def test_libsvm_to_dmatrix(target):
temp_libsvm_file = tempfile.NamedTemporaryFile(delete=False)
temp_libsvm_file_name = temp_libsvm_file.name
assert os.path.exists(temp_libsvm_file_name)
with mock.patch('sagemaker_xgboost_container.encoder.tempfile') as mock_tempfile:
mock_tempfile.NamedTemporaryFile.return_value = temp_libsvm_file
actual = encoder.libsvm_to_dmatrix(target)
assert type(actual) is xgb.DMatrix
assert not os.path.exists(temp_libsvm_file_name)
def input_fn(request_body, request_content_type):
"""
The SageMaker XGBoost model server receives the request data body and the content type,
and invokes the `input_fn`.
Return a DMatrix (an object that can be passed to predict_fn).
"""
if request_content_type == "text/libsvm":
return xgb_encoders.libsvm_to_dmatrix(request_body)
else:
raise ValueError(
"Content type {} is not supported.".format(request_content_type))
def transform_fn(model, request_body, content_type, accept_type):
"""
The SageMaker XGBoost model server receives the request data body and the content type,
we first need to create a DMatrix (an object that can be passed to predict)
"""
multiple_predictions_flag = False
if "csv" not in content_type:
# request_body is a bytes object, which we decode to a string
request_body = request_body.decode()
# request_body is of the form 'dataset, predict_function'
# e.g. 'sklearn.datasets.fetch_california_housing(), pred_contribs'
# comma separated: '[[var1, var2], [var3, var4], ..., varx]], pred_contribs'
prediction_methods = ["predict", "pred_contribs", "pred_interactions"]
if request_body.split(', ')[-1] in prediction_methods:
if "[[" in request_body:
multiple_predictions_flag = True
dataset = json.loads(", ".join(request_body.split(', ')[:-1]))
else:
# "var1, var2, var3, var4, ..., varx, pred_contribs"
dataset = ", ".join(request_body.split(', ')[:-1])
predict = request_body.split(', ')[-1]
else:
dataset = request_body
predict = "predict"
if "sklearn.datasets" in dataset:
import sklearn.datasets
try:
data = eval(dataset)
except Exception:
raise ValueError(
"Function {} is not supported. Try something like 'sklearn.datasets.fetch_california_housing()'"
.format(dataset))
X = data.data
y = data.target
dmat = xgb.DMatrix(X, y)
input_data = dmat
elif content_type == "text/libsvm":
input_data = xgb_encoders.libsvm_to_dmatrix(dataset)
elif content_type == "text/csv":
if multiple_predictions_flag:
from pandas import DataFrame
dataset = DataFrame(dataset)
# this is for the NYC Taxi columns - may have to adjust for other CSV inputs
dataset.columns = [
'f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9',
'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16'
]
input_data = xgb.DMatrix(dataset)
else:
input_data = xgb_encoders.csv_to_dmatrix(dataset)
else:
raise ValueError(
"Content type {} is not supported.".format(content_type))
"""
Now that we have the DMatrix and a prediction method,
we invoke the predict method and return the output.
"""
if "predict" in predict:
predictions = model.predict(input_data)
return str(predictions.tolist())
elif "pred_contribs" in predict:
shap_values = model.predict(input_data, pred_contribs=True)
return str(shap_values.tolist())
elif "pred_interactions" in predict:
shap_interactions = model.predict(input_data, pred_interactions=True)
return str(shap_interactions.tolist())
else:
raise ValueError(
"Prediction parameter {} is not supported.".format(predict))