def test_doubleml_exception_learner():
err_msg_prefix = 'Invalid learner provided for ml_g: '
warn_msg_prefix = 'Learner provided for ml_g is probably invalid: '
msg = err_msg_prefix + 'provide an instance of a learner instead of a class.'
with pytest.raises(TypeError, match=msg):
_ = DoubleMLPLR(dml_data, Lasso, ml_m)
msg = err_msg_prefix + r'BaseEstimator\(\) has no method .fit\(\).'
with pytest.raises(TypeError, match=msg):
_ = DoubleMLPLR(dml_data, BaseEstimator(), ml_m)
# msg = err_msg_prefix + r'_DummyNoSetParams\(\) has no method .set_params\(\).'
with pytest.raises(TypeError):
_ = DoubleMLPLR(dml_data, _DummyNoSetParams(), ml_m)
# msg = err_msg_prefix + r'_DummyNoSetParams\(\) has no method .get_params\(\).'
with pytest.raises(TypeError):
_ = DoubleMLPLR(dml_data, _DummyNoGetParams(), ml_m)
# msg = 'Learner provided for ml_m is probably invalid: ' + r'_DummyNoClassifier\(\) is \(probably\) no classifier.'
with pytest.warns(UserWarning):
_ = DoubleMLIRM(dml_data_irm, Lasso(), _DummyNoClassifier())
# ToDo: Currently for ml_g (and others) we only check whether the learner can be identified as regressor. However,
# we do not check whether it can instead be identified as classifier, which could be used to throw an error.
msg = warn_msg_prefix + r'LogisticRegression\(\) is \(probably\) no regressor.'
with pytest.warns(UserWarning, match=msg):
_ = DoubleMLPLR(dml_data, LogisticRegression(), Lasso())
# we allow classifiers for ml_m in PLR, but only for binary treatment variables
msg = (
r'The ml_m learner LogisticRegression\(\) was identified as classifier '
'but at least one treatment variable is not binary with values 0 and 1.'
)
with pytest.raises(ValueError, match=msg):
_ = DoubleMLPLR(dml_data, Lasso(), LogisticRegression())
# construct a classifier which is not identifiable as classifier via is_classifier by sklearn
# it then predicts labels and therefore an exception will be thrown
log_reg = LogisticRegression()
log_reg._estimator_type = None
msg = (
r'Learner provided for ml_m is probably invalid: LogisticRegression\(\) is \(probably\) neither a regressor '
'nor a classifier. Method predict is used for prediction.')
with pytest.warns(UserWarning, match=msg):
dml_plr_hidden_classifier = DoubleMLPLR(dml_data_irm, Lasso(), log_reg)
msg = (
r'For the binary treatment variable d, predictions obtained with the ml_m learner LogisticRegression\(\) '
'are also observed to be binary with values 0 and 1. Make sure that for classifiers probabilities and not '
'labels are predicted.')
with pytest.raises(ValueError, match=msg):
dml_plr_hidden_classifier.fit()
