def test_gradientboosted_propensity_model(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
pm = GradientBoostedPropensityModel(random_state=RANDOM_SEED)
ps = pm.fit_predict(X, treatment)
assert roc_auc_score(treatment, ps) > .5
def test_logistic_regression_propensity_model(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
pm = LogisticRegressionPropensityModel(random_state=RANDOM_SEED)
ps = pm.fit_predict(X, treatment)
assert roc_auc_score(treatment, ps) > .5
def test_elasticnet_propensity_model(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()
pm = ElasticNetPropensityModel(random_state=RANDOM_SEED)
ps = pm.fit_predict(X, treatment)
assert roc_auc_score(treatment, ps) > 0.5