Add xgboost examples for inference in script mode (aws#1341)

* Add xgboost examples for inference in script mode

* Address comments by Eric

Author: Edward J Kim

introduction_to_amazon_algorithms/xgboost_abalone/images/shap_old_abalone.png

@@ -0,0 +1,66 @@
+#  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 json
+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)
+    output = np.hstack((prediction[:, np.newaxis], feature_contribs))
+    return output
+
+
+def output_fn(prediction, content_type):
+    """
+    After invoking predict_fn, the model server invokes `output_fn`.
+    """
+    if content_type == "application/json":
+        return json.dumps(prediction.tolist())
+    else:
+        raise ValueError("Content type {} is not supported.".format(content_type))