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 xgb.DMatrix object."""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
confo_model.calibrate(data=np_2x1_with_label[0],
response=np_2x1_with_label[1])
with pytest.raises(
TypeError,
match=re.escape(
f"data is not in expected types {[np.ndarray, pd.DataFrame]}, got {list}"
),
):
confo_model.predict_with_interval([])
def test_no_leaf_node_counts_attribute_exception(
self, np_2x1_with_label, xgb_regressor_1_split_1_tree):
"""Test an exception is raised if leaf_node_counts attribute is not present."""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
assert not hasattr(
confo_model, "leaf_node_counts"
), "XGBSklearnLeafNodeScaledConformalPredictor has leaf_node_counts attribute prior to running calibrate"
with pytest.raises(
AttributeError,
match=
"XGBSklearnLeafNodeScaledConformalPredictor does not have leaf_node_counts"
" attribute, run calibrate first.",
):
confo_model.predict_with_interval(np_2x1_with_label[0])
def test_super_predict_with_interval_call(self, mocker, np_2x1_with_label,
xgb_regressor_1_split_1_tree):
"""Test that LeafNodeScaledConformalPredictor.predict_with_interval is called and the
outputs of this are returned from the method.
"""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
confo_model.calibrate(data=np_2x1_with_label[0],
response=np_2x1_with_label[1])
predict_return_value = np.array([200, 101, 1234])
mocked = mocker.patch.object(
pitci.base.LeafNodeScaledConformalPredictor,
"predict_with_interval",
return_value=predict_return_value,
)
results = confo_model.predict_with_interval(np_2x1_with_label[0])
# test output of predict_with_interval is the return value of
# LeafNodeScaledConformalPredictor.predict_with_interval
np.testing.assert_array_equal(results, predict_return_value)
assert (mocked.call_count == 1
), "incorrect number of calls to super().predict_with_interval"
call_args = mocked.call_args_list[0]
call_pos_args = call_args[0]
call_kwargs = call_args[1]
assert (
call_pos_args == ()
), "positional args incorrect in call to LeafNodeScaledConformalPredictor.predict_with_interval"
assert list(call_kwargs.keys()) == [
"data"
], "incorrect keyword args in LeafNodeScaledConformalPredictor.predict_with_interval call"
np.testing.assert_array_equal(call_kwargs["data"],
np_2x1_with_label[0])