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 {list}"
),
):
confo_model._generate_leaf_node_predictions([])
def test_output_2d(self, mocker, np_2x1_with_label,
xgb_regressor_1_split_1_tree):
"""Test the array returned from _generate_leaf_node_predictions is a 2d array
even if the output from predict is 1d.
"""
confo_model = XGBSklearnLeafNodeScaledConformalPredictor(
xgb_regressor_1_split_1_tree)
confo_model.calibrate(np_2x1_with_label[0], np_2x1_with_label[1])
# set the return value from predict to be a 1d array
predict_return_value = np.array([200, 101])
mocker.patch.object(xgb.XGBRegressor,
"apply",
return_value=predict_return_value)
results = confo_model._generate_leaf_node_predictions(
np_2x1_with_label[0])
expected_results = predict_return_value.reshape(
predict_return_value.shape[0], 1)
np.testing.assert_array_equal(results, expected_results)
def test_predict_call(self, mocker, np_2x1_with_label,
xgb_regressor_1_split_1_tree):
"""Test that the output from xgb.XGBRegressor.predict with ntree_limit = best_iteration + 1
and pred_leaf = True 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], [5, 6]])
mocked = mocker.patch.object(xgb.XGBRegressor,
"apply",
return_value=predict_return_value)
results = confo_model._generate_leaf_node_predictions(
np_2x1_with_label[0])
assert (mocked.call_count == 1
), "incorrect number of calls to xgb.XGBRegressor.apply"
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) == 0
), "incorrect number of positional args in xgb.XGBRegressor.apply call"
assert (
len(list(call_kwargs.keys())) == 2
), "incorrect number of keyword args in xgb.XGBRegressor.apply call"
assert ("ntree_limit" in call_kwargs.keys()
and "X" in call_kwargs.keys()
), "incorrect keyword args in xgb.XGBRegressor.apply call"
np.testing.assert_array_equal(call_kwargs["X"], np_2x1_with_label[0])
assert (
call_kwargs["ntree_limit"] ==
xgb_regressor_1_split_1_tree.best_iteration + 1
), "ntree_limit keyword arg incorrect in xgb.XGBRegressor.apply call"