train_test_split

Split X, y, t into random train and test subsets.

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. train_share: float, optional (default=0.7) train_share represents the proportion of the dataset to include in the train split. random_state: int, optional (default=None) random_state is the seed used by the random number generator.

Return

X_train: numpy ndarray Train matrix of features. X_test: numpy ndarray Test matrix of features. y_train: numpy array Train array of target of feature. y_test: numpy array Test array of target of feature. t_train: numpy array Train array of treatments. t_test: numpy array Test array of treatments.

Examples

from pyuplift.model_selection import train_test_split
...
for seed in seeds:
    X_train, X_test, y_train, y_test, t_train, t_test = train_test_split(X, y, t, train_share, seed)
    model.fit(X_train, y_train, t_train)
    score = get_average_effect(y_test, t_test, model.predict(X_test))
    scores.append(score)