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476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | # Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import os
import pickle as pkl
import numpy as np
import sagemaker_xgboost_container.encoder as xgb_encoders
def model_fn(model_dir):
"""
Deserialize and return fitted model.
"""
model_file = "xgboost-model"
booster = pkl.load(open(os.path.join(model_dir, model_file), "rb"))
return booster
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 predict_fn(input_data, model):
"""
SageMaker XGBoost model server invokes `predict_fn` on the return value of `input_fn`.
Return a two-dimensional NumPy array where the first columns are predictions
and the remaining columns are the feature contributions (SHAP values) for that prediction.
"""
prediction = model.predict(input_data)
feature_contribs = model.predict(input_data, pred_contribs=True, validate_features=False)
output = np.hstack((prediction[:, np.newaxis], feature_contribs))
return output
def output_fn(predictions, content_type):
"""
After invoking predict_fn, the model server invokes `output_fn`.
"""
if content_type == "text/csv" or content_type == "application/json":
return ",".join(str(x) for x in predictions[0])
else:
raise ValueError("Content type {} is not supported.".format(content_type))
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