def _handle_selectable_inference_response(predictions, accept):
"""Retrieve the additional prediction data for selectable inference mode.
:param predictions: output of xgboost predict (list of numpy objects)
:param accept: requested accept type (str)
:return: flask response with encoded predictions
"""
try:
config = ScoringService.get_config_json()
objective = config['learner']['objective']['name']
num_class = config['learner']['learner_model_param'].get(
'num_class', '')
selected_content_keys = serve_utils.get_selected_output_keys()
selected_content = serve_utils.get_selected_predictions(
predictions, selected_content_keys, objective, num_class=num_class)
response = serve_utils.encode_selected_predictions(
selected_content, selected_content_keys, accept)
except Exception as e:
logging.exception(e)
return flask.Response(response=str(e),
status=http.client.INTERNAL_SERVER_ERROR)
return flask.Response(response=response,
status=http.client.OK,
mimetype=accept)
def test_encode_selected_predictions_protobuf():
expected_predicted_labels = [[1], [0]]
expected_probabilities = [[0.4, 0.6], [0.9, 0.1]]
protobuf_response = serve_utils.encode_selected_predictions(TEST_PREDICTIONS, TEST_KEYS,
"application/x-recordio-protobuf")
stream = io.BytesIO(protobuf_response)
for recordio, predicted_label, probabilities in zip(_read_recordio(stream),
expected_predicted_labels, expected_probabilities):
record = Record()
record.ParseFromString(recordio)
assert record.label["predicted_label"].float32_tensor.values == predicted_label
assert all(np.isclose(record.label["probabilities"].float32_tensor.values, probabilities))
def test_encode_selected_content_error():
with pytest.raises(RuntimeError):
serve_utils.encode_selected_predictions(TEST_PREDICTIONS, TEST_KEYS, "text/libsvm")
def test_encode_selected_predictions_csv():
expected_csv = '1,"[0.4, 0.6]"\n0,"[0.9, 0.1]"'
assert serve_utils.encode_selected_predictions(TEST_PREDICTIONS, TEST_KEYS, "text/csv") == expected_csv
def test_encode_selected_predictions_jsonlines():
expected_jsonlines = b'{"predicted_label": 1, "probabilities": [0.4, 0.6]}\n' \
b'{"predicted_label": 0, "probabilities": [0.9, 0.1]}\n'
assert serve_utils.encode_selected_predictions(TEST_PREDICTIONS, TEST_KEYS,
"application/jsonlines") == expected_jsonlines
def test_encode_selected_predictions_json():
expected_json = json.dumps({"predictions": TEST_PREDICTIONS})
assert serve_utils.encode_selected_predictions(TEST_PREDICTIONS, TEST_KEYS, "application/json") == expected_json
