sklift.models.TwoModels

class sklift.models.models.TwoModels(estimator_trmnt, estimator_ctrl, method='vanilla')

aka naïve approach, or difference score method, or double classifier approach.

Fit two separate models: on the treatment data and on the control data.

Read more in the User Guide.

Parameters:

• estimator_trmnt (estimator object implementing 'fit') – The object to use to fit the treatment data.

• estimator_ctrl (estimator object implementing 'fit') – The object to use to fit the control data.

• method (string, 'vanilla', 'ddr_control' or 'ddr_treatment', default='vanilla') –

  Specifies the approach:

  • 'vanilla':

    Two independent models;

  • 'ddr_control':

    Dependent data representation (First train control estimator).

  • 'ddr_treatment':

    Dependent data representation (First train treatment estimator).

trmnt_preds_

Estimator predictions on samples when treatment.

Type:

array-like, shape (n_samples, )

ctrl_preds_

Estimator predictions on samples when control.

Type:

array-like, shape (n_samples, )

Example:

# import approach
from sklift.models import TwoModels
# import any estimator adheres to scikit-learn conventions
from catboost import CatBoostClassifier


estimator_trmnt = CatBoostClassifier(silent=True, thread_count=2, random_state=42)
estimator_ctrl = CatBoostClassifier(silent=True, thread_count=2, random_state=42)

# define approach
tm_ctrl = TwoModels(
    estimator_trmnt=estimator_trmnt,
    estimator_ctrl=estimator_ctrl,
    method='ddr_control'
)

# fit the models
tm_ctrl = tm_ctrl.fit(
    X_train, y_train, treat_train,
    estimator_trmnt_fit_params={'cat_features': cat_features},
    estimator_ctrl_fit_params={'cat_features': cat_features}
)
uplift_tm_ctrl = tm_ctrl.predict(X_val)  # predict uplift

References

Betlei, Artem & Diemert, Eustache & Amini, Massih-Reza. (2018). Uplift Prediction with Dependent Feature Representation in Imbalanced Treatment and Control Conditions: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part V. 10.1007/978-3-030-04221-9_5.

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

See also

Other approaches:

• SoloModel: Single model approach.

• ClassTransformation: Class Variable Transformation approach.

• ClassTransformationReg: Transformed Outcome approach.

Other:

• plot_uplift_preds(): Plot histograms of treatment, control and uplift predictions.

fit(X, y, treatment, estimator_trmnt_fit_params=None, estimator_ctrl_fit_params=None)

Fit the model according to the given training data.

For each test example calculate predictions on new set twice: by the first and second models. After that calculate uplift as a delta between these predictions.

Return delta of predictions for each example.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.

• y (array-like, shape (n_samples,)) – Target vector relative to X.

• treatment (array-like, shape (n_samples,)) – Binary treatment vector relative to X.

• estimator_trmnt_fit_params (dict, optional) – Parameters to pass to the fit method of the treatment estimator.

• estimator_ctrl_fit_params (dict, optional) – Parameters to pass to the fit method of the control estimator.

Returns:

self

Return type:

object

predict(X)

Perform uplift on samples in X.

Parameters:

X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.

Returns:

uplift

Return type:

array (shape (n_samples,))

set_fit_request(*, estimator_ctrl_fit_params: bool | None | str = '$UNCHANGED$', estimator_trmnt_fit_params: bool | None | str = '$UNCHANGED$', treatment: bool | None | str = '$UNCHANGED$') → TwoModels

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

• estimator_ctrl_fit_params (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for estimator_ctrl_fit_params parameter in fit.

• estimator_trmnt_fit_params (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for estimator_trmnt_fit_params parameter in fit.

• treatment (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for treatment parameter in fit.

Returns:

self – The updated object.

Return type:

object