lai.rst

Lai

The class which implements the Lai's approach [1].

Parameters

model : object, optional (default=sklearn.linear_model.LogisticRegression)   The classification model which will be used for predict uplift. use_weights : boolean, optional (default=False)   Use or not weights?

Methods

fit(self, X, y, t) <lai_fit>	Build a the model from the training set (X, y, t).
predict(self, X, t=None) <lai_predict>	Predict an uplift for X.

fit(self, X, y, t)

Build a the model from the training set (X, y, t).

Parameters	X: numpy ndarray with shape = [n_samples, n_features]   Matrix of features. y: numpy array with shape = [n_samples,]   Array of target of feature. t: numpy array with shape = [n_samples,]   Array of treatments.
Returns	self : object

predict(self, X, t=None)

Predict an uplift for X.

Parameters	X: numpy ndarray with shape = [n_samples, n_features]   Matrix of features. t: numpy array with shape = [n_samples,] or None   Array of treatments.
Returns	self : object   The predicted values.

References

1. A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics by Floris Devriendt, Darie Moldovan, and Wouter Verbeke

from pyuplift.transformation import Lai
...
model = Lai()
model.fit(X[train_indexes, :], y[train_indexes], t[train_indexes])
uplift = model.predict(X[test_indexes, :])
print(uplift)