def test_data_type_exception(self, np_2x1_with_label,
xgb_regressor_1_split_1_tree):
"""Test an exception is raised if data is not a np.ndarray or pd.DataFrame object."""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
with pytest.raises(
TypeError,
match=re.escape(
f"data is not in expected types {[np.ndarray, pd.DataFrame]}, got {float}"
),
):
confo_model._generate_predictions(12345.0)
def test_predict_call(self, mocker, np_2x1_with_label,
xgb_regressor_1_split_1_tree):
"""Test that the output from xgb.Booster.predict with ntree_limit = best_iteration + 1
is returned from the method.
"""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
confo_model.calibrate(np_2x1_with_label[0], np_2x1_with_label[1])
predict_return_value = np.array([200, 101])
mocked = mocker.patch.object(xgb.XGBRegressor,
"predict",
return_value=predict_return_value)
results = confo_model._generate_predictions(np_2x1_with_label[0])
assert (mocked.call_count == 1
), "incorrect number of calls to xgb.XGBRegressor.predict"
np.testing.assert_array_equal(results, predict_return_value)
call_args = mocked.call_args_list[0]
call_pos_args = call_args[0]
call_kwargs = call_args[1]
assert (
len(call_pos_args) == 1
), "incorrect number of positional args in xgb.XGBRegressor.predict call"
np.testing.assert_array_equal(call_pos_args[0], np_2x1_with_label[0])
assert call_kwargs == {
"ntree_limit": xgb_regressor_1_split_1_tree.best_iteration + 1
}, "positional args incorrect in call to xgb.XGBRegressor.predict"