial notebook (EN Open In Colab1, RU Open In Colab2) for details.

Train and predict your uplift model

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# 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

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# 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

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from sklift.viz import plot_qini_curve

plot_qini_curve(y_true=y_val, uplift=uplift_preds, treatment=treat_val)
Example of model's qini curve, perfect qini curve and random qini curve 
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from sklift.viz import plot_uplift_curve

plot_uplift_curve(y_true=y_val, uplift=uplift_preds, treatment=treat_val)
Example of model's uplift curve, perfect uplift curve and random uplift curve 
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from sklift.viz import plot_uplift_by_percentile

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