sklift.models.TwoModels
- class sklift.models.models.TwoModels(estimator_trmnt, estimator_ctrl, method='vanilla')[source]
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)[source]
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)[source]
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
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
pipeline.Pipeline
. Otherwise it has no effect.- Parameters:
estimator_ctrl_fit_params (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
estimator_ctrl_fit_params
parameter infit
.estimator_trmnt_fit_params (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
estimator_trmnt_fit_params
parameter infit
.treatment (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
treatment
parameter infit
.
- Returns:
self – The updated object.
- Return type:
object