def parse_content_data(input_data, input_content_type):
dtest = None
content_type = get_content_type(input_content_type)
payload = input_data
if content_type == CSV:
try:
decoded_payload = payload.strip().decode("utf-8")
dtest = encoder.csv_to_dmatrix(decoded_payload, dtype=np.float)
except Exception as e:
raise RuntimeError("Loading csv data failed with Exception, "
"please ensure data is in csv format:\n {}\n {}".format(type(e), e))
elif content_type == LIBSVM:
try:
decoded_payload = payload.strip().decode("utf-8")
dtest = xgb.DMatrix(_get_sparse_matrix_from_libsvm(decoded_payload))
except Exception as e:
raise RuntimeError("Loading libsvm data failed with Exception, "
"please ensure data is in libsvm format:\n {}\n {}".format(type(e), e))
elif content_type == RECORDIO_PROTOBUF:
try:
dtest = encoder.recordio_protobuf_to_dmatrix(payload)
except Exception as e:
raise RuntimeError("Loading recordio-protobuf data failed with "
"Exception, please ensure data is in "
"recordio-protobuf format: {} {}".format(type(e), e))
else:
raise RuntimeError("Content-type {} is not supported.".format(input_content_type))
return dtest, content_type
def default_input_fn(self, input_data, input_content_type):
"""Take request data and de-serializes the data into an object for prediction.
When an InvokeEndpoint operation is made against an Endpoint running SageMaker model server,
the model server receives two pieces of information:
- The request Content-Type, for example "application/json"
- The request data, which is at most 5 MB (5 * 1024 * 1024 bytes) in size.
The input_fn is responsible to take the request data and pre-process it before prediction.
Args:
input_data (obj): the request data.
input_content_type (str): the request Content-Type. XGBoost accepts CSV, LIBSVM, and RECORDIO-PROTOBUF.
Returns:
(obj): data ready for prediction. For XGBoost, this defaults to DMatrix.
"""
if len(input_data) == 0:
raise NoContentInferenceError()
try:
content_type = get_content_type(input_content_type)
except Exception:
raise UnsupportedMediaTypeInferenceError(
"Content type must be csv, libsvm, or recordio-protobuf.")
if content_type == CSV:
try:
input_data = input_data.decode('utf-8')
payload = input_data.strip()
dtest = xgb_encoder.csv_to_dmatrix(payload, dtype=np.float)
except Exception as e:
raise UnsupportedMediaTypeInferenceError(
"Loading csv data failed with "
"Exception, please ensure data "
"is in csv format: {} {}".format(type(e), e))
elif content_type == LIBSVM:
try:
input_data = input_data.decode('utf-8')
payload = input_data.strip()
dtest = xgb.DMatrix(_get_sparse_matrix_from_libsvm(payload))
except Exception as e:
raise UnsupportedMediaTypeInferenceError(
"Loading libsvm data failed with "
"Exception, please ensure data "
"is in libsvm format: {} {}".format(type(e), e))
elif content_type == RECORDIO_PROTOBUF:
try:
payload = input_data
dtest = xgb_encoder.recordio_protobuf_to_dmatrix(payload)
except Exception as e:
raise UnsupportedMediaTypeInferenceError(
"Loading recordio-protobuf data failed with "
"Exception, please ensure data is in "
"recordio-protobuf format: {} {}".format(type(e), e))
else:
raise UnsupportedMediaTypeInferenceError(
"Content type must be csv, libsvm, or recordio-protobuf.")
return dtest, content_type
def test_sparse_recordio_protobuf_to_dmatrix():
current_path = Path(os.path.abspath(__file__))
data_path = os.path.join(str(current_path.parent.parent), 'resources', 'data')
files_path = os.path.join(data_path, 'recordio_protobuf', 'sparse_edge_cases')
for filename in os.listdir(files_path):
file_path = os.path.join(files_path, filename)
with open(file_path, 'rb') as f:
target = f.read()
actual = encoder.recordio_protobuf_to_dmatrix(target)
assert type(actual) is xgb.DMatrix
def test_recordio_protobuf_to_dmatrix(target):
actual = encoder.recordio_protobuf_to_dmatrix(target)
assert type(actual) is xgb.DMatrix
